Optimum values of TMS and ps of overcurrent relays using amalgam of GA-NLP methods

Abstract

Overcurrent relays ha an enormous advantage that its time of operation can be reduced as well as its coordination can be sustained with selection of optimum values of time multiplier setting (TMS) and plug setting (PS) of OCRs. This paper presents amalgam of genetic algorithm (GA) — nonlinear programming (NLP) methods for determining optimum values of TMS and PS of overcurrent relays. Genetic algorithm (GA) has a shortcoming of converging to those values which may not be optimum, and nonlinear programming (NLP) methods have shortcoming of converging to local optimum values, in case if the initial choice is nearer to local optimum. This paper proposes amalgam method to overcome the drawback of GA and NLP method, thereby determining the optimum settings of overcurrent relays. The main crux of this paper is to formulate problem of determining optimum values of TMS and PS of overcurrent relays in distribution systems as a nonlinear optimization problem as well as to determining initial values of TMS and PS using GA technique and to find optimum values using NLP method, thus making both the methods advantageous.