使用光伏发电元件参数对逆变器效率的分析
Su-Chang Lim1, Byung-Gyu Kim2, Jong-Chan Kim1
1Department of Computer Engineering, Sunchon National University, Suncheon 57992, Republic of Korea.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了一种长短期记忆 (LSTM) 模型,用于预测光伏发电和评估逆变器效率下降. 该模型有效地识别了太阳能设备的性能下降,有助于主动维护.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 太阳能性能监测 太阳能性能监测
背景情况:
- 光伏发电容易受到环境变量和设备状况的影响.
- 保持最佳设备性能,特别是逆变器,对于持续的能源生产至关重要.
- 设备健康状况的预测性评估对于高效的光伏电站运行至关重要.
研究的目的:
- 提出和验证一种用于确定光伏系统中逆变器效率降低的方法.
- 利用长短期内存 (LSTM) 网络进行光伏设备的预测性维护.
- 量化运行时间对逆变器效率的影响.
主要方法:
- 对光伏发电和环境传感器数据进行了相关性和线性分析.
- 通过使用与发电高度相关的太阳辐射和功率数据来训练一个预测模型.
- 经过训练的LSTM模型被应用于分析2020-2022年逆变器性能数据.
主要成果:
- 预测模型实现了7.36的平均绝对百分比误差 (MAPE),27.91的根平均平方误差 (RMSE),18.43的平均绝对误差 (MAE) 和0.97.97的R平方 (R2).
- 统计分析显示,与2020年相比,2022年的平均错误率增加了159.55W.
- 这表明在三年运行期间,逆变器效率下降了0.75%.
结论:
- 开发的基于LSTM的方法是有效的分析在运行中的光伏电站的逆变器效率.
- 结果表明,长时间使用的逆变器效率有可量化的下降.
- 这种预测方法支持光伏系统的积极维护策略.
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