Distributed Power System Virtual Inertia Implemented by Grid-Connected Power Converters

Renewable energy sources (RESs), e.g., wind and solar photovoltaics, have been increasingly used to meet worldwide growing energy demands and reduce greenhouse gas emissions. However, RESs are normally coupled to the power grid through fast-response power converters without any inertia, leading to decreased power system inertia. As a result, the grid frequency may easily go beyond the acceptable range under severe frequency events, resulting in undesirable load-shedding, cascading failures, or even large-scale blackouts. To address the ever-decreasing inertia issue, this paper proposes the concept of distributed power system virtual inertia, which can be implemented by grid-connected power converters. Without modifications of system hardware, power system inertia can be emulated by the energy stored in the dc-link capacitors of grid-connected power converters. By regulating the dc-link voltages in proportional to the grid frequency, the dc-link capacitors are aggregated into an extremely large equivalent capacitor serving as an energy buffer for frequency support. Furthermore, the limitation of virtual inertia, together with its design parameters, is identified. Finally, the feasibility of the proposed concept is validated through simulation and experimental results, which indicate that 12.5% and 50% improvements of the frequency nadir and rate of change of frequency can be achieved.NRF (Natl Research Foundation, S’pore)Accepted versio

Study on Small Layers Producing Condition by Using the Method of Fuzzy Comprehensive Evaluation

Before the subdivision adjustment of single well layers carried out, we need to evaluate the producing degree of the existing layers. There are many factors affecting the properties of small layers. This paper, using fuzzy comprehensive evaluation method, calculates the comprehensive evaluation coefficient to judge the property of each small layer

A Three-Input Central Capacitor Converter for a High-Voltage PV System

High-voltage photovoltaic (PV) techniques have their own advantages in PV plants for reducing the construction cost and improving the operational efficiency. However, the high input PV voltage increases the mismatch losses of PV arrays, which is also a key factor that influences the energy yield of PV plants. This paper proposes a three-input central capacitor (TICC) dc/dc converter for a high-voltage PV system, where four low-rating cascaded buck-boost converters connect to the series-connected three low-voltage PV arrays and two capacitors and realize the maximum power point tracking independently. Meanwhile, there is a neutral point in the proposed converter, enabling it to be connected with the rear-end three-level inverter directly. It can also help balance the three-level dc-link voltage by properly regulating the transferred energy among three input sources. Compared with other transformer-less dc-dc converters, the proposed converter is able to reduce the semiconductor voltage/current stress and therefore achieve the high efficiency. Simulation and experimental results verified the performance of the proposed TICC converter

JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using RGBD diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGBD generation, dense depth prediction, depth-conditioned image generation, and coherent tile-based 3D panorama generation