Method for quickly judging over-temperature of terminal of electric energy meter

Abstract

The invention discloses a method for quickly judging the over-temperature of an electric energy meter terminal, which comprises the following steps of: arranging an in-meter temperature sensor and a terminal temperature sensor on an electric energy meter, metering the terminal current, and calculating and analyzing the temperature rise trend of the terminal temperature by using an internal arithmetic unit of the electric energy meter. Therefore, whether the over-temperature of the terminal occurs or not and the occurrence time are analyzed and judged. A terminal over-temperature monitoring and judging method which does not utilize terminal current is given by fitting a temperature change model in the meter and fitting a relevant model of the temperature in the meter and the terminal temperature; through fitting of a terminal current change model and fitting of a terminal temperature comprehensive correlation model, a terminal over-temperature monitoring and judging method using the terminal current is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent electric energy meters, and in particular to a method for quickly judging over-temperature of electric energy meter terminals. Background Art

[0002] As the power load at the user end of the power grid continues to rise, the power grid system is facing unprecedented pressure. This pressure not only exacerbates the complexity of power grid operation, but also frequently leads to safety accidents such as meter burnout due to problems such as equipment aging and overload operation, seriously threatening the power safety and stability of the majority of users.

[0003] The temperature of the energy meter and the terminal temperature are external parameters that can indirectly reflect the user's electricity consumption and the working condition of the energy meter in addition to direct electrical parameters such as current and voltage. In fact, when the contact resistance becomes larger and / or the current and voltage continue to be larger, the terminal temperature can more directly predict whether the energy meter will have a safety accident compared to the temperature inside the meter. And the monitoring and utilization of temperature information should not be processed only when the temperature is actually overheated, or even when a device failure or equipment accident occurs, but there should be a reasonable prediction of the temperature change, and accident prevention and processing should be carried out in advance.

[0004] Current electricity monitoring mainly monitors the user's electricity consumption or electrical equipment and power quality, and many have carried out internal temperature monitoring of electricity meters. However, the means of safety monitoring of the electricity meter itself are still incomplete. For example, there is a lack of effective means or methods for monitoring the terminal temperature of the electricity meter. Although some electricity meters have the function of intermittently monitoring the terminal seat temperature, they cannot quickly and accurately determine the abnormal rise in terminal seat temperature and may soon overheat. Summary of the invention

[0005] The purpose of the present invention is to provide a method for quickly judging whether an electric energy meter terminal is overheated, by using the correlation between the actual measured temperature of the terminal and the temperature inside the electric energy meter and the terminal current, to establish a prediction model to predict whether the terminal will overheat within a limited time.

[0006] To achieve the above object, the present invention designs a method for quickly judging whether an electric energy meter terminal is overheated, by setting an internal temperature sensor and a terminal temperature sensor on the electric energy meter, and using an internal computing unit of the electric energy meter such as an MCU to calculate and analyze the temperature rise trend of the terminal, thereby analyzing and judging whether the terminal will be overheated and when it will occur. The method comprises the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , n is the terminal temperature T D and the temperature inside the table T B The number of sampling groups (the number of sample groups within the window time t1), the sampling values ​​are recorded as T D1 ~TDn and T B1 ~T Bn , use T Di and T Bi Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is t D_MAX , sampling time interval Δt, that is, T D1 To T D2 or T B1 To T B2 The time interval of , and so on, the two are consistent and sampled synchronously; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn and the temperature inside the table T B1 ~T Bn The sampling value is generally the n sets of sampling values ​​starting from the current moment (n-1) Δt moment backwards; S2, the correlation model between the internal temperature and the terminal temperature is fitted, that is, T Di and T Bi Perform model fitting to obtain model parameters related to the internal temperature and terminal temperature; mainly by fitting the data within the window time, find out the correlation characteristics between the two, characterize them with model parameters, and then extrapolate the temperature rise relationship; S3, fitting the temperature change model in the table to obtain model parameters; There is no order of precedence for steps S2 and S3; S4. Calculate the upper temperature limit T in the table B_MAX , according to the terminal temperature rise upper limit T D_MAX The upper temperature limit T in the table is calculated by the model fitting result in step S2 B_MAX After obtaining the model parameters related to the temperature in the table and the terminal temperature, T D_MAX Bring it into the model to calculate T B_MAX ; S5. Calculate the upper limit of the temperature T in the table within a limited time B_MAX Arrival time t B_MAX , according to the upper temperature limit T in the table B_MAX The upper limit T of the temperature in the table is calculated by fitting the temperature change model in the table in step S3. B_MAX Arrival time t B_MAX , thus obtaining the terminal temperature upper limit reaching time t D_MAX =t B_MAX ; If B_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

[0007] When a new sampling value is received, the window keeps the same size and slides. In this way, each time a new sampling value is generated, a new set of model fitting results can be obtained, and a new terminal temperature rise prediction result can be generated. The program is executed in a loop, so that the overtemperature of the electric energy meter terminal can be continuously monitored.

