Machine Learning Techniques for Power Quality Enhancement of Power Distribution Systems with FACTS Devices
Received: 11 March 2024 | Revised: 1 May 2024 | Accepted: 3 May 2024 | Online: 2 August 2024
Corresponding author: Malladi Lakshmi Swarupa
Abstract
The power quality problem refers to the issues caused by the sudden rise of nonstandard voltage, current, or frequency. The problems that emerge from poor power quality due to non-linear loads are voltage sag, swell, interruptions, harmonics, and transients in distribution systems. Various compensation devices are used nowadays to improve power quality. The advances in power electronic technologies improve the reliability and functionality of power electronic-based controllers, resulting in increased applications of FACTS devices like DSTATCOM and Dynamic Voltage Restorer (DVR) which are fast, flexible, and efficient solutions to power quality problems. These devices are used to restore the source, load voltage, and current disturbances caused by different loads and faults. These devices were tested in a standard IEEE 14-bus system for Total Harmonic Distortion (THD) minimization while utilizing PI-based Artificial Neural Networks (ANNs) and Linear Regression (LR). The results were analyzed and compared.
Keywords:
THD, power quality, FACTS devices, types of faults, IEEE 14 bus system, linear load, non-linear load, ANN, linear regressionDownloads
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Copyright (c) 2024 Malladi Lakshmi Swarupa, Katuri Rayudu, Chava Sunil Kumar, Sree Lakshmi Gundebommu, P. Kamalakar
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