The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.
Dengan berkembangnya teknologi IC terprogram yang semakin canggih dan modern seperti FPGA, FPAA dan yang lainnya, maka pada tesis kali ini dibuat suatu modul sistem spread-spektrum dengan FPGA sebagai implementatornya.
Dalam penelitian ini dilakukan rancang bangun sistem spread spectrum dan simulasi kanal multipath berbasis FPGA (Field Programmable Gate Array), sebagai kode acak semu dipilih jenis kode Gold. Untuk melihat kinerja sistem tersebut dilakukan uji coba dan pengukuran hasil terhadap pengaturan pen-delay-an pada lintasan jamaknya.
Dari hasil percobaan yang dilakukan sifat kanal frekwensi selektif muncul saat rms delay spreadnya 215,723 nS dengan time symbol 125 nS, perbedaan yang agak jauh dikarenakan keterbatasan pembuatan delay dalam skala kecil.
Modul peralatan yang dibuat dapat digunakan sebagai simulasi secara hardware untuk membantu praktikum spread-spectrum, dimana untuk melihat sinyal-sinyal spreading-despreading, sinyal-sinyal PN serta simulasi kanal multipath spread spectrum.
Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.
Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.
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