Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

The integrations of advanced metering infrastructure and smart meters make it possible to detect electricity thieves by analyzing electricity consumption readings. However, the detection accuracies of traditional models are limited due to their difficulty in capturing the periodicity and latent features from electricity consumption readings. To solve this problem, a graph attention network (GAT)-based model is proposed to improve the detection accuracy from a fresh viewpoint on graph domains. First, a new strategy is presented to transform raw one-dimensional electricity consumption readings into dynamic graphs, which represent the features and periodicity through feature matrices and correlation matrices, respectively. Then, a GAT is migrated from traditional graph inferences into electricity theft detection, in which necessary adjustments are made on structures to capture periodicity and latent features from dynamic graphs. Case studies show that the proposed model outperforms popular baselines for a wide range of training ratios and fraudulent ratios.

Machine learning techniques have been extensively developed in the field of electricity theft detection. However, almost all typical models primarily rely on electricity consumption data to identify fraudulent users, often neglecting other pertinent household information such as gas consumption data. This article aims to explore the untapped potential of gas consumption data, a critical yet overlooked factor in electricity theft detection. In particular, we perform theoretical, qualitative, and quantitative correlation analyses between gas and electricity consumption data. Then, we propose two model-agnostic frameworks (i.e., multichannel network and twin network frameworks) to seamlessly integrate gas consumption data into machine learning models. Simulation results show a significant improvement in model performance when gas consumption data are incorporated using our proposed frameworks. Also, our proposed gas and electricity convolutional neural network, based on the proposed framework, demonstrates superior performance compared to classical and recent machine learning models on datasets with varying fraudulent ratios.

Offshore wind farms (OWFs) with modular multilevel converter high-voltage dc (MMC-HVdc) have become an important form of renewable energy utilization. However, if a fault occurs at the tie line between the MMC and the OWF, the fault steady-state current at the fault point will be equal to zero when the negative-sequence current is suppressed, so traditional differential protection may fail to operate. To cope with this issue, this article proposes a new pilot protection method based on Minkowski distance. For internal faults, OWF and MMC will have different transient currents, so the Minkowski distance will be much higher than 0, but it will be equal to 0 for external faults and normal operation since the fault currents on both sides are the opposite. Therefore, the internal fault can be detected reliably. The proposed method does not depend on the power frequency phasor extraction, so it is not affected by the frequency offset in the transient process. In addition, this method operates quickly and has a strong ability to withstand fault resistance and environmental noise. Moreover, since there is always a transient process when a fault occurs, the proposed method applies to different fault ride-through strategies. PSCAD simulation and real-time digital simulator experiments show that the proposed method is suitable for different fault locations and types.

Recent advances in neural networks have significantly improved electricity theft detection, achieving higher detection accuracy compared to earlier methods (e.g., support vector machine and decision tree). However, the performance of these networks is still restricted by the class imbalance issue, which causes the neural networks to bias toward classifying unknown users as the majority class (i.e., normal users). While previous works have developed oversampling and data augmentation techniques to alleviate this problem at the data level, these techniques usually replicate existing fraudulent samples or generate similar ones, which can lead to overfitting and, thus, limit model performance. To this end, this article aims to mitigate the class imbalance issue from a novel perspective at the algorithmic level. Specifically, a sample-weighted (SW) loss is proposed to efficiently train neural networks by assigning different weights to samples based on their importance, in contrast to most existing works, which treat all samples equally. Notably, the proposed SW loss is independent of any specific model architecture, meaning that it can be seamlessly integrated with various neural networks to update their weights for electricity theft detection. Simulation results on real-world datasets show that the proposed SW loss outperforms baselines (e.g., binary cross entropy loss, class-balanced loss, oversampling, and data augmentation), with an increase of about 0.27% to 9.78% in mean average precision and 0.14% to 2.92% in the area under the curve, respectively.

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.