Desain Optimasi PID Controller Pada Heating Furnace Temperature Menggunakan Metode Particle Swarm Optimization (PSO)

Haikal, Muhammad Agil; Herlambang, Dandy Tulus; Ali, Machrus; Muhlasin,

doi:10.36040/alinier.v2i2.5162

Cited by 2 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Untuk mendapatkan hasil kontrol suhu yang baik, optimalisasi diperlukan karena kontrol konvensional memberikan hasil yang tidak stabil dan tidak efisien. Metode yang telah diteliti dan berhasil dalam optimasi sistem kontrol PID adalah Particle Swarm Optimization (PSO) [5][6] [7], Firefly Algorithm (FA) [8][9][10] [11], Imperialist Competitive Algorithm (ICA) [12][13], Bat algorithm (BA) [14][15], Ant Colony Optimization (ACO) [16], dan Flower Pollination Algorithm (FPA) [17] [18]. Setiap metode memiliki kelebihan dan kekurangannya masing-masing.…”

Section: Pendahuluanunclassified

Flower Polination Algorithm Sebagai Optimalisasi LFC Pada Hybrid Pembangkit Wind-Diesel

Ali,

Siswanto,

Baehaqi

2024

Jurnal FORTECH

View full text Add to dashboard Cite

As input power, the amount of wind and wind speed greatly influences the wind power generation system. A combination of a wind-diesel power generation system is needed to obtain optimal power quality. A hybrid system is a controlled network of multiple renewable energy generators such as wind turbines, solar cells, micro-hydro, and so on. Gain settings that are not optimal and the time constant is small in Load Frequency Control (LFC), causing its ability to be weak (weak line). In practice, the wind-diesel system is controlled with a PID controller. Setting the gain value of the PID is still in the conventional method, so it is difficult to get the optimal value. In this research, a control design was implemented using the Smart Method to find the optimum value of the Proportional Integral Derivative (PID) based on the FPA (Flower Pollination Algorithm). For comparison, methods were used without control methods, conventional PID methods, PID Auto tuning methods, and FPA (Flower Pollination Algorithm) methods. Wind-diesel modeling uses transfer function diagrams of wind and diesel turbines. This study compares several uncontrolled methods and conventional PID, PID-Auto tuning, and PID-FPA. The results of the research that has been carried out show that the smallest undershoot is -1.187.10-04 for PID-FPA, the smallest overshot is 0 for PID-FPA, and the fastest settling time is 9.827 s for PID-FPA. So it can be concluded that PID-FPA is the best controller in this research. This research can later be continued using other artificial intelligence methods.

show abstract

Section: Pendahuluanunclassified

Flower Polination Algorithm Sebagai Optimalisasi LFC Pada Hybrid Pembangkit Wind-Diesel

Ali,

Siswanto,

Baehaqi

2024

Jurnal FORTECH

View full text Add to dashboard Cite

show abstract

“…Kontrol cerdas berbasis Artificial Intelligence telah banyak berkembang untuk meningkatkan kontrol konvensional, kontrol PID [1], kontrol Fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS) [2][3], dan jenis kontroler lainnya. Metode yang telah diteliti dan berhasil dalam optimasi sistem kontrol adalah Particle Swarm Optimization (PSO) [4][5], Firefly Algorithm (FA) [6][7] [8], Imperialist Competitive Algorithm (ICA) [9], Bat algorithm (BA) [10][11], and Ant Colony Optimization (ACO) [12][13]. Setiap metode memiliki kelebihan dan kekurangannya masingmasing.…”

Section: Pendahuluanunclassified

“…Setiap metode memiliki kelebihan dan kekurangannya masingmasing. Kontrol suhu yang telah diteliti adalah menggunakan metode PSO [4][2], dan dalam penelitian ini digunakan metode ACO.…”

Section: Pendahuluanunclassified

Desain Optimasi PID Controller Pada Temperatur Heating Furnace Berbasis Ant Colony Algorithm (ACO)

et al. 2023

View full text Add to dashboard Cite

A furnace is a tool for heating materials, oil, and so on, which usually uses gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled in order to remain stable, precise, and of course improve fuel efficiency. As technology develops, there are several methods that can be used to control temperatures that are more reliable than conventional controls. The technology is Proportional Integral Derivative (PID) controller. PID controllers have been proven to be the best controllers and are widely used in industry. But to determine the gain from the PID value is still not accurate and can affect temperature stability, the response is also still slow to reach the desired set point. Therefore, this paper is to simulate a better PID gain value by using the artificial intelligence tuning method. The artificial intelligence method is Ant Colony Optimization (ACO). The simulation results and discussion show that the best design is PID-ACO with 0.0081 overshot, no undershot, and the fastest settling time is 35 seconds

show abstract

Desain Optimasi PID Controller Pada Heating Furnace Temperature Menggunakan Metode Particle Swarm Optimization (PSO)

Cited by 2 publications

References 7 publications

Flower Polination Algorithm Sebagai Optimalisasi LFC Pada Hybrid Pembangkit Wind-Diesel

Flower Polination Algorithm Sebagai Optimalisasi LFC Pada Hybrid Pembangkit Wind-Diesel

Desain Optimasi PID Controller Pada Temperatur Heating Furnace Berbasis Ant Colony Algorithm (ACO)

Contact Info

Product

Resources

About