Utility risk: Integrating SCADA/ADMS with ML

Executive Summary (tl;dr)

• Critical gaps in risk management exist with all Utilities. They have inspection reports and SCADA/ADMS systems, but these systems don't automatically alert when sensor data deviates from norms, leaving them vulnerable to failures. 
• A solution for enhanced efficiency involves integrating inspection data with real-time monitoring using AI and ML to bridge this gap. This integration would trigger immediate alerts for deviations, reduce maintenance costs, improve compliance, and boost stakeholder confidence. This would benefit Utilities and their customers.

A critical issue highlighting a significant gap in a Utility company's current risk management practices is comprehensive inspection reports with robust SCADA (Supervisory Control and Data Acquisition) and ADMS (Advanced Distribution Management System) with a disconnect between these systems. These systems do not automatically generate alerts when sensor data deviates from the norms established in the inspection reports. This oversight leaves Utilities at risk of potential failures despite having all necessary data available.

The absence of automatic alerts for sensor deviations is a missed opportunity for proactive risk management. Inspection reports across Utilities typically contain detailed benchmarks and standard deviations that outline the normal operating thresholds for various components. When real-time sensor data falls outside these thresholds, it should trigger immediate alerts to facilitate corrective measures. Unfortunately, these critical deviations go unnoticed due to the lack of integration between the inspection data and SCADA/ADMS alerts, increasing the risk of unexpected failures and service disruptions. This costs Utilities millions of dollars each year as well for retroactive fixes that need to take place. 

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‍What can be done about this gap?

Addressing this gap requires a unified approach integrating inspection report data with real-time monitoring systems. Using artificial intelligence through machine learning, Utilities can create a seamless interface that continuously compares current sensor readings against historical inspection data. These systems become more competent and learn from the past and other aggregated utility data. This integration ensures deviations trigger immediate alerts, enabling timely interventions and mitigating risks. Such a system would enhance Utility's operations reliability and significantly reduce maintenance costs by preventing minor issues from escalating into major problems.

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Integrating inspection data with SCADA/ADMS alerts can improve regulatory compliance and reporting. Utilities can demonstrate a proactive approach to maintenance and risk management, providing auditors and regulators with clear evidence of efforts to maintain operational standards beyond the bare minimum. This transparency can lead to better stakeholder confidence and lower regulatory penalties.

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‍Conclusion

As Utilities aims to fix this gap, various AI platforms like Senpilot are helping to make the change. By implementing Senpilot in just a single weekend, Utility companies can enhance their operational efficiency, improve service reliability, and achieve substantial cost savings. This strategic investment addresses immediate data connection concerns and lays the groundwork for sustained technological advancement and a competitive edge in the future.

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