Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal deployment model of edges considering spatial constraints is proposed. In the model, the constraints including edge deployment point, line of sight, a...

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Published in:Advances in mechanical engineering Vol. 16; no. 5
Main Authors: Xu, Wenxiang, Tong, Shaocong, Xu, Shimin, Du, Baigang, Liu, Dezheng, Qin, Tao
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.05.2024
Sage Publications Ltd
SAGE Publishing
Subjects:
Algorithms
Archives & records
Clustering
Greedy algorithms
Markov analysis
Markov chains
Monitoring
Monitoring systems
Polynomials
Simulated annealing
Substations
Online monitoring equipment
edges of substations
optimal deployment
improved trending Markov chain
multi-objective simulated annealing algorithm
ISSN:1687-8132, 1687-8140
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Abstract To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal deployment model of edges considering spatial constraints is proposed. In the model, the constraints including edge deployment point, line of sight, as well as device pose, etc. are taken into account. To achieve the one-to-many collection of the deployed equipment, a mathematical model is constructed with the objectives of minimizing the shooting distance and the number of edge equipments. And an archive based multi-objective simulated annealing algorithm based on improved trending Markov chain (IAMOSA) is proposed to solve the problem. This algorithm utilizes greedy clustering to initialize deployment points, and the improved disturbance step length with tendency is used to search the neighborhood space. Besides, polynomial fitting Pareto front is also used to select and guide the Markov chain and archive population. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified through an experiment of optimal deployment of the edge equipments in a certain substation.
AbstractList To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal deployment model of edges considering spatial constraints is proposed. In the model, the constraints including edge deployment point, line of sight, as well as device pose, etc. are taken into account. To achieve the one-to-many collection of the deployed equipment, a mathematical model is constructed with the objectives of minimizing the shooting distance and the number of edge equipments. And an archive based multi-objective simulated annealing algorithm based on improved trending Markov chain (IAMOSA) is proposed to solve the problem. This algorithm utilizes greedy clustering to initialize deployment points, and the improved disturbance step length with tendency is used to search the neighborhood space. Besides, polynomial fitting Pareto front is also used to select and guide the Markov chain and archive population. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified through an experiment of optimal deployment of the edge equipments in a certain substation.
Author Xu, Shimin
Xu, Wenxiang
Du, Baigang
Tong, Shaocong
Qin, Tao
Liu, Dezheng
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Issue 5
Keywords Online monitoring equipment
edges of substations
optimal deployment
improved trending Markov chain
multi-objective simulated annealing algorithm
Language English
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– volume: 37
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  year: 2022
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Snippet To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal...
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SubjectTerms Algorithms
Archives & records
Clustering
Greedy algorithms
Markov analysis
Markov chains
Monitoring
Monitoring systems
Polynomials
Simulated annealing
Substations
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Title Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint
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