Person:
Santos Peñas, Matilde

First Name

Matilde

Last Name

Santos Peñas

URI

https://hdl.handle.net/20.500.14352/78595

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Informática

Department

Arquitectura de Computadores y Automática

Area

Ingeniería de Sistemas y Automática

Identifiers

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Search Results

Now showing 1 - 10 of 37

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends
(Wind Engineering, 2022) Ravi Pandit; Davide Astolfi; Jiarong Hong; David Infield; Santos Peñas, Matilde
This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.
Project number: 254
Diseño y aplicación al aula de un modelo de asistente semi-automático para procesos de aprendizaje presenciales
(2017) Guijarro Mata-García, María; Santos Peñas, Matilde; Fuentes Fernández, Rubén; Sáenz Pérez, Fernando; Navarro Martín, Antonio; Fernández Prados, Juan Sebastián; Vicente Hernanz, María Lina; Guijarro de Mata-García, Marta; Prieto Fernández, Lucía Amparo; Garnica Alcázar, Antonio Óscar; Jiménez Castellanos, Juan Francisco; Fernández, Isabel; Sáez Alcaide, Juan Carlos
Accounting for environmental conditions in data-driven wind turbine power models
(IEEE Transaction on Sustainable Energy, 2022) Ravi Pandit; David Infield; Santos Peñas, Matilde
Continuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves. Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activities such as early failure detection.
Project number: 377
Identificación de problemas y necesidades en la docencia virtual surgidos por la crisis sanitaria en la Facultad de Óptica y Optometría. Propuesta y planificación de soluciones y nuevas herramientas
(2021) Gutiérrez Hernández, Ángel Luis; Camargo Mínguez, Ana María; González Montero, María Guadalupe; Guijarro Mata-García, María; López Ibáñez, Manuel; Martín Pérez, Yolanda; Recas Piorno, Joaquín; Santos Peñas, Matilde
La situación de pandemia y alerta sanitaria ha obligado a todos los miembros de la comunidad educativa a adaptarse a la situación de docencia virtual ante la imposibilidad de continuar con las clases presenciales. Existe una gran variedad de necesidades debido a la diversidad de situaciones tanto personales (en cuanto a formación del profesorado y alumnos en la utilización de recursos virtuales, conocimiento de los recursos existentes y más adecuados…) como materiales (disponibilidad de material informático adecuado tanto de profesores como de alumnos). En la Facultad de Óptica y Optometría, como en el resto de centros de la UCM, se ha hecho un gran esfuerzo durante estos meses, y se sigue trabajando en ello, para conocer las necesidades de los profesores y alumnos en esta situación y poder dar respuesta a todas ellas. Nuestro proyecto persigue la identificación y análisis de esas necesidades y problemas de profesores y estudiantes, que, anque circunscrito al ámbito de la Facultad de Óptica y Optometría, quizá pueda extrapolarse en cierta medida al resto de la comunidad académica, para poder plantear y planificar estrategias para enfrentarse a las situaciones similares que pudieran darse en el futuro.
FPGA-Based Multimodal Embedded Sensor System Integrating Low- and Mid-Level Vision
(Sensors, 2011) Botella Juan, Guillermo; Martín Hernández, José Antonio; Santos Peñas, Matilde; Meyer-Baese, Uwe
Motion estimation is a low-level vision task that is especially relevant due to its wide range of applications in the real world. Many of the best motion estimation algorithms include some of the features that are found in mammalians, which would demand huge computational resources and therefore are not usually available in real-time. In this paper we present a novel bioinspired sensor based on the synergy between optical flow and orthogonal variant moments. The bioinspired sensor has been designed for Very Large Scale Integration (VLSI) using properties of the mammalian cortical motion pathway. This sensor combines low-level primitives (optical flow and image moments) in order to produce a mid-level vision abstraction layer. The results are described trough experiments showing the validity of the proposed system and an analysis of the computational resources and performance of the applied algorithms.
Trajectory tracking nonlinear hybrid control of automated guided vehicles
(Complexity, 2024) Antonio Sánchez-Rodríguez; Eduardo Bayona; Jesús Enrique Sierra García; Santos Peñas, Matilde
Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking tomake accurate stops at logistics stations, such as loading stations, or to pick up or drop of trolleys, pallets, or racks. Tis paperproposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-diferential AGV. Te control strategy combinesconventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). Te LPC is usedfor trajectory tracking while the PID is used for speed control of the robot. Te stability of the controller is demonstrated for anydiferentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for severaltrajectories, the LPC outperforms it in all cases.
