EFFICIENT MAXIMUM POWER POINT ESTIMATION MONITORING OF PHOTOVOLTAIC USING FEED FORWARD NEURAL NETWORK - Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono; Mochammad Ari Bagus Nugroho; Mentari Putri Jati (JURNAL INTEGRASI, 2020)
The development of the utilization of solar panels in the future will continue to increase. One characteristic form of solar panels is the I-V curve which can be used to analyze the amount of solar panel output power. By knowing the I-V curve, we can get Maximum Power Point Estimation (MPPE) value that can be supported by solar panels. Information about the estimated value of the maximum solar panel power is an important part in determining the loading capacity, while maintaining the life of the equipment used. Feed Forward Neural Network with Back Propagation Algorithm (FFBP) has proven to be able to provide MPPE value information on solar panel output. The input values ​​in ANN are the voltage and current of the solar panel, while the output of ANN is in the form of an estimated power value. MPPE simulation results obtained an average error of 0.04 points between actual power (MPP) and estimated power (MPPE).