Abstract
This study proposes a fuzzy self-organized neural networks (SOM) model for detecting fraud by domestic customers, the major cause of non-technical losses in power distribution networks. Using a bottom-up approach, normal behavior patterns of household loads with and without photovoltaic (PV) sources are determined as normal behavior. Customers suspected of energy theft are distinguished by calculating the anomaly index of each subscriber. The bottom-up method used is validated using measurement data of a real network. The performance of the algorithm in detecting fraud in old electromagnetic meters is evaluated and verified. Types of energy theft methods are introduced in smart meters. The proposed algorithm is tested and evaluated to detect fraud in smart meters also.
Highlights
Power grids fall into three main sectors of production, transmission and distribution
This study considers photovoltaic systems equipped with maximum power point tracking (MPPT)
The purpose of this study is to present a method for detecting fraud as the most important non-technical factor in distribution network losses
Summary
Power grids fall into three main sectors of production, transmission and distribution. Numerous proposed and tested methods have had significant results in monitoring and reducing technical losses, but technological changes such as smart grids techniques and technologies [6], digital meter technology, and the development of domestic energy generation using on-grid rooftop photovoltaic (PV) panels have created complexities in non-technical loss-detection models. Expanding the use of almost no-cost solar/mini-wind/micro-hydro power (apart from initial investment costs) will generally encourage a minimal use of the costly network energy, but the problem arises when there are similarities between the behavior of subscribers suspected of manipulating meters and subscribers using these small resources This similarity of consumption behavior makes it difficult to distinguish these two types of subscribers using the methods presented in previous research. The model is evaluated in a real sample network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
