Method for improving electric energy metering precision

Abstract

The invention discloses a method for improving electric energy metering precision, and the method comprises the following steps: S1, data collection: employing an enterprise total power supply entrance and a key power consumption module, synchronously collecting original waveform data modules of three-phase voltage and three-phase current in real time at the enterprise total power supply entrance and at least one key power consumption module, s2, a parameter analysis and identification step: a separation calculation module is included, and the separation calculation module comprises a fundamental wave component module, a sub-harmonic component module and a dynamic power factor module; according to the method for improving the electric energy metering precision, through combination of synchronous high-precision data acquisition and a dynamic compensation model, the electric energy metering precision in a complex industrial scene is remarkably improved; by introducing a historical database and model self-learning step, the metering system is endowed with self-evolution and long-term stable high-precision capability; through visualization and alarm steps, function extension from metering to energy efficiency management is realized, and energy conservation and consumption reduction are driven.

Description

Technical Field

[0001] This invention relates to the field of electrical energy calculation technology, specifically a method for improving the accuracy of electrical energy metering. Background Technology

[0002] In the cement products manufacturing industry, electricity is one of the main production costs, and accurate electricity metering is crucial for cost control, energy consumption analysis, and energy-saving renovations. However, the industry's unique production equipment and processes result in a complex power distribution network environment, posing significant challenges to traditional electricity metering methods, primarily due to the following technical issues.

[0003] The cement production process extensively utilizes high-power motors, frequency converters (such as those for raw material crushers, roller presses, and pumps), and arc-type equipment (such as calcining furnaces). These nonlinear loads generate abundant harmonics (especially characteristic harmonics such as the 5th, 7th, and 11th harmonics), leading to drastic fluctuations in the power factor. Traditional induction or electronic energy meters are mostly designed based on the fundamental power frequency, resulting in a fundamental deviation in harmonic energy metering. Furthermore, under conditions of low power factor and severe waveform distortion, the current transformer's angle difference and ratio difference characteristics undergo nonlinear changes, leading to a systematic underestimation of the metered value, resulting in under-charging of electricity and economic losses for enterprises. Metering accuracy is also insufficient under complex operating conditions.

[0004] Existing metering compensation methods are mostly static or semi-static, such as using a fixed power factor compensation coefficient or correction based on simple harmonic thresholds. However, cement production is a batch-based and intermittent process, and load characteristics change dynamically with production plans, equipment combinations, and raw material ratios. A fixed set of compensation parameters cannot adapt to different operating conditions, from start-up and full-load operation to shutdown and waiting for materials, resulting in unstable metering accuracy. As equipment ages and power grid conditions change, the original compensation model gradually becomes ineffective, requiring manual recalibration, which is costly and untimely. Furthermore, the metering system lacks adaptive and learning capabilities.

[0005] Traditional metering systems only provide the final energy reading, lacking in-depth perception and visualization of key parameters affecting metering accuracy (such as real-time dynamic power factor, harmonic content, and load fluctuation patterns). When metering anomalies or deviations occur, maintenance personnel struggle to quickly pinpoint whether the cause is a low power factor, excessive harmonics, or sudden load changes. This prevents the provision of direct and accurate data support for subsequent power factor correction device (SVG / SVC) switching, harmonic filter configuration, or production scheduling optimization. The metering system functions merely as a passive "recorder" rather than an active "diagnostic instrument" and "optimizer," leading to a disconnect between metering and energy efficiency management, and the underutilization of data value. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] 1. Technical problems to be solved:

[0008] To address the aforementioned issues of insufficient measurement accuracy under complex operating conditions, lack of adaptive and learning capabilities in the measurement system, disconnect between measurement and energy efficiency management, and insufficient exploitation of data value, this invention is proposed.

[0009] Therefore, the purpose of this invention is to provide a method for improving the accuracy of electricity metering. This invention significantly improves the accuracy of electricity metering in complex industrial scenarios and endows the metering system with self-evolution and long-term stable high-precision capabilities, realizing the functional extension from metering to energy efficiency management and driving energy conservation and consumption reduction.

