Use of power electronic converters with nonlinear loads leads to power quality problems by producing harmonic currents and drawing reactive power. A shunt active power filter provides an elegant solution for reactive ...
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ISBN:
(纸本)9781509000777
Use of power electronic converters with nonlinear loads leads to power quality problems by producing harmonic currents and drawing reactive power. A shunt active power filter provides an elegant solution for reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varied, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with hybrid artificial intelligence controller, i.e. neuro fuzzy controller for shunt active power filter. The performance of neuro fuzzy controller over PI controller is examined and tabulated. The salvation of the problem is extensively verified with various loads and plotted the worst case out of them for the sustainability of the neuro fuzzy controller.
The present paper aims to propose an approximation method of Caputo fractional operator using discretization based on quadrature theory to minimize the error function for an Artificial Neural Network (ANN) with higher...
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In this paper, a four-layer fuzzy neural network using the backpropagation （BP） algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the dens...
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In this paper, a four-layer fuzzy neural network using the backpropagation （BP） algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.
This paper presents a new robust technology for current control strategy based on artificial neural network (ANN). Development of renew able energy closely resemble with uncertainty. Implementation of state space vect...
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ISBN:
(纸本)9781509056835
This paper presents a new robust technology for current control strategy based on artificial neural network (ANN). Development of renew able energy closely resemble with uncertainty. Implementation of state space vector modulation can enhance the performance of inverter for grid interconnection. This paper shows the implementation of SPWM method for under modulation and over modulation for duty cycle of static switch. Individual training method have been adopted for training of each node of the neural network. MATLAB based Simulink method has been adopted to validated the logic and architecture. ANN tool base has been adopted for training purpose
Combustion quality in power stations plays an important role in minimizing the flue gas emissions. Complete combustion occurs when all the energy in the fuel being burnt is extracted and none of the carbon and hydroge...
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ISBN:
(纸本)9781538618882
Combustion quality in power stations plays an important role in minimizing the flue gas emissions. Complete combustion occurs when all the energy in the fuel being burnt is extracted and none of the carbon and hydrogen compounds are left unburnt. Complete combustion will occur when proper amounts of fuel and air are mixed in correct proportion under the appropriate conditions of temperature. The Monitoring process is accomplished by capturing the image of the flame with a camera and processing the image in a laptop with MATLAB code. Estimation of Sulphur Dioxide (SO_2) emissions from combustion images in thermic and gas turbine control plants is of immense significance in the field of image processing and the prime aim is the presentation, distinguishing proof and understanding of the start conditions. In this effort, virtual sensor utilizing single layer perceptron up skilled by back propagation algorithm (BPA) and Ant Colony Optimization (ACO) for blaze examination. The arrangement incorporates the Industrial Internet of Things (IIoT) where the intelligent sensors form the embedded computing system to screen the changeability in parameters relating to the flame colour.
For online control of various dynamical systems, an adaptive artificial neural network (ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID ...
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ISBN:
(纸本)9781467385886
For online control of various dynamical systems, an adaptive artificial neural network (ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID controller is suitable but they have limitations when they are required to control the plants having high non linearity or their parameters are changing with the time. In order to find the parameters of PID controller, information regarding the dynamics of the plant is essential. If perturbation occurs in plant parameter(s) then PID controller may work only if these changes are not severe. But most plants are either non linear or their parameters changes with time and this demands for a use of more robust type of controller and ANN is a suitable candidate. To use the power of PID controller and ANN, ANN based PID controller is proposed in this paper. The benefit of this combination is that it utilizes the simplicity of PID controller mathematical formula and uses the ANN powerful capability to handle parameter variations and non linearity.
This study came as an attempt to predict the foreign direct investment of the State of Qatar, depending on the model of artificial neural networks and the comparison between its models, because this type of model take...
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This study came as an attempt to predict the foreign direct investment of the State of Qatar, depending on the model of artificial neural networks and the comparison between its models, because this type of model takes into account the non-linear and stochastic characteristics that characterize the financial and economic chains in general. A multi-layer artificial neural network was built consisting of three layers (the input layer, the hidden layer, the output layer), and the number of training passes was installed 999 times, and the network learning rate was 0.6 and the activation function used is the SIGMOID function using the back propagation algorithm. The MLP (4-10-1) model gave accurate results that are close to the actual values, and it also gave the lowest values for the error measurement criteria represented in the MAE, RMSE and MAPE standards. This reflects the strength of the predicted model, which is consistent with the results of most studies that have been conducted on the subject, both Arab and foreign. It turned out that the feed-forward artificial neural network model is superior to other network models, as the outputs of the hidden layer are inputs for the time following the next time, which can be relied upon as an appropriate method for future prediction of the GDP of the State of Qatar. Also, the forecast values are positive for the period (2020-2040), which encourages increased investor attraction and market recovery in subsequent periods.
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