期刊
ENERGIES
卷 13, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/en13123110
关键词
data mining; adaptive neuro fuzzy inference system (ANFIS); non-technical losses (NTLs); power theft detection; smart grid; smart electricity metering; power distribution grids
资金
- Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant [81740]
- General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant [81740]
Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider's and nation's revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)-with many applications in many various areas-is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.
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