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This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor & adaptive learning rate, to estimate the output of the neural network correspon ding to the optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN” controller proposed in this research, independent in his work on the use of electrical measurements output of PV system to determine the duty cycle, and without the need to use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC Boost Converter, and this improves the dynamic performance of the proposed controller to determine the optimal Duty Cycle accurately and quickly. In this context, this research discusses the optimal selection of the proposed MLFFNN structure in the research in terms of determining the optimum number of hidden layers and the optimal number of neurons in them, evaluating the values of the Mean square error and the resulting Correlation Coefficient after each training of the neural network. The final network model with the optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP point of the PV system. The simulation results performed in the Matlab / Simulink environment demonstrated the best performance of the proposed DMPPT-ANN controller based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and improving the response speed of the PV system output to MPP access, , as well as finally eliminating the resulting oscillations in the steady state of the Power response curve of PV system compared with the use of a number of reference controls: an advanced tracking controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the Incremental Conductance technique INC
Modelling the relationship between drinking water turbidity and other indicators of water quality in Al-Sin drinking water purification plant using Dynamic Artificial neural networks could help in the implementation of the stabilization for the per formance of the plant because these neural networks provide efficient tool to deal with the complex, dynamic and non-linear nature of purification processes. They have the ability to response to various instant changes in parameters influencing water purification. In this research, four models of feed-forward back-propagation dynamic neural network were designed to predict the effluent turbidity from Al-Sin drinking water purification plant. The models were built based on turbidity, pH and conductivity of raw water data while the effluent turbidity data were used for verify the performance accuracy of each network. The results of this research confirm the ability of dynamic neural networks in modeling and simulating the non-linearity behavior of water turbidity as well as to predict its values. They can be used in Al-Sin drinking water purification plant in order to achieve the stabilization of its performance.
This research aims to predict the level of air pollution with a set of data used to make predictions through them and to obtain the best prediction using several models and compare them and find the appropriate solution.
the aim of this study is determination of the most influential climatic factors in the rainfall runoff relationship in Al-Kabir Al-shimalee river using artificial neural networks. The inputs included Precipitation, runoff, in different delays, in addition on لاclimate factor in each network, to determinate the best model.
This paper presents a new technique based on artificial neural networks (ANNs) to correct power factor. A synchronous motor controlled by the neural controller was used to handle the problem of reactive power compensation of the system, in order to correct power factor. In this paper, the electrical system and the neural controller were simulated using MATLAB. The results have shown that the presented technique overcomes the problems in conventional compensators (using static capacitors) such as time delay and step changes of reactive power besides to the fast compensation compared to the technique with capacitors groups.
Evaporation is a major meteorological component of the hydrologic cycle, and it plays an influential role in the development and management of water resources. The aim of this study is to predict of the monthly pan evaporation in Homs meteostation using Artificial Neural Networks (ANNs), which based on monthly air temperature and relative humidity data only as inputs, and monthly pan evaporation as output of the network. The network was trained and verified using a back-propagation algorithm with different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan evaporation with total correlation coefficient equals 96.786 % and root mean square error equals 24.52 mm/month for the total data set. This study recommends using the artificial neural networks approach to identify the most effective parameters to predict evaporation.
This paper shows a new approach to determine the presence of defects and to classify the defect type online based on Artificial Neural Networks (ANNs) in electrical power system transmission lines. This algorithm uses current and voltage signals samp led at 1 KHz as an input for the proposed ANNs without the involvement of a moving data window, so input data will be processed as a string of data. The model depends on three neural networks one for each phase and another fourth neural network for the involvement of the ground during the fault. Response time of the classifier is less than 5 ms. Moreover modern power system requires a fast, robust and accurate technique for online processing. Simulation studies show that the proposed technique is able to distinguish the fault type very accurate. Also this technique succeeded in determining of all defect types under all system conditions, so it is 100 percent accurate, so it is suitable for online application.
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