Abstract：The complete or partial loss of excitation of large hydro generators is a common and serious fault, which requires the loss of excitation protection to act more quickly. Traditional loss of excitation protection based on static boundary of terminal impedance can only judge whether or not it is loss of excitation by the final static local information after the fault, it cannot reflect the changes of measurement impedance of various disturbances in complex power grid environment, and it is difficult to satisfy the selectivity and rapidity at the same time. Recently, either some mechanism methods that reflect the change of electrical quantity or some machine learning methods are difficult to adapt to unknown scenarios. In order to improve the generalization ability of machine learning loss of excitation protection, a new data-driven loss of excitation protection based on measurement impedance dynamic trajectory recognition was proposed in this paper. Firstly, the dynamic time-series motion characteristics of the impedance trajectory measured at the terminal in the fixed time window was analyzed, and statistics was introduced to describe the distribution of the time-series characteristics; Secondly, the features were sorted by using mutual information based the minimal-redundancy-maximal-relevance criterion (mRMR); Thirdly, the weighted convex combination of global and local kernel functions was used as the multi-kernel function to construct the multiple kernel learning support vector machine (MKLSVM) model, and the Wrapper strategy was used to determine the final input features; Finally, considering the influence of the severity of generator loss of excitation fault on the measured impedance trajectory, a double time window discrimination principle based on the classification function distance was proposed to improve the reliability of loss of excitation protection. This loss of excitation discrimination model considers both global and local features, and further improves the generalization ability of SVM classification model. Simulation results of simplified equivalent hydraulic generator transmission system show that the average accuracy of verification set before and after feature selection is 99.52% and 99.43% respectively under 7 single time windows within 0.3~3 s, which shows that mRMR can retain key features well. Further, the average accuracy of identifying the loss of excitation with the double time window discriminant strategy can reach 100% in 1.5 s time, which indicates that the reliability of the loss of excitation protection has been significantly improved. The generalization ability of the previously trained discriminant model was tested using IEEE-39 Bus System with new energy access considering more disturbance conditions, and the average accuracy of the test set in 1.5 s time is above 96.95%. In addition, the generalization ability of MKLSVM composed of multi-kernel function is stronger than single-kernel SVM. Finally, the influence of time window on the loss of excitation protection scheme are investigated. The results show that the severity of loss of excitation is inversely proportional to the time window length required for identification, which just meets the rapidity nature of loss of excitation protection. The following conclusions can be drawn from the simulation analysis: (1) The proposed scheme of loss of excitation protection for hydro generators guides the design of artificial intelligence frame of loss of excitation protection by utilizing the characteristics of measured impedance change trajectory with explicit physical meaning in the mechanism-based traditional loss of excitation protection, and achieves their complementary advantages. (2) The feature selection method based on mRMR and the MKLSVM model which considers both local and global information enhance the generalization ability of the model, while the two-time window discriminant based on the distance of classification function enhances the reliability of the model. (3) The proposed principle for identifying the loss of excitation fault is independent of the strength of the external power network and the topology of the power network, and has a strong applicability.
刘超, 肖仕武. 基于测量阻抗变化轨迹智能识别的水轮发电机失磁保护[J]. 电工技术学报, 2023, 38(7): 1808-1825.
Liu Chao, Xiao Shiwu. Loss of Excitation Protection of Hydro Generator Based on Intelligent Identification of Measured Impedance Change Trajectory. Transactions of China Electrotechnical Society, 2023, 38(7): 1808-1825.
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2023, Vol. 38 
