The main function of a power system is to supply the customer load demands as economically as possible. Risk criterion is the probability of not meeting the load. This paper presents a methodology to assess probabilistic risk criteria of Al-Qudus plant before and after expansion; as this plant consists of ten generating units presently and the Ministry Of Electricity (MOE) is intending to compact four units to it in order to improve the performance of Iraqi power system especially at Baghdad region. The assessment is calculated by a program using Matlab programming language; version 7.6. Results show that the planned risk is (0.003095) that is (35 times) less than that in the present plant risk; (0.1091); which represents respectable improvement. This probabilistic method can also be used to find the planned risk level of every plant to be compact in the Iraqi electrical network on the future; or any other power systems; and compare it with the present criterion which is very useful to determine the necessary generation capacity expansion.
Polish Academy of Sciences
With their strategic and geographical positions, Japan and Iraq are one of the major
countries in the world, especially to the United States. Japan lies near the two American
Continents, separated only by the Pacific and it is only normal to have the interests of both
countries crossed and joined in the Far East region. This made the occupation of Japan an
inventible matter in the second half of the 20th century. Iraq, on the other hand, has strategic
position in the Middle East, being surrounded by many active countries like Turkey, Iran, and
Gulf countries, besides Iraq’s oil wealth which is needed by such industrial country like the
United States.
The concern of this article is the calculation of an upper bound of second Hankel determinant for the subclasses of functions defined by Al-Oboudi differential operator in the unit disc. To study special cases of the results of this article, we give particular values to the parameters A, B and λ
This research develops a new method based on spectral indices and random forest classifier to detect paddy rice areas and then assess their distributions regarding to urban areas. The classification will be conducted on Landsat OLI images and Landsat OLI/Sentinel 1 SAR data. Consequently, developing a new spectral index by analyzing the relative importance of Landsat bands will be calculated by the random forest. The new spectral index has improved depending on the most three important bands, then two additional indices including the normalized difference vegetation index (NDVI), and standardized difference built-up index (NDBI) have been used to extract paddy rice fields from the data. Several experiments being
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The research aims to explain the effect of brain
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