Search Results (69)

To address the challenges of equipment reliability assessment in the context of intelligent power systems, especially the shortcomings of traditional methods in dealing with multi-factor coupling and uncertain fault inference of CVT (capacitive voltage transformer) online monitoring devices, this study proposes a reliability analysis method based on Bayesian networks (BNs). The research aims to evaluate the reliability of CVT online monitoring devices, identify key risk factors, and optimize maintenance strategies. Firstly, a Bayesian network reliability model is constructed for the CVT online monitoring device, defining key influencing factors such as environmental factors and component quality as network nodes, and establishing conditional probability dependency relationships between nodes. Subsequently, the MATLAB R2021b simulation platform was used to simulate the system’s operating status under different combinations and scenarios. The experimental results indicate that the combination of high-temperature and high-humidity environments has the most significant impact on reliability; among the component factors, the failure of the data acquisition and processing unit has the greatest impact on system reliability; wiring process issues pose a greater threat to reliability than mechanical fixing issues; and regular maintenance can significantly improve system reliability. This method validates the effectiveness of Bayesian networks in dynamic reliability analysis of CVT online monitoring devices, which can accurately locate high-risk factors and support maintenance decision optimization. Full article

In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q_3e) is estimated from the differential pressure (ΔP) and battery voltage (V_b), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q_3e, calculated solely from ΔP and V_b, showed high agreement with the measured flow Q_3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q_3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and V_b. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information. Full article

The health state of satellite power systems plays a critical role in ensuring the normal operation of satellite platforms. This paper proposes an improved Transformer-GRU-based algorithm for satellite power status detection, which characterizes the operational condition of power systems by utilizing voltage and temperature data from battery packs. The proposed method enhances the original Transformer architecture through an integrated attention network mechanism that dynamically adjusts attention weights to strengthen feature spatial correlations. A gated recurrent unit (GRU) network with cyclic structures is innovatively adopted to replace the conventional Transformer decoder, enabling efficient computation while maintaining temporal dependencies. Experimental results on satellite power system status detection demonstrate that the modified Transformer-GRU model achieves superior detection performance compared to baseline approaches. This research provides an effective solution for enhancing the reliability of satellite power management systems and opens new research directions for future advancements in space power system monitoring technologies. Full article

With the rapid advancement of digitalization and intelligence in power systems, SF₆ high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. Their operational status directly impacts the reliability of power system protection. Therefore, real-time condition monitoring and accurate assessment of SF₆ circuit breakers along with science-based maintenance strategies derived from evaluation results hold significant engineering value for ensuring secure and stable grid operation and preventing major failures. In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF₆ high-voltage circuit breakers. To address this, we propose an operational status assessment model for SF₆ high-voltage circuit breakers based on an Integrated Attribute-Weighted Risk Model Based on the Branch–Trunk Rule (IAR-BTR), which integrates internal and environmental influences. Firstly, to tackle the issues of incomplete data and feature imbalance caused by irrelevant attributes, this study employs missing value elimination (Drop method) on the fault record database. The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. To improve the accuracy of these rules, the filtering thresholds and association metrics are refined based on seasonal distribution and the importance of time periods. This allows for the identification of spatiotemporally non-stationary factors that are strongly correlated with circuit breaker failures in low-probability seasonal conditions. Finally, a quantitative weighting method is developed for analyzing branch-trunk rules to accurately assess the impact of various factors on the overall stability of the circuit breaker. The DFP-Growth algorithm is applied to enhance the computational efficiency of the model. The case study results demonstrate that the proposed method achieves exceptional accuracy (95.78%) and precision (97.22%) and significantly improves the predictive performance of SF₆ high-voltage circuit breaker operational condition assessments. Full article

