Open AccessCANFIS based DSTATCOM modelling for solving power quality problems
Author(s) -
Bala Boyi Bukata,
R. A. Gezawa
Publication year - 2021
Publication title -
journal of advances in sciences and engineering
Language(s) - English
Resource type - Journals
ISSN - 2636-607X
DOI - 10.37121/jase.v4i2.148
Subject(s) - adaptive neuro fuzzy inference system , voltage sag , harmonics , matlab , computer science , pid controller , smart grid , control theory (sociology) , artificial neural network , fuzzy logic , electric power system , control engineering , voltage , power (physics) , power quality , engineering , artificial intelligence , fuzzy control system , control (management) , electrical engineering , temperature control , physics , quantum mechanics , operating system
Devolution of the power grid into smart grid was necessitated by the proliferation of sensitive load profiles into the system, as well as incessant environmental challenges. These two factors culminated into aggravated disturbances that cause serious havoc along the entire system structure. The traditional proportional-plus-integral-plus-derivative (PID) solution offered by the distribution synchronous compensator (DSTATCOM) could no longer hold. As such, this paper proposes some soft-computing framework for redesigning DSTATCOM to automatically deal with power quality (PQ) problems in smart distribution grids. A recipe of artificial neural network (ANN) and coactive neuro-fuzzy inference systems (CANFIS) was fabricated for the objective. The system was modelled, simulated, and validated in MATLAB/Simulink SimPowerSystems environment. The performance of the CANFIS against adaptive neuro-fuzzy inference systems (ANFIS), ANN and fuzzy logic controllers’ algorithms proved superior in handling PQ issues like voltage sag, voltage swell and harmonics.