Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems

Topic Information

Dear Colleagues,

With the continuous development of new energy power systems, the power system has gradually become a complex system that contains tens of thousands of primary and secondary power equipment. In order to ensure the safe and stable operation of the system, condition monitoring, assessment, detection and diagnosis of various equipment are particularly important. In contrast to the traditional power system, power systems that use new energy include the uncertain characteristics of new energy, which makes the operating conditions of power equipment more complex and changeable, and brings great challenges to its safe and stable operation.

In addition, with the access of distributed power generation and the increase in the number of electric vehicles, the load characteristics of power equipment become more and more variable, and the harmonic, random fluctuation and impulsive characteristics become more evident. All these aspects have a certain impact on the safe and stable operation of power equipment. In order to ensure the high and reliable operation of the new energy power system, it is necessary to focus on the monitoring, diagnosis, detection and evaluation of the power equipment, especially large-scale primary equipment, such as cables, insulators and transformers, to ensure the safety and reliability of the entire system.

This Topic aims to integrate and present the most recent advances that address the challenges in the fields of condition monitoring, assessment, fault diagnosis, defect inspection and life management of critical power equipment, such as cables, insulators, transformers, etc. Topics of interest for the publication include, but are not limited to, the following:

• Advanced condition monitoring of insulators/cable systems
• Visualization analysis and condition assessment of insulators/cable systems
• Life cycle assessment and remaining useful life estimation for power equipment
• Data/model-driven-based approaches for fault diagnosis of electrical equipment
• Non-intrusive inspection or non-destructive detection methods for high-voltage assets
• Fault mechanism modelling and analysis of power equipment
• Health assessment and management approaches for power equipment
• Reliability calculation and assessment of power equipment

Dr. Shuaibing Li
Prof. Dr. Guoqiang Gao
Dr. Guochang Li
Dr. Yi Cui
Dr. Jiefeng Liu
Dr. Guangya Zhu
Dr. Jin Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Coatings coatings	3.4	4.7	2011	13.8 Days	CHF 2600	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit
Telecom telecom	-	3.1	2020	26.1 Days	CHF 1200	Submit

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Published Papers (28 papers)

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22 pages, 2585 KiB  

Open AccessArticle

Investigation of Energy and Power Characteristics of Various Matrix Multiplication Algorithms

by Salem Alsari and Muhammad Al-Hashimi

Energies 2024, 17(9), 2225; https://doi.org/10.3390/en17092225 - 5 May 2024

Viewed by 297

Abstract

This work studied the energy behavior of six matrix multiplication algorithms with various physical asset usage patterns. Two were variants of the straight inner product of rows and columns. The rest were variants of Strassen’s divide-and-conquer. Cases varied in ways that were expected [...] Read more.

This work studied the energy behavior of six matrix multiplication algorithms with various physical asset usage patterns. Two were variants of the straight inner product of rows and columns. The rest were variants of Strassen’s divide-and-conquer. Cases varied in ways that were expected to affect energy behavior. The study collected data for square matrix dimensions up to 4000. The research used reliable on-chip integrated voltage regulators embedded in a recent HPC-class AMD CPU for power measurements. Inner product methods used much less energy than the others for small to moderately large matrices. The advantage diminished for sufficiently large dimensions. The power draw of the inner product methods was less for small dimensions. After a point, the power advantage shifted significantly in favor of the divide-and-conquer group (average of 24% better), with the more block-optimized versions showing increased power efficiency (at least 8.3% better than the base method). The study explored the interplay between algorithm design, power efficiency, and computational resources. It aims to help advance the cause of power efficiency in HPC and other scenarios that rely on this vital computation. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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22 pages, 13534 KiB  

Open AccessArticle

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

by Xiaojuan Chen and Zhaohua Zhang

Sensors 2024, 24(3), 1028; https://doi.org/10.3390/s24031028 - 5 Feb 2024

Viewed by 632

Abstract

Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper [...] Read more.

Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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15 pages, 7821 KiB  

Open AccessArticle

The Design, Fabrication, and Evaluation of a Phase-Resolved Partial Discharge Sensor Embedded in a MV-Class Bushing

by Gyeong-Yeol Lee, Nam-Hoon Kim, Dong-Eon Kim, Gyung-Suk Kil and Sung-Wook Kim

Sensors 2023, 23(24), 9844; https://doi.org/10.3390/s23249844 - 15 Dec 2023

Viewed by 636

Abstract

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses [...] Read more.

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 4329 KiB  

Open AccessArticle

High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

by Dong Yang, Baopeng Lu and Huaiwei Lu

Electronics 2023, 12(19), 4066; https://doi.org/10.3390/electronics12194066 - 28 Sep 2023

Cited by 1 | Viewed by 813

Abstract

The detection and selection of fault lines in resonant grounding distribution networks pose challenges due to the lack of sufficient state parameters and data. This paper proposes an approach to overcome these limitations by reconstructing the initial criterion for fault occurrence and fault [...] Read more.

