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

Under complex drilling working conditions, it is difficult to completely describe the working state of the drilling pump fluid end with a single-source signal to achieve a high fault diagnosis accuracy. This paper proposes to use dual-source signals (vibration signal and strain signal) to describe the working state of the fluid end. Especially, a dual-source gramian angular field (DS-GAF) method is proposed to fuse vibration-strain signals to provide more fault information. In DS-GAF method, the dual-source signal is converted to points in spherical coordinates, and the arccosine of the normalized amplitude of the two signals is assumed as the polar angle and the azimuthal angle, respectively. Three DS-GAF matrices are generated with the position of the points. These three matrices are respectively encoded as three colors (red, green, and blue) channels of the image to achieve signal fusion. A fault diagnosis method based on DS-GAF and ResNet for the drilling pump fluid end is proposed to improve the fault diagnosis accuracy under multiple working conditions. First, by the DS-GAF method, the vibration-strain signals of the fluid end are fused and converted into a fault diagnosis image dataset. Second, a fault diagnosis model based on ResNet is established and the effect of batch size on the performance of the fault diagnosis model is discussed. Finally, fault diagnosis experiments are carried out on the proposed fault diagnosis model and its performance is investigated. The results show that the diagnosis method proposed in this paper has the highest diagnostic accuracy of 98.97% for nine fault types under multiple operating conditions, with a maximum improvement of 18.91% compared to the original GAF method. The DS-GAF method for signal fusing proposed in this paper not only improves the accuracy of fluid end diagnosis, but also provides a reference for the signal-fusion diagnosis of other equipment. 

Dual-Source Gramian Angular Field Method and Its Application on Fault Diagnosis of Drilling Pump Fluid End

http://methods-x.com/article/S2215016123000237/pdf

A protection method for multi-terminal HVDC system based on fuzzy approach

High voltage direct current (HVDC) transmission systems represent a significant development for future power systems due to presenting promising solutions for long-distance power transmission. However, the protection of HVDC systems is becoming one of the most significant challenges due to the extremely high short-circuit current within a short time span and the lack of zero crossing in the DC systems. DC protection schemes must detect and isolate the fault within 4–6 ms, while AC protection systems operate with considerably longer response times. Therefore, the techniques utilized for HVDC protection systems require more attention to enhance their performance in the future. This paper provides a comprehensive review of various protection techniques for HVDC systems, highlights recent advances, and analyzes the pros and cons of each method. Initially, different challenges of HVDC protection systems are presented, and then various HVDC protection schemes are discussed and classified into two groups based on the operation time. After that existing protection schemes are investigated in detail by including practical examples. The review aims to close the gap between the presented HVDC protection methods and technical challenges in order to overcome future issues of power system protection. Moreover, signal processing methods and artificial intelligence (AI) techniques, which play a key role in the future of protection systems, are extensively investigated to highlight the possible solutions. Finally, various recommendations, key challenges, and future trends with respect to the development of HVDC protection schemes have been presented for researchers and engineers studying in this field. 

A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems

Accurate recognition of transmission line (TL) faults plays a vital role in protection systems, avoiding misoperations and false trips. Several studies in this area have revealed the limitations of existing algorithms, particularly when considering more complex electrical power systems (EPS), where some expected operations (capacitor and load switching, for example) can be misinterpreted as fault events. This work addresses the critical issue of fault recognition (FR) in TLs with a special focus on distinguishing faults from other types of disturbances. The primary objective of this study is to introduce an approach based on Convolutional Neural Networks (CNN) and Continuous Wavelet Transform (CWT) for FR in EPS. This method relies only on voltage signals acquired with a reasonable sampling frequency. The proposed method is enhanced with an Independent Component Analysis (ICA)-based preprocessing, making it robust and versatile in handling different types of disturbances, such as adjacent line faults, load switching and capacitor switching. The evaluation of the proposed method is done considering two different EPS, achieving an accuracy up to 99 %, showing the robustness of the method against different scenarios such as fault events and switching operations. 

A fault recognition method for transmission systems based on independent component analysis and convolutional neural networks

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

As the penetration of renewable energy sources (RESs) in the grid increases, the requirements for Automatic Generation Control (AGC) frequency regulation and reserve services should also be adjusted. RESs have great potential to provide auxiliary services, however, RESs also face the problem of revenue trade-off between participation in the electricity energy market and the auxiliary frequency regulation service market. Opportunity cost can help decision-makers make a reasonable allocation of resources and maximize the theoretical benefits. In this context, an opportunity cost calculation model for RESs in delivering AGC service is provided in this paper. The calculation process of the opportunity cost for RESs to provide AGC auxiliary services is proposed in this paper. To analysis the opportunity cost of the RES plants providing frequency adjustment service, the modified IEEE 6-bus system with two thermal power plants, two wind power plants, and one photovoltaic power plant, is adopted in this paper. Firstly, the clearing results of the electric market in the system is adopted. Then, the opportunity cost of the RESs with AGC is calculated. Finally, by comparing the opportunity cost per unit of capacity of frequency adjustment service and the AGC compensation price in the North Grid of China and the Northwest Grid of China, the optimal decision of AGC participation factor $a$ can be obtained. Case studies reveal that the marginal opportunity cost of AGC capacity for RESs increase with the growth of the AGC capacity. As a result, total revenue of the RESs reaches the maximum value at an appropriate ratio of AGC capacity, and this ratio is the optimal decision.

Opportunity cost analysis of renewable energy participation in AGC frequency regulation

Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.

/pdf/loss-prediction-of-ultrahigh-voltage-transmission-lines-yzf2h2oc.pdf

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

As a key power system primal frequency regulation (PFR) resource, the PFR performance evaluation of thermal power units has a significant meaning. However, most PFR performance evaluation depends on the unit performance test which leads to a result different from the actual operation performance. In this paper, a PFR performance evaluation based on frequency regulation data segment identification in daily operation is proposed. Firstly, the ramping data segments from daily operating data are selected by the improved swinging door algorithm. Secondly, PFR data segments are identified by three rules. Thirdly, three evaluation indicators including the response time, the adjustment coefficient, and the stable time of the units are calculated and a comprehensive index is formulated to evaluate the PFR performance of thermal power units by the entropy-osculating value method. Finally, the effectiveness of the proposed method is verified by a practical case.

Primary frequency regulation performance evaluation of thermal power units based on frequency regulation data segment identification using improved swinging door algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Zhenyi Wang

Author Tools

Chat about Author

Papers

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

Opportunity cost analysis of renewable energy participation in AGC frequency regulation

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

Primary frequency regulation performance evaluation of thermal power units based on frequency regulation data segment identification using improved swinging door algorithm