基于频域分解和精度加权集成的分布式风电功率预测方法

doi:10.12204/j.issn.1000-7229.2023.05.009

电力建设 ›› 2023, Vol. 44 ›› Issue (5): 84-93.doi: 10.12204/j.issn.1000-7229.2023.05.009

基于频域分解和精度加权集成的分布式风电功率预测方法

王绍敏¹^,²(), 王守相¹^,²(), 赵倩宇¹^,²(), 董逸超³()

1.教育部智能电网重点实验室(天津大学),天津市 300072
2.天津市电力系统仿真控制重点实验室(天津大学),天津市 300072
3.国网天津市电力公司经济技术研究院,天津市 300171

收稿日期:2022-06-15 出版日期:2023-05-01 发布日期:2023-04-27
通讯作者: 王守相(1973),男,博士,教授,博士生导师,主要研究方向为分布式发电与智能配电网,E-mail:sxwang@tju.edu.cn。
作者简介:王绍敏(1988),女,博士研究生,主要研究方向为源荷预测与优化调度,E-mail:wangshaomin@tju.edu.cn;
赵倩宇(1990),女,博士,讲师,主要研究方向为综合能源系统稳定性,E-mail:zhaoqianyu@tju.edu.cn;
董逸超(1992),男,博士研究生,主要研究方向为智能配电网评估与规划,E-mail:ycdong007@tju.edu.cn。
基金资助:
国家自然科学基金资助项目(52077149);国家自然科学基金资助项目(U2166202);国家电网公司总部科技项目(5400-202199280A-0-0-00)

Distributed Wind Power Forecasting Method Based on Frequency Domain Decomposition and Precision-weighted Ensemble

WANG Shaomin¹^,²(), WANG Shouxiang¹^,²(), ZHAO Qianyu¹^,²(), DONG Yichao³()

1. Key Laboratory of the Ministry of Education on Smart Power Grids (Tianjin University), Tianjin 300072, China
2. Tianjin Key Laboratory of Power System Simulation and Control (Tianjin University), Tianjin 300072, China
3. State Grid Tianjin Electric Power Company Economic and Technological Research Institute, Tianjin 300171, China

Received:2022-06-15 Online:2023-05-01 Published:2023-04-27
Supported by:
National Natural Science Founds of China(52077149);National Natural Science Founds of China(U2166202);State Grid Corporation of China Science and Technology Project(5400-202199280A-0-0-00)

摘要/Abstract

摘要：

针对具有强随机波动性的分布式小风电开展功率高精度预测研究,对增强小风机自身稳定性和配电网支撑力具有重要意义。为此,提出了基于频域分解和精度加权集成的分布式风电预测方法。首先,通过自适应噪声完备集合经验模态分解将原风电信号分解到不同频段,捕捉风电局部波动特征;然后,构建具有双层异质学习器的精度加权Stacking,实现多模型优势互补,增加应对分布式风电强波动特性的泛化能力;最后,通过4台风机的实际数据验证了所提方法优于当前用于大型风电场、风电集群及单风机的几种先进预测方法,证明了所提方法针对分布式风电功率预测的有效性和泛化性。

关键词: 分布式风电, 功率预测, 自适应噪声完备集合经验模态分解, 精度加权, 集成学习

Abstract:

Research on high-precision forecasting for distributed small wind power with strong random fluctuations is of great significance for enhancing the stability of small wind turbines and providing reliable support for distribution. Therefore, a distributed wind power forecasting method based on frequency domain decomposition and a precision-weighted ensemble was proposed. First, the original wind power signal was decomposed into different frequency bands through complete ensemble empirical mode decomposition with adaptive noise to capture the local fluctuation characteristics of wind power. Then, a precision-weighted Stacking model with two-layer heterogeneous learners was constructed to fully utilize the performance advantages of different learners and increase the generalization ability to deal with strong fluctuations. Finally, the model was verified on actual dataset from four wind turbines. It was observed that the proposed method is superior to several advanced prediction methods currently used in large wind farms, wind farm clusters, and single wind turbines, which proves the validity and generalization of the proposed method for distributed wind power forecasting.

Key words: distributed wind power, power forecasting, complete ensemble empirical mode decomposition with adaptive noise, precision weighting, ensemble learning

中图分类号:

TM614

王绍敏, 王守相, 赵倩宇, 董逸超. 基于频域分解和精度加权集成的分布式风电功率预测方法[J]. 电力建设, 2023, 44(5): 84-93.

WANG Shaomin, WANG Shouxiang, ZHAO Qianyu, DONG Yichao. Distributed Wind Power Forecasting Method Based on Frequency Domain Decomposition and Precision-weighted Ensemble[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 84-93.

图/表 10

图1 CEEMDAN-PWS模型框架示意图

Fig.1 Framework diagram of CEEMDAN-PWS model

表1 PWSj模型构建过程伪代码

Table 1 The pseudocode of PWSj building process

图2 AM-LSTM模型结构图

Fig.2 Structure diagram of AM-LSTM model

表2 各异质学习器的NRMSE指标对比

Table 2 Comparison of NRMSE for heterogeneous learners

图3 不同学习器在4台风机上的预测误差分布图

Fig.3 Error distribution of different learners for four wind turbines

表3 对比模型的构成

Table 3 Composition of comparison models

表4 不同模型的误差评估指标对比

Table 4 Error evaluation indexes comparison of different models

图4 不同模型多时间尺度预测误差分布箱线图

Fig.4 Multi-time scale error distribution of different models

表A1 各异质学习器的TIC和R2指标对比

Table A1 Comparison of TIC and R2 for heterogeneous learners

图A1 不同模型在风机WT2、WT3 和WT4上的多时间尺度预测误差箱线图

Fig.A1 Multi-time scale error distribution of different models for WT2, WT3 and WT4

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基于频域分解和精度加权集成的分布式风电功率预测方法

Distributed Wind Power Forecasting Method Based on Frequency Domain Decomposition and Precision-weighted Ensemble

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