This paper proposes an improved maximum power point tracker (MPPT) for photovoltaic (PV) system. The scheme is a hybrid between the adaptive perturb and observe (P&O) and particle swarm optimization (PSO). The algorithm incorporates the search-skip-judge (SSJ) mechanism to minimize the region within the P-V curve to be searched by the PSO. Furthermore, the PSO performance is enhanced by ensuring that the regions that have been previously explored (by other particle) will not be searched again by (another particle). Thus, the unnecessary movement of particles is minimized—leading to faster convergence. The proposed method is evaluated against four well-known MPPT techniques, namely the modified incremental conductance, the original version of SSJ, the modified cuckoo search and the hybrid PSO. In addition, an experimental prototype, which is based on PV array simulator is used to verify the simulation. The competing algorithms are tested with a buck-boost converter, driven by the TMS320F240 DSP on the dSPACE DS1104 platform. It was found that the proposed scheme converges to the global maximum power point (GMPP) most rapidly and the GMPP tracking is guaranteed even under complex partial shading conditions.

This paper proposes a fast and efficient maximum power point tracker (MPPT) for photovoltaic (PV) system under rapidly changing partial shading condition. An intelligent mechanism is adopted to systematically schedule the search for the global maximum power point (GMPP) on the P-V curve. As a result, the voltage track, i.e. the path-length that the operating point traverses along the voltage axis of the curve (until it converges to GMPP), is reduced. The search region is further minimized using a novel skipping scheme, where the voltage section that does not contain GMPP is discarded. The superiority of the proposed scheme is evaluated against two recent algorithms, namely the maximum power trapezium and the flower pollination MPPT. The performance is analyzed in terms of convergence time, voltage track and transient efficiency. The MATLAB simulation is verified experimentally using a PV array simulator, in conjunction with a buck-boost converter. The competing MPPT algorithms are implemented using the TMS320F240 DSP on the dSPACE DS1104 platform. The results indicate that, under the same operating and shading conditions, the proposed scheme is the fastest, most reliable and exhibits the highest overall transient efficiency.

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × V _oc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications.

This article proposes and experimentally validates a new chattering-free fixed-time robust sliding mode controller for the grid-connected three-phase two-level inverter through an RL filter. The proposed method addresses the reference trajectory tracking problem of grid current, enabling precise control of the power injected into the grid. Advanced nonlinear sliding surfaces and control laws are designed with the primary objective of enhancing the robustness of the closed-loop system under the proposed controller. More specifically, this work concentrates on achieving robustness against variations in filter parameters and grid impedance. The control laws are formulated to alleviate chattering in the control signal. The notion of fixed-time stability is incorporated into the controller’s design to ensure fixed-time convergence for both reaching and sliding phases and the ability to adjust the guaranteed settling time regardless of initial conditions. The fixed-time stability of the closed-loop system is proved using Lyapunov theory. The simulation and experimental results validate the effectiveness of the proposed method. It demonstrates precise tracking capability and robust performance, especially when compared with a finite-time sliding mode controller and a conventional asymptotic sliding mode controller.

Solar energy exposed its prominence to diminish the growing energy demands. But the formation of multiple peaks under partial shading conditions causes the conventional maximum power point tracking controllers to fail to track the global maximum power point (GMPP). Scanning the whole power–voltage curve to locate the GMPP takes extreme time and reduces algorithm effectiveness. Thus, the proposed method devotes the skipping and scanning mechanism incorporated with the perturb and observe (P&O) algorithm to reduce the scanning area drastically. The skipping mechanism skips over the scanning areas between successive peaks based on a specific formula that narrows down the scanning zone. Along with a buck–boost converter, the system includes a voltage control loop controlled by a proportional–integral controller. The proposed algorithm's effectiveness is tested in both simulation and experimental environments on discretized partial shading patterns, while GMPP is deployed at different locations. According to the experimental outcomes, the proposed algorithm outperforms the modified maximum power trapezium, skipping fast GMPP, modified cuckoo search, and the conventional P&O algorithm with an average convergence time of 1.02 s and average efficiency of 99.44%.