Placement of PMUs with channel limits

Korkali, Mert; Abur, Ali

doi:10.1109/pes.2009.5275529

Cited by 93 publications

(125 citation statements)

References 6 publications

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“…In [16] (Tables 4.1, 4.2, p. 32-33), similar results are reported for all systems with the exception of RTS-96, and [17] (Table I) and [19] (Tables I, III) report similar results for all systems with the exception of RTS-96 and the IEEE 300-bus. In contrast to the results reported in [16,17], which were obtained using heuristic solution procedures for consideration of zero-injection bus property, our approach guarantees globally optimal solutions for all test instances.…”

Section: Numerical Experimentssupporting

confidence: 70%

“…However, this approach cannot be generalized to multi-channel PMU placement. In [16,17], an integer programming formulation was proposed to solve the MC-PPP, and analyzed on a 14-bus test system. Several approaches to modeling the zero-injection bus property were considered.…”

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confidence: 99%

“…However, each of these approaches fails to guarantee optimal solutions for power systems with a general network topology. In [18], as an improvement of [17] without any general formulations, it accounted for the number of available channels for the chosen types of PMUs and also it presented upper limit by considering the graph sparsity. In [19], an IP model was proposed based on several complicated matrices, to consider zero-injection buses.…”

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confidence: 99%

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On integer programming models for the multi-channel PMU placement problem and their solution

Fan

Watson

2014

Energy Syst

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Phasor Measurement Units (PMUs) are starting to see increased deployment, enabling accurate measurement of power grid electrical properties to determine system health. Due to the costs associated with PMU acquisition and maintenance, it is practically important to place the minimum number of PMUs in order to achieve system complete observability. In this paper, we consider a variety of optimization models for the PMU placement problem that addresses more realistic assumptions than simple infinite-capacity placement models. Specifically, instead of assuming that a PMU can sense all lines incident to the bus at which it is placed, we impose the more realistic assumption that PMUs have restricted channel capacity, with per-unit cost given as a function of channel capacity. The optimization objective is then to minimize the total cost of placed PMUs, in contrast to their number. Further, we leverage the zero-injection bus properties to reduce the quantity and cost of placed PMUs. In formulating our optimization models, we identify a close relationship between the PMU placement problem (PPP) and a classic combinatorial problem, the set cover problem (SCP). If channel capacity limits are ignored, there is a close relationship between the PPP and the dominating set problem (DSP), a special case of the SCP. Similarly, when measurement redundancy is imposed as a design requirement, there is a close relationship between the PPP and the set multi-cover problem (SMCP), a generalized version of the SCP. These connections to well-studied combinational problems are not well-known in the power systems literature, and can be leveraged to improve solution algorithms. We demonstrate that more realistic, high-fidelity PPP optimization models can be solved to optimality using commercial integer programing solvers such as CPLEX. Specifically, run-times for all test cases, ranging from IEEE 14-bus to 300-bus test systems, are less than a second. This result indicates that the size of system that can be analyzed using state-of-the-art solvers is considerable. Further, our results call into question the need for problem-specific heuristic solution algorithms for the PPP, many of which have been proposed over the past decade. Finally, we analyze cost versus performance tradeoffs using our PPP optimization models on various IEEE test systems.

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Section: Numerical Experimentssupporting

confidence: 70%

mentioning

confidence: 99%

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confidence: 99%

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On integer programming models for the multi-channel PMU placement problem and their solution

Fan

Watson

2014

Energy Syst

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“…On the other hand, PMUs that are designed to monitor individual branches, measuring phasor voltage, and currents at the sending end of the branch can also be considered. Considering the fact that there are other types of PMUs produced by several manufacturers where the number of measurement channels per PMU will be different, the PMU placement problem can be formulated in a unified manner as follows [10]:…”

Section: Pmu Placement For Observabilitymentioning

confidence: 99%

Topology, parameter, and measurement error processing using synchronized phasors

Abur

Chen²,

Zhu³

et al. 2010

Int Trans Elec Energy Syst

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SUMMARYThis paper provides an overview of the methods developed for processing errors in network topology, parameters, and measurements, using synchronized phasors. These methods facilitate placement decisions for phasor measurements for a range of considerations. The presented overview is primarily concerned with maintaining network observability and filtering gross errors appearing in network model and measurements.

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“…The major weakness of using intelligent search algorithms is that globally optimal solutions are not guaranteed. Approaches based on deterministic methods as proposed in [19][20][21][22][23][24][25][26][27][28][29][30][31] attempt to build equivalent integer problems (EIP) to describe how the PMUs installed in the power system cause the whole network to be observable according to the observability rules and then solve the EIP using local or general optimization techniques. The method described in [6] was a breakthrough that used an EIP model to describe the task of PMU placement at strategic buses to make the entire system observable.…”

Section: Introductionmentioning

confidence: 99%

A Substation-Oriented Approach to Optimal Phasor Measurement Units Placement

Bao¹,

Guo²,

Han³

et al. 2015

Journal of Electrical Engineering and Technology

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-State Estimation (SE) is the basis of a variety of advanced applications used in most modern power systems. An SE problem formed with enough phasor measurement units (PMUs) data is simply a linear weighted least squares problem requiring no iterations. Thus, designing a minimumcost placement of PMUs that guarantees observability of a power system becomes a worthy challenge. This paper proposes an equivalent integer linear programming method for substation-oriented optimal PMU placement (SOOPP). The proposed method uses an exhaustive search to determine a globally optimal solution representing the best PMU placement for that particular power system. To obtain a more comprehensive model, contingencies and the limitation of the number of PMU measurement channels are considered and embodied in the model as changes to the original constraints and as additional constraints. The proposed method is examined for applicability using the IEEE 14-bus, 118-bus and 300-bus test systems. The comparison between SOOPP results and results obtained by other methods reveals the excellence of SOOPP. Furthermore, practical large-scale power systems are also successfully analyzed using SOOPP.

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Placement of PMUs with channel limits

Cited by 93 publications

References 6 publications

On integer programming models for the multi-channel PMU placement problem and their solution

On integer programming models for the multi-channel PMU placement problem and their solution

Topology, parameter, and measurement error processing using synchronized phasors

A Substation-Oriented Approach to Optimal Phasor Measurement Units Placement

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