Open AccessNovel Approach For Minimal Injected Harmonics At Distribution Level – Svc
Author(s) -
V. Lakshmi Devi,
T. Phanindra
Publication year - 2011
Publication title -
international journal of smart sensors and ad hoc networks
Language(s) - English
Resource type - Journals
ISSN - 2248-9738
DOI - 10.47893/ijssan.2011.1020
Subject(s) - harmonics , total harmonic distortion , control theory (sociology) , power factor , power (physics) , ac power , computer science , harmonic , distortion (music) , static var compensator , electric power system , fuzzy logic , voltage , engineering , electronic engineering , control engineering , control (management) , electrical engineering , amplifier , physics , cmos , quantum mechanics , artificial intelligence
Electrical distribution system suffers from various problems like reactive power burden, unbalanced loading, voltage regulation and harmonic distortion. Though 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 required 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 practice due to low cost and simple control strategy. These SVCs, while correcting power factor, inject harmonics into the lines causing serious concerns about quality of the distribution line supplies at PCC. This paper proposesto minimize the harmonics injected into the distribution systems by the operation of TSC-TCR type SVC used in conjunction with fast changing loads at LV distribution level. Fuzzy logic system and ANN are going to be used solve this nonlinear problem, givingoptimum triggering delay angles used to trigger switches in TCR. The scheme with Artificial Neural Network (ANN) is attractive and can be used at distribution level where load harmonics are within limits. Verification of the system and by using mat lab / simulink with proper modeling.