Power System Transient Stability Prediction And Control Based On Wide-Area Measurements

Currently, electric power networks dispersed geographically in China are being interconnected with the emphasis on transferring large amount of power from the west region to the east and keeping a generation/load balance between the south region and the north. With the huge interconnected power network in operation, complex dynamic phenomena will be witnessed and damages by the occurrence of instability events can be significantly great to the whole society. To understand and mitigate transient instability phenomena, it is necessary to develop advanced theories and implementation techniques. The dissertation sets transient stability prediction and control as its main topic, proposing effective control schemes against transient instability with a thorough utilization of computational intelligence techniques and hybrid systems theory. The control schemes are designed to use wide-area dynamics data provided by the wide-area measurement system (WAMS). The main contributions of the dissertation include the following.(1) The research background is introduced on the issue of optimal placement of wide-area measurement devices, with general placement rules and corresponding algorithms surveyed. An optimal placement method of wide-area measurement devices is proposed with the assumption that the linear observability criterion is not necessarily satisfied. The method is devised for transient stability issues, taking information content of system transient responses to disturbances as the performance criterion of placement alternatives. Through clustering analysis, coherency grouping, and intelligent search, the optimal or sub-optimal placement solution is found within limited time. The proposed method is applied to the actual Shandong power system. Simulation results show that placement sites distribute far from each other geographically and electrically across the network, placement sites expand in a successive manner facilitating future placement expansion, and the use of tabu search algorithm guarantees the efficiency of the method.(2) An online learning framework is proposed for transient stability prediction to be adaptive to the complex and time-variant operation conditions of electric power systems. The locally weighted projection regression algorithm is employed to implement online learning. A wealth of experimental evidence shows the information processing based on local receptive fields is a ubiquitous...