Although machine learning models have been widely used in electricity theft detection, most of them lack interpretability, which hinders user trust and policy enforcement. To this end, this paper aims to investigate the interpretability of machine learning models in electricity theft detection. Specifically, a comprehensive theoretical analysis is conducted to reveal why the interpretability is needed in electricity theft detection. Then, a model-agnostic explainable artificial intelligence (XAI) framework is proposed to uncover the potential start and end times of fraudulent behavior, and to clarify the rationale behind identifying fraudulent users within machine learning models by calculating the importance score of each data point. Simulation results demonstrate that the XAI framework provides class-discriminative data points to interpret fraudulent activities, enabling suspicious users to understand why the machine learning model identified them as suspicious and guiding model improvement. Moreover, compared with benchmarks (e.g., Shapley additive explanations, local interpretable model-agnostic explanations, and gradient-weighted class activation mapping techniques), the harmonic mean of overlap and coverage (HMOC) of the proposed XAI framework is improved by 10.26% to 54.73%, indicating more trustworthy interpretations.

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.

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.

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.

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.

Unique fault behaviors of renewable energy sources (RESs) may lead to the misoperation of traditional pilot protection. To cope with this issue, this article proposes a new pilot protection method using the improved Euclidean distance. For normal operation or external faults, the currents on both ends are completely opposite, so the Euclidean distance of current absolute values on both ends is equal to 0. However, it will be much larger than 0 for internal faults because transient currents on both ends will have a big difference at this time. Therefore, internal and external faults can be detected reliably. In order to facilitate the setting calculation, the Euclidean distance is normalized, and a stability factor is introduced to avoid invalid calculation results. The proposed method can be applied to different RES types and different fault ride through strategies. Meanwhile, it can withstand larger fault resistance and noise interference. Compared with other methods using the RES fault currents, this approach can operate correctly without any additional criteria when the circuit breaker recloses on a permanent fault or RESs output a low power. PSCAD simulation and real-time digital simulator experiment verify this method.

The power output generated by wind farms (WFs) is integrated directly or indirectly into the grid through power electronic devices and the fault current is significantly different from that of conventional synchronous generators) and exhibits a limited amplitude, a controlled phase angle, rich harmonics, and strong nonlinearities. Therefore, the traditional ratio braking type differential protection may have low sensitivity or may even refuse to operate. In order to solve this problem, a pilot protection method based on Spearman's rank correlation coefficient is proposed in this article. When the system operates normally or an external fault occurs, a traversing current flows through the transmission line, so the current waveforms on both sides are the opposite. When an internal fault occurs, there are large differences in transient current waveforms on both sides. Spearman's rank correlation coefficient can be used to measure the degree of correlation between the waveforms on both sides to distinguish between faults inside and outside the zone. Compared with existing protection based on fault characteristics of WFs, the proposed method shows good performance when the power output of WFs is weak and the circuit breaker recloses with permanent failure. Both simulation results and field-testing data validate the proposed method.