NEURAL OUTPUT REGULATION FOR A SOLAR POWER PLANT
نویسندگان
چکیده
منابع مشابه
Neural Output Regulation for a Solar Power Plant
In this paper the modelling capabilities of a recurrent neural network and the effectiveness and stability of the output regulation control theory are combined. The control structure consists in a neural based indirect adaptive control scheme, being the main goal to provide a viable practical control strategy suitable for real-time implementations. This control scheme was applied to the distrib...
متن کاملA Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network...
متن کاملRecurrent Neural Networks and Feedback Linearization for a Solar Power Plant Control
A feedback linearisation control scheme is proposed an implemented on a real solar power plant. This structure is based on a non-linear control methodology combined with a recurrent neural predictor. Given model plant mismatches it is crucial to provide the control system with an off-set compensation, being an internal model controller strategy used for this purpose. Experimental results collec...
متن کاملExtracting Technical Specifications of a Solar Panel Type to Design a 10 MW Hybrid Power Plant
This paper focuses on the design of a 10 MW hybrid power plant using the technical specifications (data sheet) of an industrial solar panel. The main purpose was to find out the exact electrical properties of the solar panel specialy with conjunction to its temperature, to optimize overall output energy. We first describe the most important types of solar power plants and afterwards focus on el...
متن کاملRecurrent neural networks for nonlinear output regulation
Based on a power-series approximation method, recurrent neural networks (RNN) are proposed for real-time synthesis and auto-tuning of feedback controllers for nonlinear output regulation systems. The proposed neurocontrol approach represents a novel application of recurrent neural networks to the nonlinear output regulation problem. The proposed approach completely inherits the stability and as...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC Proceedings Volumes
سال: 2002
ISSN: 1474-6670
DOI: 10.3182/20020721-6-es-1901.01037