Optimal PMU placement for state estimation robustness

Göl, Murat; Abur, Ali

doi:10.1109/isgteurope.2014.7028797

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…Using the NPCC test system with 140 buses, 171 branch PMUs are placed using the placement method of [51]. It is noted that this system has 51 ZI buses.…”

Section: Impact On Bad Data Rejectionmentioning

confidence: 99%

“…The IEEE 30-bus test system is divided into three zones and its partitions are shown in Next, a large utility system with 2917 buses and 11 zones is considered. An optimal PMU placement algorithm [51] is used to place 4393 PMUs in this system making sure that redundancy level is commensurate with the requirements of robustness.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

“…Individual area measurement configurations can be designed to meet the required local redundancy levels for bad data rejection at any location within the areas. This can be accomplished using the previous developed PMU placement method described in [51]. Nonetheless, when individual areas locally solve their state estimation problems, some of those measurements incident to inter-area tie-lines will have to be discarded since they will be unusable by either neighbor.…”

Section: Two-stage State Estimation Algorithmmentioning

confidence: 99%

“…The 3-area partition of IEEE 30-bus system is shown in Figure 5.6. 37 PMU measurements are placed in this system including 32 branch PMUs and 5 current injection PMUs using the optimal PMU placement algorithm [51]. One voltage and one current phasor measurement are placed at the sending-end of every branch PMU.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

“…A large scale utility transmission grid with 11 areas, 2917 buses and 3826 branches is used to test the proposed approach. This system is populated with 4393 PMUs by using a previously developed optimal PMU placement algorithm which can design the measurement configuration to guarantee a desired level of local measurement redundancy [51].…”

Section: -Bus Utility Systemmentioning

confidence: 99%

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Computationally efficient PMU-based L1 estimators for large power systems

Xu¹

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“…Using the NPCC test system with 140 buses, 171 branch PMUs are placed using the placement method of [51]. It is noted that this system has 51 ZI buses.…”

Section: Impact On Bad Data Rejectionmentioning

confidence: 99%

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

Section: Two-stage State Estimation Algorithmmentioning

confidence: 99%

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

Section: -Bus Utility Systemmentioning

confidence: 99%

See 3 more Smart Citations

Computationally efficient PMU-based L1 estimators for large power systems

Xu¹

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Measurement devices allocation in distribution system using state estimation: A multi‐objective approach

Marzouni

Zakariazadeh

Siano

2020

Int Trans Electr Energ Syst

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Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi-objective method.The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33-bus and IEEE 123-bus radial network.

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Groebner bases algorithm for optimal PMU placement

Becejac

Stefanov

2020

International Journal of Electrical Power & Energy Systems

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Optimal PMU placement for state estimation robustness

Cited by 11 publications

References 10 publications

Computationally efficient PMU-based L1 estimators for large power systems

Computationally efficient PMU-based L1 estimators for large power systems

Measurement devices allocation in distribution system using state estimation: A multi‐objective approach

Groebner bases algorithm for optimal PMU placement

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