By using a soft normal open point (SNOP) to connect multiple distribution networks to form a flexible interconnected distribution system (FIDS), the power distribution can be flexibly and controllably regulated among distribution stations, but it is also necessary to ensure the system’s operational efficiency and maintain voltage quality when carrying out optimal scheduling. In this paper, a FIDS optimal scheduling strategy considering dynamic weight grading is proposed. By considering the voltage overrun status of each distribution station area, the voltage level of each distribution station area is divided into three voltage overrun situations, including normal operation, safe boundary, and protection boundary levels, and an optimal scheduling model applicable to the multi-level operation of the FIDS is constructed. In order to adapt to the coordinated optimal operation objectives under different overrun levels, an optimal operation strategy considering the dynamic weights of system operation cost, voltage deviation, customer satisfaction, and SNOP regulation capability is proposed and finally simulated and verified using the improved IEEE33 node arithmetic case. The results verify the effectiveness of the method proposed in this paper in improving the system’s operational efficiency and node voltage quality. Full article

Under the transition towards electrification and intelligence in modern automotive industry, the health status of low-voltage wiring harnesses directly affects vehicle performance and safety. To address the challenge of predicting performance degradation caused by multi-physics coupling effects during wiring harness aging, this study proposes a CNN-BiLSTM-Attention hybrid neural network model. By capturing voltage, current, and temperature parameters during low-voltage system operation, the model combines CNN’s local feature extraction, BiLSTM’s temporal sequence analysis, and attention mechanisms to predict aging levels. Accelerated aging experiments were conducted to obtain wiring harnesses with different degradation levels from new to 720 h aged states, and a dedicated experimental platform was built for data collection and verification. The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance. Full article

With the rapid development of electric vehicles (EVs), vehicle-to-grid has become a common way to participate in grid regulation. However, in the traditional vehicle-to-grid strategy, the disorganized or coercive regulatory characteristics of EVs always affect the overall satisfaction of EV users and the safe and economic operation of the distribution network. It is challenging to balance the interests of road network subjects. For this reason, this paper proposes an orderly charging and discharging strategy for electric vehicles with integrated consideration of user and distribution grid benefits. First, a comprehensive EV user satisfaction model that considers the vehicle owner’s travel costs is established by considering the vehicle’s travel status and the road resistance characteristics of the road network. Further, the EV orderly charging and discharging model is established to optimize the operation cost of the distribution network, voltage deviation, and EV users’ comprehensive satisfaction, which takes into account the vehicle owner’s satisfaction and the stable operation of the distribution network. Finally, the proposed strategy is validated using the IEEE 33-node arithmetic example. The results show that the peak-to-valley load difference of the distribution network under the strategy of this paper is 29.52% lower than that under the EV non-participation regulation strategy. Compared with the EV non-participation strategy, it can effectively reduce the single-day operation cost of the system by 2.47%. Full article

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It is shown how signals generated by very sensitive piezo sensor assemblies, secured to the metallic elements, can allow for space localization of excitations and defects in the analyzed structures. The origin of the piezo excitations are acoustic modes generated by light percussive excitations whose strength is of the order of tenths of a newton and that provide piezo signal amplitudes of a few hundred millivolts. Tests of the detection scheme of the excitations are performed on steel ropes, iron pipes, and bars with lengths in the range of 1–6 m with the sensor output signal shaped in the form of a clean pulse. It is shown that the signals generated by the piezo assemblies, when adequately shaped, can feed the input of an RF transmitter, which in turn transfers information to a remote receiver whose readout allows for remotely analyzing information collected on the metallic elements. Considering the voltage amplitude of the signals (of the order of 300 mV) generated by the piezo sensors as a result of very light percussive excitations, the low power required for transmitting data, and the low cost of the sensing and transmitting assembly, it is conceivable that our devices could detect excitations generated even tens of kilometers away and allow for setting up an array of sensors for controlling in real time the status of pipe networks. Full article

