AI Applications in Power & Energy Sector

Clean, cheap, and reliable energy is essential to the development of our future and Artificial Intelligence is creating a great impact in the energy & power sector. AI systems can be particularly useful in the automation of routine and structured tasks, leaving humans to grapple with the power challenges of tomorrow.

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The major challenge in every energy sector is to identify and analyze proper data from the energy grids. In most of the scenarios the stakeholders fail to identify the proper statistics and figures about how their energy sector is functioning and what should be optimized for better efficiency and yield. There are several smart meters which are attached with the power grid of the energy sources which has an ethernet port and can be connected to the internet. Which can transmit all the real time data to the cloud, later we can integrate real time data visualization engines like Graphana or Kibana, which will monitor the powerplant performance remotely reducing the man hours and human effort followed by cutting down revenue and preventing major mishaps.

There will be various revenue driven features which will be only provided once you do real time monitoring:

Immediate Reporting: The System sends immediate notification by email or text message in case of any malfunction or value crossing the specified threshold values.
Performance Monitoring: The system ensures the monitoring of each device of the Plant and it also gives the plant performance.
Scalability and Centralized Monitoring: The Application is based on IoT and bigdata Technologies which offers scalability to monitoring millions of devices with lot of computations running at the same time. It can be used as a centralized monitoring to Solar companies to monitor power plant at single points regardless of location and technology.
Real Time Monitoring: Real-time time monitoring provides best price/performance ratio for topnotch monitoring. Monitoring also ensures to record health status of critical device like inverter.

Medium voltage overhead power lines run for hundreds of miles to supply power to cities. These great distances make it expensive to manually inspect the lines for damage that doesn't immediately lead to a power outage, such as a tree branch hitting the line or a flaw in the insulator. These modes of damage lead to a phenomenon known as partial discharge — an electrical discharge which does not bridge the electrodes between an insulation system completely. Partial discharges slowly damage the power line, so left unrepaired they will eventually lead to a power outage or start a fire. In general cases identifying loss of electricity from Power lines are very difficult. It requires a lot of manpower and time, to identify exactly from which point the loss is occurring.

By using this solution, it can detect partial discharge and help reduce maintenance costs and prevent power outages. Using machine learning algorithms, we can exactly find the point from where the signal loss is occurring.

Maintenance is the key for using solar panels for long term. Using Cameras/ Drones and machine learning algorithms we can develop a system which can monitor the solar panels and send us an alert if any panel requires maintenance, such as due to a thick layer of dust a solar panel might not perform well. So, from a real time feed it can be identified, and an alert can be sent to the authority that the dirt is needed to be cleaned. Any visual identification which might disrupt the efficiency of the Solar panels will be better managed, and longevity will be high and energy production will be capitalized.