Turbine recommender: The selection of wind turbine type using one of a machine learning technique

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program.

Measurement of Infrasound Components Contained in the Noise Emitted during a Working Wind Turbine

The research reported in this paper involves the development and refinement of methods applicable to the measurement and analysis of infrasound signals generated by the operation of wind turbines. In particular, the presentation focuses on the use of a new system that is applied for simultaneous recording of acoustic signals in the low-frequency range emitted by wind farms in three independent and identical measurement setups. A comparative analysis of the proposed new system was made with the Brüel & Kjaer measurement, a commonly used methodology, which meets the requirements of the IEC 61400-11 standard. The paper focuses on the results of frequency and time-frequency analysis of infrasound signals recorded throughout the operation of a wind turbine with a rated capacity of 2 MW. The use of a correlated system with three simultaneous measurement systems can be a new and alternative measurement method that will eliminate the drawbacks of previous approaches.

Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images

Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.