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Capacitive Energy Storage (CES) Optimization For Load Frequency Control in Micro Hydro Power Plant Using Imperialist Competitive Algorithm (ICA) Djalal, Muhammad Ruswandi; Yunus, Muhammad; Imran, Andi; Setiadi, Herlambang
EMITTER International Journal of Engineering Technology Vol 5, No 2 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

This research will discuss a strategy of frequency control at micro hydro power plant using Capacitive Energy Storage (CES). CES is a device that can store and release energy quickly. To optimize CES performance, proper tuning is required to optimize CES performance. To obtain optimal CES parameter on micro hydro, artificial intelligence method based on Imperialist Competitive Algorithm (ICA) is used. Proportional Integral Derivative Controller (PID) is still a controller that can not be separated from the system, therefore in this research will be combined with CES as the main controller for frequency control on micro hydro. The simulation results show that the application of ICA in optimizing PID-CES parameters, can well improve micro hydro performance. The control models discussed in this research are Proportional Controller (P), Proportional Integral Controller (PI), Proportional Derivative Controller (PD), PID Controller, CES Controller and PID-CES Controller. From the simulation results obtained, P controller overshoot of -0.0001254, with PI Controller -0.000125, with PD Controller -0.0001252, with PID controller -0.0001249, with CES controller -0.0001224, and with PID-CES -1.371e-05. From the results of some of the controller models, it can be concluded that the PID-CES controller proposed in this study has a very significant effect to reduce the frequency oscillation in micro hydro, and it is very suitable to be applied for frequency control at micro hydro.

Desain Sistem Kontrol Pitch Angle Wind Turbine Horizontal Axis Menggunakan Firefly Algorithm Djalal, Muhammad Ruswandi; Imran, Andi; Setiadi, Herlambang
Jurnal Teknik Elektro Vol 9, No 1 (2017): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v9i1.9710

Abstrak - Pitch variable-speed wind turbine telah berkembang pesat dalam beberapa tahun terakhir. Ada dua strategi kontrol untuk mengontrol variable kecepatan pada wind turbine. Saat kecepatan angin rendah di bawah nilai rata-rata, pengatur kecepatan harus dapat mengatur kecepatan rotor secara terus-menerus untuk mempertahankan kecepatan pada sebuah level, yang memberikan koefisien daya maksimum, sehingga efisiensi turbin akan meningkat. Pengaturan pitch angle diperlukan dalam kondisi kecepatan angin diatas yang diinginkan. Perubahan kecil pada pitch angle dapat mempengaruhi output daya. Pitch angle control adalah salah satu cara untuk menyesuaikan torsi aerodinamik pada tubin angin saat kecepatan angin berada diatas nilai kecepatan dan beberapa variable control lainnya, seperti kecepatan angin, kecepatan generator, dan daya generator. Dalam makalah ini akan akan dirancang variable control untuk memaksimalkan energi dari turbin angin. Perancangan variable control ini menggunakan PID controller. PID controller (Proporsional Integrator Diferensial) merupakan sebuah alat untuk mengontrol sebuah sistem, PID controller ini digunakan untuk mengontrol Permanent Magnet Synchronous Generator (PMSG). Hasil penelitian menunjukkan bahwa menggunakan PID controller lebih stabil dan daya output lebih optimal.Keyword â€“ turbin angin, pitch angle control,Â PID Controller

Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network DJALAL, MUHAMMAD RUSWANDI; HUTORO, KOKO; IMRAN, ANDI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 5, No 2 (2017): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v5i2.138

ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN). Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN). The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130 rad / s. The speed change was set at every 0.3 second interval and the maximum simulation time was 0.9 seconds. Case 1, without control, shows the torque response and velocity of three-phase induction motor without control. Although the speed of three phase induction motor was set to change at every 0.3 seconds, it would not affect the torque. The uncontrolled three-phase induction motors had poor performance due to induction motor speeds could not be adjusted as needed. Case 2 with backpropagation neural network control, although the speed of three phase induction motor changing at every 0.3 seconds would not affect the torque. In addition, the backpropagation neural network control had a good performance because the speed of induction motor could be adjusted as needed.Keywords: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor

Frequency stability improvement of micro hydro power system using hybrid SMES and CES based on Cuckoo search algorithm Djalal, Muhammad Ruswandi; Setiadi, Herlambang; Imran, Andi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 8, No 2 (2017)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Micro hydro has been chosen because it has advantages both economically, technically and as well as in terms of environmental friendliness. Micro hydro is suitable to be used in areas that difficult to be reached by the grid. Problems that often occur in the micro hydro system are not the constant rotation of the generator that caused by a change in load demand of the consumer. Thus causing frequency fluctuations in the system that can lead to damage both in the plant and in terms of consumer electrical appliances. The appropriate control technology should be taken to support the optimum performance of micro hydro. Therefore, this study will discuss a strategy of load frequency control by using Energy Storage. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are devices that can store energy in the form of a fast magnetic field in the superconducting coil. For the optimum performance, it is necessary to get the optimum tuning of SMES and CES parameters. The artificial intelligence methods, Cuckoo Search Algorithm (CSA) are used to obtain the optimum parameters in the micro hydro system. The simulation results show that the application of the CSA that use to tune the parameters of hybrid SMES-CES-PID can reduce overshoot oscillation of frequency response in micro hydro power plant.

