The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMM...The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.展开更多
This paper presents a method for determining specific models of overhead power lines with presence of corona phenomenon. The obtained models provide stable numerical solutions for computer simulation of transients cau...This paper presents a method for determining specific models of overhead power lines with presence of corona phenomenon. The obtained models provide stable numerical solutions for computer simulation of transients caused by direct lightning strikes. The corona non- linear charge-voltage characteristics obtained from experimental tests are used for identification of the corona parameters based on System Identification Toolbox implemented in Matlab package. Different transfer functions, which give the same waveshapes of overvoltages are de- termined using two parametric models. A circuit representation of the obtained transfer functions is proposed and the corona model is implemented in the EMTP-RV as a hierarchical structure composed of a overhead power line divided into sections with corona branches. Some computer simulations of lightning overvoltages propagated in a typical 220 kV power line due to direct lightning strikes to a line tower are presented. The proposed method and the model implemented in EMTP-RV are still valid for multi-conductor lines and for higher voltages of power lines but new corona nonlinear charge-voltage characteristics are required as an input parameter for the identification procedure.展开更多
线缆混合输电线路故障时将出现更加复杂的行波折反射现象,对于故障测距带来不小的难度。为解决此类问题,根据电缆与架空线各自的结构、特性的不同,在输电线路上安装分布式的行波检测装置将线路分成若干区间。应用皮尔逊相关系数的相关...线缆混合输电线路故障时将出现更加复杂的行波折反射现象,对于故障测距带来不小的难度。为解决此类问题,根据电缆与架空线各自的结构、特性的不同,在输电线路上安装分布式的行波检测装置将线路分成若干区间。应用皮尔逊相关系数的相关性原理,确定故障发生的区间。通过详细的公式推导,抵消掉波速对测距精度的影响,利用第二个SVD(singular value decomposition)分量标定出信号奇异点的脉冲模极大值,推导出分区间不含波速的混合线路故障定位算法。通过PSCAD仿真及MATLAB数据处理结果表明,与常规的单双端测距法应用于线缆组成的混合输电线路相比,可进一步提高测距精度。展开更多
为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测...为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测方法。首先,基于快速傅里叶变换分析特高压直流输电系统暂态故障特征,使用相模变换和小波变换提取出故障特征量作为输入数据。其次,将输入数据输入到LSTM-RNN中进行前向传播,对系统故障特征进行深度学习,同时使用反向传播方式更新网络参数,将深层的特征量输入到Softmax分类器中进行分类,把故障识别分成区外故障、母线故障和线路故障,故障分类为正极故障、负极故障和双极故障,并输出识别结果。最后,在PSCAD/EMTDC仿真条件下,搭建特高压直流输电模型。验证结果表明:所提的方法在特高压直流输电线路继电保护的故障检测、故障选极上具有更好的效果,相比于人工神经网络、卷积神经网络、支持向量机,故障识别准确率分别提升4.71%、6.57%、9.32%。展开更多
针对鸟巢、导地线、防震锤和导地线线夹多目标干扰而导致间隔棒倾斜缺陷识别易出现错检、漏检等问题,提出一种电网线路间隔棒倾斜缺陷识别方法。构建激光雷达成像模型来校正电网线路图像;采用黑、白高帽变换来区分图像潜在的黯淡、明亮...针对鸟巢、导地线、防震锤和导地线线夹多目标干扰而导致间隔棒倾斜缺陷识别易出现错检、漏检等问题,提出一种电网线路间隔棒倾斜缺陷识别方法。构建激光雷达成像模型来校正电网线路图像;采用黑、白高帽变换来区分图像潜在的黯淡、明亮区域,并输入到所设计的空间关注分类模型中,确定每个集群的最小超球体半径;根据状态判别方程,去除干扰,完成多目标干扰下的电网线路间隔棒倾斜缺陷识别。经实验验证:所提方法的平均精度均值保持在0.85以上,测试时间为600 s时的帧率(frame per second,FPS)为48.6 f/s;在间隔棒倾斜的状态下,所提方法缺陷识别的平均正确率为95.18%,平均召回率为86.37%,平均F1分数为0.90,表明所提方法不存在错检、漏检,且计算效率高、缺陷识别精度高,适用于各种环境下的电网线路间隔棒倾斜缺陷识别。展开更多
基金Projects(61004074,61134001,21076179)supported by the National Natural Science Foundation of ChinaProject(2009BAG12A08)supported by the National Key Technology Support Program of China+1 种基金Project(2010QNA5001)supported by the Fundamental Research Funds for the Central Universities of ChinaProjects(2012AA06A404,2006AA04Z184)supported by the National High Technology Research and Development Program of China
文摘The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.
