火电机组中部分信号的时序预测研究

doi:10.11930/j.issn.1004-9649.201908038

摘要/Abstract

摘要： 火电机组在升降负荷过程中，受设备性能所限，锅炉主要控制参数存在大延迟、大惯性等特点，难以平衡机组负荷快速响应与主蒸汽压力稳定控制之间的矛盾。提出一种自回归滑动平均（autoregressive moving average，ARMA）模型与粒子滤波算法相结合的综合法用于火电机组中部分信号的时序预测，旨在对锅炉侧的主蒸汽压力等信号进行超前预测，一定程度上解决锅炉侧主要参数控制迟延。该方法首先结合历史数据进行ARMA模型建模，通过粒子滤波算法对模型参数进行校正，最后利用经校正的模型计算得出信号时序预测值。利用该方法对某机组的主蒸汽压力、锅炉总煤量、主蒸汽压力设定值数据在Matlab平台进行预测仿真，结果表明，本方法在预测精度方面较ARMA模型有明显的提升。

关键词: 火电机组, 时序预测, ARMA模型, 粒子滤波, 预测精度

Abstract: Constrained by the performance of the equipment, certain issues have been existing in the main control parameters of boiler during the unit load changing process, such as large time delay and large inertia. As a result, it is not that straightforward to balance the contradiction between the needs of quick response to thermal load and stabilizing the main steam pressure. In the present work, a comprehensive method based on autoregressive moving average (ARMA) model and particle filtering is developed to perform time series forecasting on partial signals, which is designed to forecast the signals in advance such as main steam pressure such that the control delay of the main parameters could be alleviated to some extent on the boiler side. This method firstly establishes ARMA model based on historical data, then corrects the model parameters through particle filter algorithm, and at last applies the corrected model to forecast time series value. By using this method, the main steam pressure, total boiler coal quantity and main steam pressure setting value of the unit are forecasted and simulated on Matlab platform. The results show that the forecasting accuracy of this method is much better than that of ARMA model.

Key words: thermal power unit, time series forecasting, ARMA model, particle filter, prediction accuracy

王嘉兴, 王喆, 王林, 安朝榕. 火电机组中部分信号的时序预测研究[J]. 中国电力, 2020, 53(5): 164-171,178.

WANG Jiaxing, WANG Zhe, WANG Lin, AN Chaorong. Time Series Forecasting of Partial Signals in Thermal Power Units[J]. Electric Power, 2020, 53(5): 164-171,178.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201908038

https://www.electricpower.com.cn/CN/Y2020/V53/I5/164

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