scholarly journals Power enhancement in a grid connected solar PV System by using PLL-neural based converter

2019 ◽  
Vol 5 (11) ◽  
pp. 1-9
Author(s):  
Avinash Kumar ◽  
Vivek Kumar Kostha ◽  
Satyam Kumar Prasun
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Control Algorithm ◽  
Reactive Power ◽  
Network Control ◽  
Neural Network Control ◽  
Solar Pv ◽  
Pv System ◽  
The Neural Network ◽  
Solar Pv System

This work deals with neural network control algorithm-based grid connected to solar photo voltaic (PV) system consisting of DC-AC converter. The reference solar-grid current for three-leg VSC are estimated using neural network control algorithm. The neural network control algorithm based solar PV system is modeled in MATLAB R2018a along with SIMULINK.. This study presents an artificial neural network-based controller for regulating the level of active and reactive power output. First, the three phase currents from the VSI are measured and compared with the three reference currents. The neural network is trained to have minimum output error. It was concluded that the power output from the system was found to be190 KVA in case of system having no intelligent controller and 700 KVA in case of system with AAN based control. The voltage of output is maintained to be 20 kV in the grid system for analysis purpose. Thus the proposed control is expected to be implemented in the renewable energy resources for better output.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

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