Electricity theft Detection in Power Grid with a Hybrid Convolutional Neural Network - Support Vector Machine Model

Nguyen, Thi Hoai Thu; Phan, Quoc Bao

doi:10.1145/3508072.3508078

Cited by 3 publications

(1 citation statement)

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“…The addition of the inception module tackles the problem of over-fitting and computational complexity. RF (Nguyen and Phan, 2021), SVM (Lin et al, 2021), DT, and LSTMCNN (Hasan et al, 2019) perform very badly. They cannot perform on complex time series data and cause overfitting issues.…”

Section: Simulation Resultsmentioning

confidence: 99%

Novel FDIs-based data manipulation and its detection in smart meters’ electricity theft scenarios

Munawar

Khan²,

Chaudhary³

et al. 2022

Front. Energy Res.

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Non-technical loss is a serious issue around the globe. Consumers manipulate their smart meter (SM) data to under-report their readings for financial benefit. Various manipulation techniques are used. This paper highlights novel false data injection (FDIs) techniques, which are used to manipulate the smart meter data. These techniques are introduced in comparison to six theft cases. Furthermore, various features are engineered to analyze the variance, complexity, and distribution of the manipulated data. The variance and complexity are created in data distribution when FDIs and theft cases are used to poison SM data, which is investigated through skewness and kurtosis analysis. Furthermore, to tackle the data imbalance issue, the proximity weighted synthetic oversampling (ProWsyn) technique is used. Moreover, a hybrid attentionLSTMInception is introduced, which is an integration of attention layers, LSTM, and inception blocks to tackle data dimensionality, misclassification, and high false positive rate issues. The proposed hybrid model outperforms the traditional theft detectors and achieves an accuracy of 0.95%, precision 0.97%, recall 0.94%, F1 score 0.96%, and area under-the-curve (AUC) score 0.98%.

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Section: Simulation Resultsmentioning

confidence: 99%