Dynamic voltage restorer for efficient detection and compensation of voltage SAG using ANN based LMS as a new control strategy

Nowadays, innovations in the world of electrical and electronics exponentially increase sensitivity of electronic devices and control systems in daily use. Sensitivity of the devices and more dependency of industries on power systems make them more vulnerable to power quality issues such as voltage sags, spikes, interruptions, harmonics, power factor, and voltage and current unbalances. These power quality problems can lead the whole power systems towards malfunctioning, interruptions or in severe cases can damage the whole system. Whereas by IEEE reports, voltage sags are found to be most crucial and common power quality issue among all other power quality problems. Dynamic Voltage Restorer (DVR) is a cost effective, flexible and efficient compensating device for voltage related issues especially for voltage sags. This paper proposes an Artificial Neural Network (ANN) based on Least Mean Square Estimation Method (LMS) control system strategy; for detection and compensation of voltage sag with faster response time and accuracy for DVR. The proposed DVR is implemented in ATLAB6SIMULINK and tested under different power quality problems such as balanced and unbalanced voltage sags, swells and also for compensation of limited harmonics contamination with frequency shift. The achieved results validated the efficiency and performance of the system and further analysed in comparison with previous research works.

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