Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes

Jieming Ma; Ziqiang Bi; Ka Lok Man; Huan Dai; Zhentian Wu

doi:10.1109/ICRERA.2018.8566715

Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes

Jieming Ma, Ziqiang Bi, Ka Lok Man, Huan Dai, Zhentian Wu

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

8 Citations (Scopus)

Abstract

A photovoltaic (PV) plant is normally built in a fixed series-parallel configuration and its power-voltage characteristics often get complex with multiple peaks under partial shading scenarios. Therefore, identification of partial shading is important for monitoring and invoking maximum power pint estimation. This paper proposes a back-propagation neural network (BPNN) based partial shading identification method which locates shaded modules by using measured voltage data. Optimal sensor placement schemes are introduced to decrease the number of utilized voltage sensors, and meanwhile still keep a high identification performance. Experiments are conducted to evaluate the accuracy and effectiveness of the proposed identification method.

Original language	English
Title of host publication	7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	458-462
Number of pages	5
ISBN (Electronic)	9781538659823
DOIs	https://doi.org/10.1109/ICRERA.2018.8566715
Publication status	Published - 6 Dec 2018
Event	7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 - Paris, France Duration: 14 Oct 2018 → 17 Oct 2018

Publication series

Name	7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018

Conference

Conference	7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018
Country/Territory	France
City	Paris
Period	14/10/18 → 17/10/18

Keywords

Back-propagation neural network
Partial shading scenarios
Photovoltaic systems
Sensor placement

UN SDGs

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

Access to Document

10.1109/ICRERA.2018.8566715

Cite this

Ma, J., Bi, Z., Man, K. L., Dai, H., & Wu, Z. (2018). Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes. In 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 (pp. 458-462). Article 8566715 (7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA.2018.8566715

Ma, Jieming ; Bi, Ziqiang ; Man, Ka Lok et al. / Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes. 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 458-462 (7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018).

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title = "Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes",

abstract = "A photovoltaic (PV) plant is normally built in a fixed series-parallel configuration and its power-voltage characteristics often get complex with multiple peaks under partial shading scenarios. Therefore, identification of partial shading is important for monitoring and invoking maximum power pint estimation. This paper proposes a back-propagation neural network (BPNN) based partial shading identification method which locates shaded modules by using measured voltage data. Optimal sensor placement schemes are introduced to decrease the number of utilized voltage sensors, and meanwhile still keep a high identification performance. Experiments are conducted to evaluate the accuracy and effectiveness of the proposed identification method.",

keywords = "Back-propagation neural network, Partial shading scenarios, Photovoltaic systems, Sensor placement",

author = "Jieming Ma and Ziqiang Bi and Man, {Ka Lok} and Huan Dai and Zhentian Wu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 ; Conference date: 14-10-2018 Through 17-10-2018",

year = "2018",

month = dec,

day = "6",

doi = "10.1109/ICRERA.2018.8566715",

language = "English",

series = "7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018",

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Ma, J, Bi, Z, Man, KL, Dai, H & Wu, Z 2018, Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes. in 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018., 8566715, 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 458-462, 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, Paris, France, 14/10/18. https://doi.org/10.1109/ICRERA.2018.8566715

Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes. / Ma, Jieming; Bi, Ziqiang; Man, Ka Lok et al.
7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 458-462 8566715 (7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes

AU - Ma, Jieming

AU - Bi, Ziqiang

AU - Man, Ka Lok

AU - Dai, Huan

AU - Wu, Zhentian

PY - 2018/12/6

Y1 - 2018/12/6

N2 - A photovoltaic (PV) plant is normally built in a fixed series-parallel configuration and its power-voltage characteristics often get complex with multiple peaks under partial shading scenarios. Therefore, identification of partial shading is important for monitoring and invoking maximum power pint estimation. This paper proposes a back-propagation neural network (BPNN) based partial shading identification method which locates shaded modules by using measured voltage data. Optimal sensor placement schemes are introduced to decrease the number of utilized voltage sensors, and meanwhile still keep a high identification performance. Experiments are conducted to evaluate the accuracy and effectiveness of the proposed identification method.

AB - A photovoltaic (PV) plant is normally built in a fixed series-parallel configuration and its power-voltage characteristics often get complex with multiple peaks under partial shading scenarios. Therefore, identification of partial shading is important for monitoring and invoking maximum power pint estimation. This paper proposes a back-propagation neural network (BPNN) based partial shading identification method which locates shaded modules by using measured voltage data. Optimal sensor placement schemes are introduced to decrease the number of utilized voltage sensors, and meanwhile still keep a high identification performance. Experiments are conducted to evaluate the accuracy and effectiveness of the proposed identification method.

KW - Back-propagation neural network

KW - Partial shading scenarios

KW - Photovoltaic systems

KW - Sensor placement

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U2 - 10.1109/ICRERA.2018.8566715

DO - 10.1109/ICRERA.2018.8566715

M3 - Conference Proceeding

AN - SCOPUS:85060614839

T3 - 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018

SP - 458

EP - 462

BT - 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018

Y2 - 14 October 2018 through 17 October 2018

ER -

Ma J, Bi Z, Man KL, Dai H, Wu Z. Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes. In 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 458-462. 8566715. (7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018). doi: 10.1109/ICRERA.2018.8566715

Identification of Partial Shading in Photovoltaic Arrays Using Optimal Sensor Placement Schemes

Abstract

Publication series

Conference

Keywords

UN SDGs

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