Optimizing Condition Monitoring and Fault Diagnosis in High Voltage Transmission Systems: Emerging Technologies Future Directions

Abstract

In the research, it is evaluated how to monitor and diagnose the fault in the high voltage transmission networks while improving the grid reliability and increasing the failure reduction. Early fault detection is introduced through partial discharge, thermal imaging, and dissolved gas analysis and demonstrated in the paper. Artificial intelligence, and machine learning, impacts what the study refers to as the capability to predict faults, according to the study. Finally, the paper provides support for building active monitoring frameworks that enable resolving data complexity and environmental issues while improving power grid efficiency and the power supply.

1. Introduction

HV transmission systems form the backbone of the modern power grids and provide means for transmission of the electrical energy over long distances.

2.Condition Monitoring in Voltage Transmission Systems

Condition monitoring (CM) is one of the critical processes to ensure the productivity and dependability of high voltage (HV) transmission lines, which are essential for the continuous supply of electricity over extended distances.

1. Fault Diagnosis Techniques in High Voltage Transmission Systems

The fault diagnosis in a high voltage (HV) transmission system is an important stage to ensure grid reliability by identifying and categorizing faults before becoming a critical failure.

1. Current Developments in Fault Diagnosis and Condition Monitoring

Recent developments in high-voltage transmission condition monitoring and fault diagnosis have enhanced grid management in terms of problem detection, classification, and prediction by advances in high-voltage transmission condition monitoring and fault diagnosis.

1. Challenges in Fault Diagnosis

However, faults in the HV transmission systems can still not be diagnosed accurately. Lack of data homogeneity is still a thorny challenge when dealing with the heterogeneity of data coming from different sources with consequent arguable data inconsistency on fault classification (Li et al. 2022).

1. Future Directions in Fault Diagnosis

Considering these challenges, future research intends to resolve them using edge AI to minimize latency and improve real-time fault detection.

1. Conclusion

Lastly, even if condition monitoring and fault diagnosis have greatly increased the resilience and efficiency of high-voltage transmission systems, new issues need to be addressed to handle the high level of cybersecurity and data heterogeneity.