Data Analytics for Electric Utilities

Advanced Data Analytics for the Modern Power System

Emerging Data Analytics in a Changing Power Systems Industry

The power systems industry of the 21st Century is experiencing a rapid change as emerging technologies are deployed to meet the challenges of new paradigm shifts brought on by an evolving operational and regulatory environment. These new technologies have provided unprecedented availability to system and status data, enabling the practical employment of novel forms of data analytics to improve operation, planning, and improvement of the modern power grid. This opportunity in analytics brings the Big Data concept that has revolutionized other sectors of the economy to the power systems industry.  

Although system operational and status data has long been utilized in studies across the power systems industry, the monitoring capabilities of modern hardware have a number of key advantages that have opened up the potential for novel analytics.  

  • Quantity – As a general trend for modern computing hardware, storage and processing capabilities have vastly improved, greatly increasing the amount of data that may be gathered. This enables the data to cover both longer periods as well as greater resolution.  
  • Breadth – The greater availability of modern computing hardware has made it possible to add data gathering systems to more equipment than ever before. Engineers today have access to data from a multitude of monitoring and recorded sources, covering both individual field-deployed hardware as well as database systems for asset and information management. In addition to the breadth of data types, the migration to microprocessor-drive hardware and databases also vastly expands the scope of data, providing engineers with greater system coverage of data.  
  • Accessibility – Modern communication protocols and the drive for greater system integration have given engineers quicker and easier access to both monitored and stored data, potentially improving the responsiveness and relevancy of data analytics. This covers not only system data obtained from field, but also supporting records such as asset management systems and equipment technical data. 


Novel Approaches Enabled by Advanced Data Analytics 

The wide range of variety and scope, greater quantity and resolution, and improved accessibility of available data represent significant opportunities for data analytics to improve many aspects of the power systems industry. Two novel concepts made feasible by the improvements in data availability can potentially transform the way the industry approaches engineering studies and processes.  

  • Real-Time Data – The feedback loop between changing system conditions, engineering studies, and response measures has traditionally had to contend with an inherent time delay representing manual data gathering and response measure efforts on the part of field and engineering personnel. The availability of real-time (or close approximation of) data from modern monitoring and communications systems can significantly reduce this time requirement, making rapid responses to changing system conditions feasible. Advanced analyses, simulator technologies, and communications schemes can even provide near-real-time control over power system performance to an unprecedented degree, enabling employment of novel concepts such as adaptive protection or automated fault location detection.  
  • Source and Process Integration – Engineering studies and processes have typically required data from disparate sources, with manual engineer effort being required to consolidate the various information sources into forms usable by the study or process. The trend to transition both gathered data and asset technical information into machine-readable formats such as databases provides opportunities for the tighter integration of data sources. New capabilities such as rapid acquisition of data, seamless transformation of data to required formats, and automated data quality checking routines can dramatically increase efficiency and accuracy of engineering studies, analytics, and response mechanisms.  

Implementation of Data Analytics

The implementation of data analytics is typically comprised of eight stages, each representing one major task in the implementation process. These tasks are organized into three broad functional aspects, each with their own unique requirements and considerations.  

Preparation Aspect

The Preparation aspect focuses on data readiness and is comprised of the tasks that define, collect, transform, and validate the data required for the study or analysis. This aspect typically involves elements and considerations such as: 

  • Gathering the data required for the analysis from multiple individual sources, which can include both field and recorded data such as asset management systems, device settings and configuration records, system performance metrics, and equipment technical parameters. Often, the data may need to be digitized into machine-readable form, which can present significant on engineering resources and effort.  
  • Alignment and transformation of the data from the recorded format into forms that can be utilized by the analysis tools or applications. This concept known as Extract, Transform, Load (ETL), typically involves a variety of data spread across multiple sources, and can require detailed knowledge of engineering functions and formats, including methods of access and interpretation for manufacturer-specific proprietary information.  
  • Verification of the data for integrity, consistency, completeness, to ensure quality of inputs to the analysis. The drive for integration of data processes presents an opportunity for validation checks to be conducted through software automation, where vast amounts of data can be checked quickly and accurately according to pre-defined logic indicating errors or issues.  
  • Consolidation of the data into one package can be required depending on the application. This typically takes the form of a simulation or Electronic Data Warehouse (EDW) platform through which the studies or analyses will be conducted.  

Data analytics preparation aspect

Ideally, these separate elements would be integrated into a unified process, linked through software-based automation processes to enable seamless flow of data between repositories and applications. The inherent benefits of automation ensure consistent implementation and reduction of burden on engineering resources. Quanta Technology offers industry-leading solutions for the data readiness aspect, with particular emphasis on process integration with the assistance of software-based automation tools to improve efficiency and accuracy.  

Execution Aspect 

The Execution aspect focuses on performing the study or analysis through development and utilization of the applicable theories, algorithms, and processes. This aspect requires extensive industry expertise covering both the problem being studied as well as established and novel methods of addressing the concern. The specific requirements and methodologies that comprise this aspect depend heavily on the area of study and can involve enormous data sets that may call for specialized hardware solutions.  

Quanta Technology’s engineering experts are highly respected in the power systems industry with reputations for delivering cutting-edge, but and practical solutions for both existing and emerging concerns. The extensive experience, knowledge, and innovation of our experts is the core driver behind the successful development and execution of these studies and analytics made possible by the data availability of the modern power system.  

Data Analytics Risk Assessment

Presentation Aspect

The Presentation aspect focuses on the effective communication of the study or analysis findings and represents the end product of the data analytics process. Depending on the application, this aspect may take the form of reports for performance, planning, or compliance studies, interactive applications for routine ongoing analytics, or even communications and control actions if analytics are directly integrated into the hardware feedback loops. 

Although reporting and presentation of findings are long established expectations for studies and analyses, Quanta Technology’s approach to data analytics involving integration and software-based automation enables the automatic generation and presentation of technical summary or formal documentation as outputs of the process. These outputs formats can be fully customized according to the requirements and preferences of our partners, or utilize industry-standard data presentation packages to smoothly integrate with existing infrastructure.  

Data Analytics Presentation Aspect

Key Applications

Quanta Technology’s capabilities in data analytics covers the entire process from start to finish, offering a unique combination of engineering, development, integration, and applications expertise. Our experts include highly respected engineers with reputations for delivering practical results, and our data processing and management applications are industry-leading in their various fields. A selection of our innovative data analytics applications is provided below.  

  • Analysis of Intelligent Electronic Devices – Software automation-based routines to query the status and content of protection and control device settings files, from a multitude of sources such as settings repositories, software models, or even directly from field deployed devices. These routines can be configured to detect abnormal or targeted conditions depending on the application, which can vary from validation of protective relay settings according to protection philosophy to confirmation of adherence to update processes through review of time stamp data.  
  • Automated Fault Location – Determination of fault location through utilization of a custom-developed algorithm that analyzes fault event data, system status, and equipment technical parameters. This application employs an integration of data and processes through software-based automation, including automatic retrieval of fault event data from field-deployed devices, linking with software simulation packages to test fault estimations, and output to map and GIS-based resources to provide personnel with a visual estimate of fault location on a topographical map.  

Data Analytics Fault Location

  • Compliance Report Generation – Automated generation of engineering summaries and formal audit-ready reports for NERC PRC and CIP compliance standards such as PRC-023, PRC-026, PRC-027, and CIP-10. These summaries and reports are intended to both provide engineers a quick view of the compliance status of their scope of study (could be up to the entire BES system) and represent the detailed documentation required by auditors to show proof-of-compliance.  

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