Non-unit protection of parallel lines connecting solar photovoltaic plants

Fault current control by solar plant converters introduces different fault characteristics compared to conventional synchronous generator-based systems resulting in numerous issues to the available protection methods. In this article, the issues with conventional distance relaying is analyzed while protecting parallel lines connecting solar plant to grid and a new protection method is proposed using local voltage and current data. The proposed nonunit protection method derives positive sequence reactive powers for both lines for different solar plant operating conditions and uses their difference to ensure correct zone-1 protection. For faults during single circuit operation, the method includes an additional scheme comprising of instantaneous zero-sequence overcurrent check and delayed distance relaying to derive correct protection decision. Performance of the proposed nonunit protection method is tested for parallel lines connecting the solar plant in a 39-bus test system for different situations using PSCAD/EMTDC simulated data and found to be accurate. Comparative assessment reveals the high reliability of the proposed method

Adaptive unit protection for lines connecting large solar plants using incremental current ratio

Control schemes in a solar plant complying with different grid codes modulate the output voltage and current significantly during the fault. In this article, the issue with conventional current differential approaches for the line connecting the large solar plant is analyzed and a new protection technique using both end incremental current phasors is proposed. The proposed method uses two criteria to identify the internal faults in such connectivity. The first criterion is based on the ratio of both end incremental phase current phasors, and the second one uses the magnitude ratio of positive sequence incremental currents. Both the criteria are adaptive to line terminal currents and complement each other enriching the method applicable for any system condition. The performance of the proposed method is tested for different internal and external fault cases and found to be accurate. The compatibility of the proposed method is also validated using a real-time simulator. Comparative assessment with conventional current differential techniques reveals the superiority of the proposed approach

Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval

In this paper, we delve into the intricate dynamics of Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) by addressing a critical yet overlooked aspect -- the choice of viewpoint during sketch creation. Unlike photo systems that seamlessly handle diverse views through extensive datasets, sketch systems, with limited data collected from fixed perspectives, face challenges. Our pilot study, employing a pre-trained FG-SBIR model, highlights the system's struggle when query-sketches differ in viewpoint from target instances. Interestingly, a questionnaire however shows users desire autonomy, with a significant percentage favouring view-specific retrieval. To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks. Overcoming dataset limitations, our first contribution leverages multi-view 2D projections of 3D objects, instilling cross-modal view awareness. The second contribution introduces a customisable cross-modal feature through disentanglement, allowing effortless mode switching. Extensive experiments on standard datasets validate the effectiveness of our method.Comment: Accepted in European Conference on Computer Vision (ECCV) 202

What Can Human Sketches Do for Object Detection?

Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result is a sketch-enabled object detection framework that detects based on what \textit{you} sketch -- \textit{that} ``zebra'' (e.g., one that is eating the grass) in a herd of zebras (instance-aware detection), and only the \textit{part} (e.g., ``head" of a ``zebra") that you desire (part-aware detection). We further dictate that our model works without (i) knowing which category to expect at testing (zero-shot) and (ii) not requiring additional bounding boxes (as per fully supervised) and class labels (as per weakly supervised). Instead of devising a model from the ground up, we show an intuitive synergy between foundation models (e.g., CLIP) and existing sketch models build for sketch-based image retrieval (SBIR), which can already elegantly solve the task -- CLIP to provide model generalisation, and SBIR to bridge the (sketch$\rightarrow$photo) gap. In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP. We then devise a training paradigm to adapt the learned encoders for object detection, such that the region embeddings of detected boxes are aligned with the sketch and photo embeddings from SBIR. Evaluating our framework on standard object detection datasets like PASCAL-VOC and MS-COCO outperforms both supervised (SOD) and weakly-supervised object detectors (WSOD) on zero-shot setups. Project Page: \url{https://pinakinathc.github.io/sketch-detect}Comment: Accepted as Top 12 Best Papers. Will be presented in special single-track plenary sessions to all attendees in Computer Vision and Pattern Recognition (CVPR), 2023. Project Page: www.pinakinathc.me/sketch-detec