Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System.

In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way.

Redundant Fault Diagnosis for Photovoltaic Systems Based on an IRT Low-Cost Sensor.

In large solar farms, supervision is an exhaustive task, often carried out manually by field technicians. Over time, automated or semi-automated fault detection and prevention methods in large photovoltaic plants are becoming increasingly common. The same does not apply when talking about small or medium-sized installations, where the cost of supervision at such level would mean total economic infeasibility. Although there are prevention protocols by suppliers, periodic inspections of the facilities by technicians do not ensure that faults such as the appearance of hot-spots are detected in time. That is why, nowadays, the only way of continuous supervision of a small or medium installation is often carried out by unqualified people and in a purely visual way. In this work, the development of a low-cost system prototype is proposed for the supervision of a medium or small photovoltaic installation based on the acquisition and treatment of thermographic images, with the aim of investigating the feasibility of an actual implementation. The work focuses on the system's ability to detect hot-spots in supervised panels and successfully report detected faults. To achieve this goal, a low-cost thermal imaging camera is used for development, applying common image processing techniques, operating with OpenCV and MATLAB R2021b libraries. In this way, it is possible to demonstrate that it is achievable to successfully detect the hottest points of a photovoltaic (PV) installation with a much cheaper camera than the cameras used in today's thermographic inspections, opening up the possibilities of creating a fully developed low-cost thermographic surveillance system.

Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators.

In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults caused by partial shading in isolated PV installations using maximum power point tracking (MPPT) regulators. MPPT regulators are comparatively more appropriate than pulse width modulation (PWM) solar regulators in order to implement fault diagnosis systems. MPPT regulators have a physical separation between the electrical parameters belonging to the part of the solar panel with respect to the batteries part. Therefore, these electrical parameters can be used to obtain early predictive symptoms of the effects of partial shading with a greater level of observation and sensitivity. Additionally, modifications are proposed in the PV system assembly to obtain greater homogeneity of all the panels regarding the solar irradiance reception angle.

Control and Supervision Requirements for Floating Hybrid Generator Systems.

This work presents a series of devices that generate renewable energy from the marine environment which, in recent years, have aroused increasing interest. In particular, the main types of floating wind generators and marine current turbines are described. Over time, some of these floating generators have evolved in various hybrid modalities, integrating different generation devices into the same system, wind turbines, marine current turbines, wave energy converters, etc., with the objective of multiplying their generation capacity and optimizing the investment made in the floating system. However, this hybridization offers, in some cases, an opportunity to address the problem of controlling the structural stability of the system. Such stability enhancement has been considered a major challenge since the early days of floating wind turbine design. With this objective, in this work, a specific solution is proposed, consisting of a floating hybrid system composed of a wind generation subsystem and a generation subsystem with two marine current turbines. This proposal allows the development of an integrated control system which deals simultaneously with the structural stability of the system and the optimization of the generation capacity. Additionally, other requirements are also highlighted relating to the achievement of economic viability objectives, considering the reliability and availability of the system in the particularly aggressive marine environment, where maintenance operations are especially costly. In this sense, a model of intelligent integration of the tasks of supervision, diagnosis, and predictive maintenance is proposed.

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

Predictive Fault Diagnosis for Ship Photovoltaic Modules Systems Applications.

In this paper, an application for the management and supervision by predictive fault diagnosis (PFD) of solar power generation systems is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for measuring and supervising the parameters inherent to solar power generation and renewable energy supply are applied. The importance of renewable power generation systems in ships is discussed, as well as the causes of photovoltaic modules (PVMs) aging due to superimposed causes of degradation, which is a natural and inexorable phenomenon that affects photovoltaic installations in a special way. In ships, PVMs are doubly exposed to inclement weather (solar radiation, cold, rain, dust, humidity, snow, wind, electrical storms, etc.), pollution, and a particularly aggressive environment in terms of corrosion. PFD techniques for the real-world installation and safe navigation of PVMs are discussed. A specific method based on the online analysis of the time-series data of random and seasonal I-V parameters is proposed for the comparative trend analyses of solar power generation. The objective is to apply PFD using as predictor symptom parameter (PS) the generated power decrease in affected PVMs. This PFD method allows early fault detection and isolation, whose appearance precedes by an adequate margin of maneuver, from the point of view of maintenance tasks applications. This early detection can stop the cumulative degradation phenomenon that causes the development of the most frequent and dangerous failure modes of solar modules, such as hot-spots. It is concluded that these failure modes can be conveniently diagnosed by performing comparative trend analyses of the measured power parameters by NMEA sensors.

