Wind power ramp detection algorithms based on slope point correction

Zhenyu Lan; Jun Dai; Xiang Hu; Xuewu Dai; Minghai Xing; Liangyin Chen

doi:10.1109/icac55051.2022.9911117

Wind power ramp detection algorithms based on slope point correction

Zhenyu Lan, Jun Dai, Xiang Hu, Xuewu Dai, Minghai Xing, Liangyin Chen

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

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Abstract

Wind power ramp event refers to the large fluctuation of wind power in a short time interval, which will seriously affect the safe and stable operation of power grid system. In order to maintain the stable operation of power grid system, wind power ramp detection is extremely necessary. Therefore, how to improve the accuracy of wind power ramp detection is a problem worthy of study. In the existing wind power ramp detection algorithms, the accuracy of the ramp endpoint is not considered. Aiming at the problem of end-point accuracy in climbing section, this work proposes a wind power climbing detection algorithm RPCRD (ramp point correct climbing detection) based on ramp point correction, which considers the detection accuracy of wind power climbing point for the first time. In this algorithm, a merging method of climbing sections is proposed to solve the fracture problem, and a scoring mechanism for selecting climbing points is proposed to find the two extreme points that most conform to the climbing characteristics, and the climbing points at both ends of the climbing section of wind power are modified.

Original language	English
Title of host publication	2022 27th International Conference on Automation and Computing (ICAC)
Subtitle of host publication	Bristol, United Kingdom, 1-3 September 2022
Editors	Chenguang Yang, Yuchun Xu
Place of Publication	Piscataway
Publisher	IEEE
Pages	609-614
Number of pages	6
ISBN (Electronic)	9781665498074
ISBN (Print)	9781665498081
DOIs	https://doi.org/10.1109/icac55051.2022.9911117
Publication status	Published - 1 Sept 2022

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

RPCRD algorithm
Trend fitting
Wind power ramp

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10.1109/icac55051.2022.9911117

Wind power ramp detection algorithms based on slope point correction(1)Accepted author manuscript, 1.49 MB

Cite this

@inproceedings{3101ddfb2a88439098ccaee5469205ea,

title = "Wind power ramp detection algorithms based on slope point correction",

abstract = "Wind power ramp event refers to the large fluctuation of wind power in a short time interval, which will seriously affect the safe and stable operation of power grid system. In order to maintain the stable operation of power grid system, wind power ramp detection is extremely necessary. Therefore, how to improve the accuracy of wind power ramp detection is a problem worthy of study. In the existing wind power ramp detection algorithms, the accuracy of the ramp endpoint is not considered. Aiming at the problem of end-point accuracy in climbing section, this work proposes a wind power climbing detection algorithm RPCRD (ramp point correct climbing detection) based on ramp point correction, which considers the detection accuracy of wind power climbing point for the first time. In this algorithm, a merging method of climbing sections is proposed to solve the fracture problem, and a scoring mechanism for selecting climbing points is proposed to find the two extreme points that most conform to the climbing characteristics, and the climbing points at both ends of the climbing section of wind power are modified.",

keywords = "RPCRD algorithm, Trend fitting, Wind power ramp",

author = "Zhenyu Lan and Jun Dai and Xiang Hu and Xuewu Dai and Minghai Xing and Liangyin Chen",

note = "Funding information: This research was funded in part by the National Natural Science Foundation of China under Grant No.62072319, in part by Sichuan University and Luzhou Science and Technology Innovation Research and Development Project (No.2021CDLZ-11), in part by the Sichuan Science and Technology Program under Grant No.2022YF0041.",

year = "2022",

month = sep,

day = "1",

doi = "10.1109/icac55051.2022.9911117",

language = "English",

isbn = "9781665498081",

pages = "609--614",

editor = "Chenguang Yang and Yuchun Xu",

booktitle = "2022 27th International Conference on Automation and Computing (ICAC)",

publisher = "IEEE",

address = "United States",

}

Wind power ramp detection algorithms based on slope point correction. / Lan, Zhenyu; Dai, Jun; Hu, Xiang et al.
2022 27th International Conference on Automation and Computing (ICAC): Bristol, United Kingdom, 1-3 September 2022. ed. / Chenguang Yang; Yuchun Xu. Piscataway: IEEE, 2022. p. 609-614.

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

TY - GEN

T1 - Wind power ramp detection algorithms based on slope point correction

AU - Lan, Zhenyu

AU - Dai, Jun

AU - Hu, Xiang

AU - Dai, Xuewu

AU - Xing, Minghai

AU - Chen, Liangyin

N1 - Funding information: This research was funded in part by the National Natural Science Foundation of China under Grant No.62072319, in part by Sichuan University and Luzhou Science and Technology Innovation Research and Development Project (No.2021CDLZ-11), in part by the Sichuan Science and Technology Program under Grant No.2022YF0041.

PY - 2022/9/1

Y1 - 2022/9/1

N2 - Wind power ramp event refers to the large fluctuation of wind power in a short time interval, which will seriously affect the safe and stable operation of power grid system. In order to maintain the stable operation of power grid system, wind power ramp detection is extremely necessary. Therefore, how to improve the accuracy of wind power ramp detection is a problem worthy of study. In the existing wind power ramp detection algorithms, the accuracy of the ramp endpoint is not considered. Aiming at the problem of end-point accuracy in climbing section, this work proposes a wind power climbing detection algorithm RPCRD (ramp point correct climbing detection) based on ramp point correction, which considers the detection accuracy of wind power climbing point for the first time. In this algorithm, a merging method of climbing sections is proposed to solve the fracture problem, and a scoring mechanism for selecting climbing points is proposed to find the two extreme points that most conform to the climbing characteristics, and the climbing points at both ends of the climbing section of wind power are modified.

AB - Wind power ramp event refers to the large fluctuation of wind power in a short time interval, which will seriously affect the safe and stable operation of power grid system. In order to maintain the stable operation of power grid system, wind power ramp detection is extremely necessary. Therefore, how to improve the accuracy of wind power ramp detection is a problem worthy of study. In the existing wind power ramp detection algorithms, the accuracy of the ramp endpoint is not considered. Aiming at the problem of end-point accuracy in climbing section, this work proposes a wind power climbing detection algorithm RPCRD (ramp point correct climbing detection) based on ramp point correction, which considers the detection accuracy of wind power climbing point for the first time. In this algorithm, a merging method of climbing sections is proposed to solve the fracture problem, and a scoring mechanism for selecting climbing points is proposed to find the two extreme points that most conform to the climbing characteristics, and the climbing points at both ends of the climbing section of wind power are modified.

KW - RPCRD algorithm

KW - Trend fitting

KW - Wind power ramp

UR - https://www.scopus.com/pages/publications/85141149801

U2 - 10.1109/icac55051.2022.9911117

DO - 10.1109/icac55051.2022.9911117

M3 - Conference contribution

SN - 9781665498081

SP - 609

EP - 614

BT - 2022 27th International Conference on Automation and Computing (ICAC)

A2 - Yang, Chenguang

A2 - Xu, Yuchun

PB - IEEE

CY - Piscataway

ER -

Wind power ramp detection algorithms based on slope point correction

Abstract

UN SDGs

Keywords

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