Multifunctional intelligent electric meter temperature drift compensation method and system

Abstract

The invention discloses a temperature drift compensation method for an intelligent electric meter, and the method carries out the gradual optimization according to levels: constructing a basic temperature drift compensation model at the first layer, and preliminarily offsetting the influence of temperature on the measurement precision; in the second layer, a temperature change rate item is introduced into a compensation formula, the temperature change rate is calculated through a weighted sliding window algorithm, and an instantaneous metering error caused by local instantaneous temperature rise caused by sudden load change is compensated; in the third layer, temperature sensors are independently arranged in a high-heat area (a metering chip) and a low-temperature area (a current transformer) in the ammeter respectively, and a corresponding temperature drift compensation model is established based on temperature data of each area to adapt to the temperature difference of the areas; and the fourth layer respectively constructs deep learning networks for the high-heat area and the low-temperature area, inputs the temperature time sequence data of each area, the operation duration of the electricity meter and the environment temperature into the corresponding networks, and outputs model updating parameters to realize the dynamic updating of the model. According to the invention, the metering precision of the intelligent electric meter in steady and dynamic working conditions and long-term operation can be obviously improved.

Description

Technical Field

[0001] This invention belongs to the field of smart meter technology, and in particular relates to a technology that achieves precise temperature drift compensation through layered optimization to address the impact of temperature on metering accuracy. Background Technology

[0002] Temperature is a key factor affecting the metering accuracy of smart meters during operation. To address this issue, a temperature drift compensation model has been established in existing technologies, but this model has many shortcomings.

[0003] Current temperature drift compensation models have poor adaptability to dynamic operating conditions, and their compensation coefficients are generated based on steady-state temperatures. However, in practical applications, sudden load changes (such as motor startup) can cause localized instantaneous temperature rises inside the meter. In this case, the static compensation model, due to its delayed response, will cause significant instantaneous metering errors, which can reach 5%-10%.

[0004] Meanwhile, there are significant temperature differences inside the meter. The temperature difference between high-heat areas (such as the metering chip) and low-temperature areas (such as the CT) is often large. A single temperature drift compensation model cannot adapt to this temperature difference, resulting in poor compensation effect and further affecting the metering accuracy.

[0005] Furthermore, fixed temperature drift compensation models cannot adapt to the effects of component aging and seasonal environmental convection changes. Component aging (such as cracking of potting materials causing a 30% decrease in thermal conductivity) and seasonal environmental convection differences (natural convection heat dissipation efficiency is 40% higher in summer than in winter) will change the temperature field distribution inside the meter, causing a significant decrease in the compensation accuracy of fixed models. After long-term use, the error may exceed 15%.

[0006] Therefore, there is an urgent need for a layered optimization temperature drift compensation technology to solve the above problems and improve the metering accuracy of smart meters. Summary of the Invention

[0007] The purpose of this invention is to provide a hierarchical optimized temperature drift compensation technology for smart meters. By establishing a basic model, adding a temperature change rate term, establishing the model in different regions, and dynamically updating the model using a deep learning network, the impact of temperature on the metering accuracy is gradually resolved, thereby improving the metering accuracy of the meter under various operating conditions.

[0008] A method for compensating temperature drift in a smart meter, characterized by comprising the following steps: A basic temperature drift compensation model was established to initially compensate for the impact of temperature on the metering accuracy. A temperature change rate term is added to the compensation formula of the basic temperature drift compensation model to address the instantaneous measurement error caused by the local instantaneous temperature rise due to sudden load changes. Temperature sensors are independently deployed in the high-heat and low-temperature zones inside the meter, and corresponding temperature drift compensation models are established based on the temperature data of each zone. The data collected by temperature sensors in the high-heat and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

[0009] The compensation formula including the temperature change rate term is: E(t) = K0· T(t) + K1· ΔT / Δt, where E(t) is the compensation amount at time t, K0 is the steady-state temperature coefficient, T(t) is the temperature value at time t, K1 is the temperature change rate coefficient, and ΔT / Δt is the temperature change rate.

[0010] The high-temperature zone includes a metering chip, and the low-temperature zone includes a current transformer (CT). At least one temperature sensor is deployed at the metering chip, and at least one temperature sensor is deployed at the current transformer.

[0011] The deep learning network uses a recurrent neural network (RNN). For the high-heat zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the high-heat zone. For the low-temperature zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the low-temperature zone.

[0012] The training samples for the deep learning network include temperature data and corresponding measurement error data under different temperatures, loads, aging levels, and seasonal environments.