The detection and selection of fault lines in resonant grounding distribution networks pose challenges due to the lack of sufficient state parameters and data. This paper proposes an approach to overcome these limitations by reconstructing the initial criterion for fault occurrence and fault line selection. Firstly, a combination of 15% of the traditional phase voltage and the sum of the zero-sequence voltage gradient is suggested as the initial criterion for fault occurrence. This improves the speed of the line selection device. Additionally, the transient process of high-resistance grounding in a resonant grounding system is analyzed based on the impedance characteristics of high- and low-frequency lines. The line selection criterion is then established by comparing the current and voltage derivative waveforms on high- and low-frequency lines. To verify the effectiveness of the proposed method, simulations are conducted. The results demonstrate that this method can effectively handle high-resistance grounding faults under complex conditions while meeting the required speed for line selection. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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26 pages, 7048 KiB  

Open AccessReview

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

by Veeresh Ramnarine, Vidyadhar Peesapati and Siniša Djurović

Energies 2023, 16(18), 6709; https://doi.org/10.3390/en16186709 - 19 Sep 2023

Viewed by 1152

Abstract

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications [...] Read more.

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 4454 KiB  

Open AccessArticle

A Fuzzy Logic Approach to Health Index Determination for a Gas-Insulated Switchgear

by Nattapon Panmala, Thanapong Suwanasri and Cattareeya Suwanasri

Energies 2023, 16(18), 6605; https://doi.org/10.3390/en16186605 - 13 Sep 2023

Viewed by 1012

Abstract

This paper presents a fuzzy logic approach, a simplified and adaptable method, to determine the health index of gas-insulated switchgear (GIS) bay and their compartments. Since the traditional weighting and scoring method (WSM) is a subjective method, the fuzzy logic approach has been [...] Read more.

This paper presents a fuzzy logic approach, a simplified and adaptable method, to determine the health index of gas-insulated switchgear (GIS) bay and their compartments. Since the traditional weighting and scoring method (WSM) is a subjective method, the fuzzy logic approach has been applied to enhance the accuracy of the health index (HI) determination by evaluating the detectable degradation and the incorporated conditional factor (CF) considering actual operating conditions and invisible ageing. The input data are first obtained from routine inspection and time-based testing and then converted to numerical values as the fuzzy logic model input to compute the component HI. The bay HI values are further calculated by applying the WSM using the obtained component of the HI. Then, the accuracy of the obtained HI of the bay has been improved by multiplying with the CF to obtain the overall bay HI values. The proposed methodology was implemented in an independent power producer in a large industrial estate in Thailand to evaluate 175 GIS bays with actual data. The results were compared against other HI evaluation techniques with a satisfactory outcome. Finally, the overall bay HI is used to prioritize maintenance activity, to effectively allocate human resources, to prevent unplanned outages, and to achieve cost-effective, condition-based maintenance. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 1450 KiB  

Open AccessArticle

An Improved Full-Speed Domain Sensorless Control Scheme for Permanent Magnet Synchronous Motor Based on Hybrid Position Observer and Disturbance Rejection Optimization

by Yi Huang, Mi Zhao, Yunong Wang, Hong Zhang and Min Lu

Electronics 2023, 12(18), 3759; https://doi.org/10.3390/electronics12183759 - 6 Sep 2023

Cited by 2 | Viewed by 963

Abstract

A sensorless control algorithm not only reduces the cost of a permanent magnet synchronous motor (PMSM) system, but also broadens its application scope. Expanding speed threshold and enhancing dynamic performance are crucial aspects. To optimize the adaptability of observers and the immunity of [...] Read more.

A sensorless control algorithm not only reduces the cost of a permanent magnet synchronous motor (PMSM) system, but also broadens its application scope. Expanding speed threshold and enhancing dynamic performance are crucial aspects. To optimize the adaptability of observers and the immunity of the controller in a full-speed domain, an improved sensorless control scheme for a PMSM based on a hybrid position observer and disturbance compensation is proposed. Firstly, the precise detection of the initial position and the scheme of starting with the load at any position are proposed based on high-frequency rotation injection, magnetic pole direction calibration and square-wave high-frequency injection (HFI). Secondly, a higher-order sliding mode observer (HSMO) is designed to improve high-speed observation performance by introducing an extended electromotive force (EEMF). Correspondingly, a speed controller called PI plus is developed utilizing a reverse control algorithm and the observed disturbance quantity, which further enhances the system’s disturbance rejection capability. Subsequently, a linearly weighted observer switching method and a linear signal withdrawal scheme are proposed to suppress torque and speed oscillations in medium-speed threshold. Furthermore, a normalized linear extended state observer (LESO) is designed to enhance rotor information estimation accuracy and enable the observation of unknown disturbances in full-speed thresholds. Finally, the effectiveness of the proposed sensorless control system is tested through experiments involving variations in speed, load, and parameter. The experimental results indicate that the proposed sensorless strategy is capable of achieving a loaded start. The designed observer switching strategy and the scheme of injection signal withdrawal contribute to a smoother acceleration process. Furthermore, load variation test results at high-speed thresholds demonstrate that the proposed controller can reduce speed drop by 45 rpm compared to a traditional PI. Additionally, the results of parameter variation testing validate the observer’s robustness in the disturbances of ${\psi }_f$ within the range of ±0.3 pu. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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14 pages, 1629 KiB  

Open AccessArticle

Narrow Band Frequency Response Analysis of Power Transformers with Deep Learning

by Micah Phillip, Arvind Singh and Craig J. Ramlal

Energies 2023, 16(17), 6347; https://doi.org/10.3390/en16176347 - 1 Sep 2023

Viewed by 940

Abstract

Frequency response analysis (FRA) is a standard technique for monitoring the integrity of the mechanical structure of power transformer windings. To date, however, there remains no suitable method for online testing using this technique. One of the main issues that persists is that [...] Read more.

