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A comparison between parallel algorithms for system parameter estimation in dynamic linear models

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 1999年第4期15卷 369-378页

作者： Mantovan, P Pastore, A Tonellato, S Univ Venice Dipartimento Stat I-30125 Venice Italy

When dealing with high-frequency time series, statistical procedures giving reliable estimates of unknown parameters and forecasts in real time are required. This is why recursive estimation methods are usually preferred to maximum-likelihood estimators. In the paper, a recursive estimation algorithm for the system parameter of dynamic linear models is proposed. A comparison with some other algorithms is given via Monte Carlo simulations. Consistency properties of the algorithms are also empirically verified. Copyright (C) 1999 John Wiley & Sons, Ltd.

关键词： dynamic linear models system parameter estimation Kalman filter parallel algorithms

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A comparison between parallel algorithms for system parameter estimation in dynamic linear models

引用

Applied Stochastic Models in Business and Industry 2000年第4期15卷

作者： P. Mantovan A. Pastore S. Tonellato Dipartimento di Statistica ‐ Università Ca' Foscari di Venezia Campiello S. Agostin S. Polo 2347 30125 Venezia Italy

When dealing with high‐frequency time series, statistical procedures giving reliable estimates of unknown parameters and forecasts in real time are required. This is why recursive estimation methods are usually preferred to maximum‐likelihood estimators. In the paper, a recursive estimation algorithm for the system parameter of dynamic linear models is proposed. A comparison with some other algorithms is given via Monte Carlo simulations. Consistency properties of the algorithms are also empirically verified. Copyright © 1999 John Wiley & Sons, Ltd.

关键词： dynamic linear models system parameter estimation Kalman filter parallel algorithms

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Thevenin Equivalent Circuit parameter estimation for the PCC Upstream system 5

Thevenin Equivalent Circuit Parameter Estimation for the PCC...

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5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Sun, Yuanyuan Dai, Chuankai Li, Jiaqi Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China State Grid Qingdao Elect Power Co Qingdao Peoples R China

ISBN: (纸本)9781467371063

The Thevenin equivalent circuit parameters for the upstream system seen at a load bus plays an important role in the power system operation and planning. In order to accurately estimate the Thevenin equivalent parameters for the upstream supply system, an estimation method not requiring synchronized measurement is used in this paper. The characteristics of the probability curves of load voltage and impedance fluctuations have been analyzed, based on which the data selection criteria has been established. The field measurement data from different substations are processed by the proposed methods, and abnormal data has been filtered out in order to increase the accuracy of the estimated parameters. The validity and accuracy of the proposed method has been verified by both the simulation and field measurement data.

关键词： Data selection system parameter estimation Thevenin equivalent circuit synchronizing data

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Thevenin Equivalent Circuit parameter estimation for the PCC Upstream system

Thevenin Equivalent Circuit Parameter Estimation for the PCC...

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International Conference on Electric Utility Deregulation and Restructuring and Power Technologies

作者： Yuanyuan Sun Chuankai Dai Jiaqi Li Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China State Grid Qingdao Electric Power Company Qingdao China

ISBN: (纸本)9781467371070

关键词： Data selection system parameter estimation Thevenin equivalent circuit synchronizing data

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Non-quadratic quality criteria in parameter estimation of continuous-time models

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IET CONTROL THEORY AND APPLICATIONS 2011年第13期5卷 1494-1508页

作者： Kowalczuk, Z. Kozlowski, J. Gdansk Univ Technol Dept Decis Syst Fac Elect Telecommun & Informat PL-80933 Gdansk Poland

A consequent and consistent continuous-time approach to system parameter estimation is introduced. estimation algorithms, the underlying quality criteria and models of identified systems are described in the continuous-time domain, while suitable discretising operations are performed solely for the purpose of ultimate numerical realisation of estimation procedures. The considered indices of estimation quality take the form of integrals of absolute prediction errors rather than a common form of integrals or sums of square errors. In order to overcome the problem of analytical minimisation of such non-differentiable criteria, an approximate method is derived and applied in practical implementation of the resultant estimation schemes. Specific weighting mechanisms utilised in the algorithms allow for tracking the time-variant parameters of non-stationary systems, while with the employed instrumental variable the accuracy of estimates gets improved by means of suppression of the asymptotic bias. Following the so-called direct approach, an auxiliary discrete-time model that retains 'physical' parameterisation is obtained based on 'finite-horizon' spline-based integration of both sides of the presumed differential equation. In this aspect, application of splines makes the respective discrete-time processing resistant to cumulation of numerical errors. The attached numerical examples demonstrate the performance of the discussed estimation routines.

