Neural network estimation of a photovoltaic system based on the MPPT controller

Abstract

MPPT is necessary to achieve an optimal exploitation of the photovoltaic (PV) system. This paper deals with the problem of the optimization of the power, delivered by the photovoltaic panel (PVP). To achieve this aim, a neural network estimator (NNE), followed by a conversion coefficient and a calculation stage of the optimal duty cycle, has been developed. The NNE is used to calculate the open circuit voltage corresponding to each solar radiation and to a various value of temperature, based only on the standard open circuit voltage. A coefficient, determining for each solar radiation the voltage of the maximum power directly from the open circuit voltage, is estimated by a practical test. Finally, the optimal duty cycle is, next, determined by the input/output equation of the boost converter. The proposed MPPT is tested and compared with the most widely used MPPT methods by simulations using MATLAB/Simulink and real time hardware in the loop (HIL) implementation. The results obtained with the proposed MPPT show excellent dynamic performance under fast irradiation changes.

Analysis of Machine Learning in Classifying Bank Profitability with Corruption Factor

“…Therefore, predicting the bank profitability with corruption is becoming important for the stakeholders as well as the regulators. Recently, machine learning has become a vital predictive approach in a variety of domains including in education [9,10], agriculture [11], medical [12][13][14], business [15], fraud detection [16], and energy management [17]. In addition, several studies have proven the capability of machine learning in generating higher accurate results in bank profitability prediction [18][19][20].…”

Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour

“…Unlike prior studies [7], [14], [19], [21], [22] that employed traditional statistical methods, this study attempts to construct students' whistle-blowing intention model on academic dishonesty using computational intelligence approach or specifically with machine learning prediction technique. Further, despite widely use of machine learning in various domain of research including in education [23], business [24], fraud detection [25], energy management [26] and medical [27] that highlight the effectiveness of such methods to that of traditional statistical methods [28], [29], yet study on machine learning prediction and classification on whistleblowing academic fraud is limited.…”