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A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems

Maeshal Hijazi, Payman Dehghanian

TL;DR

A federated DL-based TSA framework designed to identify the operating states of the power system is introduced, which not only preserves the integrity of local utility data, making it resilient against cyber threats but also reduces the computational demands for local TSA model training.

Abstract

Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data susceptible to cyber threats and communication issues when transmitting data to a central server for training a single TSA model. Additionally, the centralized approach demands significant computational resources, which may not always be readily available. In light of these challenges, this paper introduces a federated DL-based TSA framework designed to identify the operating states of the power system. Instead of local utilities transmitting their data to a central server for centralized model training, they independently train their own TSA models using their respective datasets. Subsequently, the parameters of each local TSA model are sent to a central server for model aggregation, and the resulting model is shared back with the local clients. This approach not only preserves the integrity of local utility data, making it resilient against cyber threats but also reduces the computational demands for local TSA model training. The proposed approach is tested on four local clients each having the IEEE 39-bus test system.

A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems

TL;DR

A federated DL-based TSA framework designed to identify the operating states of the power system is introduced, which not only preserves the integrity of local utility data, making it resilient against cyber threats but also reduces the computational demands for local TSA model training.

Abstract

Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data susceptible to cyber threats and communication issues when transmitting data to a central server for training a single TSA model. Additionally, the centralized approach demands significant computational resources, which may not always be readily available. In light of these challenges, this paper introduces a federated DL-based TSA framework designed to identify the operating states of the power system. Instead of local utilities transmitting their data to a central server for centralized model training, they independently train their own TSA models using their respective datasets. Subsequently, the parameters of each local TSA model are sent to a central server for model aggregation, and the resulting model is shared back with the local clients. This approach not only preserves the integrity of local utility data, making it resilient against cyber threats but also reduces the computational demands for local TSA model training. The proposed approach is tested on four local clients each having the IEEE 39-bus test system.
Paper Structure (13 sections, 5 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 13 sections, 5 equations, 6 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Centralized DL-based TSA Models
  4. Federated DL-based TSA Models
  5. The Proposed Framework
  6. Procedures for the Federated DL-based TSA Framework
  7. The Proposed System Stability Classification Scheme
  8. Numerical Tests and Analyses
  9. Training Data Acquisition
  10. Data Pre-Processing
  11. Network Architecture and Settings
  12. Test Results
  13. Conclusion

Figures (6)

  • Figure 1: Centralized and federated DL-based TSA model schemes: The big picture.
  • Figure 2: Proposed federated DL-based TSA model framework.
  • Figure 3: 3D data matrix representation for the IEEE 39-bus test system used in the proposed framework.
  • Figure 4: Network architecture for local and global TSA models.
  • Figure 5: Confusion matrix for each electric utility client.
  • ...and 1 more figures