Optimal Model for Patrols of UAVs in Power Grid under Time Constraints

doi:10.23940/ijpe.21.01.p10.103113

Abstract

Abstract:

The safe operation of the power grid has a direct impact on the stability of the power supply with the power system. Due to the large number of equipment in the power grid, it is difficult for daily patrols. The efficiency of patrols of UAVs in power grids and the optimal configuration of UAVs are the purpose of the optimal model for patrols of UAVs in power grids under time constraints. Under time constraints, the optimization goal is to minimize the flight time of all UAVs or the longest flight time of a single UAV with the condition of completing all patrol tasks. It constructs the optimal model for patrol paths of UAVs in power grids through the space-time road network. The genetic algorithm is used to solve the optimal problem of patrol paths of UAVs. The results of scene simulation show that the patrol paths of UAVs in power grids planned by this model are more in line with actual needs and have obvious efficiency advantages.

Key words: time constraint, UAV, patrol in power grid, optimal model, time-space road network, genetic algorithm

Caiming Zhang, and Weina Fu. Optimal Model for Patrols of UAVs in Power Grid under Time Constraints [J]. Int J Performability Eng, 2021, 17(1): 103-113.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 20.

1.	G. D. L. T. Parra, P. Rad,K. K. R.Choo, “Implementation of Deep Packet Inspection in Smart Grids and Industrial Internet of Things: Challenges and Opportunities,”Journal of Network and Computer Applications, Vol. 135, pp. 32-46, 2019
2.	A. Nouri, H. Khodaei, A. Darvishan, S. Sharifiand,N. Ghadimie, “Optimal Performance of Fuel Cell-CHP-Battery based Micro-Grid under Real-Time Energy Management: An Epsilon Constraint Method and Fuzzy Satisfying Approach,”Energy, Vol. 159, pp. 121-133, 2018
3.	W. W. Jiang, E. H. M.Sha, X. Z. Chen, and L. Yang, “Optimal Functional-Unit Assignment for Heterogeneous Systems under Timing Constraint,” IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 9, pp. 2567-2580, 2017
4.	A. Kumar and L. Anjaneyulu, “Design and Development of Power Management System of Unmanned Underwater Platform for Defence Application,” Defence Science Journal, Vol. 67, No. 1, pp. 119-124, 2017
5.	M. Bajaj, “Design and Simulation of Hybrid DG System Fed Single-Phase Dynamic Voltage Restorer for Smart Grid Application,” Smart Science, Vol. 8, No. 1, pp. 24-38, 2020
6.	J. Saroha, G. Pandove,M. Singh, “Modelling and Simulation of Grid Connected SPV System with Active Power Filtering Features,”Journal of the Institution of Engineers, Vol. 99, pp. 25-35, 2018
7.	Z. L. Zhang, Y. X. Shi,L. L. Zu, “Diagnosis of State of Health for Unmanned Aerial Vehicle Battery based on SVR-PF,” Chinese Journal of Power Sources, Vol. 41, No.5, pp. 750-751+756, 2017
8.	M. A.Omar and M. M. Mahmoud, “Design and Simulation of DC/DC Boost Converter with Maximum Power Point Tracking for Grid Connected PV Inverter Considering the Nonlinearity of the PV Generator,” International Journal on Energy Conversion (IRECON), Vol. 7, No. 6, pp. 241-252, 2019
9.	Y. Lin, C. Wang, J. X. Wang,Z. Dou, “A Novel Dynamic Spectrum Access Framework based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, Vol. 16, No. 10, pp. 1675, 2016
10.	S. Liu, “Introduction of Key Problems in Long-Distance Learning and Training,” Mobile Networks and Applications, Vol. 24, No. 1, pp. 1-4, 2019
11.	S. He, A. Cozza,Y. Z. Xie, “Electromagnetic Time Reversal as a Correlation Estimator: Improved Metrics and Design Criteria for Fault Location in Power Grids,” IEEE Transactions on Electromagnetic Compatibility, Vol. 62, No. 2, pp. 598-611, 2020
12.	G. Yang and S. Liu, “Distributed Cooperative Algorithm for k-M Set with Negative Integer k by Fractal Symmetrical Property,” International Journal of Distributed Sensor Networks, Vol. 10, No. 5, pp. 398583, 2014
13.	Y. Lin, Y. Li, X. Yin,Z. Dou, “Multisensor Fault Diagnosis Modeling based on the Evidence Theory,” IEEE Transactions on Reliability, Vol. 67, No. 2, pp. 513-521, 2018
14.	A. Kaplan, N. Kingry, P. Uhing,R. Dai, “Time-Optimal Path Planning with Power Schedules for a Solar-Powered Ground Robot,” IEEE Transactions on Automation Ence and Engineering, Vol. 14, No. 2, pp. 1235-1244, 2017
15.	P. C. Sara, S. Jürgen, B. Rinner, A. L. O.José, and B. P. Eva, “UAV Trajectory Optimization for Minimum Time Search with Communication Constraints and Collision Avoidance,”Engineering Applications of Artificial Intelligence, Vol. 85, pp. 357-371, 2019
16.	L. G. Molina, P. Moreno, D. C. Esaú,A. B. Perez, “Simulation of Soil Organic Carbon Changes in Vertisols under Conservation Tillage using the RothC Model,” Scientia Agricola, Vol. 74, No. 3, pp. 235-241, 2017
17.	T. Uppal, S. Raha,S. Srivastava, “Trajectory Feasibility Evaluation using Path Prescribed Control of Unmanned Aerial Vehicle in Differential Algebraic Equations Framework,” The Aeronautical Journal, Vol. 121, No. 1240, pp. 770-789, 2017
18.	C. Wang, S. Lei, P. Ju, C. Chen, C. Peng,Y. Hou, “MDP-based Distribution Network Reconfiguration with Renewable Distributed Generation: Approximate Dynamic Programming Approach,” IEEE Transactions on Smart Grid, Vol. 11, No. 4, pp. 3620-3631, 2020
19.	F. Giudici, A. Castelletti, E. Garofalo, M. Giuliani,H. R. Maier, “Dynamic, Multi-Objective Optimal Design and Operation of Water-Energy Systems for Small, off-Grid Islands,” Applied Energy, Vol. 250, No. 15, pp. 605-616, 2019
20.	H. Sylvain, C. B. Juliette,M. Denis, “Friction Setup and Real-Time Insights of the Contact under Controlled Cold Environment: The KōRItribometer for Rubber-Ice Contact Application,” The Review of Scientific Instruments, Vol. 89, No. 12, pp. 123903-123903, 2018

