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Classification for Transient Overvoltages in Offshore Wind Farms Based on Sparse Decomposition

The transient overvoltages in offshore wind farms caused by faults or frequent operations of electrical equipment are particularly severe. In order to classify the internal transient overvoltages in offshore wind farms, this research firstly proposes a feature extraction method based on sparse decomposition using alternating direction method of multipliers, which extracts the harmonic and impulsive components from transient overvoltages. Then based on the impulsive components and transient overvoltages, two identification features are constructed. One is an impulsive feature and the other one is a ratio feature between impulsive energy values and original energy values. Finally, based on the constructed features, a support vector machine is employed to identify different types of internal transient overvoltages. Extensive simulations and experiments are performed to verify the effectiveness of the proposed feature extraction method, which is also compared with the widely used conventional wavelet algorithms and the mathematical morphology. Results show the proposed feature extraction method is capable of classifying and discriminating among various types of internal transient overvoltages with a strong self-adaptive ability and an improved accuracy.