|
|
Abstract With the rapid development of artificial intelligence, embedded computing, and Internet of Things technologies, the intelligence of power electronics systems is gradually moving from conceptual exploration toward practical implementations. This paper systematically examines the potential, implementation pathways, and future application prospects of edge intelligence in the domain of power electronics, aiming to provide both a theoretical foundation and a practical framework for the development of new generation power electronics systems.
This paper first examines the potential for edge intelligence in power electronics from the perspectives of the available data resources sampled naturally by the embedded sensors and the computational resources installed inside the equipment. On one hand, power electronics systems operate at high-frequency sampling, generating massive amounts of operational data that have yet to be fully analyzed or utilized. On the other hand, the digital signal processors (DSP) embedded in these equipment continuously upgrade in the past few decades, whose computational capability is far beyond the control consumption, in consequence, a large portion of computational resources remains underutilized while only real-time control tasks are executed. Therefore, the abundant data, coupled with redundant computational resources, provides the foundation for implementing edge intelligence in power electronics systems.
Building on this foundation, the paper proposes three typical implementation pathways for edge intelligence of power electronics: (i) a lightweight pathway based on conventional processors, which leverages the residual computational and storage resources of DSPs to perform light tasks such as intelligent power conversion control and status monitoring; (ii) a computationally enhanced pathway based on external AI chips, embedding high-performance AI processors in power electronicsequipment to enable complex intelligent reasoning and decision-making while ensuring the real-time performance of high-frequency control; and (iii) a multi-equipment collaborative pathway based on the Internet of Things, which promotes the transition of edge intelligence from individual equipment to coordinated groups through equipment interconnection and resource sharing. Subsequently, three case studies—intelligent inverter control, DC arc detection, and intelligent completion of missing data—are presented to simply demonstrate the engineering feasibility of these pathways under different computational conditions and application scenarios.
Furthermore, the paper presents an application-oriented perspective on edge intelligence across three levels. At the equipment level, it highlights the evolution from conventional control units to intelligent nodes endowed with autonomous sensing, collaborative decision-making, and security protection capabilities, thereby enabling intelligent functions of power electronics equipment. At the system level, the focus is on multi-equipment coordination and intelligent optimization to achieve system-wide adaptive control, efficient resource allocation, and enhanced overall operational performance, which could serve the intelligent operation of renewable generation station, smart factory, and etc. At the smart city level, it emphasizes the deployment of edge intelligence for real-time energy consumption monitoring, carbon emission management, and rapid emergency response, ultimately driving urban infrastructure toward greater intelligence, sustainability, resilience, and safety.
Overall, this paper provides the theoretical framework, implementation pathways, and application value of edge intelligence in power electronics systems, offering significant potential for practical engineering applications. Edge intelligence in power electronics shares both similarities and distinctions with edge intelligence in other domains. Advances in this field could provide valuable insights for intelligent systems in other areas.
|
|
Received: 01 August 2025
|
|
|
|
|
|
[1] Yuksekgonul M, Bianchi F, Boen J, et al.Optimizing generative AI by backpropagating language model feedback[J]. Nature, 2025, 639(8055): 609-616.
[2] Feng Shuo, Sun Haowei, Yan Xintao, et al.Dense reinforcement learning for safety validation of autonomous vehicles[J]. Nature, 2023, 615(7953): 620-627.
[3] Han Yaning, Chen Ke, Wang Yunke, et al.Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework[J]. Nature Machine Intelligence, 2024, 6(1): 48-61.
[4] Tashakori A, Jiang Zenan, Servati A, et al.Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves[J]. Nature Machine Intelligence, 2024, 6(1): 106-118.
[5] Ryan L.Edge Computing[R]. Richland, WA: Pacific Northwest National Laboratory, 2013.
[6] Edge Computing Consortium and Alliance of Industrial Internet: White Paper on Edge Computing Collaboration[EB/OL]. https://www.ecconsortium.org/Uploads/file/20190221/1550718911180625.pdf.
