Recent Advances in Artificial Intelligence Theory and Technology and its Application Prospects in Power System Fault Diagnosis

Authors

  • Yedong Zheng
  • Liang Hu

DOI:

https://doi.org/10.6919/ICJE.202412_10(12).0003

Keywords:

Artificial Intelligence; Industrial Chain; Deep Learning; Power System Fault Diagnosis.

Abstract

In recent years, the development of artificial intelligence (AI) theory and technology has attracted wide attention. AI has become a new focal point in international scientific and technological competition and is considered by countries worldwide as a disruptive technology that will lead major transformations in future military science and technology. This paper first introduces the concept of AI, its development history, classification, and industrial chain structure. It then analyzes the current development and application status of AI in key fields, focusing on intelligent robotics, computer vision, natural language processing, and machine learning. Finally, it discusses the prospects of applying deep learning to enhance power system fault diagnosis.

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References

[1] Feng Tianjin, Xu Jianliang. The Enlightenment of Intelligence Science History [J]. Periodical of Ocean University of China, 2009, 39(05):1042-1046.

[2] Li Xiaoli, Zhang Bo, Wang Kang, Yu Pan. Development and Application of Artificial Intelligence [J]. Journal of Beijing University of Technology, 2020, 46(06):583-590.

[3] Li Mingjie, Tao Hongzhu. The Technical Framework and Application Prospect of Artificial Intelligence Application in the Field of Power Grid dispatching and Control [J].Power System Technology,2020, 44(02):393-400.

[4] Zhang Junming, Yang Weidong. Application of Artificial Intelligence in Composite Materials [J]. Advances in Mechanics,2021,51(04):865-900.

[5] Artificial Intelligence Standardization. Website: http://www.cesi.cn/images/editor/20180124/2018012413 55 28742.pdf.

[6] Wang yang. The Origin and History of Artificial Intelligence [J]. Progress in Meteorological Technology, 2018, 8(01):63.

[7] Li Xiaoli, Zhang Bo, Wang Kang ,Yu Pan .Development and Application of Artificial Intelligence[J].Journal of Beijing University of Technology,2020,46(06):583-590.

[8] World Artificial Intelligence Rule of Law Blue Book[C],2019:158-205.

[9] China AI API Economic White Paper 2020 [C]. IResearch Consulting Series Research Report,2020(10): 180-236.

[10] Qin Mu. Standardization White Paper Released to Guide and Drive the Organic Growth of Artificial Intelligence [N]. Electromechanical Business Daily, 2018-01-29(A03).

[11] Gong Chen. Global Artificial Intelligence Governance: The Arrival of the 'Future' and the New Agenda for Global Governance [J]. International Outlook,2018,10(05):36-55+158-159.

[12] Whobrey, D. Machine Mentality and the Nature of the Ground Relation. Minds and Machines 11, 307–346 (2001).

[13] Shi Xiaolin. Denial of Criminal Subject Qualification of Artificial Intelligence Agents [J]. Journal of Henan University of Science and Technology,2020,38(02):74-82.

[14] Huang Tiejun, Shi Luping. Multimedia Technology Research: 2015- Research Progress and Development Trends of Brain Computing [J]. Chinese Journal of Image and Graphics, 2016, 21(11):1411-1424.

[15] He Dongjian, Huang Huixian, Ke Yineng, Wu Yalan Brain like computers, how to think like humans [N] .Zhejiang Daily,2020-09-04(009).

[16] Gao Yuan. Open up disciplinary boundaries and expand professional space to meet the challenges and opportunities of artificial intelligence [N].New Tsinghua University, 2018-10-26(006).

[17] Wang Lei. Logic of U.S. Competition Strategy Toward China in Artificial Intelligence[J].International Observation,2021(02):103-126.

[18] Liu Zhiyang, Wang Zemin.AI Enabling Entrepreneurship: Comparison of Theoretical Frameworks[J]. Foreign Economics & Management,2020,42(12):3-16.

[19] Zheng Jinwu. The White Paper on Standardization of Artificial Intelligence (2021 Edition) has been released [N] .China Science Journal,2021-08-06(004).

[20] Wang Sui. Panorama map of China's artificial intelligence industry in 21 years [J].Dual Use Technologies & Products, 2021(11):8-19.

[21] Report on the Development of China's AI Basic Data Service Industry [C]. IResearch Consulting Series Research Report, 2020(4):400-425.

[22] Top 10 Trends in Commercial Application of Artificial Intelligence [C]. Boao Forum for Asia,2019:104-107.

