Analyzing Substation Asset Health Information for Increased Reliability And Return on Investment

Asset Management, Substation Automation, AMI and Intelligent Grid Monitoring are common and growing investments for most utilities today. The foundation for effective execution of these initiatives is built upon the ability to efficiently collect, store, analyze and report information from the rapidly growing number of smart devices and business systems. Timely and automated access to this information is now more than ever helping drive the profitability and success of utilities. Most utilities have made significant investments in modern substation equipment but fail to continuously analyze and interpret the real-time health indicators of these assets. Continued investment in state-of-the-art operational assets will yield little return on investment unless the information can be harvested and interpreted in a meaningful way.


InStep’s eDNA (Enterprise Distributed Network Architecture) software is used by many of the world’s leading utilities to collect, store, display and report on the operational and asset health-related information produced by their intelligent assets. eDNA is a highly scalable enterprise application specifically designed for integrating data from SCADA, IEDs, utility meters and other smart devices with the corporate enterprise. This provides centralized access to the real-time, historical and asset health related data that most applications throughout a utility depend upon for managing reliability and profitability.

A real-time historian is needed for collection, organization and reporting of the substation asset measurement data. Today, asset health monitoring is often not present or it is comprised of fixed alarm limits defined within the device or historian. Additionally, fixed end-of-life calculations are used for determining an asset’s health. It is a daunting task to identify and maintain fixed limits and calculations that can be variable based on the actual device characteristics, operating history, ambient conditions and device settings. As a result, the historian alone does not provide for a complete asset monitoring strategy.


InStep’s PRiSM software is a self-learning analytic application for monitoring the real-time health of critical assets in support of Condition Based Maintenance (CBM). PRiSM uses artificial intelligence and sophisticated data-mining techniques to determine when a piece of equipment is performing poorly or is likely to fail. The early identification of equipment problems leads to reduced maintenance costs and increased availability, reliability, production quality and capacity.

The software learns from an asset’s individual operating history and develops a series of operational profiles for each piece of equipment. These operational profi es are compared to an equipment’s real-time data to identify and predict failures before they occur. Alarms and Email notification are used to alert personnel of pending problems. PRiSM includes an advanced analysis application for identifying why an asset is not performing as expected.


Utilities are rapidly replacing legacy devices and systems with modern technologies. These new systems are typically better instrumented to provide utilities with the information necessary to more effectively operate and better maintain their assets. The status of a breaker can be good information for determining the path of power fl ow but does not provide enough information to determine the health of the device or when it is likely to fail. Modern IEDs and utility meters support tens to hundreds of data points in a single device. This data is quite valuable and necessary in supporting a modern utility asset management program. Many common utility applications such as maintenance management, outage management, meter data management, capacity planning and other advanced analytical systems can be best leveraged when accurate high-resolution historical data is readily available. An intelligent condition monitoring analytical layer is needed for effective monitoring of such a large population of devices and sensors.


The need for efficient and effective data management is rapidly growing as utilities continue to update their assets and business systems. This is further driving the need for a highly scalable enterprise historian. The historian is expanding beyond the traditional role of supporting operations and is becoming a key application for effective asset management and overall business success. The historian alone does not provide for a robust real-time asset health monitoring strategy, but when combined with an advanced online condition monitoring application such as InStep’s PRiSM technology, significant savings and increased reliability can be achieved. InStep continues to play a key and growing role in supporting many of the most successful utilities in their operational, reliability and asset monitoring efforts.