scholarly journals Design and Performance Evaluation of a Step-Up DC–DC Converter with Dual Loop Controllers for Two Stages Grid Connected PV Inverter

2022 ◽  
Vol 14 (2) ◽  
pp. 811
Author(s):  
Muhammad Yasir Ali Khan ◽  
Haoming Liu ◽  
Salman Habib ◽  
Danish Khan ◽  
Xiaoling Yuan

In this work, a non-isolated DC–DC converter is presented that combines a voltage doubler circuit and switch inductor cell with the single ended primary inductor converter to achieve a high voltage gain at a low duty cycle and with reduced component count. The converter utilizes a single switch that makes its control very simple. The voltage stress across the semiconductor components is less than the output voltage, which makes it possible to use the diodes with reduced voltage rating and a switch with low turn-on resistance. In particular, performance principle of the proposed converter along with the steady state analysis such as voltage gain, voltage stress on semiconductor components, and design of inductors and capacitors, etc., are carried out and discussed in detail. Moreover, to regulate a constant voltage at a DC-link capacitor, back propagation algorithm-based adaptive control schemes are designed. These adaptive schemes enhance the system performance by dynamically updating the control law parameters in case of PV intermittency. Furthermore, a proportional resonant controller based on Naslin polynomial method is designed for the current control loop. The method describes a systematic procedure to calculate proportional gain, resonant gain, and all the coefficients for the resonant path. Finally, the proposed system is simulated in MATLAB and Simulink software to validate the analytical and theoretical concepts along with the efficacy of the proposed model.

Automation ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 220-237
Author(s):  
Farzad Mohammadzadeh Shahir ◽  
Meysam Gheisarnejad ◽  
Mohammad-Hassan Khooban

In this paper, a new structure is proposed for a boost dc–dc converter based on the voltage-lift (VL) technique. The main advantages of the proposed converter are its lack of transformer, simple structure, free and low input current ripple, high voltage gain capability by using an input source, suitable voltage stress on semiconductors and lower output capacitance. Herein, the analysis of the proposed converter operating and its elements voltage and current relations in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented, and the voltage gain of each operating mode is individually calculated. Additionally, the critical inductance, current stress of switches, calculation of passive components’ values and efficiency are analyzed. In addition, the proposed converter is compared with other studied boost converters in terms of ideal voltage gain in the CCM and the number of active and passive components, maximum voltage stress on semiconductors, and situation of input current ripples. The correctness of the theoretical concepts is examined from the experimental results using the laboratory prototype.


2016 ◽  
Vol 12 (9) ◽  
pp. 4371-4381
Author(s):  
Durairaj S ◽  
Y. Robinson ◽  
M. Muthuramalingam

A novel design of an adaptive artificial neural network technique (ANN) for controlling of the essential parameters, like as speed,  torque, flux, voltage, current, and power etc of the induction motor is presented in this paper. Induction motors are characterized by way of incredibly non-linear, complicated and time-various dynamics and inaccessibility of its states and outputs for measurements. Thus it can be considered as a challenging engineering difficulty in the industrial sector. A few of them, such as PI, fuzzy strategies, Fuzzy logic based controllers are regarded as capability candidates for such application for operating induction motor. Hence of which, the outcome of the controller is also random and high-rated results are probably not obtained. Resolution of the proper rule base application upon the drawback can be achieved by the use of an ANN controller, which becomes a built-in system of method for the manipulate purposes and yields results, which is the focus of this paper. Within the designed ANN scheme, neural community tactics are used to prefer an appropriate rule base, which is utilizing the back propagation algorithm. The simulation outcome provided on this paper is exhibit the effectiveness of the developed approach, which has acquired faster response time or settling times. Additionally, the procedure developed has got a huge number of benefits within the industrial sector will also be converted into a real time application making use of some interfacing cards.


Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2262 ◽  
Author(s):  
Hua Han ◽  
Chao Luo ◽  
Xiaochao Hou ◽  
Mei Su ◽  
Wenbin Yuan ◽  
...  

For an AC-stacked photovoltaic (PV) inverter system with N cascaded inverters, existing control methods require at least N communication links to acquire the grid synchronization signal. In this paper, a novel decentralized control is proposed. For N inverters, only one inverter nearest the point of common coupling (PCC) needs a communication link to acquire the grid voltage phase and all other N − 1 inverters use only local measured information to achieved fully decentralized local control. Specifically, one inverter with a communication link utilizes the grid voltage phase and adopts current control mode to achieve a required power factor (PF). All other inverters need only local information without communication links and adopt voltage control mode to achieve maximum power point tracking (MPPT) and self-synchronization with grid voltage. Compared with existing methods, the communication link and complexity is greatly reduced, thus improved reliability and reduced communication costs are achieved. The effectiveness of the proposed control is verified by simulation tests.


Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2704
Author(s):  
Yunhan Lin ◽  
Wenlong Ji ◽  
Haowei He ◽  
Yaojie Chen

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.


Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.


2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.


Author(s):  
Lizhi Gu ◽  
Tianqing Zheng

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.


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