Data-Driven Dynamic State Estimation of Photovoltaic Systems Using Sparse Regression and Unscented Kalman Filter

Core Concepts

This paper proposes a data-driven dynamic state estimation approach for photovoltaic (PV) energy conversion systems that utilizes sparse regression and an unscented Kalman filter to accurately estimate the system states without relying on a known physical model.

Abstract

The paper presents a two-phase methodology for data-driven dynamic state estimation of PV systems.
In the initial model identification phase, the authors use a nonlinear sparse regression technique to elucidate the dynamics of the PV systems based on state feedback data. This data-driven model identification approach can adaptively capture changes in the PV system dynamics without the need for a priori knowledge of the physical model.
Following the identification of the PV dynamics, the authors employ an unscented Kalman filter (UKF) to estimate the states of the PV system for monitoring and protection purposes. The UKF is designed to account for incomplete measurements, inherent uncertainties, and noise in the system.
The proposed sparse regression-based UKF approach is evaluated through simulation results and compared to a physics-based dynamic state estimation method. The results demonstrate the efficacy of the data-driven technique in accurately estimating the states of both single-stage and two-stage PV systems, even in the presence of parameter variations and system-level faults.
Key highlights:

Data-driven model identification using sparse regression to capture PV system dynamics
Adaptive unscented Kalman filter for state estimation to handle uncertainties and noise
Resilience to parameter variations and system-level faults
Comparative analysis with physics-based dynamic state estimation

Stats

The paper does not provide any explicit numerical data or statistics. The focus is on the methodology and simulation-based evaluation of the proposed data-driven dynamic state estimation approach.

Quotes

The paper does not contain any direct quotes that are particularly striking or supportive of the key logics.

Key Insights Distilled From

Data-Driven Dynamic State Estimation of Photovoltaic Systems via Sparse Regression Unscented Kalman Filter

by Elham Jamali... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18305.pdf

Deeper Inquiries

How can the proposed data-driven dynamic state estimation approach be extended to handle unknown noise statistics in the system

The proposed data-driven dynamic state estimation approach can be extended to handle unknown noise statistics in the system by incorporating robust techniques for handling uncertainties. One approach is to implement adaptive algorithms that can dynamically adjust to varying noise levels without prior knowledge of the noise statistics. By utilizing techniques such as adaptive filtering or online learning, the system can continuously update its estimation process based on the observed noise characteristics. Additionally, incorporating Bayesian methods that can infer the noise statistics from the data itself can also enhance the system's ability to handle unknown noise statistics effectively. By integrating these adaptive and probabilistic approaches, the data-driven dynamic state estimation framework can adapt to changing noise conditions and improve its accuracy in real-world scenarios.

What are the potential challenges and limitations of applying the sparse regression-based unscented Kalman filter to large-scale power systems with a high penetration of renewable energy sources

Applying the sparse regression-based unscented Kalman filter to large-scale power systems with a high penetration of renewable energy sources may pose several challenges and limitations. One potential challenge is the scalability of the approach to handle the increased complexity and size of large-scale power systems. As the number of variables and measurements grows, the computational burden of the sparse regression and unscented Kalman filter algorithms may become prohibitive. Additionally, the sparse regression technique relies on the assumption of sparsity in the system dynamics, which may not hold true for highly interconnected and dynamic power systems with renewable energy sources.
Moreover, the accuracy of the sparse regression model heavily depends on the selection of the sparsity tuning parameter (γ). Determining the optimal value of γ for large-scale systems with diverse and changing dynamics can be challenging and may require extensive tuning and validation. Furthermore, the unscented Kalman filter's performance can be affected by the nonlinearity and uncertainties inherent in renewable energy sources, potentially leading to suboptimal state estimation results.
To address these challenges, advanced optimization techniques, parallel computing, and distributed algorithms can be employed to enhance the scalability of the approach for large-scale power systems. Additionally, incorporating domain knowledge and system-specific constraints into the sparse regression model can improve its accuracy and robustness in capturing the system dynamics. Continuous validation and testing on realistic large-scale power system datasets are essential to ensure the effectiveness and reliability of the sparse regression-based unscented Kalman filter approach in practical applications.

How can the insights from this data-driven dynamic state estimation framework be leveraged to enhance the overall resilience and stability of modern power grids

The insights from the data-driven dynamic state estimation framework can be leveraged to enhance the overall resilience and stability of modern power grids in several ways.

Real-time Monitoring and Control: By providing accurate and timely state estimations, the framework enables operators to monitor the grid's condition continuously and make informed decisions to maintain stability and reliability.

Fault Detection and Diagnosis: The framework can be utilized for detecting and diagnosing faults in the power grid, enabling quick responses to mitigate potential disruptions and prevent cascading failures.

Optimal Resource Allocation: With precise state estimations, grid operators can optimize the allocation of resources, such as renewable energy sources and energy storage systems, to enhance grid efficiency and resilience.

Adaptive Operation: The adaptive nature of the framework allows for dynamic adjustments to changing grid conditions, such as fluctuations in renewable energy generation or unexpected disturbances, improving the grid's ability to withstand uncertainties.

Resilience Enhancement: By incorporating data-driven insights into grid operation and control strategies, the framework can enhance the overall resilience of the power grid against various challenges, including cyber-attacks, natural disasters, and system failures.

Overall, leveraging the data-driven dynamic state estimation framework can lead to more robust, efficient, and resilient modern power grids, ensuring stable and reliable electricity supply in the face of evolving energy landscapes and operational challenges.