PMU Data Recovery Using Decision Tree and SVD
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High penetration of renewable energies in power grids introduces randomness and uncertainty, leading to data loss issues in phasor measurement units (PMUs) due to communication congestion, hardware failures, and transmission delays, which restricts monitoring applications and threatens grid security.
Innovation Solution
An adaptive PMU missing data recovery method is developed, using a decision tree for identifying ambient and disturbance data, and employing improved cubic spline interpolation and singular value decomposition for accurate recovery of ambient and disturbance data, respectively, to ensure reliable PMU measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data recovery methods are applied to all PMU data uniformly, then data quality can be improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments PMU data into two categories: ambient data and disturbance data. Different recovery methods are applied to each segment - cubic spline interpolation for ambient data and singular value decomposition for disturbance data. This segmentation allows the system to optimize computational resources by applying complex methods only where necessary while maintaining overall data quality.
2Measurement precision
If advanced recovery methods like singular value decomposition are used for all data types, then recovery accuracy improves, but processing speed decreases
Solution Approach 1:
The patent applies different levels of processing quality to different data types. Ambient data, which occurs more frequently, uses the computationally efficient cubic spline interpolation method. Disturbance data, which occurs less frequently but requires higher accuracy, uses the more computationally intensive singular value decomposition method. This local quality approach optimizes the balance between processing speed and recovery accuracy.
3Productivity
If simple interpolation methods are used for disturbance data, then processing speed is maintained, but recovery accuracy deteriorates
Solution Approach 1:
The patent implements a dynamic recovery strategy that adapts the processing method based on the data type identified. The system dynamically selects between cubic spline interpolation and singular value decomposition methods according to whether the data is ambient or disturbance data. This dynamic approach ensures that processing efficiency is maintained for ambient data while recovery accuracy is preserved for disturbance data.
Data Source
AI summary
A data recovery method framework is described, in which the data is classified as either ambient or disturbance data, and recovered by different methods to achieve good performance efficiently. An approach based on decision tree is described for identifying ambient and disturbance data. Then, an improved cubic spline interpolation based on the priority allocation strategy is described for ambient data loss, which can quickly and accurately recover ambient data. Simultaneously, a disturbance data recovery method based on singular value decomposition is described. It can achieve disturbance data recovery accurately by a single channel of measurement.