Mexico has great potential for ocean energy due to the length of its coastline. This work emerged with the aim of exploring and studying the potential of wave energy conversion (WEC) in Mexico. The power intensity that can be extracted from WEC at six nodes located near the main seaports of Mexico is analyzed. We consider a power buoy arrangement as a point absorber connected to a linear permanent magnet generator (LPMG). Descriptive statistics and box plots were used to handle the measured data obtained from the moored buoys. Wave power density and wave characteristics were obtained, since the LPMG design depends on them. We emulate wave behavior and simulate the linear generator, designed with the finite element method to validate voltage and power output with regular wave excitation. This work seeks to advance our knowledge concerning the design of WEC components for the development of micro-generation systems.

Linear Electric Generators (LEGs) are attractive devices for new efficient and sustainable ways of developing power generating technologies. This research work contributes in determining the increase of energy potential of LEGs while using superconductor coils instead of stator and actuator made of heavy and high permeability materials, necessary when conventional conductive material such copper is used to generate electrical energy. The proposed work highlights the techno-economic aspects of using superconductive materials in the LEG coil. Moreover, the design features of the LEG using superconductors, the electromagnetic analysis performance with finite element simulations and the experimentation results are presented. The comparison of the power extracted using copper coil and superconducting material is established, three times more power is obtained with superconductor coil.

In power systems, transformers are fundamental components for ensuring efficient voltage regulation and distribution. Despite their robustness, these electric machines are prone to various faults, with short-circuited turns (SCTs) being one of the most common. Such faults can lead to increased vibrations, overheating, and eventual failure. To accurately diagnose these issues, vibration signature analysis has become a powerful and essential tool, enhancing preventive maintenance and minimizing downtime. Since frequency domain-based vibration signature analysis provides valuable insights into the condition of transformers, developing a model that generates the vibration frequency content of transformers under different operating conditions will be crucial for testing and enhancing diagnostic techniques. In this context, this article advances the state of the art by presenting an artificial neural network (ANN)-based experimental vibration frequency model, tested in a 1.5-kVA dry-type transformer under various conditions: different degrees of SCTs, ranging from 0 (healthy condition) to 10 SCTs, different electrical loads (no load, linear load, and nonlinear load), and various voltage imbalances (0%, 2.5%, 5%, 7.5%, and 10%, with 0% indicates no imbalance). Thus, the proposed model utilizes experimental vibration data to train the ANN, enabling it to identify and interpolate conditions not explicitly covered during training. Key results from this study demonstrate the model’s ability to achieve low root mean square error in both reconstructing known conditions and interpolating unknown ones. This demonstrates the model’s robustness and generalization capability, making it a valuable tool for different applications related to transformer diagnostics.

This paper addresses the analysis of inter-turn winding faults of a permanent magnet synchronous motor. The analysis methodology used to study inter-turn windings faults is based on the use of linear coupled circuits. The stator windings are resolved into harmonic conductor density distributions allowing the calculation of coupling impedances in the machine. Instantaneous Park's vector module was used as a diagnostic tool for incipient winding faults in steady-state operation of the permanent magnet synchronous motor.

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