Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

doi:10.23919/JSEE.2022.000048

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (2): 489-496.doi: 10.23919/JSEE.2022.000048

• • 上一篇

收稿日期:2020-07-09 接受日期:2022-02-14 出版日期:2022-05-06 发布日期:2022-05-06

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

Zheng WANG(), Zhiyuan HU(), Xuanfang YANG()

¹ School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China

Received:2020-07-09 Accepted:2022-02-14 Online:2022-05-06 Published:2022-05-06
About author:|WANG Zheng was born in 1978. He received his doctor’s degree from Huazhong University of Science and Technology in 2014. He holds a Ph.D. degree and is an associate professor in Naval University of Engineering. His research interests are intelligent control technology and network monitoring technology. E-mail: marchy618@168.com||HU Zhiyuan was born in 1993. He received his B.E. degree from Naval Academy of Aeronautical Engineering in 2014. Now he is an M.S. degree candidate at Naval University of Engineering. His research interests include control technology of unmanned underwater vehicle, intelligent control algorithm, and deep learning. E-mail: 757308793@qq.com||YANG Xuanfang was born in 1968. He received his Ph.D. degree from Huazhong University of Science and Technology in 2006. He is a doctor and a professor in Naval University of Engineering. His research interests are ship power positioning control system and intelligent control technology. E-mail: yy_xxff@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (41774021; 41974005)

摘要/Abstract

Abstract:

Electric power is widely used as the main energy source of ship integrated power system (SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization (MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.

Key words: ship integrated power system (SIPS), multi-agent and ant colony optimization (MAACO), network reconfiguration, ring grid, fault recovery

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 489-496.

Zheng WANG, Zhiyuan HU, Xuanfang YANG. Multi-agent and ant colony optimization for ship integrated power system network reconfiguration[J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 489-496.

