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Short-term electric load forecasting based on kalman filtering algorithm with moving window weather and load model

ELECTRIC POWER SYSTEMS RESEARCH 2004年第1期68卷 47-59页

作者： Al-Hamadi, HM Soliman, SA Univ Qatar Coll Engn Power Syst Res Grp Dept Elect Engn Doha Qatar

This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature. (C) 2003 Elsevier B.V. All rights reserved.

关键词： short-term electric load kalman filtering algorithm moving window

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Harmonic modeling of linear and nonlinear loads based on kalman filtering algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2004年第2期72卷 147-155页

作者： Soliman, SA Alammari, RA Univ Qatar Dept Elect Engn Doha Qatar

Most harmonic nonlinear load modeling techniques developed in the last two decades, assume that the load connected to the load bus has a constant admittance, inductive or capacitive, which depends on the mode of operation of the load (constant R, L, and C). This is not true, since the loads in most cases are nonlinear and they inject harmonics into the system. This paper presents a new application of kalman filtering (KF) to nonlinear load modeling in the presence or absence of harmonics. The proposed technique uses directly available samples of the load voltage and current to estimate, and track the variation of load parameters. The proposed algorithm can be used directly on-line for harmonics load modeling, as well as, to track the load power active and reactive. The effects of the critical parameters on the behavior of the proposed algorithm are discussed. Results for different simulated examples and practical examples from field measurements are reported in the paper. The conclusion drawn from these examples is that the algorithm is successful in estimating the load parameters. (C) 2004 Elsevier B.V. All rights reserved.

关键词： harmonics load modeling kalman filtering algorithm power in nonsinusoidal voltage

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Improving the stability of electrostatic induction dust concentration detection using kalman filtering algorithm aided by machine learning

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 174卷 882-890页

作者： Chen, Jiange Nie, Zhongheng Zhao, Fengyu Jiang, Haipeng Zhu, Li Chongqing Univ Sch Resources & Safety Engn Chongqing 400044 Peoples R China State Key Lab Gas Disaster Detecting Preventing & Chongqing 400037 Peoples R China Dalian Univ Technol Dept Chem Machinery & Safety Engn State Key Lab Fine Chem Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

The suspended dust produced in the production of process industry may cause damage to occupational health and even trigger explosion accidents. To accurately detect the dust concentration, and to reduce the noises and standard deviation volatility of the inductive signal of characterized dust concentration generated when applying the electrostatic induction method, the study designs the kalman filter based on a machine learning algorithm and realizes secondary processing of the standard deviation of inductive signal. Firstly, this study designs the dust concentration detection device using the electrostatic induction method, realizing an effective extraction and amplification of the dust inductive signal, confirming a positive correlation between the volatility of the inductive signal and dust concentration. A data processing procedure for the inductive signal is also designed. To eliminate the standard deviation volatility of inductive signal, the kalman filter aided by machine learning is selected to process mathematical models. By comparing the conventional sliding filter algorithm, median filter algorithm and kalman filtering aided by machine learning, it is confirmed that kalman filtering aided by ma-chine learning has a better effect on reducing the standard deviation of inductive signal. The standard deviation can be quickly converged to the target value through short-term iteration, effectively eliminating the fluctuation of the standard deviation value of the inductive signal, and improving the stability of the standard deviation value of the inductive signal.

关键词： Electrostatic induction method Dust concentration kalman filtering algorithm Machine learning Standard deviation

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Application of wireless sensor network in dynamic linkage video surveillance system based on kalman filtering algorithm

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JOURNAL OF SUPERCOMPUTING 2019年第9期75卷 6055-6069页

作者： Zhang, Zhicheng Zhang, Yan Henan Inst Technol Dept Basic Subjects Xinxiang 453002 Henan Peoples R China Henan Normal Univ Engn Labor Henan Prov Intelligence Business & Int Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China

The purpose of this study was to investigate the shortcomings and deficiencies of video surveillance systems, thus presenting a new method for intelligent video surveillance. The specific hardware structure and software algorithm design are adopted. And the performance of the algorithm is tested and verified through experiments. The results obtained in this study are that: combining the wireless sensor network with the traditional video surveillance system as a whole and interoperating with each other not only enables the wireless sensor network to monitor and judge live real time data, but also enables the camera to be shot and tracked when a sensitive event occurs and use the image processing algorithm to make two judgments on the warning event and issue the correct alarm. The findings of this study may serve as solving the problem that the traditional video surveillance system relies solely on image recognition technology to make an event alarm, which causes misdetection and missed detection under low illumination conditions. As a result, it improves the alarm accuracy of the system and also makes up for a single wireless sensor network. When an alarm occurs, the on-site real time screen cannot be confirmed.

