Maximum power point tracking (MPPT) is becoming more and more important in the optimization of photovoltaic systems. Several MPPT algorithms and nonlinear controllers have been developed for improving the energy yield of PV systems. On the one hand, most of the conventional algorithms such as the incremental conductance (INC) demonstrate a good affinity for the maximum power point (MPP) but often fail to ensure acceptable stability and robustness of the PV system against fast-changing operating conditions. On the other hand, the MPPT nonlinear controllers can palliate the robust limitations of the algorithms. However, most of these controllers rely on expensive solar irradiance measurement systems or complex and relatively less accurate methods to seek the maximum power voltage. In this paper, we propose a new hybrid MPPT based on the incremental conductance algorithm and the integral backstepping controller. The hybrid scheme exploits the benefits of the INC algorithm in seeking the maximum power voltage and feeds a nonlinear integral backstepping controller whose stability was ensured by the Lyapunov theory. Therefore, in terms of characteristics, the overall system is a blend of the MPP-seeking potential of the INC and the nonlinear and robust potentials of the integral backstepping controller (IBSC). It was noted that the hybrid system successfully palliates the conventional limitations of the isolated INC and relieves the PV system from the expensive burden of solar irradiance measurement. The proposed hybrid system increased the operational efficiency of the PV system to 99.94% and was found better than the INC MPPT algorithm and 8 other recently published MPPT methods. An extended validation under experimental environmental conditions showed that the hybrid system is approximately four times faster than the INC in tracking the maximum power with better energy yield than the latter.

Partial shading conditions (PSC) in photovoltaic (PV) systems degrade energy harvest by generating multi-peak power-voltage (P–V) curves, trapping conventional maximum power point tracking (MPPT) algorithms at local maxima. This paper presents a Multi-Peak to Single-Peak Conversion (MSMPPT) framework that enables conventional algorithms like Perturb & Observe (P&O) and Incremental Conductance (INC) to reliably track the global maximum power point (GMPP) under PSC without structural modifications. The framework operates via two stages: dynamic estimation of optimal voltage boundaries to shrink the GMPP search space to under 10% of the original P–V range, and active voltage regulation to enforce operation within this zone, effectively transforming the multi-peak curve into a single-peak profile. The proposed MSMPP-P&O and MSMPP-INC algorithms achieve 50% faster tracking (64 ms vs. 122 ms for P&O) and near-perfect steady-state efficiency under static shading, reducing power losses below 2%. In dynamic shading scenarios with abrupt irradiance shifts, MSMPPT maintains robustness with less than 1.5 W net loss, outperforming conventional methods that incur over 30 W of power losses. By eliminating oscillations and hotspot risks through voltage regulation, the framework retains algorithmic simplicity while enhancing performance under complex shading scenarios. Validated across benchmark shading profiles, MSMPPT demonstrates fidelity without requiring additional hardware or complex optimizers. This innovation bridges the gap between conventional MPPT simplicity and partial shading resilience, offering a cost-effective, scalable solution to boost PV system reliability in shading environments.

The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage (-) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.