Photovoltaic pumping is considered to be the most used application amongst other photovoltaic energy applications in isolated sites. This technology is developing with a slow progression to allow the photovoltaic system to operate at its maximum power. This work introduces the modified algorithm which is a perturb and observe (P&O) type to overcome the limitations of the conventional P&O algorithm and increase its global performance in abrupt weather condition changes. The most significant conventional P&O algorithm restriction is the difficulty faced when choosing the variable step of the reference voltage value, a good compromise between the swift dynamic response and the stability in the steady state. To adjust the step reference voltage according to the location of the operating point of the maximum power point (MPP), a fuzzy logic controller (FLC) block adapted to the P&O algorithm is used. This allows the improvement of the tracking pace and the steady state oscillation elimination. The suggested method was evaluated by simulation using MATLAB/SimPowerSystems blocks and compared to the classical P&O under different irradiation levels. The results obtained show the effectiveness of the technique proposed and its capacity for the practical and efficient tracking of maximum power.
With the continuous growth of energy consumption, the rationalization of energy has become a priority. The photovoltaic energy sector remains a major occupation for researchers in the field of production optimization or storage methods. The concept developed in this work is a mixed optimization approach for energy management during battery charging with a duty cycle. A selective collaborative algorithm intervenes to choose and use the appropriate results of the few techniques to optimize the charging time of a battery and estimate its state of charge by using the minimum possible tools. This is done using a collective database that is accessible in real time. It also effectively allows the synchronization of information between several customers. This approach is performed on a mobile application on android, through a Google Firebase platform that allows us to secure collaborative access between multiple customers and use the results of the calculations of some algorithms. It gives us the values obtained by the various sensors in real time to accelerate the charging speed of the battery. The validation of this approach led us to practice a few scenarios using an Arduino board to show that this approach has a better performance.
Improvements and applications of Inertial Measurement Unit (IMU) sensors have increased in several areas. They are generally used in equipment that measures orientation, gravitational force, and speed. Therefore, in this paper, we worked on improving the performance of IMU in an application on solar trackers of the Kalman filter. This work illustrates the design of an autonomous device with astronomical control of a photovoltaic (PV) panel, allowing the optimization of the orientation/energy gain ratio. The device is based on two concepts at the same time, the modeling of the solar trajectory adopted by an algorithm which calculates continuously the solar angles (elevation and azimuth) and the approval of these by the IMU in order to sweep away any climatic fluctuations and thus allow an almost perfect adjustment relative to the perpendicular axis of the rays. The tracking system is based on two joints controlled by an Arduino control board. Experiments have shown a better performance of the two-axis device: the net energy gains can be around 34% with an additional 1.1% when the Kalman filter is applied.
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