A MISO Nonlinear Model of Photovoltaic Panel Based on System Identification

The growth in the use of renewable energy sources has made photovoltaic (PV) systems increasingly popular. PV systems are mostly composed of a PV panel to generate dc power and an inverter to convert dc to ac power. Many events may occur on the PV modules, resulting in possible faults and loss of generated power, due to their exposure to environmental conditions. Thus, modeling the PV panel allows to determine the estimation of the output power of the PV system. In this context, this paper presents a nonlinear model based on the black-box modeling of System Identification. In addition, Hammerstein-Wiener (HW) model structure was selected due to its simplicity of representing the nonlinearity of the PV panel. The irradiance and temperature on the PV panel represent the input signals for the proposed model. The current and voltage are measured to calculate the output power, resulting in a Multi Input Single Output (MISO) model. The proposed HW model is compared with the classical Single Diode Model and the results show that the proposed model is more accurate.

[1]  Elyes Garoudja,et al.  An enhanced machine learning based approach for failures detection and diagnosis of PV systems , 2017 .

[2]  Dhananjay B. Talange,et al.  Modeling of solar photovoltaic module using system identification , 2017, 2017 7th International Conference on Power Systems (ICPS).

[3]  Anawach Sangswang,et al.  Modeling of Photovoltaic Grid Connected Inverters Based on Nonlinear System Identification for Power Quality Analysis , 2011 .

[4]  E. Muljadi,et al.  A cell-to-module-to-array detailed model for photovoltaic panels , 2012 .

[5]  Wooi Ping Hew,et al.  Zigbee-based data acquisition system for online monitoring of grid-connected photovoltaic system , 2015, Expert Syst. Appl..

[6]  Dan Stefanoiu,et al.  Photovoltaic Generators -- Modeling and Control , 2015, 2015 20th International Conference on Control Systems and Computer Science.

[7]  Yan Su,et al.  An ARMAX model for forecasting the power output of a grid connected photovoltaic system , 2014 .

[8]  Aissa Chouder,et al.  Monitoring, modelling and simulation of PV systems using LabVIEW , 2013 .

[9]  Violeta Holmes,et al.  Fault detection algorithm for grid-connected photovoltaic plants , 2016 .

[10]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[11]  S. R. Bull,et al.  Renewable energy today and tomorrow , 2001, Proc. IEEE.

[12]  Lennart Ljung.  Black-box models from input-output measurements , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[13]  Ren.  Renewables 2019 Global Status Report , 2012 .

[14]  Weidong Xiao,et al.  A novel modeling method for photovoltaic cells , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[15]  Abdelhamid Rabhi,et al.  Real Time Fault Detection in Photovoltaic Systems , 2017 .

[16]  William A. Beckman,et al.  Improvement and validation of a model for photovoltaic array performance , 2006 .

[17]  I. Ozturk,et al.  The effect of renewable energy consumption on economic growth: Evidence from top 38 countries , 2016 .

[19]  Bruno Ando,et al.  Sentinella: Smart Monitoring of Photovoltaic Systems at Panel Level , 2015, IEEE Transactions on Instrumentation and Measurement.

[20]  Ayedh Alqahtani,et al.  Photovoltaic Power Forecasting Model Based on Nonlinear System Identification , 2016, Canadian Journal of Electrical and Computer Engineering.

[21]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[22]  K. Zweibel,et al.  Basic photovoltaic principles and methods , 1984 .