[1]	Chunyan Xia, Xingya Wang, Yan Zhang, and Hao Yang. Using Genetic Algorithm to Augment Test Data for Penalty Prediction [J]. Int J Performability Eng, 2020, 16(7): 1078-1086.
[2]	Manish Rawat and Bhupesh Kumar Lad. Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System [J]. Int J Performability Eng, 2020, 16(6): 821-833.
[3]	You Zhou, Lecheng Sun, Xu Zhou, Milan Parmar, and Liupu Wang. An Improved Genetic Algorithm to Optimize Vehicle Scheduling for Relief Efforts [J]. Int J Performability Eng, 2019, 15(9): 2356-2363.
[4]	Manu Banga, Abhay Bansal, and Archana Singh. Proposed Hybrid Approach to Predict Software Fault Detection [J]. Int J Performability Eng, 2019, 15(8): 2049-2061.
[5]	Lei Wu and Weijian Wang. Resource Allocation Optimization of UAVs-Enabled Air-Ground Collaborative Emergency Network in Disaster Area [J]. Int J Performability Eng, 2019, 15(8): 2133-2144.
[6]	Shixin Zhang, Xiang Zan, Chunliang Chen, Weilong Chen, and Tonghan Wu. Method for Equipment Support Facility Location According to the Prescription Mission Conditions [J]. Int J Performability Eng, 2019, 15(5): 1371-1380.
[7]	Qian Wang, Zhonghua Cheng, Zhiyong Li, and Yongsheng Bai. Two-Dimensional Product Warranty Cost Model under Preventive Maintenance Time Constraints [J]. Int J Performability Eng, 2019, 15(4): 1227-1234.
[8]	Huibin Jin, Yawei Liu, Xiaomeng Mu, Mingxia Ma, and Jing Zhang. Usability Evaluation and Improvement of Mission Planner UAV Ground Control System's Interface [J]. Int J Performability Eng, 2019, 15(10): 2726-2734.
[9]	Jun Li, Ming Chen, Fei Dai, and Huibing Wang. Prioritizing-based Message Scheduling for Reliable Unmanned Aerial Vehicles Ad Hoc Network [J]. Int J Performability Eng, 2018, 14(9): 2021-2029.
[10]	Shaohua Wang, Shixin Zhang, Yong Li, Hongxiang Liu, and Zhengjun Peng. Selective Maintenance Decision-Making of Complex Systems Considering Imperfect Maintenance [J]. Int J Performability Eng, 2018, 14(12): 2960-2970.