The other day, I was watching a video clip of Boston Dynamics. How splendidly the robots were dancing, jumping or somersaulting in a near-perfect manner. These were all possible because of the development of artificial intelligence(AI) in recent times.

Amazingly, AI can also do self-learning from gathered data and through experience. Experts call it machine learning(MI) and deep learning (DP)

So, it is obvious that AI can contribute a lot to the performance improvement of wind farms as well.  

Machine learning (MI) algorithms can understand the structure & pattern of input data. Can build mathematical models out of them. And finally, use them to predict, forecast and various decisions making.

In recent evolution, AI has stepped into the arena of Deep Learning (DP), where it can mimic the human brain and process information by using ANNs(artificial neural networks).

Industries across the globe are embracing AI to improve their performances almost at every functional activity.

Though the Wind Energy sector in India had also come forward to imbibe AI about four years back, there is still an enormous untapped scope to expand it.

There is no doubt that the way we were managing our wind parks 20 years or even 10 years back, has become inept today. 

On one hand, the turbine population in India is increasing, and on the other hand, high wind sites have become scarce. So the machines are now being designed for Class II and Class III sites. At the same time, the margin of the business has also become thin.

Therefore, there is no room to accommodate any mismanagement in the operation of wind farms. Unwanted stoppages and loss of generation are unbearable.

Altogether, managing wind farms for the best possible performance is getting more challenging every day. 

In India, we are using the SCADA system (Supervisory Control and Data Acquisition) for a long time to monitor the turbine’s generation and running conditions. Wind turbines are (in general) connected to a central monitoring station via fibre-optic cables and controlled from that station.

SCADA is an efficient tool to have

  • Wind turbine overview
  • Wind turbine control
  • Wind park overview
  • Wind park control
  • Turbine log view and
  • Various reports

We now have AI, which is being used in almost every industry sector. The Decision-making process has become simplified, quick and more accurate. 

So, embracing AI in the wind energy sector in India was a prudent decision. 

AI can play a game-changer role, the way we usually manage our wind parks in India. It can benefit us in: 

  • Improved wind farm designing
  • Near accurate weather forecasting
  • Better grid management
  • Cost-effective Maintenance and 
  • Enhancing wind farm efficiency.

AI in improved wind park designing

To design a wind park AI algorithms are being used to avoid the complexity of the design process. And to get the result in a shorter time involving minimum man hr. AI does a months’ work in few minutes.

AI in weather forecasting

The unpredictability of the weather can be dealt with efficiently with the help of AI. It can offer a quick and more accurate forecast than any traditional method. 

Advanced weather visibility can yield tremendous cost and operational benefits for the park owners.

AI in better grid management

Artificial intelligence can play a big role in grid management. With the growth of renewable energy, the numbers of microgrids, battery storage, wind and solar farms and CI sector solar energy plants are increasing. All these clean energy systems will continuously deliver or draw the energy from the grid. Efficient management of these energy transactions to have an all-time balanced grid, taking early care of any forthcoming variability in demand and supply would be impossible without AI support.

AI can forecast demand and control the supply in totality by switching ‘ON’ or switching ‘OFF’ coal-based plants.  

It can also predict wind / solar power production even 36 hours in advance of the actual generation, allowing operators to schedule optimal hourly input to the power grid one day in advance.

AI in condition monitoring

Condition monitoring can be done with the help of sensors fitted in the turbine and then processing the captured data with machine learning (ML) algorithms. The result is early detection of future failures of turbine components. For example, fault in blades, high temp in generator, consistency in power curve etc.

AI in predictive maintenance

With the help of AI, wind farms can predict the need for turbine parts replacement well in advance. This will optimize the shutdown requirements resulting in an increase in energy generation and reducing maintenance costs. Enhanced turbine safety is another gain.

With the help of various sensors mounted in the turbine, AI learns to detect the advanced signs of failures, like the vibration of the nacelle, overheating of pitch bearing or failure of lubrication so on and so forth. 

One of the finest examples that AI can detect is yaw misalignment, which, if kept undetected, leads to loss of generation and undue load on the turbine. 

After getting an early warning from the AI system, more efficient predictive and preventive maintenance activities can be planned and executed. Planned activity in turn can reduce multiple travel costs of the technicians for attending turbines and minimise shut down hrs. 

Moreover, better predictive maintenance reduces prolonged loss of generation due to sudden breakdowns.

AI in enhancing  efficiency 

AI system can also determine the pattern of yawing of turbines in a coordinated and precise manner to prevent a turbine to fall under the wind shadow of another one.

As the outcome of better condition monitoring, improved weather forecast and demand management  AI enhance the efficiency of individual wind turbines as well as wind farms.

AI in making turbines bird-friendly

Germany is implementing a wildlife-friendly wind energy system. It has developed a camera system called BirdVision that uses deep learning to protect the birds from colliding turbine blades. The system switches off the turbine as soon as a bird gets near the rotating blades. In 2019  the test was successfully carried out in eight wind turbines. 

Some global Initiatives 

Google and its sister company DeepMind is working on AI. They have collected weather data and power data of 700 MW of wind power in the central USA and used these past weather forecasts and historical turbine data for machine learning algorithms and neural networks.

They have been successful in more accurate wind patterns prediction, thus getting a better generation increasing 20 per cent revenue of the wind farm.

In Germany,  SmartWind – a research project- has been taken up jointly by the Ruhr-University Bochum and a consortium of four companies. The project aims to exploit the capabilities of artificial intelligence to optimize the overall management of wind farms. 

The team will collect the real-time data in an integrated cloud platform and analyse it using AI techniques. And recommend various measures for better wind farm management to improve generation at an optimized cost.

Zorlu Enerji a wind farm operator in Turkey and also a partner in this project will apply the recommendations directly into practice. This way there will be a close loop between the research and field application to validate theoretical results in a real-world situation. 

The Indian wind energy sector can take up some endeavour in this line and draw more benefit from artificial intelligence technology.  


We can understand that AI can do wonders in the field of Indian wind energy. It can maximise clean energy production at a reduced operational cost, thus increasing revenue. It can manage the grids in a highly responsive way thus, can cut coal-based generation in a planned manner. 

There is still a lot to gain in terms of plant efficiency and cost-effectiveness. AI can participate more in this regard by developing newer techniques to exploit the full potential of wind farms. Consequently, making the wind business more attractive.