Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles

A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source

SCADA and Artificial neural networks for maintenance management

Nowadays, the reliability of the wind turbines is essential to ensure the efficiency and the benefits of the wind energy. The SCADA system installed in a wind turbine generates lot of data that need to be processed. The information obtained from these data can be used for improving the operation and management, obtaining more reliable systems. The SCADA systems operate through different control rules that are predefined. However, a static control of the wind turbine can generate a miscorrelation between the control and the real conditions of the wind turbine. For example, two wind turbines can be separated several kilometers in the same wind farm, therefore, the operation conditions must be different and the control strategy should not be unique. This research work presents a method based on neural networks for a dynamic generation of the control strategy. The method suggests that the thresholds used for generating alarms can vary and, therefore, the control of the wind turbine will be adapted to each specific wind turbine

Fuzzy Logic Applied to SCADA Systems

This article focuses on the monitoring of a wind farm in real time based on big data collected by Supervisory Control and Data Acquisition (SCADA) system. The decision- making of the type of maintenance to be applied can be insured by SCADA system. This system generates alarms based on the collected data. False alarms cause false interventions by the maintenance team resulting in loss of production and costs. The reduction of these false alarms makes it possible to contribute better to the management of the maintenance of the wind farm. In this paper, we propose a new approach for the identification of alarms by Fuzzy Logic based on the data collected by the SCADA system. The alarms generated in this case can be divided into two categories: orange alarms corresponding to faults requiring the intervention of preventive maintenance and red alarms corresponding to critical states that can cause system failures

Smart Farming: Intelligent Management Approach for Crop Inspection and Evaluation Employing Unmanned Aerial Vehicles

This work presents an unmanned aerial vehicle management platform encompassed in the concept of smart farming. Automates inspections of different crops and monitors the status of the plantation is done by IoT, analyzing an area on an online map that provides air and weather restrictions. Intelligent route management algorithms are employed to generate the optimal inspection route and waypoints, maximizing the multispectral images capture. These multispectral images can be subsequently processed according to algorithms based on phytosanitary index formulas and regressions obtained with artificial neural networks. Reports are generated with analysis of the results by this approach, for example: optimal collection time, water stress, maturity index, etc.Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant (Ref.: SBPLY/19/180501/000102).No data JCR 20190.184 SJR (2019), Q3 242/393 Computer Science (miscellaneous)No data IDR 2019UE

New Approaches on Maintenance Management for Wind Turbines Based on Acoustic Inspection

Nowadays, maintenance management is changing due to the new technologies in inspection and monitorization systems to reduce the production costs for the companies and risks for the operator. Maintenance management is a key factor in some industries as renewable energy, due to the high-cost consequences of a wrong failure detection in a wind turbine. Therefore, advances in condition monitoring systems are required for an early failure diagnosis. This paper contributes to the actual wind turbines diagnosis methods with a novel non-destructive inspection system based on acoustic analysis of the wind turbine condition. The paper presents a condition monitoring system based on an acoustic sensor embedded in an unmanned aerial vehicle to collect acoustic signals emitted by the wind turbine. The signals are sent to a ground remote-control centre, and then they are analysed. This data acquisition system needs of a qualitative and quantitative analysis to classify and identify the condition of the wind turbine. Wavelet transforms are employed for filtering the signals and pattern recognition. Several scenarios are considered and analysed considering the main mechanical parts and components of a wind turbine

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks

Machine Learning and Neural Network for Maintenance Management

A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source