A compact cable branch box adaptive control method and system

Abstract

The present application relates to the field of line state monitoring, and particularly relates to a compact cable branch box adaptive control method and system, the method comprising: obtaining the operating current, contact surface temperature and box body ambient temperature of the cable branch box and preprocessing to obtain a state vector, combining the current disturbance intensity index and contact thermal state response characteristics to construct an instantaneous urgency index, and obtaining a window cumulative urgency through fixed window accumulation; dynamically correcting the memory adjustment coefficient and iteratively updating the operation parameters, optimizing the cumulative urgency calculation result, combining the multi-level risk threshold to determine the operation risk level, outputting the corresponding early warning and control instruction, and realizing the graded early warning and adaptive control of the branch box contact electric heating abnormality. The present application overcomes the defects of inaccurate working condition judgment and easily fluctuating parameters of traditional fixed parameter evaluation by electric-thermal coupling dual feature evaluation and parameter adaptive iterative optimization, effectively improving the electric-thermal state identification accuracy and operation control adaptability of the cable branch box.

Description

Technical Field

[0001] This invention relates to the field of line condition monitoring. In particular, it relates to an adaptive control method and system for a compact cable branch box. Background Technology

[0002] Compact cable distribution boxes are important power distribution equipment in power distribution networks. With their advantages of small size, full insulation and sealing, and convenient installation, they are used in various power supply scenarios. However, they have obvious electrothermal coupling hysteresis characteristics and their operating conditions exhibit multi-time scale changes. Therefore, there is a high practical application demand for accurate monitoring and intelligent adaptive control of the equipment's operating status.

[0003] Given the confined and enclosed space of compact cable distribution boxes, heat conduction exhibits significant lag, and actual operating load conditions fluctuate widely across different time scales. Implementing adaptive control can align with the actual operating patterns of the equipment, dynamically identify operating status and autonomously optimize control parameters, promptly predict and suppress various operational risks such as overheating and overload, stabilize equipment operating conditions, and further enhance the stability of power supply at the end of the distribution network and the overall operation and maintenance management capabilities.

[0004] Currently, the mainstream monitoring and control methods for power distribution equipment have significant technical defects. Fixed threshold judgment, single-moment instantaneous parameter evaluation, and fixed attenuation coefficient estimation methods are prone to problems such as misjudgment of transient operating conditions and frequent fluctuations in control parameters. They can also cause the subsequent response capability of the algorithm to fail. Traditional static control strategies cannot adapt to the special operating physical characteristics of compact cable branch boxes and are difficult to output stable and effective end protection adaptive control basis. Summary of the Invention

[0005] To address the problems of inaccurate judgment of operating conditions and frequent fluctuations in control parameters caused by the evaluation method of fixed instantaneous parameters in the existing technology, which makes the adjustment strategy unable to adapt to the operating characteristics, the present invention provides solutions in the following aspects.

[0006] In a first aspect, an adaptive control method for a compact cable branch box includes: acquiring the operating current, contact surface temperature, and ambient temperature of the main circuit of the compact cable branch box, and preprocessing them to obtain a preprocessed state vector; extracting the operating current sequence from the state vector to construct a disturbance intensity index at the current time; based on the disturbance intensity index and combined with the thermal response characteristics formed by the change in net temperature rise of the contact at adjacent times, jointly analyzing the instantaneous urgency index of the contact at the current time, wherein the net temperature rise of the contact is the difference between the contact surface temperature and the ambient temperature of the box at the corresponding time; setting a fixed window length, calculating the sum of all instantaneous urgency indices within the window length to obtain the cumulative urgency of the window corresponding to the window length; and based on the cumulative urgency of the window... The system corrects the preset memory adjustment coefficient for the accumulated urgency and completes the adaptive update of the internal calculation parameters through a recursive algorithm. Based on the updated calculation parameters, it corrects the window accumulated urgency calculation rules in real time, synchronously and dynamically calibrates the time sequence weight and statistical interval adaptation relationship, and iteratively refreshes the accumulated urgency within the window by combining the instantaneous urgency index obtained at the latest sampling time. It outputs an optimized accumulated urgency result that conforms to the real-time electrothermal evolution law of the compact cable branch box. The optimized accumulated urgency result is compared and judged with preset multi-level risk thresholds. Based on the comparison results, the electrothermal operation risk level of the compact cable branch box connection is divided, and the corresponding status warning signal and control and handling command are output synchronously to complete the graded warning and active operation control of the abnormal heating state of the connection point.

[0007] Preferably, the method for constructing the disturbance intensity index is as follows: Extract the operating current sequence, calculate the absolute deviation between the square of the current operating current at the current moment and the square of the operating current at the previous moment, and use the ratio of the absolute deviation to the square of the rated reference current as the disturbance intensity index at the current moment.

[0008] Preferably, the method for obtaining the thermal response characteristics is as follows: Taking any given moment as the target moment, calculate the difference between the contact surface temperature and the air temperature at the target moment to obtain the net temperature rise of the contact at the target moment; and calculate the difference between the contact surface temperature and the air temperature at the moment before the target moment to obtain the net temperature rise of the contact at the moment before the target moment. Calculate the absolute value of the difference between the net temperature rise of the contact at the target time and the net temperature rise of the contact at the previous time, and perform normalization to obtain the instantaneous change amplitude of the net temperature rise of the contact. The thermal response characteristic is the instantaneous change amplitude.

[0009] The preferred method for calculating the instantaneous urgency index is as follows: The disturbance intensity index is added to the thermal response characteristics to obtain the total electrothermal coupling disturbance intensity, and the ratio of the disturbance intensity to the total electrothermal coupling disturbance intensity is used as the instantaneous urgency index of the junction at the target time.

