Power System Type Identification and Wiring Error Detection
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Solution Overview
Problem
Existing power system measurement technologies face challenges in accurately identifying the type of power system and detecting wiring or connection errors, often resulting in unusable measurements due to ambiguity in voltage and current phase relationships, especially in non-ideal systems with varying frequencies and phase angles.
Innovation Solution
A system utilizing artificial intelligence and a knowledge base to analyze measurement signals, applying rule-based methods to characterize measurements and determine the most likely power system type and connection errors, thereby providing clear remedial steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual evaluation by professionals is used to minimize wiring errors, then measurement accuracy can be improved through human judgment, but the complexity of operation increases and productivity decreases
Solution Approach 1:
The system automatically evaluates measurement signals and identifies power system types and wiring errors without requiring professional intervention. The computer executes algorithms that autonomously analyze voltage and current relationships, detect anomalies, and provide diagnostic information, enabling the system to serve itself rather than requiring external expert evaluation.
Solution Approach 2:
The patent replaces the mechanical/manual process of professional evaluation with an automated computational system. Instead of relying on human experts to manually analyze measurements and identify errors, the system uses computer-based algorithms to automatically process measurement signals, apply evaluation criteria, and generate diagnostic results, thereby substituting mechanical human operation with automated electronic processing.
2Measurement precision
If multiple possible explanations are considered for ambiguous measurement signals, then measurement precision can be improved, but device complexity increases due to multiple analysis paths
Solution Approach 1:
The evaluation process is divided into distinct segments or modules. The system separately evaluates different aspects of measurement signals (voltage relationships, current relationships, phase angles) and combines these segmented evaluations to reach a comprehensive conclusion. This modular approach allows the system to handle multiple possible explanations systematically without becoming unmanageably complex.
Solution Approach 2:
The system changes evaluation parameters dynamically based on the measurement data. Different evaluation criteria and thresholds are applied depending on the specific characteristics of the measurement signals being analyzed. This allows the system to adapt its complexity to the specific situation, using simpler evaluation paths when possible and more complex analysis only when necessary to resolve ambiguities.
3Productivity
If automated error detection is implemented, then productivity increases by eliminating manual evaluation, but measurement precision may deteriorate due to lack of human judgment
Solution Approach 1:
The system incorporates feedback mechanisms where the computer continuously refines its evaluation based on the measurement data received. The system analyzes the results of initial evaluations and uses this feedback to adjust subsequent analysis, improving accuracy through iterative refinement. This feedback loop allows automated detection to achieve precision comparable to or exceeding manual evaluation while maintaining high productivity.
Solution Approach 2:
The system performs preliminary evaluation actions automatically to identify obvious errors and patterns before more complex analysis is needed. By conducting initial assessments of measurement signals for common wiring errors and system type identification, the system establishes a foundation for more precise analysis only when necessary, thereby maintaining both speed and accuracy.
4Adaptability or versatility
If comprehensive analysis of all power system types is performed, then adaptability improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The evaluation system is designed to be dynamic rather than static. It adapts its analysis depth and methodology based on the characteristics of the measurement signals being evaluated. For common power system types with clear characteristics, the system uses simpler, faster detection methods. For less common or more ambiguous cases, the system dynamically increases analysis complexity to ensure accurate identification, thereby achieving comprehensive adaptability without consistently high complexity.
Solution Approach 2:
The system applies partial analysis for routine cases and excessive (comprehensive) analysis only when necessary. Rather than performing full comprehensive analysis on every measurement signal, the system uses streamlined evaluation for clear-cut cases and reserves detailed multi-criteria analysis for ambiguous or unusual situations, optimizing the balance between adaptability and analysis complexity.
Data Source
AI summary
A system which automatically identifies a power system type, anomalies in the output of the power system, and/or likely errors of wiring and connection to the power system or a system under test connected to the power system is presented. A measurement processing module reduces measurement signals to characterized measurements and a rule base applies knowledge bases of standard power systems and wiring and connection errors to the processed measurements to determine the power system type, the most likely anomalies in the output of the power system, and/or the most likely wiring and/or connection errors.