High-gain DC-DC converters are becoming increasingly popular in renewable energy applications and solar PV systems. This article introduces a non-isolated non-coupled inductor-based high-gain DC-DC boost converter with the desirable features of low voltage stress on controlled power switches and high voltage gain at lower duty ratios. The proposed converter is well suited for boosting the low-input DC voltage obtained from distributed generation units like photovoltaic (PV) or fuel cells to substantially higher DC voltage. The converter comprises only two switches, and a single PWM signal governs its operation. These characteristics result in a topology that is more compact, less costly, lighter weight, and easier to control. The proposed converter is compared with some previously developed topologies of high-gain boost converters across various performance parameters. This comparison illustrates that the proposed model surpasses them in every aspect. A 300 W hardware prototype of a proposed model is developed and tested in a laboratory environment to confirm the theoretical assertions of a proposed model. The proposed topology offers a promising solution and enables a high gain of approximately 12 times the input voltage at a smaller duty ratio, specifically 11.25 at a duty of 0.6 and 17.77 at a duty of 0.7. The efficiency of a proposed converter ranges between 92.5% to 94.5% making it appropriate for numerous medium-high power applications. The uniqueness of the suggested system is its simple design and greater efficiency, which provides a more considerable gain and higher output voltages for sustainable energy systems.

This study proposes a new isolated intelligent adjustable buck-boost (IIABB) converter with an intelligent control strategy that is suitable for regenerative energy systems with unsteady output voltages. It also serves as a reliable voltage source for loads such as battery systems, microgrids, etc. In addition, the hill climbing (HC) maximum power point tracking (MPPT) algorithm can be utilized with this innovative IIABB converter to capture the MPP and then enhance system performance. In this converter, five inductors (L_A, L_B, L_C, L_D, and L_E) and four power MOSFETs (S_A, S_B, S_C, and S_D) are used in the proposed novel isolated intelligent adjustable buck-boost (IIABB) converter to adjust the applied voltage across the load side. It also has a constant, stable output voltage. The new IIABB converter is simulated and verified using MATLAB R2021b, and the performances of the proposed IIABB converter and conventional SEPIC converter are compared. The solar photovoltaic module output voltages of 20 V, 30 V, and 40 V are given as inputs to the proposed IIABB converter, and the total output voltage of the proposed converter is 48 V. In the new IIABB converter, the duty cycle of the power MOSFET has a small variation. The proposed IIABB converter has an efficiency of 92~99%. On the other hand, in the conventional SEPIC converter, the duty cycle of a power MOSFET varies greatly depending on the relationship between the output and input voltage, which deteriorates the efficiency of the converter. As a result, this research contributes to the development of a novel type of IIABB converter that may be employed in renewable energy systems to considerably increase system performance and reduce the cost and size of the system.

With the rapid transformation from fossil fuel-based energy systems to renewable-based energy systems the application of a high gain boost DC-DC converter is becoming the main attraction due to its wide range of numerous applications. This specific study presents an improved high gain non isolated quadratic boost DC-DC converter (QBC) with voltage gain three times of the quadratic boost converter. The proposed non-isolated category topology is the modified version of the quadratic boost converter where the 2nd inductor of the QBC is replaced by a voltage multiplier cell (VMC) and a switch capacitor network is integrated with the VMC to increase the voltage gain as well as to reduce the switch’s voltage stress. Moreover, the continuous input current and common ground feature of this proposed circuit configuration have shaped its potential application by making it advantageous over other non-isolated based high gain boost converters. To support its feasibility a rated power of 150 W prototype is developed after validating the results by Plecs software. The voltage gain offered by this proposed circuit configuration is justified by experimental results and the software simulation. A comprehensive analysis and components design is also formulated and documented in this study.

With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.