Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7393

Share This Special Issue

Special Issue Editors

Prof. Dr. Su Su

E-Mail Website
Guest Editor

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: V2G energy management; smart distribution network planning; load forecasting

Dr. Jakub Jurasz

E-Mail Website
Guest Editor

Faculty of Environmental Engineering, Wrocław University of Science and Technology, Plac Grunwaldzki 13, 50-377 Wrocław, Poland
Interests: renewable energy; complementarity; water-food- energy nexus; solar and wind hybrid systems

Dr. Jie Yan

E-Mail Website
Guest Editor

School of New Energy, North China Electric Power University, Beijing 102206, China
Interests: renewable forecast; wind farm control
Special Issues, Collections and Topics in MDPI journals

Dr. Ning Li

E-Mail
Guest Editor

School of Electrical Engineering, Xi'an University of Technology, Xi’an 710048, China
Interests: EV battery detection in ADN; prediction of RE generation in ADN; power quality in ADN

Dr. Hao Ming

E-Mail Website
Guest Editor

Department of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: electricity market; demand response; electric vehicle

Dr. Rongsheng Chen

E-Mail Website
Guest Editor

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: traffic network modeling and optimization; mobility-on-demand system; traffic assignment; traffic flow theory

Prof. Dr. Xia Mao

E-Mail
Guest Editor

School of Electronic Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Interests: pattern recognition; affective computing; human-computer interaction

Special Issue Information

Dear Colleagues,

The last few years have seen a rapidly growing interest in technologies with renewable energy (RE) and flexible load (FL) participation in the distribution network with the development of sensor technology, which provides strong support for the active management of the distribution network, and offers the possibility of optimal scheduling of smart cities and smart transportation based on artificial intelligence. In this context, by coordinating flexible resources for smart cities and smart transportation, the carrying capacity and flexibility of the distribution network can be increased to incorporate RE and diversified loads, further improving the secure and economic operation of the distribution network.

This Special Issue aims to promote original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of RE and FL participation in the distribution network with integrated sensors.

Potential topics include, but are not limited to, the following:

Sensor and sensorless technology of RE participating in distribution network;
Sensor and sensorless technology of electric vehicles participating in distribution network;
Sensor and sensorless technology in energy optimization in the integration of road and power networks;
Sensor and sensorless technology of smart appliances participating in distribution network;
Analysis of electric vehicle user behaviors driven by sensor data;
Brainwave analysis of power users driven by sensor data;
Research on power user portrait based on smart meter data;
Data collection and competitive strategy analysis of FL in the power market;
Global carbon market and measurement and analysis of FL energy;
Sensors in smart environments for the provision and ancillary services.

Prof. Dr. Su Su
Dr. Jakub Jurasz
Dr. Jie Yan
Dr. Ning Li
Dr. Hao Ming
Dr. Rongsheng Chen
Prof. Dr. Xia Mao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

renewable energy
flexible load
distribution network
electric vehicles
energy optimization
global carbon market
smart environments

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

0 pages, 6194 KiB

Open AccessArticle

YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations

by Qian Wang, Lixin Yang, Bin Zhou, Zhirong Luan and Jiawei Zhang

Sensors 2023, 23(19), 8080; https://doi.org/10.3390/s23198080 - 26 Sep 2023

Cited by 1 | Viewed by 1504

Abstract

With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids. Full article

(This article belongs to the Special Issue Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network)

► Show Figures

Figure 1

13 pages, 5959 KiB

Open AccessArticle

Insulator Abnormal Condition Detection from Small Data Samples

by Qian Wang, Zhixuan Fan, Zhirong Luan and Rong Shi

Sensors 2023, 23(18), 7967; https://doi.org/10.3390/s23187967 - 19 Sep 2023

Cited by 1 | Viewed by 810

Abstract

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset’s optimization to demonstrate the expanded dataset’s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks. Full article

(This article belongs to the Special Issue Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network)

► Show Figures

Figure 1

20 pages, 4104 KiB

Open AccessArticle

Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving

by Zhirong Luan, Yujun Lai, Zhicong Xu, Yu Gao and Qian Wang

Sensors 2023, 23(12), 5624; https://doi.org/10.3390/s23125624 - 15 Jun 2023

Cited by 1 | Viewed by 1145

Abstract

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy. Full article

(This article belongs to the Special Issue Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network)

► Show Figures

Figure 1

20 pages, 7086 KiB

Open AccessArticle

Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar

by Jinhua Zhang, Zhengyang Zhao, Jie Yan and Peng Cheng

Sensors 2023, 23(9), 4369; https://doi.org/10.3390/s23094369 - 28 Apr 2023

Cited by 6 | Viewed by 1487

Abstract

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power. Full article

(This article belongs to the Special Issue Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network)

► Show Figures

Figure 1

17 pages, 8481 KiB

Open AccessArticle

Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian–Discrete Joint Probability Model

by Yuanchi Ma, Yongqian Liu, Zhiling Yang, Jie Yan, Tao Tao and David Infield

Sensors 2022, 22(22), 8891; https://doi.org/10.3390/s22228891 - 17 Nov 2022

Viewed by 1166

Abstract

This paper focuses on how to identify normal, derated power and abnormal data in operation data, which is key to intelligent operation and maintenance applications such as wind turbine condition diagnosis and performance evaluation. Existing identification methods can distinguish normal data from the original data, but usually remove power curtailment data as outliers. A multi-Gaussian–discrete probability distribution model was used to characterize the joint probability distribution of wind speed and power from wind turbine SCADA data, taking the derated power of the wind turbine as a hidden random variable. The maximum expectation algorithm (EM), an iterative algorithm derived from model parameters estimation, was applied to achieve the maximum likelihood estimation of the proposed probability model. According to the posterior probability of the wind-power scatter points, the normal, derated power and abnormal data in the wind turbine SCADA data were identified. The validity of the proposed method was verified by three wind turbine operational data sets with different distribution characteristics. The results are that the proposed method has a degree of universality with regard to derated power operational data with different distribution characteristics, and in particular, it is able to identify the operating data with clustered distribution effectively. Full article

(This article belongs to the Special Issue Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network)

► Show Figures

Journal Menu

Journal Browser

Sensor and Sensorless Technology with Renewable Energy and Flexible Load Participation in Active Distribution Network

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI