Existing Developments in Adaptive Smart Grid Protection: A Review

Khalid, Hammad; Shobole, Abdulfetah

doi:10.1016/j.epsr.2020.106901

Cited by 52 publications

(34 citation statements)

References 114 publications

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“…Hybrid-cloud-based big data processing, IoT technologies, advanced analytics with AI algorithms such as Blockchain and deep machine learning, and abundant communication standards used to send data from one point to another, as well as digital twins in the digital transformation of utilities and in Industry 4.0 further accelerate the intelligent metamorphosis of the grid (Ahmad et al, 2020 ; Hasankhani et al, 2021 ; Talaat et al, 2020 ). The employment of industrial IoT technologies allows asset optimisation, detection and predictive maintenance, self-healing to predict, isolate and fix grid operation issues (Khalid & Shobole, 2021 ; Kimani et al, 2019 ; Rivas & Abrão, 2020 ), restore grids or part of a grid when maintenance issues or external impacts such as cyberattacks take place, and correct and optimise renewable power quality (Gunduz & Das, 2020 ; Kotsiopoulos et al, 2021 ; Sakhnini et al, 2019 , 2021 ). The AI-supported smart grid using algorithms such as AccCap-DRL and machine learning was shown to be capable of decentralised modelling, monitoring, forecasting, detecting, integrating, managing and optimising renewable energy generation, transmission, and distribution (Hatti & Denai, 2020 ; Lin et al, 2020 ; Liu et al, 2020 ; Reddy et al, 2014 ; Sonnenschein et al, 2015 ) and connecting renewable smart grids with renewable power consumption, such as plug-in electric vehicles (Lee, 2020 ; Liu et al, 2015 ; Rahbari et al, 2017 ; Raza & Khosravi, 2015 ).…”

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

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Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.

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Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

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“…Novel microgrid protection approaches exist and are detailed in the available literature [18][19][20][21]: to a name a few, advanced optimization for relay setting calculation [22,23], application of machine-learning algorithms in adaptive protection [24,25], and distance protection on distribution voltage level [26,27]. However, many of these listed approaches are in the research or prototype phase and have not been widely implemented in the real-world microgrids.…”

Section: Microgrid Protection Strategiesmentioning

confidence: 99%

AC Microgrid Protection System Design Challenges—A Practical Experience

et al. 2021

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Alternating current (AC) microgrids are the next step in the evolution of the electricity distribution systems. They can operate in a grid-tied or island mode. Depending on the services they are designed to offer, their grid-tied or island modes could have several sub-operational states and or topological configurations. Short-circuit current levels and protection requirements between different microgrid modes and configurations can vary significantly. Designing a microgrid’s protection system, therefore, requires a thorough understanding of all microgrid operational modes, configurations, transitional states, and how transitions between those modes are managed. As part of the microgrid protection design, speed and reliability of information flow between the microprocessor-based relays and the microgrid controller, including during microgrid failure modes, must be considered. Furthermore, utility protection practices and customer requirements are not always inclusive of the protection schemes that are unique to microgrids. These and other aspects contribute to the overall complexity and challenge of designing effective microgrid protection systems. Following a review of microgrid protection system design challenges, this paper discusses a few real-world experiences, based on the authors’ own engineering, design, and field experience, in using several approaches to address microgrid protection system design, engineering, and implementation challenges.

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“…A smart grid is a next-generation power grid dependent on various sources of energy, such as renewable energy. A smart grid aims to employ energy generation and consumption data by smart meters to manage energy efficiently [5,6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

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Existing Developments in Adaptive Smart Grid Protection: A Review

Cited by 52 publications

References 114 publications

Deploying digitalisation and artificial intelligence in sustainable development research

Deploying digitalisation and artificial intelligence in sustainable development research

AC Microgrid Protection System Design Challenges—A Practical Experience

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

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