Reliability assessment of distribution system using fuzzy logic for modelling of transformer and line uncertainties

Abstract

Abstract-Reliability assessment of distribution system, based on historical data and probabilistic methods, leads to an unreliable estimation of reliability indices since the data for the distribution components are usually inaccurate or unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in reliability inputs. In this paper, the ENS index along with other commonly used indices in reliability assessment are evaluated for the distribution system using fuzzy logic. Accordingly, the influential variables on the failure rate and outage duration time of the distribution components, which are natural or human-made, are explained using proposed fuzzy membership functions. The reliability indices are calculated and compared for different cases of the system operations by simulation on the IEEE RBTS Bus 2. The results of simulation show how utilities can significantly improve the reliability of their distribution system by considering the risk of the influential variables.

Evaluation of Distribution System Reliability Indices Using Fuzzy Reasoning Approach

“…In [16], authors utilized fuzzy methods to handle uncertainties and improve system reliability planning. In [17] several factors like age, exposure and wind for weather condition are modeled using fuzzy logic to evaluate the failure rate of the components in a distribution system. In this paper, a fuzzy approach is applied to model the additional uncertainty like, the effect of operation loading condition, annual maintenance planning and overvoltage which are the reasons for the failure of line and transformers and are not considered in the fuzzy modeling in [17].…”

“…In [17] several factors like age, exposure and wind for weather condition are modeled using fuzzy logic to evaluate the failure rate of the components in a distribution system. In this paper, a fuzzy approach is applied to model the additional uncertainty like, the effect of operation loading condition, annual maintenance planning and overvoltage which are the reasons for the failure of line and transformers and are not considered in the fuzzy modeling in [17]. In the proposed method, a fuzzy inference knowledge-based IF-THEN rule is developed adding three other inputs variables like operation loading conditions, maintenance, and temperature.…”

“…Similar to ideas developed by [17] the fuzzy membership functions of three influential variables (the age of component, weather conditions, and the exposure to the risk) are modified to reflect better the impact on failure rate and average repair time of each component. Three other variables like operation loading conditions, maintenance and temperature are added.…”

“…The failure rate is less, and repair time is shorter when the weather condition is normal, in compared with an adverse weather condition [17]. The input fuzzy model of weather condition has two variables: normal and adverse weather conditions.…”

Interdependence of Transmission Branch Parameters on the Voltage Levels

“…Statistical studies on the database from historical weather data for forecasting in [29] to probabilistic methods for reliability assessment based on historical data in [30] and a data-driven analysis on capacitor bank operation in [31] show that statistics derived from realworld data are commonly used for modeling and validation in power systems. The above literature review on synthetic grid modeling suggests that there is a need for a comprehensive statistical study on realworld power systems branch electrical and nonelectrical parameters.…”