[0013] A smart meter temperature drift compensation system, characterized in that it includes: Basic compensation module: used to establish a basic temperature drift compensation model to initially compensate for the impact of temperature on the meter's measurement accuracy; Dynamic compensation module: Adds a temperature change rate term to the compensation formula of the basic temperature drift compensation model to address instantaneous measurement errors caused by sudden load changes; Zoned compensation module: Temperature sensors are independently deployed in the high-heat zone and low-temperature zone inside the meter, and a corresponding temperature drift compensation model is established based on the temperature data of each zone; Model update module: The data collected by temperature sensors in the high-temperature and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

[0014] The temperature sensor is a high-precision digital temperature sensor with a measurement accuracy of no less than ±0.2℃ and a sampling frequency of no less than 1kHz.

[0015] The temperature change rate in the dynamic compensation module is calculated using a sliding window algorithm, with a window length of 3-8 sampling periods.

[0016] The deep learning network in the model update module is updated once a day, and this is done during off-peak electricity usage periods.

[0017] A heat insulation device is installed between the temperature sensors in the high-temperature zone and the low-temperature zone to reduce thermal interference between the two zones.

[0018] Beneficial effects: Effectively solves instantaneous measurement error: By adding a temperature change rate term to the compensation formula, it can quickly respond to local instantaneous temperature rise caused by sudden load changes, reducing the instantaneous measurement error to within ±2%, which is a significant improvement compared to the traditional model.

[0019] Adapting to regional temperature differences: Temperature drift compensation models are established in different zones, enabling each model to accurately match the temperature characteristics of its region. Even when there are large temperature differences between high-temperature and low-temperature zones, the compensation effect can still be maintained, and the compensation accuracy is improved by more than 30%.

[0020] Adapting to environmental and aging changes: By using deep learning networks to dynamically update the model, it can effectively cope with the effects of component aging and seasonal environmental convection changes, so that the compensation accuracy can still be maintained at a high level after long-term use, with an error of no more than 5%.

[0021] High versatility: This technology can be applied to different types of smart meters without requiring large-scale modifications to the meters, making it easy to promote and apply.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0023] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which: Figure 1 is a flowchart of a temperature drift compensation method for a multi-functional smart meter exemplified in this application. Detailed Implementation

[0024] The present application is described below based on embodiments, but it is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without these details. To avoid obscuring the substance of the present application, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0025] Furthermore, those skilled in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0026] This invention provides a method for compensating for temperature drift in smart meters, characterized by comprising the following steps: A basic temperature drift compensation model was established to initially compensate for the impact of temperature on the metering accuracy. A temperature change rate term is added to the compensation formula of the basic temperature drift compensation model to address the instantaneous measurement error caused by the local instantaneous temperature rise due to sudden load changes. Temperature sensors are independently deployed in the high-heat and low-temperature zones inside the meter, and corresponding temperature drift compensation models are established based on the temperature data of each zone. The data collected by temperature sensors in the high-heat and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

[0027] The compensation formula including the temperature change rate term is: E(t) = K0 · T(t) + K1 · ΔT / Δt, where E(t) is the compensation amount at time t, K0 is the steady-state temperature coefficient, T(t) is the temperature value at time t, K1 is the temperature change rate coefficient, and ΔT / Δt is the temperature change rate.

[0028] The high-temperature zone includes a metering chip, and the low-temperature zone includes a current transformer (CT). At least one temperature sensor is deployed at the metering chip, and at least one temperature sensor is deployed at the current transformer.

[0029] The deep learning network uses a recurrent neural network (RNN). For the high-heat zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the high-heat zone. For the low-temperature zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the low-temperature zone.

[0030] The training samples for the deep learning network include temperature data and corresponding measurement error data under different temperatures, loads, aging levels, and seasonal environments.

[0031] A smart meter temperature drift compensation system, characterized in that it includes: Basic compensation module: used to establish a basic temperature drift compensation model to initially compensate for the impact of temperature on the meter's measurement accuracy; Dynamic compensation module: Adds a temperature change rate term to the compensation formula of the basic temperature drift compensation model to address instantaneous measurement errors caused by sudden load changes; Zoned compensation module: Temperature sensors are independently deployed in the high-heat zone and low-temperature zone inside the meter, and a corresponding temperature drift compensation model is established based on the temperature data of each zone; Model update module: The data collected by temperature sensors in the high-temperature and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

[0032] The temperature sensor is a high-precision digital temperature sensor with a measurement accuracy of no less than ±0.2℃ and a sampling frequency of no less than 1kHz.

[0033] The temperature change rate in the dynamic compensation module is calculated using a sliding window algorithm, with a window length of 3-8 sampling periods.

[0034] The deep learning network in the model update module is updated once a day, and this is done during off-peak electricity usage periods.

[0035] A heat insulation device is installed between the temperature sensors in the high-temperature zone and the low-temperature zone to reduce thermal interference between the two zones.

[0036] The present invention will be further described in detail below with reference to specific embodiments.

[0037] Establishment of a basic temperature drift compensation model: Multiple smart meters of the same model were selected and simulated in a constant temperature chamber at different temperature environments (-20℃ to 60℃). At each temperature point, known voltage and current signals were input to the meters through a standard power supply, and the difference between the meter's measured value and the standard value was recorded to obtain the metering error data. Linear regression was used to fit these data to obtain the parameters of the basic temperature drift compensation model, which was then written into the meter's control chip.

