Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency

Abstract

The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 – 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%.

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

“…It is also used to provide rapid change or PSC situation in testing the response and performance of MPPT. In general, the normal conditions of an MPPT testing environment presume that the temperature is 25 ℃, the maximum solar irradiance level is at 1000 W/m 2 and the angle of incidence α is 90° [16].…”

“…Apart from ANN, FLC is also versatile to integrate with P&O algorithm. It combines both technique advantages together [16]. FLC-based P&O has a variable step size to ensure small oscillation and faster response because big step size ensures fast response but leads to excessive oscillation whereas small step size has slow response and less oscillation [76].…”

“…In general, all conventional MPPT techniques exhibit the common disadvantages, including power fluctuation, inability to operate normally under PSC and rapid irradiance changes, trapping at one of the local MPPs and oscillation around MPP [14], [15]. Hence, AI is adopted to overcome these drawbacks [16], [17]. A typical MPPT block diagram is shown in Fig.…”

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

“…This is compared to the hundreds or thousands of repeat measurements that may be performed for unregularized ANNs. Authors are described the mathematical model and every aspect of BRANN in [36], [37] elaborately.…”

“…Smart power scanning monitors voltage to determine a partial shading condition (PSC) [35]. Apart from ANN, FLC is adaptable enough to be used with P&O algorithms which combines the benefits of both techniques [36]. The FLC-based P&O employs a variable step size to assure low oscillation and rapid reaction; a large step size provides a quick response but results in excessive fluctuation.…”