Electric meter box wire clamp fault monitoring method and system

Abstract

The invention provides an electric meter box wire clamp fault monitoring method and system, and relates to the technical field of power equipment monitoring, and the main points of the technical scheme are that the method comprises the steps: obtaining a temperature measurement value of a single position in an electric meter box, A-phase, B-phase and C-phase current measurement values of an incoming line of the electric meter box, and a neutral line current measurement value; calculating an expected in-box temperature value; calculating a temperature deviation value between the temperature measurement value and an expected temperature value in the box; heat generation index values related to the respective currents; calculating and tracking a dynamic correlation index between the time sequence of the temperature deviation value and the time sequence of the respective heating index value; and according to the change trend of the dynamic correlation degree index, early degradation of the wire clamp of the specific line is determined. The electric meter box wire clamp fault monitoring method and system provided by the invention have the advantages of low cost, capability of effectively identifying and distinguishing which phase or neutral wire wire clamp has an early deterioration sign, strong anti-interference capability and sensitivity to an early fault signal.

Description

Technical Field

[0001] This application relates to the technical field of power equipment monitoring, and more particularly, to a method and system for monitoring faults of meter box wire clamps. Background Art

[0002] In the low-voltage power supply and distribution systems of urban residential areas or commercial buildings, the meter box, as a key interface connecting the power supply network and end-users, directly affects the power supply reliability. In these application scenarios, the electrical equipment on the user side is mostly single-phase, and its power consumption behavior shows significant time-varying and random characteristics. This characteristic results in the long-term unbalanced three-phase load state of the low-voltage distribution network connected to the meter box, that is, there are obvious differences in the effective values of the currents flowing through the A-phase, B-phase, and C-phase conductors at different times, and the current of a certain phase may far exceed that of the other two phases. According to the circuit principle, unbalanced three-phase currents will generate currents on the neutral line (N line). Especially in the presence of a large number of nonlinear loads, the third-harmonic currents generated will be superimposed on the N line, which may cause the N line current to approach or even exceed the phase line current.

[0003] Inside the meter box, the power inlet cable is connected through the corresponding main wire clamps of A-phase, B-phase, C-phase, and N-phase, and then the current is distributed to single-phase or three-phase watt-hour meters through each user's front-line wire clamps. This means that all the wire clamps (main wire clamps and all the user's front-line wire clamps of this phase) of a specific phase (such as A-phase) jointly bear the total current and distributed current of this phase. Due to the long-term unbalanced three-phase load, there are continuous differences in the electrical load levels borne by each phase wire clamp. For example, for the long-term heavily loaded A-phase, its corresponding wire clamp group will experience higher average current and more severe current fluctuations, and bear greater electrothermal stress cycles. In contrast, the wire clamps of the lightly loaded C-phase bear less electrothermal stress. This phase-based differential operating condition directly leads to different deterioration speeds and modes of each phase wire clamp.

[0004] The reliability of the wire clamp connection depends on the physical and chemical state of the contact surface and the clamping force. For the wire clamps of the long-term heavily loaded phase (such as A-phase), the stronger thermal expansion and contraction effect will accelerate the evolution of the microscopic structure of the contact surface, the generation / destruction of the oxide layer, and the decrease of the pre-tightening force of the fasteners (such as bolts) due to material creep. Therefore, early deterioration signs such as a slow increase in contact resistance or unstable contact may appear earlier than those of the wire clamps of the lightly loaded phase (such as C-phase). At the same time, the N-line wire clamps not only carry the fundamental current generated by the imbalance but may also be superimposed with the zero-sequence harmonic currents generated by nonlinear loads of each phase. These high-frequency components may cause additional mechanical vibrations and non-uniform heating, resulting in unique deterioration effects.

[0005] However, this differential early deterioration between the line clamps of different phase lines (and the N line) caused by unbalanced three-phase loads is usually very weak in the initial stage of fault development, and its external physical manifestations (such as abnormal temperature rise) are extremely easy to be interfered with or masked by the natural fluctuations of the internal environment temperature of the meter box and the heat diffusion effect generated by other heating components in the box (such as energy meters, circuit breakers). Traditional monitoring methods, such as regular infrared temperature measurement, may miss early faults due to time window limitations or weak signals; and deploying high-precision dedicated sensors (such as temperature and vibration sensors) for each line clamp (a large number) faces significant obstacles in terms of economic cost, installation space, and data processing complexity, and is not feasible for large-scale promotion.

[0006] In view of the above problems, the existing technologies urgently need to be improved. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for monitoring line clamp faults in a meter box, which has the advantages of low cost, being able to effectively identify and distinguish which phase or neutral line clamp has shown early deterioration signs, strong anti-interference ability, and being sensitive to early fault signals.

[0008] In the first aspect, this application provides a method for monitoring line clamp faults in a meter box, and the technical solution is as follows: Including: Obtain the temperature measurement value at a single position inside the meter box; Obtain the current measurement values of phase A, phase B, phase C of the incoming line of the meter box and the current measurement value of the neutral line; Based on the current measurement values of phase A, phase B, phase C, the current measurement value of the neutral line, and a reference thermal model representing the health state of the line clamp, calculate an expected temperature value inside the box; Calculate the temperature deviation value between the temperature measurement value and the expected temperature value inside the box; Based on the current measurement values of phase A, phase B, phase C and the current measurement value of the neutral line, calculate the heat generation index values related to the current of each of the phase A line, phase B line, phase C line and N line respectively; Calculate and track the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of each of the phase A line, phase B line, phase C line and N line; According to the change trend of the dynamic correlation index, when the dynamic correlation index corresponding to a specific line shows an increase and reaches the preset criterion condition, it is determined that the line clamp of the specific line has early deterioration; When the meter box operates in a low - load cycle, the parameters in the reference thermal model are calibrated by using the temperature measurement values and the phase - A, phase - B, phase - C current measurement values, and the neutral - line current measurement values obtained during this cycle.

[0009] Further, in the present application, the step of calculating and tracking the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heat generation index values of the phase - A line, phase - B line, phase - C line, and N - line respectively includes: Calculating the time series of the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heat generation index values of each line; Setting a discrimination threshold for the correlation degree index; Setting a discrimination threshold for the duration; For the time series of the dynamic correlation degree index of a specific line, judging whether its value continuously exceeds the discrimination threshold of the correlation degree index within the time length defined by the discrimination threshold of the duration; When the time series of the dynamic correlation degree index of the specific line meets this continuous - exceeding condition, the enhanced trend presented by this dynamic correlation degree index is recognized as a real enhancement caused by continuous deterioration.

[0010] Further, in the present application, the step of calibrating the parameters in the reference thermal model by using the temperature measurement values and the phase - A, phase - B, phase - C current measurement values, and the neutral - line current measurement values obtained during the cycle when the meter box operates in a low - load cycle includes: Setting a first threshold for the current amplitude to define low - load; Setting a second threshold for the current change index to define load stability and the time - window length based on which the current change index is calculated; Obtaining the phase - A, phase - B, phase - C current measurement values and the neutral - line current measurement value of the incoming line of the meter box; Judging whether the current measurement value is lower than the first threshold; Calculating the current change index within the time window based on the current measurement value and the time - window length; Judging whether the current change index is lower than the second threshold; When the current measurement value is lower than the first threshold and the current change index is lower than the second threshold, the current operation cycle is determined as the low - load cycle; During the determined low - load cycle, the parameters in the reference thermal model are calibrated by using the temperature measurement values and the phase - A, phase - B, phase - C current measurement values, and the neutral - line current measurement values obtained during this cycle.

[0011] Further, in the present application, the step of calculating the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heat generation index values of the A-phase line, B-phase line, C-phase line, and N-line respectively includes: Obtain the quantization value of the historical fluctuation characteristics of the line load; Obtain the information of the identified deterioration stage where the line is currently located; According to the quantization value of the historical fluctuation characteristics and the information of the identified deterioration stage, determine the time window length for calculating the dynamic correlation degree index; Using the determined time window length, calculate the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heat generation index values of the A-phase line, B-phase line, C-phase line, and N-line respectively.

[0012] Further, in the present application, the step of determining the time window length for calculating the dynamic correlation degree index according to the quantization value of the historical fluctuation characteristics and the information of the identified deterioration stage includes: Define the discrete levels of the quantization value of the historical fluctuation characteristics and the discrete levels of the information of the identified deterioration stage; Construct a two-dimensional lookup table, use the discrete levels of the quantization value of the historical fluctuation characteristics and the discrete levels of the information of the identified deterioration stage as indexes, and preset a reference time window length value in each cell of the two-dimensional lookup table; According to the discrete level corresponding to the quantization value of the historical fluctuation characteristics and the discrete level corresponding to the information of the identified deterioration stage, obtain the reference time window length value from the two-dimensional lookup table; Calculate the change rate of the dynamic correlation degree index within a preset time period; According to the change rate of the dynamic correlation degree index, determine an adjustment factor; Perform an operation on the obtained reference time window length value and the adjustment factor to obtain the time window length for calculating the dynamic correlation degree index.

