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Zonotope and Gaussian Kalman filters based state estimation algorithm for linear system with dual noise term

IET CONTROL THEORY AND APPLICATIONS 2023年第5期17卷 516-526页

作者： Zhan, Yacong Wang, Ziyun Wang, Yan Park, Ju H. Ji, Zhicheng Jiangnan Univ Engn Res Ctr Internet Things Technol & Applicat Minist Educ Wuxi 214122 Jiangsu Peoples R China Yeungnam Univ Dept Elect Engn Kyongsan South Korea

To solve the problem of state estimation for systems with dual noise terms, a zonotope and Gaussian Kalman filters based state estimation algorithm is proposed. A state estimator is designed to obtain the estimation interval of the true state in the presence of both stochastic and unknown but bounded (UBB) uncertainties. A novel coefficient that weighs the relative influence of stochastic and UBB uncertainties is introduced, and the optimal weight solution is introduced by minimizing the polyhedron space and mean square error. Finally, two simulation examples are presented to demonstrate the accuracy and effectiveness of the proposed algorithm.

关键词： Optimisation techniques Kalman filters Simulation, modelling and identification minimisation Linear control systems polyhedron space minimization linear system state estimation algorithm dual noise term Gaussian Kalman filters unknown but bounded uncertainties UBB uncertainties state estimation estimation interval zonotope mean square error minimization Interpolation and function approximation (numerical analysis) mean square error methods linear systems stochastic uncertainties

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Condition Assessment Approach of Hydraulic Brake for Large Crane Based on state estimation algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期103卷 195-207页

作者： Lu, Houjun Zhou, Qiang Chang, Daofang Shanghai Maritime Univ Container Supply Chain Technol RC MOE Engn Shanghai Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai Peoples R China

Hydraulic brake was widely used for mechanical brake of port crane. The run-state of the brake affects the safety of the crane because of the sudden accident. Because of the complex structure and unsuited to use in real time of traditional assessment model, the condition assessment approach of hydraulic brake was constructed based on state estimation algorithm in this paper. The oil temperature, dynamic friction coefficient, spring stiffness coefficient, brake shoe clearance and contact area were chosen as the state components of the memory matrix based on the analysis of the structure and failure reasons of the brake. Considering the correlation between the state components, the Mahalanobis distance was chosen as the nonlinear operator of algorithm, and the uncertainty factors and random disturbances in state assessment were eliminated by the sliding window residual statistics method. The dynamics simulation of hydraulic brake was constructed for confirming the validity of the approach in this paper. The result shows that it can be accurately judged by the method if the brake is in abnormal state.

关键词： Dynamics simulation Hydraulic brake state estimation algorithm Sliding window statistics

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Distributed, Sustainable, and Secure Energy Future Through IT Modernization

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COMPUTER 2025年第4期58卷 65-73页

作者： Tootaghaj, Diman Zad Wu, Yiqian He, Ting Chaudhuri, Nilanjan Ray Milojicic, Dejan Strezoski, Luka Anderson, James Sharma, Puneet Hewlett Packard Labs Milpitas CA 95035 USA Hewlett Packard Labs Networking & Distributed Syst Lab Milpitas CA 95035 USA Columbia Univ New York NY 10027 USA Columbia Univ Dept Elect Engn New York NY 10027 USA Penn State Univ University Pk PA 16802 USA Penn State Univ Elect Engn Dept University Pk PA 16802 USA Univ Novi Sad Dept Power Elect & Telecommun Engn Fac Tech Sci Novi Sad 21000 Serbia DerMag Consulting Novi Sad Serbia

The decentralized and digitally interconnected future of power grids will be achieved through IT modernization to meet the unprecedented challenges and opportunities amid the rise of distributed energy resources, clim... 详细信息

关键词： Modernity Climate Change Management System Renewable Energy Energy Production Energy System Power System Forecasting Renewable Sources Distribution System Information And Communication Technologies Electric Vehicles Storage Systems Power Grid Renewable Energy Sources Communication Protocol Local Energy Wind Turbine Energy Storage Systems Solar Panels Phasor Measurement Units Microgrid Multi Agent Reinforcement Learning Smart Meters Power Line Grid Infrastructure state estimation algorithm Charge Discharge Cycles Future Grid Power Demand

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Resilient state estimation for time-varying uncertain dynamical networks with data packet dropouts and switching topology: an event-triggered method

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IET CONTROL THEORY AND APPLICATIONS 2020年第3期14卷 367-377页

