Intelligent and Integrated Condition Monitoring of Distributed Generation Systems

Lead Research Organisation: Lancaster University
Department Name: Engineering

Abstract

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.

Planned Impact

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.

Publications

10 25 50
 
Description The project was to develop and refine advanced condition monitoring technologies of distributed generation systems with particular emphasis on wind turbines. Principal achievements include: Objective 1: "To further understand the effect of time-frequency variations in sensor data, in combination with environmental data, on the conditions of the devices and vice versa using numerical modelling techniques." Computer simulations of both DFIG (doubly-fed induction generator) and PMSG (permanent magnet synchronous generator) wind turbines with grid connections have been created. Useful data at a system level under different grid and power electronic faults have been obtained to evaluate the proposed algorithms. Objective 2: "To develop and refine advanced wavelet-based methodologies for early warning of the incipient fault signals, any abnormalities and disorders irrespective of their magnitude and time duration." Using a wavelet-based singularity detection method, a fault signal of small magnitude generated at the early stage of a fault has been proven to carry same features as the signal of large magnitude at the late stage of the fault. The relationship between the Lipschitz exponent, a measure of local signal regularity, of the measurement signal and the severity of faults occurring on the grid and in the power electronics was revealed. We believe that our technology can be used to provide an early warning before a fault develops into a detrimental one. Objective 3: "To investigate intelligent methodologies to automate fault prediction algorithms." A PCA (principal component analysis) based sensor data fusion approach has been developed to identify the type and number of monitoring data that contribute the most to the system. The proposed technique can be used to reduce the number of sensors whilst still maintaining sufficient information to assess the system's conditions. The time-frequency data produced with the windowed transform techniques were found to contain more critical information and hence to be more efficient in the selection process. Model-based condition monitoring of wind turbines have been realised using multivariate artificial neural network (ANN) techniques by modelling the relationships between the measured variables. It was found that ANN incorporating the PCA technique can minimise the correlation between input parameters, thus significantly improving model fit and the accuracy of the models. We have demonstrated that non-linear state dependent parameter (SDP) 'pseudo' transfer functions can provide a more parametrically efficient representation of non-linear processes of wind turbines. Our research represented the first occasion for which SDP models were employed for a condition monitoring system. The SDP models were implemented for on-line processing of data. The developing faults have been successfully identified based upon the proposed multivariate adaptive thresholds rules. Objective 4: "To test and analyse the algorithms by using data from simulations and measurements on DG systems." The above technologies were demonstrated with simulation data from wind turbine simulation models and SCADA data obtained from an operational wind farm supplied by the industrial partners of the project. Various types of faults on the grid and in the power electronics, gearbox and generator were detected. Objective 5: "To validate the algorithms in real-time on an open embedded system relying on modular design." A prototype instrument hardware system based on a commercial control and data logging system was implemented. Wavelet based singularity detection algorithms and nonlinear SDP models were also demonstrated in real-time using a field-programmable gate array incorporated in the hardware. Overall, the project findings have been published in 6 referred journal papers and in 9 referred conference papers presented on 8 international conferences.
Exploitation Route The research activities have enabled us to assist a range of companies to improve predictive maintenance performance through the affordable condition monitoring tools and technologies. There is potential for turning research results from this project into a commercial proposition.
Sectors Aerospace

Defence and Marine

Digital/Communication/Information Technologies (including Software)

Energy

Environment

Transport

URL http://www.lancaster.ac.uk/engineering/about/people/xiandong-ma
 
Description This case study (University collaboration improves Mimic condition monitoring) was featured on the James Fisher website featured news section, and was picked up by industry specialist publications Motorship, Ship and Bunker and Green4Sea.
First Year Of Impact 2015
Sector Aerospace, Defence and Marine,Energy,Environment,Manufacturing, including Industrial Biotechology,Transport
Impact Types Societal

Economic

 
Description A data-mining approach for reliable and predictable operation of wind farms
Amount £4,948 (GBP)
Funding ID EGA6997 - Lancaster University 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Department Impact Acceleration Account Lancaster
Sector Academic/University
Country United Kingdom
Start 05/2013 
End 10/2013
 
Description An experimental test rig for wind power generation research
Amount £17,610 (GBP)
Organisation Lancaster University 
Sector Academic/University
Country United Kingdom
Start 04/2013 
End 12/2014
 
