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.
Liao, H. (2019). Review on distribution network optimization under uncertainty. Energies, 12 (17), 3369. http://doi.org/10.3390/en12173369
Liao, H., & Jovica, M. (2018). Techno-economic analysis of global power quality mitigation strategy for provision of differentiated quality of supply. International Journal of Electrical Power and Energy Systems, 107, 159-166. http://doi.org/10.1016/j.ijepes.2018.11.006
Liao, H., & Milanović, J.V. (2018). Flexibility exchange strategy to facilitate congestion and voltage profile management in power networks. IEEE Transactions on Smart Grid. http://doi.org/10.1109/TSG.2018.2868461
Milanovic, J.V., Abdelrahman, S., & Liao, H. (2018). Compound index for power quality evaluation and benchmarking. IET Generation, Transmission & Distribution, 12 (19), 4269-4275. http://doi.org/10.1049/iet-gtd.2018.5391
Liao, H., Milanovic, J.V., Rodrigues, M., & Shenfield, A. (2018). Voltage sag estimation in sparsely monitored power systems based on deep learning and system area mapping. IEEE Transactions on Power Delivery. http://doi.org/10.1109/TPWRD.2018.2865906
Liao, H., Milanović, J.V., Hasan, K.N., & Tang, X. (2018). The influence of uncertainties and parameter structural dependencies in distribution system state estimation. IET Generation, Transmission & Distribution, 12 (13), 3279-3285. http://doi.org/10.1049/iet-gtd.2017.1906
Liao, H., Abdelrahman, S., & Milanovic, J. (2017). Zonal mitigation of power quality using FACTS devices for provision of differentiated quality of electricity supply in networks with renewable generation. IEEE Transactions on Power Delivery, 32 (4), 1975-1985. http://doi.org/10.1109/TPWRD.2016.2585882
Liao, H., & Milanović, J.V. (2017). On capability of different FACTS devices to mitigate a range of power quality phenomena. IET Generation, Transmission & Distribution, 11 (5), 1202-1211. http://doi.org/10.1049/iet-gtd.2016.1017
Liao, H., & Milanović, J.V. (2017). Methodology for the analysis of voltage unbalance in networks with single phase distributed generation. IET Generation, Transmission & Distribution, 11 (2), 550-559. http://doi.org/10.1049/iet-gtd.2016.1155
Liao, H., Liu, Z., Milanović, J.V., & Woolley, N.C. (2016). Optimisation framework for development of cost-effective monitoring of voltage unbalance in distribution networks. IET Generation, Transmission & Distribution, 10 (1), 240-246. http://doi.org/10.1049/iet-gtd.2015.0757
Liao, H., Abdelrahman, S., Guo, Y., & Milanović, J.V. (2015). Identification of weak areas of the network based on exposure to voltage sags—part II: assessment to network performance using sag severity index. IEEE Transactions on Power Delivery, 30 (6), 2401-2409. http://doi.org/10.1109/TPWRD.2014.2362957
Liao, H., Abdelrahman, S., Guo, Y., & Milanović, J.V. (2015). Identification of weak areas of the network based on exposure to voltage sags—part I: sag severity index for single-event characterization. IEEE Transactions on Power Delivery, 30 (6), 2392-2400. http://doi.org/10.1109/TPWRD.2014.2362965
Liao, H., Wu, Q.H., Li, Y.Z., & Jiang, L. (2014). Economic emission dispatching with variations of wind power and loads using multi-objective optimization by learning automata. Energy Conversion and Management, 87, 990-999. http://doi.org/10.1016/j.enconman.2014.07.071
Liao, H.L., & Wu, Q.H. (2013). Multi-objective optimization by learning automata. Journal of Global Optimization, 55 (2), 459-487. http://doi.org/10.1007/s10898-012-9973-5
Ji, Z., Zhou, J.R., Liao, H.L., & Wu, Q.H. (2010). A novel intelligent single particle optimizer. Jisuanji Xuebao/Chinese Journal of Computers, 33 (3), 556-561. http://doi.org/10.3724/SP.J.1016.2010.00556
Ji, Z., Liao, H.L., Xu, W.H., & Jiang, L. (2007). Strategy of particle-pair for vector quantization in image coding. Tien Tzu Hsueh Pao/Acta Electronica Sinica, 35 (10), 1916-1920.
