Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine

Cho, Kyeong-Hee; Jo, Hyung‐Chul; Kim, Eung-Sang; Park, Hyang-A; Park, June Ho

doi:10.1007/s42835-020-00430-9

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…Many ML-based fault/defect detection [ 333 , 334 , 335 , 336 , 337 , 338 ] and quality monitoring approaches [ 336 ] have been proposed in the literature. Several examples of fault diagnostics [ 335 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 ] are also available in the literature. Bayesian approaches that enable root cause analysis of quality issues [ 350 , 351 ] have also been proposed.…”

Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization.

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Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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“…Experimental results showed that this model is superior to other traditional models. In literature [32], SVM was used as a fault diagnosis model to distinguish the normal and fault state of PV generating sets, and relevant data was used to conduct experiments. The results showed that this method could better realize the fault diagnosis of PV generating sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Liu

Zhao³

et al. 2021

Symmetry

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With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

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“…Both simulation and experimental results show that the method is effective for PV array fault diagnosis [18]. Cho et al proposed a fault diagnosis method based on support vector machine classification to perform the fault diagnosis of PV generators using real operation data [19]. Adhya et al examined the effectiveness of categorical boosting, light gradient boosting and extreme gradient boosting methods for PV array fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application

Wang

Liu

et al. 2023

Sustainability

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PV strings are essential for energy conversion in large-scale photovoltaic (PV) power plants. The operating state of PV strings directly affects the power generation efficiency and economic benefits of PV power plants. In the process of evaluating PV arrays, a reference array needs to be identified. By comparing PV arrays with the reference array, the operational status of the PV arrays can be evaluated. However, in the actual operation of PV power stations, it is difficult to directly determine the reference state of a PV array due to random fluctuations in the PV power output. In order to solve the problems mentioned above, this paper proposes a method to select the reference state and perform a grading evaluation of PV strings. Additionally, the proposed method is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, which is used to rank the performance of PV arrays to determine their status. In order to solve the problem of random fluctuations in PV power generation, a probability distribution model of the PV string conversion efficiency was built by using the kernel density estimation method. Then, the characteristic indicator of the PV string’s operating state was described by the output power of the PV string and its probability distribution model. Then, based on the operating characteristic indicator, the reference state of the PV string was determined using the TOPSIS method, and the grading evaluation of the operating state of the PV string was realized. Finally, the effectiveness of the proposed method was verified using the actual data of a PV power station.

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Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine

Cited by 15 publications

References 10 publications

A Primer on the Factories of the Future

A Primer on the Factories of the Future

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application

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