Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation Systems

Core Concepts

A novel adaptive regulated sparse regression (ARSR) algorithm is developed to efficiently identify the dynamics of single-stage and two-stage grid-connected solar photovoltaic (PV) systems from measurement data, enabling accurate data-driven modeling and control design.

Abstract

The paper introduces a novel data-driven modeling framework using adaptive regulated sparse regression (ARSR) for characterizing the dynamics of both single-stage and two-stage grid-connected photovoltaic (PV) systems. The key contributions are: Development of the ARSR algorithm that adaptively regulates the hyperparameter weights of candidate functions to best represent the PV system dynamics, addressing the limitations of conventional sparse regression techniques. Application of the ARSR approach to obtain open-loop and closed-loop data-driven models of single-stage and two-stage PV systems, which are then utilized for control design purposes. Demonstration of the ARSR approach's capability in fault analysis studies, distinguishing it from other data-driven techniques. Validation of the proposed approach through time-domain simulations and real-time simulations using an OPAL-RT real-time simulator. The results show that the ARSR-based data-driven models closely match the physical models of the PV systems, highlighting the effectiveness of the proposed approach in accurately capturing the system dynamics. The adaptive hyperparameter tuning in ARSR is crucial in enhancing the modeling accuracy, especially for the more complex two-stage PV system. The data-driven models developed using ARSR can be effectively employed for control design and fault analysis applications.

Stats

The paper does not provide any specific numerical data or statistics. The focus is on the development and validation of the ARSR algorithm for data-driven modeling and control of PV systems.

Quotes

"The key enhancement lies in the algorithm's capability to adapt the regularization parameter λ dynamically while identifying distinct states of the system." "The results obtained from the fault tests in this section demonstrate that a data-driven model can provide insights into how a physical system reacts in a fault scenario."

Key Insights Distilled From

Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

by Zhongtian Zh... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19028.pdf

Deeper Inquiries

How can the ARSR approach be extended to handle noisy measurement data and address uncertainties in the PV system parameters

To extend the Adaptive Regulated Sparse Regression (ARSR) approach to handle noisy measurement data and uncertainties in PV system parameters, several strategies can be implemented. Noise Filtering Techniques: Utilize signal processing methods like Kalman filters or wavelet denoising to preprocess the measurement data before applying the ARSR algorithm. These techniques can help reduce the impact of noise on the identification process. Robust Sparse Regression: Incorporate robust sparse regression techniques that are less sensitive to outliers in the data. Methods like LASSO with robust loss functions or Huber regression can help mitigate the effects of noisy data on the model identification. Uncertainty Quantification: Integrate uncertainty quantification methods into the ARSR framework to account for parameter uncertainties in the PV system. Techniques like Bayesian inference or Monte Carlo simulations can provide insights into the uncertainty levels and help in robust model identification. Ensemble Modeling: Implement ensemble modeling approaches where multiple data-driven models are trained on different subsets of the data to capture the variability and uncertainty in the measurements. By combining the results from multiple models, a more robust and accurate representation of the system dynamics can be achieved.

What are the potential challenges and limitations of the ARSR approach when scaling to larger, more complex power systems beyond PV applications

Scaling the ARSR approach to larger and more complex power systems beyond PV applications may present some challenges and limitations: Curse of Dimensionality: As the system complexity increases, the number of candidate functions and potential interactions between variables grow exponentially. This can lead to computational challenges and increased model complexity, making it harder to identify the relevant dynamics accurately. Data Availability: Larger power systems may require a significant amount of high-quality data for training the data-driven models effectively. Obtaining and processing such extensive datasets can be resource-intensive and may pose challenges in real-world applications. Model Interpretability: With larger systems, interpreting the results of the sparse regression models becomes more complex. Understanding the relationships between variables and the identified dynamics can be challenging, especially in highly interconnected systems. Computational Resources: Scaling the ARSR approach to larger systems may require substantial computational resources for model training and optimization. Handling the increased computational load efficiently is crucial for practical implementation. Generalization: Ensuring that the data-driven models generated using ARSR can generalize well to unseen data and different operating conditions becomes more critical as the system complexity grows. Overfitting and underfitting issues need to be carefully addressed.

Could the ARSR framework be integrated with other data-driven techniques, such as machine learning or deep learning, to further enhance the modeling and control capabilities for PV systems

Integrating the ARSR framework with other data-driven techniques like machine learning or deep learning can offer several benefits for enhancing the modeling and control capabilities of PV systems: Feature Extraction: Deep learning methods can be used for automatic feature extraction from raw data, providing more informative input representations for the sparse regression model in ARSR. This can improve the accuracy and efficiency of the identification process. Hybrid Models: Combining machine learning algorithms for pattern recognition and deep learning for feature extraction with the ARSR approach can lead to hybrid models that capture both the complex relationships in the data and the sparsity of the system dynamics. Transfer Learning: Leveraging transfer learning techniques from pre-trained deep learning models can help in initializing the sparse regression model in ARSR, especially when dealing with limited data or complex system behaviors. Nonlinear Mapping: Deep learning models can handle nonlinear relationships effectively, complementing the sparse regression in ARSR for capturing intricate dynamics in PV systems. This combination can lead to more accurate and robust models. Real-time Adaptation: Machine learning algorithms can enable real-time adaptation of the data-driven models based on changing system conditions, enhancing the control capabilities of the PV systems in dynamic environments.