Assessment Of Wind Conditions Research Articles

A Study of Frequency Domain Reflectometry Technique for High-Voltage Rotating Machine Winding Condition Assessment

Detecting and locating local degradations at an incipient stage is very important for mission-critical high-voltage rotating machines. One particular challenge in the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on the scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially created defects. These defects include turn-to-turn short, surface creepage, loose coils, insufficient end-winding spacing, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that FDR can identify and locate structural and insulation degradation in both shielded and unshielded objects with good sensitivity. This makes FDR a complementary technique for machine fault diagnosis and aging assessment.

Investigation of Distribution Transformers Vibrations in Terms of Core and Winding Condition Assessment

This work refers to the criterion values used to assess the state of the active part of the transformer based on the analysis of the effective value of the total vibration acceleration and the frequency spectrum. It was proved in the work that the criteria values should be differentiated for transformers of different rated power. Transformers with lower rated power are characterized by lower RMS values of vibration acceleration than units with higher rated power, which cannot remain without impact on the criteria values. Trend analysis of the total aRMS values may reveal increasing defects before the currently applicable criteria values are exceeded. In addition, the influence of the position of the sensor on the frequency spectrum of the obtained signals was analyzed. It was proved that the sensors should be mounted in the middle of the transformer tank, between its lower part and the cover. The dependence of RMS value of vibration acceleration on no-load losses was also determined for transformers of different types but the same rated power. This relationship will not have a large share in the total vibrations of the transformer, due to slight changes in the value of aRMS occurring for all analyzed units.

Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

Transformer Winding Fault Diagnosis Using Vibration Image and Deep Learning

Winding condition assessment is an essential task for operating transformers, and the vibration method provides a low-cost and non-intrusive approach. In this paper, a novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image. Then, a deep learning approach based on convolutional neural network (CNN) is used to classify the images belong to different classes. In the laboratory experiment, free vibration tests are performed on an on-load winding model, which are used to verify the relationship between the natural frequency and the electromagnetic force under different clamping forces. During the field experiment, transformers are divided into three categories, including normal, degraded and anomalous, and the proposed scheme is trained and tested by using the vibration samples acquired from more than 100 operating transformers. The performance of the CNN classifier under different input sizes is investigated, which achieves 98.3% overall accuracy. Besides, the confusion matrices obtained by other methods are compared, such as artificial neural network (ANN), support vector machine (SVM) and naive Bayes classifier (NBC). The results show that the proposed scheme including the vibration image extraction method and the CNN classifier offers superior performance in winding fault diagnosis.

The assessment of wind conditions by means of hot wire sensors and a modifed Wheatstone bridge architecture

In this paper we present a novel complete anemometric electronic system based on an array of hot wire sensors, provided by Telecontrolli SME, and a suitable heater feedback that achieves, thanks to a dedicated algorithm, a CMOS integrable portable solution showing a robust design, without moving parts and small dimensions. The sensor interface is implemented by means of a modified Wheatstone bridge architecture with autobalancing capability. Comparisons with a commercial cup anemometer have shown that the proposed architecture, with reduced space occupancy and low power consumption, is suitable to operate in a natural environment in the −20 to +55°C temperature range providing wind speed and direction measurement. Concerning the wind speed, a 0.6m/s sensitivity has been achieved. The integrated CMOS design of the final system is also presented and discussed in the paper.

A wind resource assessment at Aħrax Point: A node for central Mediterranean offshore wind resource evaluation

This article presents salient results from wind measurements conducted by the Malta Resources Authority at Aħrax Point on the Mediterranean island of Malta. The measurement exercise had the primary aim of enabling a better assessment of wind conditions in the near shore offshore environment, more specifically on an offshore reef known as Is-Sikka l-Bajda. The 80 metre wind monitoring mast installed at Aħrax Point generated data from various sensors at multiple monitoring levels and the analysis presented here divulges key parameters derived from this scientific study. The region lacks measured offshore wind data and the islands’ position in the central Mediterranean makes such a data bank useful for offshore wind resource assessments, as well as for comparison with results generated by mesoscale meteorological models. This study investigates wind behaviour at Aħrax Point as a multi-level measurement node and the results are also compared to a re-analysis dataset in order to establish similarities or deviations of specific wind parameters.