[23] Research Report on China's Artificial Intelligence Industry [C]. IResearch Consulting Series Research Report,2020(12):338-440.

[24] Research report on the development of China's AI infrastructure industry [C]. IResearch Consulting Series Research Report,2021(7):236-295.

[25] Report on the Development of China's AI Basic Data Service Industry [C]. IResearch Consulting Series Research Report,2020(4):400-425.

[26] Meng Lingpeng, Tian Cui, Xu Weisheng. The Pervasive Fusion Mode of Artificial Intelligence Empowering Urban Community Governance and Its Implementation Path[J]. The Journal of Shanghai Administration Institute, 2021, 22(02):83-90.

[27] Overview of China's AI industry [J]. Science and Technology in China,2019(01):63-77.

[28] Zhang Lishu. Innovative Application of Artificial Intelligence in Financial Service System [J]. Hebei Finance, 2020(03):4-6+23.

[29] Ye Qin , Xu Xiaolei, Hu Senlin, Zeng Gang, Lu Jialing. Pattern and Impact Factors of Artificial Intelligence Industries Distribution in Yangtze River Delta[J].Resources and Environment in the Yangtze Basin, 2022,31(03): 526-536.

[30] Xu Qi, Zhao Zizhong. Ecological Structure, Application Innovation, and Key Trends of Intelligent Media in China [J]. News and Writing, 2020(08):51-58.

[31] AI Application Scenario Report [N] Beijing Business Daily, 2021-08-06(009).

[32] Yu Hanchao, Liu Huihui, Wei Xiu, Yu Jiang. Analysis on AI R/D policies of developed countries and the suggestion[J]. Science and technology Guide, 2018,36(17):75-82.

[33] Zhang Junfang. Research on the Development of Artificial Intelligence Industries and the Allocation of Global Resources[C]. Proceedings of the 13th China Soft Science Academic Annual Conference,2017: 138-148.

[34] Xu Yangsheng. Intelligent robots lead the development of high-tech Science Times, 2010-08-12.

[35] K. T. Park and D. H. Kim, "Technology trend of smart mobile robot," 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013), 2013, pp. 1149-1151.

[36] Tan Min, Wang Shuo. Research Progress on Robotics[J].Acta Automatica Sinica,2013,39(07):963-972.

[37] Ma Xiangfeng. Hull Surface Measurement Positioning Identification System Design and Identification climbing Robot Research[D]. Huazhong University of Science and Technology,2016.

[38] Zhang Qingdan, Zhang Nan. From IT to RT, robots lead the future of manufacturing [N].China Science Journal, 2015-05-12(005).

[39] Si Jiannan. The Ministry of Industry and Information Technology regulates the development of industrial robots through research and development. China Industry News,2014-02-26(A01).

[40] Tu Hang, Chen Huan. An analysis of the application and development trend of artificial intelligence [J]. Network security technology and application,2019(01):96-97.

[41] Single wave Research on Object Recognition Methods in 3D Point Cloud Data [D]. Shenzhen University, 2016.

[42] Sohn JW, Kim G-W, Choi S-B. A State-of-the-Art Review on Robots and Medical Devices Using Smart Fluids and Shape Memory Alloys. Applied Sciences. 2018; 8(10):1928.

[43] Chu, WS., Lee, KT., Song, SH. et al. Review of biomimetic underwater robots using smart actuators. Int. J. Precis. Eng. Manuf. 13, 1281–1292 (2012).

[44] Hao, Y., Zhang, S., Fang, B. et al. A Review of Smart Materials for the Boost of Soft Actuators, Soft Sensors, and Robotics Applications. Chin. J. Mech. Eng. 35, 37 (2022).

[45] M. D. Lima et al., Electrically, Chemically, and Photonically Powered Torsional and Tensile Actuation of Hybrid Carbon Nanotube Yarn Muscles, Science, 338, 928-932 (2012).

[46] S. H. Kim et al., Harvesting electrical energy from carbon nanotube yarn twist. Science 357, 773–778 (2017).

[47] R. Wang et al., Torsional refrigeration by twisted, coiled, and supercoiled fibers. Science, 366, 216-221 (2019).

[48] H. Chu et al., Unipolar stroke, electroosmotic pump carbon nanotube yarn muscles. Science, 371, 494-498 (2021).

[49] Lee, H., Seichepine, F., & Yang, G. (2020). Microtentacle Actuators Based on Shape Memory Alloy Smart Soft Composite. Advanced Functional Materials, 2002510.