[0008] Furthermore, the model related to the internal temperature of the meter and the terminal temperature includes a square root function model, that is, in, , is the model parameter.

[0009] Furthermore, the model related to the internal temperature of the meter and the terminal temperature includes a mixed model of linear and square root functions, that is, in, , , is the model parameter.

[0010] Furthermore, the temperature change model in the table includes: in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation, t is the time, and the first sampling time t=0 is generally recorded. Each fitting may have different model parameters, so it is necessary to use the above sliding window for fitting to ensure real-time and accuracy; Furthermore, the number of sampling groups n is greater than or equal to the number of all model parameters, that is, it is greater than the number of model parameters related to the temperature inside the meter and the terminal temperature, and also greater than the number of model parameters of the temperature change inside the meter.

[0011] Furthermore, the limited time includes within 1 hour, half an hour or 10 minutes.

[0012] Another method for quickly judging whether the terminal of an electric energy meter is overheated is to set an internal temperature sensor and a terminal temperature sensor on the electric energy meter, measure the terminal current, and use an internal computing unit of the electric energy meter, such as an MCU, to calculate and analyze the temperature rise trend of the terminal, thereby analyzing and judging whether the terminal will overheat and when it will occur. The method includes the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , terminal current is I D , n is TD , T B and I D The number of sampling groups (the number of sample groups within the window time t1), the sampling values ​​are recorded as T D1 ~T Dn , T B1 ~T Bn and I D1 ~I Dn , use T Di , T Bi and I Di Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is t D_MAX , sampling time interval Δt, that is, T D1 To T D2 or T B1 To T B2 or I D1 to I D2 The time interval is the same as above, and so on, the three are consistent and sampled synchronously; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn , Temperature inside the table T B1 ~T Bn and terminal current I D1 ~I Dn The sampling value is generally the n sets of sampling values ​​starting from the current moment (n-1) Δt moment backwards; S2, terminal current change model fitting, obtain model parameters; according to the sampling value I D1 ~I Dn Establish terminal current I D Variation model of relative sampling time; S3, fitting the temperature change model in the table to obtain model parameters; There is no order of precedence for steps S2 and S3; S4. Fitting of terminal temperature comprehensive correlation model, establishing a comprehensive correlation model between terminal temperature, internal temperature and terminal current, and T Di , T Bi and I Di Performing model fitting to obtain parameters of the comprehensive correlation model; S5. Calculate the upper limit of the terminal temperature rise T within a limited time D_MAX Arrival time t D_MAX , based on the model parameters and the pre-set terminal temperature rise limit T D_MAX , substitute the terminal current change model and the temperature change model in the meter into the terminal current change model to calculate whether the terminal temperature can reach its temperature rise upper limit T D_MAX, and the arrival time t D_MAX ; Generally, numerical calculation can be used. Since it is not an accurate analytical solution, it is usually used when the numerical dependent variable T D Enter T D_MAX When the terminal temperature is within the range of ±5%, it is considered that the terminal temperature can reach its upper limit of temperature rise, and the corresponding time is the arrival time t D_MAX ; Note that the numerical calculation input time is gradually increased in steps, and the first time to reach T D_MAX The time allowed is t D_MAX ; If D_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

[0013] When a new sampling value is received, the window keeps the same size and slides. In this way, each time a new sampling value is generated, a new set of model fitting results can be obtained, and a new terminal temperature rise prediction result can be generated. The program is executed in a loop, so that the overtemperature of the electric energy meter terminal can be continuously monitored.

[0014] Furthermore, the terminal current variation model includes: in, , , is the model parameter, t is the time; here we directly use the quadratic polynomial, in fact, we can also use the linear polynomial, or the current model, etc.; The temperature variation model in the table includes: in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation.

[0015] Furthermore, the terminal temperature comprehensive correlation model includes: in, , , , , is the model parameter.

