<p class="Author"><span>It is known that controlling the speed of a three phase Induction Motor (IM) under different operating conditions is an important task and this can be accomplished through the process of controlling the applied voltage on its stator circuit. Conventional Proportional- Integral- Differeantional (PID) controller takes long time in selecting the error signal gain values. In this paper a hybrid Fuzzy Logic Controller (FLC) with Genetic Algorithm (GA) is proposed to reduce the selected time for the optimized error signal gain values and as a result inhances the controller and system performance. The proposed controller FL with GA is designed, modeled and simulated using MATLAB/ software under different load torque motor operating condition. The simulation result shows that the closed loop system performance efficiency under the controller has a maximum value of 95.92%. In terms of efficiency and at reference speed signal of 146.53 rad/sec, this system performance shows an inhancement of 0.67%,0.49% and 0.05% with respect to the closed loop system efficiency performance of the PID, FL, and PID with GA controllers respectively. Also the simulation result of the well designed and efficient GA in speeding up the process of selecting the gain values, makes the system to have an efficiency improvement of 14.42% with respect to the open loop system performance.</span></p>

This paper presents a comprehensive analysis of power quality for static synchronous compensator on the distribution power system (DSTATCOM) when a different types of energy sources are used to supply the dc link channel of DSTATCOM. These types of power supplies have a different effect on the compensation of DSTATCOM due to operation nature of these sources. The dynamic response of the DSTATCOM has been investigated that produced by individual and hybrid energy sources to evaluate the influence of these sources in terms of time response, compensation process and reduce the harmonics of current for source. Three cases have been considered in this study. First the photovoltaic (PV) cells alone second the battery storage alone and third a hybrid coordinated design between (PV cells with battery storage) is used. A boost Dc-Dc circuit has been connected to a photovoltaic cell with Maximum Power Point Tracking (MPPT) while a Dc-Dc buck-boost circuit is used with a battery. High coordination between PV and battery circuits in the hybrid system is used in order to improve the performance. A synchronous reference frame (SRF) with unit vector has been used to control the STATCOM circuit. The simulation results show that the hybrid design has the superiority response compared to the individual sources.

The ethanol, chloroform and acetone extracts of five species from Acacia (Acacia albidia stems, Acacia mellifera aerial parts, Acacia nubica aerial parts, Acacia seyal var. seyal stems and Acacia tortilis aerial parts) were investigated for their antimicrobial activity against two standard bacterial strains of Gram +ve bacteria (Staphylococcus aureus (ATCC 25923)), Gram −ve bacteria (Pseudomonas aeruginosa (ATCC 27853)) and standard fungi Candida albicans (ATCC 90028) using the agar-plate well diffusion method. The chloroform extract was inactive compared to ethanol and acetone extracts. But ethanol extracts showed the maximum antimicrobial activity against the test organism. Amongst the plant species screened, ethanol extract of Acacia seyal stems showed maximum inhibitory activity (38 mm) and (37 mm) against Staphylococcus aureus and Candida albicans, respectively. The ethanol, chloroform and acetone extracts of Acacia mellifera (aerial parts) did not show any activity against the test organisms. Cholorophorm and acetone extracts via DPPH, the radical scavenging activities were found to be 91 ± 0.03, 88 ± 0.01 and 85 ± 0.04, respectively. The results of phytochemical screening showed that all extracts of studied plant contain flavonoids, saponins, terpenoids, steroids, alkaloids, phenols and tannins. H. B. Abdllha et al.

Self-Excited induction generators (SEIG) display a low voltage and frequency regulation due to variable applied load and input rotation speed. Current work presents a simulation and performance analysis of a three-phase wind-driven, SEIG connect to a three-phase load. In addition, an investigation of the dynamic operation of the induction generator from starting steady state until no-load operation. It is assumed that the input mechanical power is constant where the rotor of the SEIG rotates at a constant speed. The value of the excitation capacitance which is necessary to the operation of the induction generator also computed to ensure a smooth and self-excitation starting. The output voltage of the generator is adjusted by varying the reactive power injected by STATCOM. A 3-phase IGBT voltage source inverter with a fuel cell input supply is connected as STATCOM which is used to compensate for the reduction in the supply voltage and its frequency due to variation occurred in the applied loads. This work includes introducing a neuro-fuzzyy logic controller to enhance the performance of the SEIG by regulation the generated voltage and frequency The dynamic model of SEIG with STATCOM and loads are implemented using MATLAB/SIMULINK

Multimodal biometric methods have been commonly used by several implementations because of its capability to work with a variety of important drawbacks in unimodal biometric methods, such as noise affectability, populace coverage, intraclass variety, vulnerability to spoofing, and non-universality. In this research, a multimodal biometric realtime method is suggested depending upon the design of a deep learning model for pictures of a person’s (right & left) irises. This system has been implemented by combining the characteristics of convolution neural networks and transfer learning techniques. Through this research, the training system focused on a collection of the back_propagation technique with Adam’s optimization approach utilized to modify weights and adjust learning rates during the learning process. The efficiency of the system is examined on two public datasets obtained in various conditions: IITD and CASIA-Iris-V3 Interval. The implemented system gives an accuracy of 99% for both left & right IITD iris datasets and accuracy of (94% and 93%) for the left and right iris for CASIA-iris-V3 interval datasets respectively after training. An OpenCV library for image pre-processing, Keras, and sci-kit learn python libraries for feature extraction and recognition has been utilized.