A flexible experimental laboratory for distributed generation networks based on power inverters

In the recently deregulated electricity market, distributed generation based on renewable sources is becoming more and more relevant. In this area, two main distributed scenarios are focusing the attention of recent research: grid-connected mode, where the generation sources are connected to a grid mainly supplied by big power plants, and islanded mode, where the distributed sources, energy storage devices, and loads compose an autonomous entity that in its general form can be named a microgrid. To conduct a successful research in these two scenarios, it is essential to have a flexible experimental setup. This work deals with the description of a real laboratory setup composed of four nodes that can emulate both scenarios of a distributed generation network. A comprehensive description of the hardware and software setup will be done, focusing especially in the dual-core DSP used for control purposes, which is next to the industry standards and able to emulate real complexities. A complete experimental section will show the main features of the system.Peer ReviewedPostprint (published version

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey

With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models: https://github.com/wangxiao5791509/MultiModal_BigModels_SurveyComment: Accepted by Machine Intelligence Researc

Active Management of Distributed Generation based on Component Thermal Properties

Power flows within distribution networks are expected to become increasingly congested with the proliferation of distributed generation (DG) from renewable energy resources. Consequently, the size, energy penetration and ultimately the revenue stream of DG schemes may be limited in the future. This research seeks to facilitate increased renewable energy penetrations by utilising power system component thermal properties together with DG power output control techniques. The real-time thermal rating of existing power system components has the potential to unlock latent power transfer capacities. When integrated with a DG power output control system, greater installed capacities of DG may be accommodated within the distribution network. Moreover, the secure operation of the network is maintained through the constraint of DG power outputs to manage network power flows. The research presented in this thesis forms part of a UK government funded project which aims to develop and deploy an on-line power output control system for wind-based DG schemes. This is based on the concept that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on component cooling mechanisms. The control system compares component real-time thermal ratings with network power flows and produces set points that are fed back to the DG for implementation. The control algorithm comprises: (i) An inference engine (using rule-based artificial intelligence) that decides when DG control actions are required; (ii) a DG set point calculator (utilising predetermined power flow sensitivity factors) that computes updated DG power outputs to manage distribution network power flows; and (iii) an on-line simulation tool that validates the control actions before dispatch. A section of the UK power system has been selected by ScottishPower EnergyNetworks to form the basis of field trials. Electrical and thermal datasets from the field are used in open loop to validate the algorithms developed. The loop is then closed through simulation to automate DG output control for increased renewable energy penetrations

Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work

Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field

Modelling long- and short-term structure in symbolic music with attention and recurrence

The automatic composition of music with long-term structure is a central problem in music generation. Neural network-based models have been shown to perform relatively well in melody generation, but generating music with long-term structure is still a major challenge. This paper introduces a new approach for music modelling that combines recent advancements of transformer models with recurrent networks – the long-short term universal transformer (LSTUT), and compare its ability to predict music against current state-of-the-art music models. Our experiments are designed to push the boundaries of music models on considerably long music sequences – a crucial requirement for learning long-term structure effectively. Results show that the LSTUT outperforms all the other models and can potentially learn features related to music structure at different time scales. Overall, we show the importance of integrating both recurrence and attention in the architecture of music models, and their potential use in future automatic composition systems

Ancillary Services Market Design in Distribution Networks: Review and Identification of Barriers

The high proliferation of converter-dominated Distributed Renewable Energy Sources (DRESs) at the distribution grid level has gradually replaced the conventional synchronous generators (SGs) of the transmission system, resulting in emerging stability and security challenges. The inherent characteristics of the SGs are currently used for providing ancillary services (ASs), following the instructions of the Transmission System Operator, while the DRESs are obliged to o er specific system support functions, without being remunerated for these functions, but only for the energy they inject. This changing environment has prompted the integration of energy storage systems as a solution for transfusing new characteristics and elaborating their business in the electricity markets, while the smart grid infrastructure and the upcoming microgrid architectures contribute to the transformation of the distribution grid. This review investigates the existing ASs in transmission system with the respective markets (emphasizing the DRESs’ participation in these markets) and proposes new ASs at distribution grid level, with emphasis to inertial response, active power ramp rate control, frequency response, voltage regulation, fault contribution and harmonic mitigation. The market tools and mechanisms for the procurement of these ASs are presented evolving the existing role of the Operators. Finally, potential barriers in the technical, regulatory, and financial framework have been identified and analyzed.Unión Europea (Programa Horizonte 2020) 76409