Gotowa bibliografia na temat „ASYMMETRICAL FUZZY LOGIC CONTROL (AFLC)”

This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs) devices, using adaptive fuzzy logic controller (AFLC) driven by adaptive fuzzy sets (AFSs). The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC)) and the setting of their control parameters (QSVC) are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC), driven by a fixed fuzzy set (FFS). Simulation studies were carried out and validated on the standard IEEE 30-bus test system.

Intelligent Universal Transformer (IUT) will comprise in Advanced Distribution Automation (ADA) with a new invention in control and management in future. It evolves with a high speed traditional transformer in addition to power electronic base construction will eventuate to oil elimination, dimensional size and weight reduction. Adaptive Fuzzy Logic Control (AFLC) is an adaptive progressed method with the high system performance capability being raised even on the uncertainty conditions. It enhances system stability, improves flexibility and releases designers from precise mathematical model utilization. Expert designer Knowledge is a critical requirement for conventional fuzzy logic controller (FLC), in contrast the AFLC rules and parameters are generated by adaptive model and human knowledge will downright initialize the first parameters values. In this approach four layers IUT topology is considered for developing the end user service options as 48V DC, reliable power as 240V AC 400HZ and three phase power option. AFLC schemes are proposed for employing current and voltage controllers in input output stages. Real time voltage regulation, automatic sag correction, Harmonic Filtering, energy storage option and dynamic system monitoring are the resulting benefits of using IUT four layers topology. AFLC methodology, leading the system robustness in any cases of grid and load disturbances.

Efficient control of liquid level - in a carbonization column (CCl) of soda ash production plants - is a difficult task because the plants are nonlinear, subjected to disturbances and lack a reliable mathematical model. To attain such efficient control, model-free fuzzy logic controllers (FLC) based on empirical knowledge are successfully developed and implemented, and adaptation mechanisms are added to aid the FLC tuning and compensate for plant changes. However, the stability analysis - of the adaptive FLC (AFLC) systems - is a critical issue that needs addressing. For this reason, the current investigation is devoted to the development of a method for analyzing AFLC system stability using robust stability and robust performance criteria. The suggested method is employed for the stability analysis of a designed PID AFLC utilized for liquid level control in an industrial CCl. The obtained results reveal that the AFLC preserves stability and high system performance in the whole range of adaptation and considered changes of the plant and the operation conditions. Moreover, the results unveil that the developed method can also be applied for the design of a robust FLC system that competes with adaptive counterparts.

For amelioration of tracking efficiency, the Maximum power point trackers (MPPT) are very important for photovoltaic (PV) generation. For this purpose here a reformed adaptive fuzzy logic control (FLC) MPPT tracker has been presented to enhance its overall power efficiency and gives rapid transient response under changing weather conditions. For voltage regulation at load bus, the zeta buck-boost converter has been taken for its least voltage ripple. MATLAB/SIMULINK simulation environment and dSPACE DS1104 real time control board is used to test the proposed adaptive fuzzy logic controller based MPPT in variable irradiance level and ambient tempera-ture. The tracking efficiency in this presented method is analyzed in comparison with standard fuzzy logic controller (FLC) and perturb and observe (P and O) MPPT algorithms. The modified AFLC controller gives better tracking efficiency and precise response compared to conventional fuzzy logic controller and P and O MPPT algorithms. Theoretical and experimental results obtained are demonstrated for improved functioning of the system.

Renewable Energy Resources plays an active role in standing against global warming and reduce the use of conventional energy sources. Hybrid systems formed by combining the renewable energy sources are efficient relatively. The intent of this paper is to furnish endurable power for frontier and far-off places with hybrid-system of architecture. The intended system embodying DFIG and solar PV based wind turbine. In solar systems, control mechanism is essential for improving the performance. This paper proposes a method of incremental conductance approach based MPPT Adaptive Fuzzy Logic Controller for grid connected PV system which is composed of a boost converter and a three phase inverter. Adaptive Fuzzy Logic Controller provides fast response and better %THD compared to Fuzzy and PI controllers. In solar system, MPPT will magnify solar output power value. The DFIG has two controllers Grid-Side Control (GSC) and Rotor-Side Control (RSC). The rated rotor speed and DC-link voltage are regulated by RSC and GSC through PI, Fuzzy Logic Controller and AFLC strategies. By using simulation studies performed by three control strategies, %THD analysis is carried out.

Three phases Permanent Magnet Synchronous Motors (PMSM) are non-linear resistors, resistance of stator winding, air gap flux, cross-coupling, saturation variable times and cogging torque in operation. Due to the nonlinear nature of PMSM, it is a challenge to control exactly the speed, torque and position. This paper presents two methods for speed control stabilization of the PMSM using the Adaptive Fuzzy Logic - Proportional Integral Derivative controller (AFL-PID) and Adaptive Particle Swarm Optimization - Proportional Integral Derivative controller (APSO-PID). The response results of the speed control PMSM Servo Systems use AFLC-PID and APSO-PID methods are compared and the conclusions are given.

