Energy management: Simplifying the way utility companies manage their transformer fleets, GE’s Digital Energy business announced the latest version of its Perception Fleet risk management software, version 1.14, a smart, simplified and standards-based data analysis package that provides utility operators with data on the condition of every transformer in their fleet. Perception Fleet facilitates the shift from costly time-based maintenance programs to a more cost-effective condition-based maintenance program, helping companies better understand the budget requirements to maintain and build a healthy fleet, focusing on capital expenditures and in turn reducing operating expenses.
By evaluating and risk-ranking individual transformers, Perception Fleet can determine and provide an overall fleet condition diagnosis. Pre-defined or user-defined intelligent algorithms within Perception Fleet automatically evaluate the condition of individual transformers and compare conditions to give an overall ranking of transformers. This makes it easy for system operators to determine which transformers require immediate or longer-term service to prevent failures and costly outages.
Perception Fleet also provides utility experts with the tools required to independently analyze and assess individual transformer conditions. Operators also can use the built-in internationally recognized advanced diagnostic standards tools to help interpret this data, or they can work with third-party data analysis software to provide a single solution for viewing and analyzing transformer condition data. Featuring a customizable CSV import/export function, it allows data exchange and integration with other systems such as SCADA, EMS, DMS, Historian, AM, SAS, planning, ERP, etc. Applications which support CSV file exchange can easily interface with Perception Fleet, enabling data to be effortlessly exchanged between applications.
“Transformer operators have long depended on software solutions to document critical data from their transformer fleets. That raw data would then need to be processed and interpreted by industry experts to produce meaningful and measurable results,” said Simon Phillips, general manager, monitoring & diagnostics for GE’s Digital Energy business. “Our new Perception Fleet software gives operators a tool to assess, with a single metric, the overall risk to their transformer fleet and how it changes over time, allowing them to prioritize their capital expenditures and operations and maintenance investments for the most impact.”
GE’s Perception Fleet also now includes new dashboard overviews, which provide concise and simplified critical transformer information as well as a scope overview of the entire transformer fleet in a clean display. This user friendly, intuitive design equips utility operators with a clear overview enabling a better understanding of their fleet’s condition as well as the health of their individual transformers.
This latest version of GE¹s Perception software also has the capability of delivering email notifications containing critical transformer data to technical advisors as soon as an alarm is generated and reported, reducing transformer outage time and helping prevent catastrophic failures. If an alarm is triggered on a monitoring device, user-defined experts are notified immediately with the details of the problem and a snapshot of the transformers critical data. This allows a utility company or a designated third-party transformer expert to remote diagnose a transformer condition and recommend a course of action, minimizing downtime and expediting the maintenance process once a problem has been diagnosed.
Customers with a Kelman Transformer Technical Services (KTTS) contract can elect to notify a GE transformer expert when an alarm is generated, and the expert analysis notification email will be sent to the GE expert, within an agreed time scale, enabling that expert to advise on the transformers’ condition and recommend action to be taken to prevent a fault from developing further or avoid catastrophic failure.