[0016] Furthermore, the limited time includes within 1 hour, half an hour or 10 minutes.

[0017] The advantages and beneficial effects of the present invention are as follows: the present invention provides a terminal over-temperature monitoring and judgment method that does not use terminal current by fitting the temperature change model inside the meter and the correlation model between the temperature inside the meter and the terminal temperature; by fitting the terminal current change model and the terminal temperature comprehensive correlation model, a terminal over-temperature monitoring and judgment method that uses terminal current is provided, thereby improving the self-monitoring means of the smart electric energy meter. The method of the present invention is mainly applicable to situations where the electronic components of the electric energy meter are aging but not serious, the terminal connection is loose or the contact resistance becomes large but does not affect the operation of the electronic and electrical equipment, and the electric energy meter terminal has a continuous temperature rise. If it is not handled in time, it will also cause power accidents. For rapid temperature rise caused by short circuits and other reasons, it is more appropriate to use conventional current and voltage detection alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a method for quickly judging over-temperature of the terminals of an electric energy meter; Figure 2 It is a flowchart of another method for quickly judging whether the terminals of an electric energy meter are over-temperature. DETAILED DESCRIPTION

[0019] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0020] Embodiment 1: like Figure 1 As shown, the present invention is a method for quickly judging the over-temperature of the terminals of an electric energy meter. An internal temperature sensor and a terminal temperature sensor are set on the electric energy meter. Since a general intelligent electric energy meter is provided with an internal temperature sensor, the present embodiment only needs to add a corresponding terminal temperature sensor on this basis; the internal computing unit of the electric energy meter, such as an MCU, is used to calculate and analyze the temperature rise trend of the terminal temperature, so as to analyze and judge whether the terminal will be overheated and when it will occur. The method comprises the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , n is the terminal temperature T D and the temperature inside the table T B The number of sampling groups (the number of sample groups within the window time t1), the sampling values ​​are recorded as T D1 ~T Dn and T B1 ~T Bn , use T Di and T Bi Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is tD_MAX , sampling time interval Δt, that is, T D1 To T D2 or T B1 To T B2 The time interval is the same as above, and the two are consistent and sampled synchronously; in this embodiment, the terminal temperature rise upper limit T is set D_MAX =150℃, sampling time interval 20ms, n should be large enough to ensure the credibility of the fitting result, but not too large, so that the time span of the measured data is too long, thereby losing the model fitting effect. Generally, an integer greater than or equal to 100 is taken. The maximum value of n generally controls the window time length within 1 hour. It is not meaningful to exceed the length. In this embodiment, n=500 is taken, corresponding to a window time length of 10s; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn and the temperature inside the table T B1 ~T Bn The sampling value is generally the n sets of sampling values ​​starting from the current moment (n-1) Δt moment backwards; S2, the correlation model between the internal temperature and the terminal temperature is fitted, that is, T Di and T Bi Perform model fitting to obtain model parameters related to the internal temperature and terminal temperature; mainly by fitting the data within the window time, find out the correlation characteristics between the two, characterize them with model parameters, and then extrapolate the temperature rise relationship; S3. Fitting the temperature change model in the meter to obtain model parameters. Because the temperature change in the meter is relatively stable, the model fitting accuracy is high and it is relatively easy to find suitable model parameters. The terminal temperature is not directly modeled, mainly because the terminal temperature is prone to sudden changes, and the accuracy of direct modeling and extrapolation of such sudden changes is not high. Another embodiment performs indirect modeling after considering the current factor, which is relatively more accurate and more directly applicable.

[0021] There is no order of precedence for steps S2 and S3; S4. Calculate the upper temperature limit T in the table B_MAX , according to the terminal temperature rise upper limit T D_MAX The upper temperature limit T in the table is calculated by the model fitting result in step S2 B_MAX After obtaining the model parameters related to the temperature in the table and the terminal temperature, T D_MAX Bring it into the model to calculate T B_MAX ; S5. Calculate the upper limit of the temperature T in the table within a limited time B_MAX Arrival time t B_MAX , according to the upper temperature limit T in the table B_MAX The upper limit T of the temperature in the table is calculated by fitting the temperature change model in the table in step S3.B_MAX Arrival time t B_MAX , thus obtaining the terminal temperature upper limit reaching time t D_MAX =t B_MAX ; If B_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

[0022] When a new sampling value is received, the window keeps the same size and slides. In this way, each time a new sampling value is generated, a new set of model fitting results can be obtained, and a new terminal temperature rise prediction result can be generated. The program is executed in a loop, so that the overtemperature of the electric energy meter terminal can be continuously monitored.

