Optimal Allocation of Measurement Devices for Distribution State Estimation Using Multiobjective Hybrid PSO–Krill Herd Algorithm

Prasad, Sachidananda; Kumar, D. M. Vinod

doi:10.1109/tim.2017.2674718

Cited by 38 publications

(31 citation statements)

References 26 publications

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“…Generally, to achieve the desired accuracy of DSE results, OPP algorithms attend to find the minimum possible number of PMUs and also their optimum locations [5][6][7][8][9][10][11][12][13][14][15]. As a result, the desired accuracy indices for the considered PMU configuration are achieved after receiving all real-time measured values (synchrophasors in this paper) at the monitoring system through the communication links (wired or wireless technologies) and interface systems (RTU and SCADA).…”

Section: Reliability Of Satisfying Accuracy Constraintsmentioning

confidence: 99%

“…In the literature, different objectives for minimising installation costs along with decreasing the estimation errors of the DSE outputs have been proposed [5][6][7][8][9][10][11][12][13][14][15]. However, these OPP approaches usually attend to find the minimum required number of PMUs (and other measurement devices) for satisfying the maximum acceptable estimation errors, and in cases for which PMU configurations have the same cost, select the configuration with minimum estimation errors.…”

Section: Cost Minimisation-based Opp (Cm-opp)mentioning

confidence: 99%

“…Many valuable contributions have been made to optimise the number and location of PMUs to increase the accuracy of the DSE results while restricting implementing costs [5][6][7][8][9][10][11][12][13][14][15]. In [5], a new technique for placing PMUs and smart meters (SMs) is presented using the genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

“…The proposed measurement placement problem attends to find the optimal locations and types of measurements for minimising the worst-case errors in the DSE results over different system operating conditions. The authors in [10] developed a new multi-objective optimisation model to deploy power flow meters optimally in an ADN based on the DSE results. In the proposed method, the total cost of PMUs, intelligent electronic devices (IEDs), and the RMS value of state estimation error are considered as three objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…The best-compromised solution is obtained using a multi-objective hybrid particle swarm optimisation Krill Herd algorithm. The extended version of [10] is presented in [11] which considered both synchronised and nonsynchronised measurements obtained from PMUs and IEDs located at various places in the network. Also, the research is extended to obtain a trade-offs solution between the costs of PMUs and IEDs needed for accurately estimating states of the distribution systems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal allocation of PMUs in active distribution network considering reliability of state estimation results

Gholami

Abbaspour

Fattaheian‐Dehkordi

et al. 2020

IET Generation, Transmission & Distribution

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Section: Reliability Of Satisfying Accuracy Constraintsmentioning

confidence: 99%

Section: Cost Minimisation-based Opp (Cm-opp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal allocation of PMUs in active distribution network considering reliability of state estimation results

Gholami

Abbaspour

Fattaheian‐Dehkordi

et al. 2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Measurement devices allocation in distribution system using state estimation: A multi‐objective approach

Marzouni

Zakariazadeh

Siano

2020

Int Trans Electr Energ Syst

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Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi-objective method.The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33-bus and IEEE 123-bus radial network.

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Multiobjective meter placement in active distribution system state estimation using inverse‐model ‐based multilabel Gaussian classification with adaptive reference point method

Prasad

Kumar

2021

Int Trans Electr Energ Syst

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Summary This paper proposes a new inverse‐model‐based multiobjective evolutionary algorithm for meter placement in active distribution system state estimation. The meter placement is designed as a multiobjective problem with minimizing conflict objectives such as meter cost, the relative error of voltage magnitude, and voltage angle. The multiobjective framework utilizes inverse model as a reproduction operator and maps the nondominated solution from objective space to decision space and is realized using multilabel Gaussian classification. The additional solutions are generated by sampling from inverse model that improves the search efficiency and diversity of Pareto optimal solutions. The combinatorial nature of meter placement optimization may produce a discontinuous Pareto front. The performance of multiobjective evolutionary algorithm depends on the shape of Pareto front. Therefore, to improve the performance, the adaptive reference point method is employed to adjust the reference points such that they follow the Pareto front. The proposed method is tested under different real measurement uncertainties for passive and active distribution networks. Moreover, different types of renewable sources are considered in active distribution system. The inverse model and adaptive reference point method improve the performance of multiobjective evolutionary algorithm. Therefore, the results obtained from the proposed method outperform the other existing multiobjective evolutionary algorithms, and the obtained optimal number of meters are less with the minimum voltage magnitude error and voltage angle error. The proposed method is tested on PG&E 69‐bus distribution system and Practical Indian 85‐bus distribution system.

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Optimal Allocation of Measurement Devices for Distribution State Estimation Using Multiobjective Hybrid PSO–Krill Herd Algorithm

Cited by 38 publications

References 26 publications

Optimal allocation of PMUs in active distribution network considering reliability of state estimation results

Optimal allocation of PMUs in active distribution network considering reliability of state estimation results

Measurement devices allocation in distribution system using state estimation: A multi‐objective approach

Multiobjective meter placement in active distribution system state estimation using inverse‐model ‐based multilabel Gaussian classification with adaptive reference point method

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