Controlling Techniques for STATCOM using Artificial Intelligence


  • Vidhyavati Suryawanshi
  • Dr. Surbhi Gupta


STATCOM, AI, Control Strategies, Grid connected systems, DSTATCOM


The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence.

Author Biographies

Vidhyavati Suryawanshi

Ph.D Scholar

Electrical Engineering Department

Chandigarh University

Dr. Surbhi Gupta

Associate Professor

Electrical Engineering Department

Chandigarh University


Shahgholian, G., Fazeli-Nejad, S., Moazzami, M., Mahdavian, M., Azadeh, M., Janghorbani, M., & Farazpey, S. (2016). Power system oscillations damping by optimal coordinated design between PSS and STATCOM using PSO and ABC algorithms. 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2016, 0–5.

Choubey, A., & Singh, G. (2016). A Survey on STATCOM Techniques. 4(4), 452–463.

Nusair, K. N., & Alomoush, M. I. (2017). Optimal reactive power dispatch using teaching learning based optimization algorithm with consideration of FACTS device “STATCOM.” 2017 10th Jordan International Electrical and Electronics Engineering Conference, JIEEEC 2017.

Atyia, H., & Mete, A. (2018). Shuffled Frog Leap Algorithm , and Genetic Algorithm Based. 2018.

Bakir, H., & Kulaksiz, A. A. (2020). Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA. Engineering Science and Technology, an International Journal, 23(3), 576–584.

Saxena, N. K., & Kumar, A. (2016). Reactive power control in decentralized hybrid power system with STATCOM using GA, ANN and ANFIS methods. International Journal of Electrical Power and Energy Systems, 83, 175–187.

Yu, L., Zhang, J., & Jiang, C. (2012). D-STATCOM control based on self-tuning PI with neural networks. China International Conference on Electricity Distribution, CICED, 5–6.

Shinde, S. U., Sharmila, M., Patil, R. S., & Malkhede, D. V. (2016). Performance comparison of PI & ANN based STATCOM for 132 KV transmission line. International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016, Fig 1, 2730–2734.

Ma, Y., Huang, A., & Zhou, X. (2015). A review of STATCOM on the electric power system. 2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015, 162–167.

Kow, K. W., Wong, Y. W., Rajkumar, R. K., & Rajkumar, R. K. (2016). A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events. Renewable and Sustainable Energy Reviews, 56, 334–346.

Chau, T. K., Yu, S. S., Fernando, T., Iu, H. H. C., & Small, M. (2018). A Load-Forecasting-Based Adaptive Parameter Optimization Strategy of STATCOM Using ANNs for Enhancement of LFOD in Power Systems. IEEE Transactions on Industrial Informatics, 14(6), 2463–2472.

Khan, M. T., & Siddiqui, A. S. (2016). FACTS device control strategy using PMU. Perspectives in Science, 8, 730–732.

Farahani, S. S. S., Hemati, R., & Nikzad, M. (2009). Comparison of Artificial Intelligence Strategies for STATCOM Supplementary Controller Design. World Applied Sciences Journal, 7(11), 1428–1438.

Mopidevi, R., Srinivas, L. R., babu, B. M., & Tulasiram, S. S. (2014). AI Based STATCOM for Power Quality Enhancement. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 03(08), 11315–11324.

Yenealem, M. G., Ngoo, L. M. H., Shiferaw, D., & Hinga, P. (2020). Management of voltage profile and power loss minimization in a grid-connected microgrid system using fuzzy-based STATCOM controller. Journal of Electrical and Computer Engineering, 2020.

Jin, Y., Xiao, Q., Jia, H., Mu, Y., Ji, Y., Teodorescu, R., & Dragicevic, T. (2021). A Dual-Layer Back-Stepping Control Method for Lyapunov Stability in Modular Multilevel Converter based STATCOM. IEEE Transactions on Industrial Electronics, 0046(c).

Alskran, F., & Simoes, M. G. (2021). Multilevel current source converter-based STATCOM suitable for medium-voltage applications. IEEE Transactions on Power Delivery, 36(2), 1222–1232.

Dilshad, S., Abas, N., Farooq, H., Kalair, A. R., & Memon, A. A. (2020). Neurofuzzy wavelet based auxiliary damping controls for STATCOM. IEEE Access, 8, 200367–200382.

Khurana, B. V., & Titare, L. S. (2020). Improvement of power flow and voltage stability using UPFC with artificial neural network in Matlab. Proceedings - 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security, ISSSC 2020, 5–8.




How to Cite

Suryawanshi, V. ., & Gupta, D. S. . (2022). Controlling Techniques for STATCOM using Artificial Intelligence. SMART MOVES JOURNAL IJOSTHE, 9(1), 6–14. Retrieved from