| 引用本文: | 孟贤,沈一鸣,陈宇杰,曾丕江,吴浩.基于实测数据跳变及稳态点的负荷模型参数快速辨识方法[J].电力自动化设备,2022,42(7): |
| MENG Xian,SHEN Yiming,CHEN Yujie,ZENG Pijiang,WU Hao.Fast identification method for load model parameters based on jumping and steady-state points of measured data[J].Electric Power Automation Equipment,2022,42(7): |
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| 摘要: |
| 传统总体测辨法存在暂态仿真计算量大、辨识所需时间长等问题，无法满足负荷特性记录装置在线实时辨识负荷模型参数的需求，为此，提出一种基于实测数据跳变及稳态点的负荷模型参数辨识方法。选取实测数据电压突变后1点、电压恢复前后2点以及最终稳态点作为计算点；根据感应电动机三阶微分方程，采用稳态计算法和大步长隐式梯形法计算4点的状态变量，进而得到4点的有功和无功功率；根据4点的实测功率，采用遗传粒子群混合优化算法对负荷模型重点参数进行寻优辨识。算例结果表明，所提方法辨识结果准确，所需计算量小，其计算时间不到传统总体测辨法的1/10。 |
| 关键词: 负荷模型 参数辨识 跳变点 遗传粒子群混合优化算法 负荷特性记录装置 |
| DOI：10.16081/j.epae.202204080 |
| 分类号:TM714 |
| 基金项目:国家自然科学基金资助项目(51837004)；中国南方电网云南电网有限责任公司科研项目(YNKJXM20180017) |
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| Fast identification method for load model parameters based on jumping and steady-state points of measured data |
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MENG Xian1, SHEN Yiming2, CHEN Yujie2, ZENG Pijiang3, WU Hao2
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1.Electric Power Research Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650217, China;2.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;3.Power Dispatching Control Center of Yunnan Power Grid Co.,Ltd.,Kunming 650217, China
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| Abstract: |
| The traditional measurement-based method has the problems of large calculation amount of transient simulation and long time needed for identification, so it cannot satisfy the requirement of online identification of load model parameters for load characteristic recording device, for which, an identification method of load model parameters based on jumping and steady-state points of measured data is proposed. One point after voltage sag, two points before and after voltage recovery, and the final steady-state point are selected for the calculation points. According to the third-order differential equation of induction motor model, the steady-state calculation method and implicit trapezoid integration method with large step are adopted to calculate the state variables of the four points, and then the active and reactive powers of the four points are obtained. According to the measured powers of the four points, the genetic particle swarm hybrid optimization algorithm is adopted to find the best key load model parameters. Case results show that the proposed method can obtain accurate identification results with small calculation amount, and its calculation time is less than 1/10 of the traditional measurement-based method. |
| Key words: load model parameter identification jumping point genetic particle swarm hybrid optimization algorithm load characteristic recording device |
