Error calibration method and system for intelligent electric energy metering equipment

Abstract

The invention discloses an error calibration method and system for intelligent electric energy metering equipment, and belongs to the technical field of error calibration, and the system comprises an electric energy data acquisition module which is used for collecting electric energy parameter information metered by the electric energy metering equipment in real time; the environment data acquisition module is used for acquiring related environment parameter information in real time; the analysis module carries out analysis processing according to the obtained parameter information so as to judge whether the metering of the electric energy metering equipment has errors or not; and the calibration module is used for further analyzing and generating a gain adjustment value to adjust the electric energy gain of the electric energy metering equipment. According to the invention, the operation state of the electric energy metering equipment can be monitored in real time, once errors occur, calibration can be carried out in time, error accumulation is avoided, the metering accuracy is greatly improved, manual intervention is not needed during calibration, the related electric energy gain can be automatically adjusted according to the gain adjustment value condition, environmental factors are considered, and the calibration accuracy is improved. And the measurement precision is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of error calibration, and particularly relates to a method and system for calibrating errors of an intelligent electric energy metering device. Background Art

[0002] With the rapid development of the power industry, intelligent electric energy metering devices are widely used in various links of the power grid, and their metering accuracy is directly related to the fairness of power transactions and the reliability of power grid operation. However, due to various reasons, electric energy metering devices will inevitably generate errors during long-term operation, so it is necessary to calibrate the errors of electric energy metering devices.

[0003] Existing calibration methods for electric energy metering devices mostly rely on manual regular verification, regularly detecting whether the device has errors. When errors occur, the physical knobs are gradually adjusted slightly according to the guidance in the technical manual to adjust the gain, so as to perform calibration. This method has a long inspection period and is difficult to capture the change of device errors in real time, thus affecting the metering accuracy. In addition, the errors of electric energy metering devices are mostly affected by factors such as the environment, so inaccurate calibration is likely to occur during calibration. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for calibrating errors of an intelligent electric energy metering device to solve the problems faced in the above background art.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An error calibration system for an intelligent electric energy metering device, the system includes:

[0007] An electric energy data acquisition module, which is used to collect the electric energy parameter information measured by the electric energy metering device in real time;

[0008] An environmental data acquisition module, which is used to acquire the environmental parameter information of the location where the electric energy metering device is located in real time;

[0009] An analysis module, which is used to receive the electric energy parameter information and the environmental parameter information, and perform analysis and processing according to the acquired parameter information to judge whether there is an error in the metering of the electric energy metering device;

[0010] A calibration module, which further analyzes the acquired parameter information in the case of an error in the electric energy metering device, thereby generating a gain adjustment value, and adjusts the electric energy gain of the electric energy metering device according to the gain adjustment value to perform calibration.

[0011] Further, the method for the analysis module to judge whether there is an error in the metering of the electric energy metering device is:

[0012] Set the measurement period ΔT. During the measurement period ΔT, based on the measured power parameter values, formulate the curve function Q(t) of the power parameter values changing with time;

[0013] Through the formula Obtain the error judgment value F;

[0014] When F > F th Then it is determined that there is an error in the power metering device;

[0015] Among them, t a Is the start time of the measurement period, t b Is the end time of the measurement period, Q0(t) is the standard curve function of the power parameter values set by the system changing with time within the same measurement period, and F th Is the set error judgment threshold.

[0016] Furthermore, the method for obtaining the gain adjustment value is as follows:

[0017] When it is determined that there is an error in the power metering device, at this time, obtain the curve function Q E (t) of the actual power consumption value changing with time within this measurement period from the power grid system;

[0018] Through the formula Obtain the gain adjustment value R;

[0019] Among them, σ1 and σ2 are preset coefficients, and ΔQ X And ΔQ Y Are the respective preset comparison reference values.

