Expert system for non-technical loss detection in power distribution grids using particle swarm optimization and nested power flow integration

Abstract

Non-Technical Losses in power distribution grids, primarily caused by electricity theft and meter inaccuracies, pose a significant challenge to utility companies, impacting revenue and grid stability. This paper introduces a novel expert system integrating nested Power Flow analysis with swarm intelligence for accurate Non-Technical Losses detection and localization in distribution grids. The proposed system leverages smart meter data, grid topology, and substation transformer readings to formulate an optimization problem in which the Active Power and Reactive Power consumption of one or more consumers is estimated using Particle Swarm Optimization. A key feature of the system is its adaptability to various operational scenarios, such as grid size, topology uncertainty, and distributed energy resource penetration. Extensive experimental results confirm the effectiveness of the proposed NTL detection method. In simulated scenarios, the worst-case Mean Absolute Error for Active Power estimation is limited to 0.1391 kW, with a corresponding mean actual consumption of 1.004 kW. For Reactive Power, the Mean Absolute Error does not exceed 0.0175 kVAr, relative to a mean consumption of 0.1072 kVAr. When evaluated on real consumption data, the worst-case Mean Absolute Error for Active Power across all smart meters is 0.1028 kW, with a mean actual consumption of 5.1333 kW, while for Reactive Power the worst-case Mean Absolute Error reaches 0.1385 kVAr against a mean consumption of 2.3433 kVAr. Furthermore, in the specific case where real active and reactive consumption is zero the proposed method maintains Mean Absolute Error values of 0.8095 kW for Active power and 0.0944 kVAr for Reactive power, thus verifying its reliable performance in avoiding false positives.