| EPSRC Reference: |
EP/I037326/1 |
| Title: |
Intelligent and Integrated Condition Monitoring of Distributed Generation Systems |
| Principal Investigator: |
Ma, Dr X |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Engineering |
| Organisation: |
Lancaster University |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
16 January 2012 |
Ends: |
15 May 2013 |
Value (£): |
99,024
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| EPSRC Research Topic Classifications: |
| Power Sys Man, Prot & Control |
Wind Power |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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08 Jun 2011
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Process Environment & Sustainability
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Announced
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Summary on Grant Application Form |
Distributed electricity generation (DG) will play a significant role in future electric power system, as this type of power generation technology can provide electric power by utilising a wide range of renewable energy sources at a site close to end users. Considerable advances have been achieved during past decades in the capacity, scale and location of DG systems, e.g. from onshore to offshore. One of the most critical challenges for the deployment of DG systems relates specifically to availability and reliability in order to sustain energy generation and maximise a long service life of the energy systems unattended. This has, therefore, placed higher demand on predictive maintenance from innovative condition monitoring systems and solutions to tackle new arising challenges in this area.
The research proposed in this first grant scheme application represents an effort to explore key issues of generic importance to condition monitoring techniques optimised for fault detection and diagnosis. The research is oriented towards DG systems with wind turbines being the DG sources as this particular application presents a number of realistic challenges. Firstly, measurement signals would exhibit strong non-stationary behaviour due to the intermittent nature of wind sources and fluctuations of grid system. Secondly, the signals of small magnitude may indicate a start of a significant failure, which are normally undetected by conventional methods particularly in a harsh environment. Thirdly, large volume of data needs to be processed and transmitted especially for continuous online monitoring. For example, if we assume that 250 points are required for a typical 2 MW wind turbine to monitor most subsystems of a turbine, this will give rise to 36 million data per day for a 1 GW wind farm under a sampling rate of 5 minutes. Furthermore, a critical issue needing urgent attention will be the health problems of the sensor system, which requires that the monitoring techniques should be assessing what is happening when some of the sensors read data incorrectly.
In order to meet such diversified requirements, we plan to use and apply windowed transform, a technique well known for its ability to extract nonstationary components in the measurement data. By the optimal selection of a window shape, automatic windowed wavelet transforms can be achieved to accommodate different sensor data for better feature localisation, extraction and correlation. Although an incipient fault signal is usually of low magnitude and short duration, it would essentially carry the same features as the large ones, such as the regularity. If we can design a suitable algorithm to match the local regularity or singularity of a signal, any incipient faults, abnormalities and disorders can be detected irrespective of their magnitude and time duration.
The project is also concerned with designing a hybrid neuro-fuzzy method for optimal sensor data fusion. The use of this artificial intelligence method can best correlate sensor data and predict the unknowns by systematic incorporation of priori information. Minimising the number of sensors whilst still maintaining a sufficient number to assess the system's conditions can not only minimise the complexity of sensor systems but it can also reduce data storage requirements. The final part of the project relates specially to the practical aspect, where the proposed algorithms are validated in real time for online monitoring purposes on a modular embedded system. The proposed condition monitoring system in this project would accommodate all monitoring techniques within one hardware module, which can be readily adapted to other applications.
The project will provide better sensing techniques and improved algorithms towards real applications by improving our understanding of how to engineer them in order to aid the decision making process with respect to asset maintenance and management of existing and future DG systems.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.lancs.ac.uk |