How can AI be used to provide better transformer data?

What is the benefit of using AI to provide better transformer data?

It can be challenging to maintain and manage transformers. Professionals must analyze a variety of types of data to identify and resolve problems, even when models are well-maintained.

Businesses can store transformers more efficiently and prevent failures by using artificial intelligence data analysis and new data collection methods.

Artificial intelligence can also be used by manufacturers to improve the quality of their transformers. Maintaining these machines requires companies to keep a few things in mind.

The limitations of current transformer evaluation and data collection strategies

Transformer tests and condition assessments are typically performed without considerable automation today. To ensure the normal operation of transformers, technicians conduct a variety of tests, including resistor tests and rotational rotation tests.

In addition to oil level checks, leakage checks, and silicon gel checks on respiratory transformers, transformers require daily inspections.

It is usually necessary to obtain, copy, and manually report data collected by transformer sensors, such as magnetic oil gauges. The SCADA system or the asset management platform may report important information automatically, but that is not the case with all systems.

Testing transformers daily and in rare cases requires the skills of a transformer specialist.

It is not uncommon for skilled workers to be in short supply in the energy sector, as is the case in most industries. Due to this, many companies may be concerned about properly maintaining and inspecting transformers.

Failure to maintain transformers can have some serious consequences

Failures of transformers can be very costly. It can cost up to $14 million depending on its severity and amount of downtime, and even short-term outages can cost a company as much as $1. Two million dollars is not uncommon.

It is nearly always less expensive to perform preventive maintenance than to repair a major breakdown. Still, transformer maintenance isn’t easy. Despite a theoretically efficient maintenance schedule, technicians can overlook minor issues that eventually turn into serious problems.

As companies struggle with a shortage of skilled technicians, these problems can become even more challenging.

Automating transformer monitoring and testing using artificial intelligence is one possible solution to this problem. With the combination of intelligent remote monitoring technology and artificial intelligence analysis of incoming data, businesses can identify potential transformer failures and predict failures automatically.

Using artificial intelligence, transformer data can be collected more efficiently

Now, transformers can be fitted with smart sensors that monitor important data – such as oil levels – and transmit this information to the cloud. Additionally, the data collected by these sensors can be streamed to SCADA control platform dashboards and software if needed.

The technician can access this data from anywhere so long as he or she has access to the cloud network. As a result, transformer performance can be continuously monitored. Automated analysis of incoming data further reduces the workload of business transformation specialists.

Algorithms based on artificial intelligence are excellent at detecting patterns. A sufficient amount of data can be collected to detect subtle correlations between sensor information and failure conditions. 

AI algorithms, therefore, may detect failures before a technician does – even if they look at the same data that business technicians do.

The training of these AI systems is based on a large dataset of transformer testing and monitoring data. Data sets that are comprehensive will usually include information from both new and used transformers, as well as failed transformers, exposing the AI algorithm to different operating conditions and failure scenarios.

AI could be more effective at eliminating correlations between operational variables and transformer maintenance needs when more types of data are available.

A similar approach to maintenance and testing – often called ‘predictive maintenance – is becoming more popular in manufacturing and other heavy industry sectors.

By preventing downtime, this method is a valuable cost-saving tool. In addition to expanding the use of existing IoT data collection systems used for remote monitoring, smart sensors and similar technology become more valuable investments.

Despite being still experimental, predictive maintenance seems to be more effective than preventative maintenance. In this way, businesses that already invest in effective maintenance strategies can further reduce operating costs.

Artificial intelligence can help its producers make better changes

In addition to storing and operating data, AI algorithms can also analyze it. Manufacturers can use AI to analyze factory data and production processes to identify system constraints, integrate failures into production conditions, or even create new tools to speed up transformer design.

The result is more reliable and low-cost versions that reduce the initial and operational costs of the new converter.

Transformer manufacturers can benefit from information from the field. Businesses may encounter months of sensory data leading to failure if a person fails or begins to behave abnormally.

The information may allow manufacturers to determine what may be causing the transformer to fail – and thus provide new designs and storage tips that may make them easier to maintain.

Several industries have adopted this approach to AI information as a precautionary measure. Artificial intelligence capabilities can be used to identify design errors and production bottlenecks that can reduce productivity with accurate technology.

The use of AI tools is common in quality improvement programs. Using video cameras as input, AI machine vision can edit objects dynamically. In the quality control process, these products can automate visual inspections to increase product quality without increasing staff workloads.