Dr Huilian Liao received her PhD in Electrical Engineering and Electronics from the University of Liverpool. She joined Sheffield Hallam University as a lecturer in Electrical and Electronic Engineering in September 2016. Her research interests include Electrical Power Systems, Smart Grids, Cyber-Security, and the application of Artificial Intelligence and Big Data Analytics in power grids.
Before joining Sheffield Hallam, Huilian worked as a Research Associate in the Electrical Energy and Power Systems Group within University of Manchester from May 2013 to Aug 2016. During that period, she was involved in two European projects (FP7 “Smart Distribution System Operation for Maximising the Integration of Renewable Generation” and H2020 “New Cost Efficient Business Models for Flexible Smart Grids”), researching on complex power systems (smart grids) and the application of artificial intelligence methodologies in solving power system problems.
Prior to that Huilian worked as a Lecturer in the School of Electric Power in the South China University of Technology between Jan 2012 to April 2013.
Specialist areas of interest
- The applications of artificial intelligence methodologies in future energy/power systems
- Cyber security in Smart Grid
- Modelling and state estimation of complex energy/power systems to assist the energy/power system's operation and control
- Techno-economic analysis of the impact of the received Quality of Service to customers’ equipment/devices in large scale complex energy/power systems
- Voltage control/management using power electronic devices
- Investigation of various technologies, including power electronic devices and network-based techniques, to mitigate disruptive power quality phenomena
- Analysis of the impact of various distributed generations on grid performance
- Energy/power system monitoring and optimal monitor placement strategy
- Demand side management and customers’ active involvement in future energy/power girds;• Biologically inspired computation and optimisation
- Big data analysis and machine learning methodologies including reinforcement learning and learning automata
Huilian is also part of the Geometric Modelling and Pattern Recognition Research Group (GMPR).
Power, Electrical and Control Engineering
BEng Electronic Engineering
Electrical Energy Systems (Level 7, Dr Liao is the module leader)
Electrical Power Systems (Level 6, Dr Liao is the module leader)
Electrical Power and Machines (Level 5)
Electrical Engineering principle (Level 4)
Sept 2016 - Present:
• investigate on the application of deep learning in power systems and smart grids
• Develop demand side management strategy based on distribution system rate estimation
• Research on Compound Power Quality Index to provide proper evaluation to power quality
• Application of blind signal separation algorithm for harmony analysis
Jan 2015 – Aug 2016:
• Load forecasting and big data analysis in smart grids where smart meters are fully deployed at homes;
• Energy/power system state estimation including the unbalance estimation using distribution system state estimation (DSSE) method and voltage sag estimation using Fault Location algorithm, to assist the operation and control of power systems.
May 2013 – Dec 2015:
• Analysis of the impact of power quality (including voltage sags, unbalance and harmonic phenomena) on customers’ equipment/devices in distribution networks with high penetration of renewable energy and distributed energy resources;
• Energy/power system monitoring and optimal monitor placement strategy using artificial intelligence methodologies;
• Techno-economic analysis of Quality of Service based on probabilistic modelling of uncertainty factors in power systems;
• Power quality mitigation planning based on Flexible AC Transmission Systems (FACTS) devices and network-based techniques using comprehensive multi-criteria optimisation algorithms;
• Voltage control/management in distribution networks using power electronic devices (including DVR, SVC and STATCOM);
• Financial cost assessment of power quality mitigation using Net Present Value method;
• Data analysis of load profiles and real-time load demand forecasting (both day-ahead-forecasting and half-hour-ahead-forecasting) using neural networks.
Jan 2013 – April 2014:
• Modelling of large-scale power systems;
• Application of developed optimisation methodologies in solving power system problems.
Oct 2008 – Dec 2012:
• Development of complex multi-criteria optimisation algorithms based on reinforcement learning and leaning automata, and its application in solving power system problems, including optimal load flow, economic emission dispatching and voltage stability analysis in wind power integrated systems.
July 2006 – June 2008: involved in project “Biologically Inspired Computation for Image Coding” (funded by National Natural Science Foundation of China and Royal Society U.K.) and “Research on Particle Swarm Optimisation and its Application on Image Coding” (funded by National Natural Science Foundation of China)
• Development of optimisation algorithms and classification algorithms based on particle swarm optimisation, and investigating their applications in image coding and compression of DNA data.