ABSTRAKMotor induksi secara struktur dan kendali standarnya dirancang untuk bekerja pada kecepatan nominal, sehingga sulit mengendalikan kecepatan sesuai kebutuhan karena akan mengubah konstruksi motor. Penelitian tentang pengendalian motor induksi agar semudah mengendalikan motor DC sudah banyak dilakukan oleh peneliti, salah satunya adalah dengan kendali skalar. Kendali skalar banyak digunakan karena memiliki keunggulan sederhana, biaya murah, mudah didesain dan diimplementasikan, serta yang paling penting tidak memerlukan parameter dari motor induksi. Penggunaan kendali skalar yang telah dilengkapi pengendali PID penalaan otomatis, dengan parameter yang telah dioptimalkan algoritma Particle Swarm Optimization (PSO), akan memudahkan pengendalian kecepatan motor induksi tiga fase pada kecepatan beragam. Simulasi penalaan otomatis PID menggunakan PSO telah dilakukan dengan LabView, dengan karakteristik maksimal 10% overshoot, 1% error steady state dan rise time kurang dari 2 milidetik. Sementara dalam pengujian real time dengan MyRIO hasilnya tanpa overshoot, 5.5% error steady state maksimal dan rise time maksimal 5 detik.Kata kunci: Kendali skalar, PID, Particle Swarm Optimization, LabView ABSTRACTInduction motor is designed at nominal speed as default, we have to change its stucture to obtain dessired speed. Many researchers developt method how to control induction motor as simple as DC motor, one of the methods is scalar control. Scalar control has several benefits, such as simply, low cost, easily designed and implemented, and the main banefit is no necessary motor parameters. Using scalar control with PID controller that optimized Partical Swarm Optimization (PSO) algoritm, will ease to control 3 phase induction motor variant speed. Simulation auto tunning using PSO has done on LabView, it has some characteristic, they are 10% overshoot, 1% steady state error, and rise time within 2ms. In other hand, real time test using MyRIO got no overshoot, 5.5% steady state error maximal, and rise time maximal 5 s characteristic.Keywords: Scalar control, PID, Particle Swarm Optimization, LabView
A country's economic development success is largely based on its economic growth. The human development index, labor, and local revenue are some of the indicators used to influence economic growth. This study aims to examine the factors that influence Karanganyar Regency's economic growth from 2008 to 2021. With the assistance of Microsoft Excel 2016, the time series data for 14 years are analyzed using multiple linear equations (multiple regression), and the data are processed using Eviews 12 with a significance level of 5%. Secondary data were obtained from the Republic of Indonesia's Ministry of Finance and the Central Statistics Agency of Karanganyar Regency. The study reveals that the Human Development Index and Regional Original Income have a significant impact on Economic Growth, whereas Labor does not.
A three-phase induction motor is often used in everyday life because of its high reliability. However, it is associated with some disadvantages, including difficulties in maintaining constant speed during load changes and speed regulation due to the decoupled system. Therefore, this study aims to adjust the three-phase induction motor control to become a separate amplifier DC motor by setting the vector control using the IFOC method, which changes the coupled to the decoupled system. The speed settings are equipped with a PID controller where its parameters, which are obtained using Ziegler Nichols, produce speed output with fast research time and small steady-state errors. This research was conducted to observe and analyze the performance of a controller based on the IFOC approach with a PID controller at speed differences, with static and dynamic conditions in the entire speed working area. In the first stage of the research, simulation is carried out with static conditions, namely changes in speed variations throughout the work area (low speed to high speed), the next stage is a simulation with dynamic conditions, which is to provide changes in the value of the load torque when the system is operating. The simulation result carried out with LabVIEW shows a response time of 1.13 ms, a settling time of 9.9 ms, and a steady error of 0.4% at the 500 Rpm set point. It also indicated dynamic characteristics with a recovery time of 4.9 ms at the 300 Rpm set point. When operated at low speed, IFOC with PID controller has a stable response. But In dynamic conditions, the use of a PID controller is considered unsuitable. This is because the PID controller is less fast and less robust in responding to the system when conditions change in the value of the load torque.
Perkembangan teknologi sangat berimplikasi pada kebutuhan energi listrik yang semakin meningkat. Kebutuhan energi listrik menjadi diskursus pembahasan seiring dengan ketersediaan energi listrik yang diprediksi tidak akan mampu memenuhi pasokan kebutuhan. Oleh karenanya diperlukan adanya pemanfaatan energi baru dan terbarukan dalam rangka memenuhi kebutuhan energi listrik tersebut. Energi angin yang merupakan salah satu energi baru terbaruakan yang dapat diproyeksikan menjadi energi alternatif, memiliki peluang besar dalam mambantu memenuhi kebutuhan energi listrik. Terlebih energi ini sangat mudah didapatkan dalam zonasi yang dekat dengan laut. Salah satu poin krusial dalam pemanfaatan energi angin untuk direalisasikan pada pembangit listrik tenaga angin adalah DFIG (Double-Field Induction Generator). DFIG diperlukan desain yang baik untuk mendapatkan energi angin maksimum sebelum didistribusikan ke konsumen. Pada penelitian ini membahas desain dan simulasi DFIG (Double Field Induction Generator) Wind Energy. Penelitian ini dilakukan secara simulasi pada Simulink Matlab dengan memodelkan secara matematik DFIG dari equivalent circuit.
ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView
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