A Hybrid Method for Non-Technical Loss Detection in Smart Distribution Grids

Messinis, George M.; Rigas, A.; Hatziargyriou, Nikos D.

doi:10.1109/tsg.2019.2896381

Cited by 86 publications

(47 citation statements)

References 28 publications

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“…[3] requires GIS data, quality data and TECH data to achieve the best performance. [10,12] ask large number of labeled samples, and more than 1 year span of consumption data is [11]'s necessary condition.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…However, the realistic NTL samples of which in-field inspected are rare indeed, supervised learning methods are an easier lead to overfitting. On the other hand, the artificial samples as a possible solution are adopted by some approaches [10,11]. Even though they provide lots of labeled samples to support training models, the effectiveness of such attack models is not verified by realistic cases.…”

Section: How To Obtain Satisfactory Performance Based On Limited Labementioning

confidence: 99%

“…However, it is difficult to collect enough realistic normal and abnormal cases, which makes the labeled samples are very scarce. To avoid insufficient realistic NTL samples, [10,11,13] attempt to model NTL and produce artificial NTL samples. Same as the necessary requirement of massive labeled samples, features are equally important to classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, it might be existed from the beginning, such as phase disorder. In contrast to the SMART attack defined in [11] or FDI5 defined in [10], the realistic NTL does not run in fixed artificial model and is more random and complicated. Consequently, the domain knowledge based features are very important for classifier to detect NTL.…”

Section: Phase Disordermentioning

confidence: 99%

“…Receiver Operating Characteristic (ROC) curve is a popular way to validate performance of classifier on imbalanced datasets and widely applied by [3,11,12,35]. It evaluates how fast the True Positive Rate (TPR) increases with the increase of the False Positive Rate (FPR).…”

Section: Metricsmentioning

confidence: 99%

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Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid

Zhou

Wang

et al. 2019

Energies

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Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.

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