[0010] 2. Technical Solution: To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: Includes the following steps: S1. Data acquisition steps: including the enterprise's main power supply inlet and key power consumption modules, synchronously and in real time acquire the raw waveform data of three-phase voltage and three-phase current at the power distribution nodes of the enterprise's main power supply inlet and at least one key power consumption module; S2. Parameter Analysis and Identification Steps: This includes a separation calculation module, which comprises a fundamental component module, harmonic component modules, dynamic power factor module, harmonic distortion rate module, and load fluctuation characteristic parameter module. The separation calculation module processes the acquired raw waveform data, separates and calculates the fundamental component and harmonic components, and identifies the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time. S3. Dynamic compensation model construction steps: Based on the dynamic power factor and harmonic distortion rate identified in step S2, a dynamic power metering compensation module is constructed. The dynamic power metering compensation module includes a deviation calculation module and a harmonic pollution module. The dynamic power metering compensation module comprehensively considers the metering deviation caused by low power factor and the harmonic pollution on the metering element. S4. Energy Value Calibration and Output Step: The energy value calibration and output step includes an original energy metering value module, a real-time correction module, and a real-time calibration module. The dynamic compensation model in the dynamic compensation model construction step is applied to the original energy metering value to perform real-time correction and calibration of the metering value, and outputs the final high-precision energy metering result.

[0011] As a preferred embodiment of the method for improving the accuracy of power metering according to the present invention, in step S1, "synchronous real-time acquisition" refers to the synchronous clock phase-locked data acquisition performed by deploying a high-precision sensor network at the power distribution node at a sampling frequency of not less than 2kHz, so as to ensure the consistency of the time scale of the data of each node.

[0012] As a preferred embodiment of the method for improving the accuracy of power metering according to the present invention, the specific sub-steps of "identifying the dynamic power factor under the current operating condition in real time" in step S2 include: S2.1: extracting the fundamental component from the original current and voltage waveforms; S2.2: Calculate the fundamental active power and reactive power; S2.3: The dynamic power factor is calculated according to the formula: Dynamic Power Factor = Fundamental Active Power / Apparent Power, where Apparent Power is the full-spectrum apparent power including harmonics.

[0013] As a preferred embodiment of the method for improving the accuracy of electricity metering according to the present invention, the specific sub-steps of "identifying the dynamic power factor, harmonic distortion rate and load fluctuation characteristic parameters under the current operating conditions in real time" in step S2 include: S2.4: Perform a fast Fourier transform on the original current waveform and analyze the harmonic spectrum up to the 20th to 25th orders; S2.5: Calculate the total harmonic distortion rate and the content of the main characteristic subharmonics; S2.6: Statistically measure the effective value fluctuation range and fluctuation frequency of the load current per unit time, as a load fluctuation characteristic parameter.

[0014] In a preferred embodiment of the method for improving the accuracy of electricity metering according to the present invention, the specific sub-steps of the "dynamic electricity metering compensation module" in step S3 include: S3.1: Establish a power factor compensation coefficient K_pf, the value of which is K_pf=1 / (dynamic power factor*correction factor), where the correction factor is an empirical coefficient calibrated based on historical data and used to correct the nonlinearity of the metering device under low power factor. S3.2: Establish the harmonic error compensation coefficient K_h, whose value is K_h=1+α*THDI+β*Σ(specific harmonic weight), where THD_I is the total harmonic distortion rate of the current, and α and β are the error coefficients fitted by comparing laboratory simulation with field data. S3.3: Combine K_pf and K_h to form a comprehensive compensation coefficient K_total=K_pf*K_h.

[0015] As a preferred embodiment of the method for improving the accuracy of electricity metering according to the present invention, the method further includes step S5: historical database and model self-learning step, which periodically transmits the parameters identified in step S2, the calibration results output in step S4, and the production process information for the corresponding time period.

