Hybrid Machine Learning-Based Intelligent Distance Protection and Control Schemes With Fault and Zonal Classification Capabilities for Grid- Connected Wind Farms

The poor performance of distance relays may stem from the inherently intermittent nature of wind power generation, presence of power electronic converters, the impact of power system harmonics, existence of series-compensated transmission lines, uncertainty of fault resistance, etc., which may adversely impact the performance of the distance protection relays. Thus, the distance relays in wind farms became a significant concern for utilities committed to ensuring compliance to the grid codes such as voltage-ride through capability and provide a stable and reliable power to the customers. Specialized algorithms, intelligent controllers, and advance monitoring systems must be developed to address these issues. Therefore, this article presents the analysis of several hybrid intelligent protection and control algorithms to improve the reliability of doubly fed induction generator (DFIG)-based wind farms during faults, and other dynamic operating conditions. First, a decision tree (DT) classification algorithm is developed as a fault classifier for the purpose of distinguishing between different types of faults, as well as normal operation and grid disturbances. Next, a support vector machine (SVM) as a fault location estimator and zonal protection scheme is proposed to assist with the decision-making process of distance relay by detecting the location of any type of fault on the transmission line, and precise line zoning protection with a high reliability. Lastly, a combined direct PI control-based scheme is developed for both rotor and grid side converters of the DFIG based wind energy conversion system (WECS). This scheme avoids extra PI based current loop to achieve robust performance at the time of grid side voltage dip as well as normal operating condition. The analysis of the proposed intelligent protection and control schemes exhibits satisfactory results in improving the reliability and stability of grid-connected wind farms.