| 引用本文: | 张洪嘉,戴志辉,贺欲飞,等.基于IRIME-BP-LSTM模型的继电保护装置剩余寿命预测方法[J].电力系统保护与控制,2025,53(15):125-134.[点击复制] |
| ZHANG Hongjia,DAI Zhihui,HE Yufei,et al.Remaining useful life prediction method for relay protection devices based on IRIME-BP-LSTM model[J].Power System Protection and Control,2025,53(15):125-134[点击复制] |
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| 摘要: |
| 目前继电保护装置寿命预测理论中存在缺少对单个装置状态准确评估预测、预测数据无法根据实际运行情况及时修正等问题，导致预测结果不可靠。对此，提出基于改进霜冰优化算法(improved rime optimization algorithm, IRIME)优化反向传播(backpropagation, BP)神经网络与长短期记忆网络(long short memory network, LSTM)模型的继电保护装置剩余寿命预测方法。首先，总结运维经验与规程要求，建立保护装置状态评估指标集，形成初始输入向量。其次，引入柯西变异机制改进霜冰优化算法，利用IRIME对BP神经网络初始参数进行优化。然后，将初始输入向量赋予优化后的神经网络，进行保护装置状态评估，形成装置运行状态的表征向量并构建时间序列。最后，将构建的时间序列输入到LSTM网络中进行保护装置剩余寿命的预测。案例验证结果表明，该方法在保护装置剩余寿命预测上具有更高的准确度，可以为保护装置检修运维工作提供理论指导。 |
| 关键词: 继电保护装置 剩余寿命预测 状态评估 改进霜冰优化算法 长短期记忆网络 |
| DOI：10.19783/j.cnki.pspc.241486 |
| 投稿时间：2024-11-05修订日期：2025-02-10 |
| 基金项目:国家自然科学基金项目资助(51877084) |
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| Remaining useful life prediction method for relay protection devices based on IRIME-BP-LSTM model |
| ZHANG Hongjia,DAI Zhihui,HE Yufei,JIA Wenchao |
| (Hebei Key Laboratory of Distributed Energy Storage and Microgrid (North China Electric
Power University), Baoding 071003, China) |
| Abstract: |
| Current methods for predicting the remaining useful life of relay protection device suffer from issues such as the lack of accurate assessment and prediction for individual device states, and the inability to timely correct prediction data according to actual operating conditions, resulting in unreliable prediction results. To address this, a method for predicting the remaining useful life of relay protection devices based on the IRIME-BP-LSTM model is proposed. First, operational experience and procedural requirements are summarized to establish a set of state assessment indicators for protection devices, forming the initial input vector. Then, the Cauchy mutation strategy is introduced to improve the rime optimization algorithm, which is used to optimize the initial parameters of the backpropagation (BP) neural network. Next, the initial input vector is assigned to the optimized neural network to assess the condition of the protection device, forming a representation vector of the device’s operating state and constructing a time series. Finally, the constructed time series is fed to the long short-term memory (LSTM) network for predicting the remaining useful life of the protection device. Case study results show that the proposed method has higher accuracy in predicting the remaining useful life of protection devices and can provide theoretical guidance for relay maintenance and operation decision-making. |
| Key words: relay protection device remaining useful life prediction state assessment improved rime optimization algorithm long short-term memory |
