Power transformers are ubiquitous devices of our electrical networks. They are a key component that allows the interface between networks of different voltage levels and without them, the AC networks that we know today would have not been possible. In fact, they were a deciding factor that made AC win the War of the currents.
Substation power transformers are the critical link that connects wind and solar farms to the electrical network. When transformers fail, they often cause long and costly downtimes of the entire, or part, of the farm.
To understand the economic impact that these downtimes may have let’s examine the figure below. We can see how the yearly revenue loss caused by these downtimes changes when downtime duration varies from 2 to 8 weeks and total site capacity varies from 100MW to 500MW. For example, if a site with 300MW total power capacity has one transformer which is offline for 5 weeks, this translates to a cost of 0.75M Eur/year.
Yet, despite being a critical link with such economic risk, power transformers are often “forgotten”. They tend to be pieces of equipment that are underinsured, under sensorized and under monitored. As a consequence, maintenance teams can only rely on sparse analyses such as oil and electrical tests that require bringing the power transformer offline.
We believe there is a need to improve current monitoring solutions, to reduce the risk still associated with these assets. We believe that is time to give power transformers the attention they deserve.
We offer an end-to-end solution to monitor power transformers that can be divided into two main parts: installation of sensing equipment and application of our predictive intelligence stack. Let's dive into each one of these in more detail!
We first need to collect the right data to allow proper monitoring. To do so, a sensing hardware solution is retrofitted to existing power transformers which enables the measurement of voltages and currents of both the primary and secondary sides collected at sampling frequencies in the kHz order.
The proposed sensing solution has a set of highly desirable characteristics:
Once the installation is complete and data acquisition is ongoing, the transformer health monitoring can begin. This is where Jungle’s predictive intelligence stack jumps in.
Our normality ML models allow us to understand, at each point in time, if the assets are operating as expected, e.g. internal components temperatures and pressures (see our blog post for a deep dive about our normality models) and create alarms when deviations are detected. For a more detailed explanation of our alarms mechanism please have a look at our blog posts (part 1 and part 2) dedicated to the topic.
Our normality models are very powerful and generically applicable. The same models can be employed to create a digital footprint of a power transformer enabling visibility on how each transformer sub-component should be behaving at any point in time. It is by combining the right sensing hardware solution with our state of the art predictive intelligence stack that the full monitoring potential is unleashed.
Our monitoring solution enables the unambiguous identification of more than 80% of the most common failures of power transformers as well as early detection of abnormal behaviour that precede such failures.
Curious about the insights our solution is able to distil? We have successfully identified many failures and below we show three examples of such findings.
Our system detected a problem in consecutive taps of an on-load tap changer (OLTC). This triggered a maintenance inspection where the detection was also visually confirmed. The OLTC oil was changed and a later replacement was scheduled.
Impact: Early fault detection and a greater lead time for the OLTP parts ordering was possible. Maintenance intervention scheduled to minimize cost impact.
Our system detected a degradation located in the windings of the transformer due to abnormal phenomena (partial discharges, leakage earth currents). Three months after this was confirmed via oil analysis. The power transformer has been operating with this ongoing degradation. Our system is continuously checking any potential failure developments.
Impact: Investment delayed and minimized. Maintenance intervention scheduled for a later moment to minimize cost impact.
Despite our system not detecting any abnormal behaviour, an internal failure was detected via recurring (every 3 months) oil inspections. The transformer was sent to the factory for inspection and the failure presence was not confirmed. The false positive was caused by the transformer insulating oil being contaminated.
Impact: Transformer was not operating for a long period. The maintenance intervention in-factory amounted to 750 000 €. All of which could have been avoided.
We put a spotlight on power transformers with our solution. The main advantages for our users include:
If you identified yourself with our mission and approach to predictive maintenance, feel free to reach out! Let us help you reduce uncertainty from your operations! Send us a message to firstname.lastname@example.org, and we will be in touch shortly after! It’s that easy to get started.