[0020] Furthermore, the method for the calibration module to adjust the power gain of the power metering device is as follows:

[0021] The adjustment includes a gain increase instruction and a gain decrease instruction;

[0022] When R ∈ (-∞, R1), generate a gain increase instruction. At this time, through the formula

[0023] G A = G0 + τ * (R1 - R) * ε emp Adjust the power gain to G A ;

[0024] When R ∈ (R2, +∞), generate a gain decrease instruction. At this time, through the formula

[0025] G B = G0 - τ * (R - R2) * ε emp Adjust the power gain to G B ;

[0026] Wherein, R1 and R2 are set gain adjustment judgment thresholds, G0 is the current power gain, τ is the gain conversion coefficient, and ε emp is the environmental impact value.

[0027] Furthermore, the method for obtaining the environmental impact value is as follows:

[0028] Obtain the average temperature C of the area where the power metering device is located within the current measurement period T , humidity C S , electromagnetic intensity value C H ;

[0029] Through the formula

[0030] Obtain the environmental impact value ε emp ;

[0031] Wherein, C T0 is the standard temperature set by the system, C S0 is the standard humidity set by the system, C H0 is the standard electromagnetic intensity value set by the system, C PM is the dust concentration value of the power metering device, and ΔC T , ΔC S , ΔC H , ΔC PM are respectively various preset deviation comparison values, w1, w2, w3, w4 are their respective preset coefficients, is the average dust concentration monitored on each sensor of the power metering device, maxPM is the maximum dust concentration, and a1 is the weight coefficient.

[0032] Furthermore, the environmental parameter information includes temperature information, humidity information, electromagnetic information, and dust concentration information.

[0033] Furthermore, the analysis module is also used to evaluate the potential error risk of the intelligent power metering device, and the evaluation method is as follows:

[0034] When there is no measurement error in the intelligent power metering device, obtain the magnitude of the error judgment value F obtained within n measurement periods, so as to formulate the curve function F(x) of the error judgment value changing with the measurement period;

[0035] Through the formula Obtain the risk value U;

[0036] When U > U th , it is determined that the intelligent power metering device has a potential error risk, and at this time, the power metering device is timely corrected accordingly;

[0037] Among them, x1 is the first measurement period, x2 is the last measurement period, F i is the error judgment value obtained in the i-th measurement period, and i ∈ [1, n], U th is the preset risk judgment threshold.

[0038] An error calibration method for an intelligent power metering device, the calibration method is controlled and implemented by the error calibration system of the intelligent power metering device, and the method includes:

[0039] Step 1: Collect the power parameter information measured by the power metering device and the environmental parameter information of the location where the power metering device is located;

[0040] Step 2: Send the collected relevant parameter information to the analysis module for analysis and processing to determine whether there is an error in the power metering of the power metering device;

[0041] Step 3: When it is determined that there is an error in the power metering of the power metering device, calibrate the error according to the magnitude of the gain adjustment value;

[0042] Step 4: When it is determined that there is no error in the power metering of the power metering device, evaluate the potential error risk of the power metering device based on the relevant parameter information obtained.

[0043] Advantages of the present invention:

[0044] The present invention can monitor the operating state of the power metering device in real time. Once an error occurs, it can be calibrated in time to avoid error accumulation, greatly improving the measurement accuracy. At the same time, during calibration, no manual intervention is required, and the relevant power gain can be automatically adjusted according to the gain adjustment value. Moreover, considering environmental factors, it can automatically adapt to different working conditions and environmental changes to ensure the measurement accuracy.

[0045] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 is the module block diagram of the calibration system of the present invention;

[0048] Figure 2 is the method flow chart of the calibration method in the present invention. Detailed implementation mode

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0050] In one embodiment, an error calibration system for an intelligent electric energy metering device is disclosed, as Figure 1 shown. The calibration system includes:

[0051] An electric energy data acquisition module, which is used to collect the electric energy parameter information measured by the electric energy metering device in real time;

[0052] An environmental data acquisition module, which is used to obtain the environmental parameter information of the location where the electric energy metering device is located in real time. The environmental parameter information includes temperature information, humidity information, electromagnetic information, and dust concentration information;

[0053] An analysis module, which is used to receive the electric energy parameter information and the environmental parameter information, and perform analysis and processing according to the obtained parameter information, so as to judge whether there is an error in the measurement of the electric energy metering device;

[0054] A calibration module, which further analyzes the obtained parameter information in the case of an error in the electric energy metering device, thereby generating a gain adjustment value, and adjusts the electric energy gain of the electric energy metering device according to the situation of the gain adjustment value, so as to perform calibration.