Description Reliable and predictable operation of the wind turbines: Improved condition monitoring techniques results in reduction in downtime and O&M costs
Amount £5,000 (GBP)
Funding ID Faculty's Research Impact Fund 
Organisation Lancaster University 
Sector Academic/University
Country United Kingdom
Start 06/2017 
End 07/2018
 
Description Reliable condition monitoring for fault detection and failure prognosis of marine ships
Amount £10,000 (GBP)
Funding ID EGA6989 - Lancaster University 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Department Impact Acceleration Account Lancaster
Sector Academic/University
Country United Kingdom
Start 06/2014 
End 06/2015
 
Description Renewable generation technologies for urban farming techniques
Amount £10,000 (GBP)
Funding ID EGA6986 - Lancaster University 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Department Impact Acceleration Account Lancaster
Sector Academic/University
Country United Kingdom
Start 06/2014 
End 08/2015
 
Description Study of smart DC nano-grid for self-sustainable buildings
Amount £8,958 (GBP)
Funding ID EGA6996 - Lancaster University 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Department Impact Acceleration Account Lancaster
Sector Academic/University
Country United Kingdom
Start 07/2013 
End 08/2014
 
Title University collaboration improves Mimic condition monitoring 
Description James Fisher Mimic (JFM) has completed a project to enhance its Mimic condition monitoring system in cooperation with Lancaster University in the UK. Mimic condition-monitoring software allows ship operators to base maintenance decisions on condition and performance rather than on recommended time basis. The company sought to expand its monitoring capabilities across the whole ship, with a particular focus on fuel use. The result of the collaboration is a system that the partners say could "revolutionise fuel efficiency" and offer major improvements in maintenance planning for Mimic clients. The technology can be retro-fitted to most ships, meaning a very broad potential impact from the research.The company, part of the James Fisher & Sons group, provided the university's engineering department with operational data from ships in its own fleet. The university analysed the data with a focus on three areas: fault classification using time-frequency patterns, early fault detection and the development of sensor systems and associated electronics for condition monitoring. The £20,000 project received funding from the Impact Acceleration Account of the UK's Engineering & Physical Sciences Research Council, as well JFM and the university. 
Type Of Material Data analysis technique 
Provided To Others? No  
Impact Martin Briddon, engineering manager, JFM, said: "This research has helped enormously with deeper signal processing than was currently undertaken and has resulted in a deeper understanding of machinery fault situations. We are now able to use the knowledge and information to develop new ways of searching for machinery faults amongst a clutter of normal operational data." Dr Xiandong Ma of Lancaster University's engineering department added: "The team at JFM has been really helpful and provided particular requirements in investigating condition monitoring algorithms and systems within marine ships. The experience has greatly helped to confirm the viability of the proposed project. We are looking forward to our next collaboration with them." Monitoring and diagnostics of ships play an increasingly important role in optimal scheduling of maintenance activities. Condition monitoring has previously only been used for specific aspects of a ships system. Recently, there has been a growing interest in the monitoring of fuel usage and conditions of the whole ship which JFM seek to develop and include in their software - Mimic. 
URL http://www.motorship.com/news101/industry-news/university-collaboration-improves-mimic-condition-mon...
 
Description Demand side management and energy efficiency programs 
Organisation China BBA Power
Country China 
Sector Private 
PI Contribution This grant has also lead to a research collaboration with China BBA Power Ltd, aiming to integrate the developed renewable energy models into their products in order to enhance energy efficiency and provide incentives for better use of energy.
Collaborator Contribution The project collaborator in China is currently integrating the proposed models into their products in order to enhance energy efficiency and provide incentives for better use of energy.
Impact Joint journal paper publication: "Generic model of a community-based microgrid integrating wind turbines, photovoltaics and CHP generations", Applied Energy, Vol. 112, 2013, pp. 1475-1482.
Start Year 2012
 
Description Investigations into smart control and monitoring technologies for wind turbine and wave energy devices 
Organisation Zhejiang University
Department State Key Laboratory of Fluid Power Transmission and Control
Country China 
Sector Academic/University 
PI Contribution The grant has led to a research collaboration with Zhejiang University, one of the top 3 universities in China, through a visiting PDRA (Dr Dahai Zhang) to work at Lancaster from 1st March 2012 to 28th February 2013, funded from National High-tech R&D Program of China.
Collaborator Contribution Fund (approx. £31.4k) received from National High-tech R&D Program of China (863 Program) and the China's Post-doctoral Science Fund (Grant No: 2011AA050201 and 20110491779).
Impact Joint journal paper publication: "Improved control of individual blade pitch for wind turbines", Sens. Actuators A Phys., Vo. 198, 2013, pp. 8-14.
Start Year 2012
 