A novel adaptive vehicle speed recommender fuzzy system for autonomous vehicles on conventional two‐lane roads
(Expert Systems, 2022) Barreno, Felipe; Santos Peñas, Matilde; Romana, Manuel G.
This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two-lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system.
AGV Fuzzy Control Optimized by Genetic Algorithms
(Logic Journal of the IGPL, 2024) Sierra García, Jesús Enrique; Santos Peñas, Matilde
Automated Guided Vehicles (AGV) are an essential element of transport in industry 4.0. Although they may seem simple systems in terms of their kinematics, their dynamics is very complex, and it requires robust and efficient controllers for their routes in the workspaces. In this paper, we present the design and implementation of an intelligent controller of a hybrid AGV based on fuzzy logic. In addition, genetic algorithms have been used to optimize the speed control strategy, aiming at improving efficiency and saving energy. The control architecture includes a fuzzy controller for trajectory tracking that has been enhanced with genetic algorithms. The cost function first maximizes the time in the circuit and then minimizes the guiding error. It has been validated on the mathematical model of a commercial hybrid AGV that merges tricycle and differential robot components. This model not only considers the kinematics and dynamics equations of the vehicle but also the impact of friction. The performance of the intelligent control strategy is compared with an optimized PID controller. Four paths were simulated to test the approach validity.
Explainable Anomaly Detection in Spacecraft Telemetry
(Engineering Applications of Artificial Intelligence, 2024) Gonzalo Farias; Sara Cuéllar; Santos Peñas, Matilde; Fernando Alonso-Zotes; Ernesto Fábregas
As spacecraft missions become more complex and ambitious, it becomes increasingly important to track the status and health of the spacecraft in real-time to ensure mission success. Anomaly detection is a crucial part of spacecraft telemetry analysis, allowing engineers to quickly identify unexpected or abnormal behaviour reflected on spacecraft data and take appropriate corrective action. Traditional statistical methods based on threshold setting are often inadequate for detecting anomalies in this context, requiring the development of more sophisticated techniques that can handle the high-dimensional, non-linear, and non-stationary nature of spacecraft telemetry data such as machine learning-based techniques. This article presents an approach for anomaly detection using machine-learning techniques for spacecraft telemetry. The identification of anomaly types present on two real telemetry datasets from NASA is performed to incorporate information of magnitude, frequency, and waveform from known anomalies into the feature extraction process. Then, a machine-learningbased model is trained with the obtained features and tested with unknown real data. The proposed method achieves 95.3% of precision and 100% of Recall, giving a 𝐹0.5 score of 96.2% in both datasets, outperforming the metrics obtained on the existing related works, demonstrating that the inclusion of known anomalies can improve the performance of the data-driven models. Finally, an explainability analysis is performed to understand why a particular data instance has been identified as anomalous, proving the effectiveness of the feature extraction process.
New internal clustering validation measure for contiguous arbitrary‐shape clusters
(International Journal of Intelligent Systems, 2021) Rojas Thomas, Juan Carlos; Santos Peñas, Matilde
In this study a new internal clustering validation index is proposed. It is based on a measure of the uniformity of the data in clusters. It uses the local density of each cluster, in particular, the normalized variability of the density within the clusters to find the ideal partition. The new validity measure allows it to capture the spatial pattern of the data and obtain the right number of clusters in an automatic way. This new approach, unlike the traditional one that usually identifies well-separated compact clouds, works with arbitrary-shape clusters that may be contiguous or even overlapped. The proposed clustering measure has been evaluated on nine artificial data sets, with different cluster distributions and an increasing number of classes, on three highly nonlinear data sets, and on 17 real data sets. It has been compared with nine well-known clustering validation indices with very satisfactory results. This proves that including density in the definition of clustering validation indices may be useful to identify the right partition of arbitrary-shape and different-size clusters.