An Interpretable Power System Transient Stability Assessment Method with Expert-Guided Neural-Regression-Tree

Core Concepts

An interpretable power system transient stability assessment method is proposed that combines expert knowledge, neural networks, and nonlinear regression trees to balance accuracy and interpretability.

Abstract

The content presents an interpretable power system transient stability assessment method called TSA-ENRT. The key highlights are:

Expert knowledge is extracted from a simple two-machine three-bus power system and incorporated into a nonlinear regression tree model to improve the interpretability of the generated decision logic.

The transient stability assessment problem is formulated as a regression task to retain the probability information of the neural network predictions, which can be better explained by the nonlinear regression tree.

A tree regularization technique is introduced to establish a connection between the neural network parameters and the average depth of the nonlinear regression tree, providing a better trade-off between accuracy and interpretability compared to previous methods.

Extensive experiments on a regional power system in China demonstrate that the interpretive rules generated by TSA-ENRT are highly consistent with the neural network predictions and more aligned with human expert cognition.

Stats

The following sentences contain key metrics or figures:
The accuracy of the neural network and the decision tree no longer changes, arriving at 97.3% and 75.0% respectively. The corresponding fidelity ρ is 76.1%.
The accuracy of the neural network and the decision tree were 95% and 90% respectively, and the corresponding fidelity was about 87%.
The accuracy of the neural network evaluation model in TSA-ENRT is higher than GRU-TR across different levels of the tree regularization strength.
The fidelity of the tree regularization based methods (TSA-ENRT, GRU-TR) is much higher than traditional methods.
The average error of training surrogate model without data augmentation is 1.122, while the TSA-ENRT using augmentation with Gaussian and Dirichlet distribution are 0.722 and 0.726 respectively.

Quotes

"Expert knowledge is introduced into the interpretability model. An expert knowledge base is built by extracting expert knowledge from a simple equivalent system. A nonlinear regression tree can be generated with the guide of the expert knowledge base."
"The nonlinear regression tree can alleviate the cost of the trade-off between the accuracy and interpretability of the neural network model compared with previous neural tree solutions, because the nonlinear tree model can better approximate the nonlinear behavior of the neural network."
"The TSA binary classification problem is transformed into a regression task of stability probability in TSA-ENRT. Different decision logics can be generated by the nonlinear regression tree according to the probability predicted by the network model. The probability generation behavior of the neural network can be explained by the generated decision logic."

Key Insights Distilled From

An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree

by Hanxuan Wang... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02555.pdf

Deeper Inquiries

How can the proposed TSA-ENRT framework be extended to other power system analysis tasks beyond transient stability assessment

The TSA-ENRT framework can be extended to other power system analysis tasks beyond transient stability assessment by adapting the expert guiding nonlinear regression tree to incorporate domain-specific knowledge relevant to the new task. For example, in the case of voltage stability analysis, the expert knowledge base could be updated to include nonlinear terms related to voltage stability indicators such as reactive power, voltage magnitude, and voltage angle. By extracting relevant nonlinear interactions from the power flow equations specific to voltage stability, the interpretive rules generated by the nonlinear regression tree can provide insights into the factors influencing voltage stability in the power system. This approach can be applied to various power system analysis tasks, such as fault detection, optimal power flow, and contingency analysis, by customizing the expert knowledge base to capture the key nonlinear relationships in each specific analysis domain.

What are the potential limitations or drawbacks of relying on expert knowledge extracted from a simplified power system model to guide the interpretability of a complex real-world power system

Relying on expert knowledge extracted from a simplified power system model to guide the interpretability of a complex real-world power system may have potential limitations and drawbacks. One limitation is the scalability of the expert knowledge base to capture all the intricate nonlinear interactions present in a complex power system. The simplified model may not fully represent the complexity and diversity of real-world power systems, leading to a limited scope of interpretability. Additionally, the expert knowledge base may not encompass all the nuances and variations present in different operating conditions and system configurations, potentially leading to biased or incomplete interpretive rules. Moreover, the reliance on expert knowledge from a simplified model may overlook emergent behaviors or interactions unique to the real-world system, limiting the generalizability of the interpretability framework across different scenarios and conditions.

How can the TSA-ENRT approach be further improved to provide more comprehensive and actionable insights for power system operators and engineers beyond just the interpretability of the model predictions

To enhance the TSA-ENRT approach and provide more comprehensive and actionable insights for power system operators and engineers, several improvements can be considered. Firstly, integrating real-time data streams and dynamic system responses into the framework can enhance the predictive capabilities and responsiveness of the model. By incorporating online monitoring and feedback mechanisms, the TSA-ENRT approach can adapt to changing system conditions and provide timely alerts and recommendations for system operators. Secondly, incorporating uncertainty quantification techniques, such as probabilistic modeling and sensitivity analysis, can enhance the robustness and reliability of the interpretive rules generated by the model. By accounting for uncertainties in the data and model predictions, the TSA-ENRT approach can provide more nuanced and reliable insights for decision-making. Additionally, integrating optimization algorithms and decision support systems into the framework can enable automated decision-making and scenario analysis, empowering operators to proactively manage system stability and performance. By combining these enhancements, the TSA-ENRT approach can evolve into a comprehensive decision support tool for power system operators, offering actionable insights and recommendations for enhancing system operation and resilience.