作者： Gao, Ming Hu, Jun Chen, Dongyan Jia, Chaoqing Harbin Univ Sci & Technol Dept Math Harbin 150080 Peoples R China Univ South Wales Sch Engn Pontypridd CF37 1DL M Glam Wales

In this study, the recursive state estimation method based on the event-triggered protocol is given for time-varying uncertain complex networks with switching topology (ST), data packet dropouts (DPDs), and randomly occurring non-linearities (RONs). The event-triggered strategy is given to regulate the communications and then save the limited network resources when the measurement information is transmitted. The phenomena of RONs, ST, and DPDs are depicted by some random variables obeying the Bernoulli distribution. Besides, the estimator gain perturbations are dealt with. The major aim of this study is to present a new state estimation algorithm for addressed time-varying uncertain complex networks such that, for all STs, DPDs, and RONs, a minimal upper bound of state estimation error covariance matrix is derived by designing an optimal estimator gain matrix. Finally, a numerical simulation is presented to show the effectiveness of the provided time-varying state estimation method.

关键词： discrete time systems time-varying systems matrix algebra nonlinear control systems covariance matrices uncertain systems state estimation complex networks resilient state estimation time-varying uncertain dynamical networks data packet dropouts switching topology event-triggered method recursive state estimation method event-triggered protocol ST DPDs nonlinearities RONs event-triggered strategy network resources random variables estimator gain perturbations state estimation algorithm addressed time-varying uncertain complex networks state estimation error covariance matrix optimal estimator gain matrix provided time-varying state estimation method

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Bias compensation-based parameter and state estimation for a class of time-delay non-linear state-space models

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IET CONTROL THEORY AND APPLICATIONS 2020年第15期14卷 2176-2185页

作者： Gu, Ya Zhu, Quanmin Nouri, Hassan Changshu Inst Technol Sch Elect Engn & Automat Changshu 215500 Jiangsu Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

This study presents, based on bias compensation, an integrated parameter and state estimation algorithm for a class of time-delay non-linear systems which are described by canonical observable state-space model. In technical development, the state-space system model is transformed into an input-output representation/realisation by eliminating the state variables, which is accordingly used as a feasible identification model. With such an input-output structure, directly data measurable to accommodate the estimation bias, an augmented least-squares algorithm (by adding the bias correction terms into the estimates) is proposed for estimating the parameters and states interactively. Regarding the estimator properties, the proposed algorithm is proved unbiased. The simulation results show that the proposed algorithm has good performance in estimating the parameters of state-space systems.

关键词： observers state-space methods parameter estimation delays linear systems least squares approximations nonlinear control systems state estimation time-delay nonlinear state-space models bias compensation integrated parameter state estimation algorithm time-delay nonlinear systems canonical observable state-space model state-space system model state variables feasible identification model input-output structure estimation bias least-squares algorithm bias correction estimator properties state-space systems

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state estimation via mathematical programming: a comparison of different estimation algorithms

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第6期6卷 545-553页

作者： Caro, E. Conejo, A. J. Tech Univ Madrid UPM Dept Stat Madrid Spain Univ Castilla La Mancha Dept Elect Engn E-13071 Ciudad Real Spain

Although the weighted least of squares technique is an efficient and well-established power system state-estimation procedure, a number of alternative estimation approaches have been proposed in the technical literature. This study presents and compares the most-common estimators formulating them as mathematical programming problems. The numerical accuracy and computational efficiency of the different estimators are analysed using an illustrative case study.

关键词： least squares approximations weighted least squares technique power system state-estimation procedure power system state estimation mathematical programming problems mathematical programming Interpolation and function approximation (numerical analysis) Power systems Optimisation techniques state estimation algorithm

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state filtering and parameter estimation for linear systems with d-step state-delay

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IET SIGNAL PROCESSING 2014年第6期8卷 639-646页

作者： Gu, Ya Ding, Feng Li, Junhong Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Jiangnan Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This study considers the modelling and identification problems for linear systems based on canonical state space models with d-step state-delay. A recursive least-squares parameter identification algorithm is presented. The basic idea is to drive a parameter identification model for such d-step state-delay systems, to replace the unknown noise terms and unknown state variables in the formation vector with their estimated residuals and estimated states, and to compute the state estimates of the system in the state estimation algorithm using the estimated parameters. The simulation results indicate that the proposed parameter and state estimation algorithm can capture the dynamics of the system.

