Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm
Abstract
:1. Introduction
2. Restauration Mathematical Model
2.1. Objective Function
2.2. Restoration Constraint Conditions
- (1)
- Branch Flow Constraints
- (2)
- Node Voltage Constraints
- (3)
- Line Power Constraints
- (4)
- DG Output Constraints
- (5)
- Radial Structure ConstraintsThe voltages and power around the fault area should not be affected by the restauration to ensure the safety of the power system. After islanding partition, the isolated models of the system should possess a radial topology, meaning the presence of loops should be avoided.
3. Islanding Partition Scheme Based on Kruskal Algorithm
3.1. Islanding Partition Scheme
3.2. The Kruskal Algorithm and the Island Partitioning Process
- (1)
- Initialize the distribution network by assigning appropriate weight coefficients to different loads and partitioning islands with DGs possessing independent power supply capabilities.
- (2)
- Gradually assign loads to the islanded models and verify the power balance constraints for each load inclusion. This process maximizes the added loads while adhering to the constraints. Finally, iterate through all islands based on island numbering.
- (3)
- Adhere to transmission line safety constraints by assigning allocated loads to islands with available load capacity, based on the minimum electrical distance.
- (4)
- Optimize MST by removing bus nodes that are included in the islanded partition but are not connected to loads from the tree, resulting in the desired islanding partition scheme represented by the tree .
4. Fault Restoration of Distribution Networks Based on Pigeon-Inspired Algorithm
4.1. Basic Pigeon-Inspired Algorithm (PIO)
4.2. Improved Pigeon-Inspired Optimization Algorithm (IPIO)
4.2.1. Chaos and Reverse Strategy
4.2.2. Cauchy Perturbation Redistribution Strategy
4.2.3. Iteration Factor
5. Simulation Verification and Analysis
5.1. Basic Parameter Settings for Simulation
5.2. Restauration Simulation Results
5.2.1. Fault on Branch 28
5.2.2. Simultaneous Faults on Branches 25 and 28
5.3. Algorithm Performance Comparison
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- El-Gohary, A.; Al-Ruzaiza, A.S. Chaos and adaptive control in two prey, one predator system with nonlinear feedback. Chaos Solitons Fractals 2007, 34, 443–453. [Google Scholar] [CrossRef]
- Liu, Y.; Chen, Z.; Yuan, Z. An Improved Particle Swarm Optimization and Its Application in Investment Operation. Math. Pract. Theory 2007, 1, 82–87. [Google Scholar]
- Tizhoosh, H.R. In Opposition-Based Learning: A New Scheme for Machine Intelligence. In Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), Vienna, Austria, 28–30 November 2005; pp. 695–701. [Google Scholar]
- Rudolph, G. Local convergence rates of simple evolutionary algorithms with Cauchy mutations. IEEE Trans. Evol. Comput. 1997, 1, 249–258. [Google Scholar] [CrossRef]
- Stacey, A.; Jancic, M.; Grundy, I. Particle swarm optimization with mutation. In Proceedings of the 2003 Congress on Evolutionary Computation, CEC’03, Canberra, Australia, 8–12 December 2003; Volume 2, pp. 1425–1430. [Google Scholar]
- Liu, Y.; Wu, Z.; Tu, Y.; Huang, Q.; Luo, W. A Survey on Distributed Generation and Its Networking Technology. Power Syst. Technol. 2008, 15, 71–76. [Google Scholar]
- Qian, K.; Yuan, Y.; Cheng-ke, Z. Study on Impact of Distributed Generation on Distribution System Reliability. Power Syst. Technol. 2008, 11, 74–78. [Google Scholar]