[50] Chee CYK, Tong L, Steven GP. A Review on the Modelling of Piezoelectric Sensors and Actuators Incorporated in Intelligent Structures. Journal of Intelligent Material Systems and Structures. 1998;9(1):3-19.

[51] Lu Dongping. Design of a biomimetic quadruped wheel composite mobile mechanism and research on multi motion mode gait planning [D].University of Science and Technology of China ,2015.

[52] Zhang Chengyu, Guo Sheng, Zhao Fuqun. Motion Analysis and Gait Research of a New Wheel-legged Compound Robot [J].Journal of Mechanical Engineering ,2019,55(15):145-153.

[53] WILCOX B H. ATHLETE An option for mobile lunar landers[C]// IEEE Aerospace Conference, Big City, Montana, USA, 2008:1-8.

[54] Tikanmki A, Mkel T, Pietikinen A, et al. Multi-Robot Systemn for Exploration in an Outdoor Environment[ J]. Robotics and Applications and Telematics 2007, 9(1):563-567.

[55] Qiu Zu. Review of Ultrasonic Ranging Methods and Their Current Challenges. Micromachines 2022, 13, 520.

[56] Ahmed, S.; Kallu, K.D.; Ahmed, S.; Cho, S.H. Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review. Remote Sens. 2021, 13, 527.

[57] X. Tang, Z. Zhang and Y. Qin, "On-Road Object Detection and Tracking Based On Radar and Vision Fusion: A Review," in IEEE Intelligent Transportation Systems Magazine.

[58] Bhatti, A.Q., Wahab, A. & Sindi, W. An overview of 3D laser scanning techniques and application on digitization of historical structures. Innov. Infrastruct. Solut. 6, 186 (2021).

[59] Bi, S.; Yuan, C.; Liu, C.; Cheng, J.; Wang, W.; Cai, Y. A Survey of Low-Cost 3D Laser Scanning Technology. Appl. Sci. 2021, 11, 3938.

[60] Furmonas J, Liobe J, Barzdenas V. Analytical Review of Event-Based Camera Depth Estimation Methods and Systems. Sensors 2022, 22, 1201.

[61] Xiang Xueqin, Pan Zhigeng, Tong Jing. Depth Camera in Computer Vision and Computer Graphics: An Overview[J]. Journal of Frontiers of Computer Science and Technology, 2011,5(06):481-492.

[62] Li Bei, "Study on the Intelligent Selection Model of Fuzzy Semantic Optimal Solution in the Process of Translation Using English Corpus", Wireless Communications and Mobile Computing, 2020.

[63] Hosin Lee, "Application of machine vision techniques for the evaluation of highway pavements in unstructured environments," Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments, 1991, pp. 1425-1428 vol.2.

[64] Chao Li, H., Ming Gao, H. and Wu, L. (2007), "Teleteaching approach for sensor‐based arc welding telerobotic system", Industrial Robot, Vol. 34 No. 5, pp. 423-429.

[65] Ham, M.-J.; Kim, S.; Jo, Y.-J.; Park, C.; Nam, Y.; Yoo, D.-H.; Moon, M. The Effect of a Multimodal Occupational Therapy Program with Cognition-Oriented Approach on Cognitive Function and Activities of Daily Living in Patients with Alzheimer’s Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Biomedicines 2021, 9, 1951.

[66] Ittyerah, M. Emerging Trends in the Multimodal Nature of Cognition: Touch and Handedness. Frontiers in Psychology, 8.

[67] Jiang, D., & Xiao, Y. Modelling on grain size dependent thermomechanical response of superelastic NiTi shape memory alloy. International Journal of Solids and Structures, 2020.

[68] Sun Shuang-shuang, SunGuo-jun, Du Xiao-wei, Wu Jian-sheng. Bending of Viscoelastic Beam with Embedded Shape Memory Alloy Wires[J]. Journal of Shanghai Jiaot Ong Univrsit Y,2002(11):1663-1666.

[69] Song Yaya, Huang Yanfei, Guo Weiling , Xing Zhiguo, Wang Haidou. Research Progress of Doping Modification of Potassium Sodium Niobate-based Lead-free Piezoelectric Ceramics[J].Materials Reports, 2022,36(05):34-43.

[70] Wang, Z., Huangfu, G.. Excellent thermal stability and enhanced piezoelectric performance of Bi(Ni 2/3 Nb 1/3 )O 3 ‐modified BiFeO 3 –BaTiO 3 ceramics. Journal of the American Ceramic Society, 2020.