[0013] Further, in the present application, the step of determining an adjustment factor according to the change rate of the dynamic correlation degree index includes: Preset multiple numerical intervals of the change rate of the dynamic correlation degree index in advance; Preset a corresponding adjustment factor value for each of the numerical intervals in advance; Compare the calculated change rate of the dynamic correlation degree index with the multiple numerical intervals to determine the numerical interval to which the change rate of the dynamic correlation degree index belongs; Select the preset adjustment factor value corresponding to the numerical range to which the change rate of the dynamic correlation degree index belongs as the determined adjustment factor.

[0014] Further, in the present application, the step of calculating the change rate of the dynamic correlation degree index within a preset time period includes: Obtain the dynamic correlation degree index value corresponding to the starting moment of the preset time period; Obtain the dynamic correlation degree index value corresponding to the ending moment of the preset time period; Calculate the numerical difference between the dynamic correlation degree index value at the ending moment and the dynamic correlation degree index value at the starting moment; Divide the numerical difference by the time length of the preset time period to obtain the change rate of the dynamic correlation degree index within the preset time period.

[0015] Further, in the present application, the step of calculating an expected box internal temperature value based on the measured values of the phase A, phase B, and phase C currents, the measured value of the neutral line current, and a reference thermal model representing the health state of the wire clamp includes: Adopt a physical model based on the principle of the thermal circuit network as the reference thermal model; According to the measured values of the phase A, phase B, and phase C currents, the measured value of the neutral line current, and the contact resistance values under the healthy state of each line preset, calculate the healthy heating power of each line, and set the healthy heating power as the heat input of the heat source node in the reference thermal model; Set thermal resistance nodes in the reference thermal model representing the internal heat transfer path of the meter box and the heat dissipation path between the meter box and the external environment; Set nodes in the reference thermal model representing the heat capacity of the meter box and its internal components; Solve the reference thermal model including the heat source node, the thermal resistance node, and the heat capacity node to obtain the expected box internal temperature value.

[0016] Further, in the present application, the step of calculating and tracking the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively includes: Set the length and sliding step of the sliding time window for calculating the dynamic correlation degree index; At each sliding time window position, obtain the time series segment of the temperature deviation value within the sliding time window; Obtain the time series segment of the heating index value of any one of the phase A line, phase B line, phase C line, or N line corresponding to the sliding time window position; Calculate the Pearson correlation coefficient between the time series segment of the obtained temperature deviation value and the time series segment of the obtained heating index value of the specific line; Assign the calculated Pearson correlation coefficient as the dynamic correlation index value of the specific line corresponding to the position of the sliding time window; As the sliding time window moves according to the sliding step, repeat the steps of obtaining the time series segment of the temperature deviation value, obtaining the time series segment of the heating index value of the specific line, calculating the Pearson correlation coefficient, and assigning it as the dynamic correlation index value to form the dynamic correlation index of each specific line.

[0017] In a second aspect, the present application also proposes an electric meter box clamp fault monitoring system, which includes: A temperature acquisition module for acquiring the temperature measurement value of a single position inside the electric meter box; A current acquisition module for acquiring the current measurement values of phase A, phase B, and phase C of the incoming line of the electric meter box and the current measurement value of the neutral line; An expected temperature calculation module for calculating an expected temperature value inside the box based on the current measurement values of phase A, phase B, and phase C, the current measurement value of the neutral line, and a reference thermal model representing the health state of the clamp; A temperature deviation calculation module for calculating the temperature deviation value between the temperature measurement value and the expected temperature value inside the box; A heating index calculation module for calculating the heating index values related to the current of the phase A line, phase B line, phase C line, and N line respectively based on the current measurement values of phase A, phase B, and phase C and the current measurement value of the neutral line; A correlation calculation and tracking module for calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively; A fault determination module for determining that the clamp of a specific line has early deterioration when the dynamic correlation index corresponding to a specific line shows an increase and reaches a preset criterion condition according to the change trend of the dynamic correlation index; A model calibration module for calibrating the parameters in the reference thermal model using the temperature measurement value and the current measurement values of phase A, phase B, and phase C and the current measurement value of the neutral line obtained during the low load period when the electric meter box is operating.

[0018] As can be seen from the above, a method and system for monitoring the faults of the wire clamps in an electric meter box provided by this application can effectively identify and locate the early deterioration faults of specific phase wires or neutral wire clamps in the electric meter box by using a single temperature sensor and existing current measurement data, combined with a reference thermal model and dynamic correlation analysis. It has the advantages of low cost, being able to effectively identify and distinguish which phase wire or neutral wire clamp has shown early deterioration signs, strong anti-interference ability, being sensitive to early fault signals, and the model having self-adaptability. Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of a method for monitoring the faults of the wire clamps in an electric meter box provided by this application.

[0020] Figure 2 It is a schematic structural diagram of a system for monitoring the faults of the wire clamps in an electric meter box provided by this application.

[0021] In the figure: 1. Temperature acquisition module; 2. Current acquisition module; 3. Expected temperature calculation module; 4. Temperature deviation calculation module; 5. Heat generation index calculation module; 6. Correlation calculation and tracking module; 7. Fault determination module; 8. Model calibration module. Detailed Embodiments

[0022] Next, the technical solutions in this application will be clearly and completely described in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The components of this application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application that is required to be protected, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0023] Referring to Figure 1 , this application proposes a method for monitoring the faults of the wire clamps in an electric meter box, including: S110. Obtain the temperature measurement value at a single position in the electric meter box; S120. Obtain the current measurement values of phase A, phase B, and phase C of the incoming line of the electric meter box and the current measurement value of the neutral wire; S130. Calculate an expected temperature value inside the box based on the current measurement values of phase A, phase B, and phase C, the current measurement value of the neutral wire, and a reference thermal model representing the health state of the wire clamp; S140. Calculate the temperature deviation value between the temperature measurement value and the expected temperature value inside the box; S150. Calculate the heat generation index values related to the current of the A-phase line, B-phase line, C-phase line, and N-line respectively based on the measured values of the A-phase, B-phase, and C-phase currents and the measured value of the neutral line current; S160. Calculate and track the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of the A-phase line, B-phase line, C-phase line, and N-line respectively; S170. According to the change trend of the dynamic correlation index, when the dynamic correlation index corresponding to a specific line shows an increase and reaches the preset criterion condition, it is determined that the clamp of the specific line has early deterioration; S180. When the meter box operates in a low load cycle, calibrate the parameters in the reference thermal model by using the temperature measurement values obtained during this cycle and the measured values of the A-phase, B-phase, and C-phase currents and the neutral line current.

[0024] Among them, obtaining the temperature measurement value at a single location inside the meter box means collecting the real-time temperature data of a certain point inside the meter box through a temperature sensor. This measurement value reflects the overall thermal state inside the meter box and includes the comprehensive influence of various factors such as normal heat generation, abnormal heat generation, and ambient temperature.

[0025] Among them, obtaining the measured values of the A-phase, B-phase, and C-phase currents and the neutral line current of the meter box's incoming line means collecting the real-time current data on each phase wire and the neutral line wire of the meter box's power incoming line through current sensors. These current data are the fundamental reasons for the heat generation of the main heat sources (such as clamps and wires) inside the meter box.

[0026] Among them, calculating an expected temperature value inside the box based on the measured values of the A-phase, B-phase, and C-phase currents, the neutral line current, and a reference thermal model representing the health state of the clamp means using the current data as input and predicting the temperature that should exist inside the meter box under the current load conditions through a pre-established mathematical model that simulates the thermal behavior of the meter box in a healthy state.

[0027] Among them, calculating the temperature deviation value between the temperature measurement value and the expected temperature value inside the box means calculating the difference between the actually measured temperature inside the box and the expected temperature inside the box predicted by the reference thermal model.

[0028] Among them, calculating the heat generation index values related to the current of the A-phase line, B-phase line, C-phase line, and N-line respectively based on the measured values of the A-phase, B-phase, and C-phase currents and the neutral line current means calculating the index that quantifies the contribution of the current of each line to the total heat generation inside the box according to the real-time current data of each phase and the neutral line.

[0029] Among them, calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and neutral line respectively refers to analyzing the statistical correlation between the sequence of temperature deviation representing abnormal temperature rise changing with time and the sequence of the load heating index of each line changing with time, and continuously monitoring the change of this correlation.

[0030] Among them, according to the change trend of the dynamic correlation index, when the dynamic correlation index corresponding to a specific line shows an increase and reaches the preset criterion condition, determining that the clamp of the specific line has early deterioration means that based on the observation of the change of the dynamic correlation index of each line with time, when the correlation index of a certain line continuously increases and reaches the set threshold, it is considered that the contact resistance of the clamp of this line may be increasing, resulting in an increase in abnormal heating, so it is determined that the clamp is in the early deterioration state.

