scholarly journals Control of UPQC based on steady state linear Kalman filter for compensation of power quality problems

2020 ◽  
Vol 6 (2) ◽  
pp. 52-65
Author(s):  
Sayed Javed Alam ◽  
Sabha Raj Arya
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Power Quality

Power Exchanges of Hybrid Fuel Cell-Ultracapacitor Power Sources Feeding Pulse Loads

2006 ◽  
Vol 4 (4) ◽  
pp. 516-519 ◽  
Author(s):  
D. Asprino ◽  
L. Conte ◽  
M. Pagano ◽  
G. Velotto
Keyword(s):  
Steady State ◽  
Fuel Cell ◽  
Power Quality ◽  
Primary Source ◽  
Experimental Results ◽  
Storage Device ◽  
Transient Performance ◽  
Power Sources ◽  
Hybrid Generator ◽  
Electrical Source

The paper focuses on the experimental results of a series of tests performed on a hybrid electrical source. The hybrid generator is made up of a fuel cell primary source equipped with an ultracapacitor storage device. The paper presents an examination of the steady-state and transient performance of the hybrid fuel cell-ultracapacitor source in terms of power quality. The aim is to investigate on fuel cell-ultracapacitor source’s behavior to feed pulsing loads.

scholarly journals Global Systems for Mobile Position Tracking Using Kalman and Lainiotis Filters

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nicholas Assimakis ◽  
Maria Adam
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Position Tracking ◽  
Computational Burden ◽  
Time Invariant ◽  
Global Systems

We present two time invariant models for Global Systems for Mobile (GSM) position tracking, which describe the movement inx-axis andy-axis simultaneously or separately. We present the time invariant filters as well as the steady state filters: the classical Kalman filter and Lainiotis Filter and the Join Kalman Lainiotis Filter, which consists of the parallel usage of the two classical filters. Various implementations are proposed and compared with respect to their behavior and to their computational burden: all time invariant and steady state filters have the same behavior using both proposed models but have different computational burden. Finally, we propose a Finite Impulse Response (FIR) implementation of the Steady State Kalman, and Lainiotis filters, which does not require previous estimations but requires a well-defined set of previous measurements.

Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

1994 ◽  
Vol 116 (3) ◽  
pp. 550-553 ◽  
Author(s):  
Chung-Wen Chen ◽  
Jen-Kuang Huang
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Finite Number ◽  
Discrete Time ◽  
Stochastic System ◽  
Moving Average ◽  
Ar Model ◽  
Numerical Example ◽  
Measurement Noises ◽  
Time Invariant

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

scholarly journals Performance enhancement of grid-interfaced inverter using intelligent controller

2020 ◽  
Vol 53 (3-4) ◽  
pp. 551-563 ◽  
Author(s):  
Sushma Kakkar ◽  
Rajesh Kumar Ahuja ◽  
Tanmoy Maity
Keyword(s):  
Steady State ◽  
Power Quality ◽  
Reactive Power ◽  
Performance Enhancement ◽  
Parametric Uncertainties ◽  
Inference Control ◽  
Integral Control ◽  
Proportional Integral ◽  
Active And Reactive Power ◽  
Proportional Integral Control

The high-performance grid-interfaced inverters are in demand as they are rapidly used in renewable energy systems. The main objective of grid-interfaced inverters is to inject high-quality active and reactive power with sinusoidal current. Many control schemes have been proposed earlier in the literature, but the operation under parametric uncertainties has not been given much attention. In this article, an adaptive network–based fuzzy inference control algorithm for a three-phase grid-interfaced inverter under parametric uncertainties is proposed. The main purpose of the proposed technique is to enhance the response time, decrease the steady-state oscillation in the injected active and reactive power and enhance the power quality even with parametric uncertainties. For assessment and evaluation reason, the conventional proportional–integral control is compared with the proposed controller. For a fair comparison, the gain setting for the proportional–integral control is obtained by Particle swarm optimization algorithm. The suggested system is developed and simulated in MATLAB/Simulink. Simulation results demonstrate that both the controllers work well to regulate the powers to required values, even with parametric variations. However, the proposed control demonstrates superiority in comparison to conventional proportional–integral control in terms of speedy response, decreased steady-state fluctuations, better power quality and increased robustness. The rise time and fluctuations in the per-unit active and reactive power are much less with the proposed control. Total harmonic distortion of the injected current and grid current are significantly better than the conventional proportional–integral control.

scholarly journals Two-Sample Kalman Filter for Steady-State Data Assimilation

2008 ◽  
Vol 136 (11) ◽  
pp. 4503-4516 ◽  
Author(s):  
Julius H. Sumihar ◽  
Martin Verlaan ◽  
Arnold W. Heemink
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Data Assimilation ◽  
Simple Wave ◽  
Wave Model ◽  
State Data ◽  
Error Covariance ◽  
Observation Network ◽  
Kalman Gain ◽  
Potential Applicability

Abstract In this paper, a new iterative algorithm for computing a steady-state Kalman gain is proposed. This algorithm utilizes two model forecasts with statistically independent random perturbations to determine the error covariance used to define a Kalman gain matrix for steady-state data assimilation. It is based on the assumption that the error process is weakly stationary and ergodic. The algorithm consists of an iterative procedure for improving the covariance estimate, which requires a fixed observation network. Two twin experiments using a simple wave model and an operational storm surge prediction model are performed to demonstrate the performance of the proposed algorithm. The experiments show that the results obtained by using the proposed algorithm converge to the ones produced by the classic Kalman filter algorithm. An additional experiment using the three-variable Lorenz model is also performed to demonstrate its potential applicability in unstable dynamical systems.

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