scholarly journals Intermediate-Variable-Based Distributed Fusion Estimation for Wind Turbine Systems

Actuators ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Shengwei Yang ◽  
Rusheng Wang ◽  
Jing Zhou ◽  
Bo Chen
Keyword(s):  
Wind Turbine ◽  
State Estimation ◽  
Optimization Problems ◽  
Estimation Method ◽  
The State ◽  
Intermediate Variable ◽  
System States ◽  
Augmented State ◽  
Recursive Optimization ◽  
Distributed Fusion

In wind turbine systems, the state of the generator is always disturbed by various unknown perturbances, which leads to system instability and inaccurate state estimation. In this paper, an intermediate-variable-based distributed fusion estimation method is proposed for the state estimation problem in wind turbine systems. By constructing an augmented state error system and using the idea of bounded recursive optimization, the local estimators and distributed fusion criterion are designed, which can be used to estimate the disturbance signals and system states. Then, the local estimator gains and the distributed weighting fusion matrices are obtained by solving the established convex optimization problems. Furthermore, a compensation strategy is designed by using the estimated disturbance signals, which can potentially reduce the influence of the disturbance signals on the system state. Finally, a numerical simulation is provided to show that the proposed method can effectively improve the accuracy of the estimation of the wind turbine state and disturbance, and the superiority of the proposed method is illustrated as a comparison to the Kalman fusion method.

Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

2021 ◽  
pp. 014233122110436
Author(s):  
Hao Yang ◽  
Yilian Zhang ◽  
Wei Gu ◽  
Fuwen Yang ◽  
Zhiquan Liu
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
The State ◽  
Estimation Problem ◽  
Filtering Method ◽  
Automatic Guided Vehicles ◽  
Tracking Process ◽  
Set Membership ◽  
Text Filtering ◽  
System States

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

scholarly journals Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2976 ◽  
Author(s):  
Yali Ruan ◽  
Yingting Luo ◽  
Yunmin Zhu
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Optimal Algorithm ◽  
The State ◽  
Unknown Input ◽  
Initial Value ◽  
State Estimate ◽  
Multisensor Systems ◽  
Unknown Inputs ◽  
Augmented State

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

scholarly journals AN ALGEBRAIC STATE ESTIMATION APPROACH FOR THE RECOVERY OF CHAOTICALLY ENCRYPTED MESSAGES

2006 ◽  
Vol 16 (02) ◽  
pp. 295-309 ◽  
Author(s):  
HEBERTT SIRA-RAMÍREZ ◽  
MICHEL FLIESS
Keyword(s):  
State Estimation ◽  
Chaotic System ◽  
Estimation Method ◽  
Algebraic Approach ◽  
Secret Message ◽  
Fast Time ◽  
Fast State ◽  
Computation Process ◽  
System States ◽  
Signal Time

In this article, we use a variant of a recently introduced algebraic state estimation method obtained from a fast output signal time derivatives computation process. The fast time derivatives calculations are entirely based on the consequences of using the "algebraic approach" in linear systems description (basically, module theory and non-commutative algebra). Here, we demonstrate, through computer simulations, the effectiveness of the proposed algebraic approach in the accurate and fast (i.e. nonasymptotic) estimation of the chaotic states in some of the most popular chaotic systems. The proposed state estimation method can then be used in a coding–decoding process of a secret message transmission using the message-modulated chaotic system states and the reliable transmission of the chaotic system observable output. Simulation examples, using Chen's chaotic system and the Rossler system, demonstrate the important features of the proposed fast state estimation method in the accurate extraction of a chaotically encrypted messages. In our simulation results, the proposed approach is shown to be quite robust with respect to (computer generated) transmission noise perturbations. We also propose a way to evade computational singularities associated with the local lack of observability of certain chaotic system outputs and still carry out the encrypting and decoding of secret messages in a reliable manner.

scholarly journals Analysis of Multiserver Retrial Queueing System with Varying Capacity and Parameters

