Multiple Solutions of Optimal PMU Placement Using Exponential Binary PSO Algorithm for Smart Grid Applications

Maji, Tapas Kumar; Acharjee, Parimal

doi:10.1109/tia.2017.2666091

Cited by 103 publications

(56 citation statements)

References 37 publications

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“…For instance, there are many applications about power [22][23][24], electromagnetic [25,26], and antenna [27][28][29] in the field of industrial engineering. The most popular applications in machinery are trajectory optimization [30], defect classification [31,32], and scheduling problems [33,34].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai

Zhang

2018

J Wireless Com Network

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This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai

Zhang

2018

J Wireless Com Network

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“…Therefore, the unobservability function (PU t i ) would be equal to zero, subsequently. Moreover, according to (12) and (13), if the bus i at stage t becomes observable, being affected by a ZIB, then, the auxiliary variable v t iN becomes zero. Thus, the unobservability function (PU t i ) would be equal to zero, subsequently.…”

Section: Mathematical Linear Expansion For the Problemmentioning

confidence: 99%

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

et al. 2018

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This paper presents a mathematical linear expansion model for the probabilistic Multistage Phasor Measurement Unit (PMU) Placement (MPP) in which zero-injection buses (ZIBs), as well as communication channel limitations, are taken into consideration. From the linearization perspective, presenting a model formulizing the probabilistic concept of observability while modelling the ZIB is of great significance, and has been done in this paper for the first time. More importantly, the proposed probabilistic MPP utilizes a technique disregarding the prevalent subsidiary optimizations for each planning stage. Although this technique, in turn, increases the problem complexity with manifold variables, it guarantees the global optimal solution in a wider and thorough search space; while in the prevalent methods, some parts of the search space might be missed. Furthermore, the proposed model indicates more realistic aspects of the MPP where system operators are allowed to follow their intention about the importance of buses such as strategic ones based on monitoring the priority principles. In addition, the model is capable of considering the network topology changes due to long-term expansions over the planning horizon. Finally, in order to demonstrate the effectiveness of the proposed formulation, the model is conducted on the IEEE 57-bus standard test system and the large scale 2383-bus Polish power system.

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“…Numerical algorithms [36,37] and heuristic algorithms [38][39][40][41][42] are the main optimization techniques for solving the OPP model. The numerical algorithm is mainly applicable to solve the problem whose model is simple and linear.…”

Section: Introductionmentioning

confidence: 99%

“…This performance makes the greedy algorithm attractive in typical scenarios of phased PMU placement because the performance is robust to changes in the PMU budget. Reference [39] develops an effective exponential binary particle swarm optimization algorithm to obtain multiple solutions to OPP. An exponential inertia weighting factor is introduced by the proposed algorithm to improve the group's searchability.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

et al. 2019

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A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

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Multiple Solutions of Optimal PMU Placement Using Exponential Binary PSO Algorithm for Smart Grid Applications

Cited by 103 publications

References 37 publications

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

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