[7] Jennings A, Copenhagen R V, Rusmin T.Aspects of network edge intelligence[R]. Maluku Technical Report, 2001: 1-14.
[8] Zhou Zhi, Chen Xu, Li En, et al.Edge intelligence: paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019, 107(8): 1738-1762.
[9] Mhapsekar R U, Abraham L, Davy S, et al.Application adaptive light-weight deep learning (AppAdapt-LWDL) framework for enabling edge intelligence in dairy processing[J]. IEEE Transactions on Mobile Computing, 2025, 24(2): 1105-1119.
[10] Gong Taiyuan, Zhu Li, Yu F R, et al.Edge intelligence in intelligent transportation systems: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 8919-8944.
[11] Li Yehui, Qin Dalin, Poor H V, et al.Introducing edge intelligence to smart meters via federated split learning[J]. Nature Communications, 2024, 15: 9044.
[12] Li En, Zhou Zhi, Chen Xu.Edge intelligence: on-demand deep learning model co-inference with device-edge synergy[C]//Proceedings of the 2018 Workshop on Mobile Edge Communications, Budapest, Hungary, 2018: 31-36.
[13] Wang Xiaofei, Han Yiwen, Wang Chenyang, et al.In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156-165.
[14] Straits Research. Power Electronics Market Size, Share & Trends Analysis Report By Device Type (Power Discrete, Power Module, Power IC), By Material (Silicon Carbide, Gallium Nitride, Sapphire, Other), By Application (Power Management, UPS, Transportation, Renewable, Other), By Industry Vertical (Telecommunication, Industrial, Automotive, Consumer electronics, Military & Defense, Energy & Power, Other) andBy Region (North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2025-2033[EB/OL].[2025-05-20]. https://straitsresearch. com/report/power-electronics-market.
[15] Li Shuhui, Sun Yang, Ramezani M, et al.Artificial neural networks for volt/VAR control of DER inverters at the grid edge[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 5564-5573.
[16] Dong Weizhen, Li Shuhui, Fu Xingang, et al.Control of a Buck DC/DC converter using approximate dynamic programming and artificial neural networks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(4): 1760-1768.
[17] Nie Kaizhe, Gao Feng, Wang Hanzhi, et al.Self-tuning ANN controller for grid-connected parallel-inverter[C]//2024 IEEE 9th Southern Power Electronics Conference (SPEC), Brisbane, Australia, 2024: 1-4.
[18] Maruta H, Ikeda Y, Watanabe S, et al.Implementation of neural network and bias correction controls for fast transient response of DC-DC converter[J]. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2024, 5(2): 392-401.
[19] Nie Kaizhe, Gao Feng.Single-phase grid-connection inverter control strategy based on echo state network[C]//2023 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Chongqing, China, 2023: 1328-1333.
[20] Soliman H, Abdelsalam I, Wang Huai, et al.Artificial Neural Network based DC-link capacitance estimation in a diode-bridge front-end inverter system[C]//2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017-ECCE Asia), Kaohsiung, Taiwan, China, 2017: 196-201.
[21] Soualhi A, Makdessi M, German R, et al.Heath monitoring of capacitors and supercapacitors using the neo-fuzzy neural approach[J]. IEEE Transactions on Industrial Informatics, 2018, 14(1): 24-34.
[22] Huang Zhanjun, Wang Zhanshan, Yao Xianshuang, et al.Multi-switches fault diagnosis based on small low-frequency data for voltage-source inverters of PMSM drives[J]. IEEE Transactions on Power Electronics, 2019, 34(7): 6845-6857.
[23] Qin Caiyun, Gao Feng, Zhou Kangjia.Data-driven online stability monitoring of grid-following inverters in weak grid[J]. IEEE Transactions on Industrial Informatics, 2025, 21(6): 4554-4564.
