Dynamic voltage security constrained optimal coordinated voltage control using enhanced particle swarm optimisation

A novel preventive control approach based on a newly enhanced particle swarm optimisation (PSO) is proposed for improving the dynamic voltage security of a power system. The proposed approach can be applied to handle both continuous and discrete control variables. The aim of the optimal coordinated preventive control is to optimise the terminal voltage, output power of each generator and the tap position of each on-load tap changer so as to keep the voltage secure along the trajectory of a quasi-steady-state time-domain simulation when credible contingency such as tripping of generation facility or transmission line occurs. The voltage performance is assessed via a heuristic integration of the voltage deviations of selected load buses and the optimal coordinated preventive control is formulated as a multi-objective non-linear optimisation problem solved by an enhanced PSO. A case study on the Nordic 32 test system shows that the proposed approach is capable of providing high-quality solutions when dealing with dynamic voltage security control problems.

[1]  Ieee Standards Board.  IEEE recommended practice for excitation system models for power system stability studies , 1992 .

[2]  B. Gao,et al.  Voltage Stability Evaluation Using Modal Analysis , 1992, IEEE Power Engineering Review.

[3]  P. Kundur,et al.  Voltage stability analysis using static and dynamic approaches , 1993 .

[4]  David J. Hill,et al.  Nonlinear dynamic load models with recovery for voltage stability studies , 1993 .

[5]  T. Van Cutsem.  An approach to corrective control of voltage instability using simulation and sensitivity , 1993 .

[6]  Wilsun Xu,et al.  Voltage stability analysis using generic dynamic load models , 1994 .

[7]  T. Van Cutsem,et al.  A comprehensive analysis of mid-term voltage stability , 1995 .

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  G. C. Ejebe,et al.  Preventive/corrective control for voltage stability using direct interior point method , 1997 .

[11]  T. Van Cutsem,et al.  Validation of a fast voltage stability analysis method on the Hydro-Quebec system , 1997 .

[12]  Thierry Van Cutsem,et al.  Voltage Stability of Electric Power Systems , 1998 .

[13]  Venkataramana Ajjarapu,et al.  A comprehensive approach for preventive and corrective control to mitigate voltage collapse , 2000 .

[14]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[15]  M. Larrson,et al.  Coordinated System Protection Scheme against Voltage Collapse Using Heuristic Search and Predictive Control , 2002, IEEE Power Engineering Review.

[16]  T. Van Cutsem,et al.  Preventive control of voltage security margins : a multi-contingency sensitivity-based approach , 2002, IEEE Power Engineering Society Summer Meeting,.

[17]  Gustaf Olsson,et al.  Emergency voltage control using search and predictive control , 2002 .

[18]  S.J. Cheng,et al.  Optimal coordinated voltage control for power system voltage stability , 2004, IEEE Transactions on Power Systems.

[19]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[20]  Xue Yusheng.  Space-time Cooperative Framework for Defending Blackouts Part I From Isolated Defense Lines to Coordinated Defending , 2006 .

[21]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..