为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工...为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工况、电池模组状态等27个二级指标。采用序关系分析(G1)法确定指标主观权重,采用人工鱼群算法(artificial fish swarm algorithm,AFSA)确定各评价指标客观权重,采用乘积法确定最终的组合权重,结合云模型理论建立锂电池储能电站安全风险评价模型。以某电站为例开展研究,研究结果表明:储能电站风险综合期望值为61.382,风险处于较高水平,需采取适当措施进行风险防控。模型能够有效识别关键风险路径,评价结论与现场风险表现具有较高一致性。研究结果可为锂电池储能电站风险预防和管控提供参考。展开更多
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
文摘为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工况、电池模组状态等27个二级指标。采用序关系分析(G1)法确定指标主观权重,采用人工鱼群算法(artificial fish swarm algorithm,AFSA)确定各评价指标客观权重,采用乘积法确定最终的组合权重,结合云模型理论建立锂电池储能电站安全风险评价模型。以某电站为例开展研究,研究结果表明:储能电站风险综合期望值为61.382,风险处于较高水平,需采取适当措施进行风险防控。模型能够有效识别关键风险路径,评价结论与现场风险表现具有较高一致性。研究结果可为锂电池储能电站风险预防和管控提供参考。
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
