Improved NSGA Multi-objective Optimization Based Oil-Paper Insulation Status Assessment Method

doi:10.11930/j.issn.1004-9649.202305113

Abstract

Abstract:

Oil-paper is an important component of oil-immersed power transformers, and its insulation reliability is crucial for maintaining transformer stable operation. In this paper, FDS tests on aged and damp oil-paper insulation samples are conducted, so as to obtain the relationship between the frequency domain dielectric response characteristics of oil-paper insulation and its insulation state. Based on the extended Debye model, an improved NSGA algorithm assisted recognition method for oil-paper insulation broadband dielectric characteristic parameters is proposed. The accurate extraction of the extended Debye model characteristic parameters can be achieved through the complex capacitance curve of the oil-paper insulation. The weight relationship of the characteristic parameter information entropy was considered, and the quantitative characterization equation of the model characteristic parameter and the insulation moisture content and aging was established. This method can realize the quantitative analysis of the oil-paper insulation state and provide important theoretical support for the accurate insulation state assessment of the oil-immersed power equipment.

Key words: oil-paper insulation, improve the NSGA algorithm, frequency domain dielectric response, status assessment, extended Debye model

Peng ZHANG, Jian ZHANG, Hang YANG, Limin QU, Heqian LIU, Muhe YU. Improved NSGA Multi-objective Optimization Based Oil-Paper Insulation Status Assessment Method[J]. Electric Power, 2023, 56(10): 124-132.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202305113

https://www.electricpower.com.cn/EN/Y2023/V56/I10/124

Figures/Tables 9

References 25

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模型参数	水分含量
模型参数	1.13%	2.24%	3.28%	4.08%
R²-C′	0.9962	0.9971	0.9968	0.9962
R²-C′′	0.9978	0.9985	0.9977	0.9981
C₀/nF	0.1135	0.1660	0.1165	0.1152
R₀/GΩ	899.8930	455.8450	254.5610	180.0010
C₁/nF	0.0070	0.1000	0.3199	0.5998
R₁/GΩ	174.7160	131.1760	74.9990	57.7011
C₂/nF	0.0011	0.0080	0.0376	0.0800
R₂/GΩ	124.9890	93.4900	25.9690	7.9960
C₃/nF	0.0010	0.0035	0.0080	0.0172
R₃/GΩ	62.5310	26.6790	5.6138	2.1550
C₄/nF	0.0009	0.0014	0.0040	0.0047
R₄/GΩ	8.1020	4.7950	0.5015	0.3779
C₅/nF	0.0008	0.0010	0.0030	0.0041
R₅/GΩ	0.8000	0.4898	0.1000	0.0194
C₆/nF	0.0007	0.0014	0.0030	0.0034
R₆/GΩ	0.1640	0.0445	0.0100	0.0006

模型参数	水分含量
模型参数	1.13%	2.24%	3.28%	4.08%
R²-C′	0.9962	0.9971	0.9968	0.9962
R²-C′′	0.9978	0.9985	0.9977	0.9981
C₀/nF	0.1135	0.1660	0.1165	0.1152
R₀/GΩ	899.8930	455.8450	254.5610	180.0010
C₁/nF	0.0070	0.1000	0.3199	0.5998
R₁/GΩ	174.7160	131.1760	74.9990	57.7011
C₂/nF	0.0011	0.0080	0.0376	0.0800
R₂/GΩ	124.9890	93.4900	25.9690	7.9960
C₃/nF	0.0010	0.0035	0.0080	0.0172
R₃/GΩ	62.5310	26.6790	5.6138	2.1550
C₄/nF	0.0009	0.0014	0.0040	0.0047
R₄/GΩ	8.1020	4.7950	0.5015	0.3779
C₅/nF	0.0008	0.0010	0.0030	0.0041
R₅/GΩ	0.8000	0.4898	0.1000	0.0194
C₆/nF	0.0007	0.0014	0.0030	0.0034
R₆/GΩ	0.1640	0.0445	0.0100	0.0006

模型参数	拟合方程	R²
C₁/nF	K_mc%=4.332×C₁^0.416+0.578	0.999
C₂/nF	K_mc%=8.387×C₂^0.195–1.076	0.995
R₀/GΩ	K_mc%=35.060×R₀^–0.286–3.857	0.999
R₁/GΩ	K_mc%=0.253×R₁^0.603+6.894	0.956
R₂/GΩ	K_mc%=–0.028×R₂^0.959+4.150	0.966

模型参数	拟合方程	R²
C₁/nF	K_mc%=4.332×C₁^0.416+0.578	0.999
C₂/nF	K_mc%=8.387×C₂^0.195–1.076	0.995
R₀/GΩ	K_mc%=35.060×R₀^–0.286–3.857	0.999
R₁/GΩ	K_mc%=0.253×R₁^0.603+6.894	0.956
R₂/GΩ	K_mc%=–0.028×R₂^0.959+4.150	0.966

模型参数	聚合度
模型参数	1013	624	518	389
R²-C′	0.9982	0.9967	0.9969	0.9978
R²-C′′	0.9987	0.9975	0.9971	0.9952
C₀/nF	0.1135	0.1155	0.1163	0.1161
R₀/GΩ	899.8931	758.2047	449.6251	334.9625
C₁/nF	0.0072	0.1673	0.3219	0.6613
R₁/GΩ	174.7159	156.8246	79.7452	52.2314
C₂/nF	0.0011	0.0614	0.0843	0.0900
R₂/GΩ	124.9885	43.4128	32.4279	30.7560
C₃/nF	0.0010	0.0034	0.0094	0.0627
R₃/GΩ	62.5310	38.3486	28.5869	17.1239
C₄/nF	0.0009	0.0012	0.0015	0.0031
R₄/GΩ	8.1018	7.3523	6.9395	4.6358
C₅/nF	0.0008	0.0011	0.0012	0.0030
R₅/GΩ	0.8000	0.5042	0.2703	0.1246
C₆/nF	0.0007	0.0013	0.0012	0.0022
R₆/GΩ	0.1635	0.0168	0.0054	0.0015