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Special Issue "Future Electricity Network Infrastructures"

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School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: AI applications to power systems; power system control and operation; Smart grids; renewable energy resources; energy management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guojie Li
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Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Interests: renewable energy integrated power system operation and control; renewable energy control

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform for researchers and practicing engineers to share their ideas, recent developments, and successful practices in power and electrical engineering. The issue will publish high-quality papers that are strictly related to the various theories and practical applications in the area of machine learning applications based on future power system operations with high penetration of renewable resources and its related network architecture. Topics of interest include but are not limited to:

future electricity network infrastructures
machine learning
DC network architecture
smart and intelligent buildings
smart EV charging
smart cities

Prof. Dr. Tek-Tjing Lie
Prof. Dr. Guojie Li
Guest Editors

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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.

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Published Papers (2 papers)

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Research

Open AccessArticle

Modeling of Efficient Control Strategies for LCC-HVDC Systems: A Case Study of Matiari–Lahore HVDC Power Transmission Line

Adeel Ahmed

Danish Khan

Ahmed Muddassir Khan

Muhammad Umair Mustafa

Manoj Kumar Panjwani

Muhammad Hanan

Ephraim Bonah Agyekum

Solomon Eghosa Uhunamure

and

Joshua Nosa Edokpayi

Sensors 2022, 22(7), 2793; https://doi.org/10.3390/s22072793 - 06 Apr 2022

Cited by 1 | Viewed by 1317

Abstract

With the recent development in power electronic devices, HVDC (High Voltage Direct Current) systems have been recognized as the most prominent solution to transmit electric power economically. Today, several HVDC projects have been implemented physically. The conventional HVDC systems use grid commutation converters, and its commutation relies on an AC system for the provision of voltage. Due to this reason, there are possibilities of commutation failure during fault. Furthermore, once the DC (Direct Current) system power is interrupted momentarily, the reversal of work power is likely to cause transient over-voltage, which will endanger the safety of power grid operation. Hence, it is necessary to study the commutation failure and transient over-voltage issues. To tackle the above issues, in this paper, the dynamic and transient characteristics of Pakistan’s first HVDC project, i.e., the Matiari–Lahore ±660 kV transmission line has been analyzed in an electromagnetic transient model of PSCAD/EMTDC. Based on the characteristics of the DC and the off-angle after the failure, a new control strategy has been proposed. The HVDC system along with its proposed control strategy has been tested under various operating conditions. The proposed controller increases the speed of fault detection, reduces the drop of AC voltage and DC and suppresses the commutation failure probability of LCC-HVDC (line commutated converter- high voltage direct current). Full article

(This article belongs to the Special Issue Future Electricity Network Infrastructures)

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Open AccessArticle

Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

and

Sensors 2021, 21(21), 7191; https://doi.org/10.3390/s21217191 - 29 Oct 2021

Cited by 9 | Viewed by 1753

Abstract

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches. Full article

(This article belongs to the Special Issue Future Electricity Network Infrastructures)

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