[0023] Specific methods for calculating the time when the upper temperature limit is reached include analytical methods or numerical methods. This embodiment adopts a more convenient numerical method because the prediction must be within a limited time, or within the action time of the model parameters, such as within 1 hour, half an hour, or 10 minutes, etc., leaving this time to meet the conditions for manual intervention. If this time is exceeded, even if overtemperature occurs, you can wait for the subsequent forecast cycle to complete the forecast. In addition, because it is a fitting model after all, if the forecast time is too long, the model parameters will change, and the result deviation will be large.

[0024] When the numerical method is used for calculation, the step length is generally equal to the sampling time interval Δt. Δt / 2 or Δt / 3 can also be used, depending on the computing power or the actual change curve. In this embodiment, the MCU has a strong computing power and adopts Δt / 3 to obtain a more accurate prediction result. Generally, when the numerical dependent variable T B Enter T B_MAX When the terminal temperature is within the range of ±5%, it is considered that the terminal temperature can reach its upper limit of temperature rise, and the corresponding time is the arrival time t B_MAX ; Note that the numerical calculation input time is gradually increased in steps, and the first time to reach T B_MAX The time allowed is t B_MAX .

[0025] Preferably, the model for correlation between the internal temperature and the terminal temperature includes a square root function model, that is, in, , is the model parameter.

[0026] The specific fitting method can adopt mature technology, such as obtaining the above-mentioned model parameters by the least squares method; the above is a quadratic fitting function, and a cubic function can also be used; for the traditional polynomial fitting method, although it can be used, from the actual temperature curve, the polynomial fitting is more suitable for interpolation, and the effect of extrapolation required for the present invention is not ideal. The longer the extrapolation time, the greater the numerical difference.

[0027] Generally, the temperature inside the electric energy meter is mainly related to the heating of electronic components, especially power components such as transformers, voltage stabilizers, chips, etc. Secondly, it is also related to whether the input and output connections, current sampling components, etc. are in firm contact with the terminals. When a large current flows through the sampler, if the contact with the terminal is poor, it will generate heat and burn the copper strips. Because the model affecting the temperature of the internal device is too complex and there is a lack of measured data, objectively speaking, it is impossible to accurately obtain the exact relationship between the terminal temperature and the temperature inside the meter under current conditions.

[0028] Considering that the influence of the terminal temperature on the temperature inside the meter has a relatively obvious monotonically increasing characteristic and a hysteresis characteristic, as well as the heat dissipation characteristics of the electric energy meter and the terminal itself, the present invention gives priority to using a square root function model that is fast first and then slow, or a hybrid model combining a square root function model with a linear model (see Example 2), and performs model fitting within the nearest finite time window (i.e., sampling window), so that the relationship between the temperature inside the meter and the terminal temperature can be more accurately characterized by the model. Experiments show that the model parameters of different window time fittings sometimes vary greatly, mainly because the various factors affecting the temperature inside the meter are changing with the aging of the device and the changes in the working environment.

[0029] Preferably, the temperature change model in the table includes: in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation, t is the time, and the first sampling time t=0 is generally recorded. Each fitting may have different model parameters, so it is necessary to use the above sliding window for fitting to ensure real-time and accuracy; The acquisition of the model parameters is relatively complex. It is more convenient to use numerical methods to obtain the optimal fitting model parameters, but the amount of calculation is large. It is not difficult for the current computing power. It is only when the computing power of the embedded system is limited that other alternatives need to be considered, such as handing over complex calculations to the background and only performing simple calculations on the power meter. When using the numerical method, each model parameter must have a priori value range, which has a great impact on the calculation efficiency. The range can be larger for the first few times, and the range can be gradually narrowed as experience increases.

[0030] Another method for obtaining model parameters is to linearize the above model, such as Taylor expansion, which is similar to a polynomial model. The least squares fitting method can be used to obtain the linearized model parameters and directly used in subsequent steps; the parameters of the temperature change model in the original table can also be inversely calculated for subsequent steps.

