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All Journal International Journal of Electrical and Computer Engineering Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Teknik ITS Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Fortech SinarFe7 Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Sewagati Jurnal Rekayasa elektrika Jurnal FORTECH
Ardyono Priyadi
Departemen Teknik Elekto, Fakultas Teknik Elektro, Institut Teknologi Sepuluh Nopember (ITS),Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : Jurnal Ilmiah Kursor

 1 
PARTICLE SWARM OPTIMIZATION FOR MANAGING AS INJECTION ALLOCATION Hannan Fatoni; Mauridhi Hery P; Ardyono Priyadi
Jurnal Ilmiah Kursor Vol 7 No 3 (2014)
Publisher : Universitas Trunojoyo Madura

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PARTICLE SWARM OPTIMIZATION FOR MANAGING AS INJECTION ALLOCATION aHannan Fatoni, bMauridhi Hery P, cArdyono Priyadi a Program Studi Magister ManajemenTeknologi, Institut Teknologi Sepuluh Nopember Jl. Cokroaminoto 12A, Surabaya, 60264, Indonesia b JurusanTeknikElektro, InstitutTeknologiSepuluhNopember Email: a hannanfatoni@gmail.com Abstract In oil and gas industry, the size of hydrocarbon reserves and type of the reservoir is crucial to the design methods and lifting the hydrocarbons for further processes. PT. XYZ uses the gas lift injection design to lift the oil content from the reservoir. In some conditions, the production choke valve shall be opened moreto increase the hydrocarbon production rates. However, it causes the reservoir instability, decreasing the reservoir pressure, and reducing the oil production drastically.Therefore, optimization of allocating gas lift injection rate on each of the production is needed to produce maximum oil and to improve the sustainability of oil and gas production on PT.XYZ. This paper proposes optimization technique for managing gas injection allocation using Particle Swarm Optimization (PSO). The procedure optimization can be explained as below; first step uses prosper modeling software to generate the model of production wells. Second, it obtains the curve of the gas lift injection rate against the oil production. Third, each well production model is validated by reference data from the well test result. The best PSO simulationwith limited gas injections which is 17 MMscfdresults of the gas lift injection allocation for each production wells are 0.98, 2.66, 1.39, 0.98, 3.19, 1.61, 1.78, 2.03, 1.40, and 0.98 MMscfd.With these gas injection allocations, the oil production increases to 4908.7 Barrels of oil per day (BPD). Maximum company profit after optimization reaching USD$ 578,004 compare with before optimization. The other optimization using Genetic Algorithm (GA) is also used for comparison. Keywords: Optimization, Prosper Modeling, PSO, GA.
DESIGN OPTIMIZATION OF MICRO HYDRO TURBINE USING ARTIFICIAL PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK Lie Jasa; Ratna Ika Putri; Ardyono Priyadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 7 No 3 (2014)
Publisher : Universitas Trunojoyo Madura

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Abstract

DESIGN OPTIMIZATION OF MICRO HYDRO TURBINE USING ARTIFICIAL PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK aLie Jasa, bRatna Ika Putri, cArdyono Priyadi, dMauridhi Hery Purnomo a,b,c,d Instrumentation, Measurement, and Power Systems Identification Laboratory Electrical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia a Electrical Engineering Department, Udayana University, Bali, Indonesia b Electrical Engineering Department, Politeknik Negeri Malang, Malang, Indonesia. Email: liejasa@unud.ac.id Abstrak Turbin digunakan mengkonversi energy potensial menjadi energy kinetik. Kapasitas Energy yang dihasilkan dipengaruhi oleh sudu-sudu turbin yang dipasang pada tepi. Sudu turbin dirancang seorang ahli dengan sudut kelengkungan tertentu. Efisiensi dari turbin dipengaruhi oleh besarnya sudut, jumlah dan bentuk sudu. Algoritma PSO dapat digunakan untuk komputasi dan optimasi dari design turbin mikro hidro. Penelitian ini dilakukan dengan; Pertama, Formula design turbin dioptimasi dengan PSO. Kedua, Data hasil optimasi PSO diinputkan kedalam jaringan ANN. Ketiga, training dan testing terhadap simulasi jaringan ANN. Dan yang terakhir, Analisa kesalahanr dari jaringan ANN. Data PSO sebanyak 180 record, 144 digunakan untuk training dan sisanya 40 untuk testing. Hasil penelitian ini adalah MAE= 0.4237, MSE=0.3826, dan SSE=165.2654. Error training terendah didapatkan dengan algoritma pembelajaran trainlm. Kondisi ini membuktikan bahwa jaringan ANN mampu menghasilkan desain turbin yang optimal. Kata kunci: Turbin, PSO, ANN, Energi Abstract Turbines are used to convert potential energy into kinetic energy. The blades installed on the turbine edge influence the amount of energy generated. Turbine blades are designed expertly with specific curvature angles. The number, shape, and angle of the blades influence the turbine efficiency. The particle swarm optimization (PSO) algorithm can be used to design and optimize micro-hydro turbines. In this study, we first optimized the formula for turbine using PSO. Second, we input the PSO optimization data into an artificial neural network (ANN). Third, we performed ANN network simulation testing and training. Finally, we conducted ANN network error analysis. From the 180 PSO data records, 144 were used for training, and the remaining 40 were used for testing. The results of this study are as follows: MAE = 0.4237, MSE = 0.3826, and SSE = 165.2654. The lowest training error was achieved when using the trainlm learning algorithm. The results prove that the ANN network can be used for optimizing turbine designs. Keywords: Turbine, PSO, ANN, Energy
COORDINATION CONTROLLER POWER SYSTEM IN JAVA-BALI 500 KV INTERCONECTED BASED ON BACTERIA FORAGING - PARTICLE SWARM OPTIMIZATION FOR STABILITY IMPROVEMENT IBG Manuaba; AAN Amrita; Ardyono Priyadi; Hery Purnomo
Jurnal Ilmiah Kursor Vol 8 No 2 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i2.65