[0031] Among them, when the meter box operates in a low-load cycle, calibrating the parameters in the reference thermal model by using the temperature measurement values and the phase A, phase B, phase C current measurement values, and neutral line current measurement values obtained during this cycle means that during the period when the load of the meter box is low and relatively stable, using the temperature and current data collected at this time to adjust the parameters in the reference thermal model to make its prediction results more accurately reflect the thermal behavior in the healthy state.

[0032] The core innovation of this application lies in that when only using a single temperature sensor, by constructing a reference thermal model to calculate the temperature deviation to highlight the abnormal temperature rise, and further analyzing the dynamic correlation between this temperature deviation and the heating indexes of each phase and the neutral line current, so as to realize the positioning and early warning of which specific line clamp in the meter box has early deterioration. At the same time, a model calibration mechanism under the low-load cycle is introduced to improve the accuracy of monitoring.

[0033] The working principle of this technical solution is based on the following logic: The clamp in the meter box generates heat due to carrying current, and its heating power is proportional to the square of the current and the contact resistance. Under the background of unbalanced three-phase loads, the currents borne by the clamps of each phase and the neutral line are different. In the healthy state, the total temperature rise in the box is mainly determined by the heat generated by the currents of each circuit passing through their nominal resistances and the ambient temperature. When the clamp of a certain phase (or neutral line) has early deterioration, its contact resistance increases, resulting in additional heat generation in this circuit under the same current. Although the total temperature rise caused by this part of the additional heat may be very small and easily submerged by interference, its generation mode is specific: its magnitude directly follows the change of the square of the current of this specific phase (or neutral line).

[0034] In this solution, a temperature sensor is arranged inside the box to measure the actual temperature. By using the currents of each phase and the N line collected in real time and combining with a reference thermal model that describes the relationship between heat generation and temperature in a healthy state, the expected temperature is calculated. The deviation (ΔT) between the actual temperature and the expected temperature reflects all factors that are not accurately described by the model, including model errors, environmental impacts, and potential abnormal heating caused by deterioration.

[0035] The core step is to continuously calculate the dynamic correlation coefficient between the time series of this temperature deviation ΔT and the time series of the square of the current (I_phase²) of each path (A, B, C, N). Under normal circumstances, since ΔT mainly contains random noise, model errors, or common mode effects related to all phases, its correlation with the square of the current of a specific path is usually low and unstable. However, once the clamp of a certain phase (such as phase A) deteriorates, the additional heat generated (strongly correlated with I_A²) will become a significant and specific-pattern component of ΔT, resulting in a significant and continuous increase in the correlation between ΔT and I_A² over time. By monitoring and comparing the change trends of the correlation coefficients (Corr(ΔT, I_A²), Corr(ΔT, I_B²), Corr(ΔT, I_C²), Corr(ΔT, I_N²)) corresponding to the currents of these four lines, when it is found that one of them (and usually only one) significantly increases and exceeds the preset threshold, it can be determined that the clamp on the corresponding line (phase line or N line) has early deterioration.

[0036] Through the above solution, this application solves the problem of low-cost and locatable monitoring and early warning in a specific working condition where the three-phase load in the meter box is unbalanced for a long time, resulting in different electrothermal stresses on the clamps of each phase and the neutral line, and thus causing asynchronous early deterioration. When facing multiple challenges such as severely limited monitoring resources, weak early fault signals, and being easily interfered and covered by the complex thermal environment inside the box, only using the existing current measurement data of each phase inside the meter box and adding only a single internal temperature sensor, it can effectively identify and distinguish which phase or the clamp of the neutral line has early deterioration signs. By calculating the temperature deviation, the influence of the environmental temperature and normal load heating on the measured temperature is filtered out, highlighting the abnormal temperature rise signal. By analyzing the dynamic correlation between the temperature deviation and the heating indexes of each line and tracking its change trend, it is possible to identify the abnormal heating synchronized with the load change of a specific line from the weak abnormal signals, thereby locating the specific line where early deterioration occurs. Using the low-load period for model calibration improves the accuracy of the reference thermal model, and further enhances the sensitivity and reliability of fault detection. This solution only needs to add one temperature sensor, with low cost and easy to promote and apply.

[0037] In some of the above solutions of this application, a dynamic correlation index between the time series of the temperature deviation value calculated and tracked between the temperature measurement value and the expected in-box temperature value and the time series of the heat generation index values respectively related to the currents of the phase A line, phase B line, phase C line, and neutral line is proposed to identify the early deterioration of the clamp. However, in the actual operating environment, both the internal temperature of the meter box and the line current may have instantaneous fluctuations, resulting in a possible short-term increase or fluctuation in the calculated dynamic correlation index. This instantaneous or short-term increase in the correlation index may not be caused by the continuous deterioration of the clamp, but by factors such as environmental interference or load transients. Simply judging based on the instantaneous value or short-term change trend of the dynamic correlation index is prone to false alarms or missed alarms, and it is impossible to reliably distinguish the true signal caused by continuous deterioration from the interference signal caused by noise or transient events.

[0038] In response to this, this application further proposes to calculate the time series of the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of each line; set the discrimination threshold of the correlation index; set the discrimination threshold of the duration; for the time series of the dynamic correlation index of a specific line, judge whether its value continuously exceeds the discrimination threshold of the correlation index within the time length defined by the discrimination threshold of the duration; when the time series of the dynamic correlation index of a specific line meets this continuous exceeding condition, recognize the enhancement trend presented by this dynamic correlation index as a true enhancement caused by continuous deterioration.

[0039] Among them, this solution refines the steps for calculating and tracking the dynamic correlation index, aiming to identify the signal caused by the continuous deterioration of the clamp from the time series of the dynamic correlation index and avoid misjudgment caused by instantaneous fluctuations or interference.

[0040] First, calculate the time series of the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of each line. This provides a time-varying correlation data basis for subsequent analysis. For example, the method of a sliding time window can be used to calculate the dynamic correlation index. Within a sliding time window with a preset length, obtain the time series segment of the temperature deviation value and the time series segment of the heat generation index value of a specific line within this window, calculate the correlation coefficient between these two segments, such as the Pearson correlation coefficient, and use this correlation coefficient as the value of the dynamic correlation index corresponding to the position of this time window. By moving the time window according to the preset sliding step length and repeating the calculation, the time series of the dynamic correlation index is formed.

[0041] Next, set the discrimination thresholds for the correlation index and the duration. The discrimination threshold for the correlation index is used to define the degree of correlation enhancement. For example, a value can be set such that when the dynamic correlation index exceeds this value, it is considered that the correlation may be related to deterioration. The discrimination threshold for the duration is used to define the length of time for which this enhancement needs to persist. For example, a time length can be set such that when the time for which the dynamic correlation index continuously exceeds the discrimination threshold for the correlation index exceeds this length, it is considered that this enhancement is persistent. These thresholds can be set based on historical data, expert experience, or through machine learning methods. For example, the discrimination threshold for the correlation index can be set to 0.7, and the discrimination threshold for the duration can be set to 24 hours.

[0042] Then, for the time series of the dynamic correlation index of a specific line, determine whether its value continuously exceeds the discrimination threshold for the correlation index within the time length defined by the discrimination threshold for the duration. This step introduces the concepts of continuous exceedance and duration. By requiring that the dynamic correlation index not only exceeds the discrimination threshold for the correlation index but also must continuously remain above the threshold for the time length defined by the discrimination threshold for the duration, transient and non-persistent increases in correlation are effectively filtered out. For example, a counter or a timestamp record can be maintained. When the dynamic correlation index exceeds the discrimination threshold for the correlation index, timing or counting is started. If the index falls below the threshold, the timing or counting is reset to zero. Only when the continuously exceeded time reaches or exceeds the discrimination threshold for the duration is the condition of continuous exceedance met. This persistence is a typical feature of abnormal heating caused by early deterioration of the clamp and affecting the temperature inside the box because the deterioration process is usually gradual and continuous.

[0043] Finally, when the time series of the dynamic correlation index of a specific line meets this condition of continuous exceedance, the enhancement trend presented by this dynamic correlation index is recognized as a real enhancement caused by continuous deterioration. This recognition step is based on the aforementioned continuity judgment and confirms the correlation enhancement signal that passes the duration test as being related to the real and continuous deterioration of the clamp. By combining the enhancement of the dynamic correlation index with the duration requirement, this solution can more accurately distinguish the real signal caused by continuous deterioration of the clamp from the instantaneous fluctuations caused by environmental interference or load transients, thereby improving the accuracy of fault monitoring. For example, if the dynamic correlation index of phase A line continuously exceeds 0.7 for 25 hours and the discrimination threshold for the duration is set to 24 hours, it is considered that there is a real enhancement in the phase A clamp caused by continuous deterioration. This recognition provides a more reliable basis for subsequent fault determination.

