| 引用本文: | 徐正清,肖艳炜,李群山,等.基于灵敏度及粒子群算法的输电断面功率越限控制方法对比研究[J].电力系统保护与控制,2020,48(15):177-192.[点击复制] |
| XU Zhengqing,XU Zhengqing,XU Zhengqing,et al.Comparative study based on sensitivity and particle swarm optimization algorithm for power flow over-limit control method of transmission section[J].Power System Protection and Control,2020,48(15):177-192[点击复制] |
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| 摘要: |
| 针对电力系统运行过程中由于负荷过重或支路开断引起输电断面内部线路可能出现越限的实际问题，研究了有功潮流灵敏度(节点注入有功功率对线路有功功率的灵敏度)和综合灵敏度(考虑各输电断面内部线路有功越限权重时，节点注入有功功率对输电断面有功功率的灵敏度)的计算方法。分析了粒子群优化算法，选取断面处实际功率与目标功率值的偏差为目标函数，得到发电机组出力进行断面越限控制。利用IEEE39网络模型，采用AP聚类算法进行网络的分区及输电断面的识别，对上述两种方法进行了对比分析，验证了两者均可应用于越限控制过程中。 |
| 关键词: 输电断面 综合灵敏度 粒子群算法 越限控制 有功功率的灵敏度 |
| DOI：DOI: 10.19783/j.cnki.pspc.191108 |
| 投稿时间：2019-09-11修订日期：2019-11-19 |
| 基金项目:国家电网科技项目资助(5211UZ18006K) |
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| Comparative study based on sensitivity and particle swarm optimization algorithm for power flow over-limit control method of transmission section |
| XU Zhengqing,XU Zhengqing,XU Zhengqing,XU Zhengqing,XU Zhengqing,XU Zhengqing |
| (1. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 210003, China; 2. State Grid
Zhejiang Electric Power Co., Hangzhou 310007, China; 3. State Grid Huazhong Electric Power Control Subcenter,
Wuhan 430077, China; 4. Laboratory of Geo-Exploration and the Instrumentation Ministry of Education of China,
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China) |
| Abstract: |
| In view of the practical problem that the internal line of the transmission section may exceed the limit due to excessive load or branch break during the operation of the power system. The calculation method for the active power flow sensitivity (sensitivity of node injected active power to line active power) and comprehensive sensitivity (the sensitivity of the node injected active power to the active power of the transmission section considering the over-limit weight of each transmission section's internal lines) are studied. The particle swarm optimization algorithm is analyzed. The deviation between the actual power and the target power value at the section is selected as the objective function, and the generator’s output are obtained to control the sections. With the IEEE39 network model, the AP clustering algorithm is used to partition the network and to identify the transmission sections. The two control methods are compared and analyzed. It is verified that both methods can be applied to the process of transmission-section control.
This work is supported by Science and Technology Project of State Grid Corporation of China (No. 5211UZ18006K). |
| Key words: transmission section comprehensive sensitivity particle swarm optimization algorithm over-limit control active power sensitivity |
