Research On Transient Stability Assessment Of Power Systems Based On Wide-area Dynamic Information

Transient stability assessment (TSA) has been being an important task to ensure the secure and economical operation of power systems. With interconnection of large-scale power grids, electricity market reform and growing presence of large-scale intermittent renewable energy, the dynamic behaviors of the power systems are becoming more complex and difficult to be controlled, with more serious consequences resulted from loss of stability. The existing TSA methods, such as time domain simulation methods and direct methods, can not meet the needs of online applications required by the modern power systems. In recent years, pattern recognition-based TSA (PRTSA) technology has attracted great attention of researchers in such a field at home and abroad. Its main task is to establish a mapping relationship between system state variables and system stability conclusions. Compared to the other TSA methods, the PRTSA methods have a lot of advantages, such as strong learning ability, fast assessment speed and acquisition of potentially useful information. They have a good prospect in the field of the on-line security and stability analysis of power systems.This thesis systematically studies the relative problems of PRTSA, including feature selection, classifier construction, online learning, topology change adaptation and rule extraction. First, an original feature set is extracted from the post-fault system information which can be provided by the Wide-Area Measurement Systems (WAMS) to represent the characteristics of power system transient stability, and an optimal feature subset is then selected by a proposed feature selection method to reduce the input space dimension. Then, a TSA classifier based on the optimized extreme learning machine (ELM) is constructed. After that, an online learning mechanism based on the integrated online sequential ELM is proposed. Finally, the adaptability of the proposed TSA method to the network topology changes and extraction of assessment rules from the ELM-based classifier are studied.The main work of this thesis is summarized as follows:1. A new feature selection method based on an improved maximal relevance and minimal redundancy (mRMR) criterion is proposed for TSA. First, based on the post-fault system information provided possibly by WAMS, an original feature set for stability classification is extracted. Then, the standard mRMR is improved and applied to feature selection for compressing the feature set. A group of nested candidate feature subsets are obtained by using the incremental search technique, and each candidate feature subset is evaluated by a support vector machine classifier to find the optimal feature subset with the highest classification accuracy.2. A novel TSA model based on an optimized ELM is proposed. Based on the selected optimal feature subset, ELM is used to build a TSA classifier. The parameters of the ELM model are optimized by the improved bacterial colony chemotaxis algorithm, and the classification ability of the ELM model is improved.3. A TSA online learning mechanism based on an ensemble of online sequential ELM (OS-ELM) model is proposed. In order to overcome the difficulty of on-line model updating, the OS-ELM and online boosting algorithm are employed as a weak classifier and an ensemble learning algorithm for online learning of the integrated ELM models respectively. The stability and generalization ability of the OS-ELM model is greatly improved.4. The adaptability of the proposed TSA method to the network topology changes is investigated. Based on the original feature set established, a sample set is constructed with the network topology changes in consideration, and the optimal feature subset is found by using the proposed feature selection method. The optimized ELM is then employed as a TSA classifier to evaluate the network topology adaptability of the proposed TSA method. The results show that, compared to the previous TSA methods, the topology adaptability of the proposed method is much higher.5. A novel rule extraction method for TSA is proposed by using ELM and an improved Ant-miner algorithm. In order to overcome the shortcomings of low understandability and interpretability of a’black-box’learning machine, first the basic principle of Ant-miner algorithm is studied; then based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based TSA model; and finally, a set of classification rules are obtained by the improved Ant-miner algorithm to replace the original ELM network for TSA.