Aplikasi Metode Cerdas untuk Optimasi Controller PID Motor DC Berbasis Firefly Algorithm Djalal, Muhammad Ruswandi; Nurohmah, Hidayatul; Imran, Andi; Yunus, Muhammad Yusuf
JURNAL NASIONAL TEKNIK ELEKTRO Vol 6, No 2: Juli 2017
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v6n2.393.2017

Controlling the speed of dc motor is very important to maintain the stability of motor operation. One of the most commonly used control methods is the proportional integral derivative (PID) controller. In order to operate optimally, PID controllers need the correct parameter tuning. One of the problems in using PID controllers is the determination of the proper PID parameters. In the determination of PID controller parameters is still done conventionally, so the performance of PID becomes not optimal. Therefore, in this research we will propose one of PID parameter tuning method by using intelligent method based on Firefly Algorithm (FA), to optimize and determine the proper parameters of PID. The FA is one of the smart methods inspired by firefly behavior that moves at night with flashing habits, which are then adapted and applied into intelligent algorithms to solve optimization problems. From the results obtained the Firefly method can well tune the PID parameters, so the resulting overshoot does not exist and settling time is very fast. As a comparison, in this study will also discuss the use of intelligent methods based on Bee Colony and Cuckoo Search.Keywords: PID, Bee-Colony, Cuckoo, Firefly, Settling timeAbstrak - Pengontrolan kecepatan motor dc merupakan hal yang sangat penting untuk menjaga stabilitas operasi motor. Salah satu metode pengontrolan yang sering digunakan adalah kontroler proportional integral derivative (PID). Agar dapat beroperasi dengan optimal, kontroler PID membutuhkan penalaan parameter yang tepat. Salah satu permasalahan dalam penggunaan kontroler PID adalah penentuan parameter PID yang tepat. Dalam penentuan parameter kontroler PID selama ini masih dilakukan secara konvensional, sehingga kinerja PID menjadi tidak optimal. Untuk itu pada penelitian ini akan diusulkan salah satu metode penalaan parameter PID dengan menggunakan metode cerdas berbasis Firefly Algorithm (FA), untuk mengoptimasi dan menentukan parameter yang tepat dari PID. FA adalah salah satu metode cerdas yang terinspirasi dari perilaku firefly yang bergerak dimalam hari dengan kebiasaan berkedip, yang kemudian diadaptasi dan diterapkan menjadi algoritma cerdas untuk menyelesaikan masalah optimasi. Dari hasil yang diperoleh metode Firefly dapat dengan baik menala parameter PID, sehingga overshoot yang dihasilkan tidak ada dan settling time sangat cepat. Sebagai pembanding, pada penelitian ini juga akan dibahas penggunaan metode cerdas berbasis Bee Colony dan Cuckoo Search.Kata Kunci : PID, Bee-Colony, Cuckoo, Firefly, Settling time

Application of Smart Bats Algorithm for Optimal Design of Power Stabilizer System at Sengkang Power Plant Muhammad Ruswandi Djalal; Muhammad Yunus Yunus; Herman Nawir; Andi Imran
International Journal of Artificial Intelligence Research Vol 1, No 2 (2017): December 2017
Publisher : STMIK Dharma Wacana

The problem of using Power System Stabilizer (PSS) in generator excitation is how to determine the optimal PSS parameter. To overcome these problems, the authors use a method of intelligent bats based algorithm to design PSS. Bat Algorithm is an algorithm that works based on bat behavior in search of food source. Correlation with this research is, food sources sought by bats represent as PSS parameters to be optimized. Bat's algorithm will work based on a specified destination function, namely Integral Time Absolute Error (ITAE). In this research will be seen the deviation of velocity and rotor angle of each generator, in case of disturbance in bakaru generator. The analysis results show that the uncontrolled system produces a large overshoot oscillation, and after the addition of PSS oscillation control equipment can be muted. So that the overshoot and settling time of each generator can be reduced and the generator can quickly go to steady state condition

Frequency stability improvement of micro hydro power system using hybrid SMES and CES based on Cuckoo search algorithm Muhammad Ruswandi Djalal; Herlambang Setiadi; Andi Imran
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 8, No 2 (2017)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2017.v8.76-84

