A Novel Security Framework for the Enhancement of the Voltage Stability in a High-Voltage Direct Current System

Abstract

Due to financial limitations, power systems are being operated closer to their stability boundaries. Voltage stability analysis is crucial to preserve a power system’s equilibrium. However, this impacts a system’s dependability and security, and maintaining a power system’s voltage stability is a difficult challenge. Additionally, the inverters and converters in a high-voltage direct current (HVDC) system use a significant amount of reactive power, which exacerbates voltage instability. In this study, a new algorithm called Adaptive Neural Spider Monkey (ANSMA) was developed to improve the voltage stability security in an HVDC system. Additionally, the proposed ANSMA maintains voltage stability while scheduling the loads in the generator. Moreover, applying artificial-intelligence-related energy systems to these issues is considered an efficient solution. Fuzzy, neural, ANN, and other improvements in artificial intelligence approaches, along with power semiconductor devices, have significantly impacted the ability to detect defects in HVDC systems. Furthermore, MATLAB/Simulink is used in the implementation of this developed ANSMA model. After this, the parameters are calculated, and the resulting methodology is tested on an IEEE 50-bus system. Finally, the simulation results are verified using currently used techniques to assess the effectiveness of the suggested ANSMA model.