Abstract
The percentage of losses in India in transmission and distribution sector of electricity has been fairly high. In distribution system, a considerable amount of energy is dissipated which can be categorized into technical and non-technical losses. It is possible to control and compute technical losses, provided the load quantities are known for the given power system, whereas the non-technical losses do not have any recorded information as it is difficult to track energy theft due to the act of meter tampering or bypassing the measurement system. Generally, sudden or surprise checking is done in localities, where the electricity theft is suspected by the distribution companies. However, these operations alone are not enough to identify the miscreants or to reduce the energy losses. Moreover, manual inspection is quite tedious and can be very costly. Thus, certain advance technologies like machine learning techniques need be used to counter the electrical theft more effectively. In this paper, various machine learning techniques are discussed and their performances are compared for the detection of power theft in power system.
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Banajyoti, A., Bhende, C.N. (2021). Performance Evaluation of Different Machine Learning Techniques for Detection of Non-technical Loss. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_11
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DOI: https://doi.org/10.1007/978-981-33-4866-0_11
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