关键词： identified systems continuous-time models Optimisation techniques differential equations Other topics in statistics estimation quality splines numerical errors estimation procedures square errors absolute prediction errors nonquadratic quality criteria analytical minimisation system parameter estimation infinite horizon resultant estimation schemes continuous-time approach physical parameterisation weighting mechanisms discussed estimation routines time-variant parameters auxiliary discrete-time model discrete-time processing resistant estimation algorithms splines (mathematics) ultimate numerical realisation Mathematical analysis asymptotic bias nonstationary systems Interpolation and function approximation (numerical analysis) continuous time systems Optimal control minimisation Discrete control systems continuous-time domain nondifferentiable criteria differential equation finite-horizon spline-based integration parameter estimation discrete time systems asymptotic stability

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A HIGH-ORDER YULE-WALKER METHOD FOR estimation OF THE AR parameterS OF AN ARMA MODEL

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systemS & CONTROL LETTERS 1988年第2期11卷 99-105页

作者： STOICA, P FRIEDLANDER, B SODERSTROM, T POLYTECH INST BUCHAREST DEPT AUTOMAT CONTROLBUCHARESTROMANIA SAXPY COMP CORP SUNNYVALECA 94086 UNIV UPPSALA DEPT AUTOMAT CONTROLS-75121 UPPSALASWEDEN

The high-order Yule-Walker (HOYW) method is often used to estimate the frequencies of sinusoidal signals from noisy measurements. A modification of the HOYW method is proposed for estimating the parameters of the autoregressive part of general ARMA processes. The prime application of the HOYW method introduced in this paper is the estimation of the parameters of autoregressions from noisy measurements. The performance of the proposed technique is illustrated by some numerical examples.

关键词： Signal processing ARMA systems system parameter estimation Yule-Walker equations High-order model approach

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Regression-based control of thyristor-controlled series compensators for optimal usage of transmission capacity

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第8期8卷 1444-1452页

作者： Yang, Rui Hug-Glanzmann, Gabriela Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

Flexible AC transmission system devices provide the opportunity to improve the usage of the current transmission facilities by controlling network flows and nodal voltages. The optimal steady-state settings of these devices can be determined using optimal power flow calculations. However, this requires knowledge of the system parameters, generation settings and loads. A two-stage regression-based control scheme is proposed to determine the optimal settings of thyristor-controlled series compensators (TCSCs) with the objective to optimally utilise the current transmission system in the presence of renewable energy resources. A regression function describing the relationship between a set of key measurements and the optimal device settings is determined in the offline simulation. This function is then used to calculate the optimal device settings by only receiving the information from a limited number of key measurements without solving the OPF problem in the online operation. Studies are carried out with TCSCs in three-test systems, the IEEE 14-bus system, a 28-bus system and the IEEE 118-bus system. The simulation results with one, two and four TCSCs are presented, illustrating the performance of the proposed regression-based control scheme.

关键词： regression analysis thyristor applications flexible AC transmission systems power transmission control voltage control load flow control optimal control power system parameter estimation renewable energy sources static VAr compensators thyristor controlled series compensator flexible AC transmission system device network flow voltage control nodal voltage control optimal steady-state optimal power flow calculation system parameter estimation optimal settings determination TCSC current transmission system renewable energy resources regression function optimal device OPF problem IEEE 14-bus system IEEE 28-bus system IEEE 118-bus system regression-based control scheme transmission capacity

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Physics-Informed Neural Network for Optical Fiber parameter estimation From the Nonlinear Schrodinger Equation

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第21期40卷 7095-7105页

作者： Jiang, Xiaotian Wang, Danshi Chen, Xue Zhang, Min Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