Frequency response analysis (FRA) is a standard technique for monitoring the integrity of the mechanical structure of power transformer windings. To date, however, there remains no suitable method for online testing using this technique. One of the main issues that persists is that any hardware designed to measure the frequencies in the range of interest would filter out frequency bands used for assessment by humans. The growth of pattern recognition capabilities in deep learning networks, however, now offers the possibility of detecting different types of faults in a narrow frequency band, which is simply not possible for human experts. This paper explores the ability of a selection of typical networks to classify common faults within different bands. The results show that networks are able to identify faults in bands where humans are unable to find them, which has implications for signal processing and electronics design in developing a system for online monitoring. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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17 pages, 4529 KiB  

Open AccessArticle

An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer

by Shuaibing Li, Lilong Dou, Hongwei Li, Zongying Li and Yongqiang Kang

Electronics 2023, 12(17), 3573; https://doi.org/10.3390/electronics12173573 - 24 Aug 2023

Cited by 2 | Viewed by 834

Abstract

This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve [...] Read more.

This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve the precision of fault diagnosis. To begin with, an experimental platform was used to simulate the OLTC contact, enabling the collection of mechanical vibration signals and high-frequency current signals under different operational states. These signals underwent wavelet packet transform for denoising, followed by correlation analysis to investigate their interrelationships across various states. Features were then extracted and analyzed using ensemble empirical mode decomposition and the Hilbert–Huang transform. Subsequently, a support vector machine (SVM) was employed to analyze both the mechanical vibration signal and high-frequency current signal, facilitating the classification of the OLTC contact state. The results demonstrated that the joint analysis of electrical and mechanical signals provided a comprehensive representation of the actual contact state under different conditions. The SVM classification achieved an error below 10% in predicting the values of the two signal types, validating the efficiency and feasibility of the proposed fault diagnosis method for OLTC contacts. The findings presented in this paper offer valuable insights for on-site fault diagnosis of practical OLTCs. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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15 pages, 6237 KiB  

Open AccessArticle

Effect of Interface Defects on the Harmonic Currents in Distribution Cable Accessories under Damp Conditions

by Ran Hu, Weixin Sun, Xu Lu, Feng Tang, Zhifeng Xu, Jie Tian, Daning Zhang and Guochang Li

Coatings 2023, 13(8), 1430; https://doi.org/10.3390/coatings13081430 - 15 Aug 2023

Cited by 2 | Viewed by 703

Abstract

Dampness is one of the most important factors leading to the degradation of the insulation performance of cable accessories; it may easily lead to interface defects of composite insulation. In this paper, an electromagnetic field defect model of the cable composite insulation interface [...] Read more.

Dampness is one of the most important factors leading to the degradation of the insulation performance of cable accessories; it may easily lead to interface defects of composite insulation. In this paper, an electromagnetic field defect model of the cable composite insulation interface is established, and the effects of the defect degree and the defect type on the magnetic flux and the harmonic current are studied. The results show that the magnetic field is uniformly distributed with a decreasing trend when the cable is in good condition, and the grounding current has no obvious harmonic component. For different defects, the magnetic field changes at the defect location are notably different; for water droplets and the water film, the magnetic field distortion is obvious at the location of the defect and sheath, and for water tree defects, the distortion of the magnetic field is significant. In addition, the degree of distortion caused by the same defect at different positions is also different, and the magnetic field contrast is not obvious at different degrees of moisture exposure. The distortion of the grounding current caused by the water tree is higher than that of the water droplet and the water film, and the characteristics of the harmonic components of the grounding current caused by different defects have obvious distinctions. The type of cable defect can be judged by the proportion of harmonic components. This work can be used as a reference for the assessment of the cable insulation defect status. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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18 pages, 1142 KiB  

Open AccessArticle

A Hierarchical Energy Control Strategy for Hybrid Electric Vehicle with Fuel Cell/Battery/Ultracapacitor Combining Fuzzy Controller and Status Regulator

by Xiaorui Jia and Mi Zhao

Electronics 2023, 12(16), 3428; https://doi.org/10.3390/electronics12163428 - 14 Aug 2023

Cited by 1 | Viewed by 1036

Abstract

In order to improve the fuel economy of fuel cell hybrid electric vehicles (FCHEV), a hierarchical energy management strategy (HEMS) is proposed to rationally allocate the required power to a hybrid power system with three energy sources: fuel cell, battery, and ultracapacitor. First [...] Read more.

