Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userUsing artificial neural networks for short term electrical load forecasting in Tartous province
استخدام الشبكات العصبونية في التنبؤ قصير الأمد بالحمل الكهربائي في محافظة طرطوس
1381
2 49
0.0
(
0
)
Added by
Tishreen University ورقة بحثية
Publication date
2018
fields
Electrical Power Engineering
and research's language is
العربية
Created by
Shamra Editor
Ask ChatGPT about the research
تعد التغذية الكهربائية الموثوقة و المستمرة ضرورية في ظل وظائف المجتمع الحالي المعقدة. نتيجةً للاستهلاك المتزايد و توسع شبكات التوزيع الكهربائية، فإن نظم القدرة الكهربائية تعمل بشكل قريب من حدودها الفنية، و بالتالي تتزايد احتمالية حدوث حالات التحميل الزائد، فشل التجهيزات و التعتيم. أكثر من ذلك، فإننا نواجه مشكلة أخرى تتجسد في عدم القدرة على تخزين الطاقة الكهربائية بشكل فعال و بالتالي يجب أن يتم توليد الطاقة الكهربائية عند الحاجة لها فقط. و نظراً لما يواجهه العالم من نضوب الموارد النفطية و الصعوبات المرتبطة بتأمين مصادر أخرى لتوليد الطاقة الكهربائية فإن عملية التنبؤ بالحمل الكهربائي تشكل عاملاً حاسماً في منظومة القدرة الكهربائية سواء من الناحية الاقتصادية، أو من الناحية الفنية على مستويي التشغيل و التخطيط. يقدم هذا البحث منظومة تنبؤ بالحمل الكهربائي قصير الأمد بالاعتماد على الشبكات العصبونية، مع محاكاة ضمن بيئة ماتلاب بالإضافة إلى واجهة بيانية للمنظومة اعتماداً على بيانات الأحمال السابقة و محددات الطقس في محافظة طرطوس.
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 operated 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.
References used
PAPALEXOPOULOS AD, HESTERBERG TC. A regression-based approach to short-term system load forecasting. IEEE Trans Power Syst 5(4): 1990;1535–1544
SATOH R, TANAKA E, HASEGAWA J. Daily load forecasting using a neural network combined with regression analysis. In: Proc Int Conf Intelligent System Application to Power Systems, vol. 2, Montpellier, France, 5–9 September 1994; 345–352
WANG Y, DAWA GU, JIANPING XU, JING LI, Back propagation neural network for short-term electricity load forecasting with weather features, IEEE, CSDL, Wuhan, China, 2009
rate research
Read More
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.
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".
This research was done to improve the local bitumen specifications 60-70 (Banias
refinery output) using Cement Bypass DUST(CBPD) so that it becomes more resistant to
high temperatures (Short- Term- Aging), in addition to reduce the harmful waste ma
terials.
The cement bypass was supplied by Tartous Cement Company and added to
bitumen with different percentages namely, 12%, 14%, 16% and 18% (by the weight of
bitumen). some experiments were held to determine the properties of modified and
unmodified bitumen (Penetration )25, 15, 4(∁°- Ductility- Softening point), then We
returned previous experiences after (Loss- on- heating test) and exposure to (Short- Term-
Aging) on the modified and unmodified bitumen, and the results were within specification
limits, were Compatible with previous reference studies and these additions have to
improve the resistance of bitumen to high temperatures. The optimum cement Bypass Dust
ratio was found to be 14% of the weight used bitumen.
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.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
