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Analytical Representation of Data-driven Transient Stability Constraint and Its Application in Preventive Control

Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1085-1097页

作者： Yiwei Fu Xiaohua Zhang Lei Chen Zengyao Tian Kaiyuan Hou Haikuan Wang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China State Grid Jibei Electric Power Company Beijing 100053China Northeast China Branch of State Grid Corporation of China Shenyang 110180China

Accurate transient stability assessment(TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraints of preventive control optimization, but existing methods are mostly iteration-based ones, which may result in low efficiency, sometimes even non-convergence. In this paper,an analytical representation method of data-driven transient stability constraint is proposed based on a non-parametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify *** preventive control model is then formulated as a mixed-integer linear program(MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of datadriven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

关键词： Data-driven critical clearing time transient stability constraint non-parametric regression optimal preventive control

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Comparing practice-ready forecast models for weekly and monthly fluctuations of average daily traffic and enhancing accuracy by weighting methods

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Journal of Traffic and Transportation Engineering(English Edition) 2018年第4期5卷 239-253页

作者： Andrea Pompigna Federico Rupi Department of Civil Chemical Environmental and Materials Engineering University of Bologna Bologna 40136 Italy R&M Associati Bologna 40134 Italy

Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combi- nation of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt-Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.

关键词： Short-term traffic forecasting non-parametric regression Gaussian maximum likelihood Double seasonal Holt-Winters exponential smoothing Entropy weighting method Shapley value weighting method

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Efficient estimation of smoothing spline with exact shape constraints

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Statistical Theory and Related Fields 2021年第1期5卷 55-69页

作者： Vincent Chan Kam-Wah Tsui Yanran Wei Zhiyang Zhang Xinwei Deng Department of Statistics University of Wisconsin-MadisonMadisonWIUSA Department of Statistics Virginia TechBlacksburgVAUSA

Smoothing spline is a popular method in non-parametric function *** the analysis of data from real applications,specific shapes on the estimated function are often required to ensure the estimated function undeviating from the domain *** this work,we focus on constructing the exact shape constrained smoothing spline with efficient ***‘exact’here is referred as to impose the shape constraint on an infinite set such as an interval in one-dimensional *** the estimation becomes a so-called semi-infinite optimisation problem with an infinite number of *** proposed method is able to establish a sufficient and necessary condition for transforming the exact shape constraints to a finite number of constraints,leading to efficient estimation of the shape constrained *** performance of the proposed methods is evaluated by both simulation and real case studies.

关键词： Monotonicity non-linear constraint non-parametric regression

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非参数回归在物流配送中心库存量中的应用

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物流管理 2010年第3期

作者：林森邱荣祖洪伟

以物流配送中心库存管理策略(R，S)中库存量预测的问题为研究对象，提出应用非参数回归方法对库存量进行拟合，用该方法与最小二乘法比较。并用非参数K邻近法则对库存量进行预测，丰富订货期制定方法。以某物流配送中心31天大米滞留库... 详细信息

以物流配送中心库存管理策略(R，S)中库存量预测的问题为研究对象，提出应用非参数回归方法对库存量进行拟合，用该方法与最小二乘法比较。并用非参数K邻近法则对库存量进行预测，丰富订货期制定方法。以某物流配送中心31天大米滞留库存量数据为例，研究结果表明，核回归模型拟合值较一元二次回归模型优异，得到较为理想的结果，实现通过统计模型客观地对物流配送中心库存信息智能化管理提供理论依据。

关键词：非参数回归核回归邻近法则库存量滞留期 non-parametric regression kernel regression k neighboring law inventory remaining period

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