In order to improve the fuel economy of fuel cell hybrid electric vehicles (FCHEV), a hierarchical energy management strategy (HEMS) is proposed to rationally allocate the required power to a hybrid power system with three energy sources: fuel cell, battery, and ultracapacitor. First of all, batteries and ultracapacitors are regarded as energy storage systems (ESS), which convert the distribution problem from three energy sources to two couples of energy sources. Secondly, fuzzy logic controllers are utilized in upper-layer energy management strategies (EMS) to distribute required power to fuel cell systems and the ESS. To extend the service life of the fuel cell and increase the maintenance ability of the state of charge (SOC) of the battery, a status regulation module is introduced to allocate the required power combined with fuzzy controller. Thirdly, an adaptive low-pass filter is applied to a lower-layer EMS based on the energy characteristics of the ultracapacitor, which fully utilizes the ultracapacitor. Finally, the economic and dynamic performance of the vehicle are compared between the HEMS and the power following strategy (PFS) under five typical cycle conditions: UDDS, WVUINTER, NEDC, HWFET and COMBINE. The results of the simulation show that the hydrogen consumption of the HEMS is reduced and the overall vehicle energy efficiency is increased in four operating conditions, which indicates that the proposed strategy has better economic performance. In addition, the dynamic performance of the vehicle is also improved. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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15 pages, 4332 KiB  

Open AccessArticle

A Fault Diagnosis Method Based on a Rainbow Recursive Plot and Deep Convolutional Neural Networks

by Xiaoyuan Wang, Xin Wang, Tianyuan Li and Xiaoxiao Zhao

Energies 2023, 16(11), 4357; https://doi.org/10.3390/en16114357 - 26 May 2023

Cited by 1 | Viewed by 1048

Abstract

In previous deep learning-based fault diagnosis methods for rotating machinery, the method of directly feeding one-dimensional data into convolutional neural networks can lead to the loss of important fault features. To address the problem, a novel rotating machinery fault diagnosis model based on [...] Read more.

In previous deep learning-based fault diagnosis methods for rotating machinery, the method of directly feeding one-dimensional data into convolutional neural networks can lead to the loss of important fault features. To address the problem, a novel rotating machinery fault diagnosis model based on a rainbow recursive plot (RRP) is proposed. Our main innovation and contributions are: First, a RRP is proposed to convert the one-dimensional vibration signal from the rotating machinery into a two-dimensional color image, facilitating the capturing of more significant fault information. Second, a new CNN based on LeNet-5 is devised, which extracts a feature that describes substantial fault information from the converted two-dimensional color image, thus performing fault diagnosis recognition accurately. The public rolling bearing datasets and the online fault diagnosis platform are adopted to verify proposed method performance. Experiments on public datasets show that the proposed method can improve the accurate rate of recognition to 97.86%. More importantly, online experiment on the self-made fault diagnosis platform demonstrates that our approach achieves the best comprehensive performance in terms of recognition speed and accuracy compared to mainstream algorithms. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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24 pages, 4333 KiB  

Open AccessReview

Review of Transformer Health Index from the Perspective of Survivability and Condition Assessment

by Shuaibing Li, Xinchen Li, Yi Cui and Hongwei Li

Electronics 2023, 12(11), 2407; https://doi.org/10.3390/electronics12112407 - 25 May 2023

Cited by 3 | Viewed by 3630

Abstract

As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further [...] Read more.

As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further study or to guide transformer asset management, this paper summarizes the state-of-the-art of the transformer health index, from the early proposed weighted-score-sum approaches to the more recently proposed artificial intelligence algorithm-based methods. Firstly, different methods for determining the transformer health index are reviewed. Each of these is specified as belonging to a certain type on the basis of its formulation and composition schematic. Subsequently, the steps to determine each type of health index are summarized, and examples derived from literature are provided for further illustration. Comparisons are finally carried out in order to better understand the pros and cons of different types of transformer health index, and the future development trends for transformer health indexes are also discussed. This work can serve as a valuable reference for the survivability and condition assessment of transformers in the power industry. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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17 pages, 2334 KiB  

Open AccessArticle

Analysis and Multiobjective Optimization of a Machine Learning Algorithm for Wireless Telecommunication

by Samah Temim, Larbi Talbi and Farid Bensebaa

Telecom 2023, 4(2), 219-235; https://doi.org/10.3390/telecom4020013 - 17 Apr 2023

Viewed by 2270

Abstract

There has been a fast deployment of wireless networks in recent years, which has been accompanied by significant impacts on the environment. Among the solutions that have been proven to be effective in reducing the energy consumption of wireless networks is the use [...] Read more.

