Optimal Phasor Measurement Unit Placement for Numerical Observability Using Branch-and-Bound and a Binary-Coded Genetic Algorithm

Theodorakatos, Nikolaos P.

doi:10.1080/15325008.2019.1605635

Cited by 40 publications

(40 citation statements)

References 30 publications

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“…Table 3 shows the details about the solutions for the two models under two cases. Figure 6 is the comparison between single-objective and multi- To demonstrate the effectiveness of the developed model, single-objective model minimizing the number of PMUs was solved, and then APUO value was calculated by formulation (9) and (10) and (11)- (14) to compare with that in multi-objective model. Table 3 shows the details about the solutions for the two models under two cases.…”

Section: Numerical Studymentioning

confidence: 99%

“…Apparently, the developed placement model can provide solutions with higher reliability. ,11,12,15,19,20,22,24,26,28,29,30,31, 32,33,35,36,37,38,41,45,46,47,50,51,53,54,56,57 Multi-objective 29 0.00180 1,3,4,6,9,11,12,15,19,20,22,24,27,29,30,32,33,35,36,39,41,44,46,47,49,51,53,55,57 Single-objective 29 0.00298 1,3,5,7,9,12,14,18,20,22,24,27,29,30,32,33,35,38,39,40,42,43,45,47,…”

Section: Numerical Studymentioning

confidence: 99%

“…A great number of researches have contributed to the area of the OPP problem. The model consisted of the objective of minimum PMU number and constraint of full observability, was established, and solved by mixed-integer linear programming (MILP) and nonlinear programming (NLP) in [8], the branch-and-bound algorithm (BB) and binary-bonded genetic algorithm (BCGA) in [9]. To obtain a PMU placement scheme with higher redundancy, literature [10] formulated a generalized binary integer linear programming (ILP) model minimizing weighted degrees of nodes incorporating the topology changes.…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

Huang

Liu

et al. 2019

Sustainability

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Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs in optimal phasor measurement units placement problem. Thus, in this paper, a multi-objective model was established to consider the two conflicting components simultaneously, solved by ε-constraint method and the fuzzy satisfying approach. The redundancy here was formulated as average possibility of observability including random component outages, and full possibility formula was applied to calculate the average possibility of observability in the case of single line outage. Finally, the model was employed to the IEEE-57 bus system, and the results verified that the developed model could provide a placement scheme with higher reliability.Sustainability 2019, 11, 7097 2 of 12 bee colony (ABC) algorithm, modified binary cuckoo optimization algorithm (MBCOA), weighted method, multi-objective evolutionary algorithm-based, were adopted to solve the multi-objective model in [11][12][13][14][15], respectively. In addition, to obtain the final solution from the Pareto front quickly and accurately, the fuzzy satisfying approach was applied in [16,17] and sparse neighborhood surroundings in [18]. Furthermore, the accuracy of state estimation was taken into consideration, subject to the constraint of supervisory control and data acquisition (SCADA) measurement data in [19] and the uncertainties of power system conditions in [20].Literatures aforementioned analyzed the system observability from a deterministic view, including two contrary states, i.e., observable or unobservable. However, it was investigated in a probabilistic manner in [21][22][23][24]. In [21], to obtain a PMU placement scheme with unobservable probability resulted from 1st-order and 2nd-order failures of PMUs and line less than the pre-defined threshold, the unobservable probability was calculated iteratively by increasing PMU numbers gradually on the basis of initial PMU placement scheme, until the requirements were met. A multi-objective model was proposed, simultaneously minimizing the PMU cost and unobservable probability under PMU or line failures in [22]. However, random component outages were not taken into consideration in [21] and [22]. In [23], a probabilistic manner was adopted to differentiate the multiple solutions and showed significant priority comparing to measurement redundancy. A solution with maximum observable probability considering random component outages was selected as the final optimal scheme. In addition, when considering multistage PMU placement, the observable probability was regarded as the objective to optimize at each stage [24].In this paper, firstly, APO of all buses in the network was calculated under two different cases, i.e., with and without consideration of single line outages. It is noticeable to consider the effect of single line outag...

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Section: Numerical Studymentioning

confidence: 99%

Section: Numerical Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

Huang

Liu

et al. 2019

Sustainability

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“…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].…”

mentioning

confidence: 99%

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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PMU‐based power system component monitoring scheme satisfying complete observability with multicriteria decision support

Chatterjee

Ghosh

Roy

2019

Int Trans Electr Energ Syst

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Summary Modern power system operates in stressed conditions due to fickle loads, high penetration of renewables, and complex control strategies. The failure of any power system asset may lead to protract power interruption or even catastrophic failures. Synchrophasor‐based extensive research advancements in wide area measurement, protection, and control (WAMPAC) systems are solving critical operation and control problems of the power system. This paper proposes a new combinatorial methodology for monitoring the prime power system components in preference to complete observability of the system. A multicriteria decision support (MCDS) approach, analytical hierarchy process (AHP), has been used to prepare a ranking according to component bus priority for synchrophasor measurement unit installation. The priority list is further processed with intelligent search technique (IST) to compute the optimal solution for synchrophasor meter placement, ensuring complete observability, maximum measurement redundancy, and coverage of prime system components with direct monitoring. Some practical contingencies related to synchrophasor meters placement problem like single meter loss or line outage have also been considered for the case studies. The efficiency of the proposed method has been verified with IEEE 14‐bus, 24‐bus, 30‐bus, 57‐bus, and 118‐bus and New England 39‐bus test systems. To prove the superiority of the method, in essence of synchrophasor meter placement problems, the outcome has been compared with some of the existing methods from the literature.

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Optimal Phasor Measurement Unit Placement for Numerical Observability Using Branch-and-Bound and a Binary-Coded Genetic Algorithm

Cited by 40 publications

References 30 publications

A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

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

PMU‐based power system component monitoring scheme satisfying complete observability with multicriteria decision support

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