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Miss Yuanjun (Anna) Guo Research Student |
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Biography
Yuanjun Guo(Anna) comes from the Hebei province of China.
She received her Bachelor's degree in Optoelectronic Engineering from Chongqing University in 2008, was awarded the ‘Excellent Graduate Student of Chongqing’, and then recommended to study for a Master's degree at the same university. During her Master's studies, she did a three-year project entitled 'Near-Infrared Spectrometer for Food Safety Detection' which was funded by the Chongqing Academician Foundation and also a Project in Postgraduate Innovation of Science and Technology entitled 'The Key Techniques of a Hadamard Near-Infrared Spectrometer Based on a Digital Micro Mirror'.
Anna joined the Energy, Power and Intelligent Control cluster at Queen’s University Belfast in September 2011. Her PhD studies are supported by the Chinese Scholarship Council.
PhD research area: Advanced fault detection and diagnosis methods with Smart Grid applications
Supervisor: Prof K Li
Multivariate Statistical Process Control (MSPC) refers to some statistical methods used extensively to monitor and improve the quality and productivity of manufacturing and industrial processes. There has been enormous interest in using MSPC techniques, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), to detect and diagnose abnormal conditions in process systems.
My research project has focused on the application of MSPC techniques in power systems, to improve anti-islanding detection for distributed generators. Synchrophasors from Phasor Measurement Unit (PMU) form a huge amount of data with great dimensionality and quantity. Applications of statistical monitoring techniques can be useful in extracting and interpreting process information out of massive data sets in order to discriminate between power system normal or faulty/islanding states. At the same time, pattern recognition techniques can also be used to distinguish which site of the distribution network is undergoing an islanding event.
The application of MSPC techniques based on the measurements from PMU can provide a more accurate time and location for anti-islanding detection, which results in the minimization of the amount of time spent by searching for the fault, as well as the reduction of risk of damaging utility plant and customer connected equipment.
She received her Bachelor's degree in Optoelectronic Engineering from Chongqing University in 2008, was awarded the ‘Excellent Graduate Student of Chongqing’, and then recommended to study for a Master's degree at the same university. During her Master's studies, she did a three-year project entitled 'Near-Infrared Spectrometer for Food Safety Detection' which was funded by the Chongqing Academician Foundation and also a Project in Postgraduate Innovation of Science and Technology entitled 'The Key Techniques of a Hadamard Near-Infrared Spectrometer Based on a Digital Micro Mirror'.
Anna joined the Energy, Power and Intelligent Control cluster at Queen’s University Belfast in September 2011. Her PhD studies are supported by the Chinese Scholarship Council.
PhD research area: Advanced fault detection and diagnosis methods with Smart Grid applications
Supervisor: Prof K Li
Multivariate Statistical Process Control (MSPC) refers to some statistical methods used extensively to monitor and improve the quality and productivity of manufacturing and industrial processes. There has been enormous interest in using MSPC techniques, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), to detect and diagnose abnormal conditions in process systems.
My research project has focused on the application of MSPC techniques in power systems, to improve anti-islanding detection for distributed generators. Synchrophasors from Phasor Measurement Unit (PMU) form a huge amount of data with great dimensionality and quantity. Applications of statistical monitoring techniques can be useful in extracting and interpreting process information out of massive data sets in order to discriminate between power system normal or faulty/islanding states. At the same time, pattern recognition techniques can also be used to distinguish which site of the distribution network is undergoing an islanding event.
The application of MSPC techniques based on the measurements from PMU can provide a more accurate time and location for anti-islanding detection, which results in the minimization of the amount of time spent by searching for the fault, as well as the reduction of risk of damaging utility plant and customer connected equipment.
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Contact Info
Energy, Power and Intelligent Control
School of Electronics,
Electrical Engineering and
Computer Science
Ashby Building
Stranmillis Road
Belfast
Northern Ireland
BT9 5AH
E-mail: eeecs@qub.ac.uk
Tel:+44 (0)28 9097 4669
Fax: +44 (0)28 9097 4879
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