This paper presents advantage of using a FACTS device for dynamic Reactive Power compensation. Simulation model was built in MATLAB Simulink software to prove mathematical constraints. Determination of the most favourable location and size of the compensation devices from the aspect of losses, power quality, costs are calculated as a fitness function developed by genetic algorithm. Optimisation was done by Particle swarm optimization (PSO). Finally, cut convergence time and significant potential of usage such type of PSO optimisation method for determination of future investments are shown. This algorithm is tested to determine optimal location of FACTS device in railway application, instead of the methods and algorithms in transmission or distribution power system used until now.
Słowa kluczowe: FACTS, Reactive Power Compensation, Genetic Algorithm, Particle Swarm Optimisation, Determination of Optimal Location
W artykule zaprezentowano korzyści ze stosowanie FACTS do dynamicznej kompensacji mocy biernej. Symulacje miały na celu określenie najlepszego położenia i roz,miaru urządzeń kompensujących z punktu widzenia jakości energii i kosztów. Zastosowano algorytm genetyczny PSO do optymalizacji i analizy przyszłych inwestycji.
Keywords: FACTS, kompensacja mocy biernej, algorytm genetyczny, optymalna lokalizacja
Reactive power is of great importance for the operation of the AC power system. A power system consists of different elements. Some of them can be modelled as the electricity containers, such as coils and capacities. Since the AC circuits imply continually electric current and voltage changes, these containers constantly store and release energy. During a specific time period, the total energy consumption of these components equals zero. The energy flowing between the elements for energy storage does not perform useful (active) work, but that reactive energy is inevitably a part of total energy, together with the useful (active) energy, which feeds the load. However, the flow of this energy is required in order to retain the required change of the magnetic and electrical fields of these energy containers. The energy flowing as the reaction to the activity of these elements is called reactive power. Despite the fact that the energy does not perform any useful work, it is still important for maintaining the stability of the system operation. Maintaining a balance between consumption and generation of reactive power has been the subject of numerous scientific papers since the commencement of commercial use of EPS. Simulation of the operation of the non-linear load with the active power filter in the programme package MATLAB simulink The programme package MATLAB Simulink simulates the operation of the non-linear load to the purpose of the analysis of the impact of the dynamic reactive power regulation by application of the active power filter. The model was created in line with the theory of the momentary power values. Fig. 1 shows the simulation model of the reactive power dynamic compensation of the non-linear load in the MATLAB Simulink. The power supply source is shown through the three phase source of the sinusoidal voltage, with effective value of the interphase voltage of 400 V. The phase windings are star-coupled, wh [...]
 R. Benabid, M. Boudour, M.A. Abido, Optimal location and setting of SVC and TCSC devices using non-dominated sorting Particle Swarm Optimization, Electric Power Systems Research, 79 (2009), pp. 1668-1677  Saravanan, S.M.R. Slochanal, P. Venkatesh, P.S. Abraham, Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation" Power Engineering Conference, 2 (2005), pp. 716-721  M. Saravanan, S. Mary Raja Slochanal, P. Venkatesh, J. Prince Stephen Abraham, Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability, Electric Power Systems Research, 77 (2007), pp. 276-283  J. Aghaei, M. Gitizadeh, M. Kaji, Placement and operation strategy of FACTS devices using optimal continuous power flow, Scientia Iranica, 19 (6) (December 2012), pp. 1683-1690  E. Ghahremani, I. Kamwa, Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface, IEEE Trans Power Syst, 28 (2) (2013), pp. 764-778  A. Lashkar Ara, A. Kazemi, S.A. Nabavi Niaki, Multiobjective optimal location of FACTS shunt-series controllers for power system operation planning IEEE TRANS Power Del, 27 (2) (2012), pp. 481-490  S. Gerbex, R. Cherkaoui, A.J. Germond, Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms, IEEE Transactions on Power Systems, 16 (3) (2001), pp. 537-544  A. Deihimi, H. Javaheri, A fuzzy multi-objective multi-case genetic-based optimization for allocation of FACTS devices to improve system static security, power loss and transmission line voltage profiles, International Review of Electrical Engineering (IREE), 5 (4) (2010), pp. 1616-1626  B. Singh, V. Mukherjee, P. Tiwari, A survey on impact assessment of DG and FACTS controllers in power systems, Renew. Sustain. Energy Rev, 42 (2015), pp. 846-882  M.H. Moradi, M. Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems, Int. J. Electr. Power Energy Syst, 34 (2012), pp. 66-74  S. Devi, M. Geethanjali, Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm, Int. J. Electr. Power Energy Syst, 62 (2014), pp. 562-57  Samimi A., Golkar M.A., A Novel Method for Optimal Placement of STATCOM in Distribution Networks Using Sensitivity Analysis by DIgSILENT Software, Asia-Pacific Power and Energy Engineering Conference, (2011),1-5 (Electrical Review), ISSN 0033-2097, R. 88 NR 1a/2012  Lakdja, F., Gherbi, F.Z., Zidi, S. A., OPF including TCSC devices using FACTS program software, Przegląd Elektrotechniczny 2012, R. 88, nr. 11a, p. 161-165  Eslami M., Shareef H., Mohamed A., Application of Artificial Intelligent Techniques in PSS design: A survey of the stateofthe- art methods, Przegląd Elektrotechniczny (Electr. Rev.) 87(2011) No. 4. 188-197  J. Kennedy, R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95 - International Conference on Neural Networks, 27 Nov.-1 Dec. 1995