Rancang bangun sistem pendeteksian dan monitoring harmonisa menggunakan metode DFT

Salah satu isu penting dalam analisis kualitas daya adalah untuk mendeteksi gangguan kualitas daya secara efisien dan akurat sehingga dapat menyediakan pelayanan suplai kualitas daya yang lebih baik kepada pelanggan. Keberadaan alat ukur saat ini menggunakan fast fourier transform (FFT) dalam menganalisis harmonik tetapi FFT tidak sesuai digunakan untuk mendeteksi sinyal yang non stasioner. Oleh karena itu, pada penelitian ini mengusulkan metode Fast S-Transform (FST) untuk menganalisis kualitas daya terutama harmonik karena FST memiliki komputasi yang lebih cepat. FST digunakan untuk mengubah sinyal arus dan tegangan dari daerah waktu ke daerah frekuensi untuk memperoleh komponen harmonik. Pada penelitian ini, diimplementasikan secara hardware menggunakan mikrokontroller STM32F407. Eksperimen dibandingkan dengan metode discrete fourier transform (DFT), Stockwell transform (ST), alat ukur standar FLUKE 43B dan Osiloskop DL850. Hasil yang diperoleh dari pengujian THDi pada beban non linier dan beban motor induksi memiliki kesalahan relatif sebesar 0,374284 % dan 0,548021 % sedangkan hasil yang diperoleh dari pengujian THDv pada beban non linier dan beban motor induksi memiliki kesalahan relatif sebesar 3,052288 % dan 5,073938 % dibandingkan dengan FLUKE 43B. Metode FST memiliki waktu komputasi masingmasing 0,0855 detik, 0,731 detik dan 3,7001 detik lebih cepat dengan jumlah data sebesar 2000 data, 5000 data dan 10000 data dibandingkan dengan ST. ========== One of the important issues in the analysis of power quality is to detect power quality disturbance efficiently and accurately so can provide service of power supply better quality to customers. In general, the presence of measurement tools currently using fast fourier transform (FFT) to analyze harmonics but FFT is not suitable for the detection of nonstationary signals and often occurs of leak signal. Therefore, this paper proposes a method of Fast S-Transform (FST) to analyze the power quality especially harmonics because FST has good accuracy and faster computing. FST is used to change the current and voltage signals from the voltage domain to the frequency domain to obtain the harmonic components. In this paper, implemented in hardware using microcontroller STM32F407. Experiment is compared with the method of discrete fourier transform (DFT), standard measuring tools is FLUKE 43B and oscilloscope DL850. The results obtained from testing of THDi on the non-linear load and the induction motor load has a relative error is 0.374284 % and 0.547021%, while the results obtained from testing of THDv on the non-linear load and the induction motor load has a relative error is 3,052288 % and 5,073938 % compared with FLUKE 43B. FST method has the computing time respectively is 0.0855 seconds, 0.731 seconds and 3.7001 seconds faster with the amount of 2000, 5000 and 10000 data is compared with Stockwell transform

Identifikasi Gangguan Open Circuit Dan Short Circuit Pada Instalasi Photovoltaic Array Dengan MPPT Berbasis Artificial Neural Network

In the field of photovoltaic, the last few years have been very hotly discussed and researched as a new renewable source to produce electricity that cannot be exhausted. In the development effort there must be some problems arising from the existence of a new system. As with open circuit and short circuit interference. Therefore, The Identification of Open Circuit and Short Circuit Interference in Photovoltaic Array Installation with MPPT Based Artificial Neural Network is present to solve the problem. For identification of the location of the disruption is carried out on each photovoltaic string by knowing the voltage and current when there is an open circuit or short circuit interference, as well as the output power of the MPPT is used to determine the type of interference that occurs. Identification of interference using the Artificial Neural Network method with the purpose of this system can find out the location of interference and the type of open circuit or short circuit interference in photovoltaic array installations with MPPT. So that it is easy to know the location of the disturbance that is useful to maximize handling quickly and precisely.   Keywords: Photovoltaic array, open circuit, short circuit, MPPT, Artificial Neural Network     ABSTRAK Dalam bidang photovoltaic, beberapa tahun terakhir sangat hangat menjadi perbincangan dan penelitian sebagai sumber baru terbarukan untuk menghasilkan energi listrik yang tidak bisa habis. Dalam upaya pengembangnnya pasti ada beberapa permasalahan yang timbul dari adanya sistem baru. Seperti halnya adanya gangguan open circuit dan short circuit. Maka dari itu, Identifikasi Gangguan Open Circuit dan Short Circuit pada Instalasi Photovoltaic Array dengan MPPT Berbasis Artificial Neural Network hadir untuk menyelesaikan masalah tersebut. Untuk identifikasi lokasi gangguan dilakukan pada setiap string photovoltaic dengan mengetahui tegangan dan arus ketika terjadi gangguan open circuit maupun short circuit, serta daya keluaran dari MPPT digunakan untuk mengetahui jenis gangguan yang terjadi. Identifikasi gangguan menggunakan metode Artificial Neural Network dengan tujuan sistem ini dapat mengetahui lokasi gangguan dan jenis gangguan open circuit atau short circuit pada instalasi photovoltaic array dengan MPPT. Sehingga memudahkan untuk mengetahui lokasi gangguan yang berguna untuk memaksimalkan penanganan secara cepat dan tepat.   Kata kunci: Photovoltaic array, open circuit, short circuit, MPPT, Artificial Neural Networ

