Hui-Myoung Oh’s research while affiliated with Korea Electrotechnology Research Institute-KERI and other places

With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms.

In wireless power transfer (WPT) systems, the electromagnetic fields generated by a charging module may exceed the limits set by international safety guidelines. This is a matter of concern for the safety of users of high-power WPT systems, such as electric vehicles (EVs). To address this issue, this study designed a stationary WPT system for EV charging. Furthermore, the dosimetry of the system was evaluated for two exposure scenarios. Electromagnetic field data obtained using the electromagnetic field analysis tool were employed to derive the induced quantities in the human body using the impedance method. In addition, the obtained results were compared to the values recommended by international guidelines (International Commission on Non-Ionizing Radiation Protection).

As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.