ANFIS Hybridized FACTS Controller for Voltage Stability Improvement

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 1

Year of Publication : 2025

Authors : Vaishali Chavhan, Mukesh Kumar, Sarvesh Kumar, Chetan Bobade

10.14445/23488549/IJECE-V12I1P112

How to Cite?

Vaishali Chavhan, Mukesh Kumar, Sarvesh Kumar, Chetan Bobade, "ANFIS Hybridized FACTS Controller for Voltage Stability Improvement," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 1, pp. 150-160, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P112

Abstract:

In long transmission lines, distribution network loads and generating units are all connected to a modern power system. Networks of interconnected power systems encounter a variety of instability-related problems. In a power system, dynamic instability is a problem that arises when there is a dynamic disturbance in the system network. By employing appropriate controllers to enhance system stability and synchronization, these difficulties were overcome in this study. Through individual and comparative simulation, the dynamic instability of the electrical system utilizing STATCOM and an Adaptive Neuro Fuzzy-Inference System (ANF-IS) was examined. To facilitate simulation, the IEEE 9 bus test system provided the necessary data. Profile of Voltage, Reactive power(Q), wind o/p voltage, and Active power (P) are the outcomes of the simulation. MATLAB 20 Simulink is used to simulate the systems model with grid partition in order to study the use of ANFIS. The incorporation of Adaptive Neuro-Fuzzy Inference System (ANFIS)-controlled STATCOMs aims In order to substitute the traditional PI controller, thus improving the overall performance of the STATCOM system. The study also looks into where FACTS controllers should be placed at the load side to optimize their efficiency in enhancing the voltage stability of the system.

Keywords:

Dual Fed-Induction Generator (DF-IG), Wind Energy Conversion System (WECS), IEEE 9 bus, Static synchronous Compensator (STATCOM), Adaptive Neuro-Fuzzy Inference System (ANFIS), Voltage stability.

References:

[1] Nguyen Huu Vinh, Hung Nguyen, and Him Hung Le, “Application of Anfis-Pid Controller for Statcom to Enhance Power Quality in Power System Connected Wind Energy System,” International Journal of Engineering and Technology, vol. 7, no. 4, pp. 35-37, 2018.
[CrossRef] [Publisher Link]
[2] P.A. Mohanarao, Sarat Kumar Sahu, and G. Saraswathi, “Power Quality Improvement in a Standalone Microgrid System Using Coordinated PQ Theory in UPQC-SSO,” GMSARN International Journal, vol. 17, pp. 118-140, 2023.
[Google Scholar] [Publisher Link]
[3] Vinay Kumar Polishetty, G. Balamurugan, and Kartigeyan Jayaraman, “Wind Integrated UPFC System with Cascaded Fuzzy Logic Controller for Alleviation of PQ Issues,” GMSARN International Journal, vol. 19, pp. 137-151, 2025.
[Google Scholar] [Publisher Link]
[4] Sabah Abdulkareem Yousif, and Saad Enad Mohammed, “Reactive Power Control Using STATCOM for Power System Voltage Improvement,” AI-Rafidain Engineering Journal (AREJ), vol. 26, no. 2, pp. 124-131, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] S. Felix Stephen, and I. Jacob Raglend, “A Review on PI Control of STATCOM for Voltage Regulation,” Journal of Chemical and Pharmaceutical Sciences, vol. 9, no. 1, pp. 334-340, 2016.
[Google Scholar] [Publisher Link]
[6] P.C. Pradhan et al., “A STATCOM-Control Scheme Used for Power Factor Improvement of Grid Connected Weak Bus System,” International Journal of Engineering Research & Technology, vol. 2, no. 12, pp. 3527-3534, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Nitin Saxena, and Ashwani Kumar, “Reactive Power Compensation of an Isolated Hybrid Power System with Load Interaction Using ANFIS Tuned STATCOM,” Frontiers in Energy, vol. 8, pp. 261-268, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Saurabh Mani Tripathi, and Prakash Ji Barnawal, “Design and Control of a STATCOM for Non-linear Load Compensation: A Simple Approach,” Electrical, Control and Communication Engineering, vol. 14, no. 2, pp. 172-184, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Osama Majeed Butt, Muhammad Zulqarnain, and Tallal Majeed Butt, “Recent Advancement in Smart Grid Technology: Future Prospects in the Electrical Power Network,” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 687-695, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Montaser Abdelsattar et al., “Voltage Stability Improvement of an Egyptian Power Grid Based Wind Energy System Using STATCOM,” Wind Energy, vol. 25, no. 6, pp. 1077-1120, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Aanchal Singh S. Vardhan, and Rakesh Saxena, “Vector Control of DFIG-Based Wind Turbine System,” GMSARN International Journal, vol. 16, pp. 348-358, 2022.
[Google Scholar] [Publisher Link]
[12] Mohammed Salheen Alatshan et al., “Improvement of the Performance of STATCOM in Terms of Voltage Profile Using ANN Controller,” International Journal of Power Electronics and Drive System, vol. 11, no. 4, pp. 1966-1978, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Abhishek Saware et al., “Performance Improvement of PID Controller for PMSM Using ANFIS Controller,” 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Samson Ademola Adegoke, and Yanxia Sun, “Power System Optimization Approach to Mitigate Voltage Instability Issues: A Review,” Cogent Engineering, vol. 10, no. 1, pp. 1-40, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ismail Marouani et al., “Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods,” Sustainability, vol. 15, no. 12, pp. 1-58, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Rehan Sadiq et al., “A Review of STATCOM Control for Stability Enhancement of Power Systems with wind/PV Penetration: Existing Research and Future Scope,” International Transactions on Electrical Energy Systems, vol. 31, no. 11, pp. 1-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mu’men Bodoor, Ashraf Radaideh, and Ayman Al-Quraan, “Active and Reactive Power Control for Wind Turbines Based DFIG Using LQR Controller with Optimal Gain-Scheduling,” Journal of Electrical and Computer Engineering, vol. 2021, no. 1, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Bukola Babatunde Adetokun, and Christopher Maina Muriithi, “Impact of Integrating Large-scale DFIG-based Wind Energy Conversion System on the Voltage Stability of Weak National Grids: A Case Study of the Nigerian Power Grid,” Energy Reports, vol. 7, pp. 654-666, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Parimal Borse, A.G. Thosar, and Samruddhi Shaha, “Modeling and Simulation of STATCOM,” International Journal of Engineering Research & Technology, vol. 3, no. 12, pp. 1-4, 2014.
[Google Scholar] [Publisher Link]
[20] Shrikant Chavate et al., “Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection,” Indian Journal of Science and Technology, vol. 16, no. 33, pp. 2670-2680, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] K. Sivaraman et al., “Network Failure Detection and Diagnosis by Analyzing Syslog and SNS Data: Applying Big Data Analysis to Network Operations,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 1-5, 2019.
[CrossRef] [Publisher Link]
[22] Vahid Nadimi et al., “An Adaptive-Network-based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: USA, Canada, Germany, United Kingdom, Japan, France and Italy,” 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation, Pisa, Italy, pp. 301-305, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sibonelo Motepe et al., “South African Power Distribution Network Load Forecasting using Hybrid AI Techniques: ANFIS and OP-ELM,” 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), Istanbul, Turkey, pp. 557-562, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Naji Ammar, Marizan Sulaiman, and Ahmad Fateh Mohamad Nor, “Long-Term Load Forecasting of Power Systems Using Artificial Neural Network and ANFIS,” ARPN Journal of Engineering and Applied Sciences, vol. 13, no. 3, pp. 828-834, 2018.[Google Scholar] [Publisher Link]