Work

Enhancing power quality in solar-wind grid-connected systems through soft computing techniques

Year: 2024

Type: article

Abstract: This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

DOI: 10.11591/ijpeds.v15.i4.pp2493-2500

Authors: Jayashree Kathirvel, Pushpa Sreenivasan, Soni Mohammed, Rabinarayan Sethi, Maganti Syamala

Institutions: Rajalakshmi Engineering College, Dayananda Sagar College of Engineering, Indira Gandhi Institute of Technology, Indira Gandhi Delhi Technical University for Women, Koneru Lakshmaiah Education Foundation

Language: English

Topic: Power Quality and Harmonics

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4403763189

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