Abstract

Power system stabilizer (PSS) and flexible AC transmission system (FACTS) damping controller to improve the stability of the power system has been widely used. A coordinated control method based on the combined computational evolutionary theory is proposed to overcome some of damping controllers simultaneously so as to keep the allowable level of power system damping. It works by making full use of favorable interaction between the controlling and minimizing adverse interactions so that the power system oscillations can be suppressed effectively. Proportional integral derivative (PID) controller tuning based power system stabilizer types PSS3B (PIDPSS3B), static var compensator (SVC) and automatic voltage regulator (AVR) presented in this paper. PID controller gain parameters such as proportional, integral factor, differential coefficient and get AVR selected and optimized by BF-PSOTVAC. The integral time absolut error (ITAE) standards of optimization design as objective function. The results of simulations show that performance index of system the proposed method is 42.7890. The BF-PSOTVAC method has the capability to damping optimally and suppresses error to minimum.
CRITICAL TRAJECTORY - EXTREME LEARNING MACHINE TECHNIQUE FOR COMPUTING CRITICAL CLEARING TIME Irrine Budi Sulistiawati; Ardyono Priyadi; Adi Soepriyanto
Jurnal Ilmiah Kursor Vol 8 No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i1.73

Abstract

Electric power system is called reliable if the system is able to provide power supply without interrupted. However, in large systems changing on the system or disturbance may affect the power supply. Critical clearing time is the time for deciding the system is a stable or an unstable condition. Critical clearing time has also relationship with setting relay protection to keep the system in the stable condition. Prediction of critical real time for online assessment is expected to be used for preventive action system. That’s why critical clearing time still an interesting topic to be investigated.This paper calculating time of Extreme Learning Machine to predict critical clearing tim on system. Before predicted by Extreme Learning Machine, critical clearing time calculated using numerical calculation critical trajectory method with load changing and different fault occuring. Tested by Java-Bali 500 kv 54 machine 25 bus give result that Extreme learning machine is able to perform faster prediction of neural network.
Co-Authors A.A Ngurah Amrita Adhi Kusmantoro Adi Soeprijanto Adi Soepriyanto Akbar Swandaru Almira Atha Nurhasyimi Almira Atha Nurhasyimi Anam, Sjamsul Andi Setiawan Andi Setiawan Aprilia Rahmayanti Aprilia Rahmayanti Aqsa Izza Mahendra, Rafin Arief Riambodo Ariq Arsya Nanda Arwindra Rizqiawan Asadulloh, Latief Ni'am Bernandus Anggo Seno Aji Bima Mustaqim Budiharto, Vita Lystianingrum Chandra Agung Ramadhan Daya Juang Mahaputra Diah Puspito Wulandari Dimas Anton Asfani Dimas Fajar Uman Putra Dimas Fajar Uman Putra Eko Mulyanto Yuniarno Erhankana Ardiana Putra Erlan Fajar Prihatama Fahmi Nurfaishal Farah Zakiyah Rahmanti Fath, Nifty Fauzan Fakhrul Arifin Fauzan Fakhrul Arifin Fauzan Nusyura Feby Agung Pamuji Feby Agung Pamuji Gita Dwipermata Sari Gladi Samodra Hafiz Ichwanto Hafiz Ichwanto Hannan Fatoni Hery Purnomo Heryanto Hartra M M Heryanto Hartra M M I Made Yulistya Negara IBG Manuaba Imam Abadi Imam Robandi Irrine Budi Sulistiawati Isa Hafidz Iyyaya Fariha, Nazila Jaelani Putra, Riko Satrya Fajar Januarestu, Achmad Jawahir Jumaras Situngkir Laksana, Eka Purwa Lie Jasa Lystianingrum, Vita Margo Pujiantara Mauridhi Hery P Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhy Hery Purnomo Moch. Iskandar Riansyah Mulyanto, Edy Nazila Iyyaya Fariha Nazila Iyyaya Fariha Nova Eka Budiyanta Nurio Herlambang Oddy Virgantara Putra Ontoseno Penangsang P., Mauridhi Hery Panji Setyo Suharso Prestian Rindho Saputra Pujiantara, Margo Pujiantoro, Margo Puspita Sari, Talitha Rachma Prilian Eviningsih Rafin Aqsa Izza Mahendra Rafin Aqsa Izza Mahendra Rakaditra Astungkara Ratna Ika Putri Rezi Delfianti Riambodo, Arief Risqiya Maulana Rizky Fadhli Hasben Rony Seto Wibowo Rosmaliati, Rosmaliati Rukmana, Maman Sandy, Yusdiar Sirait, Rummi Santi Rama Sitorus, Philip Nathanael Erlangga Sjamsjul Anam Soedibyo Soedibyo Soedibyo Soedibyo Suharto Suharto Sujono Sujono Sujono Talitha Puspita Sari Talitha Puspita Sari Talitha Puspita Sari Talitha Puspita Sari Talitha Puspita Sari Talitha Puspita Sari Talitha Puspita Sari Tegar Iman Ababil Tegar Iman Ababil Tri Arief Sardjono Tri Desmana Rachmildha, Tri Desmana Trihastuti Agustinah Vita Lystianingrum Vita Lystianingrum Wisbar, Andi Hidayah Yani Prabowo Yogadipha Bagas, I Gede Dyotha