| 翟晶晶,乔 阳,郝思鹏.基于深度学习的新能源场站不良数据辨识与修正方法[J].电力需求侧管理,2024,26(5):28-35 |
| 基于深度学习的新能源场站不良数据辨识与修正方法 |
| Identification and correction method of bad data of new energy plants based on deep learning |
| 投稿时间：2024-05-08 修订日期：2024-06-19 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 005 |
| 中文关键词: 深度学习 数据辨识 数据修正 新能源 |
| 英文关键词: deep learning data identification data correction new energy |
| 基金项目:江苏省科技创新项目（BA2022105） |
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| 摘要点击次数: 3 |
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| 中文摘要: |
| 针对新能源场站数据采集错误的问题,考虑到新能源场站数据具有海量和相互耦合的特点,提出一种基于深度学习的新能源场站不良数据辨识与修正方法。首先,构建长短期记忆神经网络模型,利用偏差阈值进行判断得到标识的不良数据；在此基础上,提出了萤火虫算法优化的BP修正模型,建立修正评判标准,将标识的不良数据进行修正,得到新能源场站可靠的运行数据；最后,通过实际数据集对方法的有效性进行了验证,结果表明所提方法能够有效处理新能源场站运行不良数据,具有实际应用价值。 |
| 英文摘要: |
| In view of the problem of real-time data acquisition errors in new energy plants, the data of new energy plants has mass and mutually coupled characteristics, a deep learning-based method for identifying and correcting bad data from new energy plants is proposed.Firstly, a LSTM identification model is constructed to identify the real-time bad data, and the bad data of the real-time identification is obtained. Secondly, the BP correction model optimized by the firefly algorithm is constructed to correct the bad data identified and obtain reliable data of the operation of the new energy station. The accuracy and effectiveness of the proposed method are verified by analyzing the real historical data of a typical wind farm. |
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