Sheng Su

Fault detection in users’ reactive power compensators (URPCs) remains a critical challenge, particularly for general commercial and industrial consumers lacking technical expertise. Undetected URPC malfunctions not only increase electricity costs for users but also aggravate utility power losses. To address this issue, we propose a novel remote fault detection framework that exploits the joint distribution of active load levels and power factors derived from metering data. A vision transformer with a large margin-aware focal model is then employed to effectively classify the operational states of URPCs, using joint frequency distribution matrices as characteristic representations. Unlike conventional approaches, the proposed method relies exclusively on metering data, thereby simplifying deployment and enhancing accessibility for nonspecialist users. This enables timely operation and maintenance of URPCs, reducing electricity costs and improving overall power system efficiency. The effectiveness of the proposed approach is validated through extensive simulations.

Low-voltage distribution systems (LVDS) are prone to leakage current faults, which pose significant electrical safety risks. The wiring complexity, diverse potential causes, and scarce data make identifying and troubleshooting leakage faults in LVDS a persistent challenge in operation and maintenance. A common issue encountered onsite is the incorrect connection of the neutral wire and protective earth (PE) wires within the user’s panel box. Initially, the impact and hazards associated with such wiring faults are analyzed, taking into account the graded residual current protection device and the grounding protection configuration in LVDS. Subsequently, the differential impact on the residual current of the LVDS is examined when wiring faults occur in either a single user or multiple users. A ridge regression-based identification method for detecting users with wiring faults is then proposed, utilizing the time series data of the residual current of the LVDS and the load current of users. Further, considering the impact of complex fault scenarios and the number of users in LVDS, the sliding window technique combined with the Mean-shift cluster analysis algorithm is employed to enhance the accuracy of identifying users with wiring faults. Finally, laboratory data from an LVDS model is used to conduct numerical simulations of wiring fault leakage, verifying the effectiveness and superiority of the proposed method.

The construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potential economic losses. Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. Hence, this paper proposes a method to detect abnormal residential water and electricity usage by incorporating multi-source information. Specifically, the correlation between water and electricity usage of residential customers is analyzed based on real metering data and the use of the Copula distribution function, followed by the integration of two innovative data mining techniques to form an anomaly detection framework. The distance correlation coefficient algorithm is used to measure the relevance of users’ water and electricity usage data. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the distance correlation coefficient for users and detect abnormal users whose distance correlation coefficient curves deviate from the normal user clusters. This multi-source approach avoids single-source bias by improving the data accuracy over one-dimensional methods. Experiments are implemented in a real low-voltage transformer area to prove the validity of the proposed method.

Wind power prediction plays a significant role in enhancing the effectiveness of power system operation and decision‐making. Given the inherent stochastic nature of meteorological events, achieving highly accurate forecasts for wind power poses considerable challenges. To address this challenge, this paper initially leverages the time series learning capability of recurrent neural networks (RNN) to extract sequential information from historical wind power data. Subsequently, the information extracted from the convolutional layer is transferred to the light gradient boosting machine (LGBM), utilizing the feature extraction capability of convolutional neural networks (CNN). Furthermore, an optimal weighted combination is employed for the short‐term prediction of wind power. Finally, a multi‐step wind power prediction method of integrated CNN–RNN–LGBM is proposed in this paper. Simulation results demonstrate that the proposed CNN–RNN–LGBM framework outperforms other models during global training. Meanwhile, transferring the information from CNN to LGBM can improve its performance, proving the feature extraction ability of CNN.

Wiring errors, caused by improper connections between neutral lines and protective earth (PE) lines due to negligence by electrical technicians, are a prevalent type of earth fault in low‐voltage distribution systems (LVDS). These errors can cause the load current of affected users to flow back to the transformer's neutral point via the PE line as residual current, leading to nuisance tripping of residual current devices (RCDs). To maintain normal power supply, users may disable RCDs, which compromises safety and can result in severe hazards such as electric shocks and electrical fires. This paper proposes a method to locate users with wiring errors by leveraging abundant metering data within LVDS and utilising leakage fault analysis devices. We construct a linear model of residual current considering multiple error scenarios. Based on this model, a multiple linear regression (MLR) approach is developed to identify and locate anomalous users by analysing the correlation between their load currents and the residual current of the LVDS. Experimental results under various scenarios validate the performance of the proposed method.

Distribution of lightning plays a key role in lightning protection of transmission lines. The design of wind deviation under lightning overvoltage of the transmission tower is an empirical parameter in the transmission line design code. Meteorological data from Hong Kong are investigated to analyze the joint distribution of lightning and wind speed. It has been uncovered that daily cloud-to-ground (CG) flashes follow the Burr distribution, which is highly skewed toward a few days with notable lightning. The lightning and wind follow a Gumbel copula joint distribution. According to empirical and theoretical distribution, there are 239 days with more than 1,000 CG lightning flashes per day, and approximately 20% of these days have a maximum wind speed of approximately or over 15 m/s. In 5 days with the number of CG lightning flashes above 30,000, 3 days have a maximum wind speed of over 15 m/s and the other 2 days have a maximum wind speed of over 10 m/s, which suggests that the severe convection with a squall line contributes much to the likelihood of the days with high wind and lightning storms.

Electricity theft users with zero electricity usage (UZEU) should be specifically concerned in electricity theft detection (ETD) research. The challenges are: they provide no effective information on electricity usage behaviors, and they are easily confused with vacant house users. This has caused the majority of the existing detection methods relying on single electricity usage to fail to identify UZEU accurately. Hence, this article first analyzes the underlying correlation between water and electricity (W&E) usage collected by the smart meter. This analysis then lends the theoretical basis to propose a new ETD method by comprehensively using the multisource information. More precisely, the proposed method utilizes the mutual information coefficient (MIC) to construct a correlation model between W&E usage and in turn the wavelet clustering algorithm to cluster the MIC of the power distribution users. Thereafter, the resulting weak correlations indicate the suspected users as the electricity theft UZEU in case of zero electricity usage. Finally, the proposed method is validated by numerical experiments in the real world and illustrated to be more accurate than existing methods in detecting UZEU.