- Payne, J.; Gu, F.; Razeghi, G.; Brouwer, J.; Samuelsen, S. Dynamics of high penetration photovoltaic systems in distribution circuits with legacy voltage regulation devices. Int. J. Electr. Power Energy Syst. 2021, 124, 106388. [Google Scholar] [CrossRef]
- Shen, X.; Shahidehpour, M.; Zhu, S.; Han, Y.; Zheng, J. Multi-Stage Planning of Active Distribution Networks Considering the Co-Optimization of Operation Strategies. IEEE Trans. Smart Grid 2018, 9, 1425–1433. [Google Scholar] [CrossRef]
- Wang, B.; Zhu, H.; Xu, H.; Bao, Y.; Di, H. Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network. IEEE Access 2021, 9, 90358–90365. [Google Scholar] [CrossRef]
- Fan, L.; Si, W.; Xuan, Y.; Sun, Z.; Zhao, J.; Xu, B.; Gu, Q. Multi-Objective Optimal Configuration of Multiple Switchgear Considering Distribution Network Fault Reconfiguration. IEEE Access 2021, 9, 69905–69912. [Google Scholar] [CrossRef]
- Shukla, J.; Panigrahi, B.K.; Ray, P.K. Stochastic reconfiguration of distribution system considering stability, correlated loads and renewable energy based DGs with varying penetration. Sustain. Energy Grids Netw. 2020, 23, 100366. [Google Scholar] [CrossRef]
- Senjyu, T.; Miyazato, Y.; Yona, A.; Urasaki, N.; Funabashi, T. Optimal Distribution Voltage Control and Coordination with Distributed Generation. IEEE Trans. Power Deliv. 2008, 23, 1236–1242. [Google Scholar] [CrossRef]
- Tang, Y.; Wu, Z.; Gu, W.; Yu, P.; Du, J.; Luo, X. Research on Active Distribution Network Fault Recovery Strategy Based on Unified Model Considering Reconstruction and Island Partition. Power Syst. Technol. 2020, 44, 2731–2740. [Google Scholar]
- Huang, J.; Zhang, Y.; Li, Q. Application of Ant Colony System in Distribution Reconfiguration. Proc. CSU-EPSA 2007, 19, 59–64. [Google Scholar]
- Yu, J.; Zhang, F. Distribution Network Reconfiguration Based on Improved Immune Genetic Algorithm. Power Syst. Technol. 2009, 33, 100–105. [Google Scholar]
- Li, Z.; Chen, X.; Yu, K.; Liu, H.; Zhao, B. Hybrid Particle Swarm Optimization for Distribution Network Reconfiguration. Proc. CSEE 2008, 28, 35–41. [Google Scholar]
- Lei, S.; Wang, S.; Hu, X.; Zhou, Q.; Lin, M. Chaotic Optimization Integrated with Artificial Immune Algorithm for Distribution Service Restoration after Faults. High Volt. Eng. 2009, 35, 1492–1496. [Google Scholar]
- Wang, F.; Chen, C.; Li, C.; Cao, Y.; Li, Y.; Zhou, B.; Dong, X. A Multi-Stage Restoration Method for Medium-Voltage Distribution System with DGs. IEEE Trans. Smart Grid 2017, 8, 2627–2636. [Google Scholar] [CrossRef]
- Cao, F.; Zhang, Y.; Li, S. Dynamic Reconfiguration of Active Distribution Network Based on Similarity and Adaptability of Network Structure. Autom. Electr. Power Syst. 2019, 43, 78–85. [Google Scholar]
- Duan, H.; Qiao, P. Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 2014, 7, 24–37. [Google Scholar] [CrossRef]
- Huo, M.; Wei, C.; Yu, Y.; Zhao, J. Clustering optimization algorithm for large-scale unmanned aerial vehicle based on intelligent behavior of pigeons. Sci. Sin. (Technol.) 2020, 50, 475–482. [Google Scholar]
- Pham, T.T.H.; Besanger, Y.; Hadjsaid, N. New Challenges in Power System Restoration with Large Scale of Dispersed Generation Insertion. IEEE Trans. Power Syst. 2009, 24, 398–406. [Google Scholar] [CrossRef]
- Zheng, H. Research on Fault Recovery Reconstruction of Distribution Network Based on GA-BFGS Algorithm; China University of Mining and Technology: Xuzhou, China, 2020. [Google Scholar]
