电力系统自然频率特性系数区间预测方法

doi:10.12204/j.issn.1000-7229.2021.09.011

电力建设 ›› 2021, Vol. 42 ›› Issue (9): 105-111.doi: 10.12204/j.issn.1000-7229.2021.09.011

电力系统自然频率特性系数区间预测方法

蒙永苹¹, 张明媚¹, 向明旭², 杨渝璐¹, 黄俊凯², 杨知方²

1.国网重庆市电力公司,重庆市 400014
2.输配电装备及系统安全与新技术国家重点实验室(重庆大学电气工程学院),重庆市 400044

收稿日期:2021-01-22 出版日期:2021-09-01 发布日期:2021-09-02
通讯作者: 向明旭
作者简介:蒙永苹(1974),女,硕士,高级工程师,主要研究方向为电力系统及其自动化|张明媚(1976),女,硕士,高级工程师,主要研究方向为电力系统及其自动化|杨渝璐(1982),女,硕士,高级工程师,主要研究方向为调度自动化管理|黄俊凯(1996),男,硕士研究生,主要研究方向为电力系统运行优化与分析|杨知方(1992),男,博士,研究员,主要研究方向为电力系统运行优化与分析。
基金资助:
国网重庆市电力公司科技项目“适应重庆电网运行新特性的AGC精细化智能控制策略研究”(SGCQ0000DKJS2000126)

Interval Prediction Method for Natural Frequency Characteristic Coefficient of Power System

MENG Yongping¹, ZHANG Mingmei¹, XIANG Mingxu², YANG Yulu¹, HUANG Junkai², YANG Zhifang²

1. State Grid Chongqing Electric Power Company, Chongqing 400014, China
2. State Key Laboratory of Power Transmission Equipment & System Security and New Technology (College of Electrical Engineering, Chongqing University), Chongqing 400044, China

Received:2021-01-22 Online:2021-09-01 Published:2021-09-02
Contact: XIANG Mingxu
Supported by:
Science and Technology Program of State Grid Chongqing Electric Power Company(SGCQ0000DKJS2000126)

摘要/Abstract

摘要：

电力系统自然频率特性系数β是整定区域互联电网自动发电控制(automatic generation control, AGC)策略中频率偏差系数B的重要依据。理想情况下,系数B的整定原则是使其恰好等于β系数,从而使系统AGC调节量能准确跟踪系统功率偏差。然而,电力系统β系数具有非线性与时变性,现有B系数整定方法难以有效追踪其变化。对此,提出了基于深度神经网络(deep neural network, DNN)与Bootstrap的自然频率特性系数区间预测方法。该方法利用DNN强大的非线性特征提取能力建立系统功率扰动、备用容量、机组启停方式与β系数间的映射关系,实现β系数的预测,并结合 Bootstrap方法得到β系数预测结果的置信区间,可为系统B系数的整定提供有力支撑。算例仿真结果验证了所提方法的准确性与鲁棒性。

关键词: 自然频率特性系数, 频率偏差系数, 区间预测, 深度神经网络(DNN), Bootstrap方法

Abstract:

The natural frequency characteristic coefficient (β) of the power system is a significant basis for setting the frequency bias coefficient (B) in the automatic generation control (AGC) strategy. Setting B equal to β is the ideal principle of the B coefficient setting, because the AGC power adjustment is able to exactly reflect the power mismatch under this circumstance. However, the β coefficient is nonlinear and time-varying. The existing B coefficient setting methods cannot effectively track the changes of the β coefficient. In this regard, the interval prediction method for the β coefficient based on deep neural network (DNN) and Bootstrap is proposed. With the powerful capability of nonlinear feature extracting, DNN is utilized to establish the mapping relationship among power disturbance, reserve capacity, unit commitment, and the β coefficient. Thus, the prediction of the β coefficient can be achieved. In addition, combined with the Bootstrap method, the confidence interval of the predicted β coefficient is further obtained, which provides great supports for setting the B coefficient. Finally, simulation results verify the effectiveness and robustness of the proposed method.

Key words: natural frequency characteristic coefficient, frequency bias coefficient, interval prediction, deep neural network (DNN), Bootstrap method

中图分类号:

TM712

蒙永苹, 张明媚, 向明旭, 杨渝璐, 黄俊凯, 杨知方. 电力系统自然频率特性系数区间预测方法[J]. 电力建设, 2021, 42(9): 105-111.

MENG Yongping, ZHANG Mingmei, XIANG Mingxu, YANG Yulu, HUANG Junkai, YANG Zhifang. Interval Prediction Method for Natural Frequency Characteristic Coefficient of Power System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(9): 105-111.

图/表 6

图1 自然频率特性系数随功率扰动的变化情况

Fig.1 Variation of the natural frequency characteristic coefficient with the power disturbance

表1 系统备用容量分配情况对自然频率特性系数的影响

Table 1 Impact of the system reserve allocation on the natural frequency characteristic coefficient

表2 机组启停方式对自然频率特性系数的影响

Table 2 Impact of the unit commitment on the natural frequency characteristic coefficient

图2 DNN结构

Fig.2 Structure of DNN

表3 区间预测结果对比

Table 3 Comparison of interval prediction results

表4 M1对功率扰动量预测误差的鲁棒性

Table 4 Robustness of M1 to the forecast error of power disturbance %

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电力系统自然频率特性系数区间预测方法

Interval Prediction Method for Natural Frequency Characteristic Coefficient of Power System

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