Binary particle swarm optimisation‐based optimal substation coverage algorithm for phasor measurement unit installations in practical systems

Mishra, Chetan; Jones, Kevin D.; Pal, Anamitra; Centeno, Virgilio

doi:10.1049/iet-gtd.2015.1077

Cited by 42 publications

(29 citation statements)

References 37 publications

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“…Ref. [18] had also proposed an optimal substation coverage algorithm. The differences between this paper and [18] are as follows:…”

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

“…In [18], the optimization was performed using binary particle swarm optimization (BPSO), while the proposed approach uses ILP. The latter always gives an optimal solution, while BPSO is not guaranteed to always give an optimal solution.…”

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

“…The algorithm developed in [18] was meant for optimizing locations of traditional (bus) PMUs only. The locations of the dual use line relay (branch) PMUs were assumed to be "known" in that paper.…”

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

“…To the best of our knowledge, [18] was the first paper that placed PMUs while considering the fact that some lines needed to be observed by them. In this paper, the idea is extended further by proposing two levels of line criticality: (a) Critical Lines -those lines which must be observed by PMUs under normal conditions; and (b) Super Critical Lines -those lines which must be observed by PMUs under N − 1 contingency conditions.…”

Section: Ensuring Observability Of Important Linesmentioning

confidence: 99%

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A PMU Placement Scheme Considering Realistic Costs and Modern Trends in Relaying

Pal

Vullikanti

Ravi

2017

IEEE Trans. Power Syst.

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Abstract-The primary objective of the conventional optimal phasor measurement unit (PMU) placement problem is the minimization of the number of PMU devices that, when placed in a power system, measure all bus voltages. However, due to advancements in the field of relay technology, digital relays can now act as PMUs. This has significantly reduced device costs. Moreover, although the goal is to observe all the buses, the devices themselves can only be placed in substations, whose upgrade costs are much higher than those of the devices. Considering these factors, the approach proposed here simultaneously optimizes the number of substations where traditional PMUs and dual-use line relay PMUs can be placed. The general optimal substation coverage (GOSC) algorithm presented in this paper is also able to incorporate practical requirements such as redundancy in the measurement of critical elements of the system, and estimation of the tap ratios of the transformers present. Simulation results indicate that the GOSC algorithm provides significant techno-economic benefits.

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“…Ref. [18] had also proposed an optimal substation coverage algorithm. The differences between this paper and [18] are as follows:…”

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

Section: The Optimal Pmu Placement (Opp) Problemmentioning

confidence: 99%

Section: Ensuring Observability Of Important Linesmentioning

confidence: 99%

See 2 more Smart Citations

A PMU Placement Scheme Considering Realistic Costs and Modern Trends in Relaying

Pal

Vullikanti

Ravi

2017

IEEE Trans. Power Syst.

Self Cite

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“…Binary particle swarm optimization (BPSO) algorithm is developed for the solution of the OPP problem in [19]. In [20], the authors proposed a BPSO algorithm to minimize the number of substations in which installations must be performed for making all voltage levels observable while being subject to various practical constraints. Authors of [21] proposed the binary gravitational search algorithm to solve the OPP problem.…”

mentioning

confidence: 99%

Towards Smart Energy Grids: A Box-Constrained Nonlinear Underdetermined Model for Power System Observability Using Recursive Quadratic Programming

Theodorakatos

Lytras

Babu³

2020

Energies

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This paper introduces an underdetermined nonlinear programming model where the equality constraints are fewer than the design variables defined on a compact set for the solution of the optimal Phasor Measurement Unit (PMU) placement. The minimization model is efficiently solved by a recursive quadratic programming (RQP) method. The focus of this work is on applying an RQP to attempt to find guaranteed global minima. The proposed minimization model is conducted on IEEE systems. For all simulation runs, the RQP converges superlinearly towards optimality in a finite number of iterations without to be rejected the full step-length. The simulation results indicate that the RQP finds out the minimal number and the optimal locations of PMUs to make the power system wholly observable.Energies 2020, 13, 1724 2 of 17 OPP problem is unraveled using the mixed-integer semi-definite programming approach subjected to linear matrix inequality. This technique is based on numerical observability, whereas most of the other techniques are based on topological observability, which may or may not ensure numerical observability to be executed successfully for state estimation. In [16,17], mixed-integer linear programming and nonlinear programming techniques are compared to check their suitability for networks of different sizes.Stochastic algorithms have also been used for the OPP problem. A recursive tabu search is suggested in [18], which has been claimed superior to multiple Tabu search and higher observability. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of the OPP problem in [19]. In [20], the authors proposed a BPSO algorithm to minimize the number of substations in which installations must be performed for making all voltage levels observable while being subject to various practical constraints. Authors of [21] proposed the binary gravitational search algorithm to solve the OPP problem. Authors in [22], presented a modified binary Cuckoo optimization algorithm for the solution of the OPP problem with maximum redundancy. Genetic algorithms (GA) have been applied to determine the optimal allocation of PMUs in [23,24].Accurate knowledge of transmission system parameters, such as series impedance, optimizes distance relay settings and impedance-based fault location. A new method is developed to measure transmission line impedances and admittances from synchronized phasor measurements in [25].A new on-line method for estimating transmission line constants of a power system is proposed in [26]. In [27], a new State Estimation (SE) framework is proposed for processing Remote Terminal Unit (RTU) and PMU measurements separately in order to leave the traditional weighted least square (WLS)-based SE software unchanged. Phasor measurement unit based fault location techniques are proposed in [28][29][30]. Other methods, such as an optimized extreme learning machine-based approach, use synchrophasors to ensure real-time power transient stability prediction [31].Up to now, a BILP model is written to its st...

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A critical review of methods for optimal placement of phasor measurement units

Johnson

Moger

2020

Int Trans Electr Energ Syst

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Summary Situational awareness of the electric power system is the need of the hour. This is in the wake of recent blackouts in various parts of the world. These blackouts could have been prevented with better telemetry and real‐time monitoring of power system states. A fast and reliable measuring system with devices optimally employed across the network shall enable reliable system operation. Faster situational awareness and visualization helps the system operator to take faster preventive measures and maintain system stability. Wide Area Monitoring System uses phasor measurement units (PMUs) to measure power system states. PMUs measure synchronously real‐time voltages at various buses and current phasors, which are flowing toward these buses. But, there is a need to optimally find the locations for installation of PMUs in the network for an observable system, with minimum investment. This optimal placement of PMUs (OPP) is a binary optimization problem, combinatorial in nature. In this paper, progress achieved until now in the field of OPP studies are consolidated. A discussion on the characteristics of problem formulations and shortcomings of methods and constraints considered has been done. Challenges have been highlighted to provide a good foundation for new researchers.

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Binary particle swarm optimisation‐based optimal substation coverage algorithm for phasor measurement unit installations in practical systems

Cited by 42 publications

References 37 publications

A PMU Placement Scheme Considering Realistic Costs and Modern Trends in Relaying

A PMU Placement Scheme Considering Realistic Costs and Modern Trends in Relaying

Towards Smart Energy Grids: A Box-Constrained Nonlinear Underdetermined Model for Power System Observability Using Recursive Quadratic Programming

A critical review of methods for optimal placement of phasor measurement units

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