The Power Quality Forecasting Model for Off-Grid System Supported by Multiobjective Optimization

Measurement and control of electric power quality (PQ) parameters in off-grid systems has played an important role in recent years. The purpose is to detect or forecast the presence of PQ parameter disturbances to be able to suppress or to avoid their negative effects on the power grid and appliances. This paper focuses on several PQ parameters in off-grid systems and it defines three evaluation criteria that are supposed to estimate the performance of a new forecasting model combining all the involved PQ parameters. These criteria are based on common statistical evaluations of computational models from the machine learning field of study. The studied PQ parameters are voltage, power frequency, total harmonic distortion, and flicker severity. The approach presented in this paper also applies a machine learning based model of random decision forest for PQ forecasting. The database applied in this task contains real off-grid data from long-term one-minute measurements. The hyperparameters of the model are optimized by multiobjective optimization toward the defined evaluation criteria.