Partial Discharge Positioning via Nonlinear to Linear Model Transformation
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Solution Overview
Problem
Current ultrasonic-based partial discharge (PD) positioning methods face challenges in achieving high accuracy due to non-linear equation solving complexities, iteration algorithm dependence, and interference errors, leading to long operation times and potential non-convergence issues.
Innovation Solution
A nonlinear model transformation method using multi-ultrasonic sensors, which involves constructing a spatial coordinate system, transforming nonlinear equations into linear ones by eliminating second-order terms, acquiring and filtering initial values, and employing an improved K-means clustering algorithm to determine the PD source's optimal coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a nonlinear positioning equation set is used for PD source localization, then positioning accuracy can be improved, but solving complexity increases and operation time extends
Solution Approach 1:
The patent transforms the nonlinear positioning equation set into a linear equation set by changing the mathematical parameters and structure. Specifically, it uses coordinate transformation and linearization techniques to convert the complex nonlinear equations into a simpler linear form that can be solved efficiently without iterative methods, thereby reducing solving complexity while maintaining positioning accuracy.
2Reliability
If an iteration algorithm is used to solve the nonlinear positioning equation set, then positioning can be achieved, but operation time increases and non-convergence issues may occur
Solution Approach 1:
The patent extracts and eliminates the nonlinear components from the positioning equations by removing second-order terms. This extraction process transforms the equations into a linear form that can be solved directly without requiring iterative algorithms, thereby eliminating the time-consuming iteration process and avoiding non-convergence issues while maintaining reliable positioning results.
3Ease of operation
If arrival time measurement is performed in the presence of interference factors, then ultrasonic signal detection can be implemented, but measurement accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary processing step that involves transforming the measurement equations and using multiple sensors to compensate for interference effects. By employing a linear equation system with multiple measurement points, the system can filter out interference factors and extract accurate arrival time information, thereby maintaining measurement precision despite the presence of interference.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enhances positioning accuracy, reduces operation time, avoids iteration non-convergence, and effectively addresses errors caused by arrival time inaccuracies, resulting in a more reliable and efficient PD source localization.
Implementation Method 1
An ultrasonic wave is high in anti-electromagnetic interference capability and the sound speed is relatively slow, a requirement on accuracy of arrival time is not very high, and an ultrasonic sensor is low in cost and easy to use for online monitoring.
Data Source
AI summary
A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate of the PD source. According to the present invention, the present problems of selection difficulty, iteration non-convergence, long operation time, sensitivity to an arrival time error and the like of an iteration algorithm adopted when a nonlinear model is solved are effectively solved.