Optimal placement of phasor measurement unit in distribution networks considering the changes in topology

Su, Hongzhi; Wang, Chengshan; Li, Peng; Liu, Zhelin; Yu, Li; Wu, Jianzhong

doi:10.1016/j.apenergy.2019.05.054

Cited by 50 publications

(18 citation statements)

References 32 publications

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“…In distribution networks with larger dimensions, optimization must be done in such a way that economic costs are minimized and the observability index is maximized. When the actual large-scale distribution system is considered, there is no need to install μ-PMUs in all buses, and the number of units is determined based on a trade-off between economic benefits and 4,6,7,9,11,12,14,15,17,18,20,21,23,24,26,27,28,29,31,32,34,35,36,37,39,40,42,43,45,46,47,49 observability maximization. Therefore, in this type of distribution network, there is no need for full observability, and μ-PMUs are installed only at high-sensitivity buses that are determined by optimization algorithm to achieve economic goals, simultaneously.…”

Section: Discussionmentioning

confidence: 99%

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A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

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A reliability-based optimal μ-PMU (micro-phasor measurement unit) placement scheme is suggested for efficient observability enhancement of smart distribution grids at steady-state and contingencies conditions. This article introduces a unique method for the μ-PMU allocation in reconfigurable smart distribution grids in which communication system requirements and zero injection nodes (ZINs) are considered. The original objective function and limitations are proposed aiming at minimizing the capital cost, including communication links and installation costs of μ-PMU, optical power ground wire cost, power losses cost, and reliability cost as well as obtaining the maximum number of measurement redundancy constrained to full system observability in the presence of ZINs and tie switches. The suggested method is formulated as a mixed-integer linear programming problem applied to find optimal μ-PMU locations considering the cost of communication infrastructure and co-optimize the system switching plan simultaneously. In this regard, CPLEX-a high-performance mathematical solver-is used to solve the proposed mixed-integer linear optimization problem to reach the global optimality. The simulations are performed on 33, 69, and 85-bus radial distribution networks, and comprehensive simulation studies show the effectiveness of the suggested method.

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Section: Discussionmentioning

confidence: 99%

“…Equations ( 31)-( 36) make the problem nonlinear. For this purpose, some new variables are described as Equations ( 44), (45).…”

Section: Linearization Processmentioning

confidence: 99%

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

View full text Add to dashboard Cite

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“…PO i is the possibility of observability for each bus. A ij is a constant describing the possibility of observability of bus i resulted by PMU at bus j, and is calculated by expression (10). A Vm j , A PM j , A link j Sustainability 2019, 11, 7097 5 of 12 describe the availability of voltage measurement, PMU, and communication line at bus j, respectively.…”

Section: Possibility Of Observability Without Line Outagementioning

confidence: 99%

“…In the OPP problem under constraints (2)-(5), the possibility of observability can be calculated by formulas (9) and (10). However, when constraints (6)-(8) are considered, formulas (9) and (10) will not be adaptive due to the change of connectivity parameter a ij .…”

Section: Possibility Of Observability With Line Outagementioning

confidence: 99%

“…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. Besides, a multi-objective model was also proposed to simultaneously consider installation cost and measurement redundancy.…”

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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Multiple μPMU placement solutions in active distribution networks using nonlinear programming approach

Chauhan

Dahiya

2021

Int Trans Electr Energ Syst

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In an active distribution network (ADN), the optimal micro-PMU (μPMU) placement problem (OμPP) determines the location set with the minimal number of μPMUs that make the system observable. However, most approaches that solve OμPP provide a single location set limiting electrical power monitoring planning flexibility. Therefore, this paper presents an approach based on nonlinear programming (NLP) for solving the OμPP. The objective of the approach is to provide multiple location sets for OμPP that provide flexibility to electrical power monitoring planning. New constraints of the presence of distributed generations (DGs), zero injection buses (ZIBs), and communication line unavailability are taken into account for OμPP formulation. The proposed approach is implemented on the IEEE 69 distribution feeder, Indian-85 distribution feeder, and IEEE 123 distribution feeder. The results show that the proposed approach successfully provides multiple location sets with a minimal number of μPMUs. K E Y W O R D Sbinary integer linear programming (BILP), distributed generation, micro-phasor measurement unit (μPMU), nonlinear programming (NLP)

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Optimal placement of phasor measurement unit in distribution networks considering the changes in topology

Cited by 50 publications

References 32 publications

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

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

Multiple μPMU placement solutions in active distribution networks using nonlinear programming approach

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