Big data issues in smart grid systems

Sağıroğlu, Şeref; Terzı, Ramazan; Canbay, Yavuz; Çolak, İlhami

doi:10.1109/icrera.2016.7884486

Cited by 54 publications

(28 citation statements)

References 16 publications

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“…Actually, Big Data technology has already been successfully applied as a powerful data-driven tool for solving numerous new challenges in power grid, such as price forecasting [7,8], load forecasting [9], transient stability assessment [10], outlier detection [11], and fault detection and analysis [12], among others [13,14]. Detailed discussions about Big Data issues and application are reviewed in [15], as well as the insights of Big Data-driven smart energy management in [16]. Major tasks of the architecture for these applications are similar, which focus on two major issues: big power data modeling and big power data analysis.…”

Section: Introductionmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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Section: Introductionmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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“…That is, once the erroneous data are received, they are defined, identified, corrected, documented, and modified to avoid future faults [250]. For instance, in smart grids, data cleansing helps in forecasting the generation from the PV system to decide the proper dynamic tariff rate [251,252].…”

Section: Data Preprocessingmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

et al. 2020

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The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

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“…BDA incorporates various techniques such as statistical analysis or AI to manage and derive information from big data [47]. Different aspects need to be considered in the scope of big data: volume, veracity, variety, velocity and value [48]. Commonly BDA contains multiple data formats requiring different methodologies to derive meaningful insights.…”

Section: Maintenance: a Transformation Towards Modern Strategiesmentioning

confidence: 99%

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

et al. 2020

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Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability. INDEX TERMS Bottom hole assembly, oil drilling, printed component board assembly, dynamic environments, failure modes, condition based maintenance, diagnostics, prognostics, machine learning, artificial intelligence.

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Big data issues in smart grid systems

Cited by 54 publications

References 16 publications

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

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