Search Results (980)

High-Voltage Alternating Current (HVAC) transmission systems form the backbone of modern power grids, enabling efficient long-distance and high-capacity power delivery. In Saudi Arabia, ongoing initiatives to modernize and strengthen grid infrastructure demand advanced solutions to ensure system reliability, operational stability, and the minimization of economic losses caused by faults. Traditional fault detection and classification methods often depend on the manual interpretation of voltage and current signals, which is both labor-intensive and prone to human error. Although data-driven approaches such as Artificial Neural Networks (ANNs) and Deep Learning have been applied to automate fault analysis, their performance is often constrained by the quality and size of available training datasets, leading to poor generalization and physically inconsistent outcomes. This study proposes a novel hybrid fault detection and classification framework for the 380 kV Marjan–Safaniyah HVAC transmission line by integrating Deep Learning with Physics-Informed Neural Networks (PINNs). The PINN model embeds fundamental electrical laws, such as Kirchhoff’s Current Law (KCL), directly into the learning process, thereby constraining predictions to physically plausible behaviors and enhancing robustness and accuracy. Developed in MATLAB/Simulink using the Deep Learning Toolbox, the proposed framework performs fault detection and fault type classification within a unified architecture. A comparative analysis demonstrates that the hybrid PINN approach significantly outperforms conventional Deep Learning models, particularly by reducing false negatives and improving class discrimination. Furthermore, this study highlights the crucial role of balanced and representative datasets in achieving a reliable performance. Validation through confusion matrices and KCL residual histograms confirms the enhanced physical consistency and predictive reliability of the model. Overall, the proposed framework provides a powerful and scalable solution for real-time monitoring, fault diagnosis, and intelligent decision-making in high-voltage power transmission systems. Full article

Fault detection and classification localization in photovoltaic power grids is a key challenge in photovoltaic power systems. Due to the greater fluctuation of power data in photovoltaic power grids, traditional grid fault detection methods suffer from inefficiency, low accuracy, and inaccurate fault localization in photovoltaic scenarios. In this paper, a fuzzy control technique combined with an improved GABP neural network is used to identify potential fault nodes in the photovoltaic distribution network. The symmetric crossover operator of the genetic algorithm and the symmetry constraints of the neural network weight matrix are used to improve the model’s ability to capture the symmetric fluctuation characteristics of photovoltaic data, while a classification module consisting of three fuzzy controllers is used for fault identification. The simulation results show that the recognition method proposed in this paper has good performance and the fault classification accuracy reaches 92.75%, which provides a practical reference value for the management of photovoltaic distribution network. Full article

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R² value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R² = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

In modern power systems, FACTS tools are essential for addressing voltage variation along with fault ride-through (FRT) challenges within the electrical power systems, particularly for wind generation integration. Several prominent publications emphasize that the squirrel cage induction generator (SCIG) currently comprises about 15% of operational wind turbines. This research proposes the use of FACTS devices to boost voltage stability and FRT capability. The implementation of these devices leads to improved efficiency in the electrical power system. This study considers many events, including an ideal wind profile, turbulent wind profile, symmetrical faults, and unsymmetrical faults, to support the proposed selection. Furthermore, the proposed approach is evaluated by comparison between a fixed capacitor, static synchronous compensator (STATCOM), and Static VAR Compensator (SVC) to guarantee the achievement of voltage stability, reactive power consumption, and FRT capacity under various wind speed profiles and fault conditions. An overall evaluation is conducted to compare them in all examined circumstances and to highlight their advantages and effects. The simulation findings demonstrate the efficacy and primacy of FACTS in enhancing the functioning of an integrated wind system, which is built upon a grid-connected SCIG, as well as enhancing the power system performance. The MATLAB/Simulink toolbox is used to design the models of SCIG, SVC, and STATCOM. Full article

