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Dr. Alejandro Blanco-M
University of Vic - Central University of Catalonia

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Research Keywords & Expertise

0 Deep Learning
0 Machine Learning
0 Neural Networks
0 Wind Energy
0 Wind Power

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Short Biography

Alejandro Blanco received a Ph.D. in Experimental sciences & technologies in 2018 at the University of Vic (UVic-UCC) with the developing of a machine learning system that extracts the failure patterns of the Wind Turbines. He is an experienced software engineer and embedded software with data scientist skills and the underlying hardware parallelization. In 2012 he joined the R&D Department of the Technological Center Leitat and participated in several European projects related to energy, IOT and distributed sensor networks.In 2015 he joined the Research Group in Data and Signal Processing of the UVic as a doctoral student at the company Smartive-Itestit, in charge of creating new machine learning methods for predicting failures of Wind Turbines from real data from the wind plants that the company is monitoring. His research interest is in machine/deep learning applied to the wind energy sector.

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Journal article
Published: 13 July 2021 in International Journal of Interactive Mobile Technologies (iJIM)
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Technology has become an essential element in today's digital life. The way users interact with different business services is changing thanks to Augmented Reality (AR), and the use of this technology in handhelds is gaining importance nowadays. Public and private organizations cannot be left behind, and they should strive to meet the demand for interactive services with AR. Through this review, we identified four potential major barriers to AR implementation, three of which were present in actual applications. We found that these barriers were similar across areas and that they were mainly related to the technology itself rather than to user interaction. For each of the barriers, we present a list of possible solutions or enablers that can help overcome the detected limitations.

ACS Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M; Jose Luis Eguia-Gomez. Implementation Barriers to Augmented Reality Technology in Public Services. International Journal of Interactive Mobile Technologies (iJIM) 2021, 15, 43 -56.

AMA Style

Ruth S. Contreras-Espinosa, Alejandro Blanco-M, Jose Luis Eguia-Gomez. Implementation Barriers to Augmented Reality Technology in Public Services. International Journal of Interactive Mobile Technologies (iJIM). 2021; 15 (13):43-56.

Chicago/Turabian Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M; Jose Luis Eguia-Gomez. 2021. "Implementation Barriers to Augmented Reality Technology in Public Services." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 13: 43-56.

Journal article
Published: 11 July 2021 in Applied Sciences
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Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.

ACS Style

Pere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences 2021, 11, 6405 .

AMA Style

Pere Marti-Puig, Alejandro Bennásar-Sevillá, Alejandro Blanco-M., Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences. 2021; 11 (14):6405.

Chicago/Turabian Style

Pere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. 2021. "Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model." Applied Sciences 11, no. 14: 6405.

Review
Published: 26 June 2021 in International Journal of Public Administration
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Many democracies face breaches of communication between citizens and political representatives, resulting in low engagement in political decision-making and public consultations. Gamification strategies can be implemented to generate constructive relationships and increase citizens’ motivation and participation by including positive experiences like achievements. This document contains a literature review of the gamification topic, providing a conceptual background, and presenting a selection and analysis of the applications to e-government services. The study characterises gamification element usage and highlights the need for a standardised methodology during element selection. Three research gaps were identified, with a potential impact on future studies and e-government applications.

ACS Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M. A Literature Review of E-government Services with Gamification Elements. International Journal of Public Administration 2021, 1 -17.

AMA Style

Ruth S. Contreras-Espinosa, Alejandro Blanco-M. A Literature Review of E-government Services with Gamification Elements. International Journal of Public Administration. 2021; ():1-17.

Chicago/Turabian Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M. 2021. "A Literature Review of E-government Services with Gamification Elements." International Journal of Public Administration , no. : 1-17.

Journal article
Published: 09 January 2021 in Applied Sciences
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In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.

ACS Style

Pere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences 2021, 11, 590 .

AMA Style

Pere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences. 2021; 11 (2):590.

Chicago/Turabian Style

Pere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. 2021. "Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines." Applied Sciences 11, no. 2: 590.

Conference paper
Published: 01 June 2020 in 2020 6th International Conference of the Immersive Learning Research Network (iLRN)
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ACS Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M. Gamification in E-government Platforms and Services: A Literature Review. 2020 6th International Conference of the Immersive Learning Research Network (iLRN) 2020, 1 .

AMA Style

Ruth S. Contreras-Espinosa, Alejandro Blanco-M. Gamification in E-government Platforms and Services: A Literature Review. 2020 6th International Conference of the Immersive Learning Research Network (iLRN). 2020; ():1.

Chicago/Turabian Style

Ruth S. Contreras-Espinosa; Alejandro Blanco-M. 2020. "Gamification in E-government Platforms and Services: A Literature Review." 2020 6th International Conference of the Immersive Learning Research Network (iLRN) , no. : 1.

Journal article
Published: 23 May 2019 in Energies
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Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.

ACS Style

Alejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies 2019, 12, 1982 .

AMA Style

Alejandro Blanco-M., Pere Marti-Puig, Karina Gibert, Jordi Cusidó, Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies. 2019; 12 (10):1982.

Chicago/Turabian Style

Alejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History." Energies 12, no. 10: 1982.

Journal article
Published: 31 January 2019 in Energies
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It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.

ACS Style

Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies 2019, 12, 453 .

AMA Style

Pere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies. 2019; 12 (3):453.

Chicago/Turabian Style

Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction." Energies 12, no. 3: 453.

Journal article
Published: 01 December 2018 in Environmental Modelling & Software
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The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.

ACS Style

Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software 2018, 110, 119 -128.

AMA Style

Pere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software. 2018; 110 ():119-128.

Chicago/Turabian Style

Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2018. "Effects of the pre-processing algorithms in fault diagnosis of wind turbines." Environmental Modelling & Software 110, no. : 119-128.

Journal article
Published: 22 March 2018 in Energies
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Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.

ACS Style

Alejandro Blanco-M.; Karina Gibert; Pere Marti-Puig; Jordi Cusidó; Jordi Solé-Casals. Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools. Energies 2018, 11, 723 .

AMA Style

Alejandro Blanco-M., Karina Gibert, Pere Marti-Puig, Jordi Cusidó, Jordi Solé-Casals. Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools. Energies. 2018; 11 (4):723.

Chicago/Turabian Style

Alejandro Blanco-M.; Karina Gibert; Pere Marti-Puig; Jordi Cusidó; Jordi Solé-Casals. 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools." Energies 11, no. 4: 723.