Solar Photovoltaic Panel Model Classification

How does a multi-scale network efficiently classify photovoltaic panel anomalies?

The Multi-scale network efficiently classifies photovoltaic panel anomalies. Oversampling approach overcomes the imbalanced class distribution. Multi-scale branches aim to improve the features extracted by each parallel block. Proposed method is validated with a large dataset collected from six continents.

How do we classify solar module anomalies using thermographic images?

Le et al. (2021) designed a deep neural network model to classify solar module anomalies using thermographic images in the same unbalanced dataset. A residual network structure and ensemble technique were used to design the classification network.

What are the different types of anomalies in PV panels?

Secondly, many studies have focused on datasets with either 2-class (Anomaly/No-Anomaly), 3-class, or 4-class. As the PV panels could be affected by harsh outdoor environments, there can occur various types of anomalies as cracking, diode, multi diode, hot spot, multi hot spot, soiling, vegetation, and etc.

What is PV panel soiling?

The accumulation and deposition of aerosol particles in PV panels, commonly referred to as “PV panel soiling, ”affects the performance of the PV power system. To demonstrate the efficiency of PV systems and create cost-effective mitigation, soil impact assessments were recommended at different locations and times.

Why do we need a mathematical model for PV system fault detection?

For effective fault detection methods, modelling the PV system mathematically plays an important key on the accuracy of the classification technique. This is because it has a remarkable role in obtaining the optimal parameters, design, and assessment of the PV solar system fault diagnosis methods [2, 3].

How can fault detection and classification improve the reliability of PV systems?

Proposed method is validated with a large dataset collected from six continents. Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs.