Robust design of damping controller for power system using a combination of snake optimisation algorithm and optimal control theory

被引：0

作者：

Agrawal N. ^{[1
]}

Khan F.A. ^{[1
]}

Gowda M. ^{[2
]}

机构：

[1] Department of Electrical and Electronics Engineering, Ghousia College of Engineering, Karnataka, Ramanagaram District

[2] Department of Artificial Intelligence and Data Science, BGS College of Engineering and Technology, Mahalakshmi Puram, Karnataka, Bengaluru

来源：

International Journal of Energy Technology and Policy | 2024年 / 19卷 / 1-2期

关键词：

algorithm; damping; efficient; LFO; linear quadratic regulator; low-frequency oscillations; LQR; modelling; oscillations; power system; robust; snake optimisation algorithm; SOA; stability;

D O I：

10.1504/IJETP.2024.138547

中图分类号：

学科分类号：

摘要：

Low-frequency oscillations (LFO) are created in the power system due to various disturbances. The LFO if not controlled, grows and causes the system separation. There is a huge financial loss due to the interruption of the power supply caused by disturbances. With the increasing complexity of the modern power system, there is a need for the design of a more accurate and detailed modelling. An Advanced Heffron Phillips Model (AHPM) is developed with a higher order Synchronous Generator Model 1.1, based on ten K-Constants for stability improvement. This AHPM employs the combination of snake optimisation algorithm (SOA) and linear quadratic regulator (LQR) from optimal control theory. The highest damping ratio (99.98%) is obtained by AHPM in coordination with PSS, and TCSC based on SOA and LQR. For various parameters, the settling time ranges from 1.5 to 2.0 seconds. This AHPM is robust and capable of meeting the challenges of grid integration with renewables. © 2024 Inderscience Enterprises Ltd.

引用

页码：171 / 215

页数：44

共 47 条

[1]

Abdulkader R., Ghanimi H.M., Dadheech P., Alharbi M., Fouda M.M., Aly M.H., Swaminathan D., Sengan S., Soft computing in smart grid with decentralized generation and renewable energy storage system planning, Energies, 16, 6, pp. 1-24, (2023)

[2]

Abido M.A., Pole placement technique for PSS and TCSC-based stabilizer design using simulated annealing, Electrical Power and Energy Systems, 22, 8, pp. 543-554, (2000)

[3]

Alwan H.O., A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems, Periodicals of Engineering and Natural Sciences, 11, 2, pp. 102-116, (2023)

[4]

Aribowo W., A novel improved sea-horse optimizer for tuning parameter power system stabilizer, Journal of Robotics and Control, 4, 1, pp. 12-22, (2023)

[5]

Aribowo W., Muslim S., Suprianto B., Haryudo S.I., Joko, Tunicate swarm algorithm-neural network for adaptive power system stabilizer parameter, Science and Technology Asia, 26, 3, pp. 50-63, (2021)

[6]

Aribowo W., Suprianto B., Kartini U.T., Prapanca A., Dingo optimization algorithm for designing power system stabilizer, Indonesian Journal of Electrical Engineering and Computer Science, 29, 1, pp. 1-7, (2023)

[7]

Arumugam V., Kumar A., Embedded control system for reactive power control in distributed energy resources for voltage regulation in the distributed power system, Serbian Journal of Electrical Engineering, 20, 1, pp. 107-126, (2023)

[8]

Behzadpoor S., Davoudkhani I.F., Abdelaziz A.Y., Geem Z.W., Hong J., Power system stability enhancement using robust FACTS-based stabilizer designed by a hybrid optimization algorithm, Energies, 15, 22, pp. 1-30, (2022)

[9]

Chaib L., Choucha A., Arif S., Zaini H.G., Ghoneim S.S., Robust design of power system stabilizers using improved harris hawk optimizer for interconnected power system, Sustainability, 13, 21, pp. 1-18, (2021)

[10]

Dao M.T., Truong K., Do D.N., Truong B.H., Nguyen K.P., Ngoc D., Security-constrained temperature-dependent optimal power flow using hybrid pseudo-gradient based particle swarm optimization and differential evolution, International Journal on Electrical Engineering and Informatics, 15, 1, pp. 82-105, (2023)

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