Let's delve into the details of the Power Outage Duration (SAIDI - System Average Interruption Duration Index) KPI within the Energy & Utilities industry.

Power Outage Duration (SAIDI) KPI

Data Requirements

To accurately calculate SAIDI, we need specific data points, primarily related to power outages and customer counts. Here's a breakdown:

• Outage Data:
  • Outage Start Time:

    The exact date and time when a power outage began.

  • Outage End Time:

    The exact date and time when power was restored.

  • Affected Customers:

    The number of customers impacted by each specific outage. This is crucial for weighting the outage duration.

  • Outage Location/Feeder:

    Information about the geographical area or specific feeder line affected by the outage. This helps in identifying patterns and areas with frequent issues.

  • Outage Cause Code:

    A standardized code indicating the reason for the outage (e.g., equipment failure, weather, animal contact). This is vital for root cause analysis.

  • Outage Type:

    Categorization of the outage (e.g., planned, unplanned, momentary).

• Customer Data:
  • Total Number of Customers Served:

    The total number of customers connected to the system during the reporting period. This is the denominator in the SAIDI calculation.

Data Sources:

• Outage Management System (OMS):

  The primary source for outage data, capturing real-time information about outages.

• Customer Information System (CIS):

  Provides data on the total number of customers served.

• Geographic Information System (GIS):

  Can provide location-based information about outages and affected customers.

• Supervisory Control and Data Acquisition (SCADA) System:

  Provides real-time data on system status and can help identify the start and end times of outages.

Calculation Methodology

SAIDI is calculated by dividing the total customer minutes of interruption by the total number of customers served. Here's a step-by-step breakdown:

1. Calculate the Duration of Each Outage:

   Subtract the outage start time from the outage end time for each individual outage. This gives you the outage duration in minutes.

   Example: Outage Start: 10:00 AM, Outage End: 10:30 AM. Duration = 30 minutes.

2. Calculate Customer Minutes of Interruption for Each Outage:

   Multiply the outage duration (in minutes) by the number of customers affected by that specific outage.

   Example: Outage Duration: 30 minutes, Affected Customers: 100. Customer Minutes of Interruption = 30 * 100 = 3000 minutes.

3. Calculate Total Customer Minutes of Interruption:

   Sum the customer minutes of interruption for all outages within the reporting period (e.g., a month, a quarter, a year).

   Example: If you had multiple outages, sum the customer minutes of interruption for each.

4. Calculate SAIDI:

   Divide the total customer minutes of interruption by the total number of customers served during the reporting period.

   Formula: SAIDI = (Total Customer Minutes of Interruption) / (Total Number of Customers Served)

   Example: Total Customer Minutes of Interruption: 1,000,000 minutes, Total Customers Served: 100,000. SAIDI = 1,000,000 / 100,000 = 10 minutes per customer.

Application of Analytics Model

An AI-powered analytics platform, like 'Analytics Model,' can significantly enhance the calculation and analysis of SAIDI. Here's how:

• Real-Time Querying:

  Users can use free text queries to instantly retrieve SAIDI data for specific time periods, regions, or outage types. For example, a user could ask: "Show me the SAIDI for the last quarter in the northern region" or "What was the SAIDI for outages caused by equipment failure last month?".

• Automated Insights:

  The platform can automatically identify trends and patterns in SAIDI data. For example, it can highlight areas with consistently high SAIDI values, identify common causes of outages, and detect seasonal patterns. It can also provide alerts when SAIDI exceeds predefined thresholds.

• Visualization Capabilities:

  SAIDI data can be visualized through interactive dashboards and charts. Users can see SAIDI trends over time, compare SAIDI across different regions, and drill down into specific outages. This makes it easier to understand the data and communicate findings to stakeholders.

• Data Integration:

  The platform can integrate data from various sources (OMS, CIS, GIS, SCADA) to provide a comprehensive view of outage data. This eliminates the need for manual data consolidation and ensures data accuracy.

• Predictive Analytics:

  Using machine learning algorithms, the platform can predict future SAIDI values based on historical data and other factors. This can help utilities proactively identify potential issues and take preventative measures.

Business Value

SAIDI is a critical KPI for energy and utility companies, impacting various aspects of their business:

• Reliability Assessment:

  SAIDI provides a clear measure of the reliability of the power distribution system. Lower SAIDI values indicate better system performance and fewer disruptions for customers.

• Regulatory Compliance:

  Many regulatory bodies set targets for SAIDI. Utilities must monitor and manage SAIDI to meet these requirements and avoid penalties.

• Customer Satisfaction:

  Frequent and prolonged power outages can lead to customer dissatisfaction. Monitoring and improving SAIDI helps enhance customer satisfaction and loyalty.

• Operational Efficiency:

  Analyzing SAIDI data helps identify areas where the system is underperforming. This allows utilities to focus their maintenance and investment efforts on the most critical areas, improving operational efficiency.

• Investment Decisions:

  SAIDI data can inform investment decisions related to infrastructure upgrades and grid modernization. By understanding where outages are most frequent and severe, utilities can prioritize investments that will have the greatest impact on reliability.

• Performance Benchmarking:

  SAIDI allows utilities to benchmark their performance against industry peers and identify areas for improvement.

In conclusion, SAIDI is a vital KPI for the Energy & Utilities industry. By leveraging an AI-powered analytics platform like 'Analytics Model', utilities can gain deeper insights into their outage data, make data-driven decisions, and ultimately improve the reliability of their power distribution systems.