This paper presents the design and practical implementation of a simple active dual‐axis solar tracker (DAST) to track the sun's movement by using fewer components and low‐cost as well. A dual‐axis mechanism is developed in order to tilt the PV panel by two servo motors facing the highest intensity of sunlight captured by LDR sensors, which are placed in the four corners of PV panel. The DAST prototype was constructed practically and tested using a real‐time virtual instrument based on Excel to determine its efficiency. Moreover, a comparison based on experimental results between the energy produced by the proposed smart DAST and a fixed panel shows that the smart DAST produces 36.26% more energy compared to the fixed panel. The proposed active DAST can be easily implemented without possessing deep knowledge about solar tracking technologies and electronic engineering.

The use of a maximum power point (MPP) tracking (MPPT) controller is required for photovoltaic (PV) systems to extract maximum power from PV panels. However, under partial shading conditions, the PV cells/panels do not receive uniform insolation due to several power maxima appear on the PV array's P–V characteristic, a global MPP (GMPP) and two or more local MPPs (LMPPs). In this scenerio, conventional MPPT methods, including pertub and observe (P&O) and incremental conductance (INC), fail to differentiate between a GMPP and a LMPP, as they converge on the MPP that makes contact first, which in most cases is one of the LMPPs. This results in considerable energy loss. To address this issue, this paper introduces a new MPPT method based on the Seagull Optimization Algorithm (SOA) to operate PV systems at GMPP with high efficiency. The SOA is a new member of the bio-inspired algorithms. When compared to other evolutionary techniques, it uses fewer operators and modification parameters, which is advantageous when considering the rapid design process. In this paper, the SOA-based MPPT scheme is first proposed and then implemented for an 80 W PV system using the MATLAB/SIMULINK environment. The effectiveness of the SOA based MPPT method is verified by comparing its performance with P& O and PSO (particle swarm optimization) based MPPT methods under different shading scenarios. The results demonstrated that the SOA based MPPT method performs better in terms of tracking accuracy and efficiency.

This paper presents a study of a low-cost photovoltaic (PV) emulator to test the real implementation of maximum power point tracking (MPPT) algorithm. This PV emulator is composed of a variable DC supply in series with a variable resistor; it is based on the maximum power transfer theorem in order to provide a curve that exhibits a peak which can be tracked by an MPPT algorithm. Moreover, this emulator can be used to test the performance of the MPPT algorithm under fast variation of the solar irradiance and temperature. For this reason, the P&O MPPT algorithm with a boost DC-DC converter is used in order to validate the functionality of the PV emulator. Finally, the experimental results show that our PV emulator can provide a simple, efficient and low-cost way for users (researchers, engineers, students, etc.) to test and validate their MPPT algorithms.

The real implementation of the maximum power point tracking (MPPT) controllers for the photovoltaic (PV) systems is still a big challenge for researchers working in this field. Often, they use simulation tools to assess the performance of their MPPT algorithms before actual implementation. In this context, this paper aims to propose a trusted simulation of a PV system designed under Proteus software. The proposed PV simulator can be used to verify and evaluate the performance of MPPT algorithms with a closer approximation to the real implementation. The main advantage of this model that it contains a real microcontroller, as can be found in reality, so that same code for the MPPT algorithm used in the simulation will be used in real implementation. In contrast, when using (Powersim Software) PSIM or Matlab/Simulink, the code of the algorithm must be rewritten once the real experiment begins, because these tools don’t provide a microcontroller or an electronic board in which our algorithm can be implemented and tested in the same way as the real experiment. After this section, a modified Hill-Climbing (HC) algorithm is introduced. The proposed algorithm can avoid the drift problem posed by conventional HC under a fast variation in insolation. The simulation results show that this method presents good performance in terms of efficiency (99.21%) and response time (10 ms), which improved by 1.2% and 70 ms respectively compared to the conventional HC algorithm.