Feeder Switchover Control Using SAIDI and Criticality Thresholds
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Solution Overview
Problem
Existing electrical networks lack effective systems to predict and respond to utility failures, failing to account for operational criticality, grid balancing, and feeder reliability during switchovers, leading to inefficiencies and potential outages.
Innovation Solution
A system that utilizes historical and real-time data from Advanced Distribution Management Systems and Outage Management Systems to identify impacted electrical network portions and switch them to more reliable feeders based on feeder reliability indices and operational criticality, using machine learning models to predict potential failures and adjust grid frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If switchovers are performed without considering feeder reliability indices and operational criticality, then switchover operations are simpler and faster, but system reliability and service continuity deteriorate due to potential outages
Solution Approach 1:
The system continuously monitors feeder reliability indices (FRI) and operational criticality values, using this feedback to dynamically determine optimal switchover decisions. The control system compares real-time FRI values and criticality assessments against predefined thresholds to automatically initiate or prevent switchovers, ensuring reliable power supply while adapting to changing grid conditions
Solution Approach 2:
The system performs preliminary calculations of FRI values and operational criticality values before executing switchovers. By pre-assessing feeder reliability and load criticality, the system identifies suitable candidate feeders for switchover in advance, ensuring that switchovers only occur when a more reliable alternative is confirmed available, thus preventing outages before they happen
2Measurement precision
If historical data analysis is performed to calculate ETD and MED thresholds, then prediction accuracy of utility failures is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of historical ASIDI and SAIDI data to pre-calculate ETD (Event Threshold Day) and MED (Major Event Day) thresholds in advance. These pre-computed thresholds are stored and readily available for comparison with real-time data, enabling rapid failure prediction without performing time-consuming historical analysis during critical monitoring periods
Solution Approach 2:
The system focuses computational resources on analyzing the most relevant historical parameters (ASIDI and SAIDI values) and key statistical metrics (ETD and MED thresholds) rather than processing all possible grid parameters. This selective partial analysis achieves sufficient prediction accuracy while significantly reducing processing time and computational burden
3Measurement precision
If the system monitors multiple parameters (ASIDI, SAIDI, FRI, operational criticality), then decision-making accuracy is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The monitoring system is segmented into specialized functional modules: one module calculates FRI values from SAIDI data, another assesses operational criticality values, a third computes ETD/MED thresholds from ASIDI data, and a final module integrates these parameters for switchover decisions. Each module handles a specific subset of parameters independently, reducing overall system complexity while maintaining comprehensive monitoring capability
Solution Approach 2:
The system introduces intermediary calculated parameters (FRI values, operational criticality values, ETD/MED thresholds) that serve as mediators between raw monitoring data and final switchover decisions. These intermediary metrics synthesize complex relationships between multiple parameters into single decision-relevant values, simplifying the integration process while preserving decision-making accuracy
Data Source
AI summary
A non-transitory, machine-readable medium stores instructions that, when executed by a processor, cause the processor to receive historical average system interruption duration index (ASIDI) data and calculate (1) an average of a plurality of logarithm values based on a portion of the ASIDI data that is associated with a plurality of interruption days, (2) a standard deviation of the plurality of logarithm values, and (3) an event threshold day (ETD) threshold based on the average of the plurality of logarithm values and the standard deviation of the plurality of logarithm values. An impacted electrical grid portion within an electrical grid is identified based on the ETD threshold and current ASIDI data. Switchover of the impacted electrical grid portion to a feeder from a plurality of feeders is caused based on a criticality value associated with a system that is fed by the feeder.