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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 14509

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Special Issue Editors

Dr. Li Ding

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Guest Editor

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, Hubei, China
Interests: network science and its application in power systems

Dr. Zhengmin Kong

E-Mail Website
Guest Editor

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, Hubei, China
Interests: distribution automation

Special Issue Information

Dear Colleagues,

The application of existing technologies in the power system has broad prospects for development. In addition, there remains a strong need for technological innovations (such as electric vehicles) to meet the requirements of the ever-increasing new loads as well as the high demand for power quality and power supply reliability. In the future, distribution networks will use high-speed broadband for communication between substations, utilize intelligent electronic devices for adaptive control and protection, and apply energy management systems to monitor the operation condition. Intelligent systems are also involved in the mitigation of the potential power quality issues, which consequently improves power supply reliability. Therefore, the emerging technologies and their application in the distribution network should be further studied, for example, advanced distribution network automation, feeder voltage/reactive power control, advanced sensor technology, intelligent universal transformer, multifunction solid state switch technology, fault prediction and location technology, advanced metering infrastructure, local energy network controller, distributed power generation and energy storage equipment, and cybersecurity. With these advanced technologies and the integration of artificial intelligence and big data analysis techniques, the digitally transparent distribution network is expected to become a reality.

This Special Issue aims to inspire original research on the emerging technologies in related fields to promote the application of new techniques in distribution networks. Theoretical and/or empirical studies are welcome. Contributions in the form of regular research articles on deep learning-based power demand prediction, data-driven topology identification for distribution networks, line loss calculation and anomaly analysis, and cybersecurity are of interest.

Dr. Li Ding
Dr. Zhengmin Kong
Guest Editors

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Keywords

distribution networks
topology identification
line loss
security enhancement
sensor technology
artificial intelligence
intelligence algorithm
smart device

Published Papers (9 papers)

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Research

18 pages, 4603 KiB

Open AccessArticle

Adaptive Band-Pass Filter and VMD-Esprit Based Multi-Mode Monitoring Method for Broadband Electromagnetic Oscillation in “Double High” Power Systems

by Tie Zhong, Heling Yang, Cong Sun, Chuang Liu and Junrui Chen

Energies 2023, 16(7), 3110; https://doi.org/10.3390/en16073110 - 29 Mar 2023

Viewed by 979

Abstract

With the development of new power systems with high proportions of renewable energy and high proportions of power electronics equipment, the influence of broadband electromagnetic oscillations in power systems is becoming more and more significant. In order to better grasp the dynamic characteristics of broadband oscillations, a new adaptive band-pass filter and VMD-Esprit based multi-modal monitoring method is proposed for broadband electromagnetic oscillation in “double high” power systems. First, based on the mode frequency and amplitude information provided by FFT mode detection, the proposed adaptive band-pass filter adaptively sets the center frequency, bandwidth, and other parameters and extracts or separates the voltage/current signal in each frequency band. Second, the filtered signals are corrected and compensated, and then the VMD modal decomposition of each frequency band signal is combined with Esprit for parameter identification so as to obtain the waveform and parameter information of each mode. Finally, the separation, correction, and parameter identification of multi-mode broadband oscillation waveforms are carried out. The experimental results show that frequency division processing can reduce the computation and improve real-time performance. In the processing of signals in the frequency band, the center frequency, bandwidth, and other parameters can be adjusted adaptively under different conditions of single-mode composition or multi-mode composition, which improves the accuracy of VMD decomposition and increases the flexibility of signal processing. Meanwhile, it overcomes the defects such as the inaccuracy of traditional mode recognition, which provides a new idea for broadband electromagnetic oscillation analysis. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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19 pages, 4245 KiB

Open AccessArticle

by Yong Liu, Weiwen Zhan, Yuan Li, Xingrui Li, Jingkai Guo and Xiaoling Chen

Energies 2023, 16(3), 1455; https://doi.org/10.3390/en16031455 - 01 Feb 2023

Viewed by 996

Abstract

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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15 pages, 12509 KiB

Open AccessArticle

A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example

by Yong Liu, Qingjie Xu, Xingrui Li, Weiwen Zhan, Jingkai Guo and Jun Xiao

Energies 2023, 16(3), 1361; https://doi.org/10.3390/en16031361 - 28 Jan 2023

Cited by 1 | Viewed by 1058

Abstract

Natural gas power generation has the advantages of flexible operation, short start–stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the accurate identification of borehole fluvial facies in the exploration area is one of the most important conditions affecting the success of gas field exploration. An insufficient number of drilling points in the exploration area and the accurate identification of lithological data features are key to the correct identification of borehole fluvial facies, and understanding how to achieve accurate identification of borehole fluvial facies when there are insufficient training data is the focus and challenge of research within the field of natural gas energy exploration. This paper proposes a borehole fluvial facies identification method applicable to the sparse sample size of drilling points, using the Sulige gas field in the Ordos Basin of China as the research object, with the drilling lithology data in the field as the sample data and the data augmentation and classification of the images through generative adversarial networks. The trained model was then validated on the Hangjinqi gas field with the same geological properties. Finally, this paper compares the recognition accuracy of borehole fluvial facies with that of other deep learning algorithms. It was verified that this research method can be applied to oil and gas exploration areas where the number of wells drilled is small and there are limited data, and that this method achieves accurate identification of borehole fluvial facies in the exploration area, which can help to improve the efficiency of oil and gas resources drilling identification to ensure the healthy development of the power and energy industry. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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18 pages, 4177 KiB

