Time-Synchronized Full System State Estimation Challenges Addressed by DeNSE

核心概念

Deep Neural network-based State Estimator (DeNSE) addresses challenges in time-synchronized state estimation for power systems.

要約

The article discusses the challenges of time-synchronized state estimation in power systems due to PMU placement trade-offs. It introduces the DeNSE, a Deep Neural network-based State Estimator, to overcome these challenges. The DeNSE combines SCADA and PMU data to achieve sub-second situational awareness. It addresses scalability, non-Gaussian noise, bad data detection, and correction issues. Results show its superiority over traditional methods.

1. Introduction

   • State estimation importance for power utilities.
   • Traditional methods vs. new challenges with rapid fluctuations.

2. Motivations

   • Issues with SCADA-only and hybrid state estimators.
   • Need for high-speed state estimation due to renewable energy integration.

3. Novel Contributions

   • Introduction of DeNSE for high-speed state estimation.
   • Overcoming observability issues with few PMUs.

4. Proposed Formulation

   • Bayesian approach to Transmission System State Estimation (TSSE).

5. Architecture of the DNN in the DeNSE

   • Feed-forward architecture with hidden layers.

6. Creation of Training Database

   • Unique feature of using SCADA data indirectly through MC sampling.

7. Enhancements to Proposed Formulation and Online Implementation

   • Transfer learning for topology changes.

8. Robust Bad Data Detection and Correction (BDDC)

   • Wald test methodology for bad data detection.

9. Impact of Topology Changes

   • Transfer learning efficiency demonstrated after topology changes.

10. Mitigating Impact of Bad Data

    • Proposed NOC-based BDDC methodology outperforms mean value replacement.

11. Tackling Extreme Scenarios

    • Extreme scenario filter improves performance under extreme conditions.

12. Investigating Impact of Different Database Sizes

    • Performance improvement observed with increasing database sizes.

統計

The DeNSE results in an average magnitude MAPE of 0.1676% and an average angle MAE of 0.0042%.
The proposed methodology for correcting bad data consistently outperformed the mean value replacement method.

引用

"The DeNSE employs a Bayesian framework to combine SCADA and PMU data."
"Transfer learning is used to update the DNN after topology changes."

抽出されたキーインサイト

Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

by Antos Cheera... 場所 arxiv.org 03-22-2024

https://arxiv.org/pdf/2212.01729.pdf

深掘り質問

How can the DeNSE be adapted for real-time implementation?

The adaptation of DeNSE for real-time implementation involves ensuring that the processing speed aligns with the requirements of high-speed state estimation. This includes optimizing the computational efficiency of the Deep Neural Network (DNN) used in DeNSE to handle a large number of inputs and produce outputs rapidly. Additionally, implementing mechanisms such as parallel processing or GPU acceleration can further enhance the speed at which state estimation is performed. Real-time data preprocessing techniques should also be employed to ensure that incoming measurements are processed quickly and accurately by the DNN.

What are the implications of non-Gaussian noise on state estimation accuracy?

Non-Gaussian noise in power system measurements can have significant implications on state estimation accuracy. Traditional methods like Least Squares Estimation (LSE) assume Gaussian noise characteristics, leading to potential inaccuracies when dealing with non-Gaussian noise distributions. Inaccurate modeling of non-Gaussian noise can result in biased estimates and reduced precision in determining system states. Machine learning-based approaches like DeNSE offer more flexibility in handling non-Gaussian noise due to their ability to learn complex patterns from data without strict assumptions about noise distribution, thereby improving overall state estimation accuracy under varying conditions.

How can machine learning improve other aspects of power system operations beyond state estimation?

Machine learning has vast potential to enhance various aspects of power system operations beyond state estimation:

Fault Detection: ML algorithms can analyze operational data to detect faults or anomalies in real-time, enabling proactive maintenance and minimizing downtime.
Load Forecasting: ML models can predict future load demands accurately based on historical data, facilitating efficient resource allocation and grid management.
Optimal Control Strategies: ML algorithms can optimize control strategies for generation dispatch, voltage regulation, and energy storage utilization based on dynamic operating conditions.
Grid Resilience: ML techniques enable predictive analytics for assessing grid vulnerabilities and developing resilience strategies against contingencies or cyber threats.
Energy Trading: ML algorithms support automated decision-making processes for energy trading platforms by analyzing market trends and optimizing trading strategies.

By leveraging machine learning across these areas, power utilities can achieve greater operational efficiency, reliability, and sustainability within their systems while adapting to evolving grid dynamics effectively.