There has been a fast deployment of wireless networks in recent years, which has been accompanied by significant impacts on the environment. Among the solutions that have been proven to be effective in reducing the energy consumption of wireless networks is the use of machine learning algorithms in cell traffic management. However, despite promising results, it should be noted that the computations required by machine learning algorithms have increased at an exponential rate. Massive computing has a surprisingly large carbon footprint, which could affect its real-world deployment. Thus, additional attention needs to be paid to the design and parameterization of these algorithms applied in order to reduce the energy consumption of wireless networks. In this article, we analyze the impact of hyperparameters on the energy consumption and performance of machine learning algorithms used for cell traffic prediction. For each hyperparameter (number of layers, number of neurons per layer, optimizer algorithm, batch size, and dropout) we identified a set of feasible values. Then, for each combination of hyperparameters, we trained our model and analyzed energy consumption and the resulting performance. The results from this study reveal a great correlation between hyperparameters and energy consumption, confirming the paramount importance of selecting optimal hyperparameters. A tradeoff between the minimization of energy consumption and the maximization of machine learning performance is suggested. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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19 pages, 4639 KiB  

Open AccessArticle

A Robust Fault Diagnosis Scheme for Converter in Wind Turbine Systems

by Jinping Liang and Ke Zhang

Electronics 2023, 12(7), 1597; https://doi.org/10.3390/electronics12071597 - 29 Mar 2023

Cited by 1 | Viewed by 944

Abstract

Fault diagnosis is a powerful tool to reduce downtime and improve maintenance efficiency; thus, the low management cost of wind turbine systems and effective utilization of wind energy can be obtained. However, the accuracy of fault diagnosis is extremely susceptible to the nonlinearity [...] Read more.

Fault diagnosis is a powerful tool to reduce downtime and improve maintenance efficiency; thus, the low management cost of wind turbine systems and effective utilization of wind energy can be obtained. However, the accuracy of fault diagnosis is extremely susceptible to the nonlinearity and noise in the measured signals and the varying operating conditions. This paper proposes a robust fault diagnosis scheme based on ensemble empirical mode decomposition (EEMD), intrinsic mode function (IMF), and permutation entropy (PE) to diagnose faults in the converter in wind turbine systems. Three-phase voltage signals output by the converter are used as the input of the fault diagnosis model and each signal is decomposed into a set of IMFs by EEMD. Then, the PE is calculated to estimate the complexity of the IMFs. Finally, the IMF-PE information is taken as the feature of the classifier. The EEMD addresses nonlinear signal processing and mitigates the effects of mode mixing and noise. The PE increases the robustness against variations in the operating conditions and signal noise. The effectiveness and reliability of the method are verified by simulation. The results show that the accuracy for 22 faults reaches about 98.30% with a standard deviation of approximately 2% under different wind speeds. In addition, the average accuracy of 30 runs for different noises is higher than approximately 76%, and the precision, recall, specificity, and F1-Score all exceed 88% at 10 dB. The standard deviation of all the evaluation indicators is lower than about 1.7%; this proves the stable diagnostic performance. The comparison with different methods demonstrates that this method has outstanding performance in terms of its high accuracy, strong robustness, and computational efficiency. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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19 pages, 2743 KiB  

Open AccessArticle

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

by Jinping Liang and Ke Zhang

Electronics 2023, 12(5), 1263; https://doi.org/10.3390/electronics12051263 - 6 Mar 2023

Cited by 5 | Viewed by 1215

Abstract

Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy [...] Read more.

Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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11 pages, 2685 KiB  

Open AccessArticle

Influence of Thermal Aging on Dielectric Properties of High Voltage Cable Insulation Layer

by Boyuan Liang, Rui Lan, Qian Zang, Zhen Liu, Lin Tian, Zhaochen Wang and Guochang Li

Coatings 2023, 13(3), 527; https://doi.org/10.3390/coatings13030527 - 27 Feb 2023

Cited by 4 | Viewed by 2009

Abstract

Thermal aging is one of the main reasons for the degradation of insulation properties of high voltage cable. Dielectric properties and breakdown strength are important parameters to reflect the insulation performance of the cable insulation materials. In the work, the influence of thermal [...] Read more.

Thermal aging is one of the main reasons for the degradation of insulation properties of high voltage cable. Dielectric properties and breakdown strength are important parameters to reflect the insulation performance of the cable insulation materials. In the work, the influence of thermal aging on dielectric and breakdown performance of the cable insulation layer was studied. Firstly, XLPE cable insulation samples were prepared and the thermal aging treatment was carried out. Secondly, the microstructure and molecular structure of XLPE samples under different thermal aging time were analyzed. The dielectric properties and breakdown characteristics of XLPE samples under different thermal aging times were characterized in macro aspect. Finally, the effects of different temperatures on the molecular microstructure of XLPE were studied. The results show that with the extension of thermal aging time, the microstructure of XLPE molecule is destroyed, the macromolecular chain is gradually cleaved, and the carbonyl absorption intensity increases. At power frequency, the breakdown strength decreases from 75.37 kV/mm to 62.18 kV/mm, the relative permittivity increases from 2.44 to 2.51, and the dielectric loss increases from 1.47 × 10⁻⁴ to 3.10 × 10⁻³. The free volume rate of XLPE molecules increases with the increasing temperature, and the mean square displacement gradually increases. The work has good guiding significance for the safe operation and condition assessment of high-voltage cables. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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16 pages, 3060 KiB  

Open AccessArticle

Passive Backstepping Control of Dual Active Bridge Converter in Modular Three-Port DC Converter

by Xin Li and Xiaodong Fang

Electronics 2023, 12(5), 1074; https://doi.org/10.3390/electronics12051074 - 21 Feb 2023

Cited by 1 | Viewed by 1214

Abstract

A dual active bridge (DAB) converter in a modular three-port DC converter is the key equipment to connect distributed energy and energy storage units and realize its efficient and large-scale utilization. When a DAB converter with traditional control is disturbed by the input [...] Read more.

