| 引用本文: | 韩平平,范桂军,孙维真,石博隆,张晓安.基于数据测试和粒子群优化算法的光伏逆变器LVRT特性辨识[J].电力自动化设备,2020,40(2): |
| HAN Pingping,FAN Guijun,SUN Weizhen,SHI Bolong,ZHANG Xiaoan.Identification of LVRT characteristics of photovoltaic inverters based on data testing and PSO algorithm[J].Electric Power Automation Equipment,2020,40(2): |
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| 摘要: |
| 为了对光伏逆变器低电压穿越控制精确建模，提出灵敏度分析和实测方案相结合的光伏发电单元低电压穿越控制参数辨识方法。首先对光伏单元待辨识参数进行灵敏度分析，提出辨识所用数据的测试方案；然后利用自适应惯性权重粒子群优化智能算法，结合多组实测数据对光伏并网系统低电压穿越控制参数予以辨识，从多组辨识结果中提取最优值；最后将最优值代入模型中，计算模型输出与实测数据的误差，验证了参数辨识结果的准确性。该方法考虑了逆变器功率等级不同给辨识结果带来的误差，辨识结果准确度较高并且多次辨识结果具有一致性，可用于工程实际计算。 |
| 关键词: 实测数据 粒子群优化算法 参数辨识 低电压穿越 光伏逆变器 |
| DOI：10.16081/j.epae.202001015 |
| 分类号:TM615;TM464 |
| 基金项目:国家重点研发计划项目(智能电网技术与装备重点专项)(2016YFB0900600)；国家电网公司科技项目(52094017-000W) |
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| Identification of LVRT characteristics of photovoltaic inverters based on data testing and PSO algorithm |
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HAN Pingping1, FAN Guijun1, SUN Weizhen2, SHI Bolong2, ZHANG Xiaoan3
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1.Anhui Provincial Laboratory of New Energy Utilization and Energy Conservation, Hefei University of Technology, Hefei 230009, China;2.State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000, China;3.Intelligent Manufacturing Institute of Hefei University of Technology, Hefei 230009, China
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| Abstract: |
| In order to model LVRT(Low Voltage Ride Through) control of PV(PhotoVoltaic) inverters accurately, a parameter identification method for LVRT control of PV power units based on the combination of sensitivity analysis and measured scheme is proposed. Firstly, the sensitivities of the parameters to be identified in PV cells are analyzed and the test scheme of the data used for identification is proposed. Then, the adaptive inertia weight PSO(Particle Swarm Optimization) algorithm is used to identify the LVRT control parameters of PV grid-connected system with multi-group measured data, and the optimal values are extracted from multi-group identification results. Finally, the optimal values are substituted into the model. The accuracy of the parameter identification results is verified by calculating the error between the output of the model and the measured data. The proposed method considers the error caused by different power levels of inverters. The accuracy of identification results is high and the results of multiple identification are consistent, which is applicable to practical engineering calculation. |
| Key words: measured data particle swarm optimization algorithm parameter identification LVRT PV inverter |
