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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.3 P.394-409

http://doi.org/10.1631/FITEE.1500317


Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO


Author(s):  Ali Darvish Falehi, Ali Mosallanejad

Affiliation(s):  Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983963113, Iran

Corresponding email(s):   a_darvishfalehi@sbu.ac.ir

Key Words:  Hierarchical 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


Ali Darvish Falehi, Ali Mosallanejad. Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 394-409.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500317"
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Abstract: 
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.


This article has been retracted because it shows significant overlap with another publication by the same authors [Falehi AD, Mosallanejad A, 2016. Neoteric HANFISCSSSC based on MOPSO technique aimed at oscillation suppression of interconnected multi-source power systems. IET Gener Transm Distr, 10(7):1728-1740. https://doi.org/10.1049/iet-gtd.2015.0404] without proper citation.

The authors proposed an AGC using HANFISC-TCSC based on MOPSO technique.

使用基于多目标粒子群算法多层自适应模糊推理系统晶闸管控制串联电容器补偿技术的互联多源电力系统动态稳定性增强器

概要:在互联有电力系统中,载荷条件变化引起的结线功率交流与频率动态振荡的抑制已成为自动发电控制(automatic generation control, AGC)的重要职责。为缓解因负载变化导致的系统振荡,柔性交流输电系统(flexible AC transmission systems, FACTSs)可视为一种实用有效的解决方案。本文采用了一种隶属于FACTSs族的晶闸管可控串联补偿器(thyristor-controlled series compensator, TCSC),来增强互联电力系统多源的整体动态性能。为此,我们使用一种分层自适应神经模糊推理系统控制器-晶闸管可控串联补偿器(hierarchical adaptive neuro-fuzzy inference system controller-TCSC, HANFISC-TCSC)在多区域互联电力系统中解决了两个重要的问题,即低频振荡和结线功率交换的偏差。为实现这一目标,使用多目标优化技术有其必然性。由于多目标粒子群优化算法(Multi-objective particle swarm optimization,MOPSO)在解决非线性目标问题上具有较高性能,已被用于这一优化问题中。本文对所提出的HANFISC-TCSC效能进行了精确评估,并在两个不同的互联电力系统(即双区域水柴油热和三区域水热发电系统)中,将其与传统的MOPSO-TCSC算法进行了对比。两个电力系统中仿真结果表明都可明确证实,与传统MOPSO-TCSC算法相比,HANFISC-TCSC具有更高性能。

关键词:分层自适应神经模糊推理系统控制器;晶闸管控制串联电容器补偿技术;自动发电控制(AGC);多目标粒子群优化算法;电力系统动态稳定性;相互联系的多源电力系统

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abd-Elazim, S.M., Ali, E.S., 2016. Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int. J. Electr. Power Energy Syst., 77:166-177.

[2]Abd-Elaziz, A.Y., Ali, E.S., 2015. Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. Int. J. Electr. Power Energy Syst., 73:632-643.

[3]Ali, E.S., Abd-Elazim, S.M., 2011. Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int. J. Electr. Power Energy Syst., 33(3):633-638.

[4]Ali, E.S., Abd-Elazim, S.M., 2013. BFOA based design of PID controller for two area load frequency control with nonlinearities. Int. J. Electr. Power Energy Syst., 51:224-231.

[5]Benabid, R., Boudour, M., Abido, M.A., 2009. Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization. Electr. Power Syst. Res., 79(12):1668-1677.

[6]Benítez, A.D., Casillas, J., 2013. Multi-objective genetic learning of serial hierarchical fuzzy systems for large-scale problems. Soft Comput., 17(1):165-194.

[7]Bevrani, H., Hiyama, T., Mitani, Y., 2008. Power system dynamic stability and voltage regulation enhancement using an optimal gain vector. Contr. Eng. Pract., 16(9): 1109-1119.

[8]Cai, L., Erlich, I., 2005. Simultaneous coordinated tuning of PSS and FACTS damping controllers in large power systems. IEEE Trans. Power Syst., 20(1):294-300.

[9]Chaudhuri, B., Pal, B., 2004. Robust damping of multiple swings modes employing global stabilizing signals with TCSC. IEEE Trans. Power Syst., 19(1):499-506.

[10]Chaudhuri, B., Pal, B., Zolotas, A.C., 2003. Mixed-sensitivity approach to H control of power system oscillations employing multiple FACTS devices. IEEE Trans. Power Syst., 18(3):1149-1156.

[11]Dash, P.K., Morris, S., Mishra, S., 2004. Design of a nonlinear variable-gain fuzzy controller for FACTS devices. IEEE Trans. Contr. Syst. Technol., 12(3):428-438.