[0044] In some of the above solutions of the present application, a method for monitoring the faults of the meter box wire clamp based on the dynamic correlation degree between the temperature deviation and the current heating index is proposed. This method relies on a reference thermal model representing the health state of the wire clamp to calculate the expected temperature value inside the box. However, in actual operation, the ambient temperature where the meter box is located, the heat dissipation conditions of the box body, and the heat generation of other components inside the box may change over time, resulting in a deviation between the reference thermal model and the actual thermal environment. This model deviation will make the calculated expected temperature value inside the box inaccurate, thereby affecting the accuracy of the temperature deviation value between the temperature measurement value and the expected temperature value inside the box, and ultimately may interfere with the calculation and tracking of the dynamic correlation degree index between the temperature deviation and the heating indexes of each line, reducing the accuracy and reliability of fault monitoring, and may lead to false alarms or missed alarms. Therefore, a method that can calibrate the parameters of the reference thermal model timely and accurately is needed to ensure that the model can reflect the current actual thermal environment and improve the accuracy of monitoring.

[0045] In this regard, the present application further proposes that when the meter box operates in a low-load period, the steps of calibrating the parameters in the reference thermal model by using the temperature measurement values and the current measurement values of phase A, phase B, phase C, and the neutral line current obtained during this period include: Set a first threshold for the current amplitude used to define low load; Set a second threshold for the current change index used to define load stability and the time window length based on which the current change index is calculated; Obtain the current measurement values of phase A, phase B, phase C of the incoming line of the meter box and the neutral line current measurement value; Judge whether the current measurement value is lower than the first threshold; Based on the current measurement value and the time window length, calculate the current change index within the time window; Judge whether the current change index is lower than the second threshold; When the current measurement value is lower than the first threshold and the current change index is lower than the second threshold, determine the current operating period as a low-load period; During the determined low-load period, calibrate the parameters in the reference thermal model by using the temperature measurement values and the current measurement values of phase A, phase B, phase C, and the neutral line current obtained during this period.

[0046] Among them, by setting the first threshold for the current amplitude used to define low load, this method provides a basic criterion for identifying the suitable timing for calibration. The current amplitude can take the maximum value of the currents of each phase or the neutral line current, or take the average value of the currents of each phase and the neutral line current.

[0047] Furthermore, a second threshold for defining the current change index for load stability and the length of the time window based on which the current change index is calculated are set, further refining the selection criteria for the calibration timing. The current change index can be the difference between the maximum and minimum currents within the time window, the standard deviation of the current, the rate of change of the current, etc.

[0048] The length of the time window can be set according to the actual application scenario and the data sampling frequency, for example, from a few minutes to dozens of minutes. Obtain the current measurement values of phases A, B, and C of the incoming line of the meter box and the neutral line current measurement value. These measurement values are the basic data required for load judgment and subsequent model calibration. Based on the obtained current measurement values, determine whether they are lower than the set first threshold to preliminarily screen out the periods in the low-load state.

[0049] Meanwhile, based on the current measurement values and the set length of the time window, calculate the current change index within this time window and determine whether this index is lower than the set second threshold to evaluate the stability of the current load. When the current measurement value is lower than the first threshold and the current change index is lower than the second threshold, comprehensively determine that the current operation cycle is a low-load and stable cycle suitable for model calibration. Within the determined low-load cycle, use the temperature measurement values, the current measurement values of phases A, B, and C, and the neutral line current measurement values obtained within this cycle to calibrate the parameters in the reference thermal model.

[0050] The reference thermal model can adopt a physical model based on the principle of the thermal circuit network. This model includes heat source nodes, thermal resistance nodes, and heat capacity nodes. The calibration process can adopt parameter estimation methods, such as the least squares method or optimization algorithms, and use the actual temperature and current data collected during the low-load cycle to adjust parameters such as thermal resistance and heat capacity in the model to minimize the error between the expected temperature calculated by the model and the actual measured temperature.

[0051] By performing calibration under low-load and stable conditions, the influence of the self-heating of the wire clamp (even if there is early deterioration, its abnormal heat generation is relatively small) on the temperature inside the box can be reduced, making the influence of non-wire-clamp heating factors such as the ambient temperature and the heat dissipation of the box body relatively prominent and stable. At this time, using the actual measurement data to calibrate the model can more accurately adjust the model parameters to better reflect the current actual thermal environment and the influence of non-wire-clamp heating factors.

[0052] Thus, the expected temperature value inside the box calculated by the calibrated reference thermal model will be more accurate, which directly improves the accuracy of the temperature deviation value between the measured temperature value and the expected temperature value inside the box. In the overall fault monitoring method, the accuracy of the temperature deviation value is the basis for calculating the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of each line. The accurate time series of the temperature deviation value and the heating index value can make the calculated dynamic correlation index more truly reflect the change of the health state of the clamp. Therefore, by calibrating the reference thermal model timely and accurately, this solution improves the reliability of the calculation of the temperature deviation and the dynamic correlation, thereby enhancing the accuracy and robustness of the clamp fault monitoring and reducing the risk of false alarms or missed alarms.

[0053] In some of the above solutions of this application, a dynamic correlation index between the time series of the calculated and tracked temperature deviation value and the time series of the heating index values of each line is proposed to reflect the deterioration state of the clamp. However, when calculating the correlation between time series, a time window length usually needs to be set. The load characteristics of the meter box are time-varying and random, and there are also differences in the load fluctuation characteristics of different lines. At the same time, the deterioration of the clamp is a gradual process, and the correlation performance between its abnormal heating and current may be different at different deterioration stages. If a fixed time window length is used to calculate the dynamic correlation index, it may not be able to fully adapt to these changing working conditions and deterioration stages. For example, when the load fluctuates violently, too long a window may smooth out important instantaneous correlation changes; when the amplitude of the early deterioration signal is small, too short a window may lead to unstable correlation calculation due to noise interference. Therefore, how to adaptively determine the time window length for calculating the dynamic correlation index according to the actual operating state of the meter box, especially the historical fluctuation characteristics of the line load and the current deterioration stage of the clamp, to improve the compliance of the correlation calculation with the actual situation and the detection ability of the early deterioration signal is a technical problem to be solved.

[0054] In response to this, this application further proposes that the steps for calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the A-phase line, B-phase line, C-phase line, and N-line respectively include: Obtain the quantization value of the historical fluctuation characteristics of the line load; Obtain the information of the identified deterioration stage where the line is currently located; Determine the time window length for calculating the dynamic correlation index according to the quantization value of the historical fluctuation characteristics and the information of the identified deterioration stage; Use the determined time window length to calculate the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the A-phase line, B-phase line, C-phase line, and N-line respectively.

[0055] Among them, this solution aims to solve the problem of how to adaptively select an appropriate time window length according to the actual operating conditions when calculating the dynamic correlation degree between the temperature deviation and the line heating index.

[0056] First, obtain the quantified value of the historical fluctuation characteristics of the line load, which provides information about the degree of current change in the line over a period of time in the past. The load fluctuation characteristics affect the speed and amplitude of the temperature response, thereby affecting the correlation between the temperature deviation and the current heating. This quantified value of the historical fluctuation characteristics can be calculated based on historical current measurement data. For example, indicators such as the standard deviation, change rate of the current, or the maximum change amplitude within a specific time period can be calculated to quantify the degree of load fluctuation.

[0057] Second, obtain the information on the identified deterioration stage in which the line is currently located, which reflects the current state of the deterioration degree of the clamp. In different deterioration stages, the contact resistance of the clamp may be different, and the relationship between its abnormal heating and the current may also change. This information on the deterioration stage can be identified based on the analysis of the change trend of the historical dynamic correlation degree index. For example, different correlation degree thresholds or change rate thresholds can be set to divide different deterioration stages, such as the normal stage, the early deterioration stage, the mid-term deterioration stage, etc.

[0058] Then, based on the quantified value of the historical fluctuation characteristics of the line load and the information on the identified deterioration stage obtained, determine the time window length for calculating the dynamic correlation degree index, and use the historical operation data and the current deterioration state information to guide the selection of the correlation degree calculation window.

[0059] Furthermore, in order to adjust the window length more precisely, the change rate of the dynamic correlation degree index within a preset time period can be calculated, and an adjustment factor can be determined according to this change rate.

[0060] Finally, perform an operation (such as multiplication or addition) on the obtained reference time window length value and the adjustment factor to obtain the time window length for calculating the dynamic correlation degree index. In this way, the time window length is no longer fixed, but is adjusted according to the actual situation, making the subsequent correlation degree calculation more targeted and effective.

[0061] Using the determined time window length, calculate the dynamic correlation degree index between the time series of the temperature deviation value and the time series of each line heating index value. This calculation can adopt the method of a sliding time window, setting the length of the sliding time window (i.e., the window length determined in the previous step) and the sliding step size. By using the time window adaptively determined according to the current working condition and the deterioration stage, the calculated dynamic correlation degree index can more accurately reflect the true correlation degree between the temperature deviation and the current heating of a specific line.