A dual active bridge (DAB) converter in a modular three-port DC converter is the key equipment to connect distributed energy and energy storage units and realize its efficient and large-scale utilization. When a DAB converter with traditional control is disturbed by the input voltage of distributed energy sources, some problems occur, such as large fluctuation of load voltage and slow dynamic response. In order to address such problems, this paper firstly starts with the single-phase shift control of the DAB converter, establishes the dynamic mathematical model of the DAB converter according to the nonlinear characteristics of the converter, transforms it into the passive form of Euler–Lagrange (E-L) model and designs the passive controller based on the analysis of the passive nature and stability of the converter, in order to improve the energy dissipation rate and ensure the global stability of the system. Secondly, in conjunction with the backstepping control, a passive backstepping controller is designed with the goal of shifting the comparison to eliminate errors caused by input disturbances and achieve fast-tracking of the reference voltage. Finally, a DAB simulation model based on passive backstepping control is established in Matlab/Simulink. By selecting the appropriate injection damping value, it is compared with traditional PI control and passivity-based control strategy, and the effectiveness of forward and reverse power transmission modes of the DAB converter under passive backstepping control is verified. The results show that the DAB converter with passive backstepping control has better dynamic performance and stronger robustness after sudden changes in input voltage. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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13 pages, 2086 KiB  

Open AccessArticle

Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors

by Zhiyong Zhou, Junzhong Sun, Wei Cai and Wen Liu

Energies 2023, 16(2), 699; https://doi.org/10.3390/en16020699 - 6 Jan 2023

Cited by 2 | Viewed by 1150

Abstract

In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the [...] Read more.

In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the root mean square of vibration velocity, as well as resonant demodulated spectrum analysis of vibration acceleration, have been widely used for bearing fault diagnosis. These are integrated in almost any commercially available device for diagnosing bearing faults. However, the unsuitable use of these devices results in many false diagnoses. In light of this, they were selected as research objects and were investigated experimentally. In three induction motors, faults of different severity in the bearing outer race and cage were modeled for tests, and the corresponding results were used to evaluate the performance of the selected diagnosis methods. Some vague information in engineering was clarified, and some instructive rules were outlined to improve the bearing fault diagnosis performance. Taking the kurtosis coefficient of vibration acceleration (K_u) as an example, in engineering, K_u = 4 is generally taken as the diagnostic threshold of bearing faults. This means the following rule applies: if K_u ≤ 4, the bearing is healthy; otherwise, the bearing is faulty. However, the test results in this paper show that even if K_u ≤ 4, the bearing might be faulty; if K_u > 4, the bearing is indeed faulty. Therefore, the diagnostic rule should be improved as follows: if K_u > 4, the bearing is faulty (which can be assured), and if K_u ≤ 4, the status of the bearing is still undetermined. Thus, this paper can be helpful for researchers to gain an experimental understanding of the selected diagnosis methods and provides some improved rules on their use for reducing false diagnoses. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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17 pages, 10690 KiB  

Open AccessArticle

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

by Qingzhu Wan, Yimeng Li, Runjiao Yuan, Qinghai Meng and Xiaoxue Li

Sensors 2023, 23(2), 684; https://doi.org/10.3390/s23020684 - 6 Jan 2023

Cited by 2 | Viewed by 1556

Abstract

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is [...] Read more.

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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10 pages, 13808 KiB  

Open AccessArticle

Partial Discharge Detection and Defect Location Method in GIS Cable Terminal

by Songyuan Li, Pengxian Song, Zhanpeng Wei, Xu Li, Qinghua Tang, Zhengzheng Meng, Ji Li, Songtao Liu, Yuhuai Wang and Jin Li

Energies 2023, 16(1), 413; https://doi.org/10.3390/en16010413 - 29 Dec 2022

Cited by 4 | Viewed by 1616

Abstract

The complex structure of gas-insulated switchgear (GIS) cable terminals leads to serious electric field concentration, which is a frequent fault position of a high-voltage cable system. At present, due to the differences in the frequency bands of sensors, various partial discharge detection technologies [...] Read more.

