NeuroFuzzy Wavelet Based Auxiliary Damping Controls for STATCOM

NeuroFuzzy Wavelet Based Auxiliary Damping Controls for STATCOM

The integration of renewable energy sources and varying load demands have risen reliability and stability issues that need proper attention and optimum solution. Continuous and reliable operation of the power system is the key to any country's economic and industrial growth. In this regard, a S...

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Bibliographic Details
Published in:IEEE access Vol. 8; pp. 200367 - 200382
Main Authors: Dilshad, Saad, Abas, Naeem, Farooq, Haroon, Kalair, Ali Raza, Memon, Awais Ahmed
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
adaptive NeuroFuzzy
Artificial neural networks
Automatic voltage control
Biological neural networks
Control stability
Controllers
Damping
FACTS
Flexible AC power transmission systems
Fuzzy systems
low-frequency oscillations
Mexican Hat
morlet
Neural networks
Oscillators
Performance enhancement
Performance evaluation
Performance indices
power system
Power system stability
Renewable energy sources
STATCOM
Static synchronous compensators
Systems stability
Voltage stability
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Summary:The integration of renewable energy sources and varying load demands have risen reliability and stability issues that need proper attention and optimum solution. Continuous and reliable operation of the power system is the key to any country's economic and industrial growth. In this regard, a Static synchronous compensator (STATCOM) has extensively adapted Flexible Alternating Current Transmission System (FACTS) device mainly for Volt Ampere Reactive (VAR) compensation, voltage stability, damping oscillations to improve power system stability. In this work, A NeuroFuzzy wavelets based auxiliary control for STATCOM in a two-area power system is proposed in this article. The proposed control system was tested on four different fault scenarios. A modified Takagi-Sugeno-Kang (TSK) controller by applying a Wavelet Neural Network (WNN) is used to evaluate controllers' performance. The conventional TSK control is used to assess the performance of the proposed controllers. The TSK controller is modified by applying a WNN in the consequent part. The updated parameters in Morlet and Mexican hat wavelets are updated in the WNN, and these proposed adaptive controllers are employed as damping controllers with STATCOM. Simulation results show sufficient performance enhancement using the modified WNN based control schemes. The dynamic performance improvement is demonstrated with graphical time-domain and performance indices results using MATLAB/Simulink.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3031934