Mass penetration of renewable energy sources (RES) has drastically triggered the integration of clusters of VSG-based grid-forming inverters (GFMIs) to enhance grid stability and facilitates the transition towards power electronics dominated grid (PEDG). Although high number of GFMIs provides a smoother frequency response due to higher emulated virtual inertia, considering the complex dynamic of the PEDG, estimating the emulated virtual inertia by the primary control layer of the GFMIs introduces extensive mathematical modeling and computational burden. To address the shortcomings of conventional model-based approaches, this paper proposes a data-driven methodology based on convolution neural network (CNN) to estimate effective virtual inertia by analyzing the explanatory frequency response of a cluster of GFMIs. The neural network (NN) is trained offline and evaluated on a sample cluster of 100% GFMIs through multiple case studies to validate the effectiveness of the proposed methodology. The application of the proposed data-driven tool can further facilitate network analysis by considering the corresponding aggregated model to enhance the stability analysis of the PEDG.

Power electronics dominated grid (PEDG) consisting of multiple inter-connected distributed generation (DG) units are getting more popular because of their benefits in reducing the stress on main transmission lines, limiting line losses, and improving the efficiency and stability of the power system. Often a PEDG is comprised of several energy sources such as solar, wind, and energy storage devices. For that reason, optimal power sharing between all the sources is crucial to enhance the reliability and resiliency of the network. Hence, this paper provides comprehensive insights into the operation and working principles, limitations, and challenges of the power-sharing schemes employed in PEDG. More specifically, control laws of the droop, virtual synchronous generator (VSG), and Lienard and Consensus-based oscillators are briefly overviewed. In addition, the inherent drawbacks and challenges of existing power-sharing schemes are explained for inductive, resistive, and complex line impedances. Moreover, their performance under dynamic variations of the source availability, inverter power reserve, line losses, and generation costs are discussed in detail.

Stealthy cyberattacks pose a significant threat to modern power systems by exploiting advanced techniques to manipulate system behavior while avoiding detection by traditional security measures. In this study, we focus on the impact of Deep Reinforcement Learning (DRL) based attackers on a sample microgrid and develop robust detectors to mitigate these threats. Leveraging an iterative training process, we enhance the capabilities of successive attackers and detectors, resulting in improved system security. Our experiments demonstrate that DRL-based attackers can effectively disrupt system operations, highlighting the importance of robust detection mechanisms. Subsequently, we develop robust detection mechanisms, making new attacker attempts unsuccessful. We show that detectors developed through our mechanism are more effective in mitigating system impact and quickly identifying anomalies.

Virtual reality (VR) video services require a high bitrate, and hence, viewport-adaptive streaming techniques like motion-constrained-tile-set (MCTS) have been found important to reduce streaming-rate and storage demands. The tiling scheme and streaming field of view (FOV) are among the key elements in designing an optimal VR viewport-adaptive streaming solution, in terms of rate-distortion (R-D) performance. The aim of this study is to propose an optimal configuration for the tile grid and streaming FOV, considering different VR viewing situations such as head motion speed, system delay, and head-mounted display FOV. To achieve this, a wide range of tiling schemes and streaming FOVs are examined to study the storage and streaming R-D performance of the MCTS-based technique in both viewport and non-viewport areas using a quality metric called Zonal-cubic PSNR. The findings demonstrate that for VR applications focused on preserving high viewport quality, fine tile grids lead to higher performance. In scenarios featuring small and large HMD FOV, the optimal configuration involves a small and medium streaming FOV, respectively.

Virtual reality (VR) systems require delivering high-fidelity 360° video content to immerse viewers to the captured scene. The viewport-adaptive streaming (VAS) methods have been developed to deliver 360° VR content efficiently. The Versatile Video Coding (VVC) standard introduces the subpicture picture partitioning tool, which creates isolated regions suitable for VAS. The usage of Adaptive Loop Filter (ALF) as a VVC in-loop filtering operation is limited in subpicture-based VAS. This paper aims at enabling usage of ALF in subpicture-base VAS through proposing a set of encoding constraints that are standard compliant. While ALF is activated, the proposed constrains guarantee that no coding coordination with respect to sharing of ALF parameters among subpictures is required. This further allows subpicture-based parallel encoding of high-resolution VR content. We study the performance of several methods targeting both single- and multi-thread encoding platforms. The experimental results indicate that VR content encoding can be parallelized at a subpicture-group level, while still preserving most of the ALF gain. The proposed method with subpicture-group encoding parallelization achieves on average -2.4%, -4.0%, and -4.3% Bjøntegaard delta rate reduction for Y, U, and V components respectively, compared to the case where ALF operation is deactivated.

We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is performed at encoder side to adapt the neural network to the specific content that is being encoded. In order to maximize the PSNR gain and minimize the bitrate overhead, we propose to finetune only the convolutional layers' biases. The proposed method achieves convergence much faster than conventional finetuning approaches, making it suitable for practical applications. The weight-update can be included into the video bitstream generatedby the existing video codecs. We show that our method achieves up to 9.7% average BD-rate gain when compared to the state-of-art Versatile Video Coding (VVC) standard codec on 7 test sequences.

