To assess demagnetization, a line-to-line short-circuit fault study is carried out with constant input torque and field-oriented control (FOC) for the speed of a permanent magnet synchronous generator (PMSG). For the study, a cosimulation between finite element analysis (FEA), power electronics simulation (Simplorer) and model-based control design software (Matlab Simulink) is used. The analyzed system was previously validated in the experimental set-up with DS1103 card. The initial short circuit currents, although higher than nominal, did not present a risk of demagnetization for the generator in the analyzed working conditions, namely magnets temperature and speed. Nevertheless, the acceleration during the fault, due to constant input torque, and the control scheme with a constant speed reference, provided a greater demagnetizing current while breaking the machine speed back to the pre-fault reference. The cosimulation results bring higher fidelity to the modeling and analysis of the behavior of the PMSG under FOC.

The design and analysis of a permanent magnet synchronous generator (PMSG) are presented. The interior permanent magnet (IPM) rotor was designed asymmetric and with the consequent pole approach. The basis for the design was a series-produced three-phase induction motor (IM) and neodymium iron boron (Nd-Fe-B) cuboid magnets were used for the design. For the partial demagnetization analysis, some of the magnets were extracted and the results are compared with the finite element analysis (FEA).

This manuscript analyzes the operation of an interior permanent magnet (IPM) machine working as a permanent magnet synchronous generator (PMSG). The partial demagnetization operation is analyzed. To obtain more accurate voltages and currents of the machine, finite element analysis (FEA) is used in co-simulation with the full converter and the converter’s control algorithm. Direct field oriented control (DFOC) shows robustness by maintaining the speed even with a 25% demagnetized PMSG. Also, an analysis of the rotating reference frame DQ signals is done to asses demagnetization.

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation.

For control applications, the angular velocity of the drive crank of a four-bar mechanism is traditionally assumed to be constant. In this paper, we propose control of variable velocity of the drive crank to obtain the desired output motions for the coupler point. To estimate the reference trajectory for the crank velocity, a neural network is trained with data from the kinematic model. The control law is designed from feedback linearization of the tracking error dynamics and a Proportional–Integral–Derivative (PID) controller. The applicability of the proposed scheme is validated through simulations for three variable speed profiles, obtaining excellent results from the system.

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability.