Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm
Abstract
:1. Introduction
2. Measurement of Unbalances
3. Compensation of Unbalanced Voltage
4. Improvement of Unbalanced Voltage
5. Compensation Based on PSO Algorithm
5.1. Setting of the PSO Algorithm
- Step 1: Initialize the variables in PSO, such as setting the number of particles at 20. Each particle was in six-dimensional space, denoted as . The velocity of the particle was denoted as . The iteration times, , were limited to , 20.
- Step 2: Measure the current voltages of Bus 2 for calculating the VUF. Note that the voltage could change after the response of the compensator reacted in Step 6.
- Step 3: Calculate the fitness function of each particle at the current state, as in Equation (5).
- Step 4: Check the stopping criteria:Target requirement satisfied that VUF is lower than 1%.Target requirement not satisfied but iteration times .
- Step 5: If the local optimum was less than the global optimum, the global optimum was replaced by the local optimum to maintain the current state. Then, the learning factors ( and inertia coefficient () were updated, and the powers of , and were used to accelerate the convergence rate and increase accuracy, as expressed in Equations (9)–(11). Each particle was updated as in Equations (12)–(13) to generate new optimums.If the optimums did not satisfy Equation (8), then the modification was conducted by Equation (14).
- Step 6: Submit the new current target, as solved using PSO, to the compensator.
5.2. Simulation Results
- (1)
- Case A: delta–delta connection supplying unbalanced loads
- (2)
- Case B: open–delta to open–delta connection supplying unbalanced loads
- (3)
- Case C: open–delta to open–delta connection supplying balanced loads
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Chen, T.-H.; Yang, C.-H.; Yang, N.-C. Examination of the definitions of voltage unbalance. Int. J. Electr. Power Energy Syst. 2013, 49, 380–385. [Google Scholar] [CrossRef]
- Liao, R.-N.; Yang, N.-C. Evaluation of voltage imbalance on low-voltage distribution networks considering delta-connected distribution transformers with a symmetrical NGS. IET Gener. Transm. Distrib. 2018, 12, 1644–1654. [Google Scholar] [CrossRef]
- Lee, Y.-D.; Jiang, J.-L.; Ho, Y.-H.; Lin, W.-C.; Chih, H.-C.; Huang, W.-T. Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data. Energies 2020, 13, 1844. [Google Scholar] [CrossRef]
- Tan, Y.; Wang, Z. Incorporating Unbalanced Operation Constraints of Three-Phase Distributed Generation. IEEE Trans. Power Syst. 2019, 34, 2449–2452. [Google Scholar] [CrossRef]
- Liu, Y.; Li, J.; Wu, L. Coordinated Optimal Network Reconfiguration and Voltage Regulator/DER Control for Unbalanced Distribution Systems. IEEE Trans. Smart Grid 2019, 10, 2912–2922. [Google Scholar] [CrossRef]
- Singh, B.; Solanki, J. A Comparison of Control Algorithms for DSTATCOM. IEEE Trans. Ind. Electron. 2009, 56, 2738–2745. [Google Scholar] [CrossRef]
- Salmeron, P.; Litrán, S.P. Improvement of the Electric Power Quality Using Series Active and Shunt Passive Filters. IEEE Trans. Power Deliv. 2010, 25, 1058–1067. [Google Scholar] [CrossRef]
- Wang, D.; Mao, C.; Lu, J.; He, J.; Liu, H. Auto-balancing transformer based on power electronics. Electr. Power Syst. Res. 2010, 80, 28–36. [Google Scholar] [CrossRef]
- Yan, S.; Tan, S.-C.; Lee, C.-K.; Ron Hui, S.Y. Reducing Three-Phase Power Imbalance with Electric Springs. In Proceedings of the 5th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Galway, Ireland, 24–27 June 2014; IEEE Publications: New York, NY, USA, 2014. [Google Scholar]
- Li, P.; Ji, H.; Wang, C.; Zhao, J.; Song, G.; Ding, F.; Wu, J. Optimal Operation of Soft Open Points in Active Distribution Networks Under Three-Phase Unbalanced Conditions. IEEE Trans. Smart Grid 2019, 10, 380–391. [Google Scholar] [CrossRef]
- Sun, F.; Ma, J.; Yu, M.; Wei, W. Optimized Two-Time Scale Robust Dispatching Method for the Multi-Terminal Soft Open Point in Unbalanced Active Distribution Networks. IEEE Trans. Sustain. Energy 2021, 12, 587–598. [Google Scholar] [CrossRef]
- Wang, J.; Zhou, N.; Chung, C.Y.; Wang, Q. Coordinated Planning of Converter-Based DG Units and Soft Open Points Incorporating Active Management in Unbalanced Distribution Networks. IEEE Trans. Sustain. Energy 2020, 11, 2015–2027. [Google Scholar] [CrossRef]
- Guo, P.; Xu, Q.; Yue, Y.; Ma, F.; He, Z.; Luo, A.; Guerrero, J.M. Analysis and Control of Modular Multilevel Converter With Split Energy Storage for Railway Traction Power Conditioner. IEEE Trans. Power Electron. 2020, 35, 1239–1255. [Google Scholar] [CrossRef]
- Cooper, C.B. IEEE Recommended Practice for Electric Power Distribution for Industrial Plants. IEEE Stand. 1993, 141, 658. [Google Scholar]
- Del Valle, Y.; Venayagamoorthy, G.K.; Mohagheghi, S.; Hernandez, J.C.; Harley, R.G. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Trans. Evol. Comput. 2008, 12, 171–195. [Google Scholar] [CrossRef]
- Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl. Based Syst. 2015, 89, 228–249. [Google Scholar] [CrossRef]
- Colorni, A.; Dorigo, M.; Maniezzo, V. Distributed optimization by ant colonies. In Proceedings of the First European Conference on Artificial Life, Paris, France, 11–13 December 1991; pp. 134–142. [Google Scholar]
- Holland, J.H. Genetic algorithms. Sci. Am. 1992, 267, 66–72. [Google Scholar] [CrossRef]
- Parsopoulos, K.E.; Vrahatis, M.N. Particle Swarm Optimization and Intelligence: Advances and Applications, 1st ed.; IGI Global: Hershey, PA, USA, 2007; pp. 28–29. [Google Scholar] [CrossRef]
- Huayu, F.; Hao, Z. A Fast PSO Algorithm Based on Alpha-Stable Mutation and Its Application in Aerodynamic Optimization. In Proceedings of the 9th International Conference on Mechanical and Aerospace Engineering (ICMAE), Budapest, Hungary, 10–13 July 2018; pp. 225–232. [Google Scholar]
- Sarangi, A.; Samal, S.; Sarangi, S.K. Comparative Analysis of Cauchy Mutation and Gaussian Mutation in Crazy PSO. In Proceedings of the 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 21–22 February 2019; pp. 68–72. [Google Scholar]
Case | A | B | C |
---|---|---|---|
