New approach to design SVC-based stabiliser using genetic algorithm and rough set theory

A new approach for coordinated design of a static VAR compensator-based stabiliser and a conventional power system stabiliser is proposed. The approach is based on the integration between genetic algorithm (GA) and rough-set theory. The role of rough set is to select the most dominant controller parameters that are involved in the optimisation process. The proposed approach aims to minimise the computational time and reduce the storage capacity required for the optimisation problem as well as improve the performance of power system stability of power system. The proposed rough-set-based GA is applied to select the controller parameters included in the optimisation process as well as search for their optimal setting. This study also presents a comparison between the system performances when utilising individual or coordinated controllers with those of system utilising the proposed approach. Single machine system is used to investigate the efficacy of the proposed approach and multi-machine system is used to demonstrate the applicability and scalability of the proposed method. The simulation results and comparison analysis show the effectiveness of the rough-set-based GA. In addition, a good reduction in optimisation time and size of information is achieved by applying the rough-set-based GA.

[1]  Bhim Singh,et al.  Static synchronous compensators (STATCOM): a review , 2009 .

[2]  Elsayed M. Zaki,et al.  Genetic Algorithm and Rough Sets Based Hybrid Approach for Economic Environmental Dispatch of Power Systems , 2014 .

[3]  Rudy Gianto,et al.  Optimal design for control coordination of power system stabilisers and flexible alternating current transmission system devices with controller saturation limits , 2010 .

[4]  M. A. Abido,et al.  Coordinated design of a PSS and an SVC-based controller to enhance power system stability , 2003 .

[5]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

[6]  M. A. Abido,et al.  A particle-swarm-based approach of power system stability enhancement with unified power flow controller , 2007 .

[7]  B. Chatterjee,et al.  Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques , 2013, IEEE Sensors Journal.

[8]  Bikash C. Pal,et al.  Robust and low-order design of flexible ac transmission systems and power system stabilisers for oscillation damping , 2012 .

[9]  Desire L. Massart,et al.  Rough sets theory , 1999 .

[10]  M. A. Abido,et al.  Robust coordinated design of excitation and TCSC-based stabilizers using genetic algorithms , 2004 .

[11]  Chia-Feng Juang,et al.  Coordinated Control of Flexible AC Transmission System Devices Using an Evolutionary Fuzzy Lead-Lag Controller With Advanced Continuous Ant Colony Optimization , 2013, IEEE Transactions on Power Systems.

[12]  Ji Zhu,et al.  Application of rough set and genetic algorithm to transformer fault diagnosis , 2011, The Fourth International Workshop on Advanced Computational Intelligence.

[13]  Ka Wing Chan,et al.  Enhanced particle swarm optimisation applied for transient angle and voltage constrained discrete optimal power flow with flexible AC transmission system , 2015 .

[14]  C. Rehtanz,et al.  New types of FACTS-devices for power system security and efficiency , 2007, 2007 IEEE Lausanne Power Tech.

[15]  Chao-Ming Huang,et al.  Hybrid optimisation method for optimal power flow using flexible AC transmission system devices , 2014 .