The suspension system is classified into three types passive suspension, semi-active, and active suspension.The term a quarter car model originated in the early part of the 20th century.It is considered the best way for studying the effectiveness of vehicle stability.This paper presents the modelling and control of a nonlinear active suspension system for a quarter car, the mathematical model represents a spring-mass (Quarter of the chassis) and unsprung mass (the wheel), with two degrees of freedom (2-DOF) system characterized by a pair of the differential equations.The objective of this work is to determine control strategy to deliver better performance with respect sprung displacement; sprung mass velocity; suspension deflection; peak overshoot; setting time.The active control of the suspension system is achieved using fractional-order PID (FOPID) tuned by particle swarm optimization algorithms (PSO algorithms) because the ordinary FOPID did not give good results, and linear quadratic regulator (LQR) control actions.The results are developed and simulated in MATLAB/Simulink.It is observed that the LQR controller gives better ride comfort by reducing the RMS error and the vibration of various types of road conditions as compared to an intelligent FOPID controller.
In this particular study, an artificial neural network (ANN) was utilized to effectively extract the maximum power from a photovoltaic (PV) installation.The primary objective was to assess the performance of the ANN in comparison to the commonly used perturb and observe (P&O) technique, especially when faced with varying climatic conditions.The results of the study unequivocally demonstrated the superiority of artificial intelligence, specifically the ANN, in enhancing the optimization of PV power.When compared to the traditional P&O technique, the ANN consistently outperformed it in terms of accurately tracking the maximum power point under different climatic conditions.This highlights the remarkable capabilities of artificial intelligence in improving the efficiency and performance of PV systems.
The study presented in this paper consists in the modeling and optimization of the performance of the control of an induction motor (IM) by the application of the sensorless field oriented control (SFOC), using the strategy of the discontinuous pulse width modulation (DPWM) to switch the transistors which form the three-phase voltage source inverter (3Ph_VSI).The latter gives a reduction in the number and the switching losses compared to the space vector modulation (SVPWM).This leads to improve the performance of the IM.The proposed system has many advantages; provides for the decrease in noise, presents an increase in reliability and makes the material less expensive.The study of this control is done in order to show the efficiency and robustness of the method during variations in speed and torque.
This study describes how to regulate the frequency and terminal voltage of a freestanding wind energy conversion system using an Enhanced Phase Locked Loop (EPLL)-based strategy to supply power to varied loads regardless of wind speed. In a standalone wind turbine energy conversion system, the EPLL control scheme extracts the reference source currents (SWECS). The control algorithm employs two proportional-integral (PI) controllers to create the active and reactive power components of the consumers' load currents, estimate reference source currents, and connect the zigzag transformer to PCC with VSC for neutral current compensation. To obtain optimal PI controller gains and most-suited settings to apply to SWECS, optimization approaches are used. The control algorithm is the most significant aspect of the system, and the speed with which it calculates, evaluates, and guesstimates determines the generation of source currents based on the algorithm's ideal controller PI gains. By properly estimating source currents, the EPLL control method improves dynamics and power quality issues, and the optimization technique is employed to acquire the gains of PI controllers. The proposed system employs the EPLL algorithm on a three-phase, four-wire system with changing loads to achieve ideal total harmonic distortion of source currents and voltages on the PCC, as defined by IEEE-519 standards. A battery energy storage device coupled to the VSC dc link keeps the load's necessary power constant. If the generator output exceeds the consumer demand, the excess power is delivered to BESS for temporary storage. When consumer demand exceeds generated power, a BESS delivers deficit power to the load, which adjusts and the frequency under various load conditions. The suggested system simulated results were tested with 3-phase 4-wire for harmonics reduction, load balancing, neutral wire current compensation, frequency and voltage control using MATLAB / Simulink.
Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection and localization of adjacent and distant broken bar faults in the induction machine, through a multi-winding model for the simulation of these cases. In this work, it was found that the application of Artificial Neural Networks (ANN) based on Mean Squared Error (MSE) and Random Forest (decision tree) plays an important role in detecting and locating defaults. The stator current signal Ias of a motor in the dynamic state was acquired from a healthy and faulty motor with a broken rotor bar fault. 9 statistical features and 8 wavelet packet parameters are extracted from the stator current signal. These features were employed as an input vector to train and test the ANN and random fores29t and determine whether the motor was running under normal conditions or defective. For optimizing the rotor bar defect classification procedure, feature selection algorithms are adopted, such as BBAT and BPSO. For feature reduction, we used the principal component analysis (PCA) algorithm, to reduce the number of features. The results showed that the random forest classifier based on statistical parameters and wavelet packet parameters followed by PCA can detect the defective with high accuracy (98.3333%) compared to other methods.
