Enhancing condition monitoring of distributed generation systems through optimal sensor selection

Distributed generation (DG) systems comprising of renewable energy generation technologies will play a significantly increasing role for future power systems. One of the key concerns for deployment of DG systems is specifically related to their availability and reliability, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by huge amount of data to be processed especially in real time in order to reveal healthy conditions of the system. In this paper, an optimal sensor selection method based on principal component analysis (PCA) is proposed for condition monitoring of a DG system oriented to wind turbines. The proposed method is examined with both simulation data from PSCAD/EMTDC and SCADA data of an operational wind farm in the time, frequency, and time-frequency domains. The results have shown that the proposed technique could reduce the number of sensors whilst still maintaining sufficient information to assess the system's conditions.