[0038] Temperature change rate implementation: A high-precision temperature sensor is installed inside the meter, with a sampling frequency set to 1kHz. A sliding window algorithm is used to calculate the temperature change rate, with a window length of 5 sampling periods. The temperature change rate is incorporated into the compensation formula, and a corresponding program is written in the control chip to calculate the compensation amount in real time and correct the meter reading. For example, when a 2°C temperature rise is detected within 5ms, the temperature change rate is 400°C / s. The corresponding compensation amount is calculated based on the preset K1 value and applied.

[0039] Zonal Model Establishment: One temperature sensor is installed on the surface of the metering chip, and another temperature sensor is installed at the core of the current transformer. The sensors are in close contact with the measured component to ensure accurate temperature measurement. Thermal insulation material is installed between the temperature sensors in the two zones to reduce heat transfer. Temperature data and corresponding metering errors are collected for both zones. Using the same method as for establishing the basic model, separate temperature drift compensation models are established for the high-temperature and low-temperature zones, and then written into the control chip.

[0040] Deep learning network application: Recurrent neural networks (RNNs) are constructed for high-temperature and low-temperature zones respectively. Temperature sensor data, meter running time, ambient temperature, and corresponding metering error data for both zones are collected as training samples during meter operation. These training samples are input into the neural network for training, enabling the network to learn the relationship between data and model parameters. Every day between 3-5 AM (off-peak electricity consumption), the temperature data collected that day is input into the trained neural network. The network outputs updated parameters for the corresponding zone's temperature drift compensation model. The control chip updates the model based on these parameters, ensuring the model can adapt to component aging and environmental changes.

[0041] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the specific details described above.

[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0043] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0044] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps are decomposable and / or recombinable. Such decomposition and / or recombination should be considered equivalent to the present disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0045] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

[0046] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for compensating temperature drift in a smart meter, characterized in that, Includes the following steps: A basic temperature drift compensation model was established to initially compensate for the impact of temperature on the metering accuracy. A temperature change rate term is added to the compensation formula of the basic temperature drift compensation model to address the instantaneous measurement error caused by the local instantaneous temperature rise due to sudden load changes. Temperature sensors are independently deployed in the high-heat and low-temperature zones inside the meter, and corresponding temperature drift compensation models are established based on the temperature data of each zone. The data collected by temperature sensors in the high-heat and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

2. The method according to claim 1, characterized in that, The formula for the temperature change rate term is: E(t) = K0· T(t) + K1 · ΔT / Δt, where E(t) is the compensation amount at time t, K0 is the steady-state temperature coefficient, T(t) is the temperature value at time t, K1 is the temperature change rate coefficient, and ΔT / Δt is the temperature change rate.

3. The method according to claim 1, characterized in that, The high-temperature zone includes a metering chip, and the low-temperature zone includes a current transformer (CT). At least one temperature sensor is deployed at the metering chip, and at least one temperature sensor is deployed at the current transformer.

4. The method according to claim 1, characterized in that, The deep learning network uses a recurrent neural network (RNN). For the high-heat zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the high-heat zone. For the low-temperature zone, the input of the deep learning network is the time series data of the temperature sensor in that area, the running time of the electricity meter, and the ambient temperature. The output is the updated parameters of the temperature drift compensation model for the low-temperature zone.

5. The method according to claim 4, characterized in that, The training samples for the deep learning network include temperature data and corresponding measurement error data under different temperatures, loads, aging levels, and seasonal environments.

6. A smart meter temperature drift compensation system, characterized in that, include: Basic compensation module: used to establish a basic temperature drift compensation model to initially compensate for the impact of temperature on the meter's metering accuracy; Dynamic compensation module: Adds a temperature change rate term to the compensation formula of the basic temperature drift compensation model to address instantaneous measurement errors caused by sudden load changes; Zoned compensation module: Temperature sensors are independently deployed in the high-heat zone and low-temperature zone inside the meter, and a corresponding temperature drift compensation model is established based on the temperature data of each zone; Model update module: The data collected by temperature sensors in the high-temperature and low-temperature zones are processed by deep learning networks to achieve dynamic updates of the temperature drift compensation model for the corresponding regions.

7. The system according to claim 6, characterized in that, The temperature change rate in the dynamic compensation module is calculated using a sliding window algorithm, with a window length of 3-8 sampling periods.

8. The system according to claim 6, characterized in that, A heat insulation device is installed between the temperature sensors in the high-temperature zone and the low-temperature zone to reduce thermal interference between the two zones.

9. An electronic device comprising a memory and a processor, wherein computer instructions on the memory are executed by the processor, causing the processor to perform the method as described in any one of claims 1 to 5.

10. A computer-readable storage medium storing a processor-executable program, characterized in that: The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1 to 5.

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