Hardware implementation of series DC arc fault protection using fast Fourier transform

This paper proposes method of series DC arc fault protection using low cost microcontroller. Series DC arc fault occurs when gap between conductor or wire flows a current. Series DC arc fault can cause fire hazard if do not detected and protected. However, Series DC arc fault is difficult to detected using conventional protection. To detect series DC arc fault accurately using fast Fourier transform (FFT). FFT is used to transform signal in time domain to frequency domain. Series DC arc fault has different characteristic compared by normal current in frequency domain. Therefore, the proposed algorithm for protection of series DC arc fault based on magnitudes of the current in frequency domain. Hardware system is implemented by 100 V DC power supply and DC arc fault generator. Test result is conducted experimentally under varying of load current such as 2 A, 2.5 A, 3 A, 3.5 A, 4 A and 5 A. Experimental testing results show that Series DC arc fault protection has time for trip of 0.48 s, 0.26 s, 1.04 s, 0.68 s, 0.44 s and 0.48, respectively. The fastest time for trip is 0.26 s with current of 2.5 A. Therefore, the proposed algorithm for series DC arc fault protection can operate to trip accurately and have the good performance

Implementasi Fuzzy Logic Untuk Identifikasi Jenis Gangguan Tegangan Secara Realtime

In the modern era, AC voltage variations are still often a problem. This variation causes power quality decrease even damage the equipment. Voltage variations that often occur are short and long duration. The variation consist of 6 types namely Interruption, Sag, Swell, Sustained-Interruption, Undervoltage, Overvoltage. To facilitate repairs when there is a voltage variation in the electric power system, it is necessary to have an identification that can detect and distinguish any interference that occurs. Therefore, this paper proposes a fuzzy logic method for identifying types of voltage variations. This type of voltage variation identifier requires a disturbance simulator as a voltage source with varying values. To distinguish between short duration and long duration disturbances, is the time duration of the disturbance appears. The design of the voltage variation identification algorithm uses the sugeno fuzzy inference system with 2 inputs namely magnitude vrms and timer, and 1 output is the type of voltage interference. Moreover, prototype design using AMC1200 voltage sensor, microcontroller, and display. To validate the proposed algorithm, compared with standard measuring tools and simulations. Results show that the proposed algorithm has a very good performance with an accuration compared to the standard measuring instrument of 99.8%

Identifikasi Jenis Gangguan Pada Jaringan Distribusi Menggunakan Metode Artificial Neural Network

Berkembangnya kebutuhan masyarakat terhadap tenaga listrik saat ini meningkat pesat, sehingga perlindungan terhadap jaringan distribusi sangatlah penting untuk menjamin pelayanan tenaga listrik. Paper ini menyajikan algoritma yang diusulkan untuk identifikasi variasi tegangan durasi pendek. Artificial Neural Network (ANN) digunakan untuk mengidentifikasi 7 jenis varasi tegangan durasi pendek seperti sinyal normal, sag instantaneous, sag momentary, sag temporary, swell instantaneous, swell momentary, dan juga swell temporary. Simulasi untuk membangkitkan gangguan menggunakan software MATLAB Simulink yang telah disimulasikan dan mendapat nilai untuk input data ke ANN. Hasil algoritma yang diusulkan sangatlah efektif untuk identifikasi, dimana ANN dengan 5 x 5 neuron pada lapisan tersembunyi memiliki tingkat akurasi 100%

Penggunaan Fast Fourier Transform Pada Identifikasi Arc Fault Pada Berbagai Jenis Kabel

