Synthetic data generation through power hardware-in-the-loop (PHIL) simulations

Over the past decade, artificial intelligence (AI) has played a pivotal role in driving significant advances in various technical fields, with energy management standing out as a key beneficiary of these advancements. The performance of AI and machine learning (ML) models is intricately tied to the quality and quantity of the training data; robust and stable model performance necessitates access to numerous amounts of high-fidelity data. However, the acquisition of extensive real-world datasets that encompass the entire operational spectrum of an AI system can be loaded with difficulties. These challenges arise from the limited availability of data, the potentially expensive costs of data collection, and the risks or hazards that may be associated with certain data acquisition procedures. Overcoming these obstacles, the synthesis of artificial data has emerged as a powerful solution. Synthetic data, artificially generated information that mirrors the statistical properties of real-world data, is a key tool. It is crafted using various techniques and models to ensure it closely resembles the structure, characteristics, and behavior of real data. Synthetic data is often used when real data is scarce, sensitive, or expensive to collect or when there is a need to augment existing datasets to improve the performance of ML models. The key advantage of synthetic data is its ability to be generated in large quantities while preserving privacy and confidentiality, as it does not contain any real or identifiable information. Synthetic data generation enables the augmentation of datasets with necessary additional data samples, thereby enhancing the performance and functionality of AI-based systems. This method effectively mitigates the constraints of real-world data collection, providing a controlled and adaptable approach to data generation that can be customized to meet the specific demands of the ML task at hand. Hardware-in-the-loop (HIL) simulations are advanced testing methods that combine real physical hardware components with simulated electrical systems. This approach allows for the realistic evaluation of how new equipment or control strategies will perform in an electrical grid or energy system without needing full-scale, real-world testing. The synergy between HIL simulations and AI in enhancing energy management is a powerful combination that leverages the strengths of both technologies to improve the efficiency, reliability, and sustainability of energy systems.