A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Environmental Science

Background:

• Meteorological conditions can degrade power communication equipment reliability, leading to distorted, uncertain, and incomplete fault information.
• Accurate fault diagnosis in power transmission networks is crucial for maintaining grid stability and preventing cascading failures.

Purpose of the Study:

• To propose and validate a fault diagnosis method for power transmission networks that explicitly incorporates meteorological factors.
• To enhance the robustness and accuracy of fault diagnosis under adverse weather conditions.

Main Methods:

• Development of a spiking neural P system model incorporating meteorological environmental factors and a matrix reasoning algorithm.
• Construction of a diagnosis model for suspicious transmission lines based on network topology, protection device logic, and the spiking neural P system.
• Integration of protection device action messages and temporal order information, refined using gray fuzzy theory, as input for the diagnosis model.
• Parallel execution of matrix reasoning algorithms for each model to achieve diagnosis results.

Main Results:

• Experimental validation on the IEEE 39-bus system demonstrated the feasibility of the proposed fault diagnosis method.
• The method effectively addresses the challenges posed by meteorological factors on fault information reliability.
• The proposed approach shows significant effectiveness in accurately diagnosing faults in power transmission networks.

Conclusions:

• The developed spiking neural P system-based fault diagnosis method offers a reliable solution for power transmission networks facing meteorological challenges.
• The integration of meteorological factors and gray fuzzy theory improves the accuracy and completeness of fault information.
• The study confirms the practical applicability and effectiveness of the proposed method in real-world power system scenarios.