A Low Cost Fully Integrable in a Standard CMOS Technology Portable System for the Assessment of Wind Conditions

Nowadays, the environmental meteorological monitoring is becoming a primary need in many fields of human activity. Continuous and accurate measurements are often required also in absence of local and fixed stations. This scenario leads to the development of low cost portable systems and to the design of innovative architectures, thanks to the availability of new technologies and sensors. The full system here proposed - easily integrable on a CMOS technology - is based on a hot wire anemometer, provided by Telecontrolli SME, and a suitable heater feedback that achieves, thanks to a dedicated algorithm, a novel portable solution with a robust design, without moving parts and small dimensions. It is suitable to operate up to 50̊C and provides accurate wind speed and direction measurement.

Winding Condition Assessment of Power Transformers Based on Vibration Correlation

In this paper, a winding condition assessment model using vibration signals is presented, which can be used to diagnose power transformers online. The basic principle of this model is based on the correlation analysis of winding vibrations. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the mixed signal. Then, a health parameter is proposed via principal component analysis. Another parameter is also proposed to detect the fault locations for suspected faulty transformers. In laboratory tests, the model is validated on a specifically designed 110-kV transformer. During the tests, man-made winding deformations are simulated to compare the vibrations under different conditions. The model has also been tested on several in-service power transformers. The preliminary study shows that the proposed model is feasible to assess the power transformer winding condition.

Russian

Russian

Assessment of Wind Conditions at a Fjord Inlet by Complementary Use of Sonic Anemometers and Lidars

Wind velocity measurement devices based on the remote optical sensing, lidars, are extensively applied in wind energy research and wind farm operation. The present paper demonstrates the relevance and potential of lidar measurements for other wind- sensitive structures such as long-span bridges. In a pilot study in Lysefjord, Norway, a pulsed long-range lidar and two short-range WindScanners were installed at the bridge site, together with a long-term monitoring system based on sonic anemometers. The deployment of the two types of lidars is described in more details and the complementary value of the data from all three types of the instruments is illustrated. The emphasis is on the lidars’ potential to map the wind conditions along the whole span of a bridge in a complex terrain, as opposed to “point” measurements achievable by sonic anemometers. The challenging balance between the spatial and temporal resolution of the data is discussed.

Validation of the simpleFoam (RANS) solver for the atmospheric boundary layer in complex terrain

We validate the simpleFoam (RANS) solver in OpenFOAM (version 2.1.1) for simulating neutral atmospheric boundary layer flows in complex terrain. Initial and boundary conditions are given using Richards and Hoxey proposal (1). In order to obtain stable simulation of the ABL, modified wall functions are used to set the near-wall boundary conditions, following Blocken et al remedial measures (2). A structured grid is generated with the new library terrainBlockMesher (3, 4), based on OpenFOAM's blockMesh native mesher. The new tool is capable of adding orographic features and the forest canopy. Additionally, the mesh can be refined in regions with complex orography. We study both the classical benchmark case of Askervein hill (5) and the more recent Bolund island data set (6). Our purpose is two-folded: to validate the performance of OpenFOAM steady state solvers, and the suitability of the new meshing tool to generate high quality structured meshes, which will be used in the future for performing more computationally intensive LES simulations in complex terrain. Site selection for wind energy projects is a critical step of the decision making process for possible investment in wind energy. On-site measurement campaigns over areas of several kilometers square for wind farm siting can be very costly, and can only provide a local assessment of wind conditions. To overcome this hurdle it is currently very common to recur to computational fluid dynamics (CFD) simulations that can extend meteorological mast point source measurement data over an area of several kilometers. Nowadays computational fluid dynamics (CFD) modeling for the atmospheric boundary layer (ABL) in complex terrain is becoming more ubiquitous, although it is quite time demanding to perform numerical solutions of the governing fluid equations without making simplifications to the flow dynamics. The most common approach used for ABL flows are the Reynold Averaged Navier

Transformer winding condition assessment using frequency response analysis and evidential reasoning

The study presents an evidential reasoning (ER) approach to transformer winding assessment based on frequency response analysis (FRA). A conventional FRA assessment process is firstly discussed, where frequency response comparison methods and interpretation features are briefly introduced. Then an FRA assessment process is transferred into a multiple-attribute decision-making (MADM) problem under an ER framework and an introduction to the ER algorithm is given. Subsequently, several examples of transformer winding condition assessment problems are considered using two ER evaluation analysis models, where the potential of the ER approach in combining evidence and dealing with uncertainties is demonstrated. In the case when more than one expert is involved in an FRA assessment process, the developed ER framework can be used to aggregate experts' subjective judgements and produce an overall evaluation of the condition of a transformer winding in a formalised form.