Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. 

Convolutional Neural Networks: A Survey

This article proposes a mission profile-based reliability prediction method for modular multilevel converters (MMCs). It includes key modeling steps, such as long-term mission profile, analytical power loss models, system-level and component-level thermal modeling, lifetime modeling, Monte Carlo analysis, and redundancy analysis. Thermal couplings and uneven thermal stresses among submodules are considered. A case study of a 15-kVA down-scale MMC has been used to demonstrate the proposed method and validate the theoretical analysis. The outcomes serve as a first step for developing realistic reliability analysis and model-based design methods for full-scale MMCs in practical applications.

Mission Profile-Based System-Level Reliability Prediction Method for Modular Multilevel Converters

Dynamic resistance measurement (DRM) was proved to be an important method for condition assessment of contact sets of SF6 circuit breakers (CBs). The erosion of contact sets due to arc energy is a main factor that influences the electrical lifetime of the extinguishing chamber. In this study, a high-voltage CB synthetic circuit was used to produce fault current for electrical erosion tests on the contact sets. The contact sets were close to the end of their service life after a series of erosion tests. DRMs with DC amperages of 0.5, 1.0, and 1.5 kA were conducted on the two tested breakers in the interval of each erosion test. Five essential diagnostic parameters were extracted from the contact resistance curves as a function of contact travel. The variation of these parameters during the whole erosion tests were investigated to find an effective condition assessment method for contact sets. From the power balance at the contact surface, the material erosion rate due to metal evaporation for each erosion test was calculated and in good agreement to the experimental results. Finally, the contact length and contact resistance were employed as diagnostic parameters for condition assessment of contact sets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2016.0593

Electrical erosion test and condition assessment of SF6 CB contact sets

In this paper, taking the minimum metal conductor usage of an air core reactor as optimization goal, the influence of air ducts width and encapsulation number on thermal efficiency and electromagnetic efficiency are analyzed. Combined with the equation constraint conditions for maximum temperature rise conservation, inductance conservation and structure function of reactor considering the change of air ducts width and encapsulation number, the thermal and electromagnetic combined optimization curves are formed, and design results are achieved based on the initial design parameters. Meanwhile, the temperature field simulation model of the reactor is established and the results verify the correctness of the optimization method. According to the design results, the proportionality factor of metal conductor usage is only 0.61 compared with the initial design parameters of the reactor, which shows that the proposed comprehensive optimization method can significantly reduce conductor usage, improving the metal conductor utilization ratio.

Thermal and Electromagnetic Combined Optimization Design of Dry Type Air Core Reactor

In this paper, a fluid-thermal coupled finite element model is established to calculate the temperature field of dry type air core reactor, the inner structure of reactor is built both considering the calculation accuracy and computation time. According to simulation results, the detailed temperature distribution regularity are given both with and without the rain cover, and it can be achieved that the highest temperature of inner encapsulations are different after adding rain cover, and higher compared to the situation of without the rain cover. At the same time, the reasons are given by analyzing the fluid field around reactor, which is of guiding significance to the design of reactor.

Research on temperature field simulation of dry type air core reactor

Convolutional neural network based on fast Fourier transform and gramian angle field for fault identification of HVDC transmission line

Oil-immersed transformers play an important role in the stable operation of power systems. Aiming at the low accuracy of traditional transformer fault diagnosis methods, a transformer fault diagnosis method using an improved sparrow search algorithm (ISSA) optimised support vector machine (SVM) is proposed. First, use Sin chaotic to initialise the population, then introduce the fusion of Cauchy mutation and opposition-based learning to optimise the selection of the population to improve the SSA global optimisation capability. Secondly, use the improved Sparrow algorithm to optimise the SVM kernel function parameters and penalty coefficients and establish a fault diagnosis model--ISSA-SVM for dissolved gas analysis. Enter the data into ISSA-SVM for fault diagnosis and combine it with K nearest neighbour (KNN) algorithm, gradient boosting decision tree (GBDT), sparrow search optimised deep extreme learning machine (SSA-DELM), SVM, sparrow search algorithm optimised Support vector machine (SSA-SVM), and other diagnostic results are compared. The results show that the fault diagnosis rate of ISSA-SVM is 91.43%, which is 12.86%, 7.14%, 5.71%, 2.86%, and 1.43% higher than that of KNN, GBDT, SSA-DELM, SVM, and SSA-SVM, respectively. It can more accurately judge the current operating state of the transformer. 

Fault diagnosis of oil‐immersed transformers based on the improved sparrow search algorithm optimised support vector machine

A fluid-thermal coupled analysis based on FEM is conducted. The inner structure of the coils is built with consideration of both the structural details and the simplicity; thus, the detailed heat conduction process is coupled with the computational fluid dynamics in the thermal computation of air-core reactors. According to the simulation results, 2D temperature distribution results are given and proved by the thermal test results of a prototype. Then the temperature results are used to calculate the heat flux to predict the detailed heat transfer process in the packages of the reactors. The study in this paper may be useful in the design optimization in air-core reactors.

/pdf/thermal-analysis-of-air-core-power-reactors-4ycf95wjtz.pdf

Thermal Analysis of Air-Core Power Reactors

Dissolved gas analysis in oil is an effective method for early fault diagnosis of transformers. Predicting the concentration of future characteristic gases in the transformer can assist operation and maintenance personnel in judging the operation trend of the transformer and ensure stable operation. In order to improve the prediction accuracy of dissolved gas in transformer oil based on a small number of samples, this paper proposes a VMD‐SMA‐LSSVM combined prediction model by using variational modal decomposition and least square support vector machine optimized by slime mold algorithm. First, use variational modal decomposition to decompose the gas signal. For each subsequence, a combined algorithm based on slime mold optimization and least square support vector machine is used to predict separately. Then the prediction results of each sub‐sequence are superimposed and reconstructed to obtain the final prediction value. The research results show that the prediction results obtained based on this method have better prediction effects than other models of machine learning models, other decomposition methods and optimization methods. The proposed method has good fitting characteristics when predicting seven characteristic gases, which verifies the effectiveness. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Prediction Model of Dissolved Gas in Transformer Oil Based on VMD‐SMA‐LSSVM

In this paper, a fluid-thermal coupled finite element model is established based on the design method of equal height and heat flux, the detailed temperature field distribution results of air core reactor are obtained, it can be found that the innermost and outermost encapsulation temperature rise are lower compared with the inner encapsulation. In order to improve the metal conductor utilization ratio, a thermal load optimization method is proposed by analyzing the heat dissipation characteristics of the innermost and outermost encapsulation, the optimization design results show that the metal conductor utilization ratio is increased obviously, and the temperature field simulation results verified the correctness of the optimization method.

Thermal Optimization for Dry Type Air Core Reactor Base on FEM

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