Efficiency Optimized Design of Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning

WANG Jianing; YANG Renhai; YAO Zhanghao; PENG Qiang; XIE Lüwei

doi:10.11999/JEIT221059

Volume 45 Issue 9

Sep. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(9): 3311-3320

WANG Jianing, YANG Renhai, YAO Zhanghao, PENG Qiang, XIE Lüwei. Efficiency Optimized Design of Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3311-3320. doi: 10.11999/JEIT221059

Citation:

WANG Jianing, YANG Renhai, YAO Zhanghao, PENG Qiang, XIE Lüwei. Efficiency Optimized Design of Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3311-3320. doi: 10.11999/JEIT221059

Citation:

WANG Jianing, YANG Renhai, YAO Zhanghao, PENG Qiang, XIE Lüwei. Efficiency Optimized Design of Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3311-3320. doi: 10.11999/JEIT221059

PDF( 8003 KB)

Efficiency Optimized Design of Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning

doi: 10.11999/JEIT221059

School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (52077051), The Institute of Energy, Hefei Comprehensive National Science Center Project (21KZS203), The Program of Introducing Talents of Discipline to Universities (BP0719039)

Received Date: 2022-08-11
Rev Recd Date: 2023-06-07

Available Online: 2023-06-12

Publish Date: 2023-09-27

Abstract

Abstract

The traditional power electronic converter design adopts mostly the sequential design method, which relies on manual experience. In recent years, power electronics automation design has attracted much attention by optimizing rapidly the design of power electronic systems with computers. Taking the efficiency optimized design of Active Neutral Point Clamped (ANPC) inverter as an example, a power electronics automation design method based on Deep Reinforcement Learning (DRL) is proposed, which can realize quickly to obtain the optimal design parameters according to the design objectives when the design requirements of converter change. Firstly, the overall framework of inverter efficiency optimization based on DRL is introduced; Then the efficiency model of the inverter is established; After that the agent is continuously trained through the self-learning of the Deep Deterministic Policy Gradient (DDPG) algorithm, and an optimization strategy that minimizes power loss is obtained; The strategy can quickly respond to design specification changes and provide design variables that maximize efficiency; Finally, a 140 kW experimental prototype is built, and the effectiveness of the proposed method is verified by the experimental results, which demonstrates efficiency improvements of 0.025 % and 0.025 % respectively compared to genetic algorithm and Reinforcement Learning (RL).
- Power electronics automation design,
- Active Neutral Point Clamped (ANPC) inverter,
- Efficiency,
- Deep Reinforcement Learning (DRL)

FullText(HTML)

References(20)

