基于SVR数据驱动的发电机进相极限最优化求解方法

doi:10.11930/j.issn.1004-9649.202004105

摘要/Abstract

摘要： 针对发电机进相限制条件中多变量间复杂非线性强耦合关系导致的机理建模难题，提出一种基于支持向量回归（SVR）数据驱动的发电机进相极限最优化求解方法。该方法将发电机进相极限求解问题转化为计及多个进相限制因素约束下的无功功率最小值问题。基于发电机功角方程推导建立无功功率的目标函数方程；基于SVR驱动模型建立约束变量与目标函数自变量的非线性映射关系，形成约束方程模型；采用改进的二阶振荡粒子群算法对优化模型进行求解。算例分析表明，所提方法建模简单，具有较高的精度和较强的泛化能力，可实现对任意已知有功出力工况下的发电机进相极限的快速计算，适用于发电机进相裕度在线建模和监测。

关键词: 支持向量机, 数据驱动, 进相极限, 最优化, 二阶振荡粒子群

Abstract: In view of the difficulty in modeling the mechanism caused by the complex and strong coupling nonlinearities between the multiple variables in the limiting conditions of leading phase operation, a novel method is proposed in this paper to optimize the leading phase operation limit of generator based on data-driven support vector machine regression (SVR). The limit calculation of generator leading phase operation is converted to the minimization of reactive power subject to the multiple constraints of leading phase. Based on the generator power angle equation, the objective function equation of reactive power is established. In order to formulate the constraint equation model, the nonlinear mapping relationship between constraint variables and independent variables in the objective function is constructed based on SVR data-driven model. The improved second-order oscillation particle swarm optimization algorithm is then applied to solve the optimization model. The case studies show that in addition to its modelling simplicity, the proposed method has exhibited high accuracy and strong adaptability, for the purpose of fast calculation of the generator leading phase limit under the conditions of any given active power output. Therefore it can be used for the online modeling and monitoring of the margin of generator leading phase operation.

Key words: support vector machine, data driven, leading phase limit, optimization, second-order oscillation particle swarm

李登峰, 杨旼才, 刘育明, 徐瑞林, 余霞, 李昭炯. 基于SVR数据驱动的发电机进相极限最优化求解方法[J]. 中国电力, 2021, 54(8): 136-143,153.

LI Dengfeng, YANG Mincai, LIU Yuming, XU Ruilin, YU Xia, LI Zhaojiong. SVR Data-Driven Optimization of Generator Leading Phase Operation Limit[J]. Electric Power, 2021, 54(8): 136-143,153.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004105