[0055] Through the above technical solutions, the present application first collects the electric energy parameter information measured by the electric energy metering device in real time through the electric energy data acquisition module, such as current parameter information, etc., and judges whether there is a measurement error in the electric energy metering device according to the obtained electric energy parameter information. When a measurement error occurs, further analysis is carried out in combination with the electric energy parameter information to obtain a gain adjustment value. At the same time, the environmental parameter information of the location where the electric energy metering device is located, including temperature, humidity, electromagnetic, etc., is obtained through the environmental data acquisition module, and the relevant electric energy gain of the electric energy metering device is adjusted in combination with the situation of the gain adjustment value, so as to perform error calibration. Its error calibration detection period is short, and no manual calibration is required. When it is judged that there is an error, the relevant gain can be automatically adjusted to calibrate the error, ensuring the measurement accuracy of the electric energy metering device. At the same time, when performing error calibration, environmental and other factors are taken into account, which can more accurately ensure the measurement accuracy of the subsequent electric energy metering device.

[0056] The method for the analysis module to determine whether there is an error in the power metering device is as follows: Set a measurement period ΔT. During the measurement period ΔT, according to the measured power parameter values, formulate a curve function Q(t) of the power parameter values changing with time.

[0057] Through the formula Obtain the error judgment value F;

[0058] When F > F th Then it is judged that there is an error in the power metering device.

[0059] Among them, t a Is the start time of the measurement period, t b Is the end time of the measurement period, Q0(t) is the standard curve function of the power parameter values changing with time set by the system within the same measurement period, and F th Is the set error judgment threshold.

[0060] The above technical solution provides a method for the analysis module to determine whether there is an error in the power metering device. First, set a measurement period ΔT. During the measurement period ΔT, according to the measured power parameter values, formulate a curve function Q(t) of the power parameter values changing with time. Then, through the formula Obtain the error judgment value F, where Q0(t) is the standard curve function of the power parameter values changing with time set by the system within the same measurement period, which can be determined according to a large amount of historical data within the same measurement period. Compare the obtained change situation of the power parameter values with time with the standard change situation of the power parameter values with time to obtain the error judgment value F. It can be seen that the larger the value, the greater the gap between the measurement in the current measurement period and the standard measurement, and the greater the possibility of its existence of an error. Therefore, compare the error judgment value F with the set error judgment threshold F th For comparison. When F > F th Then it is judged that there is an error in the power metering device. By this means, comparing the obtained parameter data with the corresponding standard data can monitor the operating state of the power metering device in real time. Once an error occurs, it can be calibrated in time to avoid error accumulation, greatly improving the measurement accuracy.

[0061] It should be noted that the start time t a And the end time t b Of the measurement period can both be determined artificially according to the actual situation, and the set error judgment threshold F th Is determined according to historical data combined with empirical data.

[0062] The method for obtaining the gain adjustment value is as follows: When it is judged that there is an error in the power metering device, at this time, obtain the curve function Q of the actual power consumption value changing with time within the measurement period from the power grid systemE (t);

[0063] The gain adjustment value R is obtained through the formula ;

[0064] where σ1 and σ2 are preset coefficients, ΔQ X and ΔQ Y are respective preset comparison reference values;

[0065] The method for the calibration module to adjust the power gain of the power metering device is as follows: The adjustment includes a gain increase instruction and a gain decrease instruction;

[0066] When R ∈ (-∞, R1), a gain increase instruction is generated. At this time, through the formula

[0067] G A = G0 + τ * (R1 - R) * ε emp the power gain is adjusted to G A ;

[0068] When R ∈ (R2, +∞), a gain decrease instruction is generated. At this time, through the formula

[0069] G B = G0 - τ * (R - R2) * ε emp the power gain is adjusted to G B ;

