Fuzzy logic controllers

In this paper, we presented an optimized fuzzy logic controller using particle swarm optimization for DC motor speed control. The controller model is simulated using MATLAB software and also experimentally tested on a laboratory DC motor. A comparison of the performance of different controllers such as PID controller, fuzzy logic controller and optimized fuzzy logic controller is presented as well. With reference to the results of digital simulations and experiment, the designed FLC-PSO speed controller obtains much better dynamic behavior compared to PID and the normal FLC designed. Moreover, it can acquire superior performance of the DC motor, and also perfect speed tracking with no overshoot. The optimized membership functions (MFs) are obviously proved to be able to provide a better performance and higher robustness in comparison with a regular fuzzy model, when the MFs were heuristically defined. Besides, experimental results verify the ability of proposed FLC under sudden change of the load torque which leads to speed variances. © 2012 IEEE.

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6518621

The main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

Accurate and fast islanding detection of distributed generation is highly important for its successful operation
in distribution networks. Up to now, various islanding detection technique based on communication,
passive, active and hybrid methods have been proposed. However, each technique suffers from
certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques,
due to their robustness and flexibility in dealing with complex nonlinear systems, is an option
that might solve this problem. This paper aims to provide a comprehensive review of computational
intelligence based techniques applied for islanding detection of distributed generation. Moreover, the
paper compares the accuracies of computational intelligence based techniques over existing techniques
to provide a handful of information for industries and utility researchers to determine the best method
for their respective system.

This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID,
LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers
have been tested on test bed of RIP system the controllers are compared from various aspects. The
controllers in simulink are compared with the controllers in real time.

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

Mini hydro power plants (MHPP) are gaining attraction as a cost effective source for rural electrification in developing countries due to its environmental friendly operation. These MHPP plants suffer from the speed control problem due to continuous load variation. PID controllers fail to provide optimum speed control due to its limitation of parameter tuning. Alternatively, Fuzzy logic control is commonly used for implementing intelligent control for the nonlinear system. This paper presents a new fuzzy logic based governor for frequency / speed regulation of MHPP for implementation in distribution network. For verification, the response of load frequency control using fuzzy based governor and PID based governor is compared. The simulation results show that fuzzy based governor ensures robust control by improving the frequency response as well as dynamic response in comparison to PID based governor. Furthermore, fuzzy based governor enables the generator to continuously supply power even at 25% load variation whereas generator with PID based governor fails to supply power even at 20% load variation.

This paper presents a load frequency control in four area power systems using fuzzy gain scheduling of PI controller is realized. The system simulation is realized by using Matlab/Simulink software. System dynamic performance is observed for conventional PI, fuzzy PI and fuzzy logic controllers.

Evaluation of employee performance is an important element in enhancing the quality of the work and improves employees’ motivation to perform well.  It also presents a basis for upgrading and enhancing of an organization. Periodical employees’ performance evaluation in an organization assists management to recognize its strengths and weaknesses.

This paper presents a design and implementation of a performance appraisal system using the fuzzy logic. In addition to the normal process of performance evaluation modules, the system contains step by step inference engine processes. These processes demonstrate several calculation details in relations composition and aggregation methods such as min operator, algebraic product, sup-min and sup-product. The system has foundation to add-on analysis module to analyze and report the final result using various similarity measures. MS Access database was used to maintain the data, build the inference logic and develop all setting user interfaces.

A novel pareto-optimization technique based on newly developed hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) is presented in this paper. In HFMOEA, two significant parameters such as crossover probability (P C) and mutation probability (P M) are dynamically varied during optimization based on the output of a fuzzy controller for improving its convergence performance by guiding the direction of stochastic search to reach near the true pareto-optimal solution effectively. The performance of HFMOEA is tested on three benchmark test problems such as ZDT1, ZDT2 and ZDT3 and compared with NSGA-II.