[0031] The third method to obtain model parameters is to perform FFT transformation on the time series of the measured temperature in the meter to directly obtain the fluctuation angular frequency. and the initial phase of the fluctuation ; Remaining model parameters , , Then the conventional least square method can be used to fit it. This is also a more practical method at present, and of course the amount of calculation is lower than that of the numerical method.

[0032] Since the temperature inside the table mostly fluctuates over time, when an abnormality occurs, the fluctuation will increase or decrease. Therefore, the combination of the fluctuation model and the linear model is used for fitting in a short period of time to meet its changing characteristics; the same temperature change model inside the table can also use other models such as polynomial models and simple fluctuation models. The use of sine, cosine or trigonometric functions in the fluctuation model is optional.

[0033] Preferably, the number of sampling groups n is greater than or equal to the number of all model parameters, that is, it is greater than the number of model parameters related to the temperature inside the meter and the terminal temperature, and also greater than the number of model parameters of the temperature change inside the meter. This is a requirement of analytical calculation and least squares method. Although there is no such requirement for numerical calculation, from the perspective of result credibility, the meaning and requirement of the number of sampling groups n in subsequent embodiments are the same.

[0034] The number of samples taken, the limited time includes within 1 hour, half an hour or 10 minutes.

[0035] Embodiment 2: The difference from Example 1 is that the correlation model between the internal temperature and the terminal temperature in this embodiment includes a mixed model of linear and square root functions, that is, in, , , is the model parameter.

[0036] Embodiment 3: Another method for quickly judging whether the terminal of an electric energy meter is overheated is to set an internal temperature sensor and a terminal temperature sensor on the electric energy meter, and measure the terminal current. Since generally an intelligent electric energy meter is provided with an internal temperature sensor and a current detection device is installed on the terminal, the present embodiment only needs to add a corresponding terminal temperature sensor on this basis; the internal computing unit of the electric energy meter, such as an MCU, is used to calculate and analyze the temperature rise trend of the terminal, so as to analyze and judge whether the terminal will be overheated and when it will occur. The method comprises the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , terminal current is I D , n is T D , T B and I D The number of sampling groups (the number of sample groups within the window time t1), the sampling values ​​are recorded as T D1 ~T Dn , T B1 ~T Bn and I D1 ~I Dn , use T Di , T Bi and I Di Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is t D_MAX , sampling time interval Δt, that is, T D1 To T D2 or T B1 To T B2 or I D1 to I D2 The time interval is similar to that of the above, and the three are consistent and sampled synchronously; in this embodiment, the upper limit of the terminal temperature rise T is set D_MAX =180℃, sampling time interval 100ms, take n=100, the corresponding window length is still 10s; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn , Temperature inside the table T B1 ~T Bn and terminal current I D1 ~I Dn The sampling value is generally the n sets of sampling values ​​starting from the current moment (n-1) Δt moment backwards; S2, terminal current change model fitting, obtain model parameters; according to the sampling value I D1 ~I Dn Establish terminal current I D Variation model of relative sampling time; S3, fitting the temperature change model in the table to obtain model parameters; There is no order of precedence for steps S2 and S3; S4. Fitting of terminal temperature comprehensive correlation model, establishing a comprehensive correlation model between terminal temperature, internal temperature and terminal current, and T Di , T Bi and I Di Performing model fitting to obtain parameters of the comprehensive correlation model; S5. Calculate the upper limit of the terminal temperature rise T within a limited time D_MAX Arrival time t D_MAX , based on the model parameters and the pre-set terminal temperature rise limit T D_MAX , substitute the terminal current change model and the temperature change model in the meter into the terminal current change model to calculate whether the terminal temperature can reach its temperature rise upper limit T D_MAX , and the arrival time t D_MAX ; Generally, numerical calculation can be used. Since it is not an accurate analytical solution, it is usually used when the numerical dependent variable T D Enter T D_MAX When the terminal temperature is within the range of ±5%, it is considered that the terminal temperature can reach its upper limit of temperature rise, and the corresponding time is the arrival time t D_MAX ; Note that the numerical calculation input time is gradually increased in steps, and the first time to reach T D_MAX The time allowed is t D_MAX ; If D_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

[0037] When a new sampling value is received, the window keeps the same size and slides. In this way, each time a new sampling value is generated, a new set of model fitting results can be obtained, and a new terminal temperature rise prediction result can be generated. The program is executed in a loop, so that the overtemperature of the electric energy meter terminal can be continuously monitored.

