Improve Asset Reliability with Predictive Analytics at Power Utilities

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It’s no secret that regulators and consumers are pressuring utilities to operate at the highest level of efficiency, reliability and safety. With the demand for electricity slowing, capital expenditures rising and competition from new market entrants, the utility industry is in the midst of a major financial restructuring. The growth of distributed generation and diversification of power sources bring operational system challenges. In addition, an aging infrastructure and workforce is also driving the need for asset renewal prioritization and knowledge capture.

Utilities are always looking for ways to effectively overcome these industry challenges and remain relevant in the changing energy marketplace. Adapting to new rules, innovating new offerings and investing in cost-saving technologies are just a few of the avenues for transforming these challenges into opportunities. One avenue for effectively reaching those efficiency, reliability and safety goals is through continuous online equipment health and performance monitoring with predictive analytics technology.

Predictive analytics software provides early warning of equipment failure by comparing a unique operational profile for a piece of equipment with its real-time operating data. The software is able to identify subtle changes in system behavior well before the deviating variables reach operational alarm levels, creating more time for analysis and corrective action.

Download the free paper to learn how to avoid equipment failures and reduce downtime.

Through early warning notifications, operations and maintenance personnel are able to address equipment issues days, weeks or months before they become problems that cause significant equipment damage or lead to asset failure. Instead of shutting down the plant immediately, the situation can be assessed for more convenient outcomes. Consequently, unscheduled downtime can be reduced.

Maintenance costs can also be reduced due to better planning; parts can be ordered and shipped without rush and equipment can continue running. Additionally, some suggested maintenance windows can be lengthened as determined by equipment condition and performance.

With predictive analytics, personnel know and understand the actual and expected behavior for an asset’s current ambient, loading and operating conditions. They know where inefficiencies are and their impact on financial performance and can use this information to understand the impact of performance deficiencies on current and future operations. This information also helps utilities assess the risk and potential consequences associated with each monitored asset and can be used to better prioritize operational and maintenance expenditures.

Schneider Electric’s predictive asset analytics solution, Avantis® PRiSM, is equipped with tools, templates and a database of known conditions and assets that simplify and streamline the model-building process. The intuitive process allows models to be built in minutes rather than hours and does not require any programming or specific equipment knowledge. Additionally, because PRiSM relies on existing real-time and historical sensor data, special instrumentation is not required.

Download our free paper “Predictive Asset Analytics at Power Utilities” to learn more.

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