Electrical energy has become a basic need in daily life. From industrial manufacturing to domestic applications we require this energy. But does any sector get this energy directly from generator to them? Of course not, the power so generated is at a very low level which would dissipate in form of heat if transferred to the consumers directly. So to transfer this power to the end user, it undergoes different voltage levels. The generated power is step-up to transmit over long distances and step-down to different voltage level. To perform these operations, transformers are used.
Hence, transformer plays a vital role in electric utility. Being a vital component of the transmission grid, transformers have a finite lifespan which diminishes gradually. Fault occurrences, overloading, lack of regular maintenance etc. are the factors affecting transformer life. Since it is an important asset as a long downtime may lead to huge losses in industry or complete black out of particular area, so regular maintenance is must. But even during regular huge losses of productivity or long time black out occurs as transformers remain non-operational.
Hence, utilities are looking to have tools to survey, identify, and analyse transformer health data. This approach helps to figure out what is wrong, leaving the operator to extrapolate a solution based on past experiences and guesswork. But nowadays there is an effective tool to utilize the past data. With a predictive maintenance framework organisations can reduce the risks of faults, downtime of transformer and check out the abnormality during operation which lead to big issue or may lead to complete replacement of the transformer.
But to employ predictive maintenance we need to understand what it is actually? What are its applications and approach and how organisations would be its beneficiary? So Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.
The main purpose of predictive maintenance is to reduce the regular maintenance and to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. Hence, the key of predictive maintenance is “the right information in the right time”. If user have an idea that which equipment needs maintenance, maintenance work can be better planned as per requirement not regularly (spare parts, people, etc.). This may reduce the planned maintenance time, thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimised spare parts handling.
Predictive Maintenance Approach
The process starts with the continuous or periodic conditional monitoring of the asset. Sensors continuously analyses or collects the data of the asset on the basis of minutes, hours, days, months (as per the requirements). It computes and stores the data of the parameters of the transformer to the storage cloud. From that cloud the concerned person gets this data regularly. So whenever an abnormality starts there will be some unusual or odd data and when this data reaches beyond the set limit. Alerts or warnings are sent to the analyst. So with the help of these alerts one may come to know the exact issue at the earlier stage which could become a major problem.
Hence, the predictive maintenance is a process of monitoring, analyse and action.
Predictive maintenance ensures that a transformer requiring maintenance is only shut down right before imminent failure. This reduces the total time and cost spent maintaining equipment. It amplifies performance, reducing response time to failure events. Such a holistic approach is becoming increasingly relevant in case of transformers where even a few seconds might mean the difference between a planned, agile response and major equipment failure.
A Transformer or power transformers have various aspects like predictive maintenance, Conditional monitoring, transformer fault detection, transformer condition monitoring, transformer insights, and transformer predictive management.
Transformer | Predictive Maintenance | Conditional Monitoring | Transformer Fault Detection | Transformer Condition Monitoring | Transformer Insights | Transformer Predictive Management | Power Transformers
– Deputy Manager
– Shahi Exports