[0038] This embodiment mainly considers that when the temperature of the terminal itself changes rapidly (but no obvious abnormality is found in the current and voltage), and the temperature inside the meter is difficult to accurately reflect the change of the terminal temperature in a timely manner due to the hysteresis effect (rapid change within a period of time), it is necessary to introduce the influencing factors of current and voltage to predict the terminal temperature.

[0039] The rapid temperature rise of the terminal caused by the rapid change of current and voltage is generally completed by the current and voltage detection unit. The current electric energy meter has a relatively complete current and voltage abnormality detection function. Considering that the temperature rise has an obvious hysteresis effect, it is not appropriate to use the temperature rise as the basis for emergency control or disposal judgment in such cases, but directly use the current and voltage abnormality values ​​for judgment; only when the terminal temperature rises relatively slowly under a large current and / or a large contact resistance, it is more reasonable to use the method of the present invention, which can effectively avoid the phenomenon of electric energy meter damage caused by excessive terminal temperature.

[0040] Preferably, the terminal current variation model includes: in, , , is the model parameter, t is the time; here we directly use the quadratic polynomial, in fact, we can also use the linear polynomial, or the current model, etc.; The temperature variation model in the table includes: in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation, and the first sampling time t=0 is generally recorded. Each fitting may have different model parameters, so it is necessary to use the above sliding window for fitting to ensure real-time performance and accuracy. The specific model parameter calculation method is the same as that in Example 1. This embodiment mainly adopts the third method, that is, FFT transformation is performed on the time series of the measured temperature in the meter to directly obtain the fluctuation angular frequency and the initial phase of the fluctuation ; Remaining model parameters , , It can be obtained by conventional least squares fitting.

[0041] Preferably, the terminal temperature comprehensive correlation model includes: in, , , , , is the model parameter. The least square method can be used to solve the model parameters. Here, the model focuses on the direct effects of the internal temperature and the terminal current on the terminal temperature rise. The prediction will be more accurate and belongs to an active prediction type. In principle, the accurate prediction time will be longer. Although both Embodiment 1 and Embodiment 2 belong to the active prediction type, since only the influence of the internal temperature of the meter is considered, the real-time performance and accuracy will be lacking in essence. When the model of this embodiment ignores the current I Di The influence of the terminal temperature measurement value is purely used to establish a time series prediction model, which is a pure data law model and a passive model. The prediction time of this type of model will not be too long and the accuracy will not be too high. Relatively speaking, the prediction time of this embodiment is longer and the accuracy is higher.

[0042] Preferably, the limited time includes within 1 hour, half an hour or 10 minutes, and the number of sampling groups n is greater than or equal to the number of all model parameters.

[0043] When the measured terminal temperature value directly exceeds its temperature rise upper limit, no further prediction is required and direct processing should be carried out.

[0044] The above is only for the temperature sampling and judgment of one terminal. When there are multiple terminals, each terminal can be predicted and analyzed independently, and the temperature sensor is also set independently. If the adjacent terminals share a temperature sensor, the analysis conclusion applies to the group of terminals or one of them. That is, when an alarm signal appears, the group of terminals should be inspected, confirmed and processed on site.

[0045] The present invention is mainly applicable to situations where the electronic components of the energy meter are aged but not seriously, the terminal connection is loose or the contact resistance is increased but it does not affect the operation of electronic and electrical equipment, and the continuous temperature rise of the energy meter terminal, if not handled in time, will also lead to power accidents. For rapid temperature rise caused by short circuit and other reasons, it is more appropriate to use conventional current and voltage detection alarm to deal with it.

[0046] The above is only a preferred embodiment of the present invention. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. For example, other similar model expressions can be used to replace the expressions in the embodiments of the present invention.

Claims

1. A method for quickly judging whether an electric energy meter terminal is over-temperature, characterized in that: An internal temperature sensor and a terminal temperature sensor are arranged on the electric energy meter, and the internal calculation unit of the electric energy meter is used to calculate and analyze the temperature rise trend of the terminal temperature, so as to analyze and judge whether the terminal will overheat and when it will occur. The method comprises the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , n is the terminal temperature T D and the temperature inside the table T B The number of sampling groups, the sampling values ​​are recorded as T D1 ~T Dn and T B1 ~T Bn , use T Di and T Bi Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is t D_MAX ; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn and the temperature inside the table T B1 ~T Bn Sample value; S2, fitting the correlation model between the internal temperature and the terminal temperature to obtain the model parameters; S3, fitting the temperature change model in the table to obtain model parameters; There is no order of precedence for steps S2 and S3; S4. Calculate the upper temperature limit T in the table B_MAX , according to the terminal temperature rise upper limit T D_MAX The upper temperature limit T in the table is calculated by the model fitting result in step S2 B_MAX ; S5. Calculate the upper limit of the temperature T in the table within a limited time B_MAX Arrival time t B_MAX , according to the upper temperature limit T in the table B_MAX The upper limit T of the temperature in the table is calculated by fitting the temperature change model in the table in step S3. B_MAX Arrival time t B_MAX , thus obtaining the terminal temperature upper limit reaching time t D_MAX =t B_MAX ; If B_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

