On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

Shin, Jun Hyun; Kim, Jin O.

doi:10.3390/en13174584

Cited by 15 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the algorithm is presented as a reference in Machine Learning competitions and is more efficient than decision trees [112]. The disadvantages of this algorithm lie in the fact that it is not easy to interpret (explain how the forecast is calculated) since it is obtained from a large number of trees which are very deep, it is also difficult to improve because it is considered a black box and trains slowly [118]. Random forests are tree ensemble methods, they aggregate the predictors of several trees, each of which is trained separately [28].…”

Section: Random Forest (Rf) Algorithmmentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

show abstract

Section: Random Forest (Rf) Algorithmmentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The approach automatically identified a single module failure in a PVS. The study [47] employed SVM with a greater accuracy of classification utilizing a climate corrected performance ratio, among other factors. However, the study does not specify which problem was investigated, or if it was a failure or a regular operation.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection System of Photovoltaic Based on Artificial Neural Network

Salman Zamzeer,

Farhan,

ALRikabi

2023

ejuow

View full text Add to dashboard Cite

Using PV systems, solar energy may be used to create electricity. Every year, the proportion of solar energy in the electric system increases significantly. On the other hand, photovoltaic cells are susceptible to malfunctions that diminish their efficiency and profitability. Due to the severity of the defects, fault detection and diagnosis (FDD) in the PV system have become difficult. Thus, the primary objective of the proposed study is to detect and diagnose particular types of PV system problems using an artificial neural network (ANN). This early operation is more effective for avoiding errors during PV installation and minimizing PV system power losses. A new solar cell model is created in the MATLAB/SIMULINK environment and is used to identify the fault dataset. The solar cell consists of three parallel strings and three series modules, with each module containing 20 series-connected photovoltaic cells. This model determines four parameters (V-load in Volts, I-load in Amps, Irradiance in W/m2, and Temperature in Celsius) under varying situations (five temperature values, three irradiance values, and three-time events), which are repeated for all faults evaluated. In the training and testing phases of the proposed ANN, these four parameters are utilized as effective features. Additionally, this network possesses eight outputs, one for each defect. Using implementations with three different numbers of hidden layers and an identical fault dataset, the performance of the proposed network is tested. The findings of the simulation indicate a considerable proportion of fault detection and diagnosis accuracy, ranging between 95% and 98%. The computed RMSE reveals that the training period yields 0.193% improvement, whereas the testing phase yields 0.365%.

show abstract

“…The switch ON/OFF commutation generates the duty cycle D = t ON /t C , being t ON the time ON (u = 1) and t C the switching period (0 < D < 1). The voltage output/input ratio is as expressed in Equation (1).…”

Section: The Dc/dc Convertermentioning

confidence: 99%

Grid-Connected PV Systems Controlled by Sliding via Wireless Communication

et al. 2021

View full text Add to dashboard Cite

Grid-connected photovoltaic (PV) systems are designed to provide energy to the grid. This energy transfer must fulfil some requirements such as system stability, power quality and reliability. Thus, the aim of this work is to design and control a grid-connected PV system via wireless to guarantee the correct operation of the system. It is crucial to monitor and supervise the system to control and/or detect faults in real time and in a remote way. To do that, the DC/DC converter and the DC/AC converter of the grid-connected PV system are controlled wirelessly, reducing costs in cabling installations. The used control methods are the sliding for the DC/DC converter and the Proportional-Integral (PI) for the inverter. The sliding control is robust, ensures system stability under perturbations, and is proven to work well via wireless. The PI control is simple and effective, proving its validity through wireless too. In addition, the effect of the communications is analysed in both controllers. An experimental platform has been built to conduct the experiments to verify the operation of the grid-connected PV system remotely. The results show that the system operates well, achieving the desired values for the maximum power point tracker (MPPT) sliding control and the energy transfer from the inverter to the grid.

show abstract

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

Cited by 15 publications

References 25 publications

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Fault Detection System of Photovoltaic Based on Artificial Neural Network

Grid-Connected PV Systems Controlled by Sliding via Wireless Communication

Contact Info

Product

Resources

About