Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review

• Published in: Energies (Basel) Vol. 15; no. 19; p. 7164
• Main Authors: Behara, Ramesh Kumar, Saha, Akshay Kumar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Air-turbines; Alternative energy sources; Analysis; Artificial intelligence; Computational linguistics; Datasets; Deep learning; Design optimization; doubly fed induction generator; Electric power production; Electric power systems; Electricity distribution; Electricity generation; End users; Generators; Germany; Language processing; Machine learning; Massachusetts; Natural language interfaces; power electronics; Random variables; renewable energies; Renewable resources; smart grid; Switzerland; Systems stability; Turbines; United States; Washington, D.C; Wind power; wind turbines; United States; Switzerland; Massachusetts; Germany; Washington, D.C
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15197164

The reliability assessment of smart grid-integrated distributed power-generating coordination is an operational measure to ensure appropriate system operational set-ups in the appearance of numerous issues, such as equipment catastrophes and variations of generation capacity and the connected load. The incorporation of seasonable time-varying renewable energy sources such as doubly fed generator-based wind turbines into the existing power grid system makes the reliability assessment procedure challenging to a significant extent. Due to the enormous number of associated states involved in a power-generating system, it is unusual to compute all possible failure conditions to determine the system’s reliability indicators. Therefore, nearly all of the artificial intelligence methodology-based search algorithms, along with their intrinsic conjunction mechanisms, encourage establishing the most significant states of the system within a reasonable time frame. This review’s finding indicates that machine learning and deep learning-based predictive analysis fields have achieved fame because of their low budget, simple setup, shorter problem-solving time, and high level of precision. The systems analyzed in this review paper can be applied and extended to the incorporated power grid framework for improving functional and accurate analytical tools to enrich the power system’s reliability and accuracy, overcome software constraints, and improve implementation strategies. An adapted IEEE Reliability Test System (IEEE-RTS) will be applied to authenticate the relevance and rationality of the proposed approach.