摘要
针对当前蒸汽动力系统缺乏有效稳定性评估方法的问题,提出一种适用于单个运行参数的稳定性评估方法.该方法是一种复合评估方法,首先综合运用中值回归经验模态分解和奇异值分解,对运行参数的时间序列进行分解并提取其隐藏的趋势项,然后依据各分量的最佳算法参数排列熵选取分量进行重构,最后通过非平稳时间序列分析中常用的整合滑动平均自回归模型预测趋势项和扰动项的走势并提取两者的分布特征,进而计算得到运行参数在预测趋势上各点的失稳概率并由此对其稳定性进行定量评估.经实际案例验证,证明该方法能够有效评估蒸汽动力系统单参数的运行稳定性,具有一定的理论创新性和工程应用价值.
Aimed at the lack of effective stability evaluation methods for the current steam power system,an operation stability assessment method suitable for single parameter is proposed.This method is a composite method,which first applied the midpoint and regression based empirical mode decomposition(MREMD)and singular value decomposition(SVD)to decompose the time series of operation parameters and extract their hidden trend terms.Then,the components are selected for reconstruction according to the optimal algorithm parameter permutation entropy(OAPPE)of each component.Finally,the auto-regressive integrated moving average(ARIMA)model commonly used in the non-stationary time series analysis is utilized to predict the trend and the disturbance of parameters,and their distribution characteristics are also extracted in this process,based on which,the probability of instability(PI)of operation parameters at each point on the predicted trend are calculated,and their stabilities are quantitatively evaluated.The actual case proves that this method can effectively assess the operation stability of a single parameter of the steam power system,which has a certain theoretical innovation and engineering application value.
作者
郑奕扬
倪何
金家善
ZHENG Yiyang;NI He;JIN Jiashan(No.92118 Troop of PLA Navy,Zhoushan 316000,Zhejiang,China;College of Power Engineering,Naval University of Engineering,Wuhan 430033,China;College of Naval Architecture and Marine Engineering,Naval University of Engineering,Wuhan 430033,China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2021年第11期1438-1444,共7页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(51909254)。
关键词
中值回归经验模态分解
排列熵
失稳概率
稳定性评估
midpoint and regression based empirical mode decomposition(MREMD)
permutation entropy
probability of instability(PI)
stability assessment