Open AccessArticle

A Multidimensional Adaptive Entropy Cloud-Model-Based Evaluation Method for Grid-Related Actions

by Xiaoling Chen, Weiwen Zhan, Xingrui Li, Jingkai Guo and Jianyou Zeng

Energies 2022, 15(22), 8491; https://doi.org/10.3390/en15228491 - 14 Nov 2022

Cited by 1 | Viewed by 957

Abstract

Smart grid training system needs to evaluate actions during power grid operations in order to complete training for relevant personnel. The commonly used action evaluation methods are difficult for evaluating fine actions during power grid operations, and the evaluation results are subjective. The use of an effective method to evaluate the actions of the power grid operation is important for improving the smart grid training system, enhancing the skills of the trainers, and ensuring the personal safety of operators. This paper proposes a cloud attention mechanism and an evaluation method of grid-related actions based on a multidimensional adaptive entropy cloud model to complete the evaluation of fine actions in the grid’s operation process. Firstly, the OpenCV technique is used to obtain the data related to hand actions during grid operation and to extract the action features to complete the construction of multiscale date sets; then, the adaptive entropy weight matrix at different scales is constructed based on multiscale data sets using the cloud attention mechanism, and the basic cloud model is generated from original hand-action feature data; finally, the multidimensional adaptive entropy cloud model is constructed by the adaptive entropy weight matrix and the basic cloud model, and the multidimensional adaptive entropy cloud model obtained is compared with the multidimensional adaptive entropy cloud model generated based on the standard action features in the same space to obtain the evaluation level of the hand action. The results show that the evaluation method of grid-related actions based on the multidimensional adaptive entropy cloud model can solve the mutual mapping problem between quantitative indicators and qualitative evaluation results in the evaluation of grid operation processes relatively well, and it effectively solves the subjectivity of the weight assignment of evaluation indicators, which can be used for the evaluation of fine actions in the grid’s operation processes. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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13 pages, 1793 KiB

Open AccessArticle

Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy

by Yong Sun, Huakun Que, Qianqian Cai, Jingming Zhao, Jingru Li, Zhengmin Kong and Shuai Wang

Energies 2022, 15(13), 4751; https://doi.org/10.3390/en15134751 - 28 Jun 2022

Cited by 13 | Viewed by 2393

Abstract

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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17 pages, 2787 KiB

Open AccessArticle

Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid

by Fangqiuzi He, Yong Liu, Weiwen Zhan, Qingjie Xu and Xiaoling Chen

Energies 2022, 15(6), 2071; https://doi.org/10.3390/en15062071 - 11 Mar 2022

Cited by 6 | Viewed by 1800

Abstract

The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy of test actions. First, the kernel of vectorized spatio-temporal graph convolutional of the data glove is constructed with different weights for different finger joints, and the data dimensionality reduction is also achieved. Then, different evaluation strategies are proposed for different actions. Finally, a convolution deep learning network for vectorized spatio-temporal graph is built to obtain the similarity between test actions and standard ones. The evaluation results of the proposed algorithm are compared with the subjective ones labeled by experts. The experimental results verify that the proposed action evaluation method based on the vectorized spatio-temporal graph convolutional is efficient for the manual operation accuracy evaluation in VR training systems of smart grids. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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14 pages, 808 KiB

Open AccessArticle

Spatial Interference Alignment with Limited Precoding Matrix Feedback in a Wireless Multi-User Interference Channel for Smart Grids

by Shixin Peng, Xiaohui Chen, Wei Lu, Chao Deng and Jingying Chen

Energies 2022, 15(5), 1820; https://doi.org/10.3390/en15051820 - 01 Mar 2022

Viewed by 1728

Abstract

Cellular communication provides an efficient, flexible, long-lived, and reliable communication technology for smart grids to improve the automated analysis, demand response, adoptive control, and coordination between the generator and consumers. With the expansion of wireless networks and the increase of access devices, interference has become a major problem that limits the performance of cellular wireless communication systems for smart grids. Spatial interference alignment (IA) is an effective method to eliminate interference and improve the capacity of wireless communication networks. This paper provides the sufficient conditions of spatial interference alignment operating with limited precoding matrix feedback for a K-user MIMO interference channel. Each receiver feeds the matrix index of the transmitting precoder back to the corresponding transmitter through an interference-free and error-free link. We calculated the number of feedback bits required to achieve the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as

N_{d} \geq d_{i} (M - d_{i}) {log}_{2} SNR

, where M and

d_{i}

denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results were verified by simulations for practical interference alignment schemes relying on limited precoding matrix feedback indices. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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11 pages, 1983 KiB

Open AccessArticle

Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm

by Long Peng, Jiajie Li, Jingming Zhao, Sanlei Dang, Zhengmin Kong and Li Ding

Energies 2022, 15(5), 1626; https://doi.org/10.3390/en15051626 - 22 Feb 2022

Cited by 2 | Viewed by 1161

Abstract

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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20 pages, 4681 KiB

Open AccessArticle

A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method

by Sanlei Dang, Long Peng, Jingming Zhao, Jiajie Li and Zhengmin Kong

Energies 2022, 15(2), 663; https://doi.org/10.3390/en15020663 - 17 Jan 2022

Cited by 16 | Viewed by 2545

Abstract

In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts. Full article

(This article belongs to the Special Issue Modeling, Analysis and Control of Power System Distribution Networks)

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