Search results

The purpose of this paper is to propose a novel self-adjusting proportional integral (SA-PI) controller, for controlling the active and reactive power of permanent magnet synchronous generator (PMSG) when subjected to variable wind speed and parameter variations.

The proportional and integral gains of the proposed SA-PI controller are based on tan-hyperbolic function and adjust themselves automatically within pre-fixed limits according to the error occurring during transient situations.

The proposed SA-PI controller is able to evade the problems usually encountered while using a constant gain PI controller, such as lack of robustness, adaptability and a wide range of operation. It also damps out system oscillations faster with reduced settling time and fewer overshoots.

Simulation results and comparative studies with conventional PI controller and the differential evolution–optimized PI (DE-PI) controller reveal the effectiveness of the proposed control scheme. MATLAB is used to perform the simulation studies.

This study proposes a modified sliding mode control technique having a proportional plus integral (PI) sliding surface aided by auxiliary control applied to a wind turbine driven permanent magnet synchronous generator. This paper aims to realize real and reactive power control, keeping the voltage under the desired limit during transients.

First, a PI sliding surface type sliding mode control (PISMC) is formulated, which is capable of dragging the system to the desired state and stability. Then a saturation function-based auxiliary controller is incorporated with PISMC to enhance its performance during wind speed and system parameter variations.

The proposed controller can tackle the problems faced while using a PI controller and the conventional sliding mode controller (CSMC) such as lack of robustness and requirement of unnecessary large control signals to overcome the parametric uncertainties and problem of chattering.

To justify the superior performance of the proposed controller in terms of robustness, reliability and accuracy a comparative study is done with the CSMC and PI controllers. The simulations are performed using MATLAB.

Wind power is the one of best natural resources to meet the demands of electricity in India. In this regard, one of engineering college in Visakhapatnam has procured wind turbine generators of 200 kWp and got these installed on the rooftop of the college buildings for research and power generation. After starting the mills, huge vibrations were experienced by the staff and students in the laboratories and classrooms. So, the purpose of this paper is to carry out vibration and noise studies on wind turbine generator to identify the problem for high vibrations and suggest a novel method for vibration reduction.

Experimental vibration and natural frequency investigations are carried when wind velocity around 6.0 m/s using frequency analyzer, impact hammer, condenser microphone and accelerometer. An attempt is made to reduce the vibration and noise level of wind turbine generator by inserting a steel coil spring of 300 mm length having 20 turns in series with turnbuckle D shackle assembly, which is used to connect the wind turbine generator to the hook mounted on slab.

A high vibration velocity of 9.9 mm/s was observed on at base frame of wind turbine generator. The natural frequencies of hook and slab are observed in between 15 to 20 Hz from the natural frequency test. A high noise of 94.67 dBA is observed at a distance of 1 m from the base of wind turbine generator along the rotational axis of rotor. After modification to the baseline, WTG the vibration and noise levels are reduced to 4.8 mm/sec and 77.76 dBA, respectively.

This is the first time to study the huge vibrations generated in wind turbine generators installed on the rooftop of the college. Developed a novel methodology to reduce the vibrations by inserting a steel coil springs in turnbuckle D shackle assembly of wind turbine generators. After modification, wind turbine generator are running successfully without any high vibrations.

The active power filter with two-level inverter needs a high-rating coupling transformer for high-power applications. This complicates the control and system becomes bulky and expensive. The purpose of this paper is to motivate the use of multilevel inverter as harmonic filter, which eliminates the coupling transformer and allows direct control of the power circuit. The advancement in artificial intelligence (AI) for computation is explored for controller design.

The proposed scheme has a five-level cascaded H-bridge multilevel inverter (CHBMLI) as a harmonic filter. The control scheme includes one neural network controller and two fuzzy logic-based controllers for harmonic extraction, dc capacitor voltage balancing, and compensating current adjustment, respectively. The topology is modeled in MATLAB/SIMULINK and implemented using dSPACE DS1103 interface for experimentation.

The exhaustive simulation and experimental results demonstrate the robustness and effectiveness of the proposed topology and controllers for harmonic minimization for RL/RC load and change in load. The comparison between traditional PI controller and proposed AI-based controller is presented. It indicates that the AI-based controller is fast, dynamic, and adaptive to accommodate the changes in load. The total harmonic distortion obtained by applying AI-based controllers are well within the IEEE519 std. limits.

The simulation of high-power, medium-voltage system is presented and a downscaled prototype is designed and developed for implementation. The laboratory module of CHBMLI-based harmonic filter and AI-based controllers modeled in SIMULINK is executed using dSPACE DS1103 interface through real time workshop.

