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作 者:刘涤尘[1] 王力[1,2] 赵洁[1] 张胜峰[1] 吴国旸 王骏[1] 邵尤国[1] LIU Dichen;WANG Li;ZHAO Jie;ZHANG Shengfeng;WU Guoyang;WANG Jun;SHAO Youguol(School of Electrical Engineering,Wuhan University,Wuhan 430072,China;School of Electrical & Information Engineering,Changsha University of Science & Technology,Changsha 410114,China;China Electric Power Research Institute,Beijing 100192,China)
机构地区:[1]武汉大学电气工程学院,湖北武汉430072 [2]长沙理工大学电气与信息工程学院,湖南长沙410114 [3]中国电力科学研究院,北京100192
出 处:《电力自动化设备》2018年第10期39-45,共7页Electric Power Automation Equipment
基 金:国家自然科学基金资助项目(51677137;51307123);中央高校基本科研业务费专项基金资助项目(2042018kf-0051);国家电网公司科技项目(XT71-15-060)~~
摘 要:压水堆核电机组动态模型中参数的稳定性和正确性对保证系统的暂态稳定具有重要意义,因此需要设计合适的模型参数评价方法。针对压水堆核电机组动态模型,按照内部物理边界将其分解为多个子模块模型;根据各子模块的微分方程数学模型推导,基于变量偏差的传递函数定性评价参数的改变对各子模块输出变量稳态值的影响;基于参数灵敏度的终值是否为零值和灵敏度指标大小的判定,合理选择参数的获取方案以提高参数获取效率及准确性。采用基于群体最优值摄动的粒子群优化算法获取模型参数,算例结果验证了所提参数评价方法的有效性。The stability and correctness of the parameters in the dynamic model of PWR( Pressurized Water Reactor) nuclear power unit are of great significance to ensure the transient stability of the system, so it is necessary to design a suitable parameter evaluation method. The dynamic model of PWR nuclear power unit is divided into multiple sub- module models according to its internal physical boundaries. According to the derivation for the differential equation mathematical model of each sub-module, the influence of the parameter variation on the steady-state output variables of each sub-module are estimated qualitatively based on the transfer function of variable deviation. In order to im- prove the efficiency and accuracy of parameter obtainment, a reasonable parameter acquisition scheme is chosen based on the determination of the sensitivity index value and whether the final value of the parameter sensitivity is zero. Particle swarm optimization algorithm based on the perturbation of the group optimal value is applied to obtain the model parameters, and the results of the example verify the effectiveness of the proposed parameter evaluation method.
关 键 词:压水堆核电机组 动态模型 灵敏度 粒子群优化算法 参数评价
分 类 号:TM623[电气工程—电力系统及自动化] TM74
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