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Regression WiSARD application of controller on DC STATCOM converter under fault conditions

Capable of supplying local loads, DC microgrids have received much attention in the last decade for alleviating power flow through the main power grid. This has been achieved through the use of edge devices on the control of the converters, but, among other problems, microgrids have stability issues when Constant Power Loads (CPL) are present. This problem was already solved in the literature with the DC STATCOM power converter, in normal operation mode, it can deal with the grid operation. However, in fault cases, the solutions available still fail to ignore faults or even contribute to them. The present work aims to explore the potential of a light machine learning algorithm of the type Weightless Artificial Neural Network (WANN) for predicting the output of the original controller used in the DC STATCOM on an edge device connected to a converter, and investigate its generalization capability under microgrid fault situations. The WANN used is based on the regression variant of the Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), coined as Regression WiSARD (ReW). The evaluation criteria employed measured the capability of the controller to reject the fault condition. Initial results showed surprisingly good results in comparison to the original DC STATCOM controller, indicating that a ReW-based controller plays well the role of the DC STATCOM and was able to cope with fault situations.