What are some of the different AI applications for Power Distribution?

Artificial intelligence (AI) has the potential to transform the way power is distributed and consumed. Telsa and Edison could not have imagined a hundred years later that their discoveries and invention would alter mankind as we know it. Here are some of the potential AI applications for Power Distribution that continue that trend.

Predictive maintenance: AI can be used to analyze data from sensors on power distribution infrastructure, such as transformers and transmission lines, to predict when maintenance will be required. This can help to reduce downtime and improve the reliability of the distribution system. The traditional approach would have been to have a routine schedule and physically check devices.

Demand management: AI can be used to optimize the distribution of power to meet changing demand patterns. For example, AI can be used to adjust the output of distributed energy generation sources, such as solar panels or wind turbines, to match the demand for electricity. Standard practice would require some type of human intervention.

Power quality monitoring: AI can be used to monitor the quality of the power being distributed, such as voltage and frequency, to ensure that it meets the necessary standards. This can help to improve the efficiency and reliability of the distribution system compared to having a software monitoring tool that alerts users then to take action.

Fault detection and diagnosis: AI can be used to detect and diagnose issues with the distribution system, such as power outages or equipment failures. This can help to minimize the impact of these issues and improve the overall reliability of the system. But it does require extensive amounts of data to accurately achieve this.

Renewable energy integration: AI can be used to optimize the integration of renewable energy sources, such as solar and wind, into the grid. This can help to improve the reliability and stability of the grid while also reducing our reliance on fossil fuels or other carbon-intensive fuels. Battery storage would also be included with the integration.

Overall, AI has the potential to significantly improve the efficiency, reliability, and sustainability of power distribution systems. By leveraging data and machine learning algorithms, AI can help to optimize the distribution of power and reduce waste, leading to a more efficient and reliable energy system. In some cases, as high as?10.2% to 40% for energy efficiency improvement (1). What are your thoughts?

#AI #NEMA #Renewables #PowerQuality #Faultdetection #DemandManagement #PredictiveMaintenance

References:

(1)https://www.sciencedirect.com/science/article/pii/S2352484721015055#:~:text=This%20review%20paper%20reported%20that,AI%20technology%20for%20energy%20saving.