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

doi:10.23919/JSEE.2022.000048

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

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.

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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