图/表 10

参考文献 33

1	JIN Z M, SAVAGHEBI M, VASQUEZ J C, et al Maritime DC microgrids: a combination of microgrid technologies and maritime onboard power system for future ships. Proc. of the 8th IEEE International Power Electronics and Motion Control Conference, 2016, 179- 184.
2	ZHANG S X, CHENG H Z, WANG D, et al Distributed generation planning in active distribution network considering demand side management and network reconfiguration. Applied Energy, 2018, 228, 1921- 1936. doi: 10.1016/j.apenergy.2018.07.054
3	HOSSAIN M R, GINN H L Real-time distributed coordination of power electronic converters in a DC shipboard distribution system. IEEE Trans. on Energy Conversion, 2017, 32 (2): 770- 778. doi: 10.1109/TEC.2017.2685593
4	ALAFNAN H, ZHANG M, YUAN W, et al Stability improvement of DC power systems in an all-electric ship using hybrid SMES/battery. IEEE Trans. on Applied Superconductivity, 2018, 28 (3): 5700306.
5	LI J, ZHANG Z P, LI B Y Sensor fault detection and system reconfiguration for DC-DC boost converter. Sensors, 2018, 18 (5): 1375. doi: 10.3390/s18051375
6	MORADI R, ALIKHANI A, JEGARKANDI M F Comparing the performance of reference trajectory management and controller reconfiguration in attitude fault tolerant control. Proc. of the MATEC Web of Conferences, 2018, 151, 04008. doi: 10.1051/matecconf/201815104008
7	GARAU M, GHIANI E, CELLI G, et al Co-simulation of smart distribution network fault management and reconfiguration with LTE communication. Energies, 2018, 11 (6): 1332. doi: 10.3390/en11061332
8	FENG X Y, BUTLER-PURRY K L, ZOURNTOS T Real-time electric load management for DC zonal all-electric ship power systems. Electric Power Systems Research, 2018, 154, 503- 514.
9	SUN R J, LIU Y T, ZHU H N, et al A network reconfiguration approach for power system restoration based on preference-based multi-objective optimization. Applied Soft Computing, 2019, 83, 105656. doi: 10.1016/j.asoc.2019.105656
10	SULTANA N, RUFENACHT M, SKJELLUM A, et al Failure recovery for bulk synchronous applications with MPI stages. Parallel Computing, 2019, 84, 1- 14. doi: 10.1016/j.parco.2019.02.007
11	LI W B, LI Q, GAO J J, et al Research of non-contact compensation AC voltage regulator based on fuzzy strategy. Journal of Physics: Conference Series, 2020, 1626 (1): 012070. doi: 10.1088/1742-6596/1626/1/012070
12	WANG Z, XIA L, WANG Y J Application of multi-agent and genetic algorithm in network reconfiguration of ship power system. Electronics & Electrical Engineering, 2012, 18 (9): 7- 10.
13	FOERSTER J, FARQUHAR G, AFOURAS T, et al. Counterfactual multi-agent policy gradients. Proc. of the AAAI Conference on Artificial Intelligence, arXiv preprint arXiv:1765.0892601, 2018
14	ZHOU T, LIU Q L, WANG D, et al Distributed non-fragile containment control of nonlinear multi-agent systems with time-varying delays. International Journal of Systems Science, 2020, 1- 16. doi: 10.1080/00207721.2020.1849860
15	ALI M S, AGALYA R, ORMAN Z, et al Leader-following consensus of non-linear multi-agent systems with interval time-varying delay via impulsive control. Neural Processing Letters, 2021, 53 (1): 69- 83. doi: 10.1007/s11063-020-10384-8
16	HOUSSEYNI W, MOSBAHI O, KHALGUI M, et al Multiagent architecture for distributed adaptive scheduling of reconfigurable real-time tasks with energy harvesting constraints. IEEE Access, 2017, 6, 2068- 2084.
17	LOWE R, WU Y I, TAMAR A, et al Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, 2017, 30 (1): 6379- 6390.
18	PANASETSKY D, SIDOROY D, LI Y, et al Centralized emergency control for multi-terminal VSC-based shipboard power systems. International Journal of Electrical Power & Energy Systems, 2019, 104, 205- 214.
19	LI C J, LIU G P Data-driven consensus for non-linear networked multi-agent systems with switching topology and time-varying delays. IET Control Theory & Applications, 2018, 12 (12): 1773- 1779.
20	LI C J, LIU G P Consensus for heterogeneous networked multi-agent systems with switching topology and time-varying delays. Journal of the Franklin Institute, 2018, 355 (10): 4198- 4217. doi: 10.1016/j.jfranklin.2018.04.003
21	CHNITER H, LI Y, KHALGUI M, et al Multi-agent adaptive architecture for flexible distributed real-time systems. IEEE Access, 2018, 6, 23152- 23171. doi: 10.1109/ACCESS.2018.2825023
22	BONABEAU E, DORIGO M, MARCO D, et al. Swarm intelligence: from natural to artificial systems. Oxford: Oxford University Press, 1999.
23	DORIGO M, GAMBARDELLA L M Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation, 1997, 1 (1): 53- 66. doi: 10.1109/4235.585892
24	DI CARO G, DORIGO M, GAMBARDELLA L M Ant algorithms for discrete optimization. Artificial Life, 1999, 5 (2): 137- 172. doi: 10.1162/106454699568728
25	ARIYASINGHA I, FERNANDO T G I Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem. Swarm & Evolutionary Computation, 2015, 23, 11- 26.
26	KUZNETSOV A V, SELVESIUK N I, PLATOSHIN G A, et al Application of cellular automatons and ant algorithms in avionics. Journal of Physics Conference Series, 2018, 973 (1): 012062.
27	LIU H D, SUN Y, ZHANG L Y Information reconstruction strategy for a ship power distribution system. Proc. of the Data Processing Techniques and Applications for Cyber-Physical Systems, 2020, 479- 487.
28	PAPERNOT N, MCDANIEL P. Deep k-nearest neighbors: towards confident, interpretable and robust deep learning. arXiv preprint arXiv: 1803.04765, 2018.
29	GOU J P, MA H X, OU W H, et al A generalized mean distance-based k-nearest neighbor classifier. Expert Systems with Applications, 2019, 115, 356- 372. doi: 10.1016/j.eswa.2018.08.021
30	YANG J Research on optimized reconfiguration of distributed distribution network based on ant colony optimization algorithm. Proc. of the International Conference on Computer Engineering and Application, 2020, 20- 23.
31	DORIGO M, BIRATTARI M, STUTZLE T Ant colony optimization. IEEE Computational Intelligence Magazine, 2006, 1 (4): 28- 39. doi: 10.1109/MCI.2006.329691
32	SHEN X L, SANG J S, SUN Y B, et al Application of improved ant colony algorithm in distribution network patrol route planning. Proc. of the 7th IEEE International Conference on Software Engineering and Service Science, 2016, 560- 563.
33	TIRKOLAEE E B, ALINAGHIAN M, HOSSEINABADI A, et al An improved ant colony optimization for the multi-trip capacitated arc routing problem. Computers & Electrical Engineering, 2019, 77, 457- 470.

