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

Abstract

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.

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

“…One item of perceived usefulness (PU4) excluded due to factor loading problem. e results of the pilot test (see Table 2) represent that the values of factor loading and Cronbach's alpha are within the prescribed range [24]. So, it proved the validity of the instrument.…”

“…Both the values of CR and AVE are above the threshold point [89,90]. A er that, EFA conducted to ensure that measures are according to their respective variables and factor loadings of all items are ranging from 0.772 to 0.955, above the cutoff point of 0.7 and proved that there is no issue of factor loadings [24]. Table 4 represents the values of factor loadings, CR, AVE, and Cronbach's alpha.…”

“…Several limitations are still existing, and several factors are not focused that are important for BDA adoption in the healthcare particularly the role of gender, which is important in measuring the behavioral intentions [23]. Prior literature argued that gender differences vary in different factor relationships and are needed to be studied to check the effects of the actual relationship with regard to gender [24]. e relationship between latent constructs can be positive or negative, depending on gender.…”

A survey of big data and machine learning

“…Reference [5] presents the definition of big data application scenarios through examples in different segments of transportation and energy sectors. Reference [6] proposes a platform which can provide a technical solution to multidisciplinary cooperation of smart grid (SG) monitoring and big data technology. An assessment of distinguished aspects in big data analytics developments in the domain of power systems has been presented in [7].…”

Schedulable capacity forecasting for electric vehicles based on big data analysis

“…Big data analysis and management are clear trends of future smart grids. This is challenging for traditional machine learning (ML) algorithms, since they are designed for a single machine and are not suitable to deal with big data [18]. Thus, more efficient ML algorithms for parallel computing or for big data are required.…”