Neural Network-Based Control Algorithm for DSTATCOM Under Nonideal Source Voltage and Varying Load Conditions

• Published in: Canadian journal of electrical and computer engineering Vol. 38; no. 4; pp. 307 - 317
• Main Authors: Jayachandran, J., Sachithanandam, R. Murali
• Format: Journal Article
• Language: English
• Published: IEEE Canada 01.09.2015
• Subjects: Artificial neural networks; Biological neural networks; Capacitors; Compensators; Computer simulation; Electric potential; Electric power generation; Harmonic analysis; Matlab; Neural networks; Reactive power; Strategy; Voltage; Voltage control; Distribution static compensator (DSTATCOM); neutral current mitigation; three-phase four wire (3P4W) distribution system; neural network; total harmonic distortion (THD) and unbalanced and/or distorted source; source and load
• ISSN: 0840-8688
• DOI: 10.1109/CJECE.2015.2464109

Distribution static compensator (DSTATCOM) is the optimal choice of power quality (PQ) compensator in a three-phase four-wire distribution system for the mitigation of PQ problems. The performance of the PQ compensator under varying load and nonideal source conditions relies on the control strategy. A neural network-based p-q control algorithm is proposed in this paper for the DSTATCOM, which comprises of a four-leg voltage-source converter with a dc capacitor. The proposed control strategy implements five artificial neural network controllers for, the conversion of nonideal voltage source into ideal sinusoidal voltage, the extraction of dc component p̅ of load real power supplied to the load, maintenance of the voltage across the capacitor, and mitigation of neutral current. The performance of the proposed neural network-based p-q control strategy for DSTATCOM is evaluated under various possible source and load conditions by simulating in MATLAB/Simulink environment, and the results obtained through the simulation are validated experimentally by a prototype developed in the laboratory. Both the experimental and simulation results prove that the performance of the proposed neural network-based control strategy is superior to the conventional method.