Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh, Arash; Mohammadi‐Ivatloo, Behnam; Pourhossein, Kazem; Anvari‐Moghaddam, Amjad

doi:10.1109/tpel.2021.3131293

Cited by 25 publications

(10 citation statements)

References 215 publications

(265 reference statements)

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“…The decision tree is one of the most commonly applied techniques of machine learning, which is based on inductive learning and is introduced in the form of a systematic method called recursive binary partition. Linear regression, pattern recognition, classification, and forecasting are obvious applications of this method (Moradzadeh et al 2021e ; Latif 2021 ). Like the model shown in Fig.…”

Section: Methodologiesmentioning

confidence: 99%

“…5 , tree-shaped diagrams form the composition of decision trees. This architecture consists of branches and three types of nodes named root node, inner node, and leaf node (Moradzadeh et al 2021e ). In the implementation of the training process, the input data is divided into small categories or a number of subsets with the help of some dichotomous classifications.…”

Section: Methodologiesmentioning

confidence: 99%

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Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Moradzadeh,

Moayyed,

Mohammadi-Ivatloo

et al. 2023

Environ Sci Pollut Res

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Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.

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Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Moradzadeh,

Moayyed,

Mohammadi-Ivatloo

et al. 2023

Environ Sci Pollut Res

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“…This aids significantly in device monitoring. [29][30][31][32][33][34][35][36][37][38]. The issue at hand is the substantial size of neural networks tasked with recognizing intricate data patterns from complex physical phenomena like component aging in power electronic systems.…”

Section: State Of the Art Overviewmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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“…The use of clustering-based artificial neural networks (ANNs) has been published as an ideal solution to reconfiguration PV arrays and minimize power losses based on the dynamic structure (Monteiro et al 2020). Simple structure and high accuracy are the advantages of this method, while the operation of ANNs requires a suitable database for network training (Moradzadeh et al 2020(Moradzadeh et al , 2021. Sometimes prolonged training and network processing are major problems with this method (Monteiro et al 2020).…”

Section: Related Workmentioning

confidence: 99%

Photovoltaic array reconfiguration under partial shading conditions for maximum power extraction via knight's tour technique

Rezazadeh

Moradzadeh

Pourhossein

et al. 2022

J Ambient Intell Human Comput

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This paper introduces a novel reconfiguration technique, called Knight's tour to extract maximum power from photovoltaic (PV) arrays in partial shading conditions. The Knight's tour reconfigures the PV arrays based on the Knight's movements on the chessboard. The proposed procedure achieves the maximum power values by spreading partial shadows in all rows. Knight's tour can be applied to a variety of PV arrays in different dimensions and sizes. Accordingly, the Knight's tour procedure is applied to four cases in square and rectangular shapes with different dimensions and various shading conditions in each case. To make a direct comparison and present the effectiveness of the suggested procedure, the total-cross-tied connection model and conventional methods such as SuDoKu, optimal SuDoKu, improved SuDoKu, and Skyscraper puzzle are also implemented to the introduced cases. The results of the maximum power point tracking in each case are evaluated by indicators such as global maximum power point (GMPP), fill factor, mismatch loss, and efficiency. Finally, evaluations emphasize the ability and effectiveness of the Knight's tour solution compared to other methods by achieving the GMPP values such as $$74.7\,{I}_{m}{V}_{m}$$ 74.7 I m V m , $$66.6\,{I}_{m}{V}_{m}$$ 66.6 I m V m , $$46.8\,{I}_{m}{V}_{m}$$ 46.8 I m V m , and $$109.8\,{I}_{m}{V}_{m}$$ 109.8 I m V m for cases 1 to 4, respectively. The Knight's tour method can be utilized as an efficient tool for the PV arrays in real-world systems that suffer from partial shading.

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Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Cited by 25 publications

References 215 publications

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Photovoltaic array reconfiguration under partial shading conditions for maximum power extraction via knight's tour technique

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