基于同步相量的优化极限学习机暂态稳定预测
Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
摘要：
提出了一种基于优化极限学习机（ELM）的电力系统暂态稳定预测（TSP）新方法。首先，从同步测量中提取表征电力系统暂态稳定性的输入特征。然后，ELM分类器被用来建立TSP模型。最后，利用改进的粒子群优化算法（IPSO）对模型的最优参数进行优化。该建议的新颖之处在于，它提高了基于ELM的TSP模型的预测性能，通过使用IPSO优化模型的参数与同步相量。最后，基于IEEE 39节点系统和一个大型实际电力系统的测试结果，验证了该方法的正确性和有效性。
英文摘要：
A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.
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