• Arsh Khan
• Shashwat Dhurandhar
• Rupendra Patel
• Mayank Nagwanshi
• Rose Mary Tirkey

Presented research paper delves into the pivotal realm of Power Quality Improvement, employing a sophisticated and innovative approach: the integration of Shunt Active Power Filter (SAPF) with Artificial Neural Network (ANN) and Machine Learning (ML) techniques. The convergence of SAPF technology with ANN and ML not only enhances the accuracy and efficiency of power quality monitoring but also facilitates adaptive and intelligent control strategies for optimal compensation. The analysis and modelling is done in MATLAB Simulink, A six IGBT Voltage Source Inverter (VSI) based Shunt Active Power Filter (SAPF) with hysteresis control is used to generate compensating current for harmonic mitigation at the point of common coupling (PCC). PQ method is used for this process employing a PI controller. The training data is collected from this model for our Machine Learning (ML) model. The PI controller is then replaced with the ML model to provide an improved, ANN based method for power quality improvement.

APF, SAPF, THD, IGBT, PCC, ANN, VSI, ML

"POWER QUALITY ANALYSIS AND IMPROVEMENT USING SHUNT ACTIVE POWER FILTER WITH NEURAL NETWORK & MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g1-g20, April-2024, Available :http://www.jetir.org/papers/JETIR2404601.pdf

"POWER QUALITY ANALYSIS AND IMPROVEMENT USING SHUNT ACTIVE POWER FILTER WITH NEURAL NETWORK & MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg1-g20, April-2024, Available at : http://www.jetir.org/papers/JETIR2404601.pdf