This paper aims to propose a new autonomous driving controller to calibrate the absolute heading adaptively. Besides, the second purpose of this paper is to propose a new angle-track loop with a mass regulator to improve the adaptability of the autonomous driving system under different loads and road conditions.

In this paper, the error model of heading is built and a new autonomous driving controller with heading adaptive calibration is designed. The new controller calculates the average lateral error by the self-adjusting interval window and calibrates the absolute heading through the incremental proportional–integral–derivative (PID) controller. A window-size adjustment strategy, based on the current lateral error and the derivative of lateral error, is proposed to improve both the transient and the steady-state responses. An angle-tracking loop with mass regulator is proposed to improve the adaptability of autonomous steering system under different loads and road conditions.

The experiment results demonstrate that this method can compensate the heading installation error and restrain the off-track error from 13.8 to 1.30 cm. The standard error of new controller is smaller than fuzzy-PID calibration controller and the accuracy of autonomous driving system is improved.

The accuracy of heading calibrated by the new controller is not affected by external factors and the efficiency of calibration is improved. As the model parameters of steering system can be obtained manually, the new autonomous steering controller has more simple structure and is easy to implement. Mass regulator is adjusted according to the road conditions and the mass of harvester, which can improve the system adaptability.

The power framework has become a vital part in the day-to-day life and exhibits a rapid development in this current era. Due to the fact of huge power utilization, the power frameworks fall under several power transmission-related concerns. Precisely, frequency deviation has generated a huge impact during power transmission; this in turn highly reduces the power system stability as well as reliability too.

To boost the system’s efficacy, this study proposes a neoteric closed loop feedback controller in which a control algorithm named correlative-elemental-curvature algorithm is introduced with a constant threshold.

With the aim of mitigating frequency deviation, a stability analysis technique called Retrofit Lyapunov’s method is deployed in the controller. This would simultaneously reduce the load disturbances along with tie-line synchronization issues faced with the prior controllers. Optimization is carried out with the aid of duelist optimization algorithm, which tunes the controller parameters thereby mitigating the complexities while designing a loop feedback controller power framework.

The efficacy of the proposed work is assessed with the aid of metrics, such as integral absolute error, accuracy and settling time. Thus, the proposed work enhances the system reliability as well as the stability by mitigating the frequency deviation related issues and guarantees reliable power transmission.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

Sinusoidal signals are often used as the inputs of the six degree of freedom (DOF) motion simulator platforms. The purpose of this paper is to propose a fuzzy incremental controller (FIC) to improve sinusoidal signal tracking performances of an electrohydraulic Gough-Stewart platform (GSP).

An FIC is proposed to control an electrohydraulic GSP without any model parameters. The FIC output can be self-organized by only using the hydraulic actuator position information. The control rules are determined by a systematic deterministic method.

Experimental results show that the proposed FIC is valid and can achieve better tracking performances compared with classical PID controller and a decoupling controller (a model-based controller).

An FIC using a systematic deterministic rule-base determination method is proposed to improve sinusoidal signal tracking performances of electrohydraulic GSP.

The purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.

Quadrotor design is made by using Solidworks drawing program and some mathematical performance relations. Modelling and simulation are performed in Matlab/Simulink program by using the state space model approaches with the parameters mostly taken from Solidworks. Proportional integral derivative (PID) approach is used as control technique. Morphing amount and the best PID coefficients are determined by using SPSA algorithm.

By using SPSA algorithm, the amount of morphing and the best PID coefficients are determined, and the quadrotor longitudinal and lateral flights are made most stable via morphing.

It takes quite a long time to model the quadrotor in Solidworks and Matlab/Simulink with the state space model and using the SPSA algorithm. However, this situation is overcome with the proposed model.

Optimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors.

SPSA optimization method is useful in terms of cost, time and practicality.

It is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.

The paper aims to propose a system that uses a combination of techniques to suggest weld requirements for ships parts. These suggestions are evaluated, decisions are made and then weld parameters are sent to a program generator.

A pattern recognition system recognizes shipbuilding parts using shape contour information. Fourier‐descriptors provide information and neural networks make decisions about shapes.

The system has distinguished between various parts and programs have been generated so that the methods have proved to be valid approaches.

The new system used a rudimentary curvature metric that measured Euclidean distance between two points in a window but the improved accuracy and ease of implementation can benefit other applications concerning curve approximation, node tracing, and image processing, but especially in identifying images of manufactured parts with distinct corners.

A new proposed system has been presented that uses image processing techniques in combination with a computer‐aided design model to provide information to a multi‐intelligent decision module. This module will use different criteria to determine a best weld path. Once the weld path has been determined then the program generator and post‐processor can be used to send a compatible program to the robot controller. The progress so far is described.