Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm
Received: 24 November 2021 | Revised: 9 December 2021 and 13 December 2021 | Accepted: 14 December 2021 | Online: 12 February 2022
Corresponding author: T. N. Le
Abstract
This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.
Keywords:
load shedding, ANN-PSO, BPNN, Dual Neural Network, AHPDownloads
References
N. Voropai, "Electric Power System Transformations: A Review of Main Prospects and Challenges," Energies, vol. 13, no. 21, Jan. 2020, Art. no. 5639. DOI: https://doi.org/10.3390/en13215639
C. Li et al., "Continuous Under-Frequency Load Shedding Scheme for Power System Adaptive Frequency Control," IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 950–961, Mar. 2020. DOI: https://doi.org/10.1109/TPWRS.2019.2943150
S. S. Banijamali and T. Amraee, "Semi-Adaptive Setting of Under Frequency Load Shedding Relays Considering Credible Generation Outage Scenarios," IEEE Transactions on Power Delivery, vol. 34, no. 3, pp. 1098–1108, Jun. 2019. DOI: https://doi.org/10.1109/TPWRD.2018.2884089
T. Amraee, M. G. Darebaghi, A. Soroudi, and A. Keane, "Probabilistic Under Frequency Load Shedding Considering RoCoF Relays of Distributed Generators," IEEE Transactions on Power Systems, vol. 33, no. 4, pp. 3587–3598, Jul. 2018. DOI: https://doi.org/10.1109/TPWRS.2017.2787861
N. T. Le, A. T. Nguyen, T. T. Hoang, H. M. Vu Nguyen, A. H. Quyen, and B. T. T. Phan, "Distributed Load Shedding considering the Multicriteria Decision-Making Based on the Application of the Analytic Hierarchy Process," Mathematical Problems in Engineering, vol. 2021, p. e6834501, Oct. 2021. DOI: https://doi.org/10.1155/2021/6834501
M. Hasanat, M. Hasan, I. Ahmed, M. I. Chowdhury, J. Ferdous, and S. Shatabda, "An ant colony optimization algorithm for load shedding minimization in smart grids," in 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, May 2016, pp. 176–181. DOI: https://doi.org/10.1109/ICIEV.2016.7759991
Y.-Y. Hong and C.-Y. Hsiao, "Under-frequency Load Shedding in a Standalone Power System with Wind-turbine Generators Using Fuzzy PSO," IEEE Transactions on Power Delivery, pp. 1–1, 2021. DOI: https://doi.org/10.1109/TPWRD.2021.3077668
T. Le and B. L. Nguyen Phung, "Load Shedding in Microgrids with Consideration of Voltage Quality Improvement.
M. Usman, A. Amin, M. M. Azam, and H. Mokhlis, “Optimal under voltage load shedding scheme for a distribution network using EPSO algorithm,” in 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG), Apr. 2018, pp. 1–6. DOI: https://doi.org/10.1109/ICPESG.2018.8384525
M. A. Zdiri, A. S. Alshammari, A. A. Alzamil, M. B. Ammar, and H. H. Abdallah, "Optimal Shedding Against Voltage Collapse Based on Genetic Algorithm," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7695–7701, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4448
T. L. Saaty, The Analytic Hierarchy Process. McGraw-Hill, New York, NY, USA, 1980. DOI: https://doi.org/10.21236/ADA214804
H. I. Mohammed, Z. Majid, Y. B. Yamusa, M. F. M. Ariff, K. M. Idris, and N. Darwin, "Sanitary Landfill Siting Using GIS and AHP: A Case Study in Johor Bahru, Malaysia," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4100–4104, Jun. 2019.
H. I. Mohammed, Z. Majid, Y. B. Yamusa, M. F. M. Ariff, K. M. Idris, and N. Darwin, "Sanitary Landfill Siting Using GIS and AHP: A Case Study in Johor Bahru, Malaysia," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4100–4104, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2633
H. Wang, Z. Li, L. Ma, L. Liang, G. Wu, and X. Zhang, "Prediction of CADI Chemical Composition and Heat Treatment Parameters using a BPNN Optimized with the Genetic Algorithm," IOP Conference Series: Earth and Environmental Science, vol. 233, Feb. 2019, Art. no. 052022. DOI: https://doi.org/10.1088/1755-1315/233/5/052022
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015. DOI: https://doi.org/10.1038/nature14539
J. Zhang and S. Qu, "Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm," Complexity, vol. 2021, Jul. 2021, Art. no. e1718234. DOI: https://doi.org/10.1155/2021/1718234
D. Wang, D. Tan, and L. Liu, "Particle swarm optimization algorithm: an overview," Soft Computing, vol. 22, no. 2, pp. 387–408, Jan. 2018. DOI: https://doi.org/10.1007/s00500-016-2474-6
R. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence. Amsterdam, Netherlands: Elsevier Science, 2014.
J. He, F. Zhuang, Y. Liu, Q. He, and F. Lin, "Bayesian dual neural networks for recommendation," Frontiers of Computer Science, vol. 13, no. 6, pp. 1255–1265, Dec. 2019. DOI: https://doi.org/10.1007/s11704-018-8049-1
IEEE 421.5-2005 - IEEE Recommended Practice for Excitation System Models for Power System Stability Studies. IEEE, 2006.
C. Wang, H. Yu, L. Chai, H. Liu, and B. Zhu, "Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning," IEEE Access, vol. 9, pp. 19707–19715, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3055401
Downloads
How to Cite
License
Copyright (c) 2021 L. T. H. Nhung, T. T. Phung, H. M. V. Nguyen, T. N. Le, T. A. Nguyen, T. D. Vo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.