https://www.electricpower.com.cn/CN/Y2021/V54/I8/136

参考文献

[1] 严伟, 陈俊, 沈全荣. 大型隐极发电机进相运行的探讨[J]. 电力系统自动化, 2007, 31(2):94-97
YAN Wei, CHEN Jun, SHEN Quanrong. Discussion on large non-salient pole generator phase-advancement operation[J]. Automation of Electric Power Systems, 2007, 31(2):94-97
[2] 王成亮, 王宏华. 大型同步发电机进相运行动模试验设计[J]. 电测与仪表, 2016, 53(2):118-124
WANG Chengliang, WANG Honghua. Design of dynamic simulation test for leading phase of large synchronous generator[J]. Electrical Measurement & Instrumentation, 2016, 53(2):118-124
[3] 王成亮, 王宏华. 同步发电机进相研究综述[J]. 电力自动化设备, 2012, 32(11):131-135
WANG Chengliang, WANG Honghua. Review of synchronous generator leading-phase operation[J]. Electric Power Automation Equipment, 2012, 32(11):131-135
[4] 蒲倩, 张毅威, 陈磊, 等. 容性孤网发电机进相运行与紧急电压控制[J]. 电力系统保护与控制, 2012, 40(18):24-29
PU Qian, ZHANG Yiwei, CHEN Lei, et al. Generator's leading phase operation and emergency voltage control of capacitive isolated network[J]. Power System Protection and Control, 2012, 40(18):24-29
[5] 韦延方, 卫志农, 张友强, 等. 发电机进相运行的研究现状及展望[J]. 电力系统保护与控制, 2012, 40(9):146-154
WEI Yanfang, WEI Zhinong, ZHANG Youqiang, et al. Progress and development trend on leading phase of generator[J]. Power System Protection and Control, 2012, 40(9):146-154
[6] 史家燕, 史源素, 赵肖敏, 等. 发电机工况参数模型及进相运行在线监测[J]. 中国电机工程学报, 2006, 26(11):139-143
SHI Jiayan, SHI Yuansu, ZHAO Xiaomin, et al. The new method of determining the capability of leading phase of generator and realizing the on-line monitoring[J]. Proceedings of the CSEE, 2006, 26(11):139-143
[7] 董新胜, 宁晓棠, 石海珍. 新疆主电网发电机进相试验与数据分析[J]. 华东电力, 2007, 35(4):67-70
DONG Xinsheng, NING Xiaotang, SHI Haizhen. Generator phase advance tests and data analysis for Xinjiang main grids[J]. East China Electric Power, 2007, 35(4):67-70
[8] 王成亮, 王宏华, 徐钢. 基于反向传播神经网络的发电机进相能力建模研究[J]. 电网技术, 2011, 35(11):136-140
WANG Chengliang, WANG Honghua, XU Gang. Modeling of generator leading phase ability based on back propagation neural network[J]. Power System Technology, 2011, 35(11):136-140
[9] 曹侃, 王涛, 忻俊慧. 同步发电机进相能力的快速估算方法[J]. 中国电力, 2014, 47(1):108-111
CAO Kan, WANG Tao, XIN Junhui. A fast estimation method of synchronous generator leading phase ability[J]. Electric Power, 2014, 47(1):108-111
[10] 陈波, 周宁, 舒展. 进相试验约束条件下隐极同步发电机进相深度限值分析[J]. 电机与控制应用, 2016, 43(2):30-35
CHEN Bo, ZHOU Ning, SHU Zhan. Research on leading power factor depth limit of non-salient synchronous generator with constraint in leading power factor test[J]. Electric Machines & Control Application, 2016, 43(2):30-35
[11] 刘翔宇, 何玉灵, 周文, 等. 考虑电网稳定限制的机组进相能力分析[J]. 华北电力大学学报(自然科学版), 2017, 44(1):52-57
LIU Xiangyu, HE Yuling, ZHOU Wen, et al. Analysis on unit capability of leading-phase operation considering stability restriction of power grid[J]. Journal of North China Electric Power University (Natural Science Edition), 2017, 44(1):52-57
[12] 王成亮, 王宏华, 向昌明, 等. 发电机进相能力的RBF神经网络模型[J]. 电工技术学报, 2012, 27(1):124-129
WANG Chengliang, WANG Honghua, XIANG Changming, et al. Generator leading phase ability model based on RBF neural network[J]. Transactions of China Electrotechnical Society, 2012, 27(1):124-129
[13] 翟学锋, 卫志农, 范立新, 等. 基于相关向量机的发电机进相能力建模[J]. 电力自动化设备, 2015, 35(3):146-151
ZHAI Xuefeng, WEI Zhinong, FAN Lixin, et al. Generator leading phase capability model based on relevance vector machine[J]. Electric Power Automation Equipment, 2015, 35(3):146-151
[14] 李明昌, 戴明新, 周斌, 等. 基于数据驱动模型的入海污染源排污削减优化方法研究[J]. 水资源与水工程学报, 2017, 24(2):9-18
LI Mingchang, DAI Mingxin, ZHOU Bin, et al. Study on the optimization method of pollutant emission reduction from the sea based on data-driven model[J]. Journal of Water Resources and Water Engineering, 2017, 24(2):9-18
[15] 刘俐, 李勇, 曹一家, 等. 基于支持向量机和长短期记忆网络的暂态功角稳定预测方法[J]. 电力系统自动化设备, 2020, 40(2):129-135
LIU Li, LI Yong, CAO Yijia, et al. Transient rotor angle stability prediction method based on SVM and LSTM network[J]. Electric Power Automation Equipment, 2020, 40(2):129-135
[16] 吕干云, 蒋小伟, 郝思鹏, 等. 基于半监督支持向量机的电压暂降源定位[J]. 电力系统保护与控制, 2019, 47(18):76-81
LÜ Ganyun, JIANG Xiaowei, HAO Sipeng, et al. Location of voltage sag source based on semi-supervised SVM[J]. Power System Protection and Control, 2019, 47(18):76-81
[17] 马小敏, 高剑, 吴驰, 等. 基于灰色支持向量机的输电线路覆冰厚度预测模型[J]. 中国电力, 2016(11):46-50
MA Xiaomin, GAO Jian, WU Chi, et al. Prediction model of ice thickness of transmission line based on grey support vector machine[J]. China Electric Power, 2016(11):46-50
[18] HOU K, SHAO G, WANG H, et al. Research on practical power system stability analysis algorithm based on modified SVM[J]. Protection and Control of Modern Power Systems, 2018, 3(11):1-7.
[19] 郭艳飞, 程林, 李洪涛, 等. 基于支持向量机和互联网信息修正的空间负荷预测方法[J]. 中国电力, 2019, 52(4):80-88
GUO Yanfei, CHENG Lin, LI Hongtao, et al. Spatial load forecasting method based on support vector machineand internet information correction[J]. Electric Power, 2019, 52(4):80-88
[20] 李永馨, 王鸿, 王致杰, 等. 基于ISMC-PSO的风电爬坡输出功率预测系统的研究[J]. 电力系统保护与控制, 2019, 47(18):115-119
LI Yongxin, WANG Hong, WANG Zhijie, et al. Research on ISMC-PSO based wind uphill power output prediction system[J]. Power System Protection and Control, 2019, 47(18):115-119
[21] 刘俊磊, 钱峰, 伍双喜, 等. 基于支持向量回归的直流受端电网动态无功需求在线评估[J]. 电力系统保护与控制, 2019, 47(13):37-45
LIU Junlei, QIAN Feng, WU Shuangxi, et al. Online assessment of dynamic reactive power demand of DC receiving power grid based on support vector regression[J]. Power System Protection and Control, 2019, 47(13):37-45
[22] 中国电力企业联合会. 同步发电机进相试验导则:DL/T 1523-2016[S]. 北京:中国电力出版社, 2016.
China Federation of electric power enterprises. Guidelines for phase in test of synchronous generator:DL/T1523-2016[S]. Beijing:China Electric Power Press, 2016.
[23] 张少明, 盛四清. 基于改进粒子群算法的微网优化运行[J]. 中国电力, 2020, 53(1):1-8
ZHANG Shaoming, SHENG Siqing. Optimal operation of microgrid based on improved particle swarm optimization algorithm[J]. Electric Power, 2020, 53(1):1-8
[24] CAMPOS M, KROHLING R A, ENRIQUEZ I. Bare bones particle swarm optimization with scale matrix adaptation[J]. IEEE Transactions on Cybernetics, 2014, 44(9):1567-1578.
[25] 蒋丽, 叶润舟, 梁昌勇, 等. 改进的二阶振荡粒子群算法[J]. 计算机工程与应用, 2019, 55(9):130-138
JIANG Li, YE Runzhou, LIANG Changyong, et al. Improved second-order oscillatory particle swarm optimization algorithm[J]. Computer Engineering and Application, 2019, 55(9):130-138