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Non-technical losses (NTL) refer to unrecorded power consumption generated by dishonest customers, which is a substantial issue affecting the power system stability and economic efficiency of the power grid. The detection of dishonest customers is hindered by the complexity of NTL such as data feature selection, retention time feature, and power consumption pattern judgment. This work addresses these issues using meter recording data, proposing an NTL detection approach with Gram’s angle field (GAF) and deep residual network (ResNet). Principal component analysis (PCA) method is applied to compress multiple electricity detection indexes, which aims to obtain multi-dimensional power consumption data characteristics without changing its timing characteristics. The GAF method is used to convert the time-series power features of individual users into a two-dimensional image, achieving the purpose of maintaining the user’s time-series features and user-based units. The images generated by the GAF method, which contain information about the electricity consumption characteristics of many customers, are classified by ResNet to highlight customers with NTL. The claimed algorithm was tested on a dataset consisting of both fraudulent and non-fraudulent subscriber data. The results demonstrated that the NTL detection method based on GAF and ResNet is superior to the traditional NTL detection method and has high accuracy.

• A practical solution to curb the electricity theft occurring due to hook-line activity. • The effectiveness, ease of application and flexibility of proposed electricity theft prevention technique is tested for various legal and illegal loads. • This technique curbs the non-technical losses, balances the supply demand response and reduces additional tariff posed on the genuine consumers. • It restricts the overloading of DT by preventing hook-line activity. Thus, life expectancy of DT will increase. The pilferage of electricity is a major reason for high aggregate technical and commercial losses. New technologies and methodologies incorporating anti-tampering features have been developed in metering system to arrest pilferage, but innovative means have been devised by the unscrupulous elements of society to counter the same, sometimes in connivance with those who are expected to check such activities. The proposed energy theft prevention scheme is an online approach which performs theft detection and prevention in a real-time scenario without any supply interruption. The aim is accomplished by design and implementation of electricity pilferage prevention system (EPPS) developed to provide a different amplitude of unsuitable supply voltage for random time intervals to illegally connected loads depending on the voltage regulation of power distribution network. In order to maintain uninterrupted supply to genuine consumers, consumer supervision unit (CSU) is provided by the utility. Thus, consumer appliances will operate efficiently as the voltage regulation is improved by preventing hook-line activity. The proposed theft prevention scheme benefits both utility and consumer in terms of increase in revenue and reliability of power supply respectively. The suitability, flexibility in operation and effectiveness of proposed EPPS and CSU based theft prevention scheme is experimentally demonstrated for different types of legal and illegal consumer loads under various voltage regulation scenario by using a microcontroller model STM32L151x6/8/B. In our experiments the electricity theft detects on the basis of users' data with 99% accuracy. Also the theft prevention in a real-time scenario without any supply interruption and without knowing the exact topology of the power distribution network is operated with 100% accuracy. [ABSTRACT FROM AUTHOR]