Parallel genetic algorithm based unsupervised scheme for extractionof power frequency signals in the steel industry

Abstract

A steel industry has different types of loads, and so the incoming supply voltage of some units becomes distorted thus affecting those systems that depend on a distortionless supply. A novel unsupervised scheme named the recursive hybrid parallel genetic algorithm based line enhancer (RHPGABLE) scheme is proposed, to track the desired power frequency signal from the corrupted one. The RHPGABLE scheme is based on a proposed new crossover operator known as the generalised crossover (GC) operator. The delay and the filter coefficients are estimated recursively to yield optimal solutions. In the recursion of the proposed RHPGABLE algorithm, a parallel genetic algorithm (PGA) based on a coarse-grained approach is employed to estimate the delay, while the filter coefficients are estimated by PGA and a least mean squares (LMS) algorithm. RHPGABLE is an unsupervised scheme in the sense that no a priori knowledge of delay or filter coefficients and the associated training signal component is assumed to be available. The proposed scheme has been tested successfully on both synthetic data and data obtained from the Steel Melting Shop of Rourkela Steel Plant, Orissa, India