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Asset & Grid Performance

Asset & Grid Performance

Predict and visualize asset performance. Be a reliability champion.

How SAS® Enables Superior Asset & Grid Performance

Optimized management of the distribution grid with data-driven analytics is no longer a luxury. The growth of DERs, the emergence of prosumers and the resulting grid management complexities require deeper data-driven insights to maintain and improve asset and system reliability. Our utility analytics solutions provide a broad scope of analytic and predictive capabilities to capitalize on IoT investments on the grid and give you better results.

Asset reliability analytics

Reduce the number of unplanned outages and optimize maintenance schedules for grid and renewable energy assets.

Optimal EV & DER integration

Forecast specific needs and time frames to leverage the right distributed energy resource at the right time, place and cost.

Storm analytics

Shrink outage duration by employing analytics before, during and after a storm to improve the effectiveness of your preparation and restoration efforts.

Smart meter analytics

Leverage the data-rich smart meter environment to gain customer insights and model grid and asset behavior to improve reliability.

Performance visualization

Explore and analyze data from diverse connected grid assets through robust dashboards and interactive maps.

Predictive maintenance

Integrate data from your historian, ERP and field notes to build predictive models for asset maintenance.

Why do utilities choose SAS® for asset & grid performance?

With utility analytics from SAS, you can capture and analyze data in any format from any source to gain insights and make better maintenance decisions. Know the best corrective actions to take in each situation – and when to take them. Maximize asset performance and save time and expenses by using monitoring, predictive models and alerts issued at predefined thresholds.

Accurately predict potential problems

Integrated data – from sensors, inspections, maintenance, weather, inventory, history and warranties – combines with root-cause analysis to reveal the drivers for performance issues from hundreds or thousands of measures and conditions.

Optimize maintenance plan impacts

Get ahead of the vegetation management challenges. Build predictive maintenance schedules. Predict vulnerable assets and stage resources prior to adverse events in order to minimize the impact to customers.

Manage outages before they manage you

From momentary to event-driven outages, gain insights to prevent equipment failures, speed restoration and improve customer satisfaction.

Boost uptime, performance & productivity

Get alerts so maintenance teams can make pending repairs as part of regularly scheduled maintenance. And determine the most cost-effective way to replace degrading assets.

Reduce maintenance costs & risk of revenue loss

Advanced analytics lengthens maintenance cycles without jeopardizing uptime or risking failures. Rapidly diagnose and repair issues with near-real-time insight into performance.

Customer Success

Look Who's Working Smarter With SAS®

Minimizing generator downtime & anticipating future demands on the grid

Salt River Project (SRP) uses SAS to prevent unplanned downtime by accurately determining when combustion turbines are running in order to schedule required maintenance. SAS also helps SRP predict power supply and demand, using a variety of data – e.g., weather, supply, demand and outage – to accurately purchase energy to meet customer demand or sell excess power to keep costs in line.

Explore More From SAS

IDC INFOBRIEF

Data-Driven Grid Reliability: IoT Sensing and Analytics to Enable Predictive Maintenance and Improve Resiliency

Managing utility distribution networks can have significant challenges. Data-driven grids, enabled with IoT sensing and analytics, allow utilities to better provide safe, reliable, and affordable energy to their customers.

SOLUTION BRIEF

Predictive Utility Equipment Maintenance

Learn how SAS enables predictive utility equipment maintenance using sensor data, AI and advanced analytics to predict and mitigate equipment failures before service interruptions occur.

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