[0062] In some of the above solutions of the present application, a method is proposed to determine the time window length for calculating the dynamic correlation degree index based on the historical fluctuation characteristic quantization value and the identified deterioration stage information, so as to improve the accuracy of correlation degree calculation. However, simply determining a fixed time window length based on these two pieces of information may not be able to fully adapt to the dynamic changes of the actual load fluctuation and the deterioration process, resulting in the determined time window length not being optimized enough under certain working conditions or deterioration stages, affecting the capture ability of the dynamic correlation degree index for early deterioration signals and the robustness to noise.

[0063] In response to this, the present application further proposes a method for determining the time window length for calculating the dynamic correlation degree index based on the historical fluctuation characteristic quantization value and the identified deterioration stage information. The method includes: Defining the discrete levels of the historical fluctuation characteristic quantization value and the discrete levels of the identified deterioration stage information; Constructing a two-dimensional lookup table, using the discrete levels of the historical fluctuation characteristic quantization value and the discrete levels of the identified deterioration stage information as indexes, and presetting a reference time window length value in each cell of the two-dimensional lookup table; Obtaining the reference time window length value from the two-dimensional lookup table according to the discrete level corresponding to the historical fluctuation characteristic quantization value and the discrete level corresponding to the identified deterioration stage information; Calculating the change rate of the dynamic correlation degree index within a preset time period; Determining an adjustment factor according to the change rate of the dynamic correlation degree index; Performing an operation on the obtained reference time window length value and the adjustment factor to obtain the time window length for calculating the dynamic correlation degree index.

[0064] Among them, the method first defines the discrete levels of the historical fluctuation characteristic quantization value and the identified deterioration stage information. The historical fluctuation characteristic quantization value can reflect the severity of the change of the line load current over time. For example, it can be quantified based on the standard deviation, coefficient of variation of the current, or the energy of specific frequency components, and divided into three discrete levels: low, medium, and high. The identified deterioration stage information can indicate the deterioration state of the current monitoring object. For example, it can be divided into discrete levels such as healthy, early deterioration, and medium deterioration based on historical monitoring data or preset rules.

[0065] Through discretization, continuous or complex input information is transformed into finite and manageable categories. Then, a two-dimensional lookup table is constructed. The row index of the table can use the discrete levels of the quantization values of historical fluctuation characteristics, and the column index can use the discrete levels of the identified deterioration stage information. A reference time window length value is preset in each cell of the table. For example, a longer time window (such as 24 hours) may be preset in the low-fluctuation and healthy stage to enhance the anti-noise ability; a shorter time window (such as 6 hours) may be preset in the high-fluctuation and early deterioration stage to improve the sensitivity to rapid changes. These reference values can be determined based on experience, simulation, or historical data analysis. Then, according to the current actual quantization value of historical fluctuation characteristics and the identified deterioration stage information, determine their respective discrete levels, and use these as indices to obtain the corresponding reference time window length value from the two-dimensional lookup table. This provides a preliminary time window setting based on background information.

[0066] On this basis, the method further calculates the change rate of the dynamic correlation index within a preset time period. This change rate can reflect the trend and speed of the correlation index changing over time. For example, the numerical change amount of the correlation index within the last hour can be divided by the time length. According to the calculated change rate of the dynamic correlation index, an adjustment factor is determined. This adjustment factor is used to correct the reference time window length. For example, when the correlation change rate is high, it indicates that there may be signs of rapidly developing deterioration. At this time, the adjustment factor can make the final time window length decrease to capture the signal faster; when the correlation change rate is low, it indicates that the state is relatively stable. At this time, the adjustment factor can make the final time window length increase to improve the stability of the calculation. The adjustment factor can be determined in various ways. For example, multiple numerical intervals of the change rate can be preset, and a corresponding adjustment factor value is set for each interval. The adjustment factor is obtained by looking up the determined interval; or, a function can be used to map the change rate to the adjustment factor.

[0067] Finally, the obtained base time window length value is operated with the adjustment factor determined according to the change rate of correlation to obtain the time window length finally used for calculating the dynamic correlation index. The operation method can be multiplication, addition or other combined methods. For example, the final window length can be equal to the base window length multiplied by the adjustment factor, or equal to the base window length plus a correction amount related to the adjustment factor. This way of combining the base value and dynamic adjustment enables the determination of the time window length to consider both historical and phased factors, and can be dynamically optimized according to the real-time change trend of the correlation itself. This method is combined with the method for calculating the dynamic correlation index between the time series of temperature deviation values and heating index values, and by using a time window that is more suitable for the current working condition and deterioration stage, the accuracy of correlation calculation is improved, thereby enhancing the ability of fault monitoring to capture early deterioration signals and the robustness to noise.

[0068] As a preferred embodiment, the solution of the present application is specifically implemented as follows: First, the historical fluctuation characteristic quantization value is discretized into multiple levels. For example, the standard deviation of the load current can be divided into three levels: low fluctuation, medium fluctuation, and high fluctuation. At the same time, the identified deterioration stage information is also discretized into multiple levels. For example, the deterioration stage can be divided into three levels: healthy, early warning, and warning.

[0069] Next, a two-dimensional lookup table is constructed. The row index of the table corresponds to the discrete levels of the historical fluctuation characteristic quantization value, and the column index corresponds to the discrete levels of the identified deterioration stage information. A base time window length value is preset in each cell of the table, and these values can be determined according to experience, simulation, or historical data analysis. For example, a relatively long time window (such as 24 hours) may be preset in the low fluctuation / healthy stage, and a relatively short time window (such as 6 hours) may be preset in the high fluctuation / warning stage. According to the current actual historical fluctuation characteristic quantization value and the identified deterioration stage information, determine their corresponding discrete levels, and use these as indexes to obtain the corresponding base time window length value from the two-dimensional lookup table.

[0070] On this basis, calculate the change rate of the dynamic correlation degree index within a preset time period. For example, the value of the dynamic correlation degree index at the current moment and the value of the dynamic correlation degree index at the start moment of the preset time period (such as the past 1 hour) can be obtained, the difference between the two is calculated, and then divided by the time length of the preset time period to obtain the change rate. According to the calculated change rate of the dynamic correlation degree index, an adjustment factor is determined. For example, multiple numerical intervals of the change rate (such as the change rate is less than 0, the change rate is between 0 and 0.01, the change rate is greater than 0.01) can be preset, and a corresponding adjustment factor value is preset for each interval (such as the adjustment factor is 1.2 when the change rate is less than 0, the adjustment factor is 1.0 when the change rate is between 0 and 0.01, and the adjustment factor is 0.8 when the change rate is greater than 0.01). Compare the calculated change rate with these intervals to determine the interval it belongs to, and select the corresponding preset adjustment factor value.

[0071] Finally, perform an operation on the obtained base time window length value and the determined adjustment factor, such as a multiplication operation, to obtain the time window length finally used for calculating the dynamic correlation degree index. Thus, the determination of the time window length comprehensively considers the historical load characteristics, the current deterioration state, and the dynamic change trend of the correlation degree index itself.

[0072] Through the above technical solution, the present application solves the problem that simply determining a fixed time window length based on the quantization value of historical fluctuation characteristics and the identified deterioration stage information cannot fully adapt to the actual load fluctuation and the dynamic changes in the deterioration process. By introducing the change rate of the dynamic correlation degree index itself as the adjustment basis, the determined time window length can be dynamically adjusted according to the real-time change trend of the correlation degree index. When the correlation degree index changes rapidly, the time window can be shortened to improve the capture sensitivity to early deterioration signals; when the correlation degree index changes gently, the time window can be lengthened to enhance the robustness to noise. This dynamic adaptability improves the accuracy of calculating the dynamic correlation degree index, and further enhances the accuracy and sensitivity of fault monitoring.

[0073] The steps for the present application to determine an adjustment factor according to the change rate of the dynamic correlation degree index include: presetting multiple numerical intervals of the change rate of the dynamic correlation degree index in advance; presetting a corresponding adjustment factor value for each numerical interval in advance; comparing the calculated change rate of the dynamic correlation degree index with the multiple numerical intervals to determine the numerical interval to which the change rate of the dynamic correlation degree index belongs; and selecting the preset adjustment factor value corresponding to the numerical interval to which the change rate of the dynamic correlation degree index belongs as the determined adjustment factor.

[0074] Among them, for the problem of how to determine the adjustment factor according to the change rate of the dynamic correlation degree index, a technical means based on numerical interval search is proposed. First, by presetting multiple numerical intervals of the change rate of the dynamic correlation degree index, the continuously changing change rate of the dynamic correlation degree index is discretized into different levels or states. For example, the change rate can be set to be less than a certain negative threshold (indicating a rapid decline), between the negative threshold and zero (indicating a slow decline), close to zero (indicating stability), between zero and a positive threshold (indicating a slow rise), greater than a certain positive threshold (indicating a rapid rise), and other intervals.

