Optimal substation PMU placement method for the two-level state estimator

Müller, Heloisa H.; Castro, Carlos A.

doi:10.1109/epecs.2013.6713097

Cited by 4 publications

(6 citation statements)

References 19 publications

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“…Furthermore, it is able to earmark further PMUs/measurement units to provide full observability, and the method is based on a GA with a unique performance function. The analysis of the results shows that the suggested algorithm provides a significant and reliable solution for the OPP problem in electrical grids [19].…”

Section: Mixed Heuristic/metaheuristic Methodsmentioning

confidence: 97%

“…One of the most popular numerical programing techniques is IP, which is also known as mathematical programing. This method has been utilized to solve problems with integer design elements, and has been divided to integer linear programing (ILP), integer nonlinear programing, and integer quadratic programing (IQP) on the basis of linear, nonlinear, or quadratic factor design elements, respectively [19].…”

Section: Mathematical Programming Methodsmentioning

confidence: 99%

“…Ref. [19] used the new technique in substations by applying the two-level state estimation method. To specify the optimal place and total number of PMUs, a mixed heuristic/metaheuristic technique was implemented.…”

Section: Mixed Heuristic/metaheuristic Methodsmentioning

confidence: 99%

“…The differential evolution (DE) method is suggested to solve the joint placement issue of the phasor measurements [19] of PMU devices with power flow measurements that specify the state variables in a power grid. Therefore, a joint placement problem was formulated to optimize PMU measurement redundancy and to decrease the number of measurement devices by following full network observability.…”

Section: Differential Evolutionmentioning

confidence: 99%

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Optimal PMU placement in a smart grid: An updated review

Sefid¹,

Rihan²

2018

IJSGCE

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In recent years, the monitoring and control of the power grid has become an essential objective to prevent system failure. The provision of measurements generated from phasor measurement units (PMUs) can play a significant role in achieving this goal. PMUs can offer synchronized measurements and dynamically measure voltages and current phasors. The estimation of the states of each bus via the installation of PMUs at each node of the bus is not necessary and is hindered by the high cost and complexity of the installation and communication methods. Therefore, a method that reduces the total number of PMUs without affecting the maximum observability and reliability of the power grid is needed. Achieving the minimum number of installed PMUs with maximum observability is the main target of optimal PMU placement. Several techniques are available for resolving this issue, and these techniques lie in two main categories: mathematical techniques and metaheuristic approaches. In the last few years, a huge number of publications have reported different techniques that are based on the principle used and the intended application. Some comprehensive reviews have also been reported a few years ago. This area is an intensively pursued field of study, and many new techniques and important results have been reported after the publication of these reviews. Therefore, there is a need for a comprehensive review of recent research on optimal PMU placement. The present work attempts to provide a critical review of recent literature on optimal PMU placement. We expect this review to be beneficial to researchers and industries that are studying the optimal placement of PMUs in their networks.

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Section: Mixed Heuristic/metaheuristic Methodsmentioning

confidence: 97%

Section: Mathematical Programming Methodsmentioning

confidence: 99%

Section: Mixed Heuristic/metaheuristic Methodsmentioning

confidence: 99%

Section: Differential Evolutionmentioning

confidence: 99%

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Optimal PMU placement in a smart grid: An updated review

Sefid¹,

Rihan²

2018

IJSGCE

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“…In [13] Genetic algorithm was developed to solve the proposed new model that utilizes network observability rules and determine the optimal investment decision for the placement of PMUs in the power grid. Reference [14] proposes an optimal PMU allocation method for substations based on a specialized Genetic Algorithm (GA) while in [15] the same method has been used for complete and incomplete observability. The authors in [16] presented a new optimization algorithm for optimal PMU configuration based on combination of graph theory and genetic algorithm.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Artificial Intelligence Versus Conventional Mathematical Techniques: A Review for Optimal Placement of Phasor Measurement Units

Negash

Khan

Yohannes

2016

Technol Econ Smart Grids Sustain Energy

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The Phasor Measurement Unit (PMU) is a contemporary metering device installed on system to enable the power system monitoring and control. PMUs are most accurate and advance time synchronized technology which provides measurements of voltages at the buses and also current phase values which are connected to those buses where these PMUs are located. PMU placement on each bus of a system is difficult to achieve either due to cost factor or due to non-existence limitation of communication facilities in some parts of the system. Moreover, as a consequence of Ohm's Law, when a PMU is placed at a bus, neighboring busses also become observable. This implies that a system can be made observable with a lesser number of PMUs than the number of busses. It is necessary to determine the minimum number of PMUs for entire observability of the power network. The optimal placement of PMUs (OPP) problem can be solved by various techniques. Hence in this paper a comprehensive review is presented on the OPP problems under the characterization of heuristic and mathematical methodologies. Also a comparison between

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