Applications of artificial intelligence in power electronics

Renewable energy sources rely heavily on power electronic drives and associated equipment. Transforming energy from one form to another demands high-level power converters. Several factors, such as design, control, and maintenance, influence the efficiency of a power conversion system. Artificial intelligence (AI) is an incredibly robust technology that can be utilized in various applications like power electronics, face and voice recognition, autonomous systems, and many more. It can even be used in medical diagnosis. In the field of power electronics, AI offers certain distinct advantages that make it more feasible to deploy. The application of AI is founded on three stages: design, control, and maintenance. AI has a greater effect on control and maintenance than on design. In addition, a substantial quantity of data is required to feed the AI algorithms that monitor the different power electronic stages. The most commonly used AI approaches in power electronics are fuzzy systems, machine learning, deep learning, and neural networks. Recently, machine learning algorithms have become more prominent. The machine learning model predicts the output via a learning experience. Learning occurs when a system given training input data modifies its parameters and adapts itself to produce the intended output. The machine learning algorithms are classified into three types supervised, unsupervised, and reinforcement learning. With the advent of more powerful computer networks, reinforcement learning has emerged as a promising new area in machine learning as it pertains to power electronics. The power electronics industry might greatly benefit from artificial intelligence, but more research is required to fully harness the data’s potential in training an AI model.