[0070] where R1 and R2 are set gain adjustment judgment thresholds, G0 is the current power gain, τ is the gain conversion coefficient, and ε emp is the environmental impact value. The method for obtaining the environmental impact value ε emp is as follows:

[0071] Obtain the average temperature C, humidity C T , and electromagnetic intensity value C S of the area where the power metering device is located within the current measurement period; H ;

[0072] Through the formula

[0073] the environmental impact value ε emp is obtained;

[0074] where C T0 is the standard temperature set by the system, C S0 is the standard humidity set by the system, C H0 is the standard electromagnetic intensity value set by the system, C PM is the dust concentration value of the power metering device, and ΔC T 、ΔC S 、ΔCH 、ΔC PM are respectively various preset deviation comparison values, and w1, w2, w3, w4 are their respective preset coefficients. is the average dust concentration monitored on each sensor of the power metering device, maxPM is the maximum dust concentration, and a1 is the weight coefficient.

[0075] The above technical solution provides a specific method for calibrating errors when there are errors. First, when it is determined that there is an error in the power metering of the power metering device, at this time, the curve function Q E (t) of the actual power consumption value changing with time in this measurement period is obtained from the power grid system, and then through the formula the gain adjustment value R is obtained. The formula represents the difference between the current measured value and the standard measured value. represents the difference between the current measured value and the actual consumption value. Obviously, the closer the difference is to 0, the smaller the error is, otherwise the error is larger and calibration is required. The adjustment includes a gain increase instruction and a gain decrease instruction. If the error is positive, that is, the measured value is too large, and when the error is negative, that is, the measured value is too small, calibration is required. Therefore, when R ∈ (-∞, R1), it indicates that the measured value is too small and the gain of the relevant power needs to be increased, a gain increase instruction is generated, and through the formula C A = G0 + τ * (R1 - R) * ε emp the power gain is adjusted to G A ; in the formula, ε emp is the environmental impact value, and its acquisition method is: obtaining the average temperature C T , humidity C S , and electromagnetic intensity value C H of the area where the power metering device is located in the current measurement period, and through the formula

[0076] the environmental impact value ε is obtained emp ; since the detection results are greatly affected by temperature, humidity, magnetic field strength, dust amount, etc., environmental factors need to be considered during adjustment. Therefore, the average temperature C T , humidity C S , and electromagnetic intensity value C H of the area where the power metering device is located in the current measurement period are obtained and compared with their respective standard values. The greater the difference, the greater the environmental impact in the current measurement period. Similarly, through the formula the dust concentration value of the power metering device is obtained. The greater the dust concentration value of the power metering device, the greater its impact on metering. Therefore, finally, through the formula

[0077] a comprehensive analysis is carried out to obtain the environmental impact value εemp , and then consider the environmental impact value when making gain adjustment. Specifically, through the formula G A = G0 + τ * (R1 - R) * ε emp Adjust the electrical energy gain to G A . It can be seen that the greater the difference R1 - R between the obtained gain adjustment value and the set gain adjustment judgment threshold R1, and the greater the environmental impact value, the more electrical energy gain needs to be increased. Similarly, when R ∈ (R2, +∞), it indicates that the measured value is excessive. To ensure accuracy, the gain of the relevant electrical energy needs to be reduced. Then, a gain reduction instruction is generated, and through the formula G B = G0 - τ * (R - R2) * ε emp Adjust the electrical energy gain to G B . Through this method, the operating state of the electrical energy metering device can be monitored in real time. Once an error occurs, it can be calibrated in time to avoid error accumulation, greatly improving the accuracy of metering. At the same time, during calibration, no manual intervention is required, and the size of the relevant electrical energy gain can be automatically adjusted according to the gain adjustment value situation. Moreover, considering the environmental factors, it can automatically adapt to different working conditions and environmental changes to ensure the measurement accuracy.