[24] Yao Chunxing, Xu Shuai, Ren Guanzhou, et al.Online open-circuit fault diagnosis for ANPC inverters using edge-based lightweight two-dimensional CNN[J]. IEEE Transactions on Power Electronics, 2024, 39(4): 3979-3984.
[25] Yan Junchen, Li Qiqi, Duan Shanxu.A simplified current feature extraction and deployment method for DC series arc fault detection[J]. IEEE Transactions on Industrial Electronics, 2024, 71(1): 625-634.
[26] Ge Leijiao, Xian Yiming, Yan Jun, et al.A hybrid model for short-term PV output forecasting based on PCA-GWO-GRNN[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(6): 1268-1275.
[27] Hossain M S, Mahmood H.Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast[J]. IEEE Access, 2020, 8: 172524-172533.
[28] Meng Xiangjian, Gao Feng, Xu Tao, et al.Inverter-data-driven second-level power forecasting for photovoltaic power plant[J]. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7034-7044.
[29] 朱琴跃, 于逸尘, 占岩文, 等. 基于短时傅里叶变换和深度网络的模块化多电平换流器子模块IGBT开路故障诊断[J]. 电工技术学报, 2024, 39(12): 3840-3854.
Zhu Qinyue, Yu Yichen, Zhan Yanwen, et al.IGBT open-circuit fault diagnosis of modular multilevel converter sub-module based on short-time Fourier transform and deep networks[J]. Transactions of China Electrotechnical Society, 2024, 39(12): 3840-3854.
[30] 崔曼, 胡震, 张腾飞, 等. 基于壳温信息的功率器件可靠性分析[J]. 电工技术学报, 2023, 38(24): 6760-6767.
Cui Man, Hu Zhen, Zhang Tengfei, et al.Reliability analysis of power device based on the case temperatures[J]. Transactions of China Electrotechnical Society, 2023, 38(24): 6760-6767.
[31] Wang Rui, Sun Qiuye, Zhang Huaguang, et al.Stability-oriented minimum switching/sampling frequency for cyber-physical systems: grid-connected inverters under weak grid[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 69(2): 946-955.
[32] He Shan, Zhou Dao, Wang Xiongfei, et al.Line voltage sensorless control of grid-connected inverters using multisampling[J]. IEEE Transactions on Power Electronics, 2022, 37(4): 4792-4803.
[33] Fang Jingyang, Gao Feng, Goetz S M.Symmetries in power electronics and lattice converters[J]. IEEE Transactions on Power Electronics, 2023, 38(1): 944-955.
[34] Bejerke S.Digital signal processing solutions for motor control using the TMS320F240 DSP-Controller[R]. Paris: ESIEE, 1996.
[35] Texas Instruments.TMS320C6652 and TMS320C6654 fixed and floating-point digital signal processor[R]. Dalas: TeXas Instruments, 2019.
[36] Abdi H, Williams L J.Principal component analysis[J]. Wiley interdisciplinary reviews: computational statistics, 2010, 2(4): 433-459.
[37] Wang Wei, Huang Yan, Wang Yizhou, et al.Generalized autoencoder: a neural network framework for dimensionality reduction[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 2014: 490-497.
[38] Rui Lanlan, Yang Siqi, Chen Shiyou, et al.Smart network maintenance in an edge cloud computing environment: an adaptive model compression algorithm based on model pruning and model clustering[J]. IEEE Transactions on Network and Service Management, 2022, 19(4): 4165-4175.
[39] Wang Naigang, Choi J, Brand D, et al.Training deep neural networks with 8-bit floating point numbers[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18), Montreal, Canada, 2018: 1-10.
[40] Shen Haolan, Tang Xin, Luo Yifei, et al.Online open-circuit fault diagnosis for neutral point clamped inverter based on an improved convolutional neural network and sample amplification method under varying operating conditions[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 3512612.
[41] Xing Zhikai, He Yigang, Zhang Weiwei.An online multiple open-switch fault diagnosis method for T-type three-level inverters based on multimodal deep residual filter network[J]. IEEE Transactions on Industrial Electronics, 2023, 70(10): 10669-10679.
