Power Quality Forecasting of Microgrids Using Adaptive Privacy-Preserving Machine Learning

Microgrids face challenges in monitoring and controlling the power quality (PQ) of integrated electrical systems to make timely decisions. Inverter-based technologies handle small-scale smart grids' power quality parameters (PQPs) and play an important role in condition monitoring. Accurate forecasting of such parameters is difficult due to the stochastic nature of demand, distributed generation, and weather conditions. Moreover, energy clients have concerns over growing privacy and security breaches for collaboration involving data exchanges. This study aims to predict PQPs indices of home microgrids using ANN, LSTM, and CNN-LSTM models. To preserve users' privacy, federated learning has been applied with some adaptive differential privacy on the global model and clients' data. Comparative analysis of the ML model and DP parameters shows that the LSTM model gives better results with adequate privacy parameters to predict the PQPs of five distributed microgrids. LSTM model gives the least MAE of 0.2323 for FL without privacy and 0.3256 test loss for appropriate DP level.