[0078] In the above solution, the preset coefficients σ1 and σ2, the preset coefficients w1, w2, w3, w4, the weight coefficient a1, the comparison reference value ΔQ X and ΔQ Y , the deviation comparison value ΔC T , ΔC S , ΔC H and ΔC PM , and the gain conversion coefficient τ can all be determined according to historical relevant data combined with empirical data, while the gain adjustment judgment thresholds R1 and R2, the standard temperature C T0 , the standard humidity C S0 , and the standard electromagnetic intensity value C H0 are determined according to empirical data and will not be elaborated here.

[0079] The analysis module is also used to evaluate the potential error risk of the intelligent electrical energy metering device. The evaluation method is as follows: when there is no metering error in the intelligent electrical energy metering device, obtain the magnitude of the error judgment value F obtained within n measurement cycles, and thus formulate the curve function F(x) of the error judgment value changing with the measurement cycle;

[0080] Through the formula Obtain the risk value U;

[0081] When U > U th , it is determined that the intelligent electrical energy metering device has a potential error risk, and at this time, the electrical energy metering device is timely corrected accordingly;

[0082] Among them, x1 is the first measurement period, x2 is the last measurement period, and F i is the error judgment value obtained in the i-th measurement period, and i ∈ [1, n], and U th is the preset risk judgment threshold.

[0083] The above technical solution provides a specific method for the analysis module to evaluate the potential error risk of intelligent power metering equipment. First, when there is no metering error in the intelligent power metering equipment, continuously obtain the magnitude of the error judgment value F obtained within n measurement periods, so as to formulate the curve function F(x) of the error judgment value changing with the measurement period, and through the formula obtain the risk value U. The formula represents an accumulation of the error judgment values in n measurement periods. The larger its value, the greater the possibility of potential errors. And the formula represents a fluctuation of the error judgment values in n measurement periods. The larger its value, the greater the fluctuation of the error judgment value, and the greater the possibility of potential errors. Therefore, combining the formula conduct a comprehensive analysis to obtain the risk value U, and then compare it with the preset risk judgment threshold U th When U > U th it is determined that the intelligent power metering equipment has a potential error risk. In order to avoid the occurrence of errors, at this time, the power metering equipment should be corrected in a timely manner, for example, manually correcting the relevant gain of the equipment to ensure the measurement accuracy of the equipment.

[0084] In one embodiment, an error calibration method for an intelligent power metering equipment is also disclosed. This calibration method is controlled and implemented by the above error calibration system of an intelligent power metering equipment, as Figure 2 shown, and its calibration method includes:

[0085] Step 1: Collect the power parameter information measured by the power metering equipment and the environmental parameter information of the location where the power metering equipment is located;

[0086] Step 2: Send the collected relevant parameter information to the analysis module for analysis and processing to determine whether there is an error in the power metering of the power metering equipment;

[0087] Step 3: When it is determined that there is an error in the power metering of the power metering equipment, calibrate the error according to the magnitude of the gain adjustment value;

[0088] Step 4: When it is determined that there is no error in the power metering of the power metering equipment, evaluate the potential error risk of the power metering equipment according to the relevant parameter information obtained.

[0089] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art to which the present technology pertains can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claims, they shall fall within the protection scope of the present invention.

Claims

1. An error calibration system for an intelligent electric energy metering device, characterized in that, The system includes: An electric energy data acquisition module, which is used to collect in real time the electric energy parameter information measured by an electric energy metering device; An environmental data acquisition module, which is used to obtain in real time the environmental parameter information of the location where the electric energy metering device is located; An analysis module, which is used to receive the electric energy parameter information and the environmental parameter information, and perform analysis and processing according to the obtained parameter information, so as to judge whether there is an error in the measurement of the electric energy metering device; A calibration module, which, when there is an error in the electric energy metering device, further analyzes the obtained parameter information to generate a gain adjustment value, and adjusts the electric energy gain of the electric energy metering device according to the situation of the gain adjustment value, so as to perform calibration.

2. The error calibration system of an intelligent electric energy metering device according to claim 1, wherein, The method for the analysis module to judge whether there is an error in the measurement of the electric energy metering device is: Set a measurement period ΔT. During the measurement period ΔT, according to the measured electric energy parameter value, draw a curve function Q(t) of the electric energy parameter value changing with time; Through the formula the error judgment value F is obtained; When F > F th , it is determined that there is an error in the power metering device; where t a is the start time of the measurement period, t b is the end time of the measurement period, Q0(t) is the standard curve function of the electrical energy parameter value set by the system changing with time during the same measurement period, F th is the set error judgment threshold.

