Total views : 303
Minimization of L-index using Genetic Algorithm for Improvement of Voltage profile in Power Systems
Objectives: The objective is to identify weakest buses using L-index method and to reduce the maximum value of L-index using Genetic Algorithm so as to maintain the voltage profile. Methods/Statistical Analysis: Among the various existing voltage stability indices, L-index is implemented in this paper since it involves simple calculation with high accuracy and reliability. Hence on identifying buses with maximum L-index, it becomes essential to minimize the L-index to maintain the system stability. Genetic Algorithm is chosen since it is faster and more accurate when implemented for power system problems in particular to optimize the voltage. Findings: The variations to be adopted for the control variables which are thus obtained for IEEE 30 bus system by using Genetic Algorithm are highly important to reduce L-index value. The voltage profile is improved better when Static Var Compensator is placed at the first weakest bus rather than the improvement in the voltage profile on employing a compensator in the next weakest buses which indicates the importance in identification of weakest buses. The significance of weakest buses are still emphasised on considering normal condition and contingency condition which shows that under contingency it is the weakest buses which is much more affected and hence driving the entire system towards voltage collapse. Application/Improvements: Hence by identifying the weakest buses using L-index and by minimizing the maximum value of L-index using Genetic Algorithm along with reactive power compensator improves the voltage profile better.
Genetic Algorithm, L-Index, Reactive Power, Voltage Profile.
- Bonnard G. The problems posed by electrical power supply to industrial installations. Proceedings of IEE Part B. 1985; 132(6):335–43.
- Alsac O, Scott B. Optimal load flow with steady-state security. IEEE Transactions Power Apparent System. 1974; 93(3):745–51.
- Vargas L, Quintana VH. Voltage collapse scenario in the Chilean interconnected system. IEEE Transactions on Power Systems. 1999; 14(4):1415–21.
- Vournas C. Technical summary on the Athens and southern Greece blackout of 2004; 2004. p. 1–6.
- Taylor CW. Power system voltage stability. McGraw Hill. 1994; 101(10):3830–40.
- Tamronglak S, Horowitz SH. NERC, system disturbances: Review of selected electric system disturbances in North America. 1996; 11(2):708–15.
- Choube SC, Arya LD, Kothari DP. Voltage security enhancement using static Voltage Stability Index. 11th National Power Systems Conference, Bangalore. 1992; 7(4):1529–42.
- Reis C, Babosa FPM. A comparison of Voltage Stability Indices. IEEE; Melecon, Malaga. 2006. p. 1007–10.
- Kessel P, Glavitsch H. Estimating the voltage stability of power systems. IEEE Transactions on Power System. 1986; 11(3):346–54.
- Hingorani NG. High power electronics and flexible AC transmission system. IEEE Power Engineering Review. 1988 Jul; 8(7):3–4.
- Heydt GT, Douglas GJ. Power flow control and power flow studies for systems FACTS devices. IEEE Transactions on Power Systems. 1998 Feb; 13(1):60–5.
- Hingorani NG, Gyugyi L. Understanding FACTS: Concepts and technology of flexible AC transmission systems. New York, IEEE Press. 2000; 139(4):323–31.
- Pisica C, Bulsac M, Eremia E. Optimal SVC placement in electrical power systems using a Genetic Algorithms based method. IEEE Bucharest Power Tech Conference; Bucharest. 2009. p. 1–6.
- Bansilal A, Thukaram D, Parthasarathy K. Optimal reactive power dispatch algorithm for voltage stability improvement. International Journal of Electrical Power Energy System. 1996; 18(7):461–8.
- Cai L, Erlich I, Stamtsis G. Optimal choice and allocation of FACTS devices in deregulated electricity market using Genetic Algorithms. Proceedings of IEEE PES Power Systems Conference and Exposition (PSCE); 2004. p. 201–7.
- Nanda Kumar E, Dhanasekaran R, Mani R. Optimal location and improvement of voltage stability by UPFC using Genetic Algorithm (GA). Indian Journal of Science and Technology. 2015 Jun; 8(11). DOI: 10.17485/ijst/2015/v8i11/71778.
- David GE. Genetic Algorithms in search, optimization and machine learning. Addison-Wesley. 1989; 3(2):95–9.
- Eshelman LJ, Schaffer JD. Real–coded Genetic Algorithms and interval schemata. Whitley D, editor. 1993. p. 187–202.
- Devaraj D, Yegnanarayana B. A combined Genetic Algorithm approach for optimal power flow. National Power Systems Conference, NPSC-2000. India: Bangalore. Varna, 2000; 3:1866–76.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.