In a significant stride towards enhancing power quality in renewable energy systems, researchers have developed an innovative approach that combines machine learning and optimization techniques. The study, led by Singh Manpreet from Baba Banda Singh Bahadur Engineering College in Fatehgarh Sahib, India, and affiliated with IKGPTU Jalandhar, Punjab, introduces an adaptive neuro-fuzzy inference system (ANFIS)-based firebug swarm optimization (FBSO) algorithm integrated with a unified power quality conditioner (UPQC). This advanced framework aims to address power quality (PQ) issues in hybrid renewable energy systems (HRESs), which are increasingly vital in meeting global energy demands.
The research, published in the journal “Power Electronics and Drives” (translated from its original title), focuses on the integration of photovoltaic (PV) systems, wind turbines (WTs), and battery energy storage systems (BESSs) into grid-integrated systems. These hybrid renewable energy sources are crucial for sustainable energy solutions, but they often face challenges related to power quality, such as voltage sags, current sags, and harmonic distortions.
Singh Manpreet and his team have proposed a multi-resolution proportional-integral-derivative (MRPID) controller alongside an ANFIS-FBSO-based controller to mitigate these issues. The FBSO algorithm optimizes the learning function of the ANFIS, ensuring optimal outcomes for power balancing and frequency stabilization. “The combination of machine learning and optimization techniques allows for dynamic management of energy flow and system stability in HRESs,” explains Singh Manpreet. This intelligent, adaptive, and predictive control mechanism is a significant advancement in the field of renewable energy integration.
The proposed technique was validated under various conditions, including voltage sag, current sag, real power, reactive power, and total harmonic distortions (THDs). The results were compared with existing methods, demonstrating the efficacy of the ANFIS-FBSO framework. “Our approach not only meets load demand but also enhances power quality, which is essential for the reliable operation of renewable energy systems,” adds Singh Manpreet.
The implications of this research are far-reaching for the energy sector. As the world shifts towards renewable energy sources, ensuring power quality and system stability becomes paramount. The ANFIS-FBSO framework offers a robust solution that can be implemented in various renewable energy systems, enhancing their performance and reliability. This innovation could pave the way for more efficient and stable grid-integrated hybrid renewable energy systems, ultimately contributing to a more sustainable energy future.
The study’s findings highlight the potential of combining machine learning and optimization techniques to address complex challenges in renewable energy integration. As the energy sector continues to evolve, such advancements will be crucial in shaping the future of sustainable energy solutions. The research conducted by Singh Manpreet and his team represents a significant step forward in this direction, offering valuable insights and practical solutions for the energy industry.