scholarly journals Analisis Stabilitas Sistem Tenaga Listrik Dengan Automatic Generation Control (AGC) Dua Area Menggunakan Fuzzy Logic Controller

2018 ◽  
Vol 17 (2) ◽  
pp. 263
Author(s):  
Made Dwi Noviantara ◽  
I Nengah Suweden ◽  
I Made Mataram
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Frequency Response ◽  
Fuzzy Logic Controller ◽  
Frequency Control ◽  
Stable State ◽  
Automatic Generation ◽  
Settling Time ◽  
Frequency Control System ◽  
Response Output

The power system must be able to server the load in a sustainable manner with good service quality, such as constant voltage and frequency, quickly stabilized when load changes occur. The control generator automatically changes the frequency to the highest value when the system changes every time. This is called AGC. To keep the frequency in a stable state required frequency control system. Currently developing a lot of control system with fuzzy logic method. The simulation is performed using 5 membership functions and gives a loading of 0.1 pu, using MATLAB-Simulink software. From the analysis result, the comparison of output of frequency response in overshoot condition with conventional method yielded , settling time of 20.5 second. While the fuzzy logic controller method produces frequency response output in the overshoot state that is , settling time is 12 seconds. Whit the fuzzy logic controller method produces better performance and faster than conventional methods.

Fuzzy Logic Based Load Frequency Control of Two Area Power System in Deregulated Environment

2014 ◽  
Vol 626 ◽  
pp. 219-226 ◽  
Author(s):  
Ajitha S. Priyadarsini ◽  
P. Melba Mary ◽  
N. Albert Singh
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Frequency Control ◽  
Operating Conditions ◽  
Load Frequency Control ◽  
Bilateral Contracts ◽  
Load Frequency ◽  
Frequency Control System ◽  
Tie Lines

In this paper fuzzy logic based load frequency control (LFC) of two area power system in deregulated environment was studied. A two area system is considered in which all areas are interconnected by normal AC tie-lines. In the proposed system the effects of bilateral contracts between DISCOs and GENCOs are also considered. The performance of the fuzzy logic controller is tested on a deregulated two area load frequency control system for different operating conditions using MATLAB Simulink tool.

scholarly journals Sistem Kontrol Troli Rotari sebagai Tempat Penitipan Barang Otomatis menggunakan Fuzzy Logic

2020 ◽  
Vol 8 (3) ◽  
pp. 575
Author(s):  
RISNANDA SATRIATAMA ◽  
DENNY DARLIS ◽  
PORMAN PANGARIBUAN
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Pulse Width ◽  
Completion Time ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Settling Time ◽  
Load Test ◽  
Rotary Encoder

ABSTRAKTroli rotari memerlukan sistem kontrol untuk mengatur rak ke posisi yang diinginkan. Penelitian ini berfokus pada sistem kontrol posisi rak menggunakan metode Fuzzy Logic Controller (FLC) dengan beban berbeda dari setiap pengguna. Masukan pada sistem kontrol FLC adalah error dan delta error dari sensor rotary encoder. Keluaran dari FLC adalah Pulse Width Modulation yang digunakan untuk mengontrol kecepatan motor DC. Hasil penelitian dari tiga variasi fungsi keanggotaan keluaran dengan beban pada satu rak, pengujian tanpa beban memiliki settling time antara 3,11 s hingga 3,24 s dan error steady state antara 3 hingga 8 counter. Pengujian dengan beban 250 g memiliki settling time antara 3,92 s hingga 8,80 s dan error steady state antara –5 counter hingga 4 counter. Sedangkan pengujian dengan beban 500 g memiliki settling time antara 4,66 s hingga 7,39 s dan error steady state antara 8 counter hingga 12 counter.Kata kunci: tempat penitipan barang, troli rotari, Fuzzy Logic Controller. ABSTRACTRotary trolley needs control system that used for rack control to the position. The research focused on rack position control system using the Fuzzy Logic Controller (FLC) method with different loads from each user. Inputs to the FLC control system are error and delta error from the rotary encoder sensor. The output of the FLC is Pulse Width Modulation which is used to control the speed of the DC motor. The results from 3 variations of the meeting results, the no-load test had a completion time of between 3.11 s to 3.24 s and steady-state conditions between 3 counters to 8 counters. Testing with a load of 250 g has a completion time of 3.92 s to 8.80 s and steady-state conditions between -5 counters to 4 counters. While testing with a load of 500 g has a settling time of 4.66 s to 7.39 s and steady-state conditions between 8 to 12 counters.Keywords: deposit box, rotary trolley, Fuzzy Logic Controller.

Development of Servo Drive Positioning Control System Using PID Controller and Fuzzy Logic Controller

2021 ◽  
Vol 26 (6) ◽  
pp. 583-588
Author(s):  
Zaw Myo Naing ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Settling Time ◽  
Fuzzy Pid ◽  
Servo Drive ◽  
Positioning Control ◽  
Fuzzy Pid Controller

Servo drives are one of the most widely utilized devices in various mechanical systems and industrial applications to provide precise position control. The study of servo driver produc-tiveness and performance index is the important task. In this work, PID controller and fuzzy log-ic controller (FLC) were developed to control the position of a DC servo drive. The MATLAB Simulink program was investigated and implemented to calculate the values of servo drive pa-rameters, and a scheme for simulating the operation of a servo drive using different controllers was presented. A mathematical model of a DC servo drive for a positioning control system has been proposed. The control characteristics of the PID controller, fuzzy logic controller and fuzzy PID controller are compared. The simulation results have shown that the PID controller allows for an overshoot of about 1 % with a settling time of about 4 sec. The use of the fuzzy PID con-troller reduces the maximum overshoot to 1 % and decreases the settling time to 2 sec. As a re-sult, the fuzzy PID controller allows for better performance and efficiency compared to other controllers.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Sign in / Sign up

Export Citation Format

Share Document