An amplitude-modulated pseudo-random binary sequence approach to broadband impedance spectroscopy for photovoltaic module system identification

2021 ◽  
Author(s):  
Linda Shelembe ◽  
Paul Barendse
Keyword(s):  
System Identification ◽  
Impedance Spectroscopy ◽  
Binary Sequence ◽  
Photovoltaic Module ◽  
Module System

Pseudo-random binary sequence closed-loop system identification error with integration control

2009 ◽  
Vol 223 (6) ◽  
pp. 877-884 ◽  
Author(s):  
Z Ren ◽  
G G Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Binary Sequence ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Identification Error ◽  
Closed Loop Identification ◽  
Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

scholarly journals Experimental Study of Electrical Properties of Pharmaceutical Materials by Electrical Impedance Spectroscopy

2020 ◽  
Vol 10 (18) ◽  
pp. 6576
Author(s):  
Manuel Vázquez-Nambo ◽  
José-Antonio Gutiérrez-Gnecchi ◽  
Enrique Reyes-Archundia ◽  
Wuqiang Yang ◽  
Marco-A. Rodriguez-Frias ◽  
...  
Keyword(s):  
System Identification ◽  
Electrical Properties ◽  
Impedance Spectroscopy ◽  
Electrical Impedance ◽  
Wide Frequency Range ◽  
Electrical Impedance Spectroscopy ◽  
Frequency Range ◽  
Pharmaceutical Materials ◽  
Identification Techniques

The physicochemical characterization of pharmaceutical materials is essential for drug discovery, development and evaluation, and for understanding and predicting their interaction with physiological systems. Amongst many measurement techniques for spectroscopic characterization of pharmaceutical materials, Electrical Impedance Spectroscopy (EIS) is powerful as it can be used to model the electrical properties of pure substances and compounds in correlation with specific chemical composition. In particular, the accurate measurement of specific properties of drugs is important for evaluating physiological interaction. The electrochemical modelling of compounds is usually carried out using spectral impedance data over a wide frequency range, to fit a predetermined model of an equivalent electrochemical cell. This paper presents experimental results by EIS analysis of four drug formulations (trimethoprim/sulfamethoxazole C14H18N4O3-C10H11N3O3, ambroxol C13H18Br2N2O.HCl, metamizole sodium C13H16N3NaO4S, and ranitidine C13H22N4O3S.HCl). A wide frequency range from 20 Hz to 30 MHz is used to evaluate system identification techniques using EIS data and to obtain process models. The results suggest that arrays of linear R-C models derived using system identification techniques in the frequency domain can be used to identify different compounds.

scholarly journals NNARX Networks on Didactic Level System Identification

2020 ◽  
Vol 15 ◽  
Keyword(s):  
System Identification ◽  
Polynomial System ◽  
Nonlinear System Identification ◽  
Binary Sequence ◽  
Experimental System ◽  
External Input ◽  
Open Loop ◽  
Small Scale ◽  
Water Tank ◽  
Non Linear System

This work has as main objective to propose the identification of a small scale non-linear system through the Neural Network AutoRegressive with eXternal input. The use of this network requires an adequate methodology for its configuration and, consequently, a good training set. Then, it is proposed that the main definitions of the network parameters be obtained through the analysis of nonintrusive performance indices. Additionally, using a database based on the system’s response, excited by the Pseudo-Random Binary Sequence signal. The methodology will be applied in two specific open-loop identification situations: numerical simulation of a fourth order polynomial system (Case 01), and an experimental system that controls a nonlinear water tank level (Case 02). The results of the identified models were able to represent the system dynamics with high fidelity, presenting an average identification error of less than 0:14 and 0:34% for Case 1 and 2, respectively. Also, it is observed that the learning and generalization evidence could represent the process intrinsic nonlinearities satisfactorily. Besides, it will be possible to find the potentiality and usefulness of the developed network in nonlinear system identification.

Mechanical characteristics of a new type of cable-supported photovoltaic module system

Solar Energy ◽  
2021 ◽  
Vol 226 ◽  
pp. 408-420
Author(s):  
Xu-Hui He ◽  
Hao Ding ◽  
Hai-Quan Jing ◽  
Xiao-Ping Wu ◽  
Xiao-Jun Weng
Keyword(s):  
Mechanical Characteristics ◽  
Photovoltaic Module ◽  
New Type ◽  
Module System

scholarly journals System Identification in the Delta Domain: A Unified Approach Using FAGWO Algorithm

2018 ◽  
Author(s):  
Souvik Ganguli ◽  
Gagandeep Kaur ◽  
Prasanta Sarkar
Keyword(s):  
System Identification ◽  
Binary Sequence ◽  
Search Space ◽  
Test System ◽  
Model Parameters ◽  
Gray Wolf ◽  
Wiener System ◽  
Continuous Time Systems ◽  
Gray Wolf Optimizer ◽  
Time Systems

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and latest heuristic approaches reported in the literature.

An evaluation of the influence of Eigensystem Realization Algorithm settings on multiple input multiple output system identification

2021 ◽  
pp. 107754632110300
Author(s):  
Dirceu Soares ◽  
Alberto Luiz Serpa
Keyword(s):  
System Identification ◽  
Degrees Of Freedom ◽  
Mimo Systems ◽  
Gaussian White Noise ◽  
Multiple Input Multiple Output ◽  
Binary Sequence ◽  
Response Analysis ◽  
Eigensystem Realization Algorithm ◽  
Multiple Input ◽  
Input Multiple Output

One characteristic of the Eigensystem Realization Algorithm method for system identification concerns about the difficulty of finding more appropriate parameters to run the algorithm. One of this work’s purposes is to tackle the cumbersome task of achieving the ideal algorithm settings, providing additional knowledge about algorithm parameters’ influence, and searching to improve results with quicker settings of the algorithm’s parameters, especially for Multiple Input Multiple Output (MIMO) systems. The application of a Fit Rate indicator to evaluate the identified system arises as a novelty in the Eigensystem Realization Algorithm applications, aiming to assess the system identification performance and drive the algorithm to better adjustments. Another objective of this article regards the application of a Pseudo Random Binary Sequence as excitation signals, which has not been used until now with the Eigensystem Realization Algorithm, despite being successfully applied in the system identification process. The proposed approach is verified and analyzed with numerical simulations for a mass–spring–damper model of 5 degrees of freedom. The results reported in time response analysis and frequency response analysis allow us to realize the effect of settings accordingly for the system identification improvement. The results analysis was extended to simulate and compare the Pseudo Random Binary Sequence with Gaussian white noise excitation, and the system was also submitted to the presence of measurement noise.

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