scholarly journals Comparison of Adaptive Neuro-Fuzzy Inference Systems and Echo State Networks for PV Power Prediction

2015 ◽  
Vol 53 ◽  
pp. 92-102 ◽  
Author(s):  
I. Jayawardene ◽  
G.K. Venayagamoorthy
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Power Prediction ◽  
Echo State Networks ◽  
Neuro Fuzzy ◽  
Inference Systems

scholarly journals CANFIS based DSTATCOM modelling for solving power quality problems

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
B. B. Bukata ◽  
R. A. Gezawa
Keyword(s):  
Power Quality ◽  
Fuzzy Inference ◽  
System Structure ◽  
Fuzzy Inference Systems ◽  
Neuro Fuzzy ◽  
Two Factors ◽  
Voltage Swell ◽  
Artificial Neural Network Ann ◽  
Inference Systems ◽  
Distribution Grids

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.

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