This paper presents a unique online reference control technique for distribution static compensators that use the reinforcement learning algorithm. The goal of this strategy is to address the power quality issues that develop in microgrids. The unique controller is designed to correct reactive power, harmonics, and unbalanced load current in a microgrid by using voltage and current characteristics. The current-based controller use the quadrature axis (q-axis) and the zero axis (0-axis) to correct for unequal load current in scattered resource networks. In contrast, the voltage controller is used to change the desired value of the reactive power reference. The control technique was developed and implemented in an autonomous microgrid with a weak AC-supply (non-stiff source) distribution system. It has been tested with various load circumstances and three-phase fault scenarios. Several situations are analyzed, and simulation results for a variety of settings are discussed. The report also contains a full evaluation of the effectiveness of the developed online secondary control mechanism.
DSTATCOM control, microgrid management, online control, power quality enhancement, reactive power control, reinforcement learning.
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