On-line soft-sensing model based maximum power point tracking for photovoltaic generation systems

Jieming Ma; Ziqiang Bi; Liang Zhu; Yue Jiang; Xingshuo Li; Huiqin Wen

doi:10.1109/PEDES.2016.7914274

On-line soft-sensing model based maximum power point tracking for photovoltaic generation systems

Jieming Ma
, Ziqiang Bi
, Liang Zhu
, Yue Jiang
, Xingshuo Li
, Huiqin Wen

Academy of JITRI

Suzhou University of Science and Technology
Xi'an Jiaotong-Liverpool University

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

Maximum power point tracking (MPPT) is mandatory for improving the power-conversion efficiency of photovoltaic (PV) systems. Although classical MPPT techniques are widely used by virtue of their simplicity and ease of implementation, the tracking performance is degraded compared with artificial-intelligence based techniques under rapidly changing environmental conditions. This paper proposes an on-line soft-sensing model (OLSSM) based MPPT method, which applies on-line radial basis function neural network as soft model to predict the locus of maximum power points (MPPs). The Euclidean distance between the estimated and measured duty cycle at MPPs is used to check whether the new sample will be used for model updating. Simulation and experimental results show that the OLSSM based MPPT offer enhanced tracking performance especially during transient operations.

Original language	English
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1109/PEDES.2016.7914274
Publication status	Published - 27 Apr 2017
Event	2016 IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2016 - Trivandrum, Kerala, India Duration: 14 Dec 2016 → 17 Dec 2016

Conference

Conference	2016 IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2016
Country/Territory	India
City	Trivandrum, Kerala
Period	14/12/16 → 17/12/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

MPPT
Photovoltaic systems
RBFNN
Soft-sensing

Access to Document

10.1109/PEDES.2016.7914274

Cite this

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title = "On-line soft-sensing model based maximum power point tracking for photovoltaic generation systems",

abstract = "Maximum power point tracking (MPPT) is mandatory for improving the power-conversion efficiency of photovoltaic (PV) systems. Although classical MPPT techniques are widely used by virtue of their simplicity and ease of implementation, the tracking performance is degraded compared with artificial-intelligence based techniques under rapidly changing environmental conditions. This paper proposes an on-line soft-sensing model (OLSSM) based MPPT method, which applies on-line radial basis function neural network as soft model to predict the locus of maximum power points (MPPs). The Euclidean distance between the estimated and measured duty cycle at MPPs is used to check whether the new sample will be used for model updating. Simulation and experimental results show that the OLSSM based MPPT offer enhanced tracking performance especially during transient operations.",

keywords = "MPPT, Photovoltaic systems, RBFNN, Soft-sensing",

author = "Jieming Ma and Ziqiang Bi and Liang Zhu and Yue Jiang and Xingshuo Li and Huiqin Wen",

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AU - Ma, Jieming

AU - Bi, Ziqiang

AU - Zhu, Liang

AU - Jiang, Yue

AU - Li, Xingshuo

AU - Wen, Huiqin

PY - 2017/4/27

Y1 - 2017/4/27

N2 - Maximum power point tracking (MPPT) is mandatory for improving the power-conversion efficiency of photovoltaic (PV) systems. Although classical MPPT techniques are widely used by virtue of their simplicity and ease of implementation, the tracking performance is degraded compared with artificial-intelligence based techniques under rapidly changing environmental conditions. This paper proposes an on-line soft-sensing model (OLSSM) based MPPT method, which applies on-line radial basis function neural network as soft model to predict the locus of maximum power points (MPPs). The Euclidean distance between the estimated and measured duty cycle at MPPs is used to check whether the new sample will be used for model updating. Simulation and experimental results show that the OLSSM based MPPT offer enhanced tracking performance especially during transient operations.

AB - Maximum power point tracking (MPPT) is mandatory for improving the power-conversion efficiency of photovoltaic (PV) systems. Although classical MPPT techniques are widely used by virtue of their simplicity and ease of implementation, the tracking performance is degraded compared with artificial-intelligence based techniques under rapidly changing environmental conditions. This paper proposes an on-line soft-sensing model (OLSSM) based MPPT method, which applies on-line radial basis function neural network as soft model to predict the locus of maximum power points (MPPs). The Euclidean distance between the estimated and measured duty cycle at MPPs is used to check whether the new sample will be used for model updating. Simulation and experimental results show that the OLSSM based MPPT offer enhanced tracking performance especially during transient operations.

KW - MPPT

KW - Photovoltaic systems

KW - RBFNN

KW - Soft-sensing

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DO - 10.1109/PEDES.2016.7914274

M3 - Paper

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T2 - 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2016

Y2 - 14 December 2016 through 17 December 2016

ER -

On-line soft-sensing model based maximum power point tracking for photovoltaic generation systems

Abstract

Conference

UN SDGs

Keywords

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