[71] Wang, S., Shi, Z., Liu, L., Zhou, X., Zhu, L., & Hao, Y. The design of Ti6Al4V Primitive surface structure with symmetrical gradient of pore size in biomimetic bone scaffold. Materials & Design, 2020,108830.

[72] Han, Y., Yang, J. Biomimetic injectable hydrogel microspheres with enhanced lubrication and controllable drug release for the treatment of osteoarthritis. Bioactive Materials, 2021, 6(10):3596–3607.

[73] Wei, S., Qiu, H. Promotion of Color-Changing Luminescent Hydrogels from Thermo to Electrical Responsiveness toward Biomimetic Skin Applications. ACS Nano, 2021,15(6), 10415–10427.

[74] Chen T, Chen L, Pan X, Xue C, Zhang Q. Investigation of Operative Skills and Cranioplasty Complications using Biomimetic Bone (Nano-Hap/Collagen Composites). Brain Science Advances. 2018;4(2):131-140.

[75] Chen Fenglan, Liu Xin, Tie Shengnian. Preparation and Thermal Performance of Nano- Graphene Oxide /Mirabilite Composite Phase Change Materials[J/OL].Journal of the Chinese Ceramic Society:1-10[2022-06-16].

[76] Guo Z, Ye J, Zhang S, Xu L. Effects of Individualized Gait Rehabilitation Robotics for Gait Training on Hemiplegic Patients: Before-After Study in the Same Person. Front Neurorobot. 2022 Mar 8;15:817446.

[77] Chen Teng, Li Yibin, Rong Xuewen. Design and Verification of Real-time Plantar Force Optimization for Quadruped Robots in Dynamic Gait[J].Robot,2019,41(03):307-316.DOI:10.13973/j.cnki.robot.180449.

[78] Xia Bin, Wang Fei. A Computer Vision Development Technology for Cotton Image Analysis [J]. Cotton Processing in China, 2014(05):20-22.

[79] Li Xiaoli, Zhang Bo, Wang Kang, Yu Pan 1.Development and Application of Artificial Intelligence[J]. Journal of Beijing University of Technology,2020,46(06):583-590.

[80] Guan Xiaosheng. Machine Vision and its Application[J]. Automation Panorama,2005(03):88-92.

[81] Zhao Qiaomin. Investment Analysis Report on the Machine Vision Industry [J]. Robot Technology and Applications, 2015(05):12-24.

[82] Chen Chuan, Chen Zhe, Ding Shuanghui. Innovation of Computer Vision Teaching Contents Under Development of Deep Learning[J].Jisuanji Yu Xiandaihua,2020(06):107-113.

[83] Wei Guoyizhe, Chen Siyao, Liu Yutao, Li Xiu. Survey of Underwater Image Enhancement and Restoration Algorithms [J].Application Research of Computers,2021,38(09):2561-2569+2589.

[84] Guo Pengfei. Anchored Fusion of Monte Carlo Non-local Means and Low-rank Tensor[D]. wuyi university, 2018.

[85] Yang Aiping, Wang Jinbing, Yang Bingwang, He Yuqing. Joint Deep Denoising Prior for Image Blind Deblurring [J].Acta Optica Sinica,2018,38(10):143-151.

[86] Zhu Ming, Yang Li-jie. A Printing Image Restoration Method for Multiple Degradation Factors [J].Packaging Engineering,2018,39(19):190-196.

[87] Wang Zongyue, Xia Qiming, Cai Guorong, Su Jinhe, Zhang Jiemin. Image Restoration Based on Adaptive Group Images Sparse Regularization[J].Optics and Precision Engineering,2019,27(12):2713-2721.

[88] Roth, S., & Black, M. J. (n.d.). Fields of Experts: A Framework for Learning Image Priors. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).

[89] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering," in IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, Aug. 2007,

[90] Elad, M., & Aharon, M. (2006). Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745.

[91] Gao Xinbo, Wu Mengjincheng, Wang Haitao, Leng Jiaxu. Recent Advances in Small Object Detection[J].Journal of Data Acquisition and Processing,2021,36(03):391-417.

[92] Sun Zhenana, He Ran, Wang Liang,...Overview of biometrics research[J].Journal of Image and Graphics, 2021,26(06):1254-1329.