[0010] Preferably, the preset memory adjustment coefficient is corrected based on the cumulative urgency of the window, including: The complement of the current window cumulative urgency is used as the degree of redundancy of the cable branch box without abnormal disturbance. The product of the degree of redundancy and the adjustment span coefficient is used as the dynamic compensation amount of the working condition adaptive memory. The sum of the working condition adaptive memory dynamic compensation amount and the reference memory coefficient is used as the memory adjustment coefficient at the current moment.

[0011] Preferably, the adaptive update of internal computational parameters is achieved through a recursive algorithm, including: The internal operating parameters include: recursive gain vector, thermal model parameter vector, and state confidence matrix. The thermal model parameter vector is iteratively updated based on the temperature prediction error, and the recursive gain vector and state confidence matrix respectively introduce memory adjustment coefficients to participate in adaptive correction.

[0012] Preferably, the classification of the electrical heating operation risk level of the compact cable branch box contacts includes: When the optimized cumulative urgency is less than or equal to the low-risk threshold, a normal operation signal is output and no control operation is performed. When the cumulative urgency after optimization is greater than the low-risk threshold but less than or equal to the medium-risk threshold, a first-level warning signal is output and the operation of increasing the frequency of real-time monitoring of contact temperature is initiated. When the cumulative urgency after optimization is greater than the medium-risk threshold but less than or equal to the high-risk threshold, a level-two early warning signal is output and a load adjustment command is initiated. When the risk level exceeds the high-risk threshold, a level 3 warning signal is output, and an emergency shutdown or circuit switching command is initiated.

[0013] Secondly, a compact cable branch box adaptive control system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-described compact cable branch box adaptive control method is implemented.

[0014] The present invention has the following effects: 1. This invention constructs urgent indicators by integrating the dual characteristics of electrical disturbance and contact thermal response, and completes data statistical analysis by relying on a time-series sliding window. This effectively solves the problem of large deviation in the judgment of working condition in traditional methods and greatly improves the accuracy of identifying the electric thermal operating status of cable branch boxes.

[0015] 2. This invention utilizes the window accumulation urgency to dynamically adjust the memory adjustment coefficient, and combines it with a recursive algorithm to achieve adaptive iterative updates of internal calculation parameters. This allows for real-time adaptation to the electrothermal evolution characteristics of the equipment at different operating stages, effectively suppressing frequent disorderly fluctuations in control parameters, and ensuring that the control strategy is highly consistent with the actual operating pattern of the equipment. Attached Figure Description

[0016] Figure 1 This is a flowchart of steps S1-S5 in a compact cable branch box adaptive control method according to an embodiment of the present invention.

[0017] Figure 2 This is a structural block diagram of a compact cable branch box adaptive control system according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0019] Reference Figure 1 An adaptive control method for a compact cable branch box includes steps S1-S5, as detailed below: S1: Obtain the operating current, contact surface temperature, and ambient temperature of the main circuit of the compact cable branch box, and perform preprocessing to obtain the preprocessed state vector.

[0020] High-precision current sensing units are deployed, with a fixed sampling interval of 500 milliseconds, to collect the operating current of the main power supply circuit in real time. The unit is ampere. This current value can intuitively reflect the real-time load of the line. It can not only record the stable operation status and dynamic fluctuation process of the circuit load, but also effectively characterize the intensity of conductor heating and the main source of internal thermal stress in the equipment.

[0021] Temperature detection units are attached and installed at critical connection points inside the cable branch box to synchronously collect the surface temperature of the connection points. The unit is degrees Celsius. This temperature data can accurately reflect the actual heating state of the contact area of ​​the connection point, continuously track the cumulative process of the temperature rise of the connection point and the change law of natural heat dissipation, so as to objectively reflect the degree of aging of the conductor material of the connection part and the deviation of the operating performance of the supporting insulation medium.

[0022] An ambient temperature sensor is installed inside the branch box to collect real-time data on the ambient air temperature inside the box. The unit is degrees Celsius. This ambient temperature serves as the external boundary condition during the heat dissipation process of the equipment. It can accurately reflect the temperature change patterns caused by the diurnal temperature range and seasonal temperature changes. At the same time, it can accurately characterize the quality of the natural heat dissipation environment in which the equipment is located and the thermal interference caused by the external ambient temperature and the heating state at the interface.

[0023] Data preprocessing: Instantaneous fluctuations in the power grid and the switching actions of electrical switches can easily generate momentary spike interference signals. Simultaneously, on-site mechanical vibrations and spatial electromagnetic coupling can also cause abnormal fluctuations in the test data. By employing a digital smoothing algorithm to perform noise reduction and filtering on the acquired current and temperature signals, spurious numerical abrupt changes caused by various external factors are effectively eliminated. The system retains the effective data components that truly reflect the actual load changes in the circuit and the evolution of heat conduction at the contacts, ensuring that the input test data conforms to the continuous changing characteristics of the equipment's actual physical operating state.

[0024] By combining the actual operating conditions of the cable branch box with the physical operating limits of the equipment, reasonable data value boundaries are defined to accurately identify and intercept abnormal data points that exceed the normal physical change range. For failed abnormal data, the average of the effective data at adjacent sampling times is used to replace and compensate, thereby eliminating the data distortion caused by instantaneous sensor malfunctions and brief interruptions in data transmission. This ensures the overall stability and reliability of the electrothermal operation status time sequence data and improves the credibility of the basic test data.

[0025] Using a sampling interval of 500 milliseconds, the loop operating current data, contact surface temperature data, and ambient temperature data inside the box after filtering and noise reduction are time-ordered and synchronized to achieve strict time alignment of multiple types of detection data, eliminate the data acquