How to Modernize Power Transformer Condition Monitoring? 

Transformers are one of the biggest investments in an electricity distribution system. Thus, the consequences of failure or even small faults are severe in terms of cost and power distribution.  

This makes transformer condition monitoring very critical to carry out operations. By monitoring a range of condition parameters in a transformer, such as temperature, moisture, vibrations, and more, it is possible to detect significant changes indicating the possibility of developing faults.   

As a key asset, meticulous condition monitoring can prolong equipment life and maximize reliability and availability.   

Digital technologies are being harnessed to ensure greater visibility over transformer health and performance. Remote monitoring of assets has seen a phenomenal increase in adoption, and these digital technologies drive business value.   

How do digital technologies make transformer condition monitoring easy, smart, and error-free?  

Manual monitoring and recording parameters have an associated risk of reading errors and may result in incorrect assessments. It is also overwhelming and costly as engineers are required to be at the site for the purpose.   

On the other hand, technology-led monitoring is easy, error-free, and cost-effective. Online condition monitoring enhances benefits such as critical information gathering and condition assessment, ensuring better decision-making for businesses.   

The market sales of transformer monitoring systems were $2 billion in 2021, and it is predicted to register an impressive 10.1% CAGR between 2021 – 2031. The phenomenal growth is attributed to adopting IoT-based digital technologies in the monitoring system.   

KpiX is a connected asset platform that enables industrial OEMs to integrate digital technologies with their assets.  

Many of India’s Industrial OEMS in Transformer, Compressor, Chillers, Industrial pumps, and CNC machine segments are using KpiX for the below purpose  

  • Machine data acquisition  
  • Asset tracking  
  • Real-time asset monitoring  
  • Remote service and maintenance   
  • Improve overall uptime  
  • Condition-based health monitoring   
  • Predictive maintenance
  • Downtime analysis  
  • Automated critical alerts and notification  
  • Automated reporting and dashboarding  
  • Building smart industrial equipment  
  • Enable asset visibility for end-customer  
KpiX for transformer operations

Here are the digital technologies KpiX is using to modernize Transformers and other industrial equipment.  

Industrial Internet of Things  

Usually, machine conditions and performance parameters are recorded periodically. The frequency with which a machine is reviewed will depend on its age, previous condition, cost, and impact of outages.   

An IIoT – powered remote condition monitoring system enables a continuous stream of real-time data with the sensors attached to the equipment.   

Intelligent sensors are hardware components that monitor deterioration and performance parameters. There are sensors for moisture, leakage, temperature, and voltage monitoring.   

The data collected is then redirected to a host, preferably in the cloud, for diagnostic analysis.   

Cloud Technologies and API  

Cloud computing technologies are tied directly into the transformer condition monitoring system. They help scrutinize the condition of assets by detecting critical changes to the condition of assets that could lead to outages, failures, or mishaps.    

Centralized monitoring of transformers at various locations helps establish standards of assessment and performance that aid in predictive maintenance.   

Implementing cloud computing in an industrial application requires a network architecture with three layers – sensors, gateway, and cloud service.   

The cloud is also used to store, process, monitor, and analyze data. The metrics collected by the sensors can be sent directly to the cloud or can be analyzed on edge.   

API enables raw or processed data to be retrieved from any other system. This makes asset data and insight integration with existing organizational ecosystems like ERP or collaborative applications. On top of that, API comes in handy when building custom specialized applications for internal usage, customer, or partners.  

With the influx of information from the widely deployed sensors in the system, it can be used as a computational solution to comprehend the data. Furthermore, data is exchanged between the network of sensors and the computational cloud system using a centralized control mechanism.  

Web and Mobile Applications  

Web or mobile applications are the third component in transformer condition monitoring using digital technologies.   

After the information is stored and converted into actionable data, it is delivered to the system. The machine determines the exact delivery form, the status of the transformer, and the response it needs to evoke along with alerts and notifications.  

Our web or mobile applications can accurately identify the criticality of information and take corrective action as programmed. The system sends alerts and notifications through web applications to businesses to take timely corrective action. For example, if the winding temperature exceeds a certain threshold, the system automatically sends an alert to evoke a corrective response.   

Industrial OEMs can monitor their transformers with a web or mobile application. The system will provide complete information on vital equipment parameters, network, power and availability status, load insights, and demand trends.   

The application combines different technologies such as geospatial location mapping, service instructions, and more.   

AI and Predictive Maintenance  

Gathering information is only one end of the condition monitoring spectrum. Integrating data collection with analytical solutions while harnessing diagnostic solutions using AI and ML is the optimum solution for effective monitoring.   

After developing an intelligent framework that can understand mission-critical condition information from multiple sensors, it is crucial to put the data through intelligent ML systems so that the insights are data-backed strategies rather than just opinions.   

Furthermore, by assessing the statistical distribution of data, ML systems can identify relations between parameters and thereby help make a qualified judgment regarding predictive maintenance.   

Predictive maintenance helps address developing faults, identify premature aging, recognize repairs before they impact performance, and eliminate unplanned service interruptions or outages.   

In addition, the early warning system of predictive maintenance reduces the time and cost of maintenance.   

Using the KpiX platform for Digital Transformer Condition Monitoring  

Condition monitoring aims to maximize productivity, reduce maintenance costs, and proactively manage the entire system. However, monitoring transformers and other physical assets spread across locations is not an easy task. Even a single error can cascade through the entire system when manual monitoring is undertaken.    

Remote monitoring of transformers – gathering real-time information about their status, performance, and condition – is accomplished using IoT and Cloud computing technologies. Here are the key benefits of digital transformer condition monitoring experienced by KpiX Users:  

1. Prolong the transformer Life  

Over 35% of power transformer failures are controllable through condition-based maintenance. Digital condition monitoring enables measuring health parameters that matter in real time without any error. Constant data logging and analysis facilitate taking the right action at the right time before it impacts operation or balance sheet.  

2. Helps in Implementing Reliability-centered Maintenance Strategy  

Reliability-centered maintenance strategizes the frequency of maintenance and health checks by considering the technical condition of the asset and the associated impact if it breaks down. Real-time transformer condition data helps assess the technical condition of the transformer constantly and early signs of deterioration help to define an action plan strategically.  

3. Increased Transformer Uptime  

Condition monitoring of transformers increases their uptime and performance. Timely monitoring of the condition parameters such as temperature, level, leakage, moisture, and others, can help businesses take appropriate action before the potential problem advances.   

4. Continuous Monitoring from OEM  

Once an OEM has completed the equipment installation, they can’t monitor the health condition of the equipment. Transformers can sometimes be located in remote locations such as deserts or mountains, but monitoring their conditions becomes a tough challenge.   

With remote monitoring using sensors and cloud-based storage, support engineers from OEM can monitor the equipment continuously without visiting the site.   

5. Better Demand Planning  

With the help of machine learning models, large amounts of data from the IoT sensors are analyzed to identify patterns and correlations. With this information, it is easier to match it with future demands.   

6. Prevent Sudden Equipment failures   

Without condition monitoring of transformers, businesses might encounter unplanned and sudden equipment outages. With KpiX, OEM can provide efficient service to customers by eliminating potential interruptions and outages by identifying developing faults from a very early stage.   

7. Deals Proactively with Developing Faults  

With IoT sensors sending a continuous stream of data from the transformers, every minute change in parameters can be identified and addressed before they turn serious. The data also helps OEMs detect equipment aging, untreated defects, need for repair and assess the overall health of transformers.   

Summing Up  

A digital transformer condition monitoring system helps save time and money and increases overall performance.   

Unlike traditional monitoring which requires a trained technician to be physically present at the site, digital systems such as IoT sensors and actuators send a stream of data to the cloud storage without human intervention. Moreover, it is now easy to derive patterns and valuable business-altering insights from the data using ML and AI algorithms.   

Transformer condition monitoring of systems using digital technologies is the industry’s future. Businesses at the forefront of this transformation are poised to garner huge benefits and edge out the competition.   

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