关键词： kalman filtering algorithm Wireless sensor network Dynamic linkage Video surveillance system

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Fault Detection Method of AC Charging Pile in Coastal Cities Based on kalman filtering algorithm

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JOURNAL OF COASTAL RESEARCH 2020年第Spe104期000卷 210-215页

作者： Zhang, Yuanxing Li, Taoyong Li, Bin Zhang, Jing Wang, Rijun Jiang, Linru Diao, Xiaohong China Elect Power Res Inst Co Ltd Beijing Elect Vehicle Charging Battery Swap Engn Beijing 100192 Peoples R China Jiangxi Rongxiang Sci & Technol Dev Co Ltd Jiujiang 332000 Peoples R China

Aiming at the problems of AC charging pile fault detection, such as large amount of iterative calculation and low ability of fault classification, it is difficult to adapt to the practical application environment. The fault detection method of AC charging pile in coastal cities based on kalman filter algorithm is studied. Through data fusion, algorithm fusion and other methods, the information related to the working state of AC charging pile is obtained from many aspects, and then integrated. The advantages of different fault diagnosis algorithms can improve the accuracy of fault diagnosis results;the fault tree analysis algorithm is introduced into kalman filter algorithm to improve the modeling accuracy and efficiency of AC charging pile nonlinear model, and the relationship coefficient between the performance vector variation of the whole AC charging pile and its components is analyzed, so as to solve the problem of different times of the same model and different service life of the same AC charging pile. In order to improve the accuracy of component health parameter estimation results in hardware and software design, the fault detection of AC charging pile in coastal cities is realized. The simulation results show that the final output of the charging system is more scalable and the success rate of fault detection is higher. This method has good practical application effect in coastal cities and has strong applicability.

关键词： kalman filtering algorithm coastal cities AC charging pile fault detection

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kalman filtering algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2018年第Pt.1期2018卷 1516028-1-1516028-12页

作者： He, Yonghui Jiang, Jibin Huang, Hischuan Zhuo, Shufang Wu, Yanfeng Fujian Polytech Informat Technol Dept Mechatron Engn Fuzhou 350003 Fujian Peoples R China Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350108 Fujian Peoples R China Chien Kuo Technol Univ Dept Automat Engn Changhua 50094 Taiwan

The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed to describe the random disturbances in the system model. The phenomena of missing measurements occur in a random way and the missing probability is characterized by Bernoulli distributed random variables with known conditional probabilities. Based on the projection theory, a class of kalman-type locally optimal filter is constructed and the filtering error covariance matrix is minimized in the sense of minimum mean square error principle. Also, by solving the recursive matrix equation, we can obtain the filter gain. Finally, two examples are provided: one is a numerical example to illustrate the feasibility and effectiveness of the proposed filtering scheme;the other is to solve the problem of target estimation for a tracking system considering networked phenomena.

关键词： kalman filtering algorithm Systems Stochastic Nonlinearity Functions optimal filters Mean square error Bernoulli correlated noise kalman filtering variance covariance matrix Stochastic nonlinearity numerical example

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Forecasting Gas Consumption Based on a Residual Auto-Regression Model and kalman filtering algorithm

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Journal of Resources and Ecology 2019年第5期10卷 546-552页

作者： ZHU Meifeng WU Qinglong WANG Yongqin School of Economics and Management North University of ChinaTaiyuan 030051China

Consumption of clean energy has been increasing in *** gas consumption is important to adjusting the energy consumption structure in the *** on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and kalman filtering algorithm is used to forecast gas *** results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^*** to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.

关键词： residual auto-regressive model kalman filtering algorithm inverse fitting value deviation method combined forecast

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High-precision pose and velocity measuring method for projectiles based on kalman filtering algorithm

High-precision pose and velocity measuring method for projec...

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2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015)

作者： Liang Zhang Lizhi Qian Quanli Ning Jingxiao Wang New Star Research Institude of Applied Technology

ISBN: (纸本)9781510813465

An original method is presented in this paper, which attempts to make use of the kalman filtering algorithm and maintain accuracy in position, attitude and velocity estimation for a fast moving projectile, the velocity and the pose(position and attitude) could also be obtained, then the simulation are presented and compared with the conventional least square method. The simulation result showed that the kalman filter algorithm is more accurate and stability than the conventional least squares algorithm, meanwhile, the trajectory of the projectile obtained by the kalman filter algorithm is very close to the real trajectory.

关键词： projectiles kalman filtering algorithm trajectory tracking

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Forecasting the Ethiopian Coffee Price Using kalman filtering algorithm

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Journal of Resources and Ecology 2018年第3期9卷 302-305页

作者： Tesfahun Berhane Nurilign Shibabaw Aemiro Shibabaw Molalign Adam Abera A.Muhamed Department of mathematics Bahir Dar University Bahir Dar EO.Box 3018 Ethiopia Department of mathematics Kotebe Metropolitan University Addis Ababa P.O.Box 31248 Ethiopia

Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical *** study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange（ECX） market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error（RMSE）. Based on the linear state space model and the kalman filtering algorithm, the root mean square error（RMSE） is 0.000016375, which is small enough, and it indicates that the algorithm performs well.

关键词： coffee price forecasting state space model kalman filtering algorithm Ethiopian

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Forecasting the Coke Price Based on the kalman filtering algorithm

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Journal of Resources and Ecology 2015年第1期6卷 60-64页

作者：朱美峰赵国浩山西财经大学统计学院太原030006 山西财经大学管理工程与科学学院太原030006

Research on coke price forecasting is of theoretical and practical signiifcance. Here, the kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and alows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the ifeld of coke price tracking and forecasting.

关键词： coke price forecasting state space model kalman filtering algorithm

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