[0016] As a preferred embodiment of the method for improving the accuracy of electricity metering according to the present invention, the historical database and model self-learning step further includes periodically optimizing and updating the coefficients in the dynamic compensation model using machine learning algorithms to adapt to equipment aging and process changes.

[0017] As a preferred embodiment of the method for improving the accuracy of power metering according to the present invention, the method further includes step S6: visualization and alarm step, in which the high-precision power metering results, dynamic power factor, and harmonic distortion rate are displayed in the form of real-time curves and reports.

[0018] As a preferred embodiment of the method for improving the accuracy of electricity metering according to the present invention, the visualization and alarm step further includes issuing an alarm to the energy management system when the parameter exceeds a preset threshold, prompting the system to perform power factor correction or harmonic correction.

[0019] As a preferred embodiment of the method for improving the accuracy of power metering according to the present invention, the key power-consuming modules include: a raw material crushing module, a grinding module, and a calcination module.

[0020] 3. Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are: This method for improving the accuracy of electricity metering: 1. By combining "synchronous high-precision data acquisition" with a "dynamic compensation model," this invention, for the first time in cement industry metering, systematically and simultaneously addresses two coupled technical challenges: "metering deviation caused by low power factor" and "additional errors caused by harmonic pollution." The K_pf and K_h coefficients in the model can respond in real-time to changes in operating conditions, providing refined compensation for metered values. This fundamentally overcomes the metering blind spots and errors of traditional meters under harmonic and low power factor conditions, ensuring the fairness and accuracy of electricity billing and preventing enterprises from incurring electricity cost losses due to metering system errors.

[0021] 2. By introducing a "historical database and model self-learning step," this invention enables the metrology system to learn from historical data. The system can continuously track the impact of equipment performance degradation and process adjustments, and utilize machine learning algorithms (such as linear regression and neural networks) to periodically and automatically optimize and update key coefficients (such as correction factors, α, and β) in the compensation model. This ensures that metrological accuracy does not deteriorate over time, achieving "accurate from installation and becoming increasingly intelligent with use," significantly reducing the system's later maintenance costs and calibration frequency.

[0022] 3. Through "visualization and alarm procedures," this invention deeply integrates high-precision metering results with their underlying causes (power factor, harmonic distortion rate), presenting them in intuitive curves, reports, and threshold alarms. This not only makes energy management more transparent but also directly guides production and operation: when the system alarms that the power factor is too low, it can prompt the inspection or activation of reactive power compensation devices; when an alarm indicates that a specific harmonic exceeds the standard, it can guide targeted harmonic mitigation. Thus, the electricity metering system is upgraded from a cost accounting tool to a real-time online energy quality monitoring and optimization decision support system, providing a data foundation for enterprises to explore deeper energy-saving potential. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the overall framework of a method for improving the accuracy of electricity metering according to the present invention; Figure 2 This is a schematic diagram of the data acquisition steps of a method for improving the accuracy of electricity metering according to the present invention; Figure 3 This is a schematic diagram of the parameter analysis and identification steps in a method for improving the accuracy of electricity metering according to the present invention. Figure 4 This is a schematic diagram of a dynamic energy metering compensation module for a method to improve energy metering accuracy according to the present invention; Figure 5 This is a schematic diagram of the energy value calibration and output steps in a method for improving the accuracy of energy metering according to the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0026] The orientation or positional relationship indicated in the terminology is based on the orientation or positional relationship shown in the accompanying drawings and is only for the convenience of describing the invention and simplifying the description, and is not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0027] The term "connection method" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0028] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0029] This invention provides an overall structural schematic diagram of an embodiment of a method for improving the accuracy of electricity metering, comprising: Please see Figure 1-5 This embodiment of a method for improving the accuracy of electricity metering includes the following steps: S1. Data acquisition steps: including the enterprise's main power supply inlet and key power consumption modules, synchronously and in real time acquire the raw waveform data of three-phase voltage and three-phase current at the power distribution nodes of the enterprise's main power supply inlet and at least one key power consumption module; S2. Parameter Analysis and Identification Steps: This includes a separation calculation module, which comprises a fundamental component module, harmonic component modules, dynamic power factor module, harmonic distortion rate module, and load fluctuation characteristic parameter module. The separation calculation module processes the acquired raw waveform data, separates and calculates the fundamental component and harmonic components, and identifies the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time. S3. Dynamic compensation model construction steps: Based on the dynamic power factor and harmonic distortion rate identified in step S2, a dynamic power metering compensation module is constructed. The dynamic power metering compensation module includes a deviation calculation module and a harmonic pollution module. The dynamic power metering compensation module comprehensively considers the metering deviation caused by low power factor and the additional error caused by harmonic pollution to metering components, such as current transformers and energy meters. S4. Energy Value Calibration and Output Steps: The energy value calibration and output steps include the original energy metering value module, the real-time correction module, and the real-time calibration module. The dynamic compensation model from the dynamic compensation model construction step is applied to the original energy metering value to perform real-time correction and calibration of the metering value, and outputs the final high-precision energy metering result.

[0030] It is worth noting that, specifically, in step S1, "synchronous real-time acquisition" refers to the synchronous clock phase-locked data acquisition through a high-precision sensor network deployed at the power distribution nodes, with a sampling frequency of not less than 2kHz, to ensure the consistency of the time scale of the data at each node. Among them, the specific sub-steps of "identifying the dynamic power factor under the current operating condition in real time" in step S2 include: S2.1: extracting the fundamental component from the original current and voltage waveforms; S2.2: Calculate the fundamental active power and reactive power; S2.3: The dynamic power factor is calculated according to the formula: Dynamic Power Factor = Fundamental Active Power / Apparent Power, where Apparent Power is the full-spectrum apparent power including harmonics. Specifically, step S2, "identifying the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time," includes the following sub-steps: S2.4: Perform a fast Fourier transform on the original current waveform and analyze the harmonic spectrum up to the 20th to 25th orders; S2.5: Calculate the total harmonic distortion rate and the content of major characteristic subharmonics (such as the 5th, 7th, and 11th harmonics); S2.6: Statistically measure the effective value fluctuation range and fluctuation frequency of the load current per unit time, as a load fluctuation characteristic parameter; Specifically, in step S3, the sub-steps of the "dynamic energy metering compensation module" include: S3.1: Establish the power factor compensation coefficient K_pf, whose value is K_pf=1 / (dynamic power factor*correction factor). The correction factor is an empirical coefficient calibrated based on historical data to correct the nonlinearity of the metering device under low power factor. S3.2: Establish the harmonic error compensation coefficient K_h, whose value is K_h=1+α*THDI+β*Σ(specific harmonic weight), where THD_I is the total harmonic distortion rate of the current, and α and β are the error coefficients fitted by comparing laboratory simulation with field data. S3.3: Combine K_pf and K_h to form a comprehensive compensation coefficient K_total=K_pf*K_h.

[0031] Next, specifically, the method also includes step S5: historical database and model self-learning step, periodically storing the parameters identified in step S2, the calibration results output in step S4, and the production process information for the corresponding time period, such as equipment start-up and shutdown, and production formula. The historical database and model self-learning steps also include using machine learning algorithms to periodically optimize and update the coefficients in the dynamic compensation model to adapt to equipment aging and process changes. The method also includes step S6: visualization and alarm step, which displays the high-precision power metering results, dynamic power factor, and harmonic distortion rate in the form of real-time curves and reports.

[0032] Next, the visualization and alarm steps also include issuing an alarm to the energy management system when the parameter exceeds a preset threshold, prompting the system to perform power factor correction or harmonic correction.

[0033] Furthermore, the key power-consuming modules include: raw material crushing module, grinding module, and calcination module.

