In this paper, an interline power flow controller (IPFC) is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmission line is a major problem. Thus, we use a combination of tabu search (TS) algorithm and artificial neural network (ANN) in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemented in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm (GA) and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.

In this paper, a fundamental advancement of the basic helix design which expands to array having good bandwidth is proposed. The helix is inserted in a cavity. The result is a new antenna design that offers the performance characteristics and advantages of the conventional helix but in a much more compact physical size envelope. A 4-element rectangular helical array has been designed. For miniaturization and impedance matching, the helical wire is replaced by a rectangular cross sectioned strip. It has been observed that when the helix is inserted in a cavity, it behaves differently from a normal helical antenna. The effects of the cavity on the number of turns, the impedance of total antenna, and the reflection coefficient have been analyzed. The array is designed for 2.4 GHz. The return loss obtained is less than − 10 dB and the bandwidth is more than 1.3 GHz for the array.

In this paper, digital signal processor (DSP) based fuzzy controller for series parallel resonant converter (SPRC) has been estimated, and the performance of the converter is analyzed by using state space model. The method to predict the steady-state and dynamic performance of the converter with load independent operation has been presented. The proposed converter has been analyzed with the closed-loop and open-loop conditions. The simple form of transfer function for SPRC is developed, and it is used to analyze the stability of the converter with closed-loop operation. The stability analysis of the converter is carried out by using frequency response plan. The fuzzy controller regulates the output voltage with change supply voltage and load disturbance. The controller performance of inductance capacitance inductance — T network (LCL-T) SPRC is compared with inductance inductance capacitance — T network (LLC-T) SPRC through simulation and experimental studies using TMS320F2407 processor.

This paper describes an adaptive gain sliding mode observer for brushless DC motor for large variations in speed. Sensorless brushless DC motor based on sliding mode observer exhibits multiple zero crossing in back electromotive force (EMF) which leads to commutation problems at low speed. In this paper, a modified sliding mode observer incorporating a speed component in the estimation of back EMF is proposed. It is found that after incorporating the speed component in the back EMF observer gain, multiple zero crossings at low speeds and phase shift at higher speeds are eliminated. The trapezoidal back EMF observer is implemented experimentally on a digital signal processor (DSP) board. The effectiveness of the proposed method is demonstrated through simulations and experiments.

In this article an artificial neural network (ANN) has been designed for the control of DC series motor through a DC chopper (DC-DC buck converter). The proportional-integral-derivative (PID)-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steadystate error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simulation. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller (P89V51RD2BN). It was found that the hybrid PIDANN controller with DC chopper can have better control compared with conventional PID controller.

The switched reluctance motor (SRM) is applied in various industrial applications due to its profitable advantages. However, the robustness speed of SRM is one of the major drawbacks, which greatly affects the performance of motor. Thus, the aim of this paper is to control the speed of SRM using H-infinity control strategy. This H-infinity control technique is stronger against robustness. In the proposed speed controller, the rotor position of the SRM is applied to the controller. The speed variation of the rotor is determined from the reference speed and applied to the controller as input. Then, the speed variation and the corresponding sensitivity function are determined. The sensitivity function determination is based on the input weight of the controller. The weight adjustment process is repeated until a stable speed condition is achieved. Then, the output of the proposed control technique is compared with the existing control technique and the robustness is analyzed. Here, the existing control techniques considered are proportionalintegral (PI) controller and fuzzy logic controller (FLC)-based PI gain tuning. The proposed control strategy is simulated in MATLAB working platform and the control performance is analyzed.

Awideband rectangular patch antenna resonating at 3.5 GHz and 8 GHz frequencies is developed on a flexible substrate, which can be used for wearable applications. The proposed antenna gives a wide impedance bandwidth of 116%, operating from 2.5 GHz to 9.5 GHz, covering most of the ultra-wideband (UWB) operating frequency range. A two-element multiple-input multiple-output (MIMO) system is developed using the proposed antenna, and the mutual coupling between the two antennas for various separations and frequencies is analyzed by using artificial neural networks (ANNs). The neural structure is trained by using different ANN algorithms and a comparative study is made between them. It is shown that, quasi-Newton (QN) and quasi-Newton multi layer perceptron (QN-MLP) algorithms are better in terms of training, testing errors, and correlation coefficient.

The ridge waveguide is useful in various microwave applications because it can be operated at a lower frequency and has lower impedance and a wider mode separation than a simple rectangular waveguide. An accurate model is essential for the analysis and design of ridge waveguide that can be obtained using electromagnetic simulations. However, the electromagnetic simulation is expensive for its high computational cost. Therefore, artificial neural networks (ANNs) become very useful especially when several model evaluations are required during design and optimization. Recently, ANNs have been used for solving a wide variety of radio frequency (RF) and microwave computer-aided design (CAD) problems. Analysis and design of a double ridge waveguide has been presented in this paper using ANN forward and inverse models. For the analysis, a simple ANN forward model is used where the inputs are geometrical parameters and the outputs are electrical parameters. For the design of RF and microwave components, an inverse model is used where the inputs are electrical parameters and the outputs are geometrical parameters. This paper also presents a comparison of the direct inverse model and the proposed inverse model.