Abstract
Large-scale high-voltage trip-offs (HVTOs) of wind farms are serious incidents afflicting power systems that can lead to voltage instability, power deficiency, and frequency fluctuation. In order to reduce the influence of HVTOs, it is necessary to efficiently identify the fault source after an HVTO at a wind farm. A fault source identification method for wind farm HVTOs is proposed in this work. First, the fault tree analysis (FTA) method is used to summarize the causal and logical relationships among the different factors that lead to HVTOs at wind farms. An index system is constructed according to simulations of wind farm HVTOs under multiple scenarios. Second, a set of feature indices are selected from the original index system as key criteria for wind farm HVTOs based on the symmetrical uncertainty of mutual information and the maximum relevance and minimum redundancy (SU-MRMR) method. Finally, the particle swarm optimization (PSO) method is effectively utilized in the parameter optimization of support vector machine (SVM) method, and the optimized SVM with good performance is further employed for fault source identification based on the feature indices. Both single fault source and compound fault source are identified in an actual power system, and the proposed method is verified as a reliable solution for complex fault source identification for wind farm HVTOs based on the statistical identification accuracy.
Highlights
Large-scale centralized wind farms are usually far from load center [1] and are typically located at the end of the network
In this paper, a fault source identification method for wind farm high-voltage trip-offs (HVTOs) is formed based on feature indices and the PSOSVM method
Based on the SU-MRMR method, feature indices are selected as key criteria for fault source identification of wind farm HVTOs to reduce the redundancy of machine learning samples
Summary
Large-scale centralized wind farms are usually far from load center [1] and are typically located at the end of the network. Instance, at the Jiuquan wind power base in China, a 35 kV three-phase short-circuit fault in 2011 led to the trip-offs of ten wind farms by low-voltage protection and further resulted in the trip-offs of 6 wind farms by high-voltage protection [4] In another example, on the Lingshao UHVDC transmission line in Linzhou, China, a single-pole block fault in 2013 tripped off more than 20 wind turbines by highvoltage protection [5]. Y. Wang et al.: Complex Fault Source Identification Method for HVTOs of Wind Farms Based on SU-MRMR and PSO-SVM. In this work, taking into account the complex waveform characteristics of wind farm HVTOs, a novel fault source identification method for wind farm HVTOs is illustrated based on multi-dimensional features. PSO-SVM with better classification performance is effectively applied for single/compound fault source identification of wind farm HVTOs with high accuracy. Based on SU-MRMR, most important information about wind farm HVTOs is expressed with fewer features. (2) The optimized parameters by PSO are beneficial to obtaining a better training model with a higher accuracy than that based on the SVM with default parameters. (3) The proposed method provides a high statistical accuracy for single/compound fault source identification in the balance mode as well as in the unbalance mode
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