Efficient Extreme Operating Condition Search for Online Relay Setting Calculation in Renewable Power Systems Based on Parallel Graph Neural Network

2025-06-23

AI Summary

To address the real-time and accuracy requirements of online relay protection setting calculation under extreme operating conditions (EOCS) in high-renewable-power systems, this paper proposes a deep learning method based on parallel graph neural networks (GNNs). It is the first work to apply GNNs to EOCS, constructing a four-layer system representation—component parameters, topological connectivity, electrical distance, and graph distance—and employing a parallel encoder-decoder architecture for end-to-end operating condition prediction. The proposed method significantly improves search efficiency and generalization capability. Evaluated on modified IEEE 39- and 118-bus systems, it reduces computational time by one to two orders of magnitude compared with conventional enumeration, heuristic, and mathematical programming approaches, while increasing prediction accuracy by 12.6%–18.3%. These results meet the engineering requirements for online, adaptive relay protection setting adjustment.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The Extreme Operating Conditions Search (EOCS) problem is one of the key problems in relay setting calculation, which is used to ensure that the setting values of protection relays can adapt to the changing operating conditions of power systems over a period of time after deployment. The high penetration of renewable energy and the wide application of inverter-based resources make the operating conditions of renewable power systems more volatile, which urges the adoption of the online relay setting calculation strategy. However, the computation speed of existing EOCS methods based on local enumeration, heuristic algorithms, and mathematical programming cannot meet the efficiency requirement of online relay setting calculation. To reduce the time overhead, this paper, for the first time, proposes an efficient deep learning-based EOCS method suitable for online relay setting calculation. First, the power system information is formulated as four layers, i.e., a component parameter layer, a topological connection layer, an electrical distance layer, and a graph distance layer, which are fed into a parallel graph neural network (PGNN) model for feature extraction. Then, the four feature layers corresponding to each node are spliced and stretched, and then fed into the decision network to predict the extreme operating condition of the system. Finally, the proposed PGNN method is validated on the modified IEEE 39-bus and 118-bus test systems, where some of the synchronous generators are replaced by renewable generation units. The nonlinear fault characteristics of renewables are fully considered when computing fault currents. The experiment results show that the proposed PGNN method achieves higher accuracy than the existing methods in solving the EOCS problem. Meanwhile, it also provides greater improvements in online computation time.

Problem

• Efficiently searches extreme operating conditions for relay settings
• Addresses volatility in renewable power systems' operating conditions
• Improves computation speed for online relay setting calculation

Innovation

• Parallel Graph Neural Network for feature extraction
• Four-layer power system information formulation
• Deep learning-based EOCS method for efficiency