Micro hydro has been chosen because it has advantages both economically, technically and as well as in terms of environmental friendliness. Micro hydro is suitable to be used in areas that difficult to be reached by the grid. Problems that often occur in the micro hydro system are not the constant rotation of the generator that caused by a change in load demand of the consumer. Thus causing frequency fluctuations in the system that can lead to damage both in the plant and in terms of consumer electrical appliances. The appropriate control technology should be taken to support the optimum performance of micro hydro. Therefore, this study will discuss a strategy of load frequency control by using Energy Storage. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are devices that can store energy in the form of a fast magnetic field in the superconducting coil. For the optimum performance, it is necessary to get the optimum tuning of SMES and CES parameters. The artificial intelligence methods, Cuckoo Search Algorithm (CSA) are used to obtain the optimum parameters in the micro hydro system. The simulation results show that the application of the CSA that use to tune the parameters of hybrid SMES-CES-PID can reduce overshoot oscillation of frequency response in micro hydro power plant.

Modifikasi Desain PID Controller Pada Permanent Magnet Synchronous Motor Dengan Flower Pollination Algorithm Muhammad Ruswandi Djalal; Machrus Ali; Andi Imran; Herlambang Setiadi
Jurnal Teknik Elektro Vol 6, No 2 (2017): Jurnal Teknik Elektro
Publisher : Situs resmi ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

The use of PID (Proportional-Integral-Derivative) controllers on a synchronous motor is very widely used, because of its simple, robust structure that is robust and easy to use. The use of PID controllers requires proper parameter setting for optimal performance on the motor. The most commonly used solution is the trial-error method, to determine the proper parameters for PID, but the results do not make the PID controller optimal. Lately there has been a lot of research to optimize PID controller, wrong with smart method. For this purpose, we will use Flower Pollination Algorithm (FPA) optimization method to optimize and determine the exact parameters of PID. FPA is one method that is being adapted from the process of plant pollination, so this concept is adapted and applied to be. From the results that have the FPA method can be well tuned parameters PID, so that the resulting overshoot faster and settling time is very fast. Optimization results Kp 0.9441, Ki 0.9311, Kd 0.0840. In this study will discuss uncontrolled motors, with PID trial-error controller, PID-PSO and PID-FPA.Keywords: Sync motor, PID, Trial-Error, PSO, FPA, Overshoot, Settling time

PENGEMBANGAN PENGGERAK PINTU PAGAR OTOMATIS BERBASIS MIKROKONTROLER ARDUINO UNO ATMEGA 328P Massikki Massikki; Andi Imran; Marsud Hamid; Andi Muhammad Afif; Muliaty Yantahin
Jurnal Media Elektrik Vol 18, No 3 (2021): Media Elektrik
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/metrik.v18i3.25883

Penelitian ini didasarkan dengan masalah kurangnya tingkat keamanan dan efisien dalam lingkungan rumah diakibatkan karena tidak dapat mengontrol pagar rumah sehingga diperlukan adanya pengembangan suatu alat yang dapat membuka dan menutup pagar dengan menggunakan sistem pengendali berbasis Arduino. Jenis penelitian ini menggunakan penelitian Research and Development (R&D) yang merupakan jenis penelitian untuk mengembangkan suatu produk baru atau menyempurnakan produk yang telah ada. Pengembangan ini menggunakan mikrokontroler Arduino uno ATMega 328P, Keypad dan remot control sebagai input perintah, Motor driver sebagai pengendali kecepatan dan arah putaran motor DC, motor DC sebagai alat penggerak pagar, solenoid pengunci pagar. Data dianalisis menggunakan teknik analisis validasi ahli untuk mngetahui kelayakan alat. Berdasarkan hasil penelitian yang dilakukan dapat disimpulkan bahwa kecepatan putaran motor DC yang berbeban dan tanpa beban dengan lima kali percobaan memiliki waktu yang dibutuhkan untuk membuka pagar dan menutup pagar yaitu kecepatan rpm 94,8 dengan waktu 12 detik dengan hasil uji functionality menunjukkan bahwa alat ini memiliki interpretasi sangat baik sehingga layak untuk digunakan

Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network MUHAMMAD RUSWANDI DJALAL; KOKO HUTORO; ANDI IMRAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 5, No 2 (2017): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v5i2.138

ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN). Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN). The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130 rad / s. The speed change was set at every 0.3 second interval and the maximum simulation time was 0.9 seconds. Case 1, without control, shows the torque response and velocity of three-phase induction motor without control. Although the speed of three phase induction motor was set to change at every 0.3 seconds, it would not affect the torque. The uncontrolled three-phase induction motors had poor performance due to induction motor speeds could not be adjusted as needed. Case 2 with backpropagation neural network control, although the speed of three phase induction motor changing at every 0.3 seconds would not affect the torque. In addition, the backpropagation neural network control had a good performance because the speed of induction motor could be adjusted as needed.Keywords: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor

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