This audio was created using Microsoft Azure Speech Services
The origin of connected services has brought about a meaningful rise in knowledge, recognition and application, mainly due to the concept of the Industrial Internet of Things and home control. In the realm of power distribution, IoT plays a critical role, by allowing owners of appliances (such as transformers, transfer switches, and breakers) to shift from a reactive and preventive sustaining system to a condition-based maintenance business model. Practical implementation and feasibility-related information associated with the Internet of Things functions have grown in popularity. Schneider Electric’s Power Distribution and Asset Monitoring-as-a-Service (APM-aaS), a predictive analytics software solution has been installed at numerous pilot areas.
The essential goal of Power distribution-based Asset Performance Monitoring is to offer insights concerning the well-being of electrical distribution assets. The steps involved in this process are as follows:
- Collection of behavioural data through sensors
- Integration of collected data via a dashboard
- Overlooking and interpretation of said data by power experts
- Conversion of data to action
At the pilot locations appliances such as medium voltage switchgears, dry type transformers, air circuit breakers, and high voltage oil-filled transformers were installed for supervision and analytical purposes. Additionally, wireless temperatures and partial discharge technologies were inculcated into the platform, and both medium and low-voltage circuit breakers algorithms were polished.
Sensors were infused inside of the chosen assets in order to collect performance data and help in interpreting the functional efficiency of each asset. By leveraging modern technologies in cloud systems, data was conveyed from the customer’s location to the Schneider Electric data centre to interpret optimal power management.
During this process, data is scrutinised at three levels. At the first level, analytics was made accessible through asset-specific algorithms, alongside rules and thresholds distinct to asset utilisation. The second level consisted of isolated analysts estimated at a centralised service bureau who were responsible for day-to-day services. The third level involved an array of experts – From either the product line or system side – who accredited the verdicts and collated a detailed statement along with suitable suggestions. The results were presented to the pilot consumers, with a comprehensive work plan to discuss them.
At one of the pilot sites, the team utilised a wireless temperature technology on two dry-type transformers, allowing a double-ended substation that promoted a Main-Tie-Main configuration. One side was driven by a 4-year old transformer and the adjoining transformer, which was a feature of the original installation, had been functioning for approximately 30 years.
A few weeks after the simulation started, it was observed that the heat component of the two transformers was substantially differing. The modern transformer showed an extremely tight temperature profile, with the “B” phase portraying the highest temperature out of the three phases. Simultaneously, the older transformer exhibited a heat signature that was quite unique, and the temperature difference between the phases was significant. Analysts observed that “C” phase was clearly hotter than A and B. In normal conditions if the load is equal on a dry-type transformer, the “B” phase temperature should be higher than “A” & “C” because of its physical location. It was concurred that an identically configured transformer, 4 years ago , had been replaced due to a catastrophic failure caused by a coil short circuit leading to comprehensive scale of activities undertaken for power factor correction and maintenance.
It was concluded by an expert analyst that the heat trend noticed in the older transformer was a potential warning that the insulation present was gradually fluctuating. This situation can result in short-circuit issues. The analysis presented by the Asset Performance Monitoring team was convincing enough to influence the customer’s management team to upgrade to a modern transformer, subsequently avoiding scenarios of damaging transformer loss.
Predictive maintenance and its functionality play a critical role in field services and its execution. This is how an ideal level of evaluation, management, control, and interpretation allows companies to keep their financial books healthy and ensures that their functions in power distribution and asset management remain sustainable and profitable.
