Название: Applied Artificial Intelligence (AI) to Green Power Technology
Автор: Yogesh Kumar Chauhan, Ranjan Kumar Behera, Asheesh K. Singh
Издательство: Nova Science Publishers
Серия: Computer Science, Technology and Applications
Год: 2022
Страниц: 270
Язык: английский
Формат: pdf (true)
Размер: 19.2 MB

Nowadays, Artificial Intelligence (AI) has a wider reach among all areas of engineering and technology for enhancing productivity, efficiency and performance. Green power sources are one such area. The major green power sources are solar, wind, small hydro, natural and hydrogen gas, etc. Most of these sources have issues of intermittency and non-linear relationship with input and output variables. So, there is a need to optimize the performance of these resources to improve their outlook for widespread use and acceptability.

Among the latest AI techniques, fuzzy logic and nature-inspired algorithms are leading from the front. These techniques are applied to improve the performance of renewable energy systems, green power technologies such as solar PV system/wind energy conversion system etc. and control of power electronics interfaces for renewable energy systems.

The aim of this book is create awareness and generate interest among UG/PG students, research scholars, engineers, scientists, and regulators and policy makers of government and the public and private sectors. The book will have wider reach in audience as it is application area of AI to green power technologies.

Fuzzy systems deal with imprecise/incomplete data by using a fuzzy set. Unlike the conventional set theory, an object can take any value between 0 and 1 using fuzzy membership. During fuzzification, exact (crisp) input values are transformed into fuzzy membership. Fuzzified data using a Fuzzy interference machine are combined with a prior rule to obtain fuzzy output converted to a crisp value known as defuzzification. Defuzzification can be maximum, mean of maximum and centroid.

A fuzzy logic system can handle imprecise or vague information, which is considered its major strength compared to other AI techniques. However, the significant difficulty of a fuzzy system is determining a good membership function. Also, a fuzzy system possesses no learning capability. To overcome these limitations, modelling of fuzzy systems is combined with other techniques resulting in a hybrid system.