Smart Grid Analytics for 400+ Substations

The client operates a vast network of over 419 substations. With outdated monitoring systems, it was hectic to effectively manage large-scale power transmission networks. They envisioned leveraging the benefits of big data analytics to simplify and automate the management of a vast network of substations.

Ampcome with its expertise in data analytics solutions designed, developed, and implemented highly advanced data analytics applications that improved the efficiency, reliability, and decision-making process of smart grids.

This case study explores the journey our team took to provide cutting-edge solutions aligned to their needs.

Project Overview

The client recognized the need for smart data management, automation, and data analytics to ensure seamless functioning and management of his power transmission network. He envisioned to completely automate the power grids to enhance their monitoring, efficiency, and decision-making capabilities.

Our team consisting of experienced data analytics experts devised a unified approach that would merge and integrate their separate applications into a single advanced data analytics platform. We deployed ultra-tech methodologies to provide descriptive, predictive, and prescriptive analytics to further strengthen and magnify the platform's capabilities.

Challenges & Objectives

The one true objective was to help the client leverage the benefits of a unified intelligent data management system and automation techniques in their power transmission network. This would allow them to amplify the capabilities of their substations while improving their efficiency, productivity, and reliability. Plus, the client would be able to effortlessly manage and monitor their large network of substations spanning over 50,000 MVA in capacity.

To achieve this final objective, the team had to overcome several challenges which would have been a major roadblock if our experts didn't have the knowledge and intellect to address them.

The project challenges were:

• Integrating multiple systems and applications into a single data warehouse.

• Providing comprehensive analytics capabilities, including descriptive, predictive, and prescriptive analysis.

• Ensuring real-time data acquisition and processing for timely insights.

• Creating a user-friendly interface for various stakeholders with customizable features.

Solution Implemented

We deployed a sophisticated data analytics system that included various components to help the client harness the potential of big data in their power grid.

• Data Acquisition and Integration Platform: To consolidate data from approximately ten different applications, facilitating a unified view and analysis.

• Advanced Analytics Solution: Providing dashboards and reporting at aggregated levels for key officers and stakeholders, along with an investigative workbench for operational stakeholders to take action based on insights.

• Descriptive, Predictive, & Prescriptive Analytics: Enabling comprehensive analysis of substation assets, trends, and predictions for maintenance, anomaly detection, and load forecasting, among other functionalities.

Technology Used

• Our data analytics experts used a moder tech stack to build the system including:

• • Dagster + dbt: For data integration and orchestration.

• Postgres + TimescaleDB: Chosen for data storage, given its capability to handle IT and OT data integration along with Geo-Spatial and Time series capabilities.

• MindsDB: To speed up ML development by integrating machine learning models directly into the database for time series, regression, and classification predictions.

• Apache Superset: For data exploration and visualization, enabling stakeholders to derive insights effectively.

Outcome

The introduction of the Big Data Analytics system into the Smart Grid yielded the following outcomes:

• Establishment of a consolidated platform for data analysis, amplifying operational efficiency and decision-making capabilities.

• Implementation of real-time monitoring and predictive maintenance features, resulting in reduced downtime and operational costs.

• Provision of customizable dashboards and reports, empowering stakeholders to derive personalized insights.

• Enhancement of regulatory compliance and risk management through the utilization of advanced analytical tools.

Conclusion

The integration of the Big Data Analytics system has profoundly reshaped the client's approach to overseeing their power transmission network. Through the utilization of advanced analytics and machine learning, the client now possesses the ability to anticipate and prevent issues proactively, optimize asset performance, and swiftly make well-informed decisions. This project not only signifies a significant stride towards a fully automated and intelligent grid system but also establishes a precedent for similar transformations within the energy sector.