Evangelos Papatheou

Energy harvesting from human motion and bridge vibrations: An evaluation of current nonlinear energy harvesting solutions:

A large quantity of recent research into the harvesting of electrical energy from ambient vibration sources has been focused on the improvement of device performance via the deliberate introduction of dynamic nonlinearities. In addition to this, the realisation that most of these kinetic energy sources are stochastic in nature has led to many studies focusing on the response of energy harvesters to random vibrations (often Gaussian white noise). This differs from early studies in which it was assumed that ambient vibration sources were sinusoidal. The aim of the present study is to take current nonlinear energy harvesting solutions and to numerically analyse their effectiveness when two real ambient vibration sources are used: human walking motion and the oscillation of the midspan of a suspension bridge. This study shows that the potential improvements that can be realised through the introduction of nonlinearities into energy harvesters are sensitive to the type of ambient excitation to which they are subjected. Additionally, the need for more research into the development of low-frequency energy harvesters is emphasised.

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically growing interest in exploring and investing in such offshore power plants as the continents water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an analysis of supervisory control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine-learning approach is applied, in order to produce individual and population-based power curves and then predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.

Aspects of structural health and condition monitoring of offshore wind turbines

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

A short investigation of the effect of an energy harvesting backpack on the human gait

Exploiting human motion for the purpose of energy harvesting has been a popular idea for some time. Many of the approaches proposed can be uncomfortable or they impose a significant burden on the persons gait. In the current paper a hardware in-the-loop simulator of an energy harvesting backpack is employed in order to investigate the effect of a suspended-load backpack on the human gait. The idea is based on the energy produced by a suspended-load which moves vertically on a backpack while a person walks. The energy created from such a linear system can be maximised when it resonates with the walking frequency of the person. However, such a configuration can also cause great forces to be applied on the back of the user. The system which is presented here consists of a mass attached on a rucksack, which is controlled by a motor in order to simulate the suspended-load backpack. The advantage of this setup is the ability to test different settings, regarding the spring stiffness or the damping coefficient, of the backpack harvester, and study their effect on the energy harvesting potential, as well as on the human gait. The present contribution describes the preliminary results and analysis of the testing of the system with the help of nine male volunteers who carried it on a treadmill.

Energy harvesting from human motion: an evaluation of current nonlinear energy harvesting solutions

The concept of harvesting electrical energy from ambient vibration sources has been a popular topic of research in recent years. Recently, the realisation that the majority of ambient vibration sources are often stochastic in nature has led to a large body of work which has focused on the response of energy harvesters to random excitations – most of which approximate environmental excitations as being Gaussian white noise. Of particular interest here are recent findings which demonstrate the advantages that Duffing-type nonlinearities can introduce into energy harvesters. The aim of this paper is to identify how well these results can be applied to that of a real energy harvesting scenario. More specifically, the response of an energy harvester to excitation via human motion is studied using digital simulations in conjunction with acceleration data obtained from a human participant. As well as assessing whether Duffing-type nonlinearities can have a beneficial impact on device performance this paper aims to investigate whether Gaussian white noise can indeed be used as a good approximation for this particular ambient vibration source.

Developing a hardware in-the-loop simulator for a backpack energy harvester

Energy harvesting from ambient sources has been the subject of several studies. Some of the proposed approaches attempt to generate electrical energy from the human movement. However, many of them can be uncomfortable or they impose a significant burden on the person’s gait. In the current article, the design of a hardware in-the-loop simulator for an energy-harvesting backpack is presented. The idea is based on the energy produced by a suspended load that moves vertically on a backpack while a person walks. The energy created from such a linear system can be maximised when it resonates with the walking frequency of the person. However, such a configuration can also cause great forces to be applied on the back of the user. The system that is proposed here consists of a mass attached on a rucksack, which is controlled by a motor in order to simulate the suspended-load backpack. The advantage of this setup is the ability to test different settings, regarding the spring stiffness or the damping coefficient, of the backpack harvester, and study their effect on the energy-harvesting potential, as well as on the human gait. The present contribution describes the design, analysis and preliminary testing of the hardware in-the-loop backpack system.

Genetic optimisation of a neural network damage diagnostic

This paper presents an automated optimisation procedure for the feature selection stage of a previously proposed structural health monitoring methodology using a genetic algorithm. The same diagnostic is used in the attempt to progress up the levels of damage detection to location and severity. It was validated experimentally on a Gnat aircraft wing. An artificial neural network is used as a classifier and the work is compared with the previous selection strategy based on engineering judgement.

An Experimental Investigation of Feature Availability in Nominally Identical Structures for Population-Based SHM

It is perhaps well known that the uncertainty in realistic structures may complicate most efforts for modelling and damage identification. In a population of structures which are considered identical, as in a wind farm for example, it is very often that the accurate modelling of one structure will be inadequate for the robust monitoring of the rest in an SHM approach. This paper presents an exploration of the common features which can be found in nominally identical structures and which can be used for damage identification with the ultimate purpose of population-based SHM. The concept of a population-based approach means that any additional new structures to the population will not need to be fully modelled in order to be monitored. Two different variants of the tail wing of a Piper PA-28 aircraft are used to create two pairs of nominally identical structures by separating the tail wings in half. The new population of four structures thus contains two pairs of them which are similar, but they have different length and different weight. A full modal test is performed in all of the structures and an exploration of possible common features is also done. The results show that common damage-sensitive features exist across the structures, a key requirement if population-based SHM is to be successfull.

Some Recent Developments in Structural Health Monitoring

This paper is concerned with reporting some recent developments in Structural Health Monitoring (SHM) research conducted within the Dynamics Research Group at the University of Sheffield. The particular developments discussed are concerned with arguably the two main problems facing data-based approaches to SHM, namely: how to obtain data from damage states of a structure for supervised learning and how to remove environmental and operational effects from data when unsupervised learning (novelty detection) is indicated.

Wind Turbine Health Monitoring: Current and Future Trends with an Active Learning Twist

The use of offshore wind farms has been geometrically growing in recent years. Offshore power plants move into deeper waters as European water sites offer impressive wind conditions. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an investigation of current monitoring trends for wind turbines (WTs) and will try to address the motivation and the effectiveness of Structural Health Monitoring (SHM) machine learning applications for the different components of a WTs, as well as, the novel idea of intelligent WT in terms of data knowledge transfer and learning.