The complex structure of gas-insulated switchgear (GIS) cable terminals leads to serious electric field concentration, which is a frequent fault position of a high-voltage cable system. At present, due to the differences in the frequency bands of sensors, various partial discharge detection technologies have certain differences in their scope of application and anti-interference performance, resulting in a low defect detection rate in GIS cable terminals. In this paper, a comprehensive diagnosis scheme is proposed, which integrates transient earth voltage (TEV), ultra-high frequency (UHF), high frequency (HF), and ultrasonic methods. Two abnormal discharge defects of GIS terminals in two 220 kV substations in Tianjin were tracked and monitored, and the joint diagnosis was carried out using the proposed scheme; the type of discharge defect and the phase sequence of the defect were determined, and the UHV was employed to precisely locate and analyze the defect source. Finally, through the disassembly analysis and electric field simulation of the GIS cable terminal, the accuracy and effectiveness of the discharge detection and location method were verified, providing a typical detection demonstration for the defect diagnosis of a GIS cable terminal. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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26 pages, 7475 KiB  

Open AccessFeature PaperReview

Insulation Degradation Mechanism and Diagnosis Methods of Offshore Wind Power Cables: An Overview

by Baopeng Lu, Shuaibing Li, Yi Cui, Xiaowei Zhao, Daqi Zhang, Yongqiang Kang and Haiying Dong

Energies 2023, 16(1), 322; https://doi.org/10.3390/en16010322 - 28 Dec 2022

Cited by 3 | Viewed by 3730

Abstract

The marine environment in which offshore wind turbines are located is very complex and subjected to a variety of random loads that vary with time and space. As an important component of offshore wind power, the cable also bears the impact of the [...] Read more.

The marine environment in which offshore wind turbines are located is very complex and subjected to a variety of random loads that vary with time and space. As an important component of offshore wind power, the cable also bears the impact of the environment in which most of the turbines are located. Under the long-term action of mechanical stresses such as tension, torsion, and vibration, the cable insulation will crack due to stress fatigue leading to partial discharge, which seriously affects its electrical performance. The study of the mechanism of the change of electrical properties of cable insulation due to mechanical behavior is of great theoretical guidance to improve the reliable operation of cables. This paper first introduces the basic characteristics and operating conditions of torsion-resistant cables and submarine cables. Then the mechanical behavior of the cables is summarized, and the deterioration mechanism and deterioration effect of wind power cable insulation under the influence of multiple factors such as heat, oxygen, and mechanical stress are sorted out. Then, the basic principles of wind power cable operation condition monitoring methods and their characteristics are described. Finally, the relevant methods for the detection of hidden defects inside the insulation are summarized. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 7180 KiB  

Open AccessArticle

Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images

by Junjie Wang, Li Bi, Pengxiang Sun, Xiaogang Jiao, Xunde Ma, Xinyi Lei and Yongbin Luo

Sensors 2023, 23(1), 297; https://doi.org/10.3390/s23010297 - 27 Dec 2022

Cited by 6 | Viewed by 4358

Abstract

Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks effective technical means. In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: [...] Read more.

Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks effective technical means. In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category weight assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a feature fusion method based on ResNet152–Xception. A coordinate attention (CA) mechanism is incorporated into the feature map to enhance the feature extraction capability of the existing model. The proposed model was conducted on two global publicly available PV-defective electroluminescence (EL) image datasets, and using CNN, Vgg16, MobileNetV2, InceptionV3, DenseNet121, ResNet152, Xception and InceptionResNetV2 as comparative benchmarks, it was evaluated that several metrics were significantly improved. In addition, the accuracy reached 96.17% in the binary classification task of identifying the presence or absence of defects and 92.13% in the multiclassification task of identifying different defect types. The numerical experimental results show that the proposed deep-learning-based defect detection method for PV cells can automatically perform efficient and accurate defect detection using EL images. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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17 pages, 5086 KiB  

Open AccessArticle

Study on Current-Carrying Tribological Characteristics of C-Cu Sliding Electric Contacts under Different Water Content

by Hong Wang, Guoqiang Gao, Lei Deng, Xiaonan Li, Xiao Wang, Qingsong Wang and Guangning Wu

Coatings 2023, 13(1), 42; https://doi.org/10.3390/coatings13010042 - 26 Dec 2022

Cited by 1 | Viewed by 1509

Abstract

Previous studies have often observed that moisture can promote the lubricity and wear resistance of carbon-metal contact pairs in purely mechanical conditions. However, the damage to pantograph carbon strips was found to be aggravated in rainfall conditions, leading to a much lower service [...] Read more.

Previous studies have often observed that moisture can promote the lubricity and wear resistance of carbon-metal contact pairs in purely mechanical conditions. However, the damage to pantograph carbon strips was found to be aggravated in rainfall conditions, leading to a much lower service life than anticipated. This suggests a novel influence mechanism of water on carbon-copper (C-Cu) contacts during current-carrying friction. In this paper, the influence mechanism of water on the current-carrying friction characteristics of carbon-copper contacts, including friction coefficient, wear loss, electrical contact resistance, and arc discharge characteristics, was studied under different current levels by controlling the water content of carbon sliders. The results show that the variation trend of current-carrying tribological parameters of C-Cu contacts with water content at 60–100 A is significantly different from that at 20–40 A, which is mainly the result of the competition of lubrication, cooling, and obstruction of current transmission by moisture. The abnormal wear of carbon sliders in the water environment occurs when the current is greater than 60 A, and the main reason for the abnormal wear is the intensification of discharge erosion. In addition, micro-crack propagation under high water content is an important factor in the deterioration of carbon strip properties. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 7597 KiB  

