Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest

Li, You; Ling, Zaixun; Cui, Yibo; Cai, Wei; He, Shaoming

doi:10.3390/e25050778

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The results obtained are significantly better compared to the KNN model, i.e., an accuracy of 99.4% compared to 91.8% for KNN [99]. In the literature, several reviews on the diagnostic methods for PV systems have been carried out in recent years [7,111,112]. In these reviews, the presence of hybrid models such as ANFIS, FKNN and so on is remarkable, as shown in Table 2.…”

Section: Hybrid Models For Diagnosing Pv Systemsmentioning

confidence: 99%

“…The principle of the DT algorithm consists of determining the best possible characteristic for a set of data; separating the data into subsets containing the values of the best characteristic [115]. It also allows you to recursively generate new decision trees using the subset of data created and make the decision when you can no longer classify the data [111]. In other words, the DT model has decision nodes that have several branches and are used to make decisions and leaf nodes that represent the result or class of these decisions [75].…”

Section: Hybrid Models For Diagnosing Pv Systemsmentioning

confidence: 99%

See 1 more Smart Citation

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

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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.

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Section: Hybrid Models For Diagnosing Pv Systemsmentioning

confidence: 99%

Section: Hybrid Models For Diagnosing Pv Systemsmentioning

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

A fault-tolerant topology for single-phase H-bridge inverters addressing open- and short-circuit failures for industrial applications

Djaghloul,

Tehrani,

Khan

et al. 2024

Franklin Open

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Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network

Tang,

Wang,

Qiu

et al. 2024

PLoS ONE

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The large-scale integration of offshore wind power into the power grid has brought serious challenges to the power system power quality. Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform and crested porcupine optimizer (CPO) optimized CNN. Firstly, the intrinsic mechanism and waveform characteristics of offshore wind power grid-connected disturbances are analyzed, and the simulated disturbance signals are feature extracted and time-frequency diagrams are obtained by fast S-transform. Secondly, the CPO algorithm is used to optimize the convolutional neural network and determine the best hyperparameters so that the classifier achieves the optimal classification performance. Then, the CPO-CNN classification model is used for feature extraction and feature selection of the time-frequency diagrams and classification of multiple power quality disturbances. Finally, a simulation experimental platform is established based on MATLAB to perform simulation verification and comparative analysis of power quality disturbance classification. The experimental results show that the model established in this paper is effective, and the classification accuracy is improved by 3.47% compared with the CNN method, which can accurately identify the power quality disturbance signals, and then help to assess and control the power quality problems.

show abstract

Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest

Cited by 3 publications

References 21 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

A fault-tolerant topology for single-phase H-bridge inverters addressing open- and short-circuit failures for industrial applications

Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network

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