Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

Chao-Ming Huang (), Shin-Ju Chen, Sung-Pei Yang, Yann-Chang Huang and Pao-Yuan Huang
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Chao-Ming Huang: Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan
Shin-Ju Chen: Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan
Sung-Pei Yang: Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan
Yann-Chang Huang: Department of Electrical Engineering, Cheng Shiu University, Kaohsiung 833, Taiwan
Pao-Yuan Huang: Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan

Energies, 2024, vol. 17, issue 13, 1-23

Abstract: Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.

Keywords: parameter estimation; fault diagnosis; swarm intelligence; I–V curve (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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