Transforming the energy industry with industrial IoT, IIoT

IIoT, digital substation, data, IOT asset management, field operations

By Ghian Oberholzer, Regional VP of Technical Operations APJ at Claroty

The Internet of Things (IoT) is widely deployed in the energy sector. Examples include smart electricity and water meters that remove the need for manual meter reading, and automatic control of lighting and air-conditioning in response to environmental conditions or occupancy levels, all of which are IoT-powered.

But there’s a different part of the energy sector where IoT is having a huge impact: energy production and distribution. The use of IoT technologies to monitor, control, and optimise these industrial processes is referred to as Industrial IoT (IIoT).

Monitoring and control were always a feature of industrial systems and processes, known as Operational Technology (OT). When modern IT technologies are integrated with established OT networks, energy generation and transmission can be made more efficient. But these OT systems were developed prior the internet and prior to today’s digital transformation in all aspects of industry and commerce.

Basic implementations of IIoT involves the use of sensors to gather information and smart devices to remotely control facilities. More advanced applications of IIoT include predictive and preventive maintenance, artificial intelligence and digital twins. It’s these advanced applications from which energy companies will experience very significant benefits.

As world leaders commit to reducing their greenhouse gas emissions over the course of the next 30 years, maximising the efficiency of renewable energy generation, storage and distribution will be a critical factor. IIoT is already playing a significant role in this, and it’s importance will only grow as the relatively new technology evolves.

IIoT-enabled maintenance

Artificial intelligence (AI) techniques can be applied to historical data gathered from multiple machines to enable predictive and preventative maintenance. The AI can use this data to predict the likelihood of a problem or failure, reducing the need for routine maintenance and anticipating breakdowns. These techniques are very useful when energy facilities are in remote or hard to access locations, such as in the case of wind turbines.

Operation and maintenance can account for as much as 30 per cent of the cost of generating power from a wind turbine throughout its life. And repairs are responsible for about 70 per cent of wind turbine downtime, so IIoT-enabled maintenance can bring substantial benefits in this area.

When AI meets IIoT

Maintenance support is not the only application of AI to IIoT in the energy sector. Outputs from wind and solar systems are dependent on the weather, meaning it is difficult for energy generators to pre-commit to energy deliveries. When they are able to pre-commit, they can get a higher price for their power.

AI can be used to predict outputs by analysing weather data and weather forecasts. Google’s AI division DeepMind has demonstrated this by using weather forecasts to accurately predict the output of wind farms with 700 MW capacity 36 hours in advance, lifting the value of their output by roughly 20 per cent, compared to the value without forward commitments.

The digital twin imperative

Another application of IIoT, the digital twin, is also showing great potential for the energy sector and many other industries. It is the replication, digitally, of a real-world machine or process, made possible by using IoT technologies which gather data on every component of the machine or process.

A UK company created a digital twin of its demonstration offshore wind turbine so technicians can view the operation of the turbines remotely. It has been able to reduce the need for technicians to visit the offshore wind farms, and means they are now able to inspect machines remotely, plan operations better, and train new maintenance personnel offsite.

IIoT and cybersecurity

Fundamental to IIoT delivering all these benefits is the successful integration of IT and OT networks. However, this integration exposes legacy OT networks, which used to be completely isolated, to the many cyber threats lurking in the IT world.

Most IIoT assets in the energy sector were not designed with security as a high priority, because exposure to the internet was never anticipated. To make matters worse, traditional IT security tools are incompatible with OT networks, whose devices often use obsolete and proprietary protocols.

As well as making IIoT networks vulnerable, these attributes also make attacks more difficult to detect. However, AI can be applied to better detect threats and anomalies automatically.

AI can analyse network behaviour to identify and remove false alarms that can be costly to deal with manually. It can also assign priorities to alerts and create detailed profiles of every device on the network in order to detect abnormal behaviour.

In order to fully reap the benefits that integrating IT and OT brings, energy providers must be aware of the security challenges that come along with it and take proactive steps to protect their newly interconnected networks. If the industry is successful in this endeavour, we will ultimately achieve greater reliability and efficiency of energy generation and distribution around the world.