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Alexander N. Dudin ◽  
Olga S. Dudina
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Queueing System ◽  
Optimization Problems ◽  
The State ◽  
Arrival Process ◽  
Continuous Time Markov Chain ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
System States

A multiserver queueing system, the dynamics of which depends on the state of some external continuous-time Markov chain (random environment, RE), is considered. Change of the state of the RE may cause variation of the parameters of the arrival process, the service process, the number of available servers, and the available buffer capacity, as well as the behavior of customers. Evolution of the system states is described by the multidimensional continuous-time Markov chain. The generator of this Markov chain is derived. The ergodicity condition is presented. Expressions for the key performance measures are given. Numerical results illustrating the behavior of the system and showing possibility of formulation and solution of optimization problems are provided. The importance of the account of correlation in the arrival processes is numerically illustrated.

scholarly journals A New State Estimation Method with Radar Measurement Missing

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hongjian Wang ◽  
Cun Li ◽  
Ying Wang ◽  
Qing Li ◽  
Xicheng Ban
Keyword(s):  
State Estimation ◽  
State Transition ◽  
Estimation Method ◽  
The State ◽  
Kolmogorov Equation ◽  
Transition Model ◽  
Observation Data ◽  
Adjustment Strategy ◽  
Adaptive Adjustment ◽  
State Transition Model

This paper describes a method that addresses the transient loss of observations in sea surface target state estimations. A six degrees of freedom swing platform fixed with a MiniRadaScan is used to simulate the loss of observations. The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. An observation data sliding window width adaptive adjustment strategy is proposed that can improve the fitting accuracy of the state transition model. To solve the problem where the weight value of the Gaussian components of the Gaussian mixture filter is not changed in the time update stage while the observation is missing, an adaptive adjustment strategy for the weight is proposed based on the Chapman-Kolmogorov equation, which can improve the estimation precision under the conditions of the missing observation. The simulation test demonstrates the proposed accuracy and real-time performance of the proposed algorithm.

Data Assimilation for a Coupled Ocean–Atmosphere Model. Part II: Parameter Estimation

2008 ◽  
Vol 136 (12) ◽  
pp. 5062-5076 ◽  
Author(s):  
Dmitri Kondrashov ◽  
Chaojiao Sun ◽  
Michael Ghil
Keyword(s):  
Parameter Estimation ◽  
State Estimation ◽  
Tropical Pacific ◽  
The State ◽  
Vector Model ◽  
Model Parameters ◽  
Ocean Atmosphere ◽  
Augmented State ◽  
Parameter Values ◽  
The Tropical Pacific

Abstract The parameter estimation problem for the coupled ocean–atmosphere system in the tropical Pacific Ocean is investigated using an advanced sequential estimator [i.e., the extended Kalman filter (EKF)]. The intermediate coupled model (ICM) used in this paper consists of a prognostic upper-ocean model and a diagnostic atmospheric model. Model errors arise from the uncertainty in atmospheric wind stress. First, the state and parameters are estimated in an identical-twin framework, based on incomplete and inaccurate observations of the model state. Two parameters are estimated by including them into an augmented state vector. Model-generated oceanic datasets are assimilated to produce a time-continuous, dynamically consistent description of the model’s El Niño–Southern Oscillation (ENSO). State estimation without correcting erroneous parameter values still permits recovering the true state to a certain extent, depending on the quality and accuracy of the observations and the size of the discrepancy in the parameters. Estimating both state and parameter values simultaneously, though, produces much better results. Next, real sea surface temperatures observations from the tropical Pacific are assimilated for a 30-yr period (1975–2004). Estimating both the state and parameters by the EKF method helps to track the observations better, even when the ICM is not capable of simulating all the details of the observed state. Furthermore, unobserved ocean variables, such as zonal currents, are improved when model parameters are estimated. A key advantage of using this augmented-state approach is that the incremental cost of applying the EKF to joint state and parameter estimation is small relative to the cost of state estimation alone. A similar approach generalizes various reduced-state approximations of the EKF and could improve simulations and forecasts using large, realistic models.

