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Author

Xie Xiangying

Bio: Xie Xiangying is an academic researcher from Beihang University. The author has contributed to research in topics: Photovoltaic system & Photovoltaic power station. The author has an hindex of 1, co-authored 19 publications receiving 9 citations.

Papers
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Journal ArticleDOI
01 Dec 2020
TL;DR: It can be found that some parameters like tolerance coefficient are more dominant than others for the robustness of a network and some parameters interact obviously, like capacity addition ratio and addition station ratio, which may have a negative effect on the network.
Abstract: The robustness of power grids plays a very important role in reality. The blackout of power system can make a serious damage for the normal running of whole city or whole area and the loss caused by it is inestimable. This paper aims to propose a simulation method to evaluate the robustness of a power grid and some effective strategies to improve the robustness. Complex network keeps the most important characters of a power grid, and makes the simulation process concise. This paper studies how characters influence the robustness and proposes some conclusions and suggestions for enhancing effectively the robustness by changing these characters. In this paper, for the robustness of a network, it can be found that some parameters like tolerance coefficient are more dominant than others. Some parameters interact obviously, like capacity addition ratio and addition station ratio. Improper capacity addition strategy may have a negative effect on the network. And load node with big degree and small flow is likely an excellent choice to be added capacity by distributed photovoltaic power station. The simulation method and improvement strategy can be used in the engineering of power grid and establishment of distributed photovoltaic power system.

7 citations

Journal ArticleDOI
Fan Tao1, Sun Tao, Hu Liu1, Xie Xiangying, Na Zhixiong 
TL;DR: Wang et al. as discussed by the authors proposed a novel Spatial-Temporal Genetic-based Attention Networks (STGANet), which consists of a spatial-temporal module (STM) and a genetic-based attention module (GAM).
Abstract: Photovoltaic (PV) output power is significantly random and fluctuating due to its sensitivity to meteorological factors, making PV power forecasting a big challenge. Accurate short-term PV power forecasting plays a crucial role for the stable operation and maintenance management of PV systems. To achieve this target, the paper proposes a novel Spatial-Temporal Genetic-based Attention Networks (STGANet), which consists of a spatial-temporal module (STM) and a genetic-based attention module (GAM). STM serves to predict the missing solar irradiance to support the generation forecast, and contains a graph convolutional neural network to learn the spatial and temporal dependencies between historical meteorological data, while using dilated convolution as the non-linear part to simplify the network structure. The GAM efficiently explores for potential relationships in input features with attentional mechanism and uses genetic-based operation and LSTM which takes forecasting error as reference to find global optimal solutions and to avoid getting trapped in local optimal solutions. The model is verified through comparative experiment with several benchmark models using a real-world historical meteorological dataset and a power generation dataset of PV plants in southeastern China. The results have illustrated that the proposed model can provide better prediction performance in PV systems.

4 citations

Patent
15 Feb 2019
TL;DR: In this article, a method for evaluating the state of charge of lithium batteries, including the following steps: acquiring the actual operation state data of a lithium battery, comparing the actual operator state data with the state parameters corresponding to each open circuit voltage-state of charge curve in a pre-established database to determine corresponding Open Circuit Voltage-State of Charge curves, and determining the battery according to the correspondingOpen Circuit voltage-State-of-charge curves.
Abstract: An embodiment of the invention provides a method for evaluating the state of charge of lithium batteries, including the following steps: acquiring the actual operation state data of a lithium battery;comparing the actual operation state data with the state parameters corresponding to each open circuit voltage-state of charge curve in a pre-established database to determine corresponding open circuit voltage-state of charge curves when the actual operation state data is matched with the state parameters; and determining the state of charge of the lithium battery according to the correspondingopen circuit voltage-state of charge curves. The other embodiments of the invention provide a device and equipment for evaluating the state of charge of lithium batteries. A curve cluster obtained bythe evaluation method of the invention can completely cover the operation characteristics of batteries under different working conditions, and the state of charge of lithium batteries can be correctedbased on the curve cluster. Therefore, the evaluation accuracy of the state of charge of lithium batteries can be improved.