For any system that follows rigorous mathematical and physical theories like fiber-optic system, system parameter estimation is crucial for system detection and monitoring. In this paper, a physics-informed neural network (PINN)-based method is proposed for optical fiber parameter estimation by solving the inverse problem of the nonlinear Schrodinger equation (NLSE), i.e., inferring the unknown parameters of NLSE. The proposed method considers both the prior knowledge of physical law and the characteristics of observation data from only the transmitted and received waveforms. In PINN, the NLSE and observation data are set as the loss function, and the typical fiber parameters (attenuation, dispersion, and nonlinear coefficient) are optimized iteratively until they satisfy the inferred NLSE by learning the limited observation data. Three scenarios are validated, including single Gaussian pulse propagation, ultrashort pulse propagation in highly nonlinear fiber, and optical signal transmission in standard single-mode fiber. The launch power of the signal is embedded in PINN as a parametric feature, which makes PINN learn the signal transmissions under different powers simultaneously. The weights of mean square error terms for NLSE and observation data in the loss function are properly adjusted to balance learning difficulties. Moreover, the statistical results of 128 different signals and model performance under different signal-to-noise ratios are analyzed. As an extension, fiber length estimation is also studied, and the error is 0.375% when the launch power is 0 dBm. Our work verifiably shows that the proposed approach performs well and can be further extended to multiple applications in fiber optics.

关键词： Optical fibers Optical fiber networks Optical fiber dispersion Optical fiber theory Optical fiber devices Artificial neural networks Fiber nonlinear optics Fiber-optic system nonlinear Schrodinger equation physics-informed neural network system parameter estimation

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IDENTIFICATION OF CONTINUOUS-TIME systemS FROM SAMPLES OF INPUT-OUTPUT DATA USING THE DELTA-OPERATOR

CONTROL-THEORY AND ADVANCED TECHNOLOGY

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CONTROL-THEORY AND ADVANCED TECHNOLOGY 1993年第1期9卷 113-125页

作者： STERICKER, DL SINHA, NK McMaster Univ Hamilton Canada

The use of the delta-operator for estimating the parameters of a discrete-time model for a continuous-time system is investigated. Algorithms are derived for estimating these parameters from the samples of the input-output data either for a transfer function model or for a state-space model. Results of simulation indicate that the algorithms work very well even if the sampling frequency is ten times the largest undamped natural frequency of the system to be identified, whereas severe numerical difficulties arise with models based on the unit advance (q) operator normally associated with discrete-time models at such high sampling rates.

关键词： CONTINUOUS-TIME systemS DISCRETE-TIME MODELS DELTA-OPERATOR, GENERALIZED LEAST SQUARES INSTRUMENTAL VARIABLES system parameter estimation STATE SPACE

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A systematic review on predicting PV system parameters using machine learning

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HELIYON 2023年第6期9卷 e16815页

作者： Jobayer, Md Shaikat, Md Al Hasan Rashid, Md Naimur Hasan, Md Rakibul BRAC Univ Dhaka 1212 Bangladesh

Due to the growing demand, assessing performance has become obligatory for photovoltaic (PV) energy harvesting systems. Performance assessment involves estimating different PV system parameters. Traditional ways, such as calculating solar radiation using satellite data and the IV characteristics approach as assessment methods, are no longer reliable enough to provide a reasonable projection of PV system parameters. Estimating system parameters using machine learning (ML) approaches has become a reliable and popular method because of its speed and accuracy. This paper systematically reviewed ML-based PV parameter estimation studies published in the last three years (2020 - 2022). Studies were analyzed using several criteria, including ML algorithm, outcome, experimental setup, sample data size, and error metric. The analysis revealed several interesting factors. The neural network was the most popular ML method (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%). Dataset was sourced from hardware tests and computer-based simulations: 66% of the studies used data from only computer simulation, 18% used data from only hardware setup, and the 16% experiments used data from both hardware and simulations to evaluate different system parameters. The top three most commonly used error metrics were root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%). Our systematic review will help researchers assess ML algorithms' projection in PV system parameters estimation. Consequently, scopes shall be created to establish more robust governmental frameworks, expand private financing in the PV industry, and optimize PV system parameters.

关键词： Photovoltaics system parameter estimation Machine learning systematic review

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