Liao, H., & Anani, N. (2017). Fault identification-based voltage sag state estimation using artificial neural network. Energy Procedia, 134, 40-47. http://doi.org/10.1016/j.egypro.2017.09.596
Liu, Z., Liao, H., Milanovic, J.V., Guo, T., & Tang, X. (2017). Optimal monitor placement for voltage unbalance based on distribution network state estimation. 2017 IEEE Manchester PowerTech, Powertech 2017. http://doi.org/10.1109/PTC.2017.7981036
Gasch, E., Domagk, M., Meyer, J., Abdelrahman, S., Liao, H., & Milanović, J.V. (2016). Assessment of Power quality performance in distribution networks part i - Measurement campaign and initial analysis. Proceedings of International Conference on Harmonics and Quality of Power, ICHQP, 2016-December, 164-169. http://doi.org/10.1109/ICHQP.2016.7783325
Abdelrahman, S., Liao, H., Milanović, J.V., Gasch, E., Domagk, M., & Meyer, J. (2016). Assessment of Power Quality performance in distribution networks part II - Performance Indices and ranking of network buses. Proceedings of International Conference on Harmonics and Quality of Power, ICHQP, 2016-December, 431-436. http://doi.org/10.1109/ICHQP.2016.7783326
Liao, H., & Milanović, J.V. (2016). Pathway to cost-efficient state estimation of future distribution networks. IEEE Power and Energy Society General Meeting, 2016-November. http://doi.org/10.1109/PESGM.2016.7741337
Abdelrahman, S., Liao, H., Guo, T., Guo, Y., & Milanovic, J.V. (2015). Global assessment of power quality performance of networks using the analytic hierarchy process model. 2015 IEEE Eindhoven PowerTech, PowerTech 2015. http://doi.org/10.1109/PTC.2015.7232365
Abdelrahman, S., Liao, H., & Milanovic, J.V. (2015). The effect of temporal and spatial variation of harmonic sources on annual harmonic performance of distribution networks. IEEE PES Innovative Smart Grid Technologies Conference Europe, 2015-January (January). http://doi.org/10.1109/ISGTEurope.2014.7028848
Abdelrahman, S., Liao, H., Yu, J., & Milanovic, J.V. (2014). Probabilistic assessment of the impact of distributed generation and non-linear load on harmonic propagation in power networks. Proceedings - 2014 Power Systems Computation Conference, PSCC 2014. http://doi.org/10.1109/PSCC.2014.7038371
Wu, Q.H., & Liao, H.L. (2013). Function optimisation by learning automata. Information Sciences, 220, 379-398. http://doi.org/10.1016/j.ins.2012.07.043
Liao, H., Chen, H., Wu, Q., Bazargan, M., & Ji, Z. (2012). Group search optimizer for power system economic dispatch. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7331 LNCS (PART 1), 253-260. http://doi.org/10.1007/978-3-642-30976-2_30
Li, M.S., Wu, Q.H., Liao, H.L., Tang, W.J., & Xue, Y.S. (2011). Optimal power flow with environmental constraints using paired bacterial optimizer. IEEE Power and Energy Society General Meeting. http://doi.org/10.1109/PES.2011.6039362
Liao, H.L., & Wu, Q.H. (2011). Optimal power flow in wind power integrated systems using function optimization by learning automata. IEEE Power and Energy Society General Meeting. http://doi.org/10.1109/PES.2011.6039532
Liao, H.L., Wu, Q.H., & Jiang, L. (2010). Multi-objective optimization by reinforcement learning for power system dispatch and voltage stability. IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe. http://doi.org/10.1109/ISGTEUROPE.2010.5638914
Wu, Q.H., & Liao, H.L. (2010). Function optimization by reinforcement learning for power system dispatch and voltage stability. IEEE PES General Meeting, PES 2010. http://doi.org/10.1109/PES.2010.5589845
Wu, Q.H., & Liao, H.L. (2010). High-dimensional function optimisation by reinforcement learning. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. http://doi.org/10.1109/CEC.2010.5585974
Liao, H.L., & Wu, Q.H. (2010). Multi-objective optimisation by reinforcement learning. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. http://doi.org/10.1109/CEC.2010.5585972
Ji, Z., Zhou, J., Liao, H., & Wu, Q.H. (2008). Requantization codebook design using particle-pair optimizer. 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 1851-1855. http://doi.org/10.1109/CEC.2008.4631040
Liao, H., Ji, Z., & Wu, Q.H. (2008). A novel Genetic Particle-Pair Optimizer for Vector Quantization in image coding. 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 708-713. http://doi.org/10.1109/CEC.2008.4630873
Chu, Y., Mi, H., Liao, H., Ji, Z., & Wu, Q.H. (2008). A Fast Bacterial Swarming Algorithm for high-dimensional function optimization. 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 3135-3140. http://doi.org/10.1109/CEC.2008.4631222
Ji, Z., Liao, H., Wang, Y., & Wu, Q.H. (2007). A novel intelligent particle optimizer for global optimization of multimodal functions. 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 3272-3275. http://doi.org/10.1109/CEC.2007.4424892
Liao, H., Wang, Y., Zhou, J., & Ji, Z. (2007). A novel optimizer based on particle swarm optimizer and LBG for vector quantization in image coding. Proceedings - Third International Conference on Natural Computation, ICNC 2007, 3, 416-420. http://doi.org/10.1109/ICNC.2007.120
Ji, Z., Liao, H., Zhang, X., & Wu, Q.H. (2006). Simple and efficient soft morphological filter in periodic noise reduction. IEEE Region 10 Annual International Conference, Proceedings/TENCON. http://doi.org/10.1109/TENCON.2006.343712