Intelligent and Integrated Condition Monitoring of Distributed Generation Systems

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.

As an application to the first grant scheme, it is anticipated that the research project described in this proposal will form the initial stages of a much larger project, exploring the future of advanced energy systems of generating capability with afforded and intelligent monitoring. The short-term impacts will be those that fall within the timescale of the initial stages of research, development, and validation of the algorithms in an offline monitoring context. The collaboration with industrial partners would provide an initial basis for this project, allowing for the evaluation of the proposed algorithms with real data.

It is probable that the medium and long term impacts will fall beyond the timescale of the research project. Demonstration and validation of the research outcomes in an online monitoring environment will require external investment, industrial-scale construction, and the use of live electrical grid systems such as onshore and offshore wind plants. Successful development will also require more industrial collaborators in the closely-related areas, who will be inspired by the efficiency of the concepts and instrumentation systems in laboratory to move the project forward.

Ultimately, the condition monitoring techniques may have spin-offs for industrial uses, boosting the emerging technology of electrical energy production. The main beneficiaries will be i) the condition-based maintenance and service industries, ii) the instrumentation industry and iii) the ICT industry. The combination of condition monitoring and control schemes will provide greater intelligence for the power conversion and distribution equipment. This will also require the procurement of cost-effective smart sensors to ensure continued operations and the provision of built-in sensors as standard features in key power equipment for power distribution. The potential for all these activities to create new employment on a global basis cannot be understated. The responsibility for communications and engagement will then pass to the public relations sections of the respective companies. Therefore it will impact upon governmental renewable energy targets and aid both the achievement of energy operation and the security of electrical energy production.

The potential academic impact of the proposed work is related to other researchers working in the renewable power engineering and other relevant areas. The work will provide the increased knowledge base in the dynamics of electrical grid system under disturbances and fault conditions, the formulation and validation of methods, and the development of automatic algorithms in analysing, indentifying and predicting the nature of the faulty events. Knowledge gained from the project will be disseminated to the academic and industrial communities through various channels by the project investigator and the researcher involved. Academic-level dissemination will be via the standard route of publications in general engineering journals, power and energy journals and at major conferences.

Furthermore, the Engineering Department at Lancaster has an extensive track record in hosting and contributing to workshops and symposia, which can also be brought to bear. At a more general level, knowledge and findings can be broadcasted by means of the departmental website. There are also dedicated departmental initiatives, like the Smallpeice programme, the Engineering Education Scheme (EES) and the Arkwright Scholarships, so as to increase the interest in engineering for people of school age from regional schools to participate in using our laboratory facilities. The inspiration provided by instrumentation system and user-friendly software interface from real research projects will be beneficial to future recruitment in both engineering and science in general.

Opportunities to protect key items of the IP will also be actively pursued.