Analisis Perbandingan Concentrated Winding Dan Toroidal Winding Pada Generator Axial Flux Permanent Magnet (AFPM) Tiga Fasa Menggunakan Inti Besi Pada Stator

Kebutuhan energi listrik terus meningkat seiring dengan pertumbuhan industri dan masyarakat dunia. Energi terbarukan menjadi salah satu alternatif untuk menanggulangi peningkatan penggunaan energi listrik tersebut. Belakangan ini perkembangan generator dalam memproduksi energi listrik sudah sangat beragam salah satunya menggunakan generator Axial Flux Permanent Magnet (AFPM) yang biasanya dirancang untuk memanfaatkan energi terbarukan dengan kecepatan rendah seperti air dan angin. Generator AFPM pada umumnya menggunakan stator tanpa inti dengan konfigurasi belitan concentrated. Pada pembahasan skripsi ini dibandingkan dua buah stator dengan  menggunakan inti besi yaitu konfigurasi Concentrated Winding dan konfigurasi Toroidal Winding. Generator yang digunakan memiliki spesifikasi rotor dan stator yang sama. Generator yang dirancang dengan tegangan 110 volt frekuensi 50 Hz, dan daya perhitungan sekitar 100 watt. Dari hasil pengujian generator AFPM tiga phasa dengan menjaga konstan frekuensi sebesar 50 Hz pada konfigurasi Concentrated Winding dihasilkan tegangan pengujian tanpa beban sekitar 106,5 volt, tegangan pengujian berbeban sekitar 45,6 volt, dan daya 87,15 watt dengan efisiensi generator 72,61 %. Sementara pada konfigurasi Toroidal Winding dihasilkan tegangan pengujian tanpa beban sekitar 110,6 volt, tegangan pengujian saat berbeban sekitar 42,4 volt, dan daya 77,16 watt dengan efisiensi generator 70,51 %. Berdasarkan hasil tersebut disimpulkan bahwa konfigurasi Concentrated Winding lebih baik daripada konfigurasi Toroidal Winding

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network