| 引用本文: | 刘 斌,谈竹奎,唐赛秋,等.基于数据预测启发式算法的光伏电池参数识别[J].电力系统保护与控制,2021,49(23):72-79.[点击复制] |
| LIU Bin,TAN Zhukui,TANG Saiqiu,et al.Photovoltaic cell parameter extraction using data prediction based on a meta-heuristic algorithm[J].Power System Protection and Control,2021,49(23):72-79[点击复制] |
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| 摘要: |
| 为进一步提高对太阳能的利用率以降低对于化石燃料的依赖，需要对光伏(Photovoltaic, PV)电池进行精确的参数识别和建模，以实现最优的PV系统优化运行和控制。然而，PV电池的高度非线性和多模态特性使得传统优化方法很难获得最优解，同时实测电流-电压(Current-Voltage, I-V)数据量不足也会导致建模不够精确。为此，提出了一种基于数据预测的启发式算法(Data Prediction based Meta-heuristic Algorithm, DPMhA)来实现PV电池的参数识别。特别地，利用极限学习机(Extreme Learning Machine, ELM)对实测数据进行训练和预测，为启发式算法(Meta-heuristic Algorithm, MhA)提供更为准确可靠的适应度函数，从而增强其全局探索和局部搜索能力。最后，采用双二极管PV电池模型进行参数识别，其结果表明，DPMhA具有准确性高、收敛速度快等优点。 |
| 关键词: 参数识别 光伏电池 数据预测 启发式算法 极限学习机 |
| DOI：DOI: 10.19783/j.cnki.pspc.210205 |
| 投稿时间：2021-01-26修订日期：2021-03-04 |
| 基金项目:国家自然科学基金项目资助(52067004)；贵州电网有限责任公司科技项目资助(066600KK52180051) |
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| Photovoltaic cell parameter extraction using data prediction based on a meta-heuristic algorithm |
| LIU Bin,TAN Zhukui,TANG Saiqiu,LIN Chenghui,GAO Jipu |
| (Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China) |
| Abstract: |
| To improve solar energy efficiency and reduce the dependence on fossil fuels, accurate parameter extraction and modelling of Photovoltaic (PV) cells are needed to achieve optimal design and control. However, the highly nonlinear and multi-modal characteristics of PV cells make traditional optimization methods inadequate to obtain an optimal solution. In addition, insufficient measured current-voltage (I-V) data also leads to imprecise modelling. Therefore, a Data Prediction-based Meta-Heuristic Algorithm (DPMhA) is proposed to realize the parameter extraction from a PV cell. In particular, an Extreme Learning Machine (ELM) is used to train and predict measured data, so as to provide a more accurate and reliable fitness function for the algorithm. Then global exploration and local exploitation can be simultaneously enhanced. Finally, a PV cell model with double diodes is employed to identify unknown parameters. Case studies show that DPMhA has advantages of high accuracy and fast convergence.
This work is supported by the National Natural Science Foundation of China (No. 52067004) and the Science and Technology Project of Guizhou Power Grid Co., Ltd. (No. 066600KK52180051). |
| Key words: parameter extraction PV cell data prediction meta-heuristic algorithm extreme learning machine |