[0075] Next, a corresponding adjustment factor value is preset for each set numerical interval, which establishes a clear, piecewise mapping relationship, that is, different ranges of change rates correspond to different adjustment factors. For example, for the interval of rapid decline, an adjustment factor less than 1 can be set; for the interval close to zero, an adjustment factor equal to 1 can be set; for the interval of rapid rise, an adjustment factor greater than 1 can be set. Then, the actually calculated change rate of the dynamic correlation degree index is compared with these preset numerical intervals to determine which interval the current change rate falls into. Finally, the preset adjustment factor value corresponding to the numerical interval to which the change rate belongs is selected as the finally determined adjustment factor.

[0076] Furthermore, the determined adjustment factor is used to adjust a reference time window length, so as to obtain the time window length for calculating the dynamic correlation degree index. The reference time window length can be determined according to the historical fluctuation characteristics of the line load and the identified deterioration stage information. By operating (for example, multiplying) the reference time window length with the adjustment factor determined according to the change rate of the dynamic correlation degree index, the time window length can be dynamically adjusted. When the change rate of the dynamic correlation degree index is high, it indicates that the correlation relationship between the temperature deviation and the heating index is changing rapidly. At this time, it may be necessary to shorten the time window length to capture this change faster and improve the real-time performance of monitoring; when the change rate is low, it indicates that the correlation relationship is relatively stable, and the time window length can be appropriately extended to smooth the data, reduce noise interference, and improve the stability of the correlation degree calculation. This way of adjusting the calculation time window length according to the change speed of the correlation degree itself makes the tracking process of the dynamic correlation degree index more adaptable, can better balance the real-time performance and anti-interference ability, and thus more accurately reflects the deterioration state of the clamp.

[0077] Specifically, the process of determining the adjustment factor first requires pre - defining a series of non - overlapping numerical intervals that cover the range of possible change rates of the dynamic correlation degree index. For example, the intervals can be set as (-∞, -0.02], (-0.02, -0.005], (-0.005, 0.005), [0.005, 0.02), [0.02, +∞). Subsequently, a corresponding adjustment factor value is assigned to each interval, and these values can be determined based on experience or experiments. For example, they correspond to 0.8, 0.9, 1.0, 1.1, 1.2 respectively. During the operation of the system, the change rate of the current dynamic correlation degree index within a preset time period is calculated. The calculated change rate is compared with the preset numerical intervals to determine which interval it falls into. For example, if the calculated change rate is 0.015, it falls into the interval [0.005, 0.02). Thus, the preset adjustment factor value corresponding to this interval, that is, 1.1, is selected as the adjustment factor determined this time.

[0078] In this way, this solution provides a specific and operable method to determine the adjustment factor according to the change rate of the dynamic correlation degree index, making the adjustment process of the time window length of the dynamic correlation degree index more standardized and controllable. It can systematically select an appropriate adjustment factor according to the change speed of the dynamic correlation degree index, so as to more accurately determine the time window length used to calculate the dynamic correlation degree index, improving the adaptability and accuracy of fault monitoring.

[0079] This application further proposes that the steps for calculating the change rate of the dynamic correlation degree index within a preset time period include: Obtain the value of the dynamic correlation degree index corresponding to the start time of the preset time period; Obtain the value of the dynamic correlation degree index corresponding to the end time of the preset time period; Calculate the numerical difference between the value of the dynamic correlation degree index at the end time and the value of the dynamic correlation degree index at the start time; Divide the numerical difference by the time length of the preset time period to obtain the change rate of the dynamic correlation degree index within the preset time period.

[0080] Specifically, to calculate the change rate of the dynamic correlation degree index within a preset time period, first, a preset time period needs to be determined. For example, the preset time period can be set as the past 24 hours. Then, obtain the value of the dynamic correlation degree index corresponding to the start time of this preset time period, that is, 24 hours ago. This value is read from the historical record.

[0081] Next, obtain the value of the dynamic correlation degree index corresponding to the end time of this preset time period, that is, the current time. This value is the most recently calculated.

[0082] Subsequently, calculate the numerical difference between the dynamic correlation index value at the current moment and the dynamic correlation index value 24 hours ago. For example, if the current value is 0.8 and it was 0.6 24 hours ago, the numerical difference is 0.2.

[0083] Finally, divide this numerical difference by the time length of the preset time period, which is 24 hours. Thus, the average change rate of the dynamic correlation index within the past 24 hours is obtained. For example, 0.2 / 24 ≈ 0.0083 per hour. This calculation process provides a standardized method to quantify the change speed and direction of the dynamic correlation index.

[0084] By adopting this clear calculation step, errors introduced due to inconsistent or inaccurate calculation methods are avoided. The accurately calculated change rate is then used to determine an adjustment factor, which is used to adjust the time window length for calculating the dynamic correlation index. For example, a lookup table can be preset to map different change rate value ranges to different adjustment factors. A relatively high positive change rate may correspond to an adjustment factor that shortens the time window to capture the deterioration trend more quickly; a change rate close to zero may correspond to an adjustment factor that keeps or slightly extends the time window to improve calculation stability.

[0085] In this way, the change rate of the dynamic correlation index is accurately quantified and used as a reliable basis to dynamically adjust the time window, enabling the calculation of the dynamic correlation index to better adapt to the actual deterioration development speed, thereby improving the accuracy and responsiveness of monitoring.

[0086] In some of the above solutions of this application, an expected box temperature value is calculated based on the measured values of the phase A, phase B, and phase C currents of the incoming line of the meter box, the measured value of the neutral line current, and a reference thermal model representing the health state of the clamp, and the deviation between the expected box temperature value and the actual temperature measurement value is used to monitor the clamp fault. However, the internal structure of the meter box is complex, containing various heating components, and its heat exchange with the external environment is affected by various factors, resulting in a complex and changeable internal thermal environment. A simple reference thermal model may be difficult to accurately capture the internal temperature change law under different load conditions and environmental changes, especially difficult to accurately predict the temperature in the healthy state, thus making the calculated temperature deviation value inaccurate, affecting the reliability of the subsequent fault determination link, and possibly leading to false alarms or missed alarms of early clamp deterioration. Therefore, how to construct a reference thermal model that can more accurately reflect the thermal behavior of the meter box in the healthy state to improve the accuracy of the expected box temperature calculation is a technical problem to be solved.

[0087] In response to this, the present application further proposes a step of calculating an expected temperature value inside the box based on the measured values of the phase A, phase B, and phase C currents, the measured value of the neutral line current, and a reference thermal model representing the health state of the clamp. The steps include: using a physical model based on the principle of the thermal circuit network as the reference thermal model; calculating the healthy heat generation power of each line according to the measured values of the phase A, phase B, and phase C currents, the measured value of the neutral line current, and the contact resistance values under the healthy state of each preset line, and setting the healthy heat generation power as the heat input of the heat source node in the reference thermal model; setting thermal resistance nodes in the reference thermal model to represent the heat transfer path inside the meter box and the heat dissipation path between the meter box and the external environment; setting nodes in the reference thermal model to represent the heat capacity of the meter box and its internal components; solving the reference thermal model including the heat source node, the thermal resistance node, and the heat capacity node to obtain the expected temperature value inside the box.

[0088] Among them, a physical model based on the principle of the thermal circuit network is used as the reference thermal model. This is a model constructed based on physical laws and can simulate the generation, transfer, and storage processes of heat inside the meter box. For example, the model can include multiple nodes, each node representing the temperature of a certain area or component inside the meter box, and the nodes are connected by thermal resistances, representing the heat transfer paths. The magnitude of the thermal resistance depends on the thermal conductivity of the material, the geometric shape, and the heat transfer method (conduction, convection, radiation).

[0089] Furthermore, according to the actually measured values of the phase A, phase B, and phase C currents and the neutral line current, and the contact resistance values of each line under the healthy state preset in advance, calculate the healthy heat generation power of each line under the current load. The heat generation power can be calculated according to Joule's law. For example, for a certain phase line, its healthy heat generation power is equal to the square of the measured value of the current of this phase multiplied by the preset healthy contact resistance value. These calculated healthy heat generation powers are set as the heat input of the heat source node in the reference thermal model. For example, there can be one or more heat source nodes in the model, corresponding to the heat generation of each phase and the neutral line clamp under the healthy state.

[0090] Set thermal resistance nodes in the reference thermal model to represent the heat transfer path inside the meter box and the heat dissipation path between the meter box and the external environment. These thermal resistance nodes quantify the obstacles to the transfer of heat from the heat source to the air inside the box, the box body, and the dissipation of heat from the box body to the external environment. For example, thermal resistances can be set to represent the heat convection from the clamp to the air inside the box, the heat convection from the air inside the box to the box body, the heat conduction of the box body material, and the heat convection and heat radiation from the surface of the box body to the external environment. The thermal resistance values can be obtained through theoretical calculation or experimental calibration.