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors.

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.

Evaluating the Effect of Stimuli Color and Frequency on SSVEP.

Brain-computer interfaces (BCI) can extract information about the subject's intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner's Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.

Wave Energy Assessment at Valencia Gulf and Comparison of Energy Production of Most Suitable Wave Energy Converters.

Seaports' energy strategy should rely on the use of renewable energy. Presently, the share of renewable energy used by many of the ports worldwide is negligible. Some initiatives are in the process of implementation to produce some of the energy used by the Port of Valencia, one the largest ports in the Mediterranean Basin. Among these initiatives, a photovoltaic plant with an installed capacity of 5.5 MW is under a tendering process and the assessment studies for the deployment of three to five windmills are close to being finished. However, this is not enough to make it a "zero emissions port" as some of the energy demand would still be covered by fossil fuels. Therefore, we should consider clean alternative energy sources. This article analyses the wave energy resources in the surroundings of the Port of Valencia using a 7-year series of data obtained from numerical modelling (forecast). The spatial distribution of wave power is analysed using data from 3 SIMAR points at Valencia Bay and is compared to the data obtained by the Valencia Buoy I (removed in 2005). The obtained results are used to estimate the power matrices and the average energy output of two wave energy converters suitable to be integrated into the port's infrastructure. Finally, the wave energy converters' production is compared to the average amount of energy that is forecast to be obtained from other renewable sources such as solar and wind. Due to the nature of the Gulf's wave climate (mostly low waves), the main conclusion is that the energy obtainable from the waves in the Valencia Gulf will be in correlation with such climate. However, when dealing with great energy consumers every source of production is worthwhile and further research is needed to optimize the production of energy from renewable sources and its use in an industrial environment such as ports.

Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation.

Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user's motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain-computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action-action MI strategy versus an action-relaxation one. No statistically significant difference was found between the two action-action MI strategies.

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis.

In this paper, an application for the management, supervision and failure forecast of a ship's energy storage system is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for the measurement and supervision of the parameters inherent to energy storage and energy supply are reviewed. The importance of energy storage systems in ships, the causes and models of battery aging, types of failures, and predictive diagnosis techniques for valve-regulated lead-acid (VRLA) batteries used for assisted and safe navigation are discussed. In ships, battery banks are installed in chambers that normally do not have temperature regulation and therefore are significantly conditioned by the outside temperature. A specific method based on the analysis of the time-series data of random and seasonal factors is proposed for the comparative trend analyses of both the battery internal temperature and the battery installation chamber temperature. The objective is to apply predictive fault diagnosis to detect any undesirable increase in battery temperature using prior indicators of heat dissipation process failure-to avoid the development of the most frequent and dangerous failure modes of VRLA batteries such as dry out and thermal runaway. It is concluded that these failure modes can be conveniently diagnosed by easily recognized patterns, obtained by performing comparative trend analyses to the variables measured onboard by NMEA sensors.

Sensor Buoy System for Monitoring Renewable Marine Energy Resources.

In this paper we present a multi-sensor floating system designed to monitor marine energy parameters, in order to sample wind, wave, and marine current energy resources. For this purpose, a set of dedicated sensors to measure the height and period of the waves, wind, and marine current intensity and direction have been selected and installed in the system. The floating device incorporates wind and marine current turbines for renewable energy self-consumption and to carry out complementary studies on the stability of such a system. The feasibility, safety, sensor communications, and buoy stability of the floating device have been successfully checked in real operating conditions.

A modular neural network scheme applied to fault diagnosis in electric power systems.

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.