2. A method for quickly judging over-temperature of electric energy meter terminals according to claim 1, characterized in that: The correlation model between the internal temperature and the terminal temperature includes a square root function model, that is, ,in, , is the model parameter.

3. A method for quickly judging over-temperature of electric energy meter terminals according to claim 1, characterized in that: The correlation model between the internal temperature and the terminal temperature includes a linear and square root function mixed model, that is, ,in, , , is the model parameter.

4. A method for quickly judging over-temperature of electric energy meter terminals according to claim 1, characterized in that: The temperature variation model in the table includes: ,in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation, and t is the time.

5. A method for quickly judging over-temperature of electric energy meter terminals according to claim 1, characterized in that: The number of sampling groups n is greater than or equal to the number of all model parameters, that is, it is greater than the number of model parameters related to the temperature inside the meter and the terminal temperature, and also greater than the number of model parameters of the temperature change inside the meter.

6. A method for quickly judging over-temperature of electric energy meter terminals according to claim 1, characterized in that: The limited time includes within 1 hour, half an hour or 10 minutes.

7. A method for quickly judging whether an electric energy meter terminal is over-temperature, characterized in that: An internal temperature sensor and a terminal temperature sensor are arranged on the electric energy meter, and the terminal current is measured. The internal calculation unit of the electric energy meter is used to calculate and analyze the temperature rise trend of the terminal, so as to analyze and judge whether the terminal will overheat and when it will occur. The method comprises the following steps: Record the real-time sampling terminal temperature T D , Temperature inside the table T B , terminal current is I D , n is T D , T B and I D The number of sampling groups, the sampling values ​​are recorded as T D1 ~T Dn , T B1 ~T Bn and I D1 ~I Dn , use T Di , T Bi and I Di Represents each sampling value, where i=1,…,n, the upper limit of the terminal temperature rise T D_MAX , the predicted time to reach the upper limit of temperature rise at the current sampling time is t D_MAX ; S1. Get the terminal temperature T of the most recent window time D1 ~T Dn , Temperature inside the table T B1 ~T Bn and terminal current I D1 ~I Dn The sampling value of S2, terminal current variation model fitting, obtaining model parameters; S3, fitting the temperature change model in the table to obtain model parameters; There is no order of precedence for steps S2 and S3; S4. Fitting of terminal temperature comprehensive correlation model, establishing a comprehensive correlation model between terminal temperature, internal temperature and terminal current, and T Di 、T Bi and I Di Performing model fitting to obtain parameters of the comprehensive correlation model; S5. Calculate the upper limit of the terminal temperature rise T within a limited time D_MAX Arrival time t D_MAX , based on the model parameters and the pre-set terminal temperature rise limit T D_MAX , substitute the terminal current change model and the internal temperature change model into the terminal temperature comprehensive correlation model to calculate whether the terminal temperature can reach its temperature rise upper limit T D_MAX , and the arrival time t D_MAX ; If D_MAX If there is no solution, it means that the terminals of the energy meter will not reach their upper temperature rise limit in a short period of time.

8. A method for quickly judging over-temperature of electric energy meter terminals according to claim 7, characterized in that: The terminal current variation model includes: ,in, , , is the model parameter, t is the time; The temperature variation model in the table includes: ,in, , , , , are model parameters, is the angular frequency of fluctuation, is the initial phase of the fluctuation.

9. A method for quickly judging over-temperature of electric energy meter terminals according to claim 7, characterized in that: The terminal temperature comprehensive correlation model includes: ,in, , , , , is the model parameter.

10. A method for quickly judging over-temperature of electric energy meter terminals according to claim 7, characterized in that: The limited time includes within 1 hour, half an hour or 10 minutes.

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