In islanding mode, system frequency is severely disturbed due to imbalance between generation and load demand resulting in overloading or loss of generation cases. In order to cope with these events, under-frequency load shedding scheme (UFLS) is applied to stabilize the frequency. This paper presents an intelligent under frequency load shedding scheme implemented on mini-hydro operating in islanded mode. The proposed UFLS scheme consists of a fuzzy logic load shedding controller (FLLSC) with load shedding controller module (LSCM). The FLLSC by measuring frequency and rate of change of frequency estimates the amount of load to be shed. The LSCM sheds the load estimated by FLLSC. This scheme is tested for event based and response based cases. The results have shown that proposed load shedding scheme successfully estimates the amount of load to be shed and stabilizes the frequency for these cases.

When tripping events or overloading cases occur in power system, load shedding scheme operates to shed some load and stabilize the
frequency. However, amount of load to be shed greatly depends on, how fast governor can utilizeDG spinning reserve. This paper compares the
response of DG with fuzzy based governor and PID based governor in utilization of DG spinning reserve. The simulation results show that DG with
fuzzy based governor utilizes spinning reserve more quickly and requires lesser load to be shed than a DG with PID based governor.

In islanding mode, system frequency is severely disturbed due to imbalance between generation and load demand resulting in overloading or loss of generation cases. In order to cope with these events, under-frequency load shedding scheme (UFLS) is applied to stabilize the frequency. This paper presents an intelligent under frequency load shedding scheme implemented on mini-hydro operating in islanded mode. The proposed UFLS scheme consists of a fuzzy logic load shedding controller (FLLSC) with load shedding controller module (LSCM). The FLLSC by measuring frequency and rate of change of frequency estimates the amount of load to be shed. The LSCM sheds the load estimated by FLLSC. This scheme is tested for event based and response based cases. The results have shown that proposed load shedding scheme successfully estimates the amount of load to be shed and stabilizes the frequency for these cases.

When tripping events or overloading cases occur in power system, load shedding scheme operates to shed some load and stabilize the frequency. However, amount of load to be shed greatly depends on, how fast governor can utilizeDG spinning reserve. This paper compares the response of DG with fuzzy based governor and PID based governor in utilization of DG spinning reserve. The simulation results show that DG with fuzzy based governor utilizes spinning reserve more quickly and requires lesser load to be shed than a DG with PID based governor.

This paper proposes a Fuzzy Logic based controller for implementing Active Pitch Angle Controller in horizontal axes wind turbine. A new improved model of wind turbine is presented. By using fuzzy logic based pitch angle controller, output power of wind turbine is regulated during extremely high wind speed. Based on its inputs, the controller has fast response which can decrease mechanical fatigues in wind turbine components. The proposed controller model has been successfully validated on Matlab/Simulink software.

In islanding mode, system frequency is severely disturbed due to imbalance between generation and load demand resulting in overloading or loss of generation cases. In order to cope with these events, under-frequency load shedding scheme (UFLS) is applied to stabilize the frequency. This paper presents an intelligent under frequency load shedding scheme implemented on mini-hydro operating in islanded mode. The proposed UFLS scheme consists of a fuzzy logic load shedding controller (FLLSC) with load shedding controller module (LSCM). The FLLSC by measuring frequency and rate of change of frequency estimates the amount of load to be shed. The LSCM sheds the load estimated by FLLSC. This scheme is tested for event based and response based cases. The results have shown that proposed load shedding scheme successfully estimates the amount of load to be shed and stabilizes the frequency for these cases.

A novel pareto-optimization technique based on newly developed hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) is presented in this paper. In HFMOEA, two significant parameters such as crossover probability (P C) and mutation probability (P M) are dynamically varied during optimization based on the output of a fuzzy controller for improving its convergence performance by guiding the direction of stochastic search to reach near the true pareto-optimal solution effectively. The performance of HFMOEA is tested on three benchmark test problems such as ZDT1, ZDT2 and ZDT3 and compared with NSGA-II.