离子储能电站的电压异常预测方法使用基于贝叶斯优化的告知器
Zhibo Rao1, Jiahui Wu2, Guodong Li3
1Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Connection, Xinjiang University, Urumqi, 830049, Xinjiang, People's Republic of China.
这项研究引入了贝叶斯优化的Informer神经网络,用于预测储能电池中的电压异常. 这种新的方法通过提高故障检测准确度来提高运行安全和系统稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 储能电站系统的安全稳定运行至关重要.
- 精确的电压故障检测对于识别储能电池的操作故障至关重要.
研究的目的:
- 用贝叶斯优化的Informer神经网络开发用于储能电池的电压异常预测方法.
- 提高储能系统中电压故障检测的准确性和效率.
主要方法:
- 建立了一个使用时间特征和电池管理系统 (BMS) 数据的长期运行数据集.
- 使用皮尔森相关系数 (PCC) 的量化数据相关性.
- 使用Informer神经网络与贝叶斯优化 (BO) 超参数构建了一个电压预测模型.
主要成果:
- 拟议的BO-Informer模型实现了预测误差的减少:RMSE为9.18mV,MSE为0.0831mV,MAE为6.708mV,采样间隔为1分钟,训练数据为70%.
- 分析了采样间隔和训练集比率对使用真实电网运行数据的预测准确性的影响.
- 与现有方法相比,证明了高压异常预测的准确性.
结论:
- 贝叶斯优化的Informer神经网络为储能系统中的电压异常预测提供了精确有效的方法.
- 开发的方法平衡了预测效率和准确性,有助于更安全,更稳定的储能操作.
- 进一步的分析证实了该模型在不同数据采样和培训配置中的稳定性.
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