Recently utilities and end users become more concerned about power quality issues because the load equipments are more sensitive to various power quality disturbances, such as harmonics and voltage fluctuation. Harmonic distortion and voltage flicker are the major causes in growing concern about electric power quality. Power quality disturbance monitoring plays an important role in the deregulated power market scenario due to competitiveness among the utilities. This paper presents an evolutionary algorithm approach based on Adaptive Particle Swarm Optimization (APSO) to determine the amplitude, phase and frequency of a power quality signal. In this APSO algorithm the time varying inertia weight is modified as rank based, and re-initialization is used to increase the diversity. In this paper, to the authors highlight the efficacy of different evolutionary optimization techniques like classical PSO, Constriction based PSO, Clonal Algorithm (CLONALOG), Adaptive Bacterial Foraging (ABF) and the proposed Adaptive Particle Swarm Optimization (APSO) to extract different parameters like amplitude, phase and frequency of harmonic distorted power quality signal and voltage flicker.
TopIntroduction
In recent years, power quality has become a significant issue for both utilities and customers. Power quality issues (Arrillaga, Watson, & Chen, 2000; Bollen, 2000 ; Dugan, McGranaghan, & Beaty, 2000) and the resulting problems are the consequences of the increasing use of solid state switching devices, non-linear and power electronically switched loads, unbalanced power systems, lighting controls, computer and data processing equipment as well as industrial plant rectifiers and inverters. These electronic types of loads cause quasistatic harmonic dynamic voltage distortions, inrush, pulse type current phenomenon with excessive harmonics and high distortion. A power quality (PQ) problem usually involves a variation in the electric service voltage or current, such as voltage dips and fluctuations, momentary interruptions, harmonics and oscillatory transients causing failure or mal-operation of the power service equipment. In order to improve electric power quality, the sources and causes of such disturbances must be known before appropriate mitigating action can be taken. However, in order to determine the causes and sources of disturbances, one must have the ability to detect and localize these disturbances. Estimation of amplitude and phase of fundamental, as well as harmonic signals has been one of the important tasks in measurement, control, relaying protection, distribution automation, and intelligent instrumentation of power system. Accurate power fundamental frequency is a necessity to check the state of health of the power index, and a guarantee for accurate quantitative measurement of power parameters, such as voltages, currents, active power, reactive power, and energy, and so on, in multifunction power meters under steady states. It is more difficult to precisely estimate the fundamental frequency of power systems in presence of harmonics and noises than under sinusoidal condition. It is essential to seek and develop some effective algorithms for accurate estimation of the instantaneous fundamental frequency of power systems under non-sinusoidal conditions.