A novel hybrid model-based MPPT algorithm based on artificial neural networks for photovoltaic applications

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

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Direct and indirect model based control using artificial neural networks

Industrial & Engineering Chemistry Research ◽

10.1021/ie00060a009 ◽

1991 ◽

Vol 30 (12) ◽

pp. 2564-2573 ◽

Cited By ~ 149

Author(s):

Dimitris C. Psichogios ◽

Lyle H. Ungar

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Model Based Control ◽

Model Based ◽

Artificial Neural

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A new global version of M(3000)F2 prediction model based on artificial neural networks

Advances in Space Research ◽

10.1016/j.asr.2013.11.023 ◽

2014 ◽

Vol 53 (3) ◽

pp. 371-386 ◽

Cited By ~ 6

Author(s):

S.I. Oronsaye ◽

L.A. McKinnell ◽

J.B. Habarulema

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Model ◽

Model Based ◽

Artificial Neural

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Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks

Volume 1: Turbo Expo 2003 ◽

10.1115/gt2003-38116 ◽

2003 ◽

Cited By ~ 4

Author(s):

Pernilla Olausson ◽

Daniel Ha¨ggsta˚hl ◽

Jaime Arriagada ◽

Erik Dahlquist ◽

Mohsen Assadi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Gas Turbine ◽

Hybrid Model ◽

Power Plants ◽

Computational Time ◽

Process Identification ◽

Calculation Time ◽

Artificial Neural

Traditionally, when process identification, monitoring and diagnostics are carried out for power plants and engines, physical modeling such as heat and mass balances, gas path analysis, etc. is utilized to keep track of the process. This type of modeling both requires and provides considerable knowledge of the process. However, if high accuracy of the model is required, this is achieved at the expense of computational time. By introducing statistical methods such as Artificial Neural Networks (ANNs), the accuracy of the complex model can be maintained while the calculation time is often reduced significantly reduced. The ANN method has proven to be a fast and reliable tool for process identification, but the step from the traditional physical model to a pure ANN model is perhaps too wide and, in some cases, perhaps unnecessary also. In this work, the Evaporative Gas Turbine (EvGT) plant was modeled using both physical relationships and ANNs, to end up with a hybrid model. The type of architecture used for the ANNs was the feed-forward, multi-layer neural network. The main objective of this study was to evaluate the viability, the benefits and the drawbacks of this hybrid model compared to the traditional approach. The results of the case study have clearly shown that the hybrid model is preferable. Both the traditional and the hybrid models have been verified using measured data from an existing pilot plant. The case study also shows the simplicity of integrating an ANN into conventional heat and mass balance software, already implemented in many control systems for power plants. The access to a reliable and faster hybrid model will ultimately give more reliable operation, and ultimately the lifetime profitability of the plant will be increased. It is also worth mentioning that for diagnostic purposes, where advanced modeling is important, the hybrid model with calculation time well below one second could be used to advantage in model predictive control (MPC).

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