Long-term path planning with optimal deployment of a charging station for monitoring photovoltaic solar farms

Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorit...

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Published inScientific reports Vol. 14; no. 1; pp. 17279 - 15
Main Authors Huang, Yong, Chen, Zhiyan, Chu, Jing, Wang, Haoran, Sun, Siliang
Format Journal Article
LanguageEnglish
Published England Nature Publishing Group 27.07.2024
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Abstract Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.
AbstractList Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.
Abstract Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.
Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.
ArticleNumber 17279
Author Wang, Haoran
Chu, Jing
Sun, Siliang
Huang, Yong
Chen, Zhiyan
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Snippet Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary...
Abstract Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the...
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StartPage 17279
SubjectTerms Algorithms
Automation
Linear programming
Nonlinear programming
Photovoltaics
Solar energy
Solar farms
Surveillance
Temporal logic
Unmanned aerial vehicles
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Title Long-term path planning with optimal deployment of a charging station for monitoring photovoltaic solar farms
URI https://www.ncbi.nlm.nih.gov/pubmed/39068225
https://www.proquest.com/docview/3085154061/abstract/
https://www.proquest.com/docview/3085683460/abstract/
https://pubmed.ncbi.nlm.nih.gov/PMC11283503
https://doaj.org/article/4a8efb7e15db49d785aca1dde6d8eee6
Volume 14
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