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Due to the diversity of power consumption patterns, the false positive rate (FPR) of data-driven electricity theft detection (ETD) methods is too high to meet practical needs, which severely restricts the engineering application of data-based methods. To reduce FPR of ETD methods based on advanced metering infrastructure (AMI), a deep neural network with low FPR (LFPR-DNN) is proposed in this paper. First, a deep model is constructed based on one-dimensional convolution and residual network, which can automatically extract features from consumption data. Then, a two-stage training scheme is used to train the network. In the first stage, the conventional gradient descent algorithm is used to update the network weights. To minimize the impact of data imbalance on detection performance, focal loss is used. Besides, grid search is used to optimize the hyper-parameters of the model. In the second stage, with FPR as the optimization objective, the particle swarm optimization (PSO) algorithm is used to train the network. Finally, the proposed LFPR-DNN is verified by using the open Irish data set. Compared to other state-of-the-art classifiers, LFPR-DNN has the lowest FPR with 0.29% and the highest AUC with 99.42%. The FPR is reduced by an order of magnitude, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multiple power quality disturbance (PQD) contains various single disturbances, so its classification is essentially a multilabel classification task. Due to the complexity of multilabel tasks, the performance of existing multilabel methods for PQD is hard to meet practical needs, which severely restricts the engineering application of multilabel methods. Therefore, in this article, we propose a novel multilabel method named LGAN, which incorporates deep learning and explores the correlations between PQD labels to improve performance. First, 1-D convolutional neural network extracts features automatically from PQD signals. Then, a label-guided attention module is adopted to learn the specific feature representation of each PQD category. Finally, the bidirectional recurrent neural network models the label correlations from the label-related features, and then predicts the final PQD category. Various comparative experiments show that the performance of LGAN is much better than the existing multilabel methods. Additionally, the test using real-time detection platform further verifies the availability of the proposed method. [ABSTRACT FROM AUTHOR]