OPTIMAL PLACEMENT OF PHASOR MEASUREMENT UNITS WITH LIMITED CHANNELS USING ANT COLONY OPTIMIZATION ALGORITHM

Wide-area measurement system (WAMS) is an important part of present power system structure as it provides real-time synchronized measurements of the system with the aid of phasor measurement units (PMUs). Due to economic considerations, PMUs should be installed at optimal locations. The optimal placement of PMUs (OPP) is a problem of optimally placing PMUs at strategic locations maintaining the full observability of the system. In this chapter, a novel binary whale optimization algorithm (BWOA) is applied to solve OPP problem. The maximization of measurement redundancy is considered in the objective function. The proposed algorithm is examined on five different test systems operating under normal operating conditions with or without inclusion of zero-injection buses (ZIBs) and compared with the reports available in literature. The results show the effectiveness of the proposed algorithm in solving OPPP.

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Ant colony optimizer for phasor measurement units placement

International Review of Applied Sciences and Engineering ◽

10.1556/irase.5.2014.2.4 ◽

2014 ◽

Vol 5 (2) ◽

pp. 127-134 ◽

Cited By ~ 3

Author(s):

A. Abou El-Ela ◽

A. Kinawy ◽

R. El-Sehiemy ◽

M. Mouwafi

Keyword(s):

New England ◽

System Size ◽

Phasor Measurement Units ◽

Ant Colony ◽

Optimal Placement ◽

The West ◽

Phasor Measurement ◽

Minimum Number ◽

Measurement Units ◽

Simulation Results

This paper proposes an approach for optimal placement of phasor measurement units (PMUs) for complete observability at normal and emergency conditions such as any single line outage or any single PMU loss using ant colony optimization (ACO) algorithm. The objective function is to find the optimal locations of PMUs and the minimum number of PMU channels by optimizing the logic decision of control variables, under the observability constraint. The ACO algorithm is applied to the standard IEEE 14-bus, 30-bus and New England 39-bus systems. In addition, an application of the proposed algorithm to a real power system of the west Delta network (WDN) as a part of the Unified Egyptian Network (UEN) is presented. The results obtained are compared with those obtained using other techniques. Simulation results show that the proposed ACO algorithm is more accurate and efficient for obtaining the optimal placement of PMUs with minimum number of PMU channels, especially with increasing the system size.

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Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

Finance and Credit ◽

10.24891/fc.26.11.2427 ◽

2020 ◽

Vol 26 (11) ◽

pp. 2427-2447

Author(s):

S.N. Yashin ◽

E.V. Koshelev ◽

S.A. Borisov

Keyword(s):

Ant Colony Optimization ◽

Optimization Algorithm ◽

Population Decline ◽

Innovation System ◽

Federal District ◽

Original Model ◽

Ant Colony ◽

Industrial Agglomeration ◽

Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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