Electricity Meter Impedance Learning Algorithm
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Solution Overview
Problem
Existing electricity meters cannot determine individual impedances for each phase of an electricity distribution network, which is crucial for identifying unbalances and potential breakdowns due to high peak currents.
Innovation Solution
An advanced electricity meter with an impedance learning algorithm that calculates individual phase impedances by determining correlation terms between zero sequence voltages and negative sequence currents, and between zero sequence currents and negative sequence voltages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geographic information system (GIS) data is used to estimate impedance, then the estimation process is simple and quick, but the accuracy of impedance determination is insufficient due to lack of information about wire size, connector quality or other relevant factors
Solution Approach 1:
The patent replaces the mechanical/GIS-based estimation system with an electrical measurement system. Instead of using geographic information and rough distance calculations, the invention uses actual electrical measurements of voltage and current at different phases to calculate impedance values, thereby substituting an indirect estimation method with a direct electrical measurement method that provides both speed and accuracy.
Solution Approach 2:
The patent introduces an intermediary measurement approach by using voltage and current measurements as intermediate variables to determine impedance. Rather than directly measuring impedance or relying on GIS data, the system measures voltage and current at different phases and uses these intermediate measurements to calculate the impedance values, bridging the gap between simple estimation and accurate determination.
2Loss of information
If existing meter-based impedance learning algorithm (MILA) is used, then zero sequence impedance and negative sequence impedance can be determined, but individual impedances for each phase cannot be determined
Solution Approach 1:
The patent applies segmentation by dividing the impedance measurement into individual phase components. Instead of determining only aggregate zero sequence and negative sequence impedance, the invention calculates impedance values for each phase (phase A, phase B, phase C) separately by using correlation terms that associate zero sequence voltage with negative sequence current and vice versa for each individual phase, enabling phase-specific impedance determination.
Solution Approach 2:
The patent implements local quality by providing different impedance information for different phases of the electrical system. Rather than a single overall impedance value, the system determines and provides specific impedance values for each phase (ZA, ZB, ZC), allowing localized analysis of each phase's electrical characteristics and enabling identification of phase-specific issues such as unbalances or high impedance in particular phases.
3Measurement precision
If individual phase impedances are determined using correlation terms between zero sequence voltage and negative sequence current, then accurate phase-specific impedance values are obtained, but the calculation complexity and algorithm requirements increase
Solution Approach 1:
The patent uses feedback by utilizing the relationships between different electrical quantities (zero sequence voltage, negative sequence current, and their correlations) to iteratively determine phase impedance values. The algorithm incorporates feedback loops where measured voltage and current values are processed through correlation calculations, and the results are used to refine the impedance determination for each phase, enabling accurate results through a systematic feedback-based calculation process.
Data Source
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AI summary
The present disclosure is directed to an electricity meter (7) for determining electricity consumption in a three-phase electricity distribution network (1), wherein the electricity meter (7) comprises: - a circuit (11) for determining a voltage Va, Vb, Vc and a current Ia, Ib, Ic at each phase (a, b, c) of the electricity distribution network (1), - at least one microcontroller (13) for calculating electricity consumption from the electricity distribution network (1), and - an impedance learning algorithm for being executed by the at least one microcontroller (13), wherein the impedance learning algorithm is configured to determine, based on a plurality of changes in the determined voltage ΔVa, ΔVb, ΔVc and current ΔIa, ΔIb, ΔIc at each phase of the electricity distribution network (1), a zero sequence impedance Z0 and a negative sequence impedance Z2 of the electricity distribution network (1), characterised in that the impedance learning algorithm is configured to determine an impedance value Za, Zb, Zc for each phase (a, b, c) of the electricity distribution network (1) by determining correlation terms Z20, Z02, wherein the correlation term Z20 is indicative of a correlation between a zero sequence voltage V0 and a negative sequence current I2, and wherein the correlation term Z02 is indicative of a correlation between a zero sequence current I0 and a negative sequence voltage V2.