P. Lakshman Naik, H. Jafari, T. Sudhakar Babu, A. Anil, S. Venkata Padmavathi, D. Nazarpour,
Volume 19, Issue 2 (June 2023)
Abstract
This paper demonstrates an enhancement of power quality in grid integrated systems with the help of the proposed control strategy for voltage source converter based active power filters. The Shunt Active Power filters (SAPF) are extensively utilized in modern grid integrated systems to diminish the power quality concerns associated with it. The SAPF is one of the various power filters, which has better dynamic performance. The SAPF requires an accurate control strategy that provides robust performance under source and loads unbalance conditions. The proposed control scheme is responsible for generating the gate signals to activate the operation of Voltage Source Converter (VSC) based Active Power Filter. Thus, the performance of mitigation of harmonics of source current principally depends on the adopted algorithm. The present paper represents a performance study of a control scheme to mitigate power quality issues in the grid integrated system. The proposed system is modelled and simulated in MATLAB-Simulink in Simpower system block set.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (December 2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
