Supervisory control of PV-battery systems by online tuned neural networks

The paper deals with a neural network based supervisor control system for a PhotoVoltaic (PV) plant. The aim of the work is to feed the power line with the 24 hours ahead forecast of the PV production. An on-line self-learning prediction algorithm is used to forecast the power production of the PV plant. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power feeding the electric line is scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.

[1]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[2]  Maria Letizia Corradini,et al.  Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators , 2012, IEEE Transactions on Industrial Informatics.

[3]  W. L. Kling,et al.  Technical benefits of distributed storage and load management in distribution grids , 2009, 2009 IEEE Bucharest PowerTech.

[4]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[5]  Sehyun Park,et al.  Design and implementation of intelligent energy distribution management with photovoltaic system , 2012, IEEE Transactions on Consumer Electronics.

[6]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[7]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[8]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  G. Ippoliti,et al.  Sliding mode control of permanent Magnet Synchronous Generators for wind turbines , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[10]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[11]  N. Essounbouli,et al.  Design and simulation of fuzzy controller and supervisor for a micro-hydro power plant , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[12]  Juan Gonzalez,et al.  Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation , 2012, IEEE Transactions on Smart Grid.

[13]  Basabi Chakraborty,et al.  A novel normalization technique for unsupervised learning in ANN , 2000, IEEE Trans. Neural Networks Learn. Syst..

[14]  N. Sundararajan,et al.  Fully Tuned Radial Basis Function Neural Networks for Flight Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[15]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[16]  Frank C. Walsh,et al.  Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic , 2011, IEEE Transactions on Vehicular Technology.

[17]  L Y Pao,et al.  Control of Wind Turbines , 2011, IEEE Control Systems.

[18]  G. Ippoliti,et al.  Solar irradiation forecasting using RBF networks for PV systems with storage , 2012, 2012 IEEE International Conference on Industrial Technology.

[19]  Sauro Longhi,et al.  Learning control of mobile robots using a multiprocessor system , 2006 .

[20]  G. Ippoliti,et al.  On line solar irradiation forecasting by minimal resource allocating networks , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[21]  P. Siano,et al.  Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid , 2011, IEEE Transactions on Sustainable Energy.

[22]  G. Ippoliti,et al.  Online tuned neural networks for PV plant production forecasting , 2012, 2012 38th IEEE Photovoltaic Specialists Conference.

[23]  Alvin O. Converse,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. INVITED PAPER Seasonal Energy Storage in a Renewable Energy System , 2022 .

[24]  Bimal K. Bose,et al.  Fuzzy logic and neural networks in power electronics and drives , 2000 .

[25]  G. Ippoliti,et al.  Sliding mode control based robust observer of aerodynamic torque for variable-speed wind turbines , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[26]  Sauro Longhi,et al.  On-line steam production prediction for a municipal solid waste incinerator by fully tuned minimal RBF neural networks , 2011 .

[27]  Sauro Longhi,et al.  Lyapunov-based switching control using neural networks for a remotely operated vehicle , 2007, Int. J. Control.

[28]  Maria Letizia Corradini,et al.  Neural Networks Based Control of Mobile Robots: Development and Experimental Validation , 2003, J. Field Robotics.

[29]  Gianluca Ippoliti,et al.  Intelligent control for a remotely operated vehicle , 2009, Int. J. Syst. Sci..

[30]  X. Vallve,et al.  Micro storage and demand side management in distributed PV grid-connected installations , 2007, 2007 9th International Conference on Electrical Power Quality and Utilisation.