Possible cases of non-fulfilment of the requirements of the necessary sensitivity and the selectivity of the existing protection against incomplete-phase and asymmetric modes in the electrical network under conditions of uncertainty of the initial data are determined. The paper considers the issue of intellectualization of protection from asymmetric modes based on theories of fuzzy logic, as well as machine learning models, and offers a structural diagram and an algorithm for the functioning of protection. The results of the synthesis of intelligent protection and an approach to modelling and control for controlled drive systems based on reinforcement learning are presented.

Non-holonomic wheeled robots (NWR) comprise a type of robotic system; they use wheels for movement and offer several advantages over other types. They are efficient, highly, and maneuverable, making them ideal for factory automation, logistics, transportation, and healthcare. The control of this type of robot is complicated, due to the complexity of modeling, asymmetrical non-holonomic constraints, and unknown perturbations in various applications. Therefore, in this study, a novel type-3 (T3) fuzzy logic system (FLS)-based controller is developed for NWRs. T3-FLSs are employed for modeling, and the modeling errors are considered in stability analysis based on the symmetric Lyapunov function. An observer is designed to detect the error, and its effect is eliminated by a developed terminal sliding mode controller (SMC). The designed technique is used to control a case-study NWR, and the results demonstrate the good accuracy of the developed scheme under non-holonomic constraints, unknown dynamics, and nonlinear disturbances.

With the depletion of non-renewable resources and growing public awareness about the advantages of green energy, alternative renewable sources are evolving as a significant source of energy since past few years. Furthermore, the electrical grid is on the verge of a paradigm shift, from centralized power generation, transmission, and huge power grids towards distributed generation (DG). DG fundamentally uses small-scale generators like photovoltaic (PV) panels, wind turbine, fuel cells, small and micro hydropower, diesel generator set, etc., and is limited to small distribution networks to produce power close to the end users. Renewable energy sources (RES) are essential components of DG because they are more environment friendly than conventional power generators and once established maintenance cost is also low. One of the most popular renewable energy source is solar energy because it is abundant, accessible and can be easily converted into electricity. The electricity produced from SPV system can be utilized by the local loads within the microgrid or it can be integrated with conventional grid. Microgrid (MG), which is a cluster of distributed generation, renewable sources, and local loads connected to the utility grid provides solution to manage local generations and loads as a single grid level entity. It has the potential to maximize overall system efficiency, power quality, and energy surety for critical loads. A microgrid can operate either in stand-alone mode or grid connected mode. Due to abundant availability of solar energy, an SPV based microgrid is widely used around the world. Due to intermittent nature of solar energy, stand-alone SPV based microgrid needs an energy storage system also, whereas in grid connected system, the microgrid is connected to conventional grid which takes care of the solar intermittency by having bi-directional flow of power. Depending on the technical specifications, grid-connected solar PV- based microgrid can be single-stage or double-stage. In single stage configuration, PV array is directly connected to a DC/AC converter whereas in double-stage configuration, DC/DC converter is coupled in between the solar PV array and PV inverter and provides the desired fixed DC voltage to the inverter. The present work aims at modelling, design, development and control of a solar PV vii based microgrid for enhanced performance. Also, the characterization studies of the developed system have been carried out. Modeling of the system is required in order to predict its behaviour under both steady and dynamic states. Characterization studies such as sensitivity and reliability analysis are used to evaluate the performance of the system. Sensitivity analysis is the performance evaluation technique for evaluating the change in the system’s performance with respect to the change in its parameters. The sensitivity functions for solar cell and boost converter with respect to influential parameters have been developed using first derivative of Taylor’s series. Reliability analysis for electrical and electronic components of the system have been performed using pareto analysis and reliability model of the PV based microgrid has been developed using reliability block diagram for different PV array configurations. The Fault tree analysis (FTA) model of the system has been developed to find the cause of failure and to step the events leading to failure serially. Further, Markov’s model has been used to develop the reliability functions of individual components and hence, the reliability of complete grid connected PV system has been calculated. Solar PV system gives maximum power under uniform shading. But many a times PV panels are non-uniformly irradiated and this condition is known as called partial shading condition (PSC). PSC occur due to shadow of big trees, nearby buildings and dense clouds etc. PSC in PV system is an inevasible situation and exhibits multiple peaks, consisting of a single global maximum power point and many local maximum power points, in its power-voltage curve. PSC makes tracking of global maximum power point more difficult and also reduces the efficiency of the system. The conventional MPPT control algorithms work well under uniform shading condition but under partial shading scenario, they may not be able to track global peak out of multiple peaks. Therefore, an efficient controller is required to overcome the raised issue. Further, various PV array configurations such as series, series-parallel, total cross tied, bridge linked etc. may be used to improve the system efficiency. In the present work, novel maximum power point control algorithms viz. an asymmetrical fuzzy logic control (AFLC) and asymmetrical interval type-2 FLC (AIT-2 FLC) are developed for stand-alone PV system under partial shading condition. The developed algorithms are tested for different PV array configurations. viii In stand-alone PV system, the power supplied to the load depends upon the available solar energy. The output of SPV is intermittent in nature as it depends on the environmental conditions. This intermittency problem can be addressed by adding an energy storage system along with PV system. Battery is the most commonly used energy storage device and is very pivotal in maintaining continuity of power to the load. But when two or more energy sources are connected, then control of dc link voltage at common coupling point (CCP) is an area of concern. Therefore, in a SPV system with BESS a controller is required which can maintain constant DC link voltage irrespective of system transients. The PI controller is commonly used controller for controlling dc- link voltage, but it cannot regulate DC-link voltage under dynamic operating conditions and have overshoots and long settling time in its response. Suitable intelligent controllers are designed to replace the conventional PI controller, as they provide a better transient response. In order to overcome the drawbacks of the conventional PI control algorithm, nonlinear autoregressive moving average-L2 (NARMA-L2) control algorithm is proposed and developed for the stand-alone PV system with BESS. The proposed control scheme maintains the voltage across DC-link under change in irradiation and load condition. In a grid connected SPV based microgrid, the output of boost converter i.e., DC link is connected to voltage source inverter which is connected to grid at the point of common coupling (PCC). Voltage source inverter converts the generated DC power from PV system to AC of required voltage and frequency, as well as maintains the balance of power between the SPV system, load, and grid. The inverter is regulated by the interfacing controllers for effective operation and grid synchronization. The interfacing controllers are used to control the output of PV inverter for its efficient utilization and for improving power quality at PCC by providing reactive power compensation, harmonics compensation and load balancing. Conventional control algorithm like synchronous reference frame theory (SRFT) uses proportional integral (PI) controller for DC-link voltage regulation. These controllers are not best suited for SPV based microgrid as the overshoots and long settling time in their response are inevitable. In order to overcome this, novel smooth Least Mean Square (SLMS), improved zero attracting LMS (IZALMS) and reweighted L0 norm variable step size continuous mixed p-norm (RL0-VSSCMPN) based adaptive interfacing control algorithms are proposed ix and developed for the PV based microgrid. The efficacy of the proposed control algorithms has been tested on hardware prototype developed in the laboratory using MicroLab box (dSPACE 1202). The developed prototype system acts as distribution static compensator (DSTATCOM) and consists of inverter that is tied in parallel to the grid at the point of common coupling. FLUKE power analyzer has been used to measure the response of the system. The research work presented in the thesis is expected to provide good exposure to design, development and control of the solar PV based microgrid.

