Abstract
In this paper, a hybrid deep learning model is presented to detect electricity theft in the power grids, which happens due to the Non-Technical Losses (NTLs). The NTLs emerge due to meter malfunctioning, meter bypassing, meter tampering, etc. The main focus of this study is to detect the NTLs. However, the detection of NTLs faces three major challenges: the problem of severe class imbalance, the problem of overfitting due to the highly dynamic data and poor generalization due to the usage of synthetic data. To overcome the aforementioned problems, a hybrid deep neural network is designed, which is the combination of Alexnet, Adaptive Boosting (AdaBoost) and Ant Bee Colony (ABC), termed as Alexnet-Adaboost-ABC. The Alexnet is exploited for the features’ extraction while Adaboost and ABC are used for the classification and parameters’ tuning, respectively. Moreover, the class imbalance issue is resolved using the Near Miss (NM) undersampling technique. The NM effectively reduces the majority class samples and standardize the proportion of both majority and minority classes. The model is evaluated on the real time inspected dataset released by the State Grid Corporation of China (SGCC). The performance of the proposed model is validated through the F1-score, precision, recall, Area Under Curve (AUC) and Matthew Correlation Coefficient (MCC). The simulation results depict that the proposed model outperform the existing techniques. The simulation results depict that the proposed model obtains 3%, 2% and 4% higher values of F1-score, AUC and MCC, respectively.
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References
Ding, N., Ma, H., Gao, H., Ma, Y., Tan, G.: Real-time anomaly detection based on long short-term memory and Gaussian mixture model. Comput. Electr. Eng. 79, 106458 (2019)
Yip, S.-C., Tan, W.-N., Tan, C.K., Gan, M.-T., Wong, K.S.: An anomaly detection framework for identifying energy theft and defective meters in smart grids. Int. J. Electr. Power Energ. Syst. 101, 189–203 (2018)
Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Ind. Inf. 14(4), 1606–1615 (2017)
Li, S., Han, Y., Yao, X., Yingchen, S., Wang, J., Zhao, Q.: Electricity theft detection in power grids with deep learning and random forests. J. Electr. Comput. Eng. (2019)
Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Detection of non-technical losses using smart meter data and supervised learning. IEEE Trans. Smart Grid 10(3), 2661–2670 (2018)
Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst. 35(2), 1254–1263 (2019)
Ullah, A., Javaid, N., Samuel, O., Imran, M., Shoaib, M.: CNN and GRU based deep neural network for electricity theft detection to secure smart grid. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 1598–1602. IEEE (2020)
Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., Choi, J.-G.: LSTM and bat-based RUSBoost approach for electricity theft detection. Appl. Sci. 10(12), 4378 (2020)
Khan, Z.A., Adil, M., Javaid, N., Saqib, M.N., Shafiq, M., Choi, J.G.: Electricity theft detection using supervised learning techniques on smart meter data. Sustainability 12(19), 8023 (2020)
Javaid, N., Jan, N., Javed, M.U.: An adaptive synthesis to handle imbalanced big data with deep Siamese network for electricity theft detection in smart grids. J. Parallel Distrib. Comput. 153, 44–52 (2021)
Ramos, C.C., Rodrigues, D., de Souza, A.N., Papa, J.P.: On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization. IEEE Trans. Smart Grid 9(2), 676–683 (2016)
Hasan, M., Toma, R.N., Nahid, A.A., Islam, M.M., Kim, J.M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)
Avila, N.F., Figueroa, G., Chu, C.C.: NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting. IEEE Trans. Power Syst. 33(6), 7171–7180 (2018)
Gul, H., Javaid, N., Ullah, I., Qamar, A.M., Afzal, M.K., Joshi, G.P.: Detection of non-technical losses using SOSTLink and bidirectional gated recurrent unit to secure smart meters. Appl. Sci. 10(9), 3151 (2020)
Aslam, Z., Javaid, N., Ahmad, A., Ahmed, A., Gulfam, S.M.: A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids. Energies 13(21), 5599 (2020)
Maamar, A., Benahmed, K.: A hybrid model for anomalies detection in AMI system combining K-means clustering and deep neural network. Comput. Mater. Continua 60(1), 15–39 (2019)
Viegas, J.L., Esteves, P.R., Vieira, S.M.: Clustering-based novelty detection for identification of non-technical losses. Int. J. Electr. Power Energ. Syst. 101, 301–310 (2018)
Coma-Puig, B., Carmona, J.: Bridging the gap between energy consumption and distribution through non-technical loss detection. Energies 12(9), 1748 (2019)
Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645–9657 (2019)
Huang, Y., Qifeng, X.: Electricity theft detection based on stacked sparse denoising autoencoder. Int. J. Electr. Power Energ. Syst. 125, 106448 (2021)
Li, W., Logenthiran, T., Phan, V.T., Woo, W.L.: A novel smart energy theft system (SETS) for IoT-based smart home. IEEE Internet Things J. 6(3), 5531–5539 (2019)
Ghasemi, A.A., Gitizadeh, M.: Detection of illegal consumers using pattern classification approach combined with Levenberg-Marquardt method in smart grid. Int. J. Electr. Power Energ. Syst. 99, 363–375 (2018)
Zheng, K., Chen, Q., Wang, Y., Kang, C., Xia, Q.: A novel combined data-driven approach for electricity theft detection. IEEE Trans. Ind. Inf. 15(3), 1809–1819 (2018)
Wang, X., Yang, I., Ahn, S.-H.: Sample efficient home power anomaly detection in real time using semi-supervised learning. IEEE Access 7, 139712–139725 (2019)
Razavi, R., Gharipour, A., Fleury, M., Akpan, I.J.: A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energ. 238, 481–494 (2019)
Punmiya, R., Choe, S.: Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans. Smart Grid 10(2), 2326–2329 (2019)
Bitam, S., Batouche, M., Talbi, E.G.: A survey on bee colony algorithms. In: 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Ph.D. Forum (IPDPSW), pp. 1–8. IEEE (2010)
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Asif, M. et al. (2021). Alexnet-Adaboost-ABC Based Hybrid Neural Network for Electricity Theft Detection in Smart Grids. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_24
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