Volume 12, Issue 1

The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.

In this paper describes the operation of power system networks to be nearest to stability rated values limits. State estimation for monitoring and protection power system is very important because it provides a real-time (RT) Phase angle of different nodes of accuracy and then analysis and decided to choose control way (methods). In order to detect the exact situation (instant state) for power system networks parameters. In this paper proposes a new monitoring and analysis system state estimation method integrating with MATLAB environment ability, by using phasor measurement units (PMU’s) technology, by this system the estimation problem, iterations numbers, and processing time will reduce. The measurements of phasors value of voltage signal and current estimated and analyzed. Mat lab/PSAT package use as a tool to design and simulate four electrical power systems networks such as INSG 24 buses, IEEE14 bus, Diyala city 10buses (IRAQ), and IEEE6 bus and then installed and applied PMU’s devices to each system. Simulation results show that the PMU’s performances effectiveness appear clearly. All results show the validation of PMU’s devices as an estimator to power system networks states and a significant improvement in the accuracy of the calculation of network status. All results achieved and discussed through this paper setting up mathematical models with Graph Theoretic Procedure algorithm.

In this paper, a semi-elliptical annular slot loaded trapezoidal dipole antenna with band-notched characteristics for UWB applications is designed. A microstrip feedline consisting of multiple feedline sections is used for improving the impedance matching. The band-notched characteristics for WLAN band are achieved by loading the trapezoidal dipole arms with semi-elliptical annular slots. The designed antenna structure has an operating range from 3.5-12.4 GHz(109%) with band-rejection in the frequency range of 5-6 GHz. Nearly omnidirectional patterns are achieved for the designed antenna structure. The designed antenna structure provided an average peak gain of 2.12 dB over the entire frequency range except in the notched band where it reduced to -2.4 dB. The experimental and simulation results are observed to be in good agreement. An improved bandwidth performance with miniaturized dimensions as compared to earlier reported antenna structures is achieved.

This paper investigates Load Frequency Control of multi area inter connected power system having different turbines with PID controller. The gain values of controller are optimized using different Metaheuristic Algorithms. The performance and validity of designed controllers were checked on multi area interconnected power system with various Step Load Perturbations. Finally, the performance of proposed controllers was compared with conventional controller and from the result it was proved that the proposed controller exhibits superior performance than conventional controller for various Step Load Perturbations.

A Matlab/Simulink model for the Finite Control Set Model Predictive current Control FCS-MPC based on cost function optimization, with current limit constraints for four-leg VSI is presented in this paper, as a new control algorithm. The algorithm selects the switching states that produce minimum error between the reference currents and the predicted currents via optimization process, and apply the corresponding switching control signals to the inverter switches. The new algorithm also implements current constraints which excludes any switching state that produces currents above the desired references. Therefore, the system response is enhanced since there is no overshoots or deviations from references. Comparison is made between the Space Vector Pulse Width Modulation SVPWM and the FCS-MPC control strategies for the same load conditions. The results show the superiority of the new control strategy with observed reduction in inverter output voltage THD by 10% which makes the FCS-MPC strategy more preferable for loads that requires less harmonics distortion.

The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID-like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system’s robustness.

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

The autonomous navigation of robots is an important area of research. It can intelligently navigate itself from source to target within an environment without human interaction. Recently, algorithms and techniques have been made and developed to improve the performance of robots. It’s more effective and has high precision tasks than before. This work proposed to solve a maze using a Flood fill algorithm based on real time camera monitoring the movement on its environment. Live video streaming sends an obtained data to be processed by the server. The server sends back the information to the robot via wireless radio. The robot works as a client device moves from point to point depends on server information. Using camera in this work allows voiding great time that needs it to indicate the route by the robot.

The thermal dependence of the spectral response (i.e. transmission, reflection and time delay ( r) responses) of uniform polymer optical fiber (POF) Bragg gratings has been investigated. In addition to the temperature dependence, the effects of grating strength (kLg) and fiber index modulation ( n) have been investigated. Besides high capability of tunable wavelength due to the unique large and negative thermo-optic coefficient of POF, the spectral response for POF Bragg gratings show high stability and larger spectrum bandwidth with temperature variation compare with the silica optical fiber (SOF) Bragg gratings, especially with the increase of the kLg value. It was found that by increasing kLg, the peak reflectance value increases and the bandwidth of the Bragg reflector become narrower. Also it’s shown by increasing the kLg value, r deceasing significantly and reach its minimum value at the designed wavelength ( B). Furthermore, the r for POF Bragg gratings is less than that for SOF Bragg gratings at the same value of kLg. Also it’s found that the peak reflectivity value increases to around 60% when the n value increases from 1*10-⁴ to 5*10^-4.

Data-intensive science is a critical science paradigm that interferes with all other sciences. Data mining (DM) is a powerful and useful technology with wide potential users focusing on important meaningful patterns and discovers a new knowledge from a collected dataset. Any predictive task in DM uses some attribute to classify an unknown class. Classification algorithms are a class of prominent mathematical techniques in DM. Constructing a model is the core aspect of such algorithms. However, their performance highly depends on the algorithm behavior upon manipulating data. Focusing on binarazaition as an approach for preprocessing, this paper analysis and evaluates different classification algorithms when construct a model based on accuracy in the classification task. The Mixed National Institute of Standards and Technology (MNIST) handwritten digits dataset provided by Yann LeCun has been used in evaluation. The paper focuses on machine learning approaches for handwritten digits detection. Machine learning establishes classification methods, such as K-Nearest Neighbor(KNN), Decision Tree (DT), and Neural Networks (NN). Results showed that the knowledge-based method, i.e. NN algorithm, is more accurate in determining the digits as it reduces the error rate. The implication of this evaluation is providing essential insights for computer scientists and practitioners for choosing the suitable DM technique that fit with their data.

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user’s equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.