关键词： state estimation linear systems state-space methods filtering theory least mean squares methods modelling and identification problem linear systems canonical state space model recursive least squares parameter identification algorithm parameter identification model d-step state delay system formation vector residual estimation state estimation algorithm state filtering

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Simultaneous spatiotemporal bias and state estimation for asynchronous multi-sensor system

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 5824-5828页

作者： Bu, Shizhe Zhou, Chang Zhou, Gongjian Harbin Inst Technol Sch Elect & Informat Engn Harbin Heilongjiang Peoples R China Changchun Univ Sci & Technol Sch Optoelect Engn Changchun Jilin Peoples R China

Elimination of spatiotemporal bias of asynchronous multiple sensors is the foundation of accurate multi-sensor data fusion. In this article, a sequential processing simultaneous spatiotemporal bias and state estimation algorithm for asynchronous multi-sensor system is proposed. The spatiotemporal bias is combined with target state to obtain an augmented state vector, and the augmented state model is formulated. For asynchronous multi-sensor with known but different sampling periods, a sequential processing method is proposed to handle the multiple measurements. The relationship of sensor measurements, target state and spatiotemporal bias is analysed, and the corresponding measurement model is formulated. A novel algorithm, in which the unscented Kalman filter is used to handle the nonlinearity between the augmented measurement and state vectors, is proposed to jointly estimate the spatiotemporal bias and target state. Simulation results verify the effectiveness of the proposed algorithm.

关键词： target tracking state estimation nonlinear filters Kalman filters sensor fusion target state asynchronous multisensor system asynchronous multiple sensors accurate multisensor data fusion sequential processing simultaneous spatiotemporal bias state estimation algorithm augmented state vector augmented state model sensor measurements augmented measurement state vectors

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Pollution level estimation for tunnel ventilation control

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JSME INTERNATIONAL JOURNAL SERIES B-FLUIDS AND THERMAL ENGINEERING 2003年第2期46卷 278-286页

作者： Hong, DH Chu, BS Kim, WD Chung, JT Kim, TH Korea Univ Dept Mech Engn Seoul 136701 South Korea Dukin Co Ltd Software Team Taejon 305348 South Korea Korea Inst Construct Technol Urban Util & Facil Res Group Koyong 411712 Kyonggi Do South Korea

Traffic tunnels built in recent years are equipped with traffic counters and pollution sensors. Utilizing these built-in sensors, it is possible to develop an algorithm to estimate the amount of pollutants exhausted from the various types of vehicles passing through the tunnel. Also, with this estimated data and the sensor outputs, more accurate pollution levels can be assessed utilizing a Kalman filter. The diffusion of pollutants in a tunnel can be described with a one-dimensional diffusion and advection equation. This equation is approximated with interpolation functions and a weighted residual method converts to an adequate form for. standard state estimate algorithms. With this converted equation, a least square based, algorithm is developed, whose outputs are the estimated amounts of pollutants emitted from each type of vehicles. Also, a Kalman filter is utilized to more accurately estimate the pollution levels of the tunnel under the existence of model and measurement uncertainties. In order, to verify the feasibility of the developed algorithms, experiments and simulations are performed. The real data is acquisitioned from the Dunnae tunnel located in Young-Dong highway in Korea. The estimated emission rates and the computer simulated concentration levels agree reasonably to the measured values.

关键词： tunnel ventilation Kalman filter state estimation algorithm least square diffusion-advection equation

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Validation of a PMU-based fault location identification method for smart distribution network with photovoltaics using real-time data

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第21期12卷 5824-5833页

作者： Usman, Muhammad Usama Faruque, Md. Omar Florida State Univ Elect & Comp Sci 2000 Levy Ave Tallahassee FL 32310 USA Florida State Univ Ctr Adv Power Syst 2000 Levy Ave Tallahassee FL 32310 USA

A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS). The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG). This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network. SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm. The method was validated using the IEEE 37 node test feeder with DGs. PMUs are placed on the real-time model of the system. The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37.118 protocol. OpenPDC is used to collect real-time PMU data coming from the DRTS. Microsoft SQL is used as a database management server to store data coming from OpenPDC. In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault. Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved.

关键词： power engineering computing smart power grids phasor measurement fault location SQL distributed power generation protocols distribution networks power system measurement DG PMUs real-time model DRTS streams phasor data OpenPDC real-time PMU data real-time simulator store data Matlab-based FL identification algorithm balanced fault types unbalanced fault types PMU-based fault location identification method smart distribution network real-time data distributed generation state estimation algorithm SE simulated phasor measurement units fault currents generators voltage measurements FL algorithm IEEE 37 node test feeder operating distribution grid

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