Search Results (370)

In the modern era of smart grids, integrating advanced Geographic Information Systems (GISs) with protection coordination functionalities is pivotal for enhancing the reliability and efficiency of distribution networks. This paper presents an implementation of a web-based distribution network GIS platform that seamlessly integrates distribution system feeder GIS monitoring with the system model file layout, fault current analysis, and coordination simulation functions. The system can provide scalable and accessible solutions for power utilities, ensuring that protective devices operate in a coordinated manner to minimize outage impacts and improve service restoration times. The proposed GIS platform has demonstrated significant improvements in fault management and relay coordination through extensive simulation and field testing. This research advances the capabilities of distribution network management and sets a foundation for future enhancements in smart grid technology. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

As the key non-electric protection equipment of an oil-immersed transformer, the gas relay plays an important role in ensuring the safe operation of the transformer. To further enhance the sensitivity of gas relays for the heavy gas alarm, this paper takes the BF type double float gas relay as the research object and proposes a new method for heavy gas setting, which is based on the internal oil flow acceleration characteristics of the gas relay. Firstly, the analytical derivation of the force acting on the gas relay baffle is carried out, and through theoretical analysis, the internal mechanism of heavy gas action under transient oil flow excitation is revealed. Then, the numerical simulation and experimental research on the variation of oil flow velocity and acceleration under different fault energies are carried out. The results show that with the increase of fault energy, the oil flow velocity fluctuates up and down during heavy gas action, but the oil flow acceleration shows a linear correlation. The oil flow acceleration can be set as the threshold of heavy gas action, and the severity of the fault can be judged. At the same time, the alarm time of the heavy gas setting method based on the oil flow acceleration characteristics is greatly shortened, which can reflect the internal fault of the transformer in time and significantly improve the sensitivity of the heavy gas alarm. Full article

In order to improve the reliability evaluation accuracy of a new generation of substation relay protection equipment under small-sample failure rate data, a Generative Adversarial Network (GAN) model based on the Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) to optimize the Long Short-Term Memory (LSTM) network is proposed. Because of the adaptability of LSTM for processing time series, LSTM is embedded into the GAN, and the LSTM optimized by ASFSSA is used as the generator of GAN. The trained model is used to expand the original data samples, and the least squares method is used to estimate the distribution model parameters, to obtain the reliability function of the relay protection equipment, and to predict the operating life of the equipment. The results show that compared with other methods, the correlation coefficient of the expanded data samples is closer to the original data, and the life estimation of the equipment is more accurate. The model can be used as a reference for reliability assessment and acceptance testing of the new generation of substation relay protection equipment. Full article

The increasing digitalization of power systems has revolutionized the functionality and efficiency of electrical protection relays. These digital relays enhance fault detection, monitoring, and response mechanisms, ensuring the reliability and stability of power networks. However, their connectivity and reliance on communication protocols introduce significant cybersecurity risks, making them potential targets for malicious attacks. Cyber threats against digital protection relays can lead to severe consequences, including cascading failures, equipment damage, and compromised grid security. This paper presents a comprehensive review of cybersecurity challenges in digital electrical protection relays, focusing on four key areas: (1) a taxonomy of cyber attack models targeting protection relays, (2) the associated risks and their potential impact on power systems, (3) existing mitigation strategies to enhance relay security, and (4) future research directions to strengthen resilience against cyber threats. Full article

The increasing attention to medium-voltage direct current (MVDC) distribution networks is motivated by the need to efficiently connect renewable energy sources and DC loads. However, fast and reliable protection strategies remain a key challenge due to the rapid rise and high magnitude of DC fault currents. This paper proposes a protection strategy for MVDC distribution networks considering network reconfiguration. The strategy integrates a fault-detection scheme based on the product of the rate of change in current and voltage (ROCOC × ROCOV) and a fault-identification scheme based on the ratio of the magnitudes of the positive and negative pole voltages. In a radial topology, the sign of ROCOC × ROCOV provides selectivity between internal and external faults. In multi-terminal topologies under network reconfiguration, external faults can present characteristics similar to those of internal faults. To ensure selectivity, communication is introduced between protective relays that share the same protection zone. Thresholds were set without large-scale simulations. The protection strategy was implemented in PSCAD/EMTDC and evaluated in a 37.4 kV MVDC distribution network. The strategy was validated under various fault conditions in radial and multi-terminal MVDC distribution networks, demonstrating fast, sensitive, and selective performance. The proposed strategy can contribute to the stable operation of MVDC distribution networks. Full article