[12]del Rosso, A.D., Canizares, C.A., Dona, V.M., 2003. A study of TCSC controller design for power system stability improvement. IEEE Trans. Power Syst. 18(4):1487-1496.

[13]Divya, K.C., Nagendra Rao, P.S., 2005. A simulation model for AGC studies of hydro-hydro systems. Int. J. Electr. Power Energy Syst., 27(5-6):335-342.

[14]Eberhart, R.C., Shi, Y.H., Kennedy, J., 2001. Swarm Intelligence. Academic Press, San Diego, CA.

[15]Elshafei, A.L., El-Metwally, K.A., Shaltout, A.A., 2005. A variable-structure adaptive fuzzy-logic stabilizer for single and multi-machine power systems. Contr. Eng. Pract., 13(4):413-423.

[16]Falehi, A.D., 2012. Simultaneous coordinated design of TCSC-based damping controller and AVR based on PSO technique. Electr. Rev., 88(5):136-140.

[17]Falehi, A.D., 2013. Design and scrutiny of maiden PSS for alleviation of power system oscillations using RCGA and PSO techniques. J. Electr. Eng. Technol., 8(3):402-410.

[18]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.

[19]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.

[20]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.

[21]Goshal, S.P., 2004. Optimization of PID gains by particle swarm optimization in fuzzy based automatic generation control. Electr. Power Syst. Res., 72(3):203-212.

[22]Gyugyi, L., 1992. Unified power-flow control concept for flexible AC transmission systems. IEE Proc. C, 139(4): 323-331.

[23]Gyugyi, L., Schauder, C.D., Sen, K.K., 1997. Static synchronous series compensator: a solid-state approach to the series compensation of transmission lines. IEEE Trans Power Del., 12(1):406-417.

[24]Hingorani, N.G., Gyugyi, L., 2000. Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press, New York.

[25]Iracleous, D.P., Alexandridis, A.T., 2005. A multi-task automatic generation control for power regulation. Electr. Power Syst. Res., 73(3):275-285.

[26]Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern., 23(3):665-685.

[27]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.

[28]Kazemi, A., Jahed Motlagh, M.R., Naghshbandy, A.H., 2007. Application of a new multi-variable feedback linearization method for improvement of power systems transient stability. Int. J. Electr. Power Energy Syst., 29(4): 322-328.

[29]Kikuchi, H., Otake, A., Nakanishi, S., 1998. Functional completeness of hierarchical fuzzy modeling. Inform. Sci., 110(1-2):51-60.

[30]Kundur, P., Klein, M., Rogers, G.J., et al., 1989. Application of power system stabilizers for enhancement of overall system stability. IEEE Trans. Power Syst., 4(2):614-626.

[31]Larsen, E.V., Sanchez-Gasca, J.J., Chow, J.H., 1995. Concepts of design of FACTS controllers to damp power swings. IEEE Trans. Power Syst., 10(2):948-956.

[32]Lee, M.L., Chung, H.Y., Yu, F.M., 2003. Modeling of hierarchical fuzzy systems. Fuzzy Sets Syst., 138(2):343-361.

[33]Li, B.H., Wu, Q.H., Turner, D.R., et al., 2000. Modeling of TCSC dynamics for control and analysis of power system stability. Int. J. Electr. Power Energy Syst., 22(1):43-49.

[34]Mattavelli, P., Verghese, G.C., Stankovic, A.M., 1997. Phasor dynamics of thyristor-controlled series capacitor systems. IEEE Trans. Power Syst., 12(3):1259-1267.

[35]Moradi, A., Shirazi, K.H., Keshavarz, M., et al., 2014. Smart piezoelectric patch in non-linear beam: design, vibration control and optimal location. Trans. Instit. Meas. Contr., 36(1):131-144.

[36]Panda, S., Padhy, N.P., 2008. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Appl. Soft Comput., 8(4):1418-1427.

[37]Raju, G., Zhou, J., Kisner, R., 1991. Hierarchical fuzzy control. Int. J. Contr., 54(5):1201-1216.

[38]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 IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks, p.551-556.

[39]Soliman, H.M., Dabroum, A., Mahmoud, M.S., et al., 2011. Guaranteed-cost reliable control with regional pole placement of a power system. J. Franklin Instit., 348(5): 884-898.

[40]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.

[41]Talaat, H.E.A., Abdennour, A., Al-Sulaiman, A.A., 2010. Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer. Int. J. Electr. Power Energy Syst., 32(7):751-759.

[42]Tan, W., Xu, Z., 2009. Robust analysis and design of load frequency controller for power systems. Electr. Power Syst. Res., 79(5):846-853.

[43]Zhang, Y., Zhou, Q., Sun, C.X., et al., 2008. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst., 23(3):853-858.

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