[0034] Example 1: S1. Data acquisition steps: including the enterprise's main power supply inlet and key power consumption modules, synchronously and in real time acquire the raw waveform data of three-phase voltage and three-phase current at the power distribution nodes of the enterprise's main power supply inlet and at least one key power consumption module; S2. Parameter Analysis and Identification Steps: This includes a separation calculation module, which comprises a fundamental component module, harmonic component modules, dynamic power factor module, harmonic distortion rate module, and load fluctuation characteristic parameter module. The separation calculation module processes the acquired raw waveform data, separates and calculates the fundamental component and harmonic components, and identifies the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time. S3. Dynamic compensation model construction steps: Based on the dynamic power factor and harmonic distortion rate identified in step S2, a dynamic power metering compensation module is constructed. The dynamic power metering compensation module includes a deviation calculation module and a harmonic pollution module. The dynamic power metering compensation module comprehensively considers the metering deviation caused by low power factor and the additional error caused by harmonic pollution to metering components, such as current transformers and energy meters. S4. Energy Value Calibration and Output Steps: The energy value calibration and output steps include the original energy metering value module, the real-time correction module, and the real-time calibration module. The dynamic compensation model from the dynamic compensation model construction step is applied to the original energy metering value to perform real-time correction and calibration of the metering value, and outputs the final high-precision energy metering result.

[0035] It is worth noting that, specifically, in step S1, "synchronous real-time acquisition" refers to the synchronous clock phase-locked data acquisition through a high-precision sensor network deployed at the power distribution nodes, with a sampling frequency of not less than 2kHz, to ensure the consistency of the time scale of the data at each node. Among them, the specific sub-steps of "identifying the dynamic power factor under the current operating condition in real time" in step S2 include: S2.1: extracting the fundamental component from the original current and voltage waveforms; S2.2: Calculate the fundamental active power and reactive power; S2.3: The dynamic power factor is calculated according to the formula: Dynamic Power Factor = Fundamental Active Power / Apparent Power, where Apparent Power is the full-spectrum apparent power including harmonics. Specifically, step S2, "identifying the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time," includes the following sub-steps: S2.4: Perform a fast Fourier transform on the original current waveform and analyze the harmonic spectrum up to the 25th order; S2.5: Calculate the total harmonic distortion rate and the content of major characteristic subharmonics (such as the 11th harmonic); S2.6: Statistically measure the effective value fluctuation range and fluctuation frequency of the load current per unit time, as a load fluctuation characteristic parameter; Specifically, in step S3, the sub-steps of the "dynamic energy metering compensation module" include: S3.1: Establish the power factor compensation coefficient K_pf, whose value is K_pf=1 / (dynamic power factor*correction factor). The correction factor is an empirical coefficient calibrated based on historical data to correct the nonlinearity of the metering device under low power factor. S3.2: Establish the harmonic error compensation coefficient K_h, whose value is K_h=1+α*THDI+β*Σ(specific harmonic weight), where THD_I is the total harmonic distortion rate of the current, and α and β are the error coefficients fitted by comparing laboratory simulation with field data. S3.3: Combine K_pf and K_h to form a comprehensive compensation coefficient K_total=K_pf*K_h.

[0036] Next, specifically, the method also includes step S5: historical database and model self-learning step, periodically storing the parameters identified in step S2, the calibration results output in step S4, and the production process information for the corresponding time period, such as equipment start-up and shutdown, and production formula. The historical database and model self-learning steps also include using machine learning algorithms to periodically optimize and update the coefficients in the dynamic compensation model to adapt to equipment aging and process changes. The method also includes step S6: visualization and alarm step, which displays the high-precision power metering results, dynamic power factor, and harmonic distortion rate in the form of real-time curves and reports.

[0037] Next, the visualization and alarm steps also include issuing an alarm to the energy management system when the parameter exceeds a preset threshold, prompting the system to perform power factor correction or harmonic correction.