In a phase-locked loop (PLL) synchronized inverter grid-connected system, its equivalent damping coefficient is nonlinearly coupled with the operation power angle. Therefore, under a large disturbance, the indefinite damping increases the risk of transient instability in the system. To address this issue, firstly, a structurally modified IPLL is designed by removing the proportional coefficient branch of the traditional PLL and introducing a positive damping feedback branch. This design eliminates the coupling between the equivalent damping coefficient and the power angle, ensuring that the equivalent damping remains consistently positive. Secondly, based on the principle of energy dissipation via positive damping, a damping coefficient switching control strategy is developed. This strategy adaptively adjusts the damping during faults to rapidly dissipate excess kinetic energy, ensuring that the system returns to stability after fault clearance. Notably, the damping coefficient is pre-designed offline without relying on real-time grid parameters or operating data, enhancing the engineering practicability. Lastly, hardware-in-the-loop (HIL) experiments validate the strategy under extreme conditions. Full article

In order to meet the needs of grid integration of various renewable energy sources and promote long-distance power transmission, a hybrid multi-infeed DC system architecture consisting of a line-commutated converter (LCC) and a modular multilevel converter (MMC) is constructed. Focusing on the issue of traditional differential protection refusing to operate under high-resistance grounding faults and failing under symmetrical faults, a dual-criteria protection mechanism is proposed in this paper. By integrating current differential and voltage criterion, the accurate identification of various types of AC line faults can be realized. A hybrid DC system simulation model was built on MATLAB, the sampled data was decoupled, and the differential quantity was calculated to test the dual-criteria protection mechanism. The simulation results show that the proposed protection mechanism can effectively identify various faults within the hybrid DC multi-feed system area and faults outside the area and has robustness to complex working conditions such as high-resistance grounding and three-phase short circuits, which improves the sensitivity, selectivity, and adaptability of the protection. This method is designed for AC line protection under the disturbance of multi-infeed DC systems. It is not directly applicable to pure DC microgrids. The concept can be extended to AC/DC hybrid microgrids by adding DC-side protection criteria and re-calibrating thresholds. Full article

Compared to the traditional unidirectional power supply system, the bidirectional traction power supply system in an electrified railway offers advantages like improved traction voltage and reduced energy losses, making it more suitable for steep gradient routes. However, its increased electrical complexity necessitates advanced catenary-rail short-circuit fault calculations and relay protection calibration. This paper proposes a fault calibration approach based on deriving electrical quantities with fault distance in the railway bidirectional traction grid system. A multi-loop circuit modeling method is used to accurately model the traction grid system and impedance parameters, incorporating real loop circuits formed by the grid transmission and return conductors for the first time. The approach is validated through real-life experiments on a Chinese railway line. A case study of a direct power supply system with a return cable is used to derive electrical quantities. Faults are categorized into two sections: between the substation and the parallel station (PS), and between the PS and the section post (SP). For each section, electrical quantities are derived under unidirectional substation excitation, and the results are superimposed to obtain fault distance variation curves for currents and voltages of substation, PS, SP, and Thévenin impedance. Finally, a calibration strategy for relay protection is presented. Full article

The substation grounding grid, as the primary path for fault current dissipation, is crucial for ensuring the safe operation of the power system and requires regular inspection. The pulsed eddy current method, known for its non-destructive and efficient features, is widely used in grounding grid detection. However, during the parameter identification process, it is prone to local minima or no solution. To address this issue, this paper first develops a pulsed eddy current forward response model for the substation grounding grid based on the magnetic dipole superposition principle, with accuracy validation. Then, a variable dimensional Bayesian parameter identification method is introduced, utilizing the Reversible-Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using nonlinear optimization results as the initial model and introducing a dual-factor control strategy to dynamically adjust the sampling step size, the model enhances coverage of high-probability regions, enabling effective estimation of grounding grid parameter uncertainties. Finally, the proposed method is validated by comparing the forward response model with field test results, showing that the error is within 10%, demonstrating both the accuracy and practical applicability of the proposed parameter identification method. Full article