Open AccessArticle

Feasibility of Photovoltaic Module Single-Diode Model Fitting to the Current–Voltage Curves Measured in the Vicinity of the Maximum Power Point for Online Condition Monitoring Purposes

by Heidi Kalliojärvi, Kari Lappalainen and Seppo Valkealahti

Energies 2022, 15(23), 9079; https://doi.org/10.3390/en15239079 - 30 Nov 2022

Cited by 4 | Viewed by 1172

Abstract

Photovoltaic system condition monitoring can be performed via single-diode model fitting to measured current–voltage curves. Model parameters can reveal cell aging and degradation. Conventional parameter identification methods require the measurement of entire current–voltage curves, causing interruptions in energy production. Instead, partial curves measured [...] Read more.

Photovoltaic system condition monitoring can be performed via single-diode model fitting to measured current–voltage curves. Model parameters can reveal cell aging and degradation. Conventional parameter identification methods require the measurement of entire current–voltage curves, causing interruptions in energy production. Instead, partial curves measured near the maximum power point offer a promising option for online condition monitoring. Unfortunately, measurement data reduction affects fitting and diagnosis accuracy. Thus, the optimal selection of maximum power point neighbourhoods used for fitting requires a systematic analysis of the effect of data selection on the fitted parameters. To date, only one published article has addressed this issue with a small number of measured curves using symmetrically chosen neighbourhoods with respect to the maximum power. Moreover, no study has determined single-diode fit quality to partial curves constructed via other principles, e.g., as a percentage of the maximum power point voltage. Such investigation is justified since the voltage is typically the inverter reference quantity. Our work takes the study of this topic to a whole new scientific level by systematically examining how limiting the current–voltage curve measuring range to maximum power point proximity based on both power and voltage affects single-diode model parameters. An extensive dataset with 2400 measured curves was analysed, and statistically credible results were obtained for the first time. We fitted the single-diode model directly to experimental curves without measuring outdoor conditions or using approximations. Our results provide clear guidance on how the choices of partial curves affect the fitting accuracy. A significant finding is that the correct selection of maximum power point neighbourhoods provides promising real-case online aging detection opportunities. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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18 pages, 5406 KiB  

Open AccessArticle

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

by Aaron J. Wilson, Bruce R. J. Warmack, Ali Riza Ekti and Yilu Liu

Sensors 2022, 22(22), 8827; https://doi.org/10.3390/s22228827 - 15 Nov 2022

Viewed by 1141

Abstract

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. [...] Read more.

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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17 pages, 5020 KiB  

Open AccessArticle

Influence of Interface Temperature on the Electric Contact Characteristics of a C-Cu Sliding System

by Hong Wang, Guoqiang Gao, Deng Lei, Qingsong Wang, Song Xiao, Yunlong Xie, Zhilei Xu, Yaguang Ma, Keliang Dong, Qichen Chen and Guangning Wu

Coatings 2022, 12(11), 1713; https://doi.org/10.3390/coatings12111713 - 10 Nov 2022

Cited by 3 | Viewed by 2074

Abstract

Electrical contact resistance (ECR) and discharge are the key parameters of electrical contact performance for carbon-copper (C-Cu) contacts in the pantograph-contact line system. The change in physical and chemical properties of the C-Cu interface caused by interface temperature is the main reason for [...] Read more.

Electrical contact resistance (ECR) and discharge are the key parameters of electrical contact performance for carbon-copper (C-Cu) contacts in the pantograph-contact line system. The change in physical and chemical properties of the C-Cu interface caused by interface temperature is the main reason for the variation in ECR and discharge. In this paper, an electric contact test platform based on interface temperature control was established. The influence of interface temperature on ECR and the discharge characteristics under different current amplitudes were studied. There are opposite trends in the change in ECR and the discharge characteristics with interface temperature under different currents, which results from the competition between interface oxidation and a softening of the contact spots caused by high temperature. The trend of interface oxidation with temperature was analyzed via the quantitative analysis of the composition and content of the oxides at the C-Cu contact interface and is discussed here. The relationship between interface oxidation, ECR, and discharge characteristics was studied. Furthermore, a finite element simulation model was established for estimating the temperature distribution throughout the C-Cu contact spots. The competitive process of the softening and oxidation of the contact spots at different temperatures and currents was analyzed, and the variation mechanism of the ECR and discharge characteristics with interface temperature was studied. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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21 pages, 7384 KiB  

Open AccessArticle

A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators

by Luis O. S. Grillo, Carlos A. C. Wengerkievicz, Nelson J. Batistela, Patrick Kuo-Peng and Luciano M. de Freitas

Sensors 2022, 22(22), 8631; https://doi.org/10.3390/s22228631 - 9 Nov 2022

Viewed by 1571

Abstract

Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this [...] Read more.

Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected. Full article

(This article belongs to the Topic Condition Monitoring and Diagnostic Methods for Power Equipment in New Energy Power Systems)

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