[0038] Furthermore, the key power-consuming modules include: raw material crushing module, grinding module, and calcination module.

[0039] Example 2: High-precision three-phase voltage sensors and Rogowski coil current sensors are installed in the 0.4kV low-voltage distribution cabinets of the raw material crushing workshop, raw material grinding workshop, and clinker calcination kiln head, corresponding to the raw material crushing module, grinding module, and calcination kiln head, respectively, at the 10kV busbar side (main power supply inlet) of the enterprise's main substation and in the three key power-consuming processes. All sensors are connected to a central data processing server via shielded twisted-pair cables or optical fibers. The server has a built-in high-precision synchronization clock card that sends synchronization clock signals to each data acquisition terminal via the IRIG-B or PTP protocol to ensure uniform time stamp of the collected data across the entire network. The sensor sampling frequency is set to 4kHz to meet the requirements for accurate analysis of harmonics up to the 25th order (according to the Nyquist sampling theorem) to complete the deployment of the system hardware.

[0040] S1. Data Acquisition Steps: The central data processing server instructs each acquisition terminal to synchronously acquire the instantaneous value sequence (i.e., raw waveform data) of the three-phase voltages Ua, Ub, Uc and the three-phase currents Ia, Ib, Ic at a frequency of 4kHz. Each data packet is tagged with a high-precision time stamp (accurate to the microsecond level) and uploaded to the server's memory buffer via industrial Ethernet.

[0041] S2. Parameter Analysis and Identification Steps: The "separate computing module" in the server processes the data of each power distribution node in parallel. First, it preprocesses the raw waveform data, such as denoising and eliminating DC offset.

[0042] Sub-steps S2.1 and S2.2: Use digital filters (such as FFT-based frequency domain filtering or designed FIR / IIR filters) to accurately extract the 50Hz fundamental components U1 and I1 from the preprocessed U and I signals. Subsequently, calculate the fundamental active power P1=mean(U1*I1) and the fundamental reactive power Q1 (which can be calculated using Hilbert transform or a method based on a 90° phase shift).

[0043] Sub-step S2.3: The "Dynamic Power Factor Module" calculates the dynamic power factor (DPF). The calculation formula is DPF = P1 / S. The apparent power S is calculated using a full-spectrum method including harmonics: S = Urms * Irms, where Urms and Irms are the effective values ​​of the original voltage and current signals, respectively. This calculation method more realistically reflects the actual load conditions of equipment such as transformers and lines.

[0044] Sub-steps S2.4 and S2.5: The "Harmonic Distortion Rate Module" performs a 4096-point FFT operation on the original current waveform I to analyze its spectrum. The total harmonic distortion rate (THD_I) is calculated as THD_I = sqrt(Σ(Ih^2)) / I1*100%, where Ih is the effective value of the h-th harmonic current (h = 2~25). Simultaneously, the content rate of characteristic harmonics (such as the 5th, 7th, and 11th harmonics) is recorded as HR_h = Ih / I1*100%.

[0045] Sub-step S2.6: The "Load Fluctuation Characteristic Parameter Module" uses a 1-minute window to count the maximum and minimum values ​​of the effective current value Irms within the window and calculates the fluctuation range ΔI; at the same time, it calculates the main load fluctuation frequency f_fluctuation through zero-crossing detection or spectrum analysis.

[0046] S3. Steps for constructing a dynamic compensation model The "Dynamic Energy Metering and Compensation Module" receives parameters such as DPF, THD_I, and HR_h from step S2.

[0047] Sub-step S3.1: The "Calculate Deviation Module" calculates the power factor compensation coefficient according to the formula K_pf=1 / (DPF*C). Here, C is the correction factor. The initial value of C can be obtained through laboratory simulation: On a standard power source, simulate loads with different power factors (e.g., 0.6–0.95), record the standard meter reading W_std and the original reading W_raw of the calibrated metrology system, and obtain the initial value by fitting the average trend of C=W_std / (W_raw*DPF). The initial value can be set to an empirical value between 1.02 and 1.05.

