Fault Diagnosis Method for Photovoltaic Systems Based on Multi-strategy Fusion

doi:10.16182/j.issn1004731x.joss.25-0471

Abstract

Abstract:

To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize parameters such as the number of neurons in the hidden layer of BiGRU and the learning rate of the model, and the weight assignment of fault-sensitive features was enhanced by combining the assistive technology. The results of simulation experiments have shown that the diagnostic accuracies of the proposed model simulation and the measured data reach 97.9% and 95.4%, respectively.Compared with the comparative models, the diagnostic accuracy is improved by up to 4.1%. The proposed model provides a new technical path for intelligent operation and maintenance of PV systems.

Key words: Markov transition field, BiGRU, improved lemming algorithm, fault diagnosis, maximum power point tracking (MPPT) fault, inverter fault

CLC Number:

TP391.9

Li Bin, Wang Yuchuo. Fault Diagnosis Method for Photovoltaic Systems Based on Multi-strategy Fusion[J]. Journal of System Simulation, 2025, 37(12): 3018-3032.

Figures/Tables 19

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Fig. 6

Table 2

Fig. 7

Table 3

Fig. 8

Table 4

Photovoltaic system working condition

标签	工作状态	主要影响参数
K0	正常运行
K1	光伏阵列故障	$U p v, I p v, U d c$
K2	逆变器故障	$U d c, I a b c, U a b c$
K3	并网异常	$I a b c, U a b c$
K4	MPPT轻度故障	$U d c, I a b c, U a b c$
K5	MPPT严重故障	$U p v, U d c, I a b c, U a b c$
K6	光伏阵列及逆变器故障	$U p v, I p v, U d c, I a b c, U a b c$
K7	光伏阵列及MPPT轻度故障	$U p v, I p v, U d c, I a b c, U a b c$

Table 4

Fig. 9

Table 5

Fig. 10

Table 6

Table 7

Fig. 11

Fig. 12

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测试函数	理论值	测试函数	理论值
F1	100	F4	1 700
F2	1 100	F5	2 200
F3	700	F6	2 400

函数	指标	IALA	ALA	BWO	SSOA	HHO	WOA	IVY
F1	最优值	1.07×10²	1.10×10²	4.13×10⁹	4.84×10⁹	1.64×10⁵	8.26×10⁵	2.04×10²
	平均值	3.56×10²	7.85×10²	7.72×10⁹	1.09×10¹⁰	6.82×10⁵	9.98×10⁶	8.76×10⁷
	标准差	7.42×10²	1.11×10³	2.07×10⁹	2.81×10⁹	9.20×10⁵	1.69×10⁷	4.50×10⁸
F2	最优值	1.21×10³	1.22×10³	2.20×10³	2.43×10³	1.57×10³	1.50×10³	1.23×10³
	平均值	1.62×10³	1.62×10³	2.56×10³	3.32×10³	2.08×10³	2.26×10³	2.20×10³
	标准差	2.25×10²	2.31×10²	2.80×10²	2.95×10²	2.60×10²	2.94×10²	3.97×10²
F3	最优值	7.09×10²	7.15×10²	7.73×10²	7.97×10²	7.20×10²	7.40×10²	7.31×10²
	平均值	7.25×10²	7.27×10²	7.99×10²	8.27×10²	7.92×10²	7.83×10²	7.87×10²
	标准差	6.12	6.40	9.87	1.66×10¹	2.17×10¹	3.32×10¹	2.60×10¹
F4	最优值	1.73×10³	1.74×10³	7.16×10³	2.48×10⁵	3.98×10³	3.15×10³	4.09×10⁴
	平均值	1.81×10³	1.82×10³	2.15×10⁵	5.42×10⁵	5.55×10⁴	2.24×10⁵	4.50×10⁵
	标准差	5.78×10¹	6.70×10¹	1.15×10⁵	1.25×10⁵	4.43×10⁴	5.09×10⁵	2.41×10⁵
F5	最优值	2.20×10³	2.21×10³	2.36×10³	2.70×10³	2.31×10³	2.27×10³	2.30×10³
	平均值	2.29×10³	2.30×10³	2.62×10³	3.18×10³	2.42×10³	2.41×10³	2.32×10³
	标准差	2.64×10¹	1.64×10¹	1.90×10²	3.46×10²	3.87×10²	3.35×10²	2.39×10¹
F6	最优值	2.50×10²	2.73×10³	2.59×10³	2.84×10³	2.75×10³	2.54×10³	2.50×10³
	平均值	2.64×10³	2.75×10³	2.73×10³	2.95×10³	2.82×10³	2.77×10³	2.71×10³
	标准差	3.43×10¹	7.34	8.02×10¹	5.12×10¹	4.00×10¹	4.76×10¹	9.57×10¹

模型	原模型	无AT模型
训练量	640	640
测试量	160	160
训练时间/min	6.2	5.4
测试时间/s	1.7	1.1
参数量	2.76×10⁷	2.48×10⁷
准确率/%	98.1	92.5

参数	理论值
功率/W	1 050
开路电压/V	109.23
短路电流/A	13.82
工作电压/V	90.84

参数	理论值
功率/W	350
开路电压/V	36.41
短路电流/A	13.82
工作电压/V	30.28
功率容差/%	±5