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Register a new userOperational Electrical Load Forecasting By Using Artificial Neural Networks
التنبؤ العملياتي بالحمولات الكهربائية باستخدام الشبكات العصبونية
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سنقدم في هذا البحث منهجية علمية للتنبؤ قصيرة جدا بالحمولات الكهربائية للمنظومة الكهربائية السورية أي التنبؤ بهذه الحمولات لعدة ساعات قادمة و قد أطلقنا على هذا النوع من التنبؤ التنبؤ العملياتي, تعتمد هذه المنهجية على استخدام الشبكات العصبية الصناعية.
In this paper, we presented a scientific methodicalness in very short term load forecasting depends on back propagation artificial neural networks, and we relied upon real data of Syrian electrical power system.
References used
ABDUL HAMID, M, ABDUL RAHMAN, T, 2010 -Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm. International Confernce on Computer Modeling and Simulation, IEEE, UK, 82p
BADRI, A, AMELI, Z, 2012 -Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting, Procedia, 534p
HAYKIN ,S, 2009 -Neural networks and Learning Machine. (3rd edition), pearson-prentice Hall Upper Saddle River, 315p
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In this research, We present a scientific advanced developed
study and keeping up with new studies and technologies of very
short-term electrical load forecasting and applying this study for
electrical load forecasting of basic Syrian electrical p
ower system
by studying this prediction for next four hours according to the
criterion applied in the Syrian Electricity Ministry with ten minutes
intervals ,we call it "Instant electrical load forecasting".
A reliable and continuous supply of electrical energy is necessary for the functioning
of today’s complex society. Because of the increasing consumption and the extension of
existing electrical transmission networks and these power systems are oper
ated closer and
closer to their limits accordingly the possibilities of overloading, equipment failures and
blackout are also increasing, furthermore, we have an additional obstacle which is that
electrical energy cannot be stored efficiently, so, electrical energy should be generated only
when it's needed.
Due to the fact that world is facing a lack of oil reserves and the difficulties related to
have alternative sources to generate electrical power, then, electrical load forecasting is
considered as a crucial factor in electrical power system either from economical or
technical point of view on both planning and operating levels.
This research introduces a short term electrical load forecasting system by using
artificial neural networks with a simulation in Matlab environment in addition to an
interface for the system and all that is depending on previous load data and weather
parameters in Tartous province.
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.
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 contribution of our research include building an artificial neural
network in MATLAB program environment and improvement of
maximum loading point algorithm, to compute the most critical
voltage stability margin, for on-line voltage stability a
ssessment,
and a method to approximate the most critical voltage stability
margin accurately. a method to create a (ARTIFICIAL NEURAL
NETWORK) approach.
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