This paper presents an optimal design of a surface-based polynomial fitting for tracking the maximum power point (MPPT) of a photovoltaic (PV) system, here named surface-based polynomial fitting (MPPT-SPF). The procedure of the proposed MPPT-SPF strategy is based on a polynomial model to characterize data from the PV module with a global fit. The advantage of using polynomials is that they provide a good fit within a predefined data range even though they can diverge greatly from that range. The MPPT-SPF strategy is integrated with a DC-DC boost converter to verify its performance and its interaction with different control loops. Therefore, the MPPT strategy is applied to the reference outer PI control loop, which in turn provides the current reference to the inner current loop based on a discrete-time sliding current control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The proposed strategy does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared with a conventional perturb and observe method to prove its effectiveness under demanding tests.

Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

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The use of wind energy is a decisive factor for human development, associated with the environment and capacity of people exploiting this vital resource. The investigation is part of predicting the behavior of the temperatures inside the nacelle of the Goldwind wind turbines S50 / 750 models installed in the Gibara Wind Farm. It supports its theories and analysis of computer-aided design (CAD), by monitoring the working temperatures, which interact with the forced ventilation system in the studied devices. The values analyzed result from continuous measurements carried out by the SCADA systems (Supervisory Control and Data Acquisition) of the studied machines. Which allows the diagnosis and early visualization of unforeseen incipient failures that currently occur under operating conditions standardized by the manufacturer. When the wind reaches speeds that exceed 11.5 m/s, major aggregates such as the gearbox and the generator fail. The results obtained in the investigation will allow to correct the program in the PLC (Logical Control Program) for the start-up and stop of the cooling system of the wind turbines in order to generate an ideal thermal balance for the working condition activities in Cuba, inside the nacelle, reducing the frequent occurrence of critical temperature values in the maximum limit that put the wind turbines out of service and therefore providing the wind farm with a higher coefficient of technical availability.

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