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    Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Bozkurt, Helin; Macit, Ramazan; Celik, Ozgur; Teke, Ahmet
    Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.
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    Öğe
    Power Quality Enhancement in Hybrid PV-BES System based on ANN-MPPT
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Bozkurt, Helin; Celik, Ozgur; Teke, Ahmet
    Battery energy systems (BESs) assisted photovoltaic (PV) plants are among the popular hybrid power systems in terms of energy efficiency, energy management, uninterrupted power supply, grid-connected and off-grid availability. The primary objective of this study is to enhance the power quality of a grid-tied PV-BES hybrid system by developing an operational strategy based on artificial neural network (ANN) based maximum power point tracking (MPPT) method. A test system comprising a 10-kWh BES and a 12.4 kW PV plant is structured and simulated on the MATLAB/Simulink platform. The hybrid system is validated with three different cases: constant radiation, rapid changing radiation, and real-day solar radiation data from the Turkish State Meteorological Service of Tarsus (Mersin, Turkiye) employing the developed operational strategy. These cases involve the examination of three distinct MPPT methods, analyzing DC-link voltage, battery state of charge (SOC), current, voltage, and system total harmonic distortion (THD). The simulation results indicate that the developed operational strategy with the ANN-MPPT method yields superior THD results in output current and a more stable DC-link voltage. Furthermore, the strategy shows improved convergence speed and reduced oscillations to achieve diverse reference operating points under varying atmospheric conditions compared to conventional MPPT methods. Numerical results demonstrate that the developed operational strategy with the ANN-MPPT consistently maintains THD values below 3% and exhibits a stable DC-link voltage deviation of 1.42% in various charging modes for both rapidly changing radiation and real-day solar radiation data.