Randomized modulation of power converters via Markov chains

被引:19
|
作者
Stankovic, AM [1 ]
Verghese, GC [1 ]
Perreault, DJ [1 ]
机构
[1] MIT, ELECTROMAGNET & ELECT SYST LAB, CAMBRIDGE, MA 02139 USA
基金
美国国家科学基金会;
关键词
power electronics; Markov processes; switching circuits; modulation; spectral analysis; frequency-domain analysis; time domain analysis;
D O I
10.1109/87.553665
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Randomized modulation of switching in power converters holds promise for reducing filtering requirements and reducing acoustic noise in motor drive applications, This paper is devoted to issues in analysis and synthesis of randomized modulation schemes based on finite Markov chains, The main advantage of this novel type of randomized modulation is the availability of an explicit control of time-domain performance, in addition to the possibility of shaping the power spectra of signals of interest, We focus on the power spectra of the switching functions that govern converter operation, and on the power spectra of certain associated waveforms. Numerical (Monte Carlo) and experimental verifications for our power spectral formulas are presented, We also formulate representative narrow- and wideband synthesis problems in randomized modulation, and solve them numerically. Our results suggest that randomized modulation is very effective in satisfying narrow-band constraints, but has limited effectiveness in meeting wide-band signal power constraints.
引用
收藏
页码:61 / 73
页数:13
相关论文
共 50 条
  • [21] Estimation of finite probabilities via aggregation of Markov chains
    Karmanov, AV
    Karmanova, LA
    AUTOMATION AND REMOTE CONTROL, 2005, 66 (10) : 1640 - 1646
  • [22] A fundamental limitation to the reduction of Markov chains via aggregation
    Kotsalis, Georgios
    Shamma, Jeff S.
    2012 50TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON), 2012, : 1449 - 1454
  • [23] Learning Multiple Markov Chains via Adaptive Allocation
    Talebi, Mohammad Sadegh
    Maillard, Odalric-Ambrym
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [24] Estimating statistics of neuronal dynamics via Markov chains
    Froyland, G
    Aihara, K
    BIOLOGICAL CYBERNETICS, 2001, 84 (01) : 31 - 40
  • [25] CORRELATION DECAY FOR HARD SPHERES VIA MARKOV CHAINS
    Helmuth, Tyler
    Perkins, Will
    Petti, Samantha
    ANNALS OF APPLIED PROBABILITY, 2022, 32 (03): : 2063 - 2082
  • [26] Modeling uncertainty of directed movement via Markov chains
    Yin, Zhangcai
    Sun, Huatao
    Chen, Xuefei
    Liu, Qingquan
    Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 2015, 44 (10): : 1160 - 1166
  • [27] MODELING OF TANK ELECTROLYZERS VIA MARKOV-CHAINS
    FAHIDY, TZ
    JOURNAL OF APPLIED ELECTROCHEMISTRY, 1987, 17 (04) : 841 - 848
  • [28] Extracting patterns in music for composition via Markov chains
    Verbeurgt, K
    Dinolfo, M
    Fayer, M
    INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE, 2004, 3029 : 1123 - 1132
  • [29] Accelerating distributed consensus via lifting Markov chains
    Li, Wenjun
    Dai, Huaiyu
    2007 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY PROCEEDINGS, VOLS 1-7, 2007, : 2881 - 2885
  • [30] Fast algorithms at low temperatures via Markov chains
    Chen, Zongchen
    Galanis, Andreas
    Goldberg, Leslie A.
    Perkins, Will
    Stewart, James
    Vigoda, Eric
    RANDOM STRUCTURES & ALGORITHMS, 2021, 58 (02) : 294 - 321