李彬, 刘玉明, 尹义武, 朱斯, 许颜贺. 基于可分割云模型的抽水蓄能电站电气设备状态评估[J]. 失效分析与预防, 2024, 19(4): 250-257. DOI: 10.3969/j.issn.1673-6214.2024.04.004
    引用本文: 李彬, 刘玉明, 尹义武, 朱斯, 许颜贺. 基于可分割云模型的抽水蓄能电站电气设备状态评估[J]. 失效分析与预防, 2024, 19(4): 250-257. DOI: 10.3969/j.issn.1673-6214.2024.04.004
    LI Bin, LIU Yu-ming, YIN Yi-wu, ZHU Si, XU Yan-he. State Evaluation of Electrical Equipment in Pumped Storage Power Station Based on Divisible Cloud Model[J]. Failure Analysis and Prevention, 2024, 19(4): 250-257. DOI: 10.3969/j.issn.1673-6214.2024.04.004
    Citation: LI Bin, LIU Yu-ming, YIN Yi-wu, ZHU Si, XU Yan-he. State Evaluation of Electrical Equipment in Pumped Storage Power Station Based on Divisible Cloud Model[J]. Failure Analysis and Prevention, 2024, 19(4): 250-257. DOI: 10.3969/j.issn.1673-6214.2024.04.004

    基于可分割云模型的抽水蓄能电站电气设备状态评估

    State Evaluation of Electrical Equipment in Pumped Storage Power Station Based on Divisible Cloud Model

    • 摘要: 鉴于抽水蓄能电站电气设备评估指标的复杂性以及传统评估方式的维度局限性,提出一种主客观耦合权重和二维可分割云模型的电站一次、二次设备状态联合评估方法。首先,基于抽水蓄能电站实际巡检数据与缺陷报告,搭建一套系统性涵盖发电电动机、主变压器、高压电气设备及其相关二次辅助设备的综合评估体系;其次,提出基于改进自调节层次分析法和CRITIC权重法的计算指标耦合权重赋值方法,计算各评估指标二维云模型参数矩阵,通过云模型发生器直观呈现出电站核心电气设备的健康状态;最后,通过建立分割面,划分高风险区以快速筛选高风险指标。基于构建的评估体系与评价方法,对近段时间电站主要电气设备风险进行评估,能够快速有效筛选高风险设备。该方法具有较强的现场可操作性,能够有效对电站核心一次设备及其二次配套设备进行状态评估,为电站检修决策提供支撑。

       

      Abstract: Considering the complexity of assessment indicators for electrical apparatus in pumped storage power plants and the limitations of scope of conventional evaluation methodologies, a joint evaluation method of primary and secondary equipment status of pumped storage power stations with subjective and objective coupling weights and two-dimensional divisible cloud model is proposed. This methodology is grounded in the empirical inspection data and flaw reports from a representative Chinese pumped storage power plant to establish a systematic evaluation system covering generator motor, main transformer, high-voltage electrical equipment and its related secondary auxiliary equipment. The improved self-adjusting analytic hierarchy process and the objective weighting method of CRITIC are utilized to calculate the coupling weights of indicators. Subsequently the two-dimensional cloud model parameter matrix of each evaluation indicator is calculated. The input cloud model generator visually presents the health state of the core electrical equipment of the power station. Finally, by establishing the segmentation plane, the high risk index can be quickly identified to reflect the status of parts. In the light of the constructed evaluation system and evaluation method, the risk of the main electrical equipment of the power station can be evaluated in the coming future, and the results show that the method can rapidly and effectively pinpoint the high-risk equipment. The presented study demonstrates the simplicity, feasibility, and efficacy of the methodology in assessing the operational status of both the core primary machinery and the auxiliary secondary infrastructure within a pumped-storage power plant, which offers valuable guidance for maintenance and decision-making of the power plants.

       

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