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This paper investigates the sparse recovery models for bad data detection and state estimation in power networks. Two sparse models, the sparse L1-relaxation model (L1-R) and the multi-stage convex relaxation model (Capped-L1), are compared with the weighted least absolute value (WLAV) in the aspects of the bad data processing capacity and the computational efficiency. Numerical tests are conducted...
Although much of today's power system remains steady, the integrations of wind power make a portion of the system become fluctuant. To monitor these fluctuations, we need to utilize phasor measurements and shorten the time interval for state estimation. On the other hand, however, this means huge increase in computation burden. As a tradeoff between accuracy and computation efficiency, a multi-time...
A distribution management system based on loop analysis method is presented in this paper. Firstly, the configuration of the newly developed DMS system is introduced which accommodates DERs. The realization methods of key functions are presented include state estimation, power flow analysis, network optimization, network restoration and reconfiguration, which are based on loop analysis method. An...
A substation three-phase nonlinear state estimation method based on KCL is presented. Measurements from different apparatus in substation are utilized, such as RTU and PMU three-phase data. Substation state estimation is established on three-phase breaker-oriented model using KCL, in which analog bad data and topology error identification can be decoupled, realizing real-time three-phase unbalanced...
The mathematical base of state estimation (SE) method for power systems is studied based on information theory. A more general SE method based on the minimum information loss (MIL) principle is proposed. The new method is suitable for different probability distributions of measurement errors, it can utilize different types of information including that of analog and digital measurements, and a unified...
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