[1] 蔡斌, 吴素农, 王诗明, 等. 电网在线安全稳定分析和预警系统[J]. 电网技术, 2007, 31(2): 36-41. CAI Bin, WU Sunong, WANG Shiming, et al.Power Grid On-line Security and Stability Analysis and Forewarning System[J]. Power System Technology, 2007, 31(2): 36-41. [2] Park D C, Elsharkawi M A, Marks R J I, et al. Electric load forecasting using an artificial neural network[C]// IEEE PES 1990 Summer Meeting, 1991: 442-449. [3] 顾雪平, 曹绍杰, 张文勤. 基于神经网络暂态稳定评估方法的一种新思路[J]. 中国电机工程学报, 2000, 20(4): 77-82. GU Xueping, CAO Shaojie, ZHANG Wenqin.A NEW FRAMEWORK FOR TRANSIENT STABILITY ASSESSMENT BASED ON NEURAL NETWORKS[J]. Proceedings of the Csee, 2000, 20(4): 77-82. [4] El-Sharkawi M A, Huang S J. Development of genetic algorithm embedded Kohonen neural network for dynamic security assessment[C]//International Conference on Intelligent Systems Applications To Power Systems, 1996. Proceedings, Isap. IEEE, 1996: 44-49. [5] 于之虹, 郭志忠. 遗传算法在暂态稳定评估输入特征选择中的应用[J]. 电力系统保护与控制, 2004, 32(1): 16-20. YU Zhihong, GUO Zhizhong.Feature selection based on genetic algorithm for transient stability assessment[J]. Relay, 2004, 32(1): 16-20. [6] Mohammadi M, Gharehpetian G B.Power System On-Line Static Security Assessment by Using Multi- Class Support Vector Machines[J]. Journal of Applied Sciences (S0255-8297), 2008, 8(12). [7] Kalyani S, Swarup K S.Support vector machine based pattern recognition approach for static security assessment[J]. International Journal of Artificial Intelligence (S0004-3702), 2010, 5(10): 17-36. [8] 马骞, 杨以涵, 刘文颖, 等. 多输入特征融合的组合SVM电力系统暂态稳定评估[J]. 中国电机工程学报, 2005, 25(6): 17-23. MA Qian, YANG Yihan, LIU Wenying, et al.POWER SYSTEM TRANSIENT STABILITYASSESSMENT WITH COMBINED SVM METHOD MIXING MULTIPLE INPUT FEATURES[J]. Zhongguo Dianji Gongcheng Xuebao/proceedings of the Chinese Society of Electrical Engineering, 2005, 25(6): 17-23. [9] Wang X, Ye M, Duanmu C J.Classification of data from electronic nose using relevance vector machines[J]. Sensors & Actuators B Chemical (S0925-4005), 2009, 140(1): 143-148. [10] Widodo A, Yang B S.Application of relevance vector machine and survival probability to machine degradation assessment[J]. Expert Systems with Applications (S0957-4174), 2011, 38(3): 2592-2599. [11] 段青. 基于稀疏贝叶斯学习方法的回归与分类在电力系统中的预测研究[D]. 济南: 山东大学, 2010. DUAN Qing.Study of Forecasting in Power Systems with Regression and Classification Based on Sparsity Bayesian Learning[D]. Ji’nan: Shandong University, 2010. [12] Salman S K, Teo A L J. Investigation into the estimation of the critical clearing time of a grid connected wind power based embedded generator[C]//Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES. 2002: 975-980. [13] 邵雅宁, 唐飞, 刘涤尘, 等. 一种适用于WAMS量测数据的系统暂态功角稳定评估方法[J]. 电力系统保护与控制, 2015, 43(6): 33-39. SHAO Yaning, TANG Fei, LIU Dichen, et al.An approach of transient angle stability assessment in power system for WAMS measured data[J]. Power System Protection & Control, 2015, 43(6): 33-39. [14] 李大虎, 曹一家. 基于PMU和混合支持向量机网络的电力系统暂态稳定性分析[J]. 电网技术, 2006, 30(9): 46-52. LI Dahu, CAO Yijia.Power System Transient Stability Analysis Based on PMU and Hybrid Support Vector Machine[J]. Power System Technology, 2006, 30(9): 46-52. [15] 李海英, 刘中银, 宋建成. 电力系统静态安全状态实时感知的相关向量机法[J]. 中国电机工程学报, 2015, 35(2): 294-301. LI Haiying, LIU Zhongyin, SONG Jiancheng.Real-time Static Security Situational Awareness of Power Systems Based on Relevance Vector Machine[J]. Proceedings of the Csee, 2015, 35(2): 294-301. [16] Guindon S, Gascuel O.PhyML—A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood[J]. Systematic Biology (S1063-5157), 2003, 52(5): 696-704. [17] Burden F R, And M G F, Whitley D C, et al. Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks[J]. Journal of Chemical Information & Computer Sciences(S0095-2338), 2000, 40(6): 1423-1430. [18] Tipping M.Relevance vector machine: US, US 6633857 B1[P]. 2003. [19] Processor O, Matlab T, Introduction P.Power System Analysis Toolbox[K]. Tailieu Vn. [20] Wong T T.Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation[J]. Pattern Recognition(S0031-3203), 2015, 48(9): 2839-2846. |