[0048] Sub-step S3.2: The "Harmonic Pollution Module" calculates the harmonic error compensation coefficients according to the formula K_h=1+α*THD_I+β*(0.3*HR_5+0.3*HR_7+0.4*HR_11). Here, α and β are error coefficients. The calibration method is as follows: In a laboratory setting, a mixed signal of the 5th, 7th, and 11th harmonics with the fundamental frequency in known proportions is injected into the system. The measurement errors under different THD_I and harmonic combinations are recorded, and α and β are fitted using a multiple linear regression algorithm. For example, for a certain type of current transformer, the fitting result might be α=0.002 and β=0.0015.

[0049] Sub-step S3.3: The module calculates the comprehensive compensation coefficient K_total=K_pf*K_h.

[0050] S4. Power Value Calibration and Output Procedure This step includes the "Original Energy Meter Value Module", the "Real-time Correction Module", and the "Real-time Calibration Module".

[0051] First, the "raw energy metering module" uses a traditional energy integration algorithm (such as Σ(U_sample*I_sample*Δt)) to calculate the raw energy value E_raw within one integration period (such as 1 second).

[0052] Then, the "real-time correction module" multiplies E_raw with K_total for the current cycle to obtain the corrected electrical energy E_corrected = E_raw * K_total.

[0053] Finally, the "real-time calibration module" outputs E_corrected as the final high-precision energy metering result to the database and display interface. Simultaneously, the system accumulates the calibrated energy from each node to obtain the company's total energy consumption and the energy consumption of each process item.

[0054] S5, Historical Database and Model Self-Learning Steps The system establishes a historical database, storing one record every 15 minutes. The record includes: timestamp, DPF, THD_I, HR_h, K_pf, K_h, K_total, calibrated energy value, and associated production information (e.g., "Grinding mill No. 1 is running," "Calcination kiln speed 85%"). Weekly, the system initiates a self-learning task. A machine learning algorithm (e.g., a multiple linear regression model with DPF, THD_I, and HR_h as input features and (standard table reference value / E_raw) as the target value) is trained on the historical data from the past week. The newly trained model coefficients are used to update the values ​​of C, α, and β. In this way, the compensation model can automatically adapt to the effects of seasonal changes, equipment performance degradation, or the commissioning of new equipment.

[0055] S6. Visualization and Alarming Steps.

[0056] The system features a human-computer interface. The main interface displays real-time changes in total power consumption, dynamic power factor (DPF), and total harmonic distortion (THD_I) in the form of trend curves. Alarm rules can be set, such as: triggering a "low power factor alarm" when the DPF remains below 0.85 for 5 consecutive minutes and sending a message to the energy management personnel's mobile app; triggering a "characteristic harmonic exceedance alarm" when HR_5 or HR_7 exceeds 8%. Simultaneously, the system can generate daily and monthly reports, detailing power consumption for each process, average power factor, and duration of harmonic exceedances, providing accurate data reports for enterprises to conduct power consumption audits, energy-saving project assessments, and power equipment maintenance.

[0057] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for improving the accuracy of electricity metering, characterized in that, Includes the following steps: S1. Data acquisition steps: including the enterprise's main power supply inlet and key power consumption modules, synchronously and in real time acquire the raw waveform data of three-phase voltage and three-phase current at the power distribution nodes of the enterprise's main power supply inlet and at least one key power consumption module; S2. Parameter Analysis and Identification Steps: This includes a separation calculation module, which comprises a fundamental component module, harmonic component modules, dynamic power factor module, harmonic distortion rate module, and load fluctuation characteristic parameter module. The separation calculation module processes the acquired raw waveform data, separates and calculates the fundamental component and harmonic components, and identifies the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time. S3. Dynamic compensation model construction steps: Based on the dynamic power factor and harmonic distortion rate identified in step S2, a dynamic power metering compensation module is constructed. The dynamic power metering compensation module includes a deviation calculation module and a harmonic pollution module. The dynamic power metering compensation module comprehensively considers the metering deviation caused by low power factor and the harmonic pollution on the metering element. S4. Energy Value Calibration and Output Step: The energy value calibration and output step includes an original energy metering value module, a real-time correction module, and a real-time calibration module. The dynamic compensation model in the dynamic compensation model construction step is applied to the original energy metering value to perform real-time correction and calibration of the metering value, and outputs the final high-precision energy metering result.