[93] Hao Sun ,Zhipeng Deng, Lin Lei. Fast Object Detection in Remote Sensing Images via Light Weight Deep Networks[C]//.Proceedings of the 5th Academic Conference on High Resolution Earth Observation, 2018:2-21.

[94] Shao Jiaqi, Qu Changwen, Li Jianwei. A Performance Analysis of Convolutional Neural Network Models in SAR Target Recognition [J].Radar Sclence and Technology,2018,16(05):525-532.

[95] Andrew G. Howard, Menglong Zhu, Bo Chen. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[J]. Computer Vision and Pattern Recognition.2017.

[96] Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C].Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848-6856.

[97] Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang. Interleaved Group Convolutions for Deep Neural Networks[J]. Computer Vision and Pattern Recognition.2017.

[98] Ren Chao . The Realization of A Face Detection System Based on Adaboost Algorithm With Using DSP[D].Harbin Engineering University,2014.

[99] Girshick R, Donahue J. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.

[100] P. Felzenszwalb, D. McAllester, D.Ramanan A Discriminatively Trained, Multiscale, Deformable Part Model IEEEConference on Computer Vision and Pattern Recognition (CVPR), 2008.

[101] Huang Tongyu, Hu Bingjie, Zhu Tingting. Lightweight object detection method for road traffic scene[J].Modern Electronics Technique ,2022,45(03):88-95.

[102] Xie Yuhong, Xie Yuan, Chen Liang1, Li Cuihua1, Qu Yanyun1.Object Detection in Real-World Hazy Scene[J]. Journal of Computer-Aided Design & Computer Graphics ,2021,33(05):733-745.

[103] Bao Xiaomin, Wang Siqi. Survey of object detection algorithm based on deep learning[J].Transducer and Microsystem Technologies,2022,41(04):5-9.

[104] Hu Fuyuan, Wang Xinjun. Survey Progresson Image Instance Segmentation Methods of Deep Convolutional Neural Network[J].Computer Science ,2022,49(05):10-24.

[105] Tian Xuan, Tian Liang, Ding Qi.Review of Image Semantic Segmentation Based on Deep Learning[J].Journal of Software,2019,30(02):440-468.

[106] Li Pu, Chen Li. Natural Scene Image Segmentation Method Based on Super-pixel Random Walk[J]. Computer Technology and Development,2021,31(12):61-66.

[107] Han Shoudong, Zhao Yong, Tao Wenbing, Sang Nong. Gaussian Super-pixel Based Fast Image Segmentation Using Graph Cuts[J]. Acta Automatica Sinica ,2011,37(01):11-20.

[108] Li Xuehong, Zhao Ying. Research progress on coastline extraction technology based on remote sensing images [J]. Marine surveying and mapping, 2016,36(04):67-71.

[109] Wang Zhengxu, Zhao Wenbin, Cai Yuejiang. Efficient segmentation of hippocampus in brain MRI based on 3D convolutional neural network[J]. China Digital Medicine, 2022,17(01):8-14.

[110] Wang Yingjie, Zhu Jiuqi, Wang Zumin, Bai Fengbo, Gong Jian. Review of applications of natural language processing in text sentiment analysis[J].Journal of Computer Applications,2022,42(04):1011-1020.

[111] Che Wangxiang, Liu Ting. New Paradigm of Natural Language Processing: A Method Based onPre-Trained Models[J]. ZTE Technologies, 2022,28(02):3-9.

[112] Zhang Qinli. Natural language processing tends towards greater intelligence [N]. Chinese Journal of Social Sciences,2015-07-15(002).

[113] Li Deyi, Yu Jian, China Association of Artificial Intelligence, Introduction to Artificial Intelligence in the New Generation Information Technology Series of China Association for Science and Technology, China Science and Technology Press, 2018.08.

[114] Chen Deguang, Ma Jinlin, Ma Zilin, Zhou Jie. Review of Pre-training Techniques for Natural Language Processing[J].Journal of Frontiers of Computer Science and Technology,2021,15(08):1359-1389.

[115] Yu Tongrui, Jin Ran, Han Xiaoqin, Li Jiahui, Yu Ting. Review of Pre-training Models for Natural Language Processing[J].Computer Engineering and Applications,2020,56(23):12-22.

[116] Gao Bo. Evidence Analysis of Uncertain Artificial Intelligence Data[J]. Journal of Jinan University,2022,44(02):73-82.

[117] Arthur, S. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 1959, 3: 211-229.

