Anand Budholia, Reliance Power
Aug 30th 2017

Digital Innovation Strategy

The project by Reliance Power is about identifying and avoiding critical equipment failure using a predictive diagnostic software. The solution helps in making informed maintenance decisions using operating data and converting maintenance activities from a reactive mode to proactive mode, thus extending error detection to its diagnosis and management.

The actionable information/alerts provided by the solution gives an advance notice to the Operation & Maintenance team at site  to accomplish the necessary maintenance, before these problems can compromise its operating results. It has improved the plant operability by giving early warnings for all critical failures. One unit tripping for a typical Reliance Power’s 300 MW coal based plant can cost crores of rupees for the company. The project has effectively now reduced maintenance costs through fewer trips and equipment degradation.

The various IT constituents involved in the project are Condition Monitoring & Diagnostic System having predictive analytics, Distributed Control System (DCS), Data Historian Application, etc. The Distributed Control System (DCS) is connected to plant equipment sensors and actuators which capture the equipment parameter values. The Historian Application takes the data of the identified critical parameters from DCS through OPC connectivity and stores it in a time series database.

Predictive Analytics Tool has its own models developed for power plant equipment based on equipment design, physics, and empirical regression. The tool analyses the current / historical data provided by Historian, and using advanced algorithms it generates the early warning (advisories) along with its priority. These advisories are notified to a team/personnel through a work flow for corrective and preventive action.