Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network

2018 ◽  
Vol 144 ◽  
pp. 316-333 ◽  
Author(s):  
Shi-Jie Cao ◽  
Chen Ren
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Ventilation Control ◽  
Artificial Neural ◽  
Low Dimensional

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

scholarly journals Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller

2020 ◽  
Vol 93 (1-4) ◽  
pp. 31-38
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Energy ◽  
Control Strategy ◽  
Energy System ◽  
Maximum Power ◽  
Point Tracking ◽  
Pi Controllers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.

A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

Development of a real-time control strategy with artificial neural network for automatic control of a continuous-flow sequencing batch reactor

2001 ◽  
Vol 44 (1) ◽  
pp. 95-104 ◽  
Author(s):  
B. C. Cho ◽  
S.-L. Liaw ◽  
C.-N. Chang ◽  
R.-F. Yu ◽  
S.-J. Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Control Strategy ◽  
Continuous Flow ◽  
Prediction Models ◽  
Process Models ◽  
Real Time Control ◽  
Time Control ◽  
Artificial Neural

The purpose of this study is to develop a reliable and effective real-time control strategy by integrating artificial neural network (ANN) process models to perform automatic operation of a dynamic continuous-flow SBR system. The ANN process models, including ORP/pH simulation models and water quality ([NH4+-N] and [NOx--N]) prediction models, can assist in real-time searching the ORP and pH control points and evaluating the operation performances of aerobic nitrification and anoxic denitrification operation phases. Since the major biological nitrogen removal mechanisms were controlled at nitritification (NH4+-N→NO2--N) and denitritification (NO2--N→N2) stages, as well as the phosphorus uptake and release could be completely controlled during aerobic and anoxic operation phases, the system operation performances under this ANN real-time control system revealed that both the aeration time and overall hydraulic retention time could be shortened to about 1.9-2.5 and 4.8-6.2 hrs/cycle respectively. The removal efficiencies of COD, ammonia nitrogen, total nitrogen, and phosphate were 98%, 98%, 97%, and 84% respectively, which were more effective and efficient than under conventional fixed-time control approach.

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