基金Project supported by the National Science Center, Poland
文摘This paper presents a method for determining specific models of overhead power lines with presence of corona phenomenon. The obtained models provide stable numerical solutions for computer simulation of transients caused by direct lightning strikes. The corona non- linear charge-voltage characteristics obtained from experimental tests are used for identification of the corona parameters based on System Identification Toolbox implemented in Matlab package. Different transfer functions, which give the same waveshapes of overvoltages are de- termined using two parametric models. A circuit representation of the obtained transfer functions is proposed and the corona model is implemented in the EMTP-RV as a hierarchical structure composed of a overhead power line divided into sections with corona branches. Some computer simulations of lightning overvoltages propagated in a typical 220 kV power line due to direct lightning strikes to a line tower are presented. The proposed method and the model implemented in EMTP-RV are still valid for multi-conductor lines and for higher voltages of power lines but new corona nonlinear charge-voltage characteristics are required as an input parameter for the identification procedure.
文摘线缆混合输电线路故障时将出现更加复杂的行波折反射现象,对于故障测距带来不小的难度。为解决此类问题,根据电缆与架空线各自的结构、特性的不同,在输电线路上安装分布式的行波检测装置将线路分成若干区间。应用皮尔逊相关系数的相关性原理,确定故障发生的区间。通过详细的公式推导,抵消掉波速对测距精度的影响,利用第二个SVD(singular value decomposition)分量标定出信号奇异点的脉冲模极大值,推导出分区间不含波速的混合线路故障定位算法。通过PSCAD仿真及MATLAB数据处理结果表明,与常规的单双端测距法应用于线缆组成的混合输电线路相比,可进一步提高测距精度。
文摘为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测方法。首先,基于快速傅里叶变换分析特高压直流输电系统暂态故障特征,使用相模变换和小波变换提取出故障特征量作为输入数据。其次,将输入数据输入到LSTM-RNN中进行前向传播,对系统故障特征进行深度学习,同时使用反向传播方式更新网络参数,将深层的特征量输入到Softmax分类器中进行分类,把故障识别分成区外故障、母线故障和线路故障,故障分类为正极故障、负极故障和双极故障,并输出识别结果。最后,在PSCAD/EMTDC仿真条件下,搭建特高压直流输电模型。验证结果表明:所提的方法在特高压直流输电线路继电保护的故障检测、故障选极上具有更好的效果,相比于人工神经网络、卷积神经网络、支持向量机,故障识别准确率分别提升4.71%、6.57%、9.32%。
文摘针对鸟巢、导地线、防震锤和导地线线夹多目标干扰而导致间隔棒倾斜缺陷识别易出现错检、漏检等问题,提出一种电网线路间隔棒倾斜缺陷识别方法。构建激光雷达成像模型来校正电网线路图像;采用黑、白高帽变换来区分图像潜在的黯淡、明亮区域,并输入到所设计的空间关注分类模型中,确定每个集群的最小超球体半径;根据状态判别方程,去除干扰,完成多目标干扰下的电网线路间隔棒倾斜缺陷识别。经实验验证:所提方法的平均精度均值保持在0.85以上,测试时间为600 s时的帧率(frame per second,FPS)为48.6 f/s;在间隔棒倾斜的状态下,所提方法缺陷识别的平均正确率为95.18%,平均召回率为86.37%,平均F1分数为0.90,表明所提方法不存在错检、漏检,且计算效率高、缺陷识别精度高,适用于各种环境下的电网线路间隔棒倾斜缺陷识别。