2. The method for improving the accuracy of electricity metering according to claim 1, characterized in that, In step S1, "synchronous real-time acquisition" refers to the synchronous clock phase-locked data acquisition performed by deploying a high-precision sensor network at the power distribution node at a sampling frequency of not less than 2kHz, to ensure the consistency of the time scale of the data at each node.

3. The method for improving the accuracy of electricity metering according to claim 2, characterized in that, In step S2, the specific sub-steps of "identifying the dynamic power factor under the current operating condition in real time" include: S2.1: Extracting the fundamental component from the original current and voltage waveforms; S2.2: Calculate the fundamental active power and reactive power; S2.3: The dynamic power factor is calculated according to the formula: Dynamic Power Factor = Fundamental Active Power / Apparent Power, where Apparent Power is the full-spectrum apparent power including harmonics.

4. The method for improving the accuracy of electricity metering according to claim 3, characterized in that, In step S2, the specific sub-steps for "identifying the dynamic power factor, harmonic distortion rate, and load fluctuation characteristic parameters under the current operating conditions in real time" include: S2.4: Perform a fast Fourier transform on the original current waveform and analyze the harmonic spectrum up to the 20th to 25th orders; S2.5: Calculate the total harmonic distortion rate and the content of the main characteristic subharmonics; S2.6: Statistically measure the effective value fluctuation range and fluctuation frequency of the load current per unit time, as a load fluctuation characteristic parameter.

5. The method for improving the accuracy of electricity metering according to claim 4, characterized in that, In step S3, the specific sub-steps of the "dynamic energy metering compensation module" include: S3.1: Establish a power factor compensation coefficient K_pf, the value of which is K_pf=1 / (dynamic power factor*correction factor), where the correction factor is an empirical coefficient calibrated based on historical data and used to correct the nonlinearity of the metering device under low power factor. S3.2: Establish the harmonic error compensation coefficient K_h, whose value is K_h=1+α*THDI+β*Σ(specific harmonic weight), where THD_I is the total harmonic distortion rate of the current, and α and β are the error coefficients fitted by comparing laboratory simulation with field data. S3.3: Combine K_pf and K_h to form a comprehensive compensation coefficient K_total=K_pf*K_h.

6. The method for improving the accuracy of electricity metering according to claim 1, characterized in that, The method further includes step S5: historical database and model self-learning step, which periodically transmits the parameters identified in step S2, the calibration results output in step S4, and the production process information for the corresponding time period.

7. The method for improving the accuracy of electricity metering according to claim 6, characterized in that, The historical database and model self-learning steps also include periodically optimizing and updating the coefficients in the dynamic compensation model using machine learning algorithms to adapt to equipment aging and process changes.

8. The method for improving the accuracy of electricity metering according to claim 1, characterized in that, The method further includes step S6: visualization and alarm step, which displays the high-precision power metering results, dynamic power factor, and harmonic distortion rate in the form of real-time curves and reports.

9. The method for improving the accuracy of electricity metering according to claim 8, characterized in that, The visualization and alarm steps also include issuing an alarm to the energy management system when the parameter exceeds a preset threshold, prompting the system to perform power factor correction or harmonic correction.

10. The method for improving the accuracy of electricity metering according to claim 1, characterized in that, The key power-consuming modules include: raw material crushing module, grinding module and calcination module.

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