| 引用本文: | 林 苑,赵晋斌,孙明琦,等.新型电力系统下基于物理信息LSTM网络的电力变压器状态评估方法[J].电力系统保护与控制,2025,53(14):133-141.[点击复制] |
| LIN Yuan,ZHAO Jinbin,SUN Mingqi,et al.Power transformer condition assessment method based on physics-informed LSTM network in the context of new power systems[J].Power System Protection and Control,2025,53(14):133-141[点击复制] |
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| 摘要: |
| 新型电力系统下电网具有间歇性、波动性特征，在此背景下电力变压器运行将面临多物理场耦合作用的复杂工况。针对传统的电力变压器状态评估模型存在无法有效挖掘缺陷和失效机理、无法有效利用多源异构数据的问题，提出了一种基于物理信息长短期记忆(long short-term memory, LSTM)网络的电力变压器状态评估方法。融入模型-数据驱动思想，为电力变压器在新型电力系统背景下的精细化管理和智能运维提供新的研究思路。首先，分析电力变压器在电磁-流体-温度多物理场耦合下的物理模型，建立变压器的状态评估先验知识函数。然后，将该知识函数嵌入LSTM网络损失函数中，惩罚违反物理先验知识的“黑箱”数据，有效挖掘多物理场耦合复杂工况下电力变压器的演化规律，通过Softmax分类器获取电力变压器健康指数和寿命预测模型。最后，根据变压器实际运行情况验证所提算法的有效性，实现对电力变压器关键性能及其相关性分析的综合状态评估。 |
| 关键词: 新型电力系统 物理信息LSTM网络 电力变压器 状态评估 |
| DOI：10.19783/j.cnki.pspc.240533 |
| 投稿时间：2024-05-02修订日期：2024-11-07 |
| 基金项目:国家自然科学基金项目资助(51777120) |
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| Power transformer condition assessment method based on physics-informed LSTM network in the context of new power systems |
| LIN Yuan1,2,ZHAO Jinbin1,SUN Mingqi2,YAN Yinbei3 |
| (1. Shanghai University of Electric Power, Shanghai 200090, China; 2. State Grid Shanghai Shinan Power Supply Company,
Shanghai 200030, China; 3. State Grid Shanghai Shibei Power Supply Company, Shanghai 200072, China) |
| Abstract: |
| In the context of new power systems, the grid exhibits intermittent and fluctuating characteristics, subjecting power transformers to complex working conditions involving multi-physical field coupling. To address the limitations of traditional power transformer condition assessment models, such as their inability to effectively uncover defect and failure mechanisms or to make full use of multi-source heterogeneous data, a power transformer condition assessment based on physics-informed LSTM network is proposed. The approach integrates model-driven and data-driven concepts, offering a new research direction for the refined management and intelligent operation and maintenance of power transformers under new power system conditions. First, the physical model of power transformers under electromagnetic-fluid- temperature multi-physics coupling is analyzed to establish a prior knowledge function for transformer condition assessment. Then, the knowledge function is embedded into the loss function of the LSTM network to penalize “black-box” data that violate physical prior knowledge. This enables effectively discovery of the evolution patterns of power transformers under complex coupled conditions. A Softmax classifier is used to obtain the health index and life prediction model of power transformers. Finally, the effectiveness of the proposed algorithm is verified based on the actual transformer operation data, achieving a comprehensive condition assessment of key performance indicators and their interrelationships. |
| Key words: new power systems physics-informed LSTM network power transformer condition assessment |
