Special lssues

Fault Diagnosis and State Evaluation of New Power Grid

Submission Deadline: 15 June 2023 (closed)

Guest Editors

Bo Yang, Kunming University of Science and Technology, China. Email: yangbo_ac@outlook.com
Yaxing Ren, University of Lincoln, United Kingdom. Email: YRen@lincoln.ac.uk
Yixuan Chen, University of Hong Kong, Hong Kong. Email: yxchen@eee.hku.hk
Xiaoshun Zhang, Northeastern University, China. Email: zhangxiaoshun@mail.neu.edu.cn

Summary

The distributed generation mode of renewable energy will inevitably lead to the rapid growth of the operating equipment of new power systems. Therefore, it is necessary to combine digital technology and artificial intelligence to realize efficient, accurate, and timely perception and intelligent decision-making of power grid equipment status, so as to meet the new challenges brought by the operation and maintenance of new power system equipment. Meanwhile, with the continuous expansion of the power system scale and the increasing complexity of the structure, a large amount of alarm information flows into the dispatching center in a short time, which far exceeds the processing capacity of the operators. To adapt to the rapid and accurate identification of faults under various simple and complex accident situations, the power system fault diagnosis system is crucial for decision-making reference.

This research topic aims to collate original papers about state perception and fault diagnosis of new power systems, which aims to provide a platform to promote up-to-date research and share promising ideas in related fields. Review articles describing the state of the art are also welcomed. Potential topics include but are not limited to the following:
• Model-based and model-free fault detection and isolation, fault threshold selection for renewable energy systems, such as wind, solar, fuel cells, and so on;
• Fault-tolerant control and reconfiguration of renewable energy systems and grids;
• Artificial intelligence methods for fault detection and isolation;
• Parameters identification of renewable energy systems, such as solar, fuel cells, wave energy, etc;

• Life cycle prediction and management of batteries and energy storage;

• Multi-source fusion for state monitoring of renewable energy systems and grids;
• The application of machine learning technology and digital twins in smart grids;
• Construction of knowledge graph of HVDC transmission system.


Keywords

Renewable energy systems, New power grid, Fault diagnosis/fault detection and isolation, Fault-tolerant control and reconfiguration, Parameter identification, Life cycle prediction and management, Machine learning technology, Digital twins, Knowledge graph.

Published Papers


  • Open Access

    ARTICLE

    Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

    Yongcan Zhu, Jiawen Wang, Ye Zhang, Long Zhao, Botao Jiang, Xinbo Huang
    Energy Engineering, Vol.121, No.4, pp. 895-911, 2024, DOI:10.32604/ee.2023.041002
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment… More >

  • Open Access

    ARTICLE

    Modeling of Large-Scale Hydrogen Storage System Considering Capacity Attenuation and Analysis of Its Efficiency Characteristics

    Junhui Li, Haotian Zhang, Cuiping Li, Xingxu Zhu, Ruitong Liu, Fangwei Duan, Yongming Peng
    Energy Engineering, Vol.121, No.2, pp. 291-313, 2024, DOI:10.32604/ee.2023.027593
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract In the existing power system with a large-scale hydrogen storage system, there are problems such as low efficiency of electric-hydrogen-electricity conversion and single modeling of the hydrogen storage system. In order to improve the hydrogen utilization rate of hydrogen storage system in the process of participating in the power grid operation, and speed up the process of electric-hydrogen-electricity conversion. This article provides a detailed introduction to the mathematical and electrical models of various components of the hydrogen storage unit, and also establishes a charging and discharging efficiency model that considers the temperature and internal gas… More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

    Shumin Sun, Peng Yu, Jiawei Xing, Yan Cheng, Song Yang, Qian Ai
    Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification… More >

  • Open Access

    ARTICLE

    Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

    Xueting Cheng, Ziqi Zhang, Yueshuang Bao, Huiping Zheng
    Energy Engineering, Vol.120, No.11, pp. 2517-2529, 2023, DOI:10.32604/ee.2023.042633
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk More >

  • Open Access

    ARTICLE

    Optimization of DC Resistance Divider Up to 1200 kV Using Thermal and Electric Field Analysis

    Dengyun Li, Baiwen Du, Kai Zhu, Jicheng Yu, Siyuan Liang, Changxi Yue
    Energy Engineering, Vol.120, No.11, pp. 2611-2628, 2023, DOI:10.32604/ee.2023.028282
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract Self-heating and electric field distribution are the primary factors affecting the accuracy of the Ultra High Voltage Direct Current (UHVDC) resistive divider. Reducing the internal temperature rise of the voltage divider caused by self-heating, reducing the maximum electric field strength of the voltage divider, and uniform electric field distribution can effectively improve the UHVDC resistive divider’s accuracy. In this paper, thermal analysis and electric field distribution optimization design of 1200 kV UHVDC resistive divider are carried out: (1) Using the proposed iterative algorithm, the heat dissipation and temperature distribution of the high voltage DC resistive… More >

  • Open Access

    ARTICLE

    Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade

    Wenyang Tang, Cong Liu, Bo Zhang
    Energy Engineering, Vol.120, No.11, pp. 2667-2681, 2023, DOI:10.32604/ee.2023.040743
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain… More >

  • Open Access

    ARTICLE

    Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy

    Zhixian Qi, Shuohe Wang, Qiang Xue, Haiting Mi, Jian Wang
    Energy Engineering, Vol.120, No.9, pp. 2059-2077, 2023, DOI:10.32604/ee.2023.028595
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract A current identification method based on optimized variational mode decomposition (VMD) and sample entropy (SampEn) is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current. This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD; the optimal VMD for DC feeder current is decomposed into the intrinsic modal function (IMF) of different frequency bands. The sample entropy algorithm is used to perform feature extraction of each IMF, and More >

  • Open Access

    ARTICLE

    Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

    Guobin He, Rui Su, Jinxin Yang, Yuanping Huang, Huanlin Chen, Donghui Zhang, Cangtao Yang, Wenwen Li
    Energy Engineering, Vol.120, No.9, pp. 2133-2154, 2023, DOI:10.32604/ee.2023.029007
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization, multi-resource penetration in active distribution networks has been advancing fiercely. In particular, distributed generation (DG) based on renewable energy is critical for active distribution network operation enhancement. To comprehensively analyze the accessing impact of DG in distribution networks from various parts, this paper establishes an optimal DG location and sizing planning model based on active power losses, voltage profile, pollution emissions, and the economics of DG costs as well as meteorological conditions. Subsequently, multi-objective particle swarm optimization (MOPSO) is… More >

  • Open Access

    EDITORIAL

    Assessment on Fault Diagnosis and State Evaluation of New Power Grid: A Review

    Bo Yang, Yulin Li, Yaxing Ren, Yixuan Chen, Xiaoshun Zhang, Jingbo Wang
    Energy Engineering, Vol.120, No.6, pp. 1287-1293, 2023, DOI:10.32604/ee.2023.027801
    (This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)
    Abstract This article has no abstract. More >

Share Link