A linear method to extract diode model parameters of solar panels from a single I–V curve

The I-V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I-V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I-V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed

Non-Contact Measurement of POA Irradiance and Cell Temperature for PV Systems

This paper presents a non-contact measurement of irradiance on plane of array (POA) and cell temperature for PV systems. The idea is motivated from the diode model of PV, where POA irradiance and cell temperature are proportional to the photocurrent and modified ideality factor, respectively. Based on the recent progress of diode model identification, the photocurrent and modified ideality factor can be linearly determined from I-V characteristics, which makes it feasible to develop a non-contact measurement approach for POA irradiance and cell temperature, i.e., both of them will be derived completely from the diode mode parameter identification without the need of any sensors. The calibration of the proportional factors is done from the indoor module flash test and then applied to outdoor module testbed to show the accuracy and effectiveness of the proposed method

A hands-on approach to teaching system identification using first order plus dead time (FOPDT) modelling of step response data

This paper describes three step response-based system identification methods of increasing complexity, together with a range of exercises that will enhance student understanding of this area in an engaging and practical way. For illustration purposes and practicality, it is assumed that the model to be identified is of the first order plus dead time (FOPDT) type. The first method uses a popular graphical technique, which is easy to understand and apply, but inaccurate when the response data is not ideal. The second uses the Nelder-Mead simplex method, which is a more powerful technique and has the added benefit of introducing undergraduate students to the concepts of numerical optimisation. The third uses an integral equation (IE) algorithm. The latter two methods, which can be readily extended to other model structures and input types, are also demonstrated using experimental data obtained from a tank level control system

Close loop step test used for tuning PID controller by genetic algorithms

The identification of multiple points on the process frequency response from a single step feedback test is used. These identified points are there employed to design a PID controller using the multiple-point fifting controller design method. The PID controller is design by minimizing the error between the actual and desired close-loop response in a certain frequency region. The control problem is stated as a nonlinear least squares unconstrained minimization problem. The optimization problem is solved with a simplegenetic algorithm.Keywords: FFT, genetic algorithm, nonlinear least squares optimization, PIDcontroller

A linear identification of diode models from single I-V characteristics of PV panels

This paper presents a novel approach on diode model parameters identification from the I-V characteristics of PV panels. Other than the prevailing methodology of solving a group of nonlinear equations from a few points on the I-V curve, the proposed one views the diode model as the equivalent output of a dynamic system. From this new viewpoint, diode model parameters are linked to the transfer function (after Laplace transform) of the same dynamic system whose parameters are then identified by a simple integral-based linear square. Indoor flash test shows the accuracy and effectiveness of the proposed method, and outdoor module testing shows its ability of online monitoring and diagnostics. Comparisons to the methods of Lambert W function and evolution algorithms are also included

Robust identification of continuous systems with dead-time from step responses

10.1016/S0005-1098(00)00177-1Automatica373377-390ATCA