Structure | Transformer Δ–Δ connection supplies unbalance load | Transformer V–V connection supplies unbalance load | Transformer V–V connection supplies balance load |
Power factor (pf) | 1 | 1 | 0.8 lag |
phase load (a, b, c) (%) | 95, 65, 80 | 90, 70, 80 | 100, 100, 100 |
(%) | 10.56 | 7.03 | 0 |
(%) | 0.34 | 1.67 | 1.88 |
Power at Secondary Side of Transformer (pu) | Power at Compensator (pu) | Power at Load (pu) | |
---|---|---|---|
Phase a | 0.0° | 0.00° | 0° |
Phase b | 0.0° | −180° | 0° |
Phase c | 0.0° | 0.00° | 0° |
Three-phase power | 0° | 0 | 0° |
Change | ||||
---|---|---|---|---|
(%) | 10.56 | 0.10 | −10.46 | |
(%) | 0.34 | 0.01 | −0.33 | |
Voltage | Phase a | 0.910 ∠ −5.2° | 1.002 ∠ −1.7° | 0.092 |
Phase b | 1.095 ∠ −124.3° | 1.002 ∠ −121.8° | −0.093 | |
Phase c | 1.011 ∠ 124.2° | 1.003 ∠ 118.3° | −0.007 | |
Variation (%) | Phase a | −9.00 | 0.18 | −8.82 |
Phase b | 9.51 | 0.19 | −9.32 | |
Phase c | 1.05 | 0.34 | −0.71 |
Power at Secondary of Transformer (pu) | Power at Compensator (pu) | Power at Load (pu) | |
---|---|---|---|
Phase a | 0.262 ∠ 0° | 0.048 ∠ −1.3° | 0.310 ∠ 0° |
Phase b | 0.261 ∠ 0° | 0.048 ∠ 179.3° | 0.214 ∠ 0° |
Phase c | 0.263 ∠ 0° | 0.001 ∠ 123.3° | 0.263 ∠ 0° |
Three phase | 0.787 ∠ 0° | 0 ∠ 0° | 0.787 ∠ 0° |
Change | ||||
---|---|---|---|---|
(%) | 7.03 | 0.11 | −6.92 | |
(%) | 1.67 | 0.58 | −1.09 | |
Voltage | Phase a | 0.927 ∠ −4.4° | 0.997 ∠ −1.7° | 0.070 |
Phase b | 1.068 ∠ −122.9° | 1.005 ∠ −121.3° | −0.063 | |
Phase c | 1.016 ∠ 121.5° | 1.009 ∠ 118.2° | −0.007 | |
Variation (%) | Phase a | −7.28 | −0.28 | −7.00 |
Phase b | 6.79 | 0.53 | −6.24 | |
Phase c | 1.64 | 0.87 | −0.75 |
Power at Secondary of Transformer (pu) | Power at Compensator (pu) | Power at Load (pu) | |
---|---|---|---|
Phase a | 0.235 ∠ −15.3° | 0.092 ∠ 42.3° | 0.294 ∠ 0° |
Phase b | 0.338 ∠ −0.1° | 0.104 ∠ 179.8° | 0.234 ∠ 0° |
Phase c | 0.240 ∠ 15.0° | 0.072 ∠ −59.8° | 0.268 ∠ 0° |
Three phase | 0.796 ∠ 0.0° | 0 ∠ 0° | 0.796 ∠ 0° |
Mean/ Standard | Particle Number | ||||
---|---|---|---|---|---|
10 | 20 | 40 | 80 | ||
Max Iterations | 10 | NaN | 5.6/2.3 * | 6.7/1.6 | 5.5/1.6 |
20 | 14.6/4.3 * | 11.7/2.2 | 8.6/1.8 | 7.0/1.7 | |
40 | 12.6/2.9 | 13.6/4.4 | 9.7/2.8 | 6.41/9 | |
80 | 16.5/5.5 | 15.2/4.2 | 10.8/2.0 | 7.3/2.4 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 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
Chang, C.-K.; Cheng, S.-T.; Boyanapalli, B.-K. Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm. Energies 2022, 15, 3460. https://doi.org/10.3390/en15093460
Chang C-K, Cheng S-T, Boyanapalli B-K. Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm. Energies. 2022; 15(9):3460. https://doi.org/10.3390/en15093460
Chicago/Turabian StyleChang, Chien-Kuo, Shih-Tang Cheng, and Bharath-Kumar Boyanapalli. 2022. "Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm" Energies 15, no. 9: 3460. https://doi.org/10.3390/en15093460
APA StyleChang, C. -K., Cheng, S. -T., & Boyanapalli, B. -K. (2022). Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm. Energies, 15(9), 3460. https://doi.org/10.3390/en15093460