Adaptive Electrical Meter Using Statistical Variability for Noise
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Solution Overview
Problem
Conventional meters for measuring electrical parameters in noisy environments require extended measurement periods and multiple readings to ensure reliability, which is time-consuming and prone to uncertainty due to noise fluctuations.
Innovation Solution
A method involving a hand-held digital meter that samples electrical parameter outputs, calculates statistical variability, and uses filtering techniques like median filtering to discard outliers, allowing for adaptive measurement time and confidence level estimation, thereby ensuring reliable and efficient measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an extended measurement period is used to produce an averaged value, then measurement reliability is improved, but measurement time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing statistical parameters (mean, standard deviation) from initial samples, then using these to estimate the number of additional samples needed to achieve the desired confidence level. This allows the system to adaptively determine measurement duration based on actual noise conditions rather than using a fixed extended measurement period, thereby reducing unnecessary measurement time while maintaining reliability.
Solution Approach 2:
The patent implements feedback by continuously monitoring the statistical variability of samples and using this information to dynamically adjust the measurement process. The system calculates the standard deviation from collected samples, compares it against threshold values, and determines whether to continue sampling or finalize the measurement. This feedback mechanism enables the system to achieve reliable measurements in the minimum necessary time by stopping sampling as soon as the confidence level requirement is met.
2Reliability
If multiple measurements are performed to gain confidence in reliability, then measurement reliability is improved, but operational complexity increases
Solution Approach 1:
The patent applies self-service by implementing automatic statistical analysis and confidence level assessment within the measurement system. The meter autonomously collects samples, calculates statistical parameters (mean, standard deviation), determines the number of samples needed for the desired confidence level, and generates the final measurement result without requiring external intervention. This eliminates the need for operators to manually perform multiple measurements and analyze results, reducing operational complexity while maintaining high reliability through automated statistical validation.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the number of samples based on the observed statistical variability. Instead of performing a fixed number of measurements or using a fixed measurement time, the system modifies the sampling count according to the calculated standard deviation and desired confidence level. This adaptive parameter adjustment simplifies the operational process while ensuring that sufficient measurements are taken to achieve reliable results.
3Measurement precision
If statistical analysis is performed on all samples, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by performing statistical analysis on a strategically selected subset of samples rather than processing all collected data. The system uses the mean and standard deviation calculated from initial samples to estimate the required confidence level and determine whether additional sampling is needed. This partial statistical processing achieves sufficient measurement precision without the computational overhead of analyzing every single sample, thereby reducing processing time while maintaining accuracy.
Data Source
AI summary
In a meter for performing a measurement of an electrical parameter, an output from a sensor is sampled to produce at least one sample, and an iterative method is performed comprising: producing further samples; holding in memory a stored array of samples comprising the at least one sample and each of the further samples from each iteration; determining a measure of statistical variability of a mean for the respective iteration from a measure of statistical variability and from the number of samples used to generate the measure of statistical variability; comparing the measure of statistical variability of the mean with a pre-determined threshold; and generating an electrical signal indicating a state of the measurement if the measure of statistical variability of the mean of the samples taken during the measurement is less than or equal to the pre-determined threshold.