Title University collaboration improves Mimic condition monitoring 
Description The company, part of the James Fisher & Sons group, provided the university's engineering department with operational data from ships in its own fleet. The university analysed the data with a focus on three areas: fault classification using time-frequency patterns, early fault detection and the development of sensor systems and associated electronics for condition monitoring. Martin Briddon, engineering manager, JFM, said: "This research has helped enormously with deeper signal processing than was currently undertaken and has resulted in a deeper understanding of machinery fault situations. We are now able to use the knowledge and information to develop new ways of searching for machinery faults amongst a clutter of normal operational data." 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2015 
Impact Mimic condition-monitoring software allows ship operators to base maintenance decisions on condition and performance rather than on recommended time basis. The company sought to expand its monitoring capabilities across the whole ship, with a particular focus on fuel use. The result of the collaboration is a system that the partners say could "revolutionise fuel efficiency" and offer major improvements in maintenance planning for Mimic clients. The technology can be retro-fitted to most ships, meaning a very broad potential impact from the research. 
URL http://www.motorship.com/news101/industry-news/university-collaboration-improves-mimic-condition-mon...
 
Description Condition monitoring and fault diagnosis for wind turbines 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact The latest work on wind turbine condition monitoring research at Lancaster was presented in this workshop. Several signal processing and data mining techniques were demonstrated for the audiences for condition monitoring and fault diagnosis/prognosis of wind turbines. This is the first time for us to disseminate our wind energy research outcomes to one of the largest wind turbine manufacturers in China. It was also an excellent opportunity for us to exchange ideas with a wind turbine manufacturer and to explore potential R&D collaborations with them.

An invited talk was given at XEMC Windpower CO.LTD, Xiangtan, Hunan Province, China on 27th May 2013

After my talk, a link has been built between Lancaster and XEMC Windpower for research activities of common interests.
Year(s) Of Engagement Activity 2013
 
Description Condition monitoring for reliable and predictable operation of wind turbines 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact International Workshop on Health Monitoring of Offshore Wind Farms (HEMOW), Nanjing, China
Year(s) Of Engagement Activity 2014
URL http://www.hemow.eu/2nd-Hemow-workshop-programme-final.pdf
 
Description Electromagnetic NDT and Condition Monitoring 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The 2nd International Conference on Structural Health Monitoring and Integrity Management (ICSHMIM 2014), Nanjing, China
Year(s) Of Engagement Activity 2014
URL https://books.google.co.uk/books/about/Structural_Health_Monitoring_and_Integri.html?id=m8MMrgEACAAJ...
 
Description New Software Aims to improve Fuel Efficiency, Reduce Unnecessary Maintenance 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This case study was featured on the James Fisher website featured news section, and was picked up by industry specialist publications Motorship, Ship and Bunker and Green4Sea.
Year(s) Of Engagement Activity 2015
URL http://shipandbunker.com/news/world/948932-new-software-aims-to-improve-fuel-efficiency-reduce-unnec...
 
Description The state-of-the-art methods for intelligent and integrated condition monitoring 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact The presentation described the development of condition monitoring techniques and the associated instruments, which the presenter has been closely involved in over the past decade. The talk started with the monitoring and characterisation of materials degradation using electromagnetic NDT methods. It then addressed condition monitoring techniques from fundamental aspects in the high voltage insulation diagnosis through applications in large-scale rotary machines in conventional power plants to the latest applications in distributed generation systems with wind turbines. Several signal processing and data mining techniques have been proposed to realise the intelligent and integrated monitoring systems for fault diagnosis and prognosis. The talk described the journey in the development of new technologies from conception in an academic environment to practical deployment.

An invited research seminar given to the staff and students in the National University of Defence Technology on 28th May 2013

Student exchange scheme has been implemented between NUDT and Lancaster after my talk.
Year(s) Of Engagement Activity 2013
 
Description University collaboration improves Mimic condition monitoring 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This case study was featured on the James Fisher website featured news section, and was picked up by industry specialist publications Motorship, Ship and Bunker and Green4Sea.
Year(s) Of Engagement Activity 2015
URL http://www.motorship.com/news101/industry-news/university-collaboration-improves-mimic-condition-mon...