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.
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 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.
