Adapting big data standards, maturity models to smart grid distributed generation: critical review

Sundararajan, Aditya; Hernandez, Alexander; Sarwat, Arif I.

doi:10.1049/iet-stg.2019.0298

Cited by 18 publications

(9 citation statements)

References 61 publications

(64 reference statements)

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“…The transfer learning capabilities were studied first in the SolarTech Lab context following the different domain combinations defined in the previous Section 2. 3. The CNN+LSTM model also described in Section 2.3 was first trained and validated on the SolarTech dataset in a bare learning scenario.…”

Section: Resultsmentioning

confidence: 99%

“…All these features, among others, are leading to a very significant growth in the global solar market, particularly in an aspect that is especially relevant to this work: distributed power generation. Data engineering and deep-learning are key techniques to find open and low-cost solutions, as powerful tools that are able to manage the smart grids that enable distributed generation [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

et al. 2022

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New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

et al. 2022

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“…2) Requirements: To deal with the massive volume of data generated due to constant communication and connectivity in the smart grid, sophisticated techniques that can analyze the data and can assist in the decision-making process are required [166]. ML can solve the problems arising due to the large volumes of data generated from smart grids and can assist the smart grids in the collection of data, analyze the patterns existing in the data and also in making decisions to run the smart grid.…”

Section: ) How Xai Can Helpmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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“…Fig. 9 depicts the infrastructure of the entire networked smart grid, identified by the National Institute of Standards and Technologies (NIST), which comprises 7-domains of generation, transmission, distribution, electricity markets, operation, service providers, along with customers [79], [80].…”

Section: Cp Viewpoint Of Networked Smart Grid Security a Smart Gmentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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Adapting big data standards, maturity models to smart grid distributed generation: critical review

Cited by 18 publications

References 61 publications

Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

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