Arc merupakan loncatan bunga api yang disebabkan karena adanya pelepasan energi dari kabel penghantar. Arc fault menghasilkan panas yang dapat merusak isolasi kawat sehingga dapat menyebabkan terjadinya bahaya kebakaran. Namun keterbatasan akan hal memonitoring seluruh jalur pengawatan menjadi kendala dalam pendeteksian secara dini adanya gangguan arcing. Dirancang sebuah alat identifikasi arc fault pada kabel berjenis serabut dan pejal, yang mana dapat mencegah kebakaran dikarenakan keterlambatan untuk mengamankan bahaya arcing. Pada alat ini memanfaatkan AMC1301 sebagai sensor tegangan dan sensor arus. Sistem ini bekerja mengamankan instalasi saat terjadi gangguan serta dapat mengirim kondisi secara real status dari jalur pengawatan (ada gangguan arc atau tidak). Kondisi dari sistem instalasi yang terbaca oleh sensor diolah oleh mikrokontroler dan metode yang digunakan adalah mendeteksi munculnya komponen frekuensi tinggi pada arus sistem menggunakan Fast Fourier Transform (FFT). Apabila mendeteksi adanya gangguan busur seri AC, maka mikrokontroler akan mengolah data dengan FFT dan diidentifikasi jenis kabel uji sesuai karakteristiknya ketika terjadi gangguan. Penelitian ini dibangun pada sistem tegangan rendah 220V/50Hz dengan arus gangguan sebesar 0,83A dengan beban resistif. Data pengujian menunjukkan bahwa AFCI dengan metode FFT mampu mendeteksi gangguan busur seri AC dan memberikan proteksi pada sistem dengan rata-rata waktu pemutusan 872 ms

Global Maximum Power Point Tracking of PV Array Under Non-Uniform Irradiation Condition Using Adaptive Velocity Particle Swarm Optimization

Non-uniform irradiation condition (NUIC) is a condition of differences irradiation level received by each Photovoltaic (PV) on PV array. NUIC of PV array causes the emergence of several power peaks (consisting of several local peaks and one global peak) in the power-voltage (P-V) characteristic curve. This condition can cause several algorithms (hill-climbing / P&O, IC) that are unable to reach the global peak as they are trapped at a local peak. This paper proposes an Adaptive Velocity Particle Swarm Optimization (AVPSO) algorithm to search the global peaks/Global Maximum Power Point (GMPP) of PV arrays under NUIC. The proposed algorithm is a modification of the PSO algorithm. AVPSO algorithm able to adjust its own weight factor values and cognitive acceleration coefficients depend on the distance of the particle's position now with the global best position during the tracking process. Adaptive weight factors can reduce the level of power or voltage oscillation during the tracking process until convergent, while the cognitive acceleration coefficient can prevent particles trapped at the local peak. Thus, the proposed AVPSO algorithm can reach GMPP with faster tracking time and low oscillation rates. In addition, this paper proposed an algorithm that can work both in static and dynamic NUIC patterns; thus, the proposed algorithm can track again when there is a change in global peak value in the PV array

Rancang Bangun 3 Phase Energy Meter Untuk Analisis Kualitas Daya Di Industri

The quality of electric power is one of the most important things in the industrial world, with good power quality, industrial efficiency will also increase which will result in savings in production costs. Energy consumption must also be monitored in such a way for the purposes of analyzing electrical energy efficiency. Therefore, the author designed a tool called a 3 phase energy meter that can monitor energy consumption and power quality for industry with a 3 phase electrical system. The 3 phase energy meter is equipped with an IoT system so that the observation of the measurement results of every electrical machine system in the industry can be done at one point without having to go to the location. The hope is that with the 3 phase energy meter, it can improve production efficiency through power quality analysis.Kualitas daya listrik merupakan salah satu hal yang sangat penting dalam dunia industri, dengan kualitas daya yang baik maka efisiensi industri pun juga akan meningkat yang akan berakibat pada penghematan biaya produksi. Pemakaian energi pun juga harus di pantau sedemikian rupa untuk keperluan analisis efisiensi energi listrik. Oleh karena itu penulis merancang sebuah alat yang bernama 3 phase energy meter yang dapat memonitor pemakaian energi dan kualitas daya untuk industri dengan sistem kelistrikan 3 phasa. 3 phase energy meter di lengkapi dengan sistem IoT sehingga pengamatan hasil pengukuran setiap sistem kelistrikan mesin yang ada di industri dapat di lakukan di satu titik tanpa harus mendatangi lokasi. Harapanya dengan adanya 3 phase energy meter ini dapat meningkatkan efisiensi produksi melalui analisis kualitas daya

Parallel Balancing Battery using Adaptive Power Sharing and ANN SOC Estimator

The battery balancing method is commonly used in cell circuits and battery circuits to maintain the power continuity on the DC Bus. The power continuity on the DC Bus is guaranteed if the load continues to get a power source, even if either the battery or power supply malfunctions. Besides, the battery balancing method is also used to protect the battery from excessive charging current pliers flowing into the battery. Therefore, the State-of-Charge (SoC) should be concern in balancing the maintained battery condition on both systems and avoiding overcharging. Artificial Neural Network (ANN) is used in this paper to determine the value of battery SoC. Based on simulations using MATLAB 2018, SoC values with ANN showed accurate results with error values below 0.1%. Based on the simulation results, the two batteries, which are arranged to have a difference of SoC value of 0.3%, will achieve a balanced SoC value for 28.45 seconds from the simulation

Load Identification Using Harmonic Based on Probabilistic Neural Network

Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attention  in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic loa