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Mr. Mattia Beretta
UTGAC - UPC

Basic Info


Research Keywords & Expertise

0 Predictive Maintenance
0 Wind Energy
0 fault detection
0 Reneawable Energy System
0 Data Science

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Short Biography

Mattia Beretta received the B.Sc. degree in Mechanical Engineering from Politecnico di Milano (POLIMI), in 2015, and the Double M.Sc. degree in Renewable Energies Engineering from Kungliga Tekniska Högskolan (KTH), Sweden and Universitat Politècnica de Catalunya (UPC), Spain in 2017. He is currently pursuing a Ph.D. degree in Environmental Engineering at Universitat Politècnica de Catalunya. His researches focus on the design and implementation of predictive maintenance systems for wind turbines applying machine learning, deep learning and statistics to monitor and analyze sensors’ data.

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Journal article
Published: 31 August 2021 in Applied Sciences
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SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.

ACS Style

Mattia Beretta; Karoline Pelka; Jordi Cusidó; Timo Lichtenstein. Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data. Applied Sciences 2021, 11, 8065 .

AMA Style

Mattia Beretta, Karoline Pelka, Jordi Cusidó, Timo Lichtenstein. Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data. Applied Sciences. 2021; 11 (17):8065.

Chicago/Turabian Style

Mattia Beretta; Karoline Pelka; Jordi Cusidó; Timo Lichtenstein. 2021. "Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data." Applied Sciences 11, no. 17: 8065.

Journal article
Published: 20 August 2021 in Energies
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Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally.

ACS Style

Jordi Cusidó; Arnau López; Mattia Beretta. Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning. Energies 2021, 14, 5167 .

AMA Style

Jordi Cusidó, Arnau López, Mattia Beretta. Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning. Energies. 2021; 14 (16):5167.

Chicago/Turabian Style

Jordi Cusidó; Arnau López; Mattia Beretta. 2021. "Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning." Energies 14, no. 16: 5167.

Journal article
Published: 17 August 2021 in Applied Sciences
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The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines.

ACS Style

Mattia Beretta; Yolanda Vidal; Jose Sepulveda; Olga Porro; Jordi Cusidó. Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis. Applied Sciences 2021, 11, 7523 .

AMA Style

Mattia Beretta, Yolanda Vidal, Jose Sepulveda, Olga Porro, Jordi Cusidó. Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis. Applied Sciences. 2021; 11 (16):7523.

Chicago/Turabian Style

Mattia Beretta; Yolanda Vidal; Jose Sepulveda; Olga Porro; Jordi Cusidó. 2021. "Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis." Applied Sciences 11, no. 16: 7523.

Journal article
Published: 22 February 2021 in Sensors
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A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.

ACS Style

Mattia Beretta; Anatole Julian; Jose Sepulveda; Jordi Cusidó; Olga Porro. An Ensemble Learning Solution for Predicitive Manintenance of Wind Turbines Main Bearing. Sensors 2021, 21, 1512 .

AMA Style

Mattia Beretta, Anatole Julian, Jose Sepulveda, Jordi Cusidó, Olga Porro. An Ensemble Learning Solution for Predicitive Manintenance of Wind Turbines Main Bearing. Sensors. 2021; 21 (4):1512.

Chicago/Turabian Style

Mattia Beretta; Anatole Julian; Jose Sepulveda; Jordi Cusidó; Olga Porro. 2021. "An Ensemble Learning Solution for Predicitive Manintenance of Wind Turbines Main Bearing." Sensors 21, no. 4: 1512.

Journal article
Published: 03 December 2020 in Applied Sciences
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A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario.

ACS Style

Mattia Beretta; Juan José Cárdenas; Cosmin Koch; Jordi Cusidó. Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders. Applied Sciences 2020, 10, 8649 .

AMA Style

Mattia Beretta, Juan José Cárdenas, Cosmin Koch, Jordi Cusidó. Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders. Applied Sciences. 2020; 10 (23):8649.

Chicago/Turabian Style

Mattia Beretta; Juan José Cárdenas; Cosmin Koch; Jordi Cusidó. 2020. "Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders." Applied Sciences 10, no. 23: 8649.