Plastic recycling is an important but complicated issue. Some plastics are currently readily recyclable with existing technologies, whereas others are not. However, the general public often does not appreciate the benefits and costs associated with hard-to-recycle (e.g., multi-material) plastics, potentially causing confusion and, in some cases, backlash. While some methods of relaying information to the public have been previously studied (e.g., infographics, descriptive labeling), educational videos have not. We created an educational video on the properties and functions of single- and multi-material plastic. Participants who viewed the educational video were more knowledgeable about multi-material plastic recycling and had higher intentions to use and be satisfied with a hypothetical recycling company that intends to begin recycling multi-material plastic. Our data suggest that education interventions have the potential to inform and empower the public while protecting common values. Full article

State evaluation of relay protection equipment constitutes a crucial component in ensuring the stable, secure, and symmetric operation of power systems. Current methodologies predominantly encompass fuzzy-rule-based control systems and data-driven machine learning approaches. The former relies on manual experience for designing fuzzy rules and membership functions and exhibits limitations in high-dimensional data integration and analysis. The latter predominantly formulates state evaluation as a classification task, which demonstrates its ineffectiveness in identifying equipment at boundary states and faces challenges in model parameter selection. To address these limitations, this paper proposes a quantitative state evaluation method for relay protection equipment based on a two-stage artificial protozoa optimizer (two-stage APO) optimized improved Conformer (two-stage APO-IConf) model. First, we modify the Conformer architecture by replacing pre-layer normalization (Pre-LN) in residual networks with post-batch normalization (post-BN) and introducing dynamic weighting coefficients to adaptively regulate the connection strengths between the first and second feed-forward network layers, thereby enhancing the capability of the model to fit relay protection state evaluation data. Subsequently, an improved APO algorithm with two-stage optimization is developed, integrating good point set initialization and elitism preservation strategies to achieve dynamic equilibrium between global exploration and local exploitation in the Conformer hyperparameter space. Experimental validation using operational data from a substation demonstrates that the proposed model achieves a RMSE of 0.5064 and a MAE of 0.2893, representing error reductions of 33.6% and 35.0% compared to the baseline Conformer, and 9.1% and 15.2% error reductions over the improved Conformer, respectively. This methodology can provide a quantitative state evaluation and guidance for developing maintenance strategies for substations. Full article

Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the Kopli Solar Power Plant, Estonia, by integrating controlled laboratory simulations with extensive field monitoring. Initially, detailed laboratory experiments were conducted to replicate critical DC-side short-circuit scenarios, particularly focusing on negative DC input terminal faults. The results consistently showed these faults rapidly escalating into multi-phase short-circuits and sustained ground-fault arcs due to inadequate internal protection mechanisms, semiconductor breakdown, and delayed relay response. Subsequently, extensive field-based waveform analyses of multiple inverter failure events captured identical fault signatures, thereby conclusively validating laboratory-identified failure mechanisms. Critical vulnerabilities were explicitly identified, including insufficient isolation relay responsiveness, inadequate semiconductor transient ratings, and ineffective internal insulation leading to prolonged arc conditions. Based on the validated findings, the paper proposes targeted inverter design enhancements—particularly advanced DC-side protective schemes, rapid fault-isolation mechanisms, and improved internal insulation practices. Additionally, robust operational and monitoring guidelines are recommended for industry-wide adoption to proactively mitigate future inverter failures. The presented integrated methodological framework and actionable recommendations significantly contribute toward enhancing inverter reliability standards and operational stability within grid-connected photovoltaic installations. Full article

As the proportion of new energy generation continues to rise, power systems are confronted with novel challenges. Grid-forming converters, which possess voltage source characteristics and can support the grid, typically employ a VSG control strategy during normal operation to emulate the behavior of synchronous generators. This approach enhances frequency response and system stability in modern power systems. This review article systematically examines two typical fault control strategies for grid-forming converters: the switching strategy and the virtual impedance strategy. These different control strategies result in distinct fault response characteristics of the converter. Based on the analysis of fault control strategies for grid-forming converters, this study investigates the impact of the converter’s fault response characteristics on overcurrent protection, pilot protection, distance protection, and differential protection and investigates and prospects corresponding countermeasures. Finally, through simulation modeling, the fault response characteristics under different control strategies and their effects on protection are verified and analyzed. Focusing on grid-forming converters, this paper dissects the influence of their fault control strategies on relay protection, providing strong support for the wide application and promotion of grid-forming converters in new types of power systems. Full article

Fiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and phase angle characteristics of fault currents, which makes the sensitivity of fiber differential protection decline and even increases the risk of failure to operate. In view of this phenomenon, combined with the digital and intelligent development of the new energy power system, this study integrates deep learning with relay protection to propose a protection algorithm based on a two-dimensional spatial current trajectory image and deep learning. In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. Under different fault conditions, they have obvious differences. A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers. Full article

Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) due to the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while, in reality, it is not using anonymous routing and is forwarding its networking directly to the destination peer to reduce networking overheads. Using profile detection based on deterministic features to detect anonymous routing and false claims is vulnerable to spoofing, especially in permissionless cryptocurrency bypassing registration control. We thus designed and built a method of network fingerprinting, using networking behaviors to detect and classify networking types. We built a network sensor to collect data on an active Bitcoin node connected to the Mainnet and applied supervised machine learning to identify whether a peer node was using IP (direct forwarding without the relays for anonymity protection), Tor, or I2P. Our results show that our scheme is effective in accurately detecting networking types and identifying spoofing attempts through supervised machine learning. We tested our scheme using multiple supervised learning models, specifically CatBoost, Random Forest, and HistGradientBoosting. CatBoost and Random Forest performed best and had comparable accuracy performance in effectively detecting false claims, i.e., they classified the networking types and detected fake claims of Tor usage with 93% accuracy and false claims of I2P with 94% accuracy in permissionless Bitcoin. However, CatBoost-based detection was significantly quicker than Random Forest and HistGradientBoosting in real-time testing and detection. Full article

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the DeepFace library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances, and it sends real-time remote monitoring messages. Some of the most important things that have been achieved include adaptive facial recognition, ensuring that the system changes as it is used, and integrating real-time notifications and emotion detection without any problems. Face recognition worked well in many settings. Modular architecture facilitated hardware–software integration and scalability for various applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology’s potential in modern security systems. Full article

With the rapid development of renewable energy technologies, large numbers of photovoltaic (PV) and battery energy storage systems (BESS) have been connected to distribution networks. However, both PV and the BESS are inverter interfaced power sources, which may cause the traditional protection relays to mis-operate or mal-operate. Moreover, according to the latest grid connection specifications, PV and BESS are required to absorb negative sequence current during asymmetric faults of distribution networks, indicating that they both must adopt new control strategies during the fault ride through period. In response to the above challenges, this work first studies the fault ride through control strategies of PV and BESS when different phase-to-phase faults occur according to the latest grid connection requirements. Second, it analyzes the negative sequence impedance characteristics of PV and BESS under asymmetric faults and quantitatively calculates its variation range. Third, during symmetric faults, the differences in fault current provided by PV and BESS and those provided by the large power grid are compared. Then, this work proposes a fault direction detection principle for the distribution network with PV and BESS. For asymmetric phase-to-phase faults, this principle detects the fault direction by using the negative sequence power angle; for symmetric faults, it detects the fault direction by using the reactive current and active current. Finally, simulation tests are carried out to verify the operation performance of the proposed principle. Full article

Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and generate fault currents that remain within normal operating thresholds. As a result, conventional protection schemes like overcurrent relays, which are tuned for high-magnitude faults, fail to detect such internal anomalies. Moreover, frequency response deviations caused by TWFs often resemble those introduced by routine phenomena such as tap changer operations, load variation, or core saturation, making accurate diagnosis difficult using traditional FRA interpretation techniques. This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. The proposed system employs region-based statistical deviation labeling to enhance interpretability across five well-defined frequency bands. It is validated on five real FRA datasets obtained from operating transformers in Gauteng Province, South Africa, covering a range of MVA ratings and configurations, thereby confirming model transferability. The system supports post-processing but is lightweight enough for near real-time diagnostic use, with average execution time under 12 s per case on standard hardware. A custom graphical user interface (GUI), developed in MATLAB R2022a, automates the diagnostic workflow—including region identification, wavelet-based decomposition visualization, and PDF report generation. The complete framework is released as an open-access toolbox for transformer condition monitoring and predictive maintenance. Full article