1 citations

Patent
04 Jan 2019
TL;DR: In this article, the authors proposed a fault detection method for photovoltaic power stations in various environments at provincial or national scales connected to the State Grid Distributed Photovolta Cloud Network.
Abstract: Embodiments of the present application provide a fault detection method, apparatus, apparatus, and storage medium for a photovoltaic power station. The fault detection method comprises the following steps of: acquiring photovoltaic power generation and environmental parameters of the photovoltaic power station in a specified period of time; According to the neutralization of the environmental parameters and the neural network model trained in advance, the power prediction value and prediction error of the photovoltaic power station in a specified period of time are determined. According to thepower of photovoltaic power generation, power prediction value and prediction error, whether the photovoltaic power station has a fault in a specified period of time is judged. The embodiment of thepresent application realizes accurate detection of photovoltaic power station faults, is applicable to photovoltaic power stations in various environments at provincial or national scales connected tothe State Grid Distributed Photovoltaic Cloud Network, and can realize effective dispatch of a large number of photovoltaic power stations at provincial or national scales.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: The results show that policy guidance has a significant impact on the evolution of China's CRIG and that given this influence, the degree of CRIG distribution will have an “edge-multi-core” shape and the network density will show an inverted “N” curve shape.

9 citations

Journal ArticleDOI
14 Jun 2022-Energies
TL;DR: In this article , the authors present a compendium of references published since 2011 on spatio-temporal methods for global horizontal irradiance and photovoltaic generation, categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon).
Abstract: To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

6 citations

Journal ArticleDOI
22 Aug 2021-Energies
TL;DR: The algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model and can effectively measure the short-term vulnerability of power grid units under extreme weather.
Abstract: The large-scale interconnection of the power grid has brought great benefits to social development, but simultaneously, the frequency of large-scale fault accidents caused by extreme weather is also rocketing. The power grid is regarded as a representative complex network in this paper to analyze its functional vulnerability. First, the actual power grid topology is modeled on the basis of the complex network theory, which is transformed into a directed-weighted topology model after introducing the node voltage together with line reactance. Then, the algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model. The product of unit reliability and topology model’s characteristic parameters under extreme weather is used as the index to measure the functional vulnerability of the power grid, which considers the extreme weather of freezing and gale and quantifies the functional vulnerability of lines under wind load, ice load, and their synergistic effects. Finally, a simulation using the IEEE-30 node system is implemented. The result shows that the proposed method can effectively measure the short-term vulnerability of power grid units under extreme weather. Meanwhile, the example analysis verifies the different effects of normal and extreme weather on the power grid and identifies the nodes and lines with high vulnerability under extreme weather, which provides theoretical support for preventing and reducing the impact of extreme weather on the power grid.

5 citations

TL;DR: This pioneering study takes the I - V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN) that can achieve end-to-end fault diagnosis and is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function (SAMME-CART).
Abstract: : With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Consid-ering that all the fault information of the PV module is contained in the current-voltage ( I - V ) curve, this pioneering study takes the I - V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been verified in this study, which is of great significance for realizing the edge diagnosis of PV fault.

5 citations

Proceedings ArticleDOI
12 Mar 2021
TL;DR: In this paper, a critical node identification algorithm based on PageRank algorithm is proposed to identify critical nodes in power systems and improve system security. But, this algorithm is not suitable for power line power systems.
Abstract: There are some critical nodes in power system operation, and the failure of these nodes can easily aggravate the propagation of faults and lead to serious power outage accidents. In order to identify critical nodes in power systems and improve system security, this paper proposes a critical node identification algorithm based on PageRank algorithm. A model of the impact of node failure on system line power is proposed based on tide calculation, and this model is combined with the PageRank algorithm to construct point-to-point links to achieve effective identification of critical nodes. Finally, simulation analysis is performed on IEEE-39 system, and the topology and capacity and load are verified and compared with existing methods by simulating deliberate attacks on critical nodes, which proves that the proposed method is useful for identifying nodes in critical positions in the system and can provide guidance for safe and stable operation of the power system.

2 citations