Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements
Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements
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DOI：
10.1109/tpwrs.2010.2082575
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发表时间：
2011-08-01
影响因子：
6.6
通讯作者：
Fernando, Ioni T.
中科院分区：
文献类型：
--
作者：
Gomez, Francisco R.;Rajapakse, Athula D.;Fernando, Ioni T.
本文首先表明，电力系统的暂态稳定状态下的大的干扰，如故障可以早期预测的基础上测量故障后的发电机电压，速度，或转子角度的值。同步采样值提供的相量测量单元（PMU）的发电机电压，频率，或转子角清除故障后立即收集被用作输入的支持向量机（SVM）分类器，预测的暂态稳定状态。对新英格兰39节点测试系统和委内瑞拉电网的研究表明，使用故障后恢复电压幅值测量值作为输入，可以进行更快、更准确的预测。在平衡和不平衡故障条件下，以及在不同的操作条件下，测量误差的存在下，电压敏感负载，和网络拓扑结构的变化的准确性和鲁棒性的暂态稳定预测算法与电压幅值测量进行了广泛的测试。在使用新英格兰39节点测试系统进行的各种测试中，所提出的算法总是可以预测电力系统何时接近暂态不稳定，成功率超过95%。
The paper first shows that the transient stability status of a power system following a large disturbance such as a fault can be early predicted based on the measured post-fault values of the generator voltages, speeds, or rotor angles. Synchronously sampled values provided by phasor measurement units (PMUs) of the generator voltages, frequencies, or rotor angles collected immediately after clearing a fault are used as inputs to a support vector machines (SVM) classifier which predicts the transient stability status. Studies with the New England 39-bus test system and the Venezuelan power network indicated that faster and more accurate predictions can be made by using the post-fault recovery voltage magnitude measurements as inputs. The accuracy and robustness of the transient stability prediction algorithm with the voltage magnitude measurements was extensively tested under both balanced and unbalanced fault conditions, as well as under different operating conditions, presence of measurement errors, voltage sensitive loads, and changes in the network topology. During the various tests carried out using the New England 39-bus test system, the proposed algorithm could always predict when the power system is approaching a transient instability with over 95% success rate.