Fuzzy Logic-Driven Reliability Improvement in Power Systems under Environmental Conditions

Abstract

The Energy Not Supplied (ENS) index is an important indicator for determining the economic performance of power systems. This index analyzes the losses sustained as a result of network faults that prevent the delivery of electrical energy, as well as the economic impact on consumers during power outages. As a direct indication of system reliability, ENS sheds light on the resilience of distribution networks. However, a significant issue in dependability assessments is a lack of adequate statistical data. To address these concerns, fuzzy logic has evolved as a reliable method for modeling uncertainty in engineering parameters. Distribution networks, which operate in a wide range of environmental and geographic conditions—from urban to rural—are frequently vulnerable to natural disasters such as storms, heavy rainfall, and snow, as well as contaminants such as salt, dust, and humidity. Using fuzzy logic, this study analyzes how these ambient and natural elements affect the ENS index. The ENS index is calculated using MATLAB simulations on the RBTS-bus2 network. Results show that by addressing environmental and operational uncertainties, up to 31% improvement in both ENS and SAIFI indices can be achieved, with an overall enhancement of 41.88% in system reliability. These findings demonstrate the value of fuzzy logic in enhancing reliability evaluations even when statistical data is limited.