博士
國立臺灣科技大學
電機工程系
103
In this dissertation, an asymmetrical fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target a cost function and optimization, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. The proposed asymmetrical FLC-based MPPT algorithm is implemented using a low cost digital signal controller dsPIC33FJ16GS502. To validate the correctness and the effectiveness of the proposed method, a 300 W prototyping circuit is built and simulations as well as experiments are carried out accordingly. Compared with the symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are improved by 25.8% and 0.93% under STC, respectively. Moreover, since the symmetrical FLC-based MPPT method fails to track the real MPP when irradiance level is low, the proposed asymmetrical FLC-based MPPT method can successfully deal with this problem. The advantages of the first MF design method are that it is simple and easy to adopt. The second MF design method applies the PSO technique to obtain the optimized input MF setting values. Compared with the first design method, the transient time and the MPP tracking accuracy can further be improved by 0.88% and 0.98%, respectively. This proves that PSO can be successfully applied to obtain the optimized MF setting values. In addition, the PSO optimized asymmetrical FLC-based MPPT method has the highest fitness value compared with other implemented methods.

Abstract This paper is concerned with robust longitudinal control of vehicles in the Intelligent Vehicle Highway Systems (IVHS) by adaptive vehicle traction force control (TFC). Two different traction force controllers, adaptive fuzzy logic control (AFLC) and adaptive sliding mode control (ASMC), are proposed and applied to the fastest stable acceleration/deceleration and robust vehicle platooning problems. The motivation for investigating adaptive techniques arises from the unknown time-varying nature of the tire/road surface interaction that governs vehicle traction. Synchronous application of the engine or brake torques is also proposed for more stable vehicle maneuvers. The lack of controllability during braking (only one net input torque for the two control objectives, i.e. front and rear wheel slips) is partly overcome by applying auxiliary engine torque. Simulations of the two control methods are conducted using a complex nonlinear vehicle model which fully describes the dynamic behavior of the vehicle. Both controllers result in good performance under time-varying operating conditions.

Abstract The electro-hydraulic servo systems have been frequently used in the fin position servo system of a missile because of their high power and good positioning capabilities. This paper presents a neuro-fuzzy control scheme for an electro-hydraulic fin position servo system. In this paper, we have explained a fuzzy control scheme which has multidimensional and asymmetrical membership functions, and we have designed a neuro-fuzzy controller which include parameters of the fuzzy logic controller. The effectiveness of this control scheme is verified by comparison with the conventional control algorithm through a series of simulation studies.