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NEW DELHI : The electrical grid infrastructure is important for all countries today. However, with climate change, advent of renewables, such as wind and solar,  and higher demand, the pressure on the 100-year-old grid infrastructure has increased. Companies, such as GE Electric, are turning to advanced and emerging technologies, like artificial intelligence (AI), machine learning (ML) and blockchain, to build smarter electrical grids. In an interview, Vera Silva, chief technology officer, grid solutions, GE Renewable Energy, explains how the grid will become more intelligent with tech innovations. Edited excerpts: 


How smart are electrical grids in India? 

The grids in India don’t have enough resiliency. There are interruptions, and flickering, etc. There’s quite a lot of work that must be done to improve the power quality of the grids, partly because  it has not kept pace with growing electricity demand. We also do not have a lot of redundancy in the grid yet due to the infra. Distribution substations and even some transmission substations are ageing and don’t have the latest technology. There’s a strong push on costs that may bring shorter benefits, but can add to the costs later. 


What must be improved to address these issues? 

Digitization of the grid is a journey. It started in the 60s and 70s with analogue, relays. Transmission grids were more advanced by default. India has one of the most sophisticated energy management systems at the transmission level with the most number of phasor measurement units—sensors to help keep the grid stable. The area we need maximum progress is distribution grid because it was not designed to host generation, control it, or behave and modify, the way you regulate (energy). What we are doing today is getting more observability of distribution via sensors, monitoring, communication, and management systems based on software. There’s a lot of development in some areas of Europe in what we call self-healing grids, that reconfigure automatically when they notice a fault. Hence, digital orchestration and automation of the grid are key areas. Last, but not the least, is the protection of the grid since we need to protect equipment and users.

Can you give us examples of technologies used in smart grids? 

The backbones of the smart grid are the software that is used to orchestrate the grid—to manage distribution, controls, monitoring, and the data from grid. All the factors help you operate (more efficiently). Having more data, information, and visibility will open new opportunities. That is why AI and ML are critical. For example, to operate the grid, the better we can forecast the generation and demand to situate the storms, the better we can prepare. What we are collecting a lot of data to predict what photovoltaic will produce, how demand will behave, etc. This is where combining imaging, deep learning, and other AI techniques is beneficial to make predictions about (things like) distributed energy resource management and wind demand forecasting. The second is asset performance management. We are adding sensors in every equipment that goes into the grid. Accessing data from these sensors allows us to be more effective on how we do more predictive maintenance rather than time-based one. 

Last, but not the least, is that on a decentralized grid, you could create islands and                micro-grids that could survive and bring electricity faster locally (in case of a grid failure), rather than waiting for the whole grid to be back. This is another area where a lot of the techniques on machine learning can be used. And that’s when I am talking about the self-healing network.

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