[118] Wang Yian, Zhao Wenjun, Liang Lianguo. A Review on Optimal Design of Fluid Machinery Based on Machine Learning Method[J].Chinese Journal of Turbomachinery,2020,62(05):77-90.

[119] Liu Yan, Zhou Shuse.Discriminative Deep Belief Networks for Visual Data Classification[J].Pattern Recognition, 2010, 44(10):2287-2297.

[120] Luo H, Shen R, Niu C, et al. Sparse Group Restricted Boltzmann Machines [C]//Proceedings of the AAAI,2011.

[121] Yu Dong, Deng Li. Deep Convex net: a Scalable Architecture for Speech Pattern Classification[C] //Proc of the 12th Annual Conference of International Speech Commnication Association. 2011:2296-2299.

[122] Huang G B, Lee H, LEARNED MILLER E. Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks[C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition.2012:2518-2525.

[123] Zhou Shu sen, Chen Qing cai, Wang Xiao long. Convolutional Deep Networks for Visual Data Classification [J]. Neural Process Lett, 2013,38:17 -27.

[124] He K, Zhang X, Ren S, etal. Spatial Pyramid Pooling[M]/ /Computer Vision -ECCV 2014:13th European Conference, Zurich, Switzerland, Set 6-12, 2014, Proceedings, Part Ⅲ, 2014:346-361.

[125] Wong W K, Sun M. Deep Learning Regularized Fisher Mappings [J]. IEEE Trans on Neural Networks,2011 ,22(10) :1668-1675.

[126] Collobert RT R. Deep Learning for Efficient Discriminative Parsing [C] //Proc of the 14th International Conference on Artificial Intelligence and Statistics.2011 :224 - 232.

[127] Hinton G E, Srivastava N, Krizhevsky A, et al. Improving Neural Networks by Preventing Co-adaption of Features Detectors [J] .arXiv Preprint arXiv:1207 ,2012.

[128] Duchi J, Hazan E, Singer Y. Adaptive Subgradient Methods for Online I earning and Stochastic Optimization[J]. Journal of Machine Learning Research,2011,12(2) :2121 -2159.

[129] Sun Zhijun, Xue Lei, Xu Yangming.Marginal Fisher Feature Extraction Algorithm Based on Deep Learning[J].Journal of Electronics & Information Technology,2013,35(4):805:811.

[130] Zhou Shu-sen, Chen Qing-cai, Wang Xiao-long. Active Deep Learning Method for Semi-supervised Sentiment Classification[J]. NeuroComputing,2013,120:536-547.

[131] Tom A. Stanford Algorithm Analyzes Sentenence Sentiment, Advanced Machine Learning [N].Stanford University ,2013.

[132] SCHAUL T,ZHANG S,Le CUN Y. No More Pesky Learning Rates [C] / /Proc of International Conference on Machine Learning.2013: 343-351.

[133] Wang Jingdong, Zhang Ting, Roger Bo. Communication of the Chinese Computer Society.2015, P72.

[134] Zhang J ,Wang Y ,Yang Y , et al. Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization[J]. Thermal Science and Engineering Progress,2024,55.

[135] Wu H ,Arellano D R A ,Martín O D , et al. Channel parallel virus machine for power system fault diagnosis[J]. Journal of Membrane Computing,2024(prepublish).

[136] Wu Yue, Lv Jing. Analysis of Optimization of Power System Fault Diagnosis and Prediction Model Based on Deep Learning[J].Electronic Technique,2024,53(07):226-227.

[137] Pi Xiaoliang. Power System Fault Diagnosis Technology Based on Big Data [J]. China Paper Industry, 2024, 45(07): 121-124.

[138] Yang Jie, Wu Hao, Dong Xingxing. Transmission line fault type identification based on the characteristics of current fault components and random forest[J]. Power System Protection and Control, 2021,49(13):53-63.

[139] Zhou Yu. Analysis of Fault Diagnosis and Recovery Mechanism in Power System Based on Intelligent Technology[J]. Integrated Circuit Applications,2024,41(07):140-141.

[140] Tang Wenhu, Huang Wenwei, Guo Caishan .Exploration on Theoretical and Technological Framework of Distributed Smart Grid[J/OL]. Power System Technology:1-13[2024-10-15].

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2024-11-19

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Zheng, Yedong, and Liang Hu. 2024. “Recent Advances in Artificial Intelligence Theory and Technology and Its Application Prospects in Power System Fault Diagnosis”. International Core Journal of Engineering 10 (12): 18-33. https://doi.org/10.6919/ICJE.202412_10(12).0003.