In this paper, the distribution of the electric field of medium voltage (MV) underground cable insulation made of polyethylene/copper nanoparticles (XLPE/Cu) is investigated. Three methods were established for copper nanoparticles (CuNPs) synthesis such as the alkaline reduction process, electrolysis process and metal displacement reaction. Ultraviolet-visible spectra, X-ray diffraction, and transmission electron microscopy (TEM) were used to classify the obtained CuNPs. The XLPE/Cu films with different CuNP weight content (0%, 1%, 3% and 5%) were subjected to a 3 MeV electron beam (EB) with different doses (0, 15, 20 and 25 kGy). The optimum AC/DC conductivity (AC 2 × 10 −3 S m−1 , DC 4.63 × 10 −2 S m −1 ) in minimum relative permittivity (2.05) achieved for (XLPE/5-Cu) 25 kGy that is has a CuNP content of 5 wt-% and irradiated at 25 kGy. The electric field distribution in the middle of the (XLPE/5-Cu) 25 kGy sample is maximum uniform than at the top or bottom.
In this paper, forecasting estimation for medium-term period by using Artificial Neural Network (ANN) techniques has been implemented. The actual data are obtainedfrom The Egyptian Electric Holding Company (EEHC). Thestudy focused on unexpected peaks on working days ofsummer seasons that have the dangerous effect on safety ofelectric power system. Medium-term loadforecasting is established by two plans, One-year plan and Five-years plan with different designed techniques. The input consists of many parameters such as past annual loads, population, oil price and so on. The outputs are annual loads or the energy sales or both. The results ofthese techniques are compared with output of the models calculated by EEPU. So, valuable conclusions and recommendations are obtained.
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