Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing...Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.展开更多
针对传统谐波责任划分方法需采用专门同步设备监测数据,且需基于等值电路模型划分谐波责任,工程应用较为复杂等不足,采用现有谐波监测装置非同步测量数据,提出一种综合考虑了数据非同步性、场景划分和数据相关性的谐波责任划分方法。首...针对传统谐波责任划分方法需采用专门同步设备监测数据,且需基于等值电路模型划分谐波责任,工程应用较为复杂等不足,采用现有谐波监测装置非同步测量数据,提出一种综合考虑了数据非同步性、场景划分和数据相关性的谐波责任划分方法。首先,对原始非同步监测数据集采用分段聚合近似算法进行降噪预处理,利用形状动态时间规整算法(shape dynamic time warping,ShapeDTW)实现数据匹配对齐;然后,利用点排序识别聚类结构的聚类算法(ordering points to identify the clustering structure,OPTICS)划分场景以处理电力系统中因负荷投切和无功补偿装置切换等情况导致的谐波责任变化;最后,基于相关性分析构建场景谐波责任和总谐波责任指标,在指标构建的过程中引入了场景时长占比这一因素以得到更加科学合理的总谐波责任值。通过仿真验证和电网实例验证,该方法能基于现有非同步性监测数据实现各用户合理时间尺度动态谐波责任划分,可为工程上的快速谐波责任划分提供一定的新思路和新方法。展开更多
电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先...电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。展开更多
在碳达峰、碳中和等重大战略决策的推动下,新能源汽车的产销规模不断创历史新高。针对实车运行过程中电池健康状态(state of health,SOH)难以准确估计的问题,基于实车短时充电片段数据,采用安时积分法计算电池的当前最大可用容量,并采...在碳达峰、碳中和等重大战略决策的推动下,新能源汽车的产销规模不断创历史新高。针对实车运行过程中电池健康状态(state of health,SOH)难以准确估计的问题,基于实车短时充电片段数据,采用安时积分法计算电池的当前最大可用容量,并采用箱型图剔除由传感器噪声、复杂工况等因素导致的容量离群点。基于易获取的电流、电压、温度、电池荷电状态(state of charge,SOC)等数据字段提取电池衰退特征,以相关系数分析各特征与健康状态之间的相关性。通过主成分分析法进行特征参数降维,以降低计算复杂度。基于长短期记忆神经网络构建实车动力电池健康状态预测模型,通过灰狼优化算法(grey wolf optimizer,GWO)确定最优的模型超参数。结果表明:基于实车充电过程中80%~90%SOC区间的监测数据,模型进行电池健康状态预测绝对误差为0.27 A·h,模型拟合优度为0.89,可以实现实车动力电池健康状态的准确估计。展开更多
A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle(PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model s...A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle(PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.展开更多
文摘Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.
文摘针对传统谐波责任划分方法需采用专门同步设备监测数据,且需基于等值电路模型划分谐波责任,工程应用较为复杂等不足,采用现有谐波监测装置非同步测量数据,提出一种综合考虑了数据非同步性、场景划分和数据相关性的谐波责任划分方法。首先,对原始非同步监测数据集采用分段聚合近似算法进行降噪预处理,利用形状动态时间规整算法(shape dynamic time warping,ShapeDTW)实现数据匹配对齐;然后,利用点排序识别聚类结构的聚类算法(ordering points to identify the clustering structure,OPTICS)划分场景以处理电力系统中因负荷投切和无功补偿装置切换等情况导致的谐波责任变化;最后,基于相关性分析构建场景谐波责任和总谐波责任指标,在指标构建的过程中引入了场景时长占比这一因素以得到更加科学合理的总谐波责任值。通过仿真验证和电网实例验证,该方法能基于现有非同步性监测数据实现各用户合理时间尺度动态谐波责任划分,可为工程上的快速谐波责任划分提供一定的新思路和新方法。
文摘电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。
文摘在碳达峰、碳中和等重大战略决策的推动下,新能源汽车的产销规模不断创历史新高。针对实车运行过程中电池健康状态(state of health,SOH)难以准确估计的问题,基于实车短时充电片段数据,采用安时积分法计算电池的当前最大可用容量,并采用箱型图剔除由传感器噪声、复杂工况等因素导致的容量离群点。基于易获取的电流、电压、温度、电池荷电状态(state of charge,SOC)等数据字段提取电池衰退特征,以相关系数分析各特征与健康状态之间的相关性。通过主成分分析法进行特征参数降维,以降低计算复杂度。基于长短期记忆神经网络构建实车动力电池健康状态预测模型,通过灰狼优化算法(grey wolf optimizer,GWO)确定最优的模型超参数。结果表明:基于实车充电过程中80%~90%SOC区间的监测数据,模型进行电池健康状态预测绝对误差为0.27 A·h,模型拟合优度为0.89,可以实现实车动力电池健康状态的准确估计。
基金co-supported by the National Nature Sciences Foundation of China (Nos. 61503369 and 61528303)the State Key Laboratory of Roboticsthe Chinese National Key Technology R&D Program (No. Y4A12081010)
文摘A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle(PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.