Standards describing the test procedures recommended to investigate the shielding effectiveness of enclosures have two major issues: they generally prescribe the assessment of the electromagnetic field of empty cavities, and they do not deal with very small enclosures. However, the dimensions of some very common shielded apparatus are smaller than those considered in the standards and the electromagnetic field distribution inside the shielded structure is strongly affected by the enclosure content. In this paper, both issues have been investigated for two components commonly used in medium voltage/low voltage (MV/LV) substations: a mini personal computer used to store, process, and transmit relevant data on the status of the electric network, with these aspects being essential in smart grids, and an electronic relay which is ubiquitous in MV/LV substations. Both components are partially contained in a metallic enclosure which provides a certain amount of electromagnetic shielding against external interferences. It is observed that an electrostatic discharge may cause a failure and/or a loss of data, requiring an improvement of shielding characteristics or a wise choice of the positions where the most sensitive devices are installed inside the enclosure. Since the dimensions of very small enclosures, fully occupied by their internal components, do not allow for the insertion of sensors inside the protected volume, numerical analysis is considered as the only way for the appraisal of the effects induced by a typical source of interference, such as an electrostatic discharge. Full article

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality. Full article

This work proposes multi-objective two-stage distribution optimal power flow (D-OPF) to coordinate the use of smart inverters (SIs) and existing voltage control legacy devices. The first stage of multi-objective D-OPF aims to solve a mixed-integer nonlinear programming (MINLP) formulation that minimizes both voltage variation and active power loss, with SI modes, SI settings, voltage regulator (VR) taps, and capacitor bank (CB) status as control variables. The Pareto Optimal Solutions obtained from the first-stage MINLP are used to determine the optimal active–reactive power dispatch from the SIs by solving a nonlinear programming formulation in the second stage of the proposed D-OPF. This model guarantees that the setpoints for active–reactive power align with the droop characteristics of the SIs, ensuring practicability and the autonomous dispatch of active–reactive power by the SIs according to IEEE 1547-2018. The effectiveness of the proposed method is tested on the IEEE 123 distribution network by contrasting the two proposed D-OPF models, with one prioritizing SIs for voltage control and power loss minimization and the other not prioritizing SIs. The simulation results demonstrate that prioritizing SIs with optimal mode and droop settings can improve voltage control and power loss minimization. The proposed model (with SI prioritization) also reduces the usage of traditional grid control devices and optimizes the dispatch of active–reactive power. The POS also shows that the SI modes, droops, and legacy device settings can be effectively obtained based on the desired objective priority. Full article

This paper presents a proof-of-concept of a versatile Power Quality (PQ) analyser for tracking the voltage supply in industrial and residential sectors. It implements 2D Higher-Order Statistics (HOS) to assess voltage quality, based more on the sinusoidal waveform than on power fluctuations. Beyond the second-order parameters and permissible deviations regulated by the norm, EN 50160, the two-dimensional traces and probability density functions, along with a previously studied differential index, manage to identify different states of the electrical grid. Waveforms were measured in the wall plugs of a public building. In regard to analysing reliability and voltage waveform, the results corroborate that incorporating skewness and kurtosis indicators improves the characterisation, as well as extracting the customers’ supply behaviour under normal and anomalous operations. The instrument showed good behaviour in site characterisation, and the implemented method was considered as a probabilistic approach for the risk assessment of an installation. The prototype was tested in the facilities of a public building of the university, being able to detect deviations in 10 s traces of 3.9% in variance and 0.6% in kurtosis. Full article

An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction. Full article

The continuously growing penetration of renewable energy sources (RESs) in electrical networks provides increasing challenges and critical situations to be managed by worldwide system operators. Due to their features and variability, non-programmable RES power plants, whose increasing penetration reduces the inertia level of the power system, may determine the instability effects on the grids, especially from the frequency and voltage regulation standpoints. The present study focuses on the support that advanced FACTS (Flexible Alternating Current Transmission System) devices, such as STATCOMs (Static Synchronous Compensators), can provide to the power system operation in terms of system inertia improvement, frequency stability, and voltage stability. In particular, a review of the scientific literature and practice is performed, with the aim of benchmarking the ongoing evolution of these technologies, also comparing them with different options based on synchronous condensers, synchronous condensers integrated with flywheels, and STATCOMs with supercapacitors. The outcome of the analysis consists of an updated evaluation of the state-of-the-art technological development in the field and of a comparison between different FACTSs with the purpose of identifying the most suitable solutions for different practical situations, also taking account of synergies across various options. This study includes an updated overview regarding the status of STATCOM installation in the Italian power grid. Full article