With the implementation of China’s “dual carbon” strategy, the installed capacity of new energy has grown rapidly. Wind power and photovoltaic power have accounted for more than 40%, but the integration of power electronic apparatus into the grid has resulted in the manifestation of a system with “low inertia and weak damping”, which can easily lead to transient overvoltage problems at transmitters when high-voltage direct-current (HVDC) latching faults occur. Although a variety of dynamic reactive power optimization strategies have been proposed in the existing research, most of them are aimed at single equipment, and multi-reactive power source collaborative control schemes are lacking. In this paper, we innovatively establish a transient voltage analysis model for a new energy transmitter, derive the expression of overvoltage amplitude, and propose a method for the construction of a multi-reactive source collaborative optimization model, which can effectively suppress transient overvoltage through capacity and initial output configuration. We provide a new idea for the safe operation of a significant percentage of new energy grids. The case analysis shows that the co-optimization method outlined in this paper is an effective solution to suppress the transient overvoltage triggered by AC faults and has wide application value. Full article

With the large-scale integration of renewable energy devices into the power grid, the voltage stability of the renewable energy base is becoming increasingly weak, and the problem of transient overvoltage is becoming increasingly severe. Grid-forming (GFM) converters can provide strong voltage support. When GFM converters are paralleled with grid-following (GFL) converters, they can effectively reduce transient overvoltage. However, hybrid systems involve many parameters and exhibit complex dynamics, making assessment of transient overvoltage difficult. To address this, this paper first uses Thevenin’s theorem to reduce the renewable transmission system to an equivalent model. Next, the voltage assessment of the hybrid system is analyzed across the pre-fault, mid-fault, and post-fault stages of a short-circuit fault. Then, based on the characteristics of a phase-locked loop (PLL), this paper innovatively derives an assessment method for transient overvoltage at the common coupling point (PCC) under different PLL stability conditions. Additionally, the influence of GFL converter parameters, GFM converter parameters, the GFM capacity ratio on transient overvoltage, and the external system reactance are analyzed. Finally, the proposed evaluation method and factor analysis are validated through electromechanical transient simulation using the simulation software STEPS v2.2.0. Full article

The flexible DC transmission project of renewable energy has become an inevitable development trend for large-scale renewable energy grid connection. A two-terminal weak feed (TTWF) AC system is often composed of 100% power electronic equipment. The traditional fault control strategy adopted after a fault in the converter at both terminals of the line limits the fault current and controls the phase, resulting in a decrease in the time-domain distance protection performance. This paper first analyzes the adaptability challenges of time-domain distance protection in TTWF. Based on detailed fault characteristic studies, two improvement approaches are proposed: (1) accounting for phase control effects by equivalently modeling the fault impedance as a series combination of fault resistance and inductance; and (2) incorporating distributed capacitance effects through fault differential equation derivation based on π-type line equivalent models. A novel time-domain distance protection method is subsequently developed, comprehensively considering control strategy impacts and distributed capacitive currents. Simulation tests verify that the proposed method maintains reliable operation under severe conditions, including 300 Ω fault resistance and 30 dB white noise interference, demonstrating significantly improved resistance to fault impedance and noise compared to conventional solutions. Full article

The rapid and accurate identification of power system transient stability status is a fundamental prerequisite for ensuring the secure and reliable operation of large-scale power grids. With the increasing complexity and heterogeneity of modern power system components, system nonlinearity has grown significantly, rendering traditional time-domain simulation and direct methods unable to meet accuracy and efficiency requirements simultaneously. To further improve the prediction accuracy of power system transient stability and provide more refined assessment results, this paper integrates deep learning with power system transient stability and proposes a transient stability assessment of power systems built upon a deep spatio-temporal feature extraction network method. First, a spatio-temporal feature extraction module is constructed by combining an improved graph attention network with a residual bidirectional temporal convolutional network, aiming to capture the spatial and bidirectional temporal characteristics of transient stability data. Second, a classification module is developed using the Kolmogorov–Arnold network to establish the mapping relationship between spatio-temporal features and transient stability states. This enables the accurate determination of the system’s transient stability status within a short time after fault occurrence. Finally, a weighted cross-entropy loss function is employed to address the issue of low prediction accuracy caused by the imbalanced sample distribution in the evaluation model. The feasibility, effectiveness, and superiority of the proposed method are validated through tests on the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system. Full article

The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing. Full article

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article