scholarly journals Study on the Initial Value Problem for Fractional-order Cubature Kalman Filters of Nonlinear Continuous-time Fractional-order Systems

2021 ◽  
Author(s):  
Chuang Yang ◽  
Zhe Gao ◽  
Yue Miao ◽  
Tao Kan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Fractional Order ◽  
Continuous Time ◽  
Kalman Filters ◽  
The State ◽  
Order System ◽  
Initial Value ◽  
Cubature Kalman Filter ◽  
Augmented State

Abstract To realize the state estimation of a nonlinear continuous-time fractional-order system, two types of fractional-order cubature Kalman filters (FOCKFs) designed to solve problem on the initial value influence. For the first type of cubature Kalman filter (CKF), the initial value of the estimated system are also regarded as the augmented state, the augmented state equation is constructed to obtain the CKF based on Grünwald-Letnikov difference. For the second type of CKF, the fractional-order hybrid extended-cubature Kalman filter (HECKF) is proposed to weaken the influence of initial value by the first-order Taylor expansion and the third-order spherical-radial rule. These two methods can effectively reduce the influence of initial value on the state estimation. Finally, the effectiveness of the proposed CKFs is verified by two simulation examples.

Position Sensorless Control Based on Augmented Extended Kalman Filter for Permanent Magnet Linear Synchronous Motor

2011 ◽  
Vol 88-89 ◽  
pp. 350-354
Author(s):  
Hua Cai Lu ◽  
Ming Jiang ◽  
Li Sheng Wei ◽  
Bing You Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Variable ◽  
Detailed Explanation ◽  
System States ◽  
Position Sensorless

In order to achieve position sensorless control for PMLSM drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter (AEKF) estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter (EKF). The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

Research of Robust State Estimation Method and Program Implementation Considering Large-Scale Wind Power Integration

2014 ◽  
Vol 672-674 ◽  
pp. 361-366
Author(s):  
Ya Di Luo ◽  
Jing Li ◽  
Zi Ming Guo ◽  
Gui Rong Shi ◽  
Dong Sheng Wang ◽  
...  
Keyword(s):  
State Estimation ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Estimation Method ◽  
Wind Farms ◽  
The State ◽  
Wind Power Integration ◽  
The Impact ◽  
Bad Data

According to the characteristics of the wind farm measuration and the impact of bad data on the state estimation, this paper introduces the reference value of measurement type and the bad data reference factor into the weight function, and then presents the calculation method of state estimation method for solving residual contamination problem caused by large-scale wind power integration. In order to improve the software computing speed and the data section real-time performance of robust state estimation, using parallel algorithms to do Givens transformation. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the State Estimation.

scholarly journals A New Constrained State Estimation Method Based on Unscented H∞ Filtering

2020 ◽  
Vol 10 (23) ◽  
pp. 8484
Author(s):  
Yuanyuan Liu ◽  
Yaqiong Fu ◽  
Huipin Lin ◽  
Jingbiao Liu ◽  
Mingyu Gao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
State Constraints ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
The State ◽  
Soc Estimation ◽  
Non Gaussian ◽  
Physical Laws ◽  
Broad Overview

The unscented Kalman filter (UKF) is widely used in many fields. When the unscented Kalman filter is combined with the H∞ filter (HF), the obtained unscented H∞ filtering (UHF) is very suitable for state estimation of nonlinear non-Gaussian systems. However, the application of state estimation is often limited by physical laws and mathematical models on some occasions. The standard unscented H∞ filtering always performs poorly under this situation. To solve this problem, this paper improves the UHF algorithm based on state constraints and studies the UHF algorithm based on the projection method. The standard UHF sigma points that violate the state constraints are projected onto the constraint boundary. Firstly, the paper gives a broad overview of H∞ filtering and unscented H∞ filtering, then addresses the issue of how to add constraints using the UHF approach, and finally, the new method is tested and evaluated by the gas-phase reversible reaction and the State of Charge (SOC) estimation examples. Simulation results show the validity and feasibility of the state-constrained UHF algorithm.

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