Electricity Distribution IoT solution: real-time transformer monitoring + fault detection.
The solution
We built our Electricity Distribution IoT solution on our world-leading Dynamix integrated data, AI + IoT platform to help our client collect real-time data from Power Distribution Transformers’ PLCs, weather reports and from 4G energy meters, combe the data sets to identify new performance metrics, maintenance insights and improve fault detection.
As Power Distribution Transformer types vary, one of the key features of our solution was that it be extremely flexible.
Defining what data to collect is critical
In order to create the Electricity Distribution IoT solution, we started by identifying the data needed to deliver the desired business outcomes. In this solution these included:
- Energy consumption
- Transformer voltages
- Current and predicted temperature and humidity
- Oil level
- Operating status
- Weather data.
Initial goals of the Electricity Distribution IoT solution
The first goal of the Electricity Distribution IoT solution was to connect it to the transformers, PLCs and external weather service in order to collect data in a consistent and reliable manner, and ensure its quality and integrity. To do this, we included the following features in the solution:
- Monitoring of critical operational data received from the equipment via a web-based system
- Implementation of device management monitoring
- Creating business logic for the application to meet the business objectives
- Providing alarm and alert notifications via email or SMS messages to identify issues
- Collection of historical data to enable energy consumption forecasting and fault detection
- Multiple dashboards to provide the right users with the right insights
- Testing of the application, making sure all of the above goals are met.
Before go-live, we tested four critical aspects of the Electricity Distribution IoT solution:
Security
The Rayven Dynamix data, AI + IoT platform is built with security as a top priority.
Rayven’s proprietary security architecture ensures data is secure at all points of the IoT environment. The solution includes data encryption in transit and is encrypted from device to cloud (device-dependent). Devices are authenticated using device keys (device-dependent) and 256-bit SSL encryption is used between end-user devices (PCs, tablets, mobile phones) and the cloud, which protects confidentiality, data integrity and availability. In transit from device to cloud, we have SHA-256 with RSA Encryption, automated at-rest encryption using 256-bit AES encryption (optional), and during use (from cloud to screen) SHA-256 with RSA Encryption. The solution additionally conducts device security checks via automated polling and/or pull request as well as having security (Bearer) tokens that authenticate devices and services, meaning that keys don’t need to be sent on the network.
Connectivity
Once securely online with the Electricity Distribution IoT solution, the PLC’s were connected to the 4G energy meter, which acts also as a gateway. This configuration means that you only need a small, one-off investment with cabling and gateway connection to set up the solution, and then incur only minimal costs to maintain the connection in the form of a monthly 4G data sim fee. The combination of a secure and encrypted transmission path together with a dedicated, direct connection means that the solution has a fast and secure connection without needing to involve their internal IT team.
Data integrity
Your ability to make the right decisions is reliant on the integrity of the data that you collect. Ensuring that you have reliable connectivity means that your data is always up-to-date and that you can rely on it to make critical business decisions in real-time. It was, therefore, essential for us to test that the raw data matched the dashboards and to build-in capability to back-fill data in sequence in the event that communication ever goes down, so that there would never be gaps in data flows to the IoT platform.
Industrial Data Science
The objective of the exploratory data analysis was to identify trends in the data, which included:
- Energy usage patterns compared with temperature and humidity
- Maintenance and failure alerts
What’s next?
We are gathering data that will help us to build models of energy forecasting, fault detection and predictive maintenance, further improving the way that the company maintains their distribution assets.
