On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Wang, Jiawei; Chatzivasileiadis, Spyros; Panteli, Mathaios; Štrbac, Goran; Terzija, Vladimir

doi:10.1109/tste.2022.3194728

Cited by 33 publications

(7 citation statements)

References 86 publications

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“…This parameter is influenced by factors such as the standard of production of added value per person (B 1 ), the rate of new business registration (B 2 ), the ratio of employment based on the workplace in the public sector (B 3 ), the ratio of employment based on the workplace in the private sector of knowledge-based sectors (B 4 ), the ratio of employment to the workplace in manufacturing (B 5 ), and gross weekly wages by workplace (B 6 ). 55,[90][91][92] Supportability. One of the factors effective risk factors is the system's supportability.…”

Section: Sense Of Ownershipmentioning

confidence: 99%

“…Nevertheless, there were limitations. First, based on the review of the related literature provided in the next chapter, most of the studies have been focused on energy systems, [51][52][53] transportation networks, [54][55][56] and water supply networks, [57][58][59][60] and there are few quantitative scientific literatures in the field of mines and coal, while coal mining is facing numerous disturbances. Second, most of the studies have evaluated the conceptual frameworks and assessment approaches.…”

Section: Introductionmentioning

confidence: 99%

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A novel index for shearer system resilience in underground coal mines based on the operational environment

Norouzi Masir,

Ataei,

Sereshki

2024

Proceedings of the Institution of Mechanical Engineers, Part O:

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The use of resilience during the mining period by guaranteeing the safety and performance of the systems greatly helps the managers and engineers with their decision-making and designing different systems. During the last two decades, various studies have been conducted in this regard. However, the analysis of underground coal mining systems’ resilience has been rarely addressed by these studies. The present study aims to provide a quantitative index for resilience in the environment of underground coal mining operations based on the risk factors. To do so, in a case study of Tabas Mechanized Coal Mine, the Failure of shearer data and the stoppages caused by it (for 2 years and a half, from March 2017 to June 2019) were studied to analyze the system’s resilience. Then, the experts were surveyed. After collecting and categorizing the data, the data analysis calculations were done for indicators of reliability, maintainability, supportability, organizational resilience, PHM system efficiency, and economic resilience. Finally, a new quantitative index named the “Shearer System Resilience Index” was introduced. The results indicated that resilience is reduced over time. They also shows that in order to improve resilience, project managers should pay more attention to these indicators, especially the indicators of maintainability, and organizational resilience. Therefore, regarding the scope of the data investigated in the current study, the use of Failure of Shearer data-based risk factors and experts’ opinions for the analysis of resilience in the case study is strongly recommended.

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Section: Sense Of Ownershipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel index for shearer system resilience in underground coal mines based on the operational environment

Norouzi Masir,

Ataei,

Sereshki

2024

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Classification results for various relays in a DC microgrid and necessity of adaptive feature of the protection scheme has been discussed by Jing Wang [16]. ML methods can handle extremely complex system and application of ML for power system network reliability, stability and security has been discussed [17]. In this work ML technique is applied for power system.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Protection Challenges and Solution

Modi,

Usha

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Microgrids are embryonic as inspiring solution to the various concerns such as environmental, economic, depletion of the resources for the fuel availability and power mismatch. Microgrids are going to become one of the core components of the upcoming power system. So, it is essential to understand various issues and challenges in microgrid. Power management, constant voltage and frequency, control of various distributed generators and Protection of the microgrid are major areas of concern. Not much work has been done on the protection side of the microgrid. Detecting fault and obtaining the appropriate setting of the protective gear for unconventional topology of the microgrid is very challenging. In this work a solution has been proposed and implemented in microgrid controller (MGC) for fault detection and classification in a microgrid. Effectiveness of proposed method is tested in MATLAB /SIMULINK environment.

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“…In particular, Machine Learning (ML) models have proven effective in learning these nonlinear relationships and can be used for optimal pre-and post-disaster resource allocation [16], [17]. However, while ML models have been mostly applied to individual infrastructure systems, including energy networks [18]- [20] and road networks [21], [22], their application to interdependent infrastructure systems is limited due to data availability constraints.…”

Section: Introductionmentioning

confidence: 99%

Predicting Resilience of Interdependent Urban Infrastructure Systems

Cassottana¹,

Biswas

Balakrishnan³

et al. 2022

IEEE Access

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Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions. To mitigate these impacts, models and methods are needed to support rapid decision-making for optimal resource allocation in the aftermath of a disruption and to substantiate investment decisions for the structural reconfiguration of these systems. In this paper, we leverage infrastructure simulation models and Machine Learning (ML) algorithms to develop resilience prediction models. First, we employ an interdependent infrastructure simulation model to generate infrastructure disruption and recovery scenarios and compute the resilience value for each scenario. The infrastructure-, disruption-, and recovery-related attributes are recorded for each scenario and ML algorithms are employed on the synthetic dataset to develop accurate resilience prediction models. The results of the prediction models are analyzed and possible design strategies suggested based on the resilience enhancement attributes. The proposed methodology can support infrastructure agencies in the resource-allocation process for pre-and post-disaster interventions.

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On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Cited by 33 publications

References 86 publications

A novel index for shearer system resilience in underground coal mines based on the operational environment

A novel index for shearer system resilience in underground coal mines based on the operational environment

Microgrid Protection Challenges and Solution

Predicting Resilience of Interdependent Urban Infrastructure Systems

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