- Chen, Q.; Wang, W.; Wang, H.; Wu, J.; Li, X.; Lan, J. A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals. IEEE Access 2020, 8, 204987–205013. [Google Scholar] [CrossRef]
- Liu, Z.; Bao, Q.; Sun, C.; Wu, X. Islanding algorithm of distribution system with distributed generations based on improved Kruskal algorithm. Trans. China Electrotech. Soc. 2013, 28, 164–171. [Google Scholar]
- Hao, Z.; Ji-hong, S.; Tie-nan, Z.; Yang, L. An Improved Chaotic Particle Swarm Optimization and Its Application in Investment. In Proceedings of the International Symposium on Computational Intelligence and Design, Washington, DC, USA, 17–18 October 2008; pp. 124–128. [Google Scholar]
- Tian, S.; Liu, L.; Wei, S.; Fu, Y.; Yang, M.; Liu, S. Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm. Power Syst. Prot. Control 2021, 49, 1–11. [Google Scholar]
- Xu, Z.; Pan, J.; Fan, S.; Song, X.; Zhai, S.; Gong, M. A distribution network reconfiguration method with distributed generation based on improved firefly algorithm. Power Syst. Prot. Control 2018, 46, 26–32. [Google Scholar]
- Chen, J.; Xiao, Z. Research on adaptive genetic algorithm based on multi-population elite selection strategy. In Proceedings of the IEEE International Conference on Computational Intelligence and Applications (ICCIA), Beijing, China, 8–11 September 2017; pp. 108–112. [Google Scholar]
DG | Location | Capacity/kW | Power Factor |
---|---|---|---|
1 | 6 | 700 | 0.85 |
2 | 13 | 500 | 0.9 |
3 | 24 | 1200 | 0.8 |
4 | 31 | 650 | 0.9 |
Load Level | Node | |
---|---|---|
Level 1 load | 100 | 5, 6, 12, 13, 23, 24, 29, 31 |
Level 2 load | 10 | 7, 11, 15, 22, 26, 30, 32 |
Level 3 load | 1 | 1~4, 8, 9, 10, 14, 16~20, 21, 25, 27, 28 |
Number | Island Load Point | Island Load/kV | Disconnected Switch |
---|---|---|---|
DG2 | 10, 11, 12, 13, 14, 15, 16 | 465 kW + j230 kvar | S10, S16 |
DG3 | 22, 23, 24 | 930 kW + j450 kvar | S22 |
DG4 | 29, 30, 31, 32 | 620 kW + j810 kvar | S29 |
Algorithm | Minimum Voltage/pu | Active Power Loss/kW | Disconnected Switch Number | Switching Operations Count |
---|---|---|---|---|
Before | 0.9683 | 49.3339 | S33, S34, S35, S36, S37 | -- |
IPIO | 0.9746 | 42.9386 | S29, S36, S7, S11, S13 | 8 |
PIO | 0.9646 | 52.2051 | S29, S36, S14, S33, S35 | 4 |
GA | 0.9729 | 43.4675 | S29, S36, S7, S34, S35 | 4 |
Algorithm | Minimum Voltage/pu | Active Power Loss/kW | Disconnected Switch Number | Switching Operations Count |
---|---|---|---|---|
Before | 0.9715 | 42.2289 | S33, S34, S35, S36, S37 | -- |
IPIO | 0.9754 | 38.1056 | S29, S36, S7, S11, S13 | 8 |
PIO | 0.9677 | 45.0615 | S29, S36, S15, S33, S35 | 4 |
GA | 0.9731 | 38.6342 | S29, S36, S7, S34, S35 | 4 |
Algorithm Name | Average Number of Iterations | Number of Times Trapped in Local Optima |
IPIO | 15.8 | 0 |
PSO | 26.5 | 5 |
GAPSO | 10 | 1 |
ISSA | 98.2 | 5 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, M.; Wu, J.; Zhang, Q.; Zheng, H. Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm. Electronics 2024, 13, 886. https://doi.org/10.3390/electronics13050886
Liu M, Wu J, Zhang Q, Zheng H. Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm. Electronics. 2024; 13(5):886. https://doi.org/10.3390/electronics13050886
Chicago/Turabian StyleLiu, Mingyang, Jiahui Wu, Qiang Zhang, and Hongjuan Zheng. 2024. "Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm" Electronics 13, no. 5: 886. https://doi.org/10.3390/electronics13050886
APA StyleLiu, M., Wu, J., Zhang, Q., & Zheng, H. (2024). Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm. Electronics, 13(5), 886. https://doi.org/10.3390/electronics13050886