Capacitor Bank Switching for Distribution Reactive Power Anomalies
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Solution Overview
Problem
Existing systems fail to detect degraded power quality at the distribution portion of an electrical grid, as they primarily monitor the transmission portion, leading to a need for systems and methods that can detect and mitigate reactive power anomalies within the distribution portion.
Innovation Solution
A method involving a processor that receives electrical measurement data from a distribution portion of an electrical grid, uses a machine learning model to detect reactive power anomalies, and identifies a capacitor bank associated with the transmission portion to mitigate the anomalies by electrically coupling or decoupling the capacitor bank.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monitoring is performed at the transmission portion of the electrical grid, then the monitoring system can detect transmission-level power quality issues, but it fails to detect degraded power quality at the distribution portion
Solution Approach 1:
The patent uses machine learning models as an intermediary to bridge the gap between transmission-level measurements and distribution-level anomaly detection. The ML model processes transmission portion measurement data to infer and detect reactive power anomalies at the distribution portion, enabling indirect observation of distribution quality through transmission data analysis
Solution Approach 2:
The patent replaces physical measurement devices at the distribution portion with a virtual sensing approach using machine learning. Instead of installing sensors directly at the distribution level (mechanical/physical approach), the system uses computational models to substitute and infer distribution conditions from transmission measurements
2Reliability
If capacitor banks are deployed at the transmission portion to mitigate reactive power anomalies, then power quality can be improved, but the system complexity increases due to coordination between transmission and distribution portions
Solution Approach 1:
The patent implements a closed-loop feedback system where the machine learning model continuously monitors transmission measurement data, detects reactive power anomalies, identifies appropriate capacitor banks, triggers their operation, and verifies the mitigation effect through continued monitoring. This feedback mechanism coordinates transmission and distribution portions automatically, managing system complexity through intelligent control
Solution Approach 2:
The system enables self-service operation where the machine learning model autonomously detects anomalies, selects capacitor banks, and triggers mitigation actions without requiring manual intervention or complex centralized coordination. The system serves itself by automatically managing the entire reactive power correction process
Data Source
AI summary
A non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to receive electrical measurement data measured at a distribution network of an electrical grid and not a transmission network of the electrical grid. The electrical measurement data is provided as input to a machine learning model to detect a reactive power anomaly that is at the distribution network, and in response to detecting the reactive power anomaly, identify a capacitor bank from a plurality of capacitor banks and associated with the transmission network of the electrical grid. In response to identifying the capacitor bank, the instructions cause the processor to cause the capacitor bank to be one of electrically coupled to the transmission network or electrically decoupled from the transmission network, to mitigate the reactive power anomaly.