No abstract

Transmission line fault detection is a complex, high-cost and time-consuming work for deep-sea offshore wind farms. Therefore, rapid intelligent on-line fault detection and classification of submarine transmission lines play a very important role in the offshore wind farm reliability improving and operating costs reduction. This paper presents a novel hybrid on-line detection method that combines Wavelet noise Reduction, Clarke transform, Stockwell transform and Decision Tree (WRC-SDT). First, the measured Wind Turbine (WT) voltage signals are processed through wavelet noise reduction and Clarke transform to get the gradient of the voltage component. The gradient of the voltage component is then monitored in order to detect faults. Second, the recorded WT current date are processed through Stockwell transform with a view to obtaining the transmission line fault eigenvalues. The fault eigenvalues are then taken as the input of decision tree in order to classify different types of faults. To verify the feasibility and performance of the proposed method, the comparison of a detection and classification result is presented based on more than 1600 fault simulation data. The results show that WRC-SDT method is immune to fault resistance, starting angle and location. The proposed method is also robust to measurement noise. INDEX TERMS Clarke transform, transmission line, fault detection, fault classification, Stockwell transform, offshore wind turbine.