No.	Device	Power/kW	Level	No.	Device	Power/kW	Level	No.	Device	Power/kW	Level	No.	Device	Power/kW	Level
1	G1	21	0	25	M7	1.270 5	2	49	G7	3.75	0	73	P18	0.005	0
2	G2	3.75	0	26	I6	0.769 2	2	50	G8	21	0	74	I20	0.05	3
3	G3	3.75	0	27	P10	0.005	0	51	P23	0.005	0	75	M8	1.270 5	2
4	G4	21	0	28	I7	0.05	3	52	M1	20	1	76	I8	3.0	2
5	P1	0.005	0	29	M8	1.270 5	2	53	M2	20	1	77	P19	0.005	0
6	M1	20	1	30	I8	3.0	2	54	P24	0.005	0	78	I21	0.05	3
7	M2	20	1	31	P11	0.005	0	55	P25	0.005	0	79	M9	1.270 5	2
8	P2	0.005	0	32	I9	0.05	3	56	P26	0.005	0	80	I10	1.493 7	2
9	P3	0.005	0	33	M9	1.270 5	2	57	P27	0.005	0	81	P20	0.005	0
10	P4	0.005	0	34	I10	1.493 7	2	58	P28	0.005	0	82	I22	0.005	3
11	P5	0.005	0	35	P12	0.005	0	59	M3	20	1	83	M10	0.729 5	2
12	P6	0.005	0	36	I11	0.005	3	60	M4	20	1	84	I12	1.270 5	2
13	M3	20	1	37	M10	0.7295	2	61	P15	0.005	0	85	P21	0.005	0
14	M4	20	1	38	I12	1.270 5	2	62	I17	0.05	3	86	I23	0.05	3
15	P7	0.005	0	39	P13	0.005	0	63	M5	1.270 5	2	87	M11	1.270 5	2
16	I1	0.05	3	40	I13	0.05	3	64	I2	1.102 4	2	88	I14	1.392	2
17	M5	1.270 5	2	41	M11	1.270 5	2	65	P15	0.005	0	89	P22	0.005	0
18	I2	1.102 4	2	42	I14	1.392	2	66	I18	0.05	3	90	I24	0.005	3
19	P10	0.005	0	43	P14	0.005	0	67	M6	0.897 6	2	91	M12	0.769 2	2
20	I3	0.05	3	44	I15	0.005	3	68	I4	1.493 7	2	92	I16	1.230 8	2
21	M6	0.897 6	2	45	M12	0.7692	2	69	P17	0.005	0	93	L1	0.005	0
22	I4	1.493 7	2	46	I16	1.230 8	2	70	I19	0.05	3	94	L2	0.005	0
23	P9	0.005	0	47	G5	21	0	71	M7	1.270 5	2	95	L3	0.005	0
24	I5	0.05	3	48	G6	3.75	0	72	I6	0.769 2	2	96	L4	0.005	0

Algorithm	Fitness value			SOT			Best iteration
Algorithm	Maximum	Minimum	Average	Maximum	Minimum	Average	Best iteration
Traditional ACO	5.632 55	5.508 03	5.586 26	26	1	5.8	29
MAACO	5.632 85	5.508 32	5.623 04	23	1	3.9	19

Algorithm	Fitness value			SOT			Best iteration
Algorithm	Maximum	Minimum	Average	Maximum	Minimum	Average	Best iteration
Traditional ACO	5.602 15	5.506 82	5.545 87	25	6	8.8	37
MAACO	5.610 25	5.554 64	5.591 93	19	3	5.4	28

Algorithm	Average fitness value	Average SOT	Best iteration
GA	5.587 36	4.5	32
PSO	5.608 79	4.2	28
MAACO	5.623 04	3.9	19

Algorithm	Average fitness value	Average SOT	Best iteration
GA	5.584 39	7.9	39
PSO	5.620 54	6.8	35
MAACO	5.591 93	5.4	28

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 33

相关文章 0

编辑推荐

Metrics

本文评价