Flexible AC transmission system (FACTS) controller play an essential role in increasing the penetration level of renewable energy resources owing to their ability in continuously controlling the active and reactive power flow in the network. This paper presents a probabilistic multi-objective optimization approach to obtain the optimal sizes and locations of static var compensators (SVCs) and thyristor-controlled series capacitors (TCSCs) in a power transmission network with high penetration level of wind generation. The objective of the problem is to maximize the system loadability while minimizing the network power losses and the installation cost of the FACTS controllers. In this study, the uncertainties associated with wind power generation and the correlated load demand are considered. The uncertainties are handled in this work using the points estimation method. Moreover, the dynamic line ratings (DLRs) of the transmission lines are considered in this work. In this case, the maximum transmission capacity of transmission lines is estimated dynamically according to the weather conditions. Considering the DLRs or transmission lines is expected to avoid unrealistic congestion in the network, and hence, improve its loadability. The optimization problem is solved using the multi-objective teaching-learning based optimization (MO-TLBO) algorithm to find the best locations and ratings for the FACTS controllers. Additionally, a technique based on the fuzzy decisionmaking approach is employed to extract one of the Pareto optimal solutions as the best compromise. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results demonstrate the effectiveness of the proposed approach and show that the maximum loadability limit of the study system increases when considering the DLR. This limit can be enhanced to 123.0% without FACTS controller and 137.0 % ,130 % and 132.0% by SVC, TCSC and (SVC-TCSC) respectively.

This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.

SUMMARYThe power transfer capability (PTC) of AC transmission lines is increased by using series capacitor and shunt reactor compensation. This paper presents a method to calculate the degree of series capacitor and shunt reactor compensation in order to increase the power transfer capability of overhead transmission lines. This method is based on linear programming which takes into consideration line modelling, series and shunt compensation, voltage and reactive power constraints, and power factor before and after compensation. The proposed method has been applied to the existing 500 kV transmission line interconnected between the High-Dam generation station and Cairo load center in Egypt.

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Effect of biscuit processing on the destruction of aflatoxins B1 and G1 with and/or without some commonly leavening agents used namely sodium bicarbonate, ammonium bicarbonate and sodium bisulfite and sodium chloride.It was found that mixing step reduced the concentration of aflatoxins B1 and G1 by 80.7% and 82.7%, while the effect of baking step being 28.9% and 21.5%. The effect of mixing was found to be more pronounced than that baking step.The highest destruction effect on aflatoxin B1 was observed by adding a mixture composed of sodium and ammonium bicarbonate and sodium bisulfite followed by sodium chloride, sodium bisulfite, ammonium bicarbonate and/or sodium bicarbonate alone, where the reduction values of toxin after mixing were 93.4,91.9,91.7, 88.8 and 86.6% respectively, while the baking effect ranged 17.2 to 34.5% in the presence of different leaving agents added.Concerning aflatoxin G1; the highest destructive effect of toxin was adsorbed by adding a mixture of sodium and ammonium bicarbonate and sodium bisulfite followed by sodium bisulfite, sodium chloride, ammonium bicarbonate and/or sodium bicarbonate alone since the destruction values of such toxin after mixing were 96.2%, 92.8%, 92.6%, 89.0% and 87.7% respectively, while the baking effect ranged 20.9 to 34.5% in all leavening agents added.