[0091] Meanwhile, nodes representing the heat capacities of the meter box and its internal components are set in the baseline thermal model. Heat capacity reflects an object's ability to store heat and affects the dynamic response of temperature over time. For example, heat capacity nodes can be set to represent the heat capacities of the main heat-generating or heat-storing components such as the air inside the box, the meter, the circuit breaker, and the wire clamp. The heat capacity value can be calculated through material density, specific heat capacity, and volume, or obtained through experimental calibration.

[0092] Finally, the baseline thermal model containing heat source nodes, thermal resistance nodes, and heat capacity nodes is solved to obtain the expected temperature value inside the box. The solution process can be a steady-state analysis (ignoring the influence of heat capacity and calculating the stable temperature) or a transient analysis (considering the influence of heat capacity and calculating the change of temperature over time). By solving this physical model, the predicted temperature value at a certain position inside the box (for example, the position where the temperature sensor is located) when the meter box is in a healthy state under the current load can be obtained.

[0093] Thus, by adopting a thermal circuit network model based on physical principles and combining the actual current input and health state parameters, the thermal behavior of the meter box can be more accurately simulated, and the temperature inside the box under healthy conditions can be predicted. When this more accurate expected temperature value is compared with the actually measured temperature value inside the box, the resulting temperature deviation value can more effectively reflect whether there is an actual abnormal temperature rise, thereby improving the accuracy of the subsequent fault determination process. For example, when the actual temperature measurement value is significantly higher than this accurately predicted healthy state temperature value, a larger deviation value is more likely to indicate abnormal heating rather than normal temperature changes caused by inaccurate model prediction or environmental fluctuations. This accurate calculation of the deviation value provides a reliable basis for subsequent localization of the faulty line by analyzing the correlation between the deviation value and the current heating indicators of each line.

[0094] In some of the above solutions of this application, a dynamic correlation index between the time series of the calculated and tracked temperature deviation value and the time series of the heating indicators of each line is proposed to determine the wire clamp fault according to its change trend. However, in practical applications, how to specifically and effectively calculate and track this dynamic correlation index so that it can accurately reflect the enhanced dynamic correlation between the weak abnormal heating caused by the early deterioration of the wire clamp and the current load, and overcome the influence of environmental temperature fluctuations, interference from other heat sources inside the box, and signal noise, is the key challenge for reliable fault monitoring.

[0095] In response to this, the present application further proposes steps for calculating and tracking the dynamic correlation index between the time series of temperature deviation values and the time series of heat generation index values of the A-phase line, B-phase line, C-phase line, and N-line respectively, including: setting the length and sliding step of the sliding time window for calculating the dynamic correlation index; at each sliding time window position, obtaining the time series segment of temperature deviation values within the sliding time window; obtaining the time series segment of the heat generation index value of any one of the A-phase line, B-phase line, C-phase line, or N-line corresponding to the sliding time window position; calculating the Pearson correlation coefficient between the obtained time series segment of temperature deviation values and the time series segment of the heat generation index value of the specific line; assigning the calculated Pearson correlation coefficient as the dynamic correlation index value of the specific line corresponding to the sliding time window position; as the sliding time window moves according to the sliding step, repeating the steps of obtaining the time series segment of temperature deviation values, obtaining the time series segment of the heat generation index value of the specific line, calculating the Pearson correlation coefficient, and assigning it as the dynamic correlation index value to form the dynamic correlation index of each specific line.

[0096] Among them, this solution provides a specific method for calculating and tracking the dynamic correlation index between the temperature deviation value and the heat generation index value of each line, aiming to solve the problem of how to effectively quantify and track this dynamic relationship to support early fault diagnosis. By adopting the method based on the sliding time window and Pearson correlation coefficient, this solution can capture the increasing correlation between the abnormal temperature rise caused by the early deterioration of the clamp and the current load, even if this abnormal temperature rise signal is weak and affected by environmental interference.

[0097] Specifically, first of all, setting the length and sliding step of the sliding time window for calculating the dynamic correlation index defines the time scale and update frequency for analyzing the dynamic correlation. The length of the sliding time window determines the amount of historical data considered each time the correlation is calculated, affecting the sensitivity to short-term fluctuations and the smoothness of long-term trends; the sliding step determines the update frequency of the correlation index, affecting the fineness of dynamic tracking. For example, the length of the sliding time window can be set to the data volume from several hours to several days, and the sliding step can be set to several minutes to several hours. By reasonably setting these two parameters, the calculated correlation index can better reflect the gradual process caused by the deterioration of the clamp.

[0098] Next, at each sliding time window position, the steps of obtaining the time series segment of the temperature deviation value within the sliding time window and obtaining the time series segment of the heating index value of any one of the phase A line, phase B line, phase C line or neutral line corresponding to the sliding time window position ensure that the temperature deviation data and the heating index data of a specific line are extracted within the same time window. This is the basis for performing correlation analysis and ensures the temporal correspondence of the analysis objects. The time series of the temperature deviation value is calculated from the temperature measurement value and the expected temperature value inside the box, and the time series of the heating index value is calculated from the current measurement values of each phase or the neutral line.

[0099] Then, calculating the Pearson correlation coefficient between the obtained time series segment of the temperature deviation value and the obtained time series segment of the heating index value of the specific line is the core step of quantifying the correlation. The Pearson correlation coefficient is a statistic used to measure the strength and direction of the linear association between two sets of data. Here, calculating the Pearson correlation coefficient between the temperature deviation (representing abnormal temperature rise) and the line heating index (representing normal heating and potential abnormal heating caused by current load) within a specific time window can effectively quantify the degree to which the temperature deviation inside the box changes when the current of the line changes. For a healthy clamp, the temperature deviation is mainly affected by the environment and other factors, and the correlation with the current of a single line is weak; while when the clamp of a certain line deteriorates, resulting in an increase in contact resistance, the additional heat generated will increase significantly with the increase in current, thus enhancing the correlation between the temperature deviation and the heating index related to the current of this line. The value range of the Pearson correlation coefficient is between -1 and 1. A value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates a weak correlation. The deterioration of the clamp usually manifests as an increase in contact resistance, resulting in the heating power increasing with the square of the current, which will make the positive correlation between the temperature deviation and the current-related heating index increase.

[0100] The step of assigning the calculated Pearson correlation coefficient as the dynamic correlation degree index value of the specific line corresponding to the sliding time window position clarifies the specific numerical source of the dynamic correlation degree index, that is, the Pearson correlation coefficient calculated in each time window.

[0101] Finally, as the sliding time window moves according to the sliding step, repeat the steps of obtaining the time series segment of the temperature deviation value, obtaining the time series segment of the heating index value of the specific line, calculating the Pearson correlation coefficient, and assigning it as the dynamic correlation degree index value to form the dynamic correlation degree index of each specific line, which describes the process of dynamic tracking. By continuously moving the sliding window and repeating the calculation, the system generates a sequence of correlation degree indexes that change with time for each line. Tracking the change trend of this sequence, especially its increasing trend, can effectively indicate whether the clamp of a specific line is undergoing early deterioration, thus realizing early and locatable monitoring of faults.

[0102] In a second aspect, referring to Figure 2 , the present application further proposes an electric meter box wire clamp fault monitoring system, which includes: A temperature acquisition module 1 for acquiring the temperature measurement value at a single position inside the electric meter box; A current acquisition module 2 for acquiring the current measurement values of phase A, phase B, phase C of the incoming line of the electric meter box and the current measurement value of the neutral line; An expected temperature calculation module 3 for calculating an expected temperature value inside the box based on the current measurement values of phase A, phase B, phase C, the current measurement value of the neutral line, and a reference thermal model representing the health state of the wire clamp; A temperature deviation calculation module 4 for calculating the temperature deviation value between the temperature measurement value and the expected temperature value inside the box; A heating index calculation module 5 for calculating the heating index values related to the current of the phase A line, phase B line, phase C line, and N line respectively based on the current measurement values of phase A, phase B, phase C, and the current measurement value of the neutral line; A correlation calculation and tracking module 6 for calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively; A fault determination module 7 for determining that the wire clamp of a specific line undergoes early deterioration when the dynamic correlation index corresponding to a specific line shows an increase and reaches the preset criterion condition according to the change trend of the dynamic correlation index; A model calibration module 8 for calibrating the parameters in the reference thermal model by using the temperature measurement values and the current measurement values of phase A, phase B, phase C, and the current measurement value of the neutral line obtained during the low load period when the electric meter box is operating.

[0103] By using a single temperature sensor and existing current measurement data, combining the reference thermal model and dynamic correlation analysis, it is possible to effectively identify and locate the early deterioration faults of the wire clamps of specific phase lines or neutral lines inside the electric meter box, which has the advantages of low cost, being able to effectively identify and distinguish which phase line or neutral line wire clamp has shown early deterioration signs, strong anti-interference ability, being sensitive to early fault signals, and the model having self-adaptability.

[0104] In addition, in some preferred embodiments, an electric meter box wire clamp fault monitoring system proposed by the present application can execute any one of the steps in the above method.