[42] 单南良, 徐兴华, 鲍先强, 等. 基于边缘智能的电磁能装备轻量化故障诊断方法[J]. 电工技术学报, 2025, 40(3): 821-831.
Shan Nanliang, Xu Xinghua, Bao Xianqiang, et al.The lightweight fault diagnosis method of electromagnetic energy equipment based on edge intelligence[J]. Transactions of China Electrotechnical Society, 2025, 40(3): 821-831.
[43] 徐小华, 周长兵, 胡忠旭, 等. 轻量级深度神经网络模型适配边缘智能研究综述[J]. 计算机科学, 2024, 51(7): 257-271.
Xu Xiaohua, Zhou Zhangbing, Hu Zhongxu, et al.Lightweight deep neural network models for edge intelligence: a survey[J]. Computer Science, 2024, 51(7): 257-271.
[44] de Arquer Fernández P, Fernández M Á, Carús Candás J L, et al. An IoT open source platform for photovoltaic plants supervision[J]. International Journal of Electrical Power & Energy Systems, 2021, 125: 106540.
[45] Hossain M L, Abu-Siada A, Muyeen S M, et al.Industrial IoT based condition monitoring for wind energy conversion system[J]. CSEE Journal of Power and Energy Systems, 2021, 7(3): 654-664.
[46] Sikimić M, Amović M, Vujović V, et al.An overview of wireless technologies for IoT network[C]//2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, 2020: 1-6.
[47] Khanh Q V, Hoai N V, Manh L D, et al.Wireless communication technologies for IoT in 5G: vision, applications, and challenges[J]. Wireless Communications and Mobile Computing, 2022, 2022(1): 3229294.
[48] Petrariu A I, Lavric A.SigFox wireless communication enhancement for Internet of Things: a study[C]//2021 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, 2021: 1-4.
[49] Gkotsiopoulos P, Zorbas D, Douligeris C.Performance determinants in LoRa networks: a literature review[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1721-1758.
[50] Al-Falahy N, Alani O Y.Technologies for 5G networks: challenges and opportunities[J]. IT Professional, 2017, 19(1): 12-20.
[51] Shafi M, Molisch A F, Smith P J, et al.5G: a tutorial overview of standards, trials, challenges, deployment, and practice[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(6): 1201-1221.
[52] Xing E P, Ho Q, Xie Pengtao, et al.Strategies and principles of distributed machine learning on big data[J]. Engineering, 2016, 2(2): 179-195.
[53] Yun S, Kang J M, Choi S, et al.Cooperative inference of DNNs over noisy wireless channels[J]. IEEE Transactions on Vehicular Technology, 2021, 70(8): 8298-8303.
[54] Li M, Andersen D G, Park J W, et al.Scaling distributed machine learning with the parameter server[C]//11th USENIX Symposium on operating systems design and implementation (OSDI 14), Berkeley, LA, USA, 2014: 583-598.
[55] Nie Kaizhe, Gao Feng, Jiang Yu.A lightweight ANN controller for grid-tied inverters with strong adaptability[J]. IEEE Open Journal of Power Electronics, 2025, 6: 1803-1814.
[56] Yan Junchen, Li Qiqi, Duan Shanxu.A simplified current feature extraction and deployment method for DC series arc fault detection[J]. IEEE Transactions on Industrial Electronics, 2024, 71(1): 625-634.
[57] Zhou Kangjia, Gao Feng, Hou Zhenyu, et al.Power conversion Internet of Things: architecture, key technologies and prospects[J]. IEEE Transactions on Industrial Informatics, 2024, 20(8): 10587-10598.
[58] Zhou Kangjia, Gao Feng, Zhang Yusen, et al.Missing data imputation for photovoltaic station based on spatiotemporal features[C]//2024 CPSS & IEEE International Symposium on Energy Storage and Conversion (ISESC), Xi’an, China, 2024: 355-359. |
|
|
2026, Vol. 41 
