A Fuzzy-Controlled Differential Evolution Integrated Static Synchronous Series Compensator to Enhance Power System Stability

Abstract

Static Synchronous Series Compensator (SSSC) is the fact device that provides stable and controlled power transmission. The unequal load, power disturbances, and fault can cause power fluctuations in the system. In this paper, a more intelligent and optimized SSSC system design is presented with the integration of a fuzzy controlled differential evolution method. The method has analyzed the disturbance and variation occurs in the system. The fuzzy controlled analysis and dynamic search are applied to multiple parameters for observing the behavior of the system. The impurities and load evaluation-based fitness rule is defined with the DE algorithm to provide control over the power system. The fuzzy rules are applied for tuning the parameters dynamically for different scenarios and unequal load. The designed and optimized power system model is tested against the three-phase fault and unequal load conditions for single and multiple machines. The simulation results show that the proposed work model has improved stability and reduced fluctuations. The comparative evaluation is also conducted against the Particle Swarm Optimization (PSO), Genetics, Differential Evolution (DE), and Gravitational Search Algorithm (GSA). The experimentation results verified that the proposed fuzzy-controlled DE method improved the performance of the system in varying load and fault situations. The proposed integrated-SSSC model has reduced the fluctuations and achieved the stability effectively against the other optimization models.

KEYWORDS:

• Differential evolution
• Fault
• Fuzzy
• Power system
• SSSC
• VSC

Additional information

Notes on contributors

Kapil Juneja

Kapil Juneja is currently doing PhD in computer science and engineering at the MD University, Rohtak, Haryana, India. His research interests include image processing, biometrics, pattern recognition, computer networks, software engineering and machine learning. He is currently doing research on various image, network security and data classification applications.