[0105] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for monitoring the failure of wire clamps in an electric meter box, characterized in that, Including: Obtaining the temperature measurement value at a single position inside the meter box; Obtaining the current measurement values of phase A, phase B, phase C of the incoming line of the meter box and the current measurement value of the neutral line; Based on the current measurement values of phase A, phase B, phase C, the current measurement value of the neutral line and a reference thermal model representing the health state of the wire clamp, calculating an expected temperature value inside the box; Calculating the temperature deviation value between the temperature measurement value and the expected temperature value inside the box; Based on the current measurement values of phase A, phase B, phase C and the current measurement value of the neutral line, respectively calculating the heat generation index values related to the current of phase A line, phase B line, phase C line and N line; Calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of phase A line, phase B line, phase C line and N line respectively; According to the change trend of the dynamic correlation index, when the dynamic correlation index corresponding to a specific line shows an increase and reaches the preset criterion condition, determining that the wire clamp of the specific line has early deterioration; When the meter box is operating in a low-load period, using the temperature measurement value and the current measurement values of phase A, phase B, phase C and the current measurement value of the neutral line obtained during this period to calibrate the parameters in the reference thermal model.

2. The fault monitoring method for the wire clamp of an electricity meter box according to claim 1, characterized in that, The step of calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of phase A line, phase B line, phase C line and N line respectively includes: Calculating the time series of the dynamic correlation index between the time series of the temperature deviation value and the time series of the heat generation index values of each line; Setting the discrimination threshold of the correlation index; Setting the discrimination threshold of the duration; For the time series of the dynamic correlation index of a specific line, judging whether its value continuously exceeds the discrimination threshold of the correlation index within the time length defined by the discrimination threshold of the duration; When the time series of the dynamic correlation index of the specific line meets the continuous exceeding condition, recognizing the increasing trend presented by the dynamic correlation index as a real increase caused by continuous deterioration.

3. A method for monitoring the failure of an electric meter box wire clamp according to claim 1, characterized in that, The step of, when the meter box is operating in a low-load period, using the temperature measurement value and the current measurement values of phase A, phase B, phase C and the current measurement value of the neutral line obtained during this period to calibrate the parameters in the reference thermal model includes: Setting the first threshold for defining the current amplitude of the low load; Setting the second threshold of the current change index for defining the load stability and the time window length for calculating the current change index; Obtaining the current measurement values of phase A, phase B, phase C of the incoming line of the meter box and the current measurement value of the neutral line; Judging whether the current measurement value is lower than the first threshold; Based on the current measurement value and the time window length, calculating the current change index within the time window; Judging whether the current change index is lower than the second threshold; When the measured current value is lower than the first threshold and the current change index is lower than the second threshold, determine the current operating cycle as the low-load cycle; During the determined low-load cycle, use the temperature measurement value, the measured current values of phase A, phase B, and phase C, and the measured neutral line current value obtained during this cycle to calibrate the parameters in the reference thermal model.

4. A method for monitoring faults of a wire clamp of an electric meter box according to claim 1, characterized in that, The step of calculating the dynamic correlation degree index between the time series of the calculated temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively includes: Obtain the quantization value of the historical fluctuation characteristics of the line load; Obtain the information of the identified deterioration stage where the line is currently located; According to the quantization value of the historical fluctuation characteristics and the information of the identified deterioration stage, determine the time window length for calculating the dynamic correlation degree index; Use the determined time window length to calculate the dynamic correlation degree index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively.

5. A method for monitoring the failure of an electric meter box wire clamp according to claim 4, characterized in that, The step of determining the time window length for calculating the dynamic correlation degree index according to the quantization value of the historical fluctuation characteristics and the information of the identified deterioration stage includes: Define the discrete levels of the quantization value of the historical fluctuation characteristics and the discrete levels of the information of the identified deterioration stage; Construct a two-dimensional lookup table, use the discrete levels of the quantization value of the historical fluctuation characteristics and the discrete levels of the information of the identified deterioration stage as indexes, and preset a reference time window length value in each cell of the two-dimensional lookup table; According to the discrete level corresponding to the quantization value of the historical fluctuation characteristics and the discrete level corresponding to the information of the identified deterioration stage, obtain the reference time window length value from the two-dimensional lookup table; Calculate the change rate of the dynamic correlation degree index within a preset time period; Determine an adjustment factor according to the change rate of the dynamic correlation degree index; Perform an operation on the obtained reference time window length value and the adjustment factor to obtain the time window length for calculating the dynamic correlation degree index.

6. The method for monitoring the failure of the wire clamp of the electric meter box according to claim 5, characterized in that, The step of determining an adjustment factor according to the change rate of the dynamic correlation degree index includes: Preset multiple numerical intervals of the change rate of the dynamic correlation degree index in advance; Preset a corresponding adjustment factor value for each of the numerical intervals in advance; Compare the calculated change rate of the dynamic correlation degree index with the multiple numerical intervals to determine the numerical interval to which the change rate of the dynamic correlation degree index belongs; Select the preset adjustment factor value corresponding to the numerical interval to which the change rate of the dynamic correlation degree index belongs as the determined adjustment factor.

7. A method for monitoring faults of an electric meter box wire clamp according to claim 5, characterized in that, The step of calculating the change rate of the dynamic correlation degree index within a preset time period includes: Obtain the value of the dynamic correlation degree index corresponding to the start time of the preset time period; Obtain the value of the dynamic correlation degree index corresponding to the end time of the preset time period; Calculate the numerical difference between the value of the dynamic correlation degree index at the end time and the value of the dynamic correlation degree index at the start time; Dividing the numerical difference by the time length of the preset time period to obtain the change rate of the dynamic correlation index within the preset time period.

8. A method for monitoring faults of a wire clamp of an electric meter box according to claim 1, characterized in that, The step of calculating an expected temperature value inside the box based on the measured values of the currents of phases A, B, and C, the measured value of the neutral line current, and a reference thermal model representing the health state of the clamp includes: Using a physical model based on the principle of the thermal circuit network as the reference thermal model; Calculating the healthy heating power of each line according to the measured values of the currents of phases A, B, and C, the measured value of the neutral line current, and the contact resistance values under the healthy state of each preset line, and setting the healthy heating power as the heat input of the heat source node in the reference thermal model; Setting thermal resistance nodes representing the heat transfer path inside the meter box and the heat dissipation path between the meter box and the external environment in the reference thermal model; Setting nodes representing the heat capacity of the meter box and its internal components in the reference thermal model; Solving the reference thermal model including the heat source node, the thermal resistance node, and the heat capacity node to obtain the expected temperature value inside the box.

9. A method for monitoring faults of wire clamps in an electric meter box according to claim 1, characterized in that, The step of calculating and tracking the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the phase A line, phase B line, phase C line, and N line respectively includes: Setting the length and sliding step of the sliding time window for calculating the dynamic correlation index; At each sliding time window position, obtaining a time series segment of the temperature deviation value within the sliding time window; Obtaining a time series segment of the heating index value of any one of the phase A line, phase B line, phase C line, or N line corresponding to the sliding time window position; Calculating the Pearson correlation coefficient between the obtained time series segment of the temperature deviation value and the obtained time series segment of the heating index value of the specific line; Assigning the calculated Pearson correlation coefficient as the dynamic correlation index value of the specific line corresponding to the sliding time window position; As the sliding time window moves according to the sliding step, repeating the steps of obtaining the time series segment of the temperature deviation value, obtaining the time series segment of the heating index value of the specific line, calculating the Pearson correlation coefficient, and assigning it as the dynamic correlation index value to form the dynamic correlation index of each specific line.

10. A fault monitoring system for an electric meter box wire clamp, characterized in that, The system includes: A temperature acquisition module for acquiring the temperature measurement value at a single position inside the meter box; A current acquisition module for acquiring the measured values of the currents of phases A, B, and C and the measured value of the neutral line current of the incoming line of the meter box; An expected temperature calculation module for calculating an expected temperature value inside the box based on the measured values of the currents of phases A, B, and C, the measured value of the neutral line current, and a reference thermal model representing the health state of the clamp; A temperature deviation calculation module for calculating the temperature deviation value between the temperature measurement value and the expected temperature value inside the box. A heating index calculation module, configured to calculate the heating index values related to the currents of the A-phase line, B-phase line, C-phase line, and N-line respectively based on the measured values of the A-phase, B-phase, and C-phase currents and the measured value of the neutral line current; A correlation calculation and tracking module, configured to calculate and track the dynamic correlation index between the time series of the temperature deviation value and the time series of the heating index values of the A-phase line, B-phase line, C-phase line, and N-line respectively; A fault determination module, configured to determine that the clamp of a specific line undergoes early deterioration when the dynamic correlation index corresponding to the specific line shows an increase and reaches a preset criterion condition according to the change trend of the dynamic correlation index; A model calibration module, configured to calibrate the parameters in the reference thermal model by using the temperature measurement values and the measured values of the A-phase, B-phase, and C-phase currents and the measured value of the neutral line current obtained during the low-load period when the meter box is operating in the low-load period.

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