ANOMALY DETECTION
Enhancing Efficiency in Hydro-Electric Grid Operations through AI-Driven Anomaly Detection
A prominent hydro-electric grid operator faced a significant challenge in efficiently managing and maintaining its electricity production. This operator relied heavily on sensors for operational control and monitoring. Unfortunately, those sensors were error prone : different types of anomalies could arise at any given time. Those errors were so critical that a dedicated team was engaged in scrutinizing sensor outputs daily, highlighting the need for a more streamlined and automated solution.
Anomaly Overload
The sheer volume of anomalies from sensors overwhelmed the manual analysis team, leading to potential oversight and delays in corrective actions.
Resource Intensity
Human-intensive anomaly detection and correction processes were time-consuming and resource-draining, affecting overall operational efficiency.
Precision Requirement
Accuracy in identifying and addressing anomalies was paramount to ensure the reliability and stability of the hydro-electric grid.
Confidence Level Estimation
Our solution provided a degree of confidence for each detected anomaly, aiding operators in prioritizing and responding to potential issues effectively.
Time-Series Modelling
Time-series models were employed to accurately interpolate missing information, ensuring a continuous and reliable flow of data even in the presence of sensor gaps.
Human-in-the-Loop UI
To maintain human oversight, we developed a user-friendly interface allowing operators to review detected anomalies swiftly. This interface facilitated quick decision-making, ensuring that human expertise complemented AI outputs
The implemented AI solution successfully transformed the hydro-electric grid operator's anomaly detection and correction processes. By significantly reducing the manual workload and enhancing accuracy, the system allowed the client to proactively manage potential issues, preventing disruptions in electricity production. The human-in-the-loop approach ensured that the AI system worked collaboratively with human operators, combining the strengths of automation and human expertise for optimal results.