A novel protection scheme for transmission lines connected to solar photovoltaic and wind turbine farms using fuzzy logic systems and bagged ensemble learning

The integration of renewable energy sources (RESs), such as solar and wind power, into power systems presents unique challenges for transmission line protection. Traditional distance protection schemes may not be adequately sensitive or adaptable to the dynamic characteristics of RES-connected lines. To address these challenges, this paper proposes an intelligent novel protection scheme that combines fuzzy logic system for fault detection/classification with regression-based bagged ensemble learning for fault location estimation. The proposed scheme utilizes voltage signals of the bus connected to renewable energy sources processed with discrete Fourier transform (DFT) to extract relevant features for fault diagnosis. A Mamdani-based fuzzy inference system is implemented to analyze the DFT-extracted features and make decisions regarding fault occurrence and type. A bagged ensemble learning approach, incorporating multiple regression trees, is employed to accurately estimate the fault location along the transmission line. The performance and efficacy of the proposed protection scheme are verified through extensive MATLAB/SIMULINK simulations on the transmission line model integrated with renewable energy sources (solar and wind) considering the variations in different fault parameters with different solar irradiations and wind speeds. The results demonstrate that the scheme effectively detects and classifies various fault types in one cycle time, even under dynamic RES generation conditions. The proposed scheme achieved 99.56% accuracy in fault detection/classification confirming its reliable operation. Further, the proposed fault location estimation approach approximates the fault location within ± 5% error band, and the Chi-square test is performed to assess its reliability.