面向全钒液流电池储能系统运维的故障风险评估

张宇; 谢冰; 李家珏; 韩子娇; 李爱魁

doi:10.19799/j.cnki.2095-4239.2026.0717

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面向全钒液流电池储能系统运维的故障风险评估

储能XXXX | 更新时间：2026-10-21

- 面向全钒液流电池储能系统运维的故障风险评估
- Fault Risk Assessment for Operation and Maintenance of Vanadium Flow Battery Energy Storage Systems
- 储能科学与技术 2026年页码：1-15
- 作者机构：
  
  1.大连理工大学，辽宁大连116024
  2.国网辽宁省电力有限公司，辽宁沈阳 110006
- 作者简介：
  
  张宇（2001—），男，硕士研究生，研究方向为电化学储能技术，E-mail：yuzhang0227@163.com；
  李爱魁，研究员，研究方向为储能电站应用技术及全钒液流电池储能技术，E-mail：liaikui@dlut.edu.cn。
- 基金信息：
  
  大容量电化学储能电站城市应急供电能力提升技术研究(4000-202428085A-1-1-ZN)
- DOI：10.19799/j.cnki.2095-4239.2026.0717
  中图分类号： TM 911
- 收稿：2026-08-18，
  
  修回：2026-10-19，
  
  网络首发：2026-10-21，
- 稿件说明：
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张宇, 谢冰, 李家珏, 等. 面向全钒液流电池储能系统运维的故障风险评估[J]. 储能科学与技术, XXXX, XX(XX): 1-15.

ZHANG Yu, XIE Bing, Li Jiayu, et al. Fault Risk Assessment for Operation and Maintenance of Vanadium Flow Battery Energy Storage Systems[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15.
张宇, 谢冰, 李家珏, 等. 面向全钒液流电池储能系统运维的故障风险评估[J]. 储能科学与技术, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0717.

ZHANG Yu, XIE Bing, Li Jiayu, et al. Fault Risk Assessment for Operation and Maintenance of Vanadium Flow Battery Energy Storage Systems[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0717.

摘要

全钒液流电池（vanadium flow battery，VFB）储能系统包含电堆、储液罐及管路系统等特有设备，潜在故障点较多，系统运行可靠性面临挑战。本文以可用功率和可用容量的异常程度衡量VFB储能系统故障风险，提出一种基于模糊多态贝叶斯网络的故障风险评估方法。首先，分析系统内的典型故障与传播机理并构建多态贝叶斯网络。然后，采用三角模糊数表征专家评价以弥补故障样本不足的问题，实现定性经验向模糊参数的转换；分别根据专家专业水平和评价相似度确定静态权重和动态共识权重，并通过融合权重聚合专家意见，获得网络概率参数。此外，引入Leaky Noisy-Max模型简化条件概率表构建流程。最后，在GeNIe软件中搭建网络，开展双向推理与敏感性分析。正向推理结果表明，系统运行异常基准概率为5.06%，温度控制系统故障与管道系统漏液组合场景下升至90.87%。以某光储一体化电站故障事件为例进行逆向推理，模型识别的首要根因与公开故障分析报告一致，均为电堆制造缺陷。结合敏感度与影响强度两类指标构建四象限运维决策矩阵，为VFB储能系统的故障因素分级管控及差异化运维提供决策支持。

Abstract

Vanadium flow battery (VFB) energy storage systems contain specific components such as stacks

electrolyte tanks and pipelines

which introduce numerous potential fault points and challenge operational reliability. A fault risk assessment method based on a fuzzy multi-state Bayesian network is proposed

in which the fault risk is measured by the abnormality degrees of available power and available capacity. First

typical faults and their propagation mechanisms are analyzed

and a multi-state Bayesian network is constructed. Then

triangular fuzzy numbers are adopted to represent expert judgments and compensate for the scarcity of fault samples

converting qualitative experience into fuzzy parameters. Static weights and dynamic consensus weights are determined according to expert competence and judgment similarity

respectively

and expert opinions are aggregated through the fused weights to obtain the network probability parameters. In addition

the Leaky Noisy-Max model is introduced to simplify the construction of conditional probability tables. Finally

the network is built in GeNIe for bidirectional inference and sensitivity analysis. Forward inference results show that the baseline probability of abnormal system operation is 5.06%

and it rises to 90.87% under the combined scenario of temperature control system failure and pipeline leakage. Backward inference is performed on a fault event of a photovoltaic-storage integrated power station

and the primary root cause identified by the model

a stack manufacturing defect

is consistent with the published fault analysis report. A four-quadrant operation and maintenance decision matrix is established by combining sensitivity and influence intensity indices

providing decision support for classified management of fault factors and differentiated maintenance of VFB energy storage systems.

关键词

Keywords

references

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