Artificial Neural Network Based SVC Switching At Distribution System for Minimal Injected Harmonics

Abstract

Electrical distribution system grieves from various problems like reactive power burden, unbalanced loading, voltage regulation and harmonic distortion. However DSTATCOMS are ideal solutions for such systems, they are not popular because of the cost and complexity of control involved. Phase wise balanced reactive power compensations are essential for fast changing loads needing dynamic power factor correcting devices leading to terminal voltage stabilization. Static Var Compensators (SVCs) remain ideal choice for such loads in exercise due to low cost and simple control strategy. These SVCs, while correcting power factor, inject harmonics into the lines producing serious concerns about quality of the distribution line supplies at PCC. This study proposes to minimize the harmonics injected into the distribution systems by the operation of TSC-TCR type SVC used in combination with fast changing loads at LV distribution level. The system with Artificial Neural Network (ANN) is smart and can be used at distribution level where load harmonics are within limits. Substantiation of the system and by using Matlab/Simulink with proper modeling.

Keywords: ANN, SVC, harmonic distortion, Matlab, minimal injected harmonics.

Cite this Article

K.J. Bhayani, A.H. Budhrani. Artificial Neural Network Based SVC Switching At Distribution System for Minimal Injected Harmonics. Trends in Electrical Engineering. 2019; 9(1): 41–47p.