3. The error calibration system of an intelligent power metering device according to claim 2, characterized in that, The method for obtaining the gain adjustment value is: When it is determined that there is an error in the measurement of the electrical energy metering device, at this time, the curve function Q of the actual power consumption value changing with time within the measurement period is obtained from the power grid system. E (t); Obtain the gain adjustment value R through the formula ​ wherein, σ1 and σ2 are preset coefficients, ΔQ X and ΔQ Y are respective preset comparison reference values.

4. The error calibration system of an intelligent electric energy metering device according to claim 3, characterized in that, The method for the calibration module to adjust the electric energy gain of the electric energy metering device is: The adjustment includes a gain increase instruction and a gain decrease instruction; When R ∈ (-∞, R1), a gain increase instruction is generated. At this time, through the formula G A = G0 + τ * (R1 - R) * ε emp the electrical energy gain is adjusted to G A ; When \(R\in(R_2, +\infty)\), a gain reduction instruction is generated. At this time, through the formula \(G\) B = G_0 - \(\tau\)*(R - R_2)*\(\varepsilon\) emp the electrical energy gain is adjusted to \(G\) B ; Among them, R1 and R2 are set gain adjustment judgment thresholds, G0 is the current power gain, τ is the gain conversion coefficient, and ε emp is the environmental impact value.

5. The error calibration system of an intelligent electric energy metering device according to claim 4, characterized in that, The method for obtaining the environmental influence value is: Obtain the average temperature C of the area where the power metering device is located during the current measurement period T , humidity C S , electromagnetic intensity value C H ; Through the formula Obtain the environmental impact value ε emp ; Among them, C T0 is the standard temperature set by the system, in °C S0 is the standard humidity set by the system, in °C H0 is the standard electromagnetic intensity value set by the system, in °C PM is the dust concentration value of the electric energy metering device, and ΔC T 、ΔC S 、ΔC H 、ΔC PM are respectively various preset deviation comparison values, and w1, w2, w3, w4 are their respective preset coefficients. is the average dust concentration monitored on each sensor of the electric energy metering device, maxPM is the maximum dust concentration, and a1 is the weight coefficient.

6. The error calibration system of an intelligent electric energy metering device according to claim 5, characterized in that The environmental parameter information includes temperature information, humidity information, electromagnetic information, and dust concentration information.

7. An error calibration system for an intelligent electric energy metering device according to claim 2, characterized in that, The analysis module is also used to evaluate the potential error risk of the intelligent electric energy metering device. The evaluation method is: When there is no measurement error in the intelligent electric energy metering device, obtain the magnitude of the error judgment value F obtained within n measurement periods, so as to draw a curve function F(x) of the error judgment value changing with the measurement period; Derive the risk value U through the formula ​ When U > U th it is determined that there is a potential error risk in the intelligent power metering device, and at this time, corresponding corrections are made to the power metering device in a timely manner; Among them, x1 is the first measurement period, x2 is the last measurement period, F i is the error judgment value obtained in the i-th measurement period, and i ∈ [1, n], U th is the preset risk judgment threshold.

8. A method for error calibration of an intelligent power metering device, wherein the calibration method is controlled and implemented by the error calibration system of the intelligent power metering device according to any one of claims 1-7, characterized in that, The method includes: Step 1, collect the electric energy parameter information measured by the electric energy metering device and the environmental parameter information of the location where the electric energy metering device is located; Step 2, send the collected relevant parameter information to the analysis module for analysis and processing to judge whether there is an error in the measurement of the electric energy metering device; Step 3, when it is judged that there is an error in the measurement of the electric energy metering device, calibrate the error according to the magnitude of the gain adjustment value; Step 4, when it is judged that there is no error in the measurement of the electric energy metering device, evaluate the potential error risk of the electric energy metering device according to the relevant parameter information obtained.

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