scholarly journals Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2456 ◽  
Author(s):  
Yassine Amirat ◽  
Zakarya Oubrahim ◽  
Hafiz Ahmed ◽  
Mohamed Benbouzid ◽  
Tianzhen Wang
Keyword(s):  
Kalman Filter ◽  
Likelihood Function ◽  
Least Square ◽  
Computation Complexity ◽  
Real Power ◽  
Power Monitoring ◽  
Phasor Estimation ◽  
Newton Raphson ◽  
Events Data ◽  
Maximum Likelihood Technique

This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton–Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.

Machining Process Power Monitoring: Bayesian Update of Machining Power Model

2012 ◽  
Author(s):  
Parikshit Mehta ◽  
Mathew Kuttolamadom ◽  
Laine Mears
Keyword(s):  
Cutting Force ◽  
Likelihood Function ◽  
Control Point ◽  
Least Square ◽  
Machining Process ◽  
Prior Belief ◽  
Monitoring And Control ◽  
Cnc Machine ◽  
Power Monitoring ◽  
Bayesian Update

Monitoring the CNC machine tool power provides valuable information that aids condition based maintenance, machine efficiency and machining process monitoring. Cutting force in machining process is an interesting variable to measure from monitoring and control point of view. Although the direct methods of measuring the cutting force exist, prohibitive costs do not allow deployment in industrial environment. In the indirect methods of measuring force, measuring the spindle motor current to estimate the cutting power and consequently the cutting force is popular. This work discusses the calibration of spindle current based torque sensor for the estimation of the cutting force in turning operation. The work undertakes handling uncertainty in measurement of the cutting torque measurement. Considering the steady state value, the cutting torque is represented as a polynomial function of the speed and measured power. Though the identification of the unknown coefficients can be done based on the offline tests, in current work, the Bayesian update of coefficients is proposed. This method allows online learning of these coefficients. The cutting torque value based on the model has some variability due to variation in the coefficients and unmodeled dynamics. The iterative learning happens in three stages, namely — Prior belief, likelihood function establishment and update in prior belief with observed data producing posterior belief. The establishment of the priors is done through some offline tests. The likelihood function accounts for noise in the measurement of torque. And finally, Markov Chain Monte Carlo (MCMC) simulations help sampling from unknown posterior distribution. This scheme has ability to sample from any distribution. A single update cycle shows high reduction in the variability of the torque. Experimental data is produced to verify the effectiveness of method; the Bayesian update scheme outperforms least-square polynomial fit method consistently for different cutting speeds and cutting load values.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Phasor estimation in power transmission lines by using the Kalman filter

2021 ◽  
Author(s):  
Ronaldo F. R. Pereira ◽  
Felipe P. Albuquerque ◽  
Luisa H. B. Liboni ◽  
Eduardo C. M. Costa ◽  
Mauricio C. de Oliveira
Keyword(s):  
Kalman Filter ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Phasor Estimation

scholarly journals RSS Based Wi-Fi Positioning Method Using Recursive Least Square (RLS) Algorithm

2018 ◽  
Vol 7 (2.24) ◽  
pp. 492
Author(s):  
Sreevardhan Cheerla ◽  
D Venkata Ratnam
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Positioning Method ◽  
Accurate Position ◽  
Software And Hardware ◽  
Newton Raphson ◽  
Raphson Method

Due to rapid increase in demand for services which depends upon exact location of devices leads to the development of numerous Wi-Fi positioning systems. It is very difficult to find the accurate position of a device in indoor environment due to substantial development of structures. There are many algorithms to determine the indoor location but they require expensive software and hardware. Hence receiving signals strength (RSS) based algorithms are implemented to find the self-positioning. In this paper Newton-Raphson, Gauss-Newton and Steepest descent algorithms are implemented to find the accurate location of Wi-Fi receiver in Koneru Lakshmaiah (K L) University, Guntur, Andhra Pradesh, India. From the results it is evident that Newton -Raphson method is better in providing accurate position estimations. 

A new robust Kalman filter with measurement loss based on mixing distribution

2021 ◽  
pp. 014233122110549
Author(s):  
Chenghao Shan ◽  
Weidong Zhou ◽  
Yefeng Yang ◽  
Hanyu Shan
Keyword(s):  
Kalman Filter ◽  
Likelihood Function ◽  
State Space Model ◽  
Random Variable ◽  
Superior Performance ◽  
Gaussian State ◽  
Bernoulli Random Variable ◽  
Mixing Distribution ◽  
Robust Kalman Filter ◽  
Heavy Tailed

A new robust Kalman filter (KF) based on mixing distribution is presented to address the filtering issue for a linear system with measurement loss (ML) and heavy-tailed measurement noise (HTMN) in this paper. A new Student’s t-inverse-Wishart-Gamma mixing distribution is derived to more rationally model the HTMN. By employing a discrete Bernoulli random variable (DBRV), the form of measurement likelihood function of double mixing distributions is converted from a weighted sum to an exponential product, and a hierarchical Gaussian state-space model (HGSSM) is therefore established. Finally, the system state, the intermediate random variables (IRVs) of the new STIWG distribution, and the DBRV are simultaneously estimated by utilizing the variational Bayesian (VB) method. Numerical example simulation experiment indicates that the proposed filter in this paper has superior performance than current algorithms in processing ML and HTMN.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

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