References

[1]	BURKART R M and KOLAR J W. Comparative η-ρ-σ Pareto optimization of Si and SiC multilevel dual-active-bridge topologies with wide input voltage range[J]. IEEE Transactions on Power Electronics, 2017, 32(7): 5258–5270. doi: 10.1109/TPEL.2016.2614139
[2]	YU Ruiyang, PONG B M H, LING B W K, et al. Two-stage optimization method for efficient power converter design including light load operation[J]. IEEE Transactions on Power Electronics, 2012, 27(3): 1327–1337. doi: 10.1109/TPEL.2011.2114676
[3]	BUSQUETS-MONGE S, SOREMEKUN G, HEFIZ E, et al. Power converter design optimization[J]. IEEE Industry Applications Magazine, 2004, 10(1): 32–38. doi: 10.1109/MIA.2004.1256250
[4]	袁立强, 陆子贤, 戴宇轩, 等. 高性能电力电子设计自动化求解器关键因素与解决方法[J]. 中国电机工程学报, 2021, 41(20): 7055–7068. doi: 10.13334/j.0258-8013.pcsee.202413 YUAN Liqiang, LU Zixian, DAI Yuxuan, et al. Key factors and methodology of high-performance design automation solver for power electronics[J]. Proceedings of the CSEE, 2021, 41(20): 7055–7068. doi: 10.13334/j.0258-8013.pcsee.202413
[5]	DE LEÓN-ALDACO S E, CALLEJA H, and AGUAYO ALQUICIRA J. Metaheuristic optimization methods applied to power converters: A review[J]. IEEE Transactions on Power Electronics, 2015, 30(12): 6791–6803. doi: 10.1109/TPEL.2015.2397311
[6]	BIELA J, KOLAR J W, STUPAR A, et al. Towards virtual prototyping and comprehensive multi-objective optimisation in power electronics[C]. The Power Conversion and Intelligent Motion Conference Europe, Nuremberg, Germany, 2010: 515–538.
[7]	BURKART R M and KOLAR J W. Comparative life cycle cost analysis of Si and SiC PV converter systems based on advanced η-ρ-σ multiobjective optimization techniques[J]. IEEE Transactions on Power Electronics, 2017, 32(6): 4344–4358. doi: 10.1109/TPEL.2016.2599818
[8]	赵斌, 王刚, 宋婧妍, 等. 基于粒子群算法的LCLC谐振变换器优化设计[J]. 电子与信息学报, 2021, 43(6): 1622–1629. doi: 10.11999/JEIT190337 ZHAO Bin, WANG Gang, SONG Jingyan, et al. Optimal design method of the LCLC resonant converter based on particle-swarm-optimization algorithm[J]. Journal of Electronics &Information Technology, 2021, 43(6): 1622–1629. doi: 10.11999/JEIT190337
[9]	LI Xinze, ZHANG Xin, LIN Fanfan, et al. Artificial-intelligence-based design for circuit parameters of power converters[J]. IEEE Transactions on Industrial Electronics, 2022, 69(11): 11144–11155. doi: 10.1109/TIE.2021.3088377
[10]	CHEN Xin, QU Guannan, TANG Yujie, et al. Reinforcement learning for selective key applications in power systems: Recent advances and future challenges[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 2935–2958. doi: 10.1109/TSG.2022.3154718
[11]	陈学松, 杨宜民. 强化学习研究综述[J]. 计算机应用研究, 2010, 27(8): 2834–2838,2844. doi: 10.3969/j.issn.1001-3695.2010.08.006 CHEN Xuesong and YANG Yimin. Reinforcement learning: Survey of recent work[J]. Application Research of Computers, 2010, 27(8): 2834–2838,2844. doi: 10.3969/j.issn.1001-3695.2010.08.006
[12]	LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[C]. The 4th International Conference on Learning Representations, San Diego, America, 2016.
[13]	乔骥, 王新迎, 张擎, 等. 基于柔性行动器–评判器深度强化学习的电–气综合能源系统优化调度[J]. 中国电机工程学报, 2021, 41(3): 819–832. doi: 10.13334/j.0258-8013.pcsee.201704 QIAO Ji, WANG Xinying, ZHANG Qing, et al. Optimal dispatch of integrated electricity-gas system with soft actor-critic deep reinforcement learning[J]. Proceedings of the CSEE, 2021, 41(3): 819–832. doi: 10.13334/j.0258-8013.pcsee.201704
[14]	TANG Yuanhong, HU Weihao, CAO Di, et al. Artificial intelligence-aided minimum reactive power control for the DAB converter based on harmonic analysis method[J]. IEEE Transactions on Power Electronics, 2021, 36(9): 9704–9710. doi: 10.1109/TPEL.2021.3059750
[15]	TANG Yuanhong, HU Weihao, ZHANG Bin, et al. Deep reinforcement learning-aided efficiency optimized dual active bridge converter for the distributed generation system[J]. IEEE Transactions on Energy Conversion, 2022, 37(2): 1251–1262. doi: 10.1109/TEC.2021.3126754
[16]	SUTTON R S and BARTO A G. Reinforcement Learning: An Introduction[M]. 2nd ed. Cambridge: MIT Press, 2018: 47–50.
[17]	JIAO Yang and LEE F C. LCL filter design and inductor current ripple analysis for a three-level NPC grid interface converter[J]. IEEE Transactions on Power Electronics, 2015, 30(9): 4659–4668. doi: 10.1109/TPEL.2014.2361907
[18]	WANG Jianing, XUN Yuanwu, LIU Xiaohui, et al. Soft switching circuit of high-frequency active neutral point clamped inverter based on SiC/Si hybrid device[J]. Journal of Power Electronics, 2021, 21(1): 71–84. doi: 10.1007/s43236-020-00166-9
[19]	ELIZONDO D, BARRIOS E L, URSÚA A, et al. Analytical modeling of high-frequency winding loss in round-wire toroidal inductors[J]. IEEE Transactions on Industrial Electronics, 2023, 70(6): 5581–5591. doi: 10.1109/TIE.2022.3192689
[20]	SHIMIZU T and IYASU S. A practical iron loss calculation for AC filter inductors used in PWM inverters[J]. IEEE Transactions on Industrial Electronics, 2009, 56(7): 2600–2609. doi: 10.1109/TIE.2009.2018436