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Decreasing Induction Motor Loss Using Reinforcement Learning

Mohammad Bagher Naghibi Sistani 1 and Sadegh Hesari 2
1. Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2. Bojnourd Branch, Islamic Azad University, Young Researcher and Elite Club, Bojnourd, Iran
Abstract—In this paper, we have tried to reduce the induction motor losses by controlling the magnetic currents in different torque loads. Reinforcement learning is a method where an agent considers the environment state chooses one action among all possible actions, and the environment returns a numerical signal as a reward for that action. The agent aims at finding a policy by trial-and-error method to reach the maximum sum of rewards. The main proposed idea of this paper is implementing Q-Learning algorithm to find the optimal action in every state of the environment. In this method, quantized amounts of electromagnetic Torque and motor speed are considered as states, and magnetic current is considered as action. Simulation results shows that this method can reduce the power loss about 50% in comparison with the standard driver of motor (FOC) when the motor works in low loads.

Index Terms—reinforcement learning, Q-Learning algorithm, induction motor, decreasing loss

Cite: Mohammad Bagher Naghibi Sistani and Sadegh Hesari, "Decreasing Induction Motor Loss Using Reinforcement Learning," Jounal of Automation and Control Engineering, Vol. 4, No. 1, pp. 13-17, February, 2016. doi: 10.12720/joace.4.1.13-17
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