Despite the advances in maintenance management in the wind energy industry, it remains challenging to combine the new technologies and information systems with advanced analytics. The next generation of this industry is facing new opportunities employing the emergent advancement in information and communication technologies to maintain their competency and market needs. These challenges for transforming the industry into the next level require the integration of advanced analytics, called Cyber-Physical Systems (CPS). CMS and SCADA in WT generate a worldwide data volume potential of 25 TB per day, 4 bigger times than Twitter (8 TB/day). The massive data is transformed into useful information by CPS, e.g., analyzing patterns of degradations and inefficiencies to optimize decision-making based on a correct maintenance policy. It will support the actions required to maximize the uptime, productivity, and efficiency of the industrial systems. Nowadays, researchers are focused on the development of new sensors, CMS, algorithms, approaches, etc., to detect and diagnose faults, i.e., alarms. It is also generating a large number of false alarms and, therefore, downtimes and lost production in this industry.

The acoustic signals are not being considered yet in this industry due to the signal analysis and pattern recognition complexity regarding conventional CMS. Any fault in WTs in rotatory components or blades generates noises. Noise has been demonstrated to be useful for the early detection of some faults or abnormal conditions in other industries, but it can be also employed for false alarms detection and diagnosis (FADD). We have found that it has not been done yet. We have been employing unmanned aerial vehicles (UAVs) for CMS of similar devices, creating patents (ES 2768778 & ES2580302).

WindSound will develop a new False Alarm Detection and Diagnosis (FADD) methodology for improving offshore WTs maintenance, by using acoustics sensors embedded in UAVs. The acoustic dataset will be analysed with the SCADA and CMS datasets from the industry. SCADA and CMS in WTs generate a large volume and variety of data, i.e., Big Data. Artificial Neural Network will be employed for the multivariable analysis. There are not enough studies in FADD in this industry and no FADD using acoustics signals collected by CMS embedded in UAV due to the complexity of the signal processing and pattern recognition. Acoustic sensors will collect complementary information to CMS and SCADA data, with the objective of validating False Alarms. We have been focused on this topic for the last 5 years: We have demonstrated in references the need of this research, and we have begun to solve it by Artificial Neural Network and Fuzzy Logic. The reduction of false alarms will lead to reducing O&M costs while improving availability, safety, and reliability, and it will increase the safety of the workers. A new challenge ahead is to harness all the information obtained from WindSound that has to be integrated and analysed with current data to get optimal decision-making. It will require creating new importance measures to study the effect of the components over the system in a certain time and to discriminate the components. A novel decision-making analysis will be done considering the endogenous variables, such as economics, resources and reliability, and exogenous variables, such as weather, market opportunities, and legal issues, using new importance indexes, qualitatively solved by logical decision trees and quantitatively by binary decision diagrams, where we have worked for the last 10 years.

WindSound proposes, for the first time, the use of acoustic sensors in drones for CMS of WTs. The information will be analysed with the data from the SCADA and CMS of the offshore WTs to analyse the alarms, i.e., it will replace human resources to access the WTs to verify the alarms in favour of their safety. This information will be finally studied with endogenous (resources, budgets for maintenance tasks) and exogenous variables (electricity price, weather conditions, legal issues). WindSound presents a new holistic multidisciplinary approach to this paradigm in favour of the competitiveness and reliability of this industry that has not been previously studied. We have experience accredited by papers in most reference journals, leading projects, international awards, collaborations with other universities, etc.

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References

101 entries « 2 of 7 »

2022

Marugán, A. Pliego; Márquez, F. P. García; Pérez, J. M. Pinar: A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers. In: vol. 168, 2022. (Type: Journal Article | Links | BibTeX)
Gonzalo, A. Peinado; Benmessaoud, T.; Entezami, M.; Márquez, F. P. García: Optimal maintenance management of offshore wind turbines by minimizing the costs. In: vol. 52, 2022. (Type: Journal Article | Links | BibTeX)
Márquez, F. P. García; Gonzalo, A. Peinado: A Comprehensive Review of Artificial Intelligence and Wind Energy. In: vol. 29, no. 5, pp. 2935-2958, 2022. (Type: Journal Article | Links | BibTeX)
Chacón, A. M. Peco; Ramírez, I. Segovia; Márquez, F. P. García: State of the Art of Artificial Intelligence Applied for False Alarms in Wind Turbines. In: vol. 29, no. 5, pp. 2659-2683, 2022. (Type: Journal Article | Links | BibTeX)
Acaroğlu, H.; Márquez, F. P. García: High voltage direct current systems through submarine cables for offshore wind farms: A life-cycle cost analysis with voltage source converters for bulk power transmission. In: vol. 249, 2022. (Type: Journal Article | Links | BibTeX)
Sabhahit, J. N.; Solanke, S. S.; Jadoun, V. K.; Malik, H.; Márquez, F. P. G.; Pinar-Pérez, J. M.: Contingency Analysis of a Grid of Connected EVs for Primary Frequency Control of an Industrial Microgrid Using Efficient Control Scheme. In: vol. 15, no. 9, 2022. (Type: Journal Article | Links | BibTeX)
Márquez, F. P. García; Sánchez, P. J. Bernalte; Ramírez, I. Segovia: Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring. In: vol. 21, no. 2, pp. 485-500, 2022. (Type: Journal Article | Links | BibTeX)
Segovia, I.; Bernalte, P. J.; Márquez, F. P. G.: Wind Turbine Alarm Management with Artificial Neural Networks. In: vol. 436, pp. 1-11, 2022. (Type: Journal Article | Links | BibTeX)
Chacon, A. M. P.; Márquez, F. P. G.: Ensembles Learning Algorithms with K-Fold Cross Validation to Detect False Alarms in Wind Turbines. In: vol. 144, pp. 450-464, 2022. (Type: Journal Article | Links | BibTeX)
Marugan, A. P.; Marquez, F. P. G.; Pinar-Perez, J. M.: A Dynamic Multi-objective Model for Improving Maintenance Management of Offshore Wind Turbines. In: vol. 144, pp. 112-123, 2022. (Type: Journal Article | Links | BibTeX)
Sanchez, P. J. B.; Ramirez, I. S.; Marquez, F. P. G.: Deep Learning for Acoustic Pattern Recognition in Wind Turbines Aerial Inspections. In: vol. 144, pp. 350-362, 2022. (Type: Journal Article | Links | BibTeX)
Ramirez, I. S.; Márquez, F. P. García: Classification Learner Applied to False Alarms for Wind Turbine Maintenance Management. In: vol. 273, pp. 113-121, 2022. (Type: Journal Article | Links | BibTeX)
Ramírez, I. Segovia; Sánchez, P. J. Bernalte; Márquez, F. P. García: A Supervisory Control and Data Acquisition System Filtering Approach for Alarm Management with Deep Learning. In: vol. 273, pp. 86-95, 2022. (Type: Journal Article | Links | BibTeX)
Chacón, A. M. P.; Ramirez, I. S.; Márquez, F. P. García: False Alarm Detection in Wind Turbine Management by K-Nearest Neighbors Model. In: vol. 273, pp. 106-112, 2022. (Type: Journal Article | Links | BibTeX)

2021

Padmaja, A.; Shanmukh, A.; Mendu, S. S.; Devarapalli, R.; González, J. Serrano; Márquez, F. P. García: Design of capacitive bridge fault current limiter for low-voltage ride-through capacity enrichment of doubly fed induction generator-based wind farm. In: vol. 13, no. 12, 2021. (Type: Journal Article | Links | BibTeX)
101 entries « 2 of 7 »