Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors

Use of Fuzzy Logic for Design and Control of Nonlinear MIMO Systems

Standard analytical methods are often ineffective or even useless for design of nonlinear control systems with imprecisely known parameters. The use of fuzzy logic principles presents one possible way to control such systems which can be used both for modeling and design of the control. The advantage of using this method consists in its simplicity and easy way of developing the algorithm, which in the phase of designing the controllers and also for modeling the features of the designed structures, allows the use of computer technology. Simplicity of the proposed structure (usually with the PI controllers) and determination of their parameters without any need for complex mathematical description present another considerable advantage of the used method. This chapter presents two typical examples of designing the control of nonlinear multi‐input multi‐output (MIMO) systems from the field of mechatronic systems based on fuzzy logic principles

Torque Control

This book is the result of inspirations and contributions from many researchers, a collection of 9 works, which are, in majority, focalised around the Direct Torque Control and may be comprised of three sections: different techniques for the control of asynchronous motors and double feed or double star induction machines, oriented approach of recent developments relating to the control of the Permanent Magnet Synchronous Motors, and special controller design and torque control of switched reluctance machine

Improvements the direct torque control performance for an induction machine using fuzzy logic controller

This article examines a solution to the major problems of induction machine control in order to achieve superior dynamic performance. Conventional direct torque control and indirect control with flux orientation have some drawbacks, such as current harmonics, torque ripples, flux ripples, and rise time. In this article, we propose a comparative analysis between previous approaches and the one using fuzzy logic. Results from the simulation show that the direct torque control method using fuzzy logic is more effective in providing a precise and fast response without overshooting, and it eliminates torque and flux fluctuations at low switching frequencies. The demonstrated improvements in dynamic performance contribute to increased operational efficiency and reliability in industrial applications

Serangga dan mitos suku kaum jakun, Kampung Peta, Mersing Johor

This study focuses on seeing insects from the mythical perspective of the Orang Asli tribe of Jakun, Kampung Peta, Mersing Johor. The existence of insects in the life of every ethnic in Malaysia has brought various elements of myths. Therefore, when combining myths and insects, it could be said that myth is a human way of understanding, expressing and linking insects to him/herself as well as a group/culture. The practice of using insects among ethnic groups in daily life is called etnoentomology. In this study, the insects studied are the butterfly (Lepidoptera), the odonates (Odonata) and the cicadas (Homoptera). This is because these insects are very popular in the community and have their own myths that are brought into the local culture of belief

Direct space vector modulation for matrix converter fed dual star induction machine and neuro-fuzzy speed controller

This paper presents the modeling, design, and simulation of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling the speed of the Double Star induction Machine (DSIM), the machine is fed by three phase direct matrix converter which makes directly AC-AC power conversion is modeled using Direct Space Vector Modulation technique(DSVM)  for direct matrix converter. Double star Induction motor is characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements. Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques has been devised by various researchers across the world. Some of them are based on the neuro-fuzzy techniques. The main advantage of designing the ANFIS coordination scheme is to control the speed of the DSIM to increase the dynamic performance, to provide good stabilization. To show the effectiveness of our scheme, the proposed method was simulated on an electrical system composed of a 4.5 kW six-phase induction machine and its power inverter. Digital simulation results demonstrate that the deigned ANFIS speed controller realize a good dynamic of the DSIM, a perfect speed tracking with no overshoot, give better performance and high robustness

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

Performance Analysis of Adaptive Fuzzy Sliding Mode for Nonlinear Control of the Doubly Fed Induction Motor

In this article, we propose a contribution to the control of a doubly fed induction motor by sliding mode with adaptive fuzzy logic. The technique of vector-control by classical field oriented applied to the doubly fed induction motor (DFIM) with mechanical sensors made it possible to have performances comparable with that of the direct current motor. However, it very sensitive to the parametric variations of the machine. The regulation speed by a classical regulator (PI) presents disadvantages: Poor robustness against parametric uncertainties of modeling and no the considering of the disturbances and little degree of freedom for the regulation. Because this effect, several robust controls were proposed in the technical literature to ensure the decoupling of the currents of the DFIM in a reference (d, q) leading to calculate simplified correctors. Among them, the variable structure control by sliding mode. It uses algorithms of regulations which ensure the robustness of the behavior of the process compared to the parametric variations and disturbances. Also, the impact of regulators based on artificial intelligence techniques such as adaptive fuzzy sliding mode controller are studied. In terms of results obtained, good dynamic performance and robustness with respect to load disturbances and parametric variation has been observed