Predictive Maintenance: How to Manage Electrical Grids

Product news

Predictive maintenance is a widely adopted technique across sectors and during the last period, its adoption is starting to spread across new markets such as the Energy Sector.

Nowadays, predictive maintenance is commonly adopted in the energy sector to manage infrastructures, networks, and electrical grids.

The reasons that are driving the adoption of predictive maintenance techniques by Transmission System Operators (TSO) and hardware manufacturers across the world are:

The increasing availability of IoT devices connected to the network through cloud or fog technologies;
The rising offer of predictive maintenance software in the market equipped with artificial intelligence that allows an easier and faster real-time data analysis for plants and devices’ maintenance;
The accelerated transformation of the electricity market is moving the energy production from centralized models to distributed models

Predictive maintenance software and Smart IoT devices

In this context, electrification is driving the change towards CO2 reduction and a more sustainable world. The electricity grids are increasing their relevance as critical infrastructures because they provide a fundamental service to society. Thus, reducing anomalies and service interruptions is crucial to keep the world moving.

Indeed, safeguarding electricity grids has become a must, and IoT, software, and Artificial Intelligence have become crucial tools to improve grids’ resilience. The best way to achieve these results is to leverage predictive and preventive maintenance.

To adopt predictive maintenance on grids companies use Remote Terminal Unit (RTU), an electronic remote control device, which allows communication between the elements of the grids and the distributed control software, helping companies to prevent critical issues by monitoring real-time data and reducing maintenance costs.

Predictive Maintenance: the transition of the energy market

Another factor as important as the technological equipment is the transformation that the energy industry is experiencing. The strong push towards electrification and thus the change of energy production models is moving the industry from a centralized system to a distributed and flexible production scheme.

Therefore, it is becoming increasingly difficult to safely manage the electricity system which is increasingly subjected to stress and variations in production and consumption.

An efficient predictive maintenance program is a strategic element for Utilities to reduce costs and increase grids’ resilience and at the same time ensure service continuity.

Maintenance Software: expected investments

As mentioned above the attention towards predictive maintenance is rising in the energy market and for this reason, TSO and governments around the world are investing heavily in the digitalization of electric grids to enable the use of Artificial Intelligence and Machine Learning to predict failures and reduce breakdowns.

For example, Terna, the Italian Transmission System Operator (TSO), has included in its strategic plan 2020-2024 2.3 billion euros in investments for the digitization of the grid and the adoption of innovative or sustainable solutions to increase the quality of the service.

At the same time, ENEL, the main Italian Distribution System Operator (DSO), with its subsidiary e-Distribuzione, the second-largest utility in the world by capitalization, has included in its 2020-2022 strategic plan to invest 11.8 billion euros for the digitization and automation of networks.

Furthermore, thanks to the new Recovery and Resilience Plan the Italian government is planning to invest an additional stake of 4.11 billion euros to strengthen and digitize the infrastructure network.

Thanks to these investments it will be possible to enhance Smart Grids, strengthen the resilience of electrical grids and digitize infrastructures, allowing a smarter use of big data and real-time monitoring thanks to predictive maintenance software and the millions of field devices distributed on the power grid.

Consequently, the ability to manage these innovative technologies will be fundamental and improve awareness of the new opportunities and risks that digital transformation entails it will be as essential as the technologies.

In conclusion, the digitalization process of electrical networks and the adoption of predictive maintenance models requires that utilities acquire new skills and capabilities. Thus, companies in the energy sector have two strategies to do that “Make or Buy”! The “Make Strategy” requires the acquisition of new talents and skills while the “Buy strategy” requires the entrance into the market of new players such as software providers.

Discover more about our solution for predictive maintenance for power grids: ROSE Intelligent Energy Management System.