Event-triggered multi-area state estimation in power systems

Kashyap, Neelabh; Werner, Stefan; Huang, Yih-Fang

doi:10.1109/camsap.2011.6135905

Cited by 11 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…] × 100%, (4) where τ j (t) is the time error of the latest bus j measurements, P i j (t − τ j (t)) and Q i j (t − τ j (t)) represent the latest active and reactive power flow measurements at bus j,…”

Section: State Estimation Based On Time Skew Oriented Weight Adaptationmentioning

confidence: 99%

“…Hence, SE solutions aimed at a tighter and resource-efficient monitoring of system evolution are greatly desired. In [4], the event-triggered multi-area state estimation is introduced. In this scenario, the local areas update their state estimates only when needed, which is judged by the square root error of the current measurement and the previous estimate.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of updating state vector by whether there is considerable "novelty" in the received measurements [3], [4], [5], we transmit data and implement SE by evaluating whether it is necessary or not. A comprehensive summary of SE-demand situations is included in this architecture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On-demand state estimation with sampling time skew in power systems

Liu

et al. 2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new architecture to transfer data and execute SE on demand. A list of situations are summarized to direct the SE-demand generator in system control center. As S-CADA and PMU measurements are co-exist in realistic power systems, time skew problem is inevitable. To mitigate the influence of time skew, a state estimator based on time skew oriented weight adaptation is considered. In each SE circle, the weights assigned to the measurements not only correspond to their noise, but also the time offsets relative to the SE-demand point. Numerical examples demonstrate the improved accuracy of our estimator compared with the conventional hybrid SE when measurements time skew is present.

show abstract

Section: State Estimation Based On Time Skew Oriented Weight Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On-demand state estimation with sampling time skew in power systems

Liu

et al. 2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

show abstract

“…The results obtained with the developed SE technique are more promising than the existing WLS SE. In [93] an event-triggered MASE is developed which is able to perform event- issue is addressed by [104], in which the authors propose a Seidel-type recursive Bayesian approach, in which it is shown that the convergence speed is improved even in the presence of noise. Very recently, an SOR-based RBA is proposed in [105], which has further increased the convergence speed of both basic-RBA and Seidel-type RBA.…”

Section: Event-triggered Dsse Techniquesmentioning

confidence: 99%

“…Later, the consensus-algorithm initiates local communication among the neighboring network-areas to exchange state information. Another work on MASE technique can be found in [93], in which event-driven sensing, estimation and communication is implemented to minimize the data exchange and thus, reduce the dependency on the communication network.…”

Section: Multi-area Dssementioning

confidence: 99%