Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera $KC200GT$ and Solarex $MSX60$, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.

Validating photovoltaic maximum power point tracking (MPPT) algorithms with Hardware-in-the-Loop (HIL) usually requires expensive simulators. This paper characterizes an event-driven WiFi-HIL co-simulation platform in which unmodified MPPT firmware runs on a low-cost ESP32 and exchanges measurements and references with a switched converter model over HTTP on WiFi. The architecture rests on timescale separation: the converter settles in τconv ≈ 0.29 ms, the median closed-loop round-trip time (RTT) is 28 ms, and irradiance transients evolve over τG ≥ 500 ms. A 2×3×3 factorial campaign (P&O and PSO; local, emulated and physical channel; three profiles; 90 runs) shows that the physical WiFi channel lowers mean tracking efficiency by 3.5–7.6 percentage points (pp) versus a latency-free loop and widens its spread. A fixed-delay control condition attributes 0–3.1 pp of this gap to the slower update rate and 1.5–6.0 pp to the jitter, tail and losses of the real channel, where 2–3 % of exchanges exceed 0.5 s. The Wokwi emulator returns the physical board's control references (all P&O steps; PSO up to its first random draw) but not its timing. Delay injection and a quasi-static Monte Carlo loss study show that P&O has no systematic dependence on latency between 40 and 550 ms and loses at most 0.6 pp at up to 20 % loss, whereas PSO degrades with both. On-chip measurements give 31 µs per algorithm step and 0.75 ms local turnaround. The platform therefore suits outer-loop MPPT validation, provided channel latency and loss are reported with the efficiency.

This study numerically evaluated the effect of internal structural optimization on the moment of inertia and self-starting behavior of an H-Darrieus hydrokinetic turbine with a NACA 0018 blade profile. A one-way fluid--structure interaction (FSI) methodology based on CFD--FEA coupling was implemented under transient operating conditions representative of hydrokinetic systems. Pressure contours obtained from CFD simulations were transferred to structural analyses to determine the critical loading condition and perform topology optimization of the blade internal structure. The optimization process preserved the external hydrodynamic profile while redistributing internal material through a rib-based reinforcement strategy derived from cooperating-section analysis. The optimized configuration achieved safety factors above 1.3 while reducing blade mass by 81.71\% and total turbine mass by 39\%. Consequently, the rotor moment of inertia decreased from 25.5858 to 7.7831 $kg\cdot m^{2}$, reducing the estimated self-starting time from 1.8 s to 0.55 s. Modal and harmonic analyses confirmed the absence of critical resonance conditions under the evaluated operating range. Additionally, the optimized design reduced embodied energy and greenhouse gas emissions associated with turbine manufacturing. The results demonstrate that internal structural optimization is an effective strategy for improving the dynamic and sustainable performance of H-Darrieus hydrokinetic turbines.

This work presents a methodology for tracking the maximum power point in a photovoltaic (PV) panel through the setup of a converter and battery; in this context, the search for the Maximum Power Point Tracking (MPPT) is carried out by integrating an improved Incremental Conductance (INC) algorithm with a Nonlinear Model Predictive Controller (NMPC). This approach uses voltage and current data to adjust system behavior under partial shading conditions, improving accuracy in Global Maximum Power Point Tracking (GMPPT). The methodology is implemented on a DC/DC boost converter connected to a battery and tested under five irradiance scenarios. The INC algorithm is enhanced by a random exploration strategy to escape local maxima and a moving average filter to stabilize voltage tracking. The NMPC controller optimizes the control signals in real time, enabling precise adjustment despite nonlinear behavior. The proposed system achieves over 99% accuracy and response times under 0.05 seconds. The results indicate better performance than traditional algorithms, especially under dynamic irradiance conditions, validating the suitability of this control strategy for real-world PV applications.

Context: The need for efficient energy harvesting in photovoltaic (PV) systems requires advanced maximum power point tracking (MPPT) techniques. Traditional methods like perturb and observe (P&O) exhibit limitations under varying conditions, leading to less efficient energy extraction. Method: This study compares a model-predictive controller (MPC) combined with optimization algorithms such as the particle swarm optimizer (PSO), the vortex search algorithm (VSA), and the salp swarm algorithm (SSA) to improve MPPT performance in PV systems. These algorithms are tested under different irradiance conditions to assess their efficiency and adaptability. Results: The results show that PSO, when used with MPC, greatly enhances energy extraction compared to the conventional P&O method. Additionally, VSA and SSA also perform well in adapting to rapid irradiance changes, ensuring an optimal power output. Conclusions: Optimization-based MPPT methods, especially those using PSO, offer significant efficiency improvements for PV systems, making them strong alternatives to traditional techniques.