Tile-based viewport-adaptive streaming methods have been used in delivering omnidirectional video for virtual reality applications. In these methods, the 360° video is encoded in multiple quality versions by using the motion constrained tile set (MCTS) technique. A set of high-quality and low-quality tiles, corresponding to viewport and non-viewport areas, respectively, are selected and transmitted to the user. However, these methods require frequent intra random access points to ensure seamless viewport switching capability, very high decoding complexity, or a multi-layer coding scheme. The frequent intra random access points include very high bitrate in viewport switching points. The high decoding complexity and multi-layer decoder requirements are not aligned with the omnidirectional media format (OMAF) standard. Such requirements make these methods sub-optimal or impractical for streaming the omnidirectional video. This paper studies the current tile-based solutions for delivering the omnidirectional content. Moreover, the OMAF-compliant shared coded picture (SCP)-based scheme is proposed in this paper for streaming the omnidirectional video. The core concept of the SCP-based method is to manipulate the switching point pictures in a way that the frequent intra-coded pictures are no longer required for the viewport switching operations between different quality versions of the content. The experiments illustrated that the SCP-based method outperforms the MCTS-based method on average by 11% to 14% in terms of streaming bitrate reduction with only 4% extra decoding complexity.

The recent Omnidirectional MediA Format (OMAF) standard, which specifies the delivery of 360° video content, supports only equirectangular projection (ERP) and cubemap projection and their region-wise packing with a limitation on video decoding capability to the maximum resolution of 4K (e.g., 4,096 × 2,048). Streaming of 4K ERP content allows only a limited viewport resolution, which is lower than the resolution of many current head-mounted displays (HMDs). Therefore, to take full advantage of high-resolution HMDs, delivery of 360° video content beyond 4K resolution needs to be enabled. In this regard, we propose two specific mixed-resolution packing schemes of 6K (e.g., 6,144 × 3,072) and 8K (e.g., 8,192 × 4,096) ERP content and their realization in tile-based streaming, while complying with the 4K decoding constraint and the High Efficiency Video Coding standard. The proposed packing schemes offer 6K and 8K effective resolution at the viewport. Using our proposed test methodology, experimental results indicate that the proposed layouts significantly decrease streaming bitrates when compared to mixed-quality viewport-adaptive streaming of 4K ERP. Our results further indicate that 8K-effective packing outperforms 6K-effective packing especially in high-quality videos.

Virtual reality (VR) head-mounted display (HMD) requires spherical panoramic contents with high-spatial and temporal fidelity to immerse the viewers into the captured scene. Hereby, VR contents are extremely bandwidth intensive and impose technical challenges for the design of a VR streaming system. A bandwidth-efficient VR streaming system can be achieved using the viewport-aware adaptation techniques, in which part of the sphere within the viewer's field of view is presented at higher quality. In this paper, two recently emerged viewport-adaptive streaming methods so-called tile-based method and truncated square pyramid (TSP) projection, a well-studied viewport-dependent projection, are compared using a proposed quality assessment methodology. The comparison is made in terms of storage and streaming bitrate performances. The simulation results indicate that the tile-based approach has slightly lower streaming performance, while offering a significant storage and encoding time saving at the server side, compared to TSP-based streaming.

Virtual reality applications make use of 360-degree panoramic or omnidirectional video with high resolution and high frame rate in order to create the immersive experience to the user. The user views only a portion of the captured 360-degree scene at each time instant, hence streaming the whole omnidirectional video in highest quality is not efficient. In order to alleviate the problem of bandwidth wastage, viewport-adaptive encoding and streaming schemes have been proposed. In these schemes, part of the captured scene that is within the viewer's field of view is delivered at highest quality while the rest of the scene in a lower quality. In this work, three tile-based viewport-adaptive methods using motion-constrained tile sets (MCTS), region-of-interest scalability and simulcast approach have been studied for streaming omnidirectional content. In the performed experiments with various tiling arrangements, MCTS-based scheme required highest bitrate compared to other methods. The scalable coding scheme provided the highest performance in terms of streaming bitrate saving on average up to 53% and 35% compared to streaming the whole omnidirectional video and MCTS-based method, respectively.

Delivering wide-angle and high-resolution spherical panoramic video content entails a high streaming bitrate. This imposes challenges when panorama clips are consumed in virtual reality (VR) head-mounted displays (HMD). The reason is that the HMDs typically require high spatial and temporal fidelity contents and strict low-latency in order to guarantee the user's sense of presence while using them. In order to alleviate the problem, we propose to store two versions of the same video content at different resolutions, each divided into multiple tiles using the High Efficiency Video Coding (HEVC) standard. According to the user's present viewport, a set of tiles is transmitted in the highest captured resolution, while the remaining parts are transmitted from the low-resolution version of the same content. In order to enable randomly choosing different combinations, the tile sets are encoded to be independently decodable. We further study the trade-off in the choice of tiling scheme and its impact on compression and streaming bitrate performances. The results indicate streaming bitrate saving from 30% to 40%, depending on the selected tiling scheme, when compared to streaming the entire video content.

Virtual reality (VR) provides unprecedented immersive experience using high-resolution spherical stereoscopic panoramic video. Such an experience is achieved by using head-mounted display (HMD) which has very strict latency bounds in order to respond promptly to user movements. Conventional streaming of VR video requires large bandwidth because the entire captured panorama is transmitted. However, only a limited field-of-view (FOV) is displayed by an HMD, resulting in wastage of bandwidth. To alleviate the problem, this paper proposes a High Efficiency Video Coding (HEVC) compliant approach for efficient coding and streaming of stereoscopic VR content. The proposed method is based on partitioning video pictures into tiles, where only the required tiles corresponding to the primary viewport are transmitted in high resolution, while the remaining parts are transmitted in low resolution. Furthermore, this method enables coding stereoscopic video contents using a conventional HEVC codec, while still achieving significant compression gain by means of adopting inter-view prediction only in intra random access point (IRAP) pictures. Using this method, the predicted view can be decoded independently of the main view, hence allowing simultaneous decoding instances. Experimental results demonstrate that the proposed approach is able to substantially improve compression efficiency and streaming bitrate performance.