AI and ML for Power Systems

Applying AI and ML to Power Systems

ML and AI algorithms in recent years have affected nearly every industry, and there are significant opportunities for these algorithms to be deployed to help improve utility operations. These algorithms’ ability to find complex relationships that may be difficult or impossible to physically model has great potential value in the utility space where relationships between grid conditions and the devices connected to the grid are enormously complex.

Sample Use Cases

The fact that AI is not being applied in the power industry extensively, presents unique opportunities to explore and leverage the new frontier it offers in solving previously challenging problems faced by utilities and OEMs in their daily grid enhancement activities.

Transformer Heath Management
Monitoring and diagnostics system generating real-time data from sensors, historical and external lab report data are fed into a combination of a machine learning algorithm and rule engine to detect anomalies and incipient faults ahead of any imminent dysfunction or failure. The cost benefit stems from the elimination of potential losses the system could have unleashed to the operator in terms of maintenance, time-to repair, and environmental costs.
Wind Energy Forecasting
Despite the recent surge in renewable energy generation, the intermittency of these resources poses many challenges to utilities. Utilities must be able to predict wind speeds and direction with high accuracy in order to rely on this renewable resource. AI technologies can be used to create a high-resolution wind forecasting hybrid model based on data on wind and power output from sensors on wind turbines combined with data collected from local weather stations, satellites, and so on. The cost saving stems from a reduction in the wind forecast margin error.
Identification of Aging Degradation
with a comprehensive, image-based understanding of visually identifiable indications of plant equipment aging. An AI-enabled solution utilizes plant images to illustrate the effects of aging in the most common plant materials including metals, concrete, polymers, coatings, electrical, and I&C equipment. to identify an array of potential aging issues during scheduled or opportunistic walkdowns or inspections. The cost-saving capable of identifying potential aging issues and entering them into the corrective action program.
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Our Consulting Approach

Leverage measurements from weather resources and power systems, to forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support grid operations and planning. Our consulting approach is articulated in the following 4 steps:

STEP 1: Use Case Identification

Take steps with the customer to formulate the business challenges and agree on the SOW. Outline the development of cost-saving use cases of AI/ML that addresses the challenges.

STEP 2: Proof of concept

Research and Develop a methodology that address the customer complex needs immediately. Perform a rapid prototyping of the solutions, including simulation scenarios and cost outcomes.

STEP 3: Live Deployment

Connect operational data sources, and collect data from different sources. Benchmark the key performance indicators, refine, and adjust. Establish a production report, and reflect on the results

STEP 4: Lifecycle Management

Establish an agreement on the management of the deployed AI-enabled solutions. Take steps to monitor the AI Infrastructure regularly. Ensure the AI infrastructure ability to withstand growing operation changes so that the technology fits in properly.

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