K. H. Chong

Modified Direct Torque Control using Algorithm Control of Stator Flux Estimation and Space Vector Modulation Based on Fuzzy Logic Control for Achieving High Performance from Induction Motors

Direct torque control based on space vector modulation (SVM-DTC) protects the DTC transient merits. Furthermore, it creates better quality steady-state performance in a wide speed range. The modified method of DTC using SVM improves the electrical magnitudes of asynchronous machines, such as minimizing the stator current distortions, the stator flux with electromagnetic torque without ripple, the fast response of the rotor speed, and the constant switching frequency. In this paper, the proposed method is based on two new control strategies for direct torque control with space vector modulation. First, fuzzy logic control is used instead of the PI torque and a PI flux controller to minimizing the torque error and to achieve a constant switching frequency. The voltages in the direct and quadratic reference frame (V d ,V q ) d q are achieved by fuzzy logic control. In this scheme, the switching capability of the inverter is fully utilized, which improves the system performance. Second, the close loop of stator flux estimation based on the voltage model and a low pass filter is used to counteract the drawbacks in the open loop of the stator flux such as the problems saturation and dc drift. The response of this new control strategy is compared with DTC-SVM. The experimental and simulation results demonstrate that the proposed control topology outperforms the conventional DTC-SVM in terms of system robustness and eliminating the bad outcome of dc-offset.

Hybrid Artificial Immune System-Genetic Algorithm optimization based on mathematical test functions

This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; firstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efficiency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To differentiate between the results in terms of achieving the minimum value for these functions, eight mathematical test functions are being used to make comparison.

Characterization of a submicron PMOS in mixer circuits

In this paper, we investigate the properties of a submicron pMOS with a single layer of metallization. The fabrication process and electrical characterization of the device were simulated using the SILVACO TCAD tools. We have applied constant field scaling on the effective channel length, the density of ion implantation for threshold voltage adjustment, and gate oxide thickness. To suppress short channel effects, we have also applied retrograde well implantation, Shallow Trench Isolation (STI), sidewall spacer deposition, silicide formation, and Lightly Doped Drain implantation in our process simulation. We have validated the electrical performance of the device by plotting and analyzing the ID - Vg relationship

The implementation of crossover factor, fTIG in the Finite Persisting Sphere Genetic Algorithm

In this paper, a crossover factor, fTIG is introduced to the Finite Persisting Sphere Genetic Algorithm (FPSGA). The factor provides a variable range of the loop in the process of Finite Persisting Sphere. By the existing of the variable range, the risk to have too large number of loop or too small number of loop in the FPSGA can be reduced. Too large number of loop will risk of repeating using the same data and too small number of loop will cause the loss of good genes in the FPSGA. By the proposed approach, potential to achieve the global solution in a small number of population will be increased and at the same time less time required running the process in the loop. This paper show that FPSGA with fTIG has higher global solution compared to other method and this method has faster converges to the global solution. The experiment result revealed the superiority of fTIG in FPSGA.

Optimization Variation for Multiple Heuristic Approaches in Solar Tracking

The solar tracker, a device that keeps PV or photo-thermal panels in an optimum position perpendicular to the solar radiation during daylight hours, increases the collected energy and can be considered as one of the economical renewable energy in generating electricity. The current trend in solar concentrator tracking system is to use an open-loop local controller that computes the direction of the solar vector based on geographical location and time. It is not accurate because it has error from computing the intensity variation. Literature suggested that the photovoltaic panels could produce maximum power if the panels have angle of inclination zero degree to the sun position. In this research, genetic algorithm is one of the optimization techniques used to maximize the performance of solar tracking system. Research shows that among the heuristic approaches within 50 generation, Genetic Algorithm reaches the best fitness value at 0.01706 with optimum voltage 10.05V.

Comparative Analysis of Selective Clonal Mutation with Conventional GA Operators in Solar Tracking Environment

Genetic Algorithm (GA) belongs to elementary stochastic optimization algorithms inspired by evolution.It points out the ability of simple representations using bit strings to encode complicated structures and the power of simple transformations to reach the desired solution. Research shows that a new operator namely Selective Clonal Mutation (SCM) for better genetic solutions has been successfully developed so that faster convergence to the best desired solution could be obtained. This operator has produced the best fitness value as compared to the conventional genetic algorithm result within 50 generation, Selective Clonal Mutation (SCM) is able to produce the best fitness value at 0.01731 with optimum voltage 10.05V in solar tracking environment.

Algorithmic precision liquid dispensing (APLD) system

Liquid dispensing can be a very tough job especially when dealing with various liquids of different viscosity. Furthermore, to reach a precise result, it is very difficult to achieve. This paper presents a liquid dispensing system with real-time automatic control by using an analytical balancer reading as a feedback with additional mathematical PWM algorithm to optimize the dispensing process. Using the specifically custom build nozzle, the dispensing process can be accomplished in shorter duration together with higher accuracy and precision.

Path optimization using genetic algorithm evolution

Gantry robots are widely used in the industries for various material handling applications. The robot capable of moving in the Cartesian makes it very flexible and efficient at big or small areas. Path optimization to the robot would make it more efficient and easier automation. In this paper, genetic algorithm (GA) has been proposed to optimize the traveling sequence making the movement more efficient and economic as the total travel length is shortened.

Full wave analysis of a superconducting stripline with large width-to-separation ratio

This paper presents a full-wave analysis on the wave propagating in a superconducting stripline with a large width-to-separation ratio. A set of transcendental equation is derived, by matching the ratio of the tangential electric and magnetic fields, respectively, with the surface impedance of the strip. The propagation constant can be obtained by substituting the complex conductivity of the superconducting strip into the equation and numerically solving for the root. We have computed the attenuation constant and phase velocity using our method and compared with the analytical equation formulated by Matick using the general transmission line model, which is applied by Kautz in a superconducting stripline. The results for both methods agree exceedingly well.