RETRACTED ARTICLE: Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO
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Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control (AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems (FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator (TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC (HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization (MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.
Key wordsHierarchical adaptive neuro-fuzzy inference system controller (HANFISC) Thyristor-controlled series compensator (TCSC) Automatic generation control (AGC) Multi-objective particle swarm optimization (MOPSO) Power system dynamic stability Interconnected multi-source power systems
CLC numberTM76 TP391
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- Eberhart, R.C., Shi, Y.H., Kennedy, J., 2001. Swarm Intelligence. Academic Press, San Diego, CA.Google Scholar
- Falehi, A.D., 2012. Simultaneous coordinated design of TCSC-based damping controller and AVR based on PSO technique. Electr. Rev., 88(5): 136–140.Google Scholar
- Falehi, A.D., Rostami, M., 2011. Design and analysis of a novel dual-input PSS for damping of power system oscillations employing RCGA-optimization technique. Int. Rev. Electr. Eng., 6(2): 938–945.Google Scholar
- Falehi, A.D., Dankoob, A., Amirkhan, S., et al., 2011. Coordinated design of STATCOM-based damping controller and dual-input PSS to improve transient stability of power system. Int. Rev. Electr. Eng., 6(3): 1308–1318.Google Scholar
- Falehi, A.D., Rostami, M., Doroudi, A., et al., 2012. Optimization and coordination of SVC-based supplementary controllers and PSSs to improve the power system stability using genetic algorithm. Turk. J. Electr. Eng. Comput. Sci., 20(5): 639–654. http://dx.doi.org/10.3906/elk-1010-838Google Scholar
- Gyugyi, L., 1992. Unified power-flow control concept for flexible AC transmission systems. IEE Proc. C, 139(4): 323–331. http://dx.doi.org/10.1049/ip-c.1992.0048Google Scholar
- Hingorani, N.G., Gyugyi, L., 2000. Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press, New York.Google Scholar
- Karnavas, Y.L., Papadopoulos, D.P., 2000. Excitation control of a power-generating system based on fuzzy logic and neural networks. Int. Trans. Electr. Energy Syst., 10(4): 233–241. http://dx.doi.org/10.1002/etep.4450100406Google Scholar
- Rojas, I., Bernier, J.L., Rodriguez-Alvarez, R., et al., 2000. What are the main functional blocks involved in the design of adaptive neuro-fuzzy inference systems? IEEEINNS-ENNS Int. Joint Conf. on Neural Networks, p.551–556. http://dx.doi.org/10.1109/IJCNN.2000.859453Google Scholar
- Takagi, T., Sugeno, M., 1983. Derivation of fuzzy control rules from human operator’s control actions. IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis, p.55–60.Google Scholar