Distributed mitigation of voltage sag by optimal placement of series compensation devices based on stochastic assessment

Central mitigation does not necessarily provide the most economical solution to voltage-sag problems. Distributed mitigation with strategically placed compensation devices should provide a better alternative. Placement studies of this nature usually involve extensive evaluations of candidate compensation schemes, which can become unmanageable for large systems with stochastic voltage-sag data. A probability-based technique known as the weighted sampling method is proposed to simplify the problem. The technique is applied to a distribution system to optimize the cost of placing series compensation. The cost has two components: device cost and cost reduction to consumers due to implementation of the device(s). Series compensation devices being optimized are the two distinctly different dynamic voltage restorer and thyristor voltage regulator. These devices are placed and optimized to best complement each other. To achieve reliable and fast convergence, a genetic algorithm with innovative coding is proposed. A 34-node distribution system is studied with a wide range of voltage-sag data and consumer tolerance characteristics.

[1]  Larry Conrad,et al.  with Remote Fault-Clearing Voltage Dips , 1991 .

[2]  Barry W. Kennedy.  Power Quality Primer , 2000 .

[3]  C. Grigg,et al.  Proposed chapter 9 for predicting voltage sags (dips) in revision to IEEE Std 493, the gold book , 1994, Proceedings of IEEE/PES Transmission and Distribution Conference.

[4]  Goran Strbac,et al.  Mitigation of voltage sags embracing a prediction technique and the use of a dynamic voltage restorer , 2001 .

[5]  G. Darling,et al.  Capacitor placement, replacement and control in large-scale distribution systems by a GA-based two-stage algorithm , 1997 .

[6]  J.M. Salzer.  Worldwide review of power disturbances , 1988, IEEE Aerospace and Electronic Systems Magazine.

[7]  L. Morgan,et al.  Experience with an inverter-based dynamic voltage restorer , 1999 .

[8]  C. S. Chang,et al.  Stochastic multiobjective generation dispatch of combined heat and power systems , 1998 .

[9]  V.E. Wagner,et al.  Power quality and factory automation , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[10]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[11]  Matti Lehtonen,et al.  Estimating the annual frequency and cost of voltage sags for customers of five Finnish distribution companies , 2001 .

[12]  Yann-Chang Huang,et al.  Solving the capacitor placement problem in a radial distribution system using Tabu Search approach , 1996 .

[13]  T. S. Chung,et al.  Optimal placement of FACTS controller in power system by a genetic-based algorithm , 1999, Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems. PEDS'99 (Cat. No.99TH8475).

[14]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[15]  C. S. Chang,et al.  Optimal multiobjective planning of dynamic series compensation devices for power quality improvement , 2001 .

[16]  M. Lehtonen,et al.  Static Security Enhancement via Optimal Utilization of Thyristor Controlled Series Capacitors , 2002, IEEE Power Engineering Review.

[17]  C. S. Chang,et al.  Optimal multiobjective SVC planning for voltage stability enhancement , 1998 .