This paper is a further study of two papers [1] and [2], which were related to Ill-Conditioned Load Flow Problems and were published by IEEE Trans. PAS. The authors of this paper have some different opinions, for exam...This paper is a further study of two papers [1] and [2], which were related to Ill-Conditioned Load Flow Problems and were published by IEEE Trans. PAS. The authors of this paper have some different opinions, for example, the 11-bus system is not an ill-conditioned system. In addition, a new approach to solve Load Flow Problems, E-ψtc, is introduced. It is an explicit method;solving linear equations is not needed. It can handle very tough and very large systems. The advantage of this method has been fully proved by two examples. The authors give this new method a detailed description of how to use it to solve Load Flow Problems and successfully apply it to the 43-bus and the 11-bus systems. The authors also propose a strategy to test the reliability, and by solving gradient equations, this new method can answer if the solution exists or not.展开更多
This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is m...This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT;similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.展开更多
跟-构网型(grid following and grid-forming,GFL-GFM)变流器混合并网系统中多类型控制策略及控制切换行为使电压响应特性复杂多变,难以快速、准确评估其暂态电压稳定状态。为此,该文提出一种基于切换系统最大李雅普诺夫指数(switching ...跟-构网型(grid following and grid-forming,GFL-GFM)变流器混合并网系统中多类型控制策略及控制切换行为使电压响应特性复杂多变,难以快速、准确评估其暂态电压稳定状态。为此,该文提出一种基于切换系统最大李雅普诺夫指数(switching system maximum Lyapunov exponent,SSMLE)的跟-构网型变流器混合并网系统暂态电压稳定评估方法。首先,计及系统多运行状态和运行参数对暂态电压响应特性影响,建立混合并网系统不同运行工况下电压轨线变分方程;然后,在各运行状态下通过变分方程分段求解SSMLE,并采用切换补偿矩阵修正控制切换时刻积分终值矩阵偏差,提升电压稳定状态判别速度和准确度;其次,利用SSMLE分析系统关键参数对暂态电压稳定性的影响并确定暂态电压稳定参数域,可为调度人员获取系统运行状态、更新电压稳控策略提供参考;最后,通过GFL-GFM变流器混合并网系统和多机硬件在环仿真系统的仿真分析,验证所提方法的准确性和有效性。展开更多
文摘This paper is a further study of two papers [1] and [2], which were related to Ill-Conditioned Load Flow Problems and were published by IEEE Trans. PAS. The authors of this paper have some different opinions, for example, the 11-bus system is not an ill-conditioned system. In addition, a new approach to solve Load Flow Problems, E-ψtc, is introduced. It is an explicit method;solving linear equations is not needed. It can handle very tough and very large systems. The advantage of this method has been fully proved by two examples. The authors give this new method a detailed description of how to use it to solve Load Flow Problems and successfully apply it to the 43-bus and the 11-bus systems. The authors also propose a strategy to test the reliability, and by solving gradient equations, this new method can answer if the solution exists or not.
文摘This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT;similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.
文摘跟-构网型(grid following and grid-forming,GFL-GFM)变流器混合并网系统中多类型控制策略及控制切换行为使电压响应特性复杂多变,难以快速、准确评估其暂态电压稳定状态。为此,该文提出一种基于切换系统最大李雅普诺夫指数(switching system maximum Lyapunov exponent,SSMLE)的跟-构网型变流器混合并网系统暂态电压稳定评估方法。首先,计及系统多运行状态和运行参数对暂态电压响应特性影响,建立混合并网系统不同运行工况下电压轨线变分方程;然后,在各运行状态下通过变分方程分段求解SSMLE,并采用切换补偿矩阵修正控制切换时刻积分终值矩阵偏差,提升电压稳定状态判别速度和准确度;其次,利用SSMLE分析系统关键参数对暂态电压稳定性的影响并确定暂态电压稳定参数域,可为调度人员获取系统运行状态、更新电压稳控策略提供参考;最后,通过GFL-GFM变流器混合并网系统和多机硬件在环仿真系统的仿真分析,验证所提方法的准确性和有效性。
