电力系统状态估计常用加权最小二乘(W LS)法处理,这种方法中量测权值的悬殊和大量的注入量测会导致信息矩阵出现病态问题,降低算法的收敛性。综合带约束的正规方程(NE/C)法和海克特(H ach te l)法数值稳定性好的优点,把量测量合理分类...电力系统状态估计常用加权最小二乘(W LS)法处理,这种方法中量测权值的悬殊和大量的注入量测会导致信息矩阵出现病态问题,降低算法的收敛性。综合带约束的正规方程(NE/C)法和海克特(H ach te l)法数值稳定性好的优点,把量测量合理分类构建信息矩阵,并采用分块稀疏矩阵技术,形成了一种计算速度快、数值稳定性好的状态估计新算法。理论和算例分析验证了该算法的有效性。展开更多
最小均方(Least Mean Square, LMS)算法因其计算复杂度低、稳定性好的特点已广泛应用于谐波检测领域中。但为了避免权重偏移,进一步提高收敛速度,提出了一种基于线性约束最小均方(Linearly Constrained Least Mean Square, LCLMS)的谐...最小均方(Least Mean Square, LMS)算法因其计算复杂度低、稳定性好的特点已广泛应用于谐波检测领域中。但为了避免权重偏移,进一步提高收敛速度,提出了一种基于线性约束最小均方(Linearly Constrained Least Mean Square, LCLMS)的谐波检测算法。该算法在LMS算法的基础上,对权重变量加入了一个线性约束条件,并应用于不同高斯白噪声环境下谐波、间谐波信号的幅值和相角参数评估。最后又在稳态信号、动态信号和电弧炉算例下检验了该算法的可行性。实验结果表明,该算法可以快速准确地检测不同环境下谐波的相关信息,且相比LMS算法有较快的收敛速度和较高的抗干扰能力。展开更多
Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWL...Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.展开更多
文摘电力系统状态估计常用加权最小二乘(W LS)法处理,这种方法中量测权值的悬殊和大量的注入量测会导致信息矩阵出现病态问题,降低算法的收敛性。综合带约束的正规方程(NE/C)法和海克特(H ach te l)法数值稳定性好的优点,把量测量合理分类构建信息矩阵,并采用分块稀疏矩阵技术,形成了一种计算速度快、数值稳定性好的状态估计新算法。理论和算例分析验证了该算法的有效性。
文摘最小均方(Least Mean Square, LMS)算法因其计算复杂度低、稳定性好的特点已广泛应用于谐波检测领域中。但为了避免权重偏移,进一步提高收敛速度,提出了一种基于线性约束最小均方(Linearly Constrained Least Mean Square, LCLMS)的谐波检测算法。该算法在LMS算法的基础上,对权重变量加入了一个线性约束条件,并应用于不同高斯白噪声环境下谐波、间谐波信号的幅值和相角参数评估。最后又在稳态信号、动态信号和电弧炉算例下检验了该算法的可行性。实验结果表明,该算法可以快速准确地检测不同环境下谐波的相关信息,且相比LMS算法有较快的收敛速度和较高的抗干扰能力。
基金supported by the National Natural Science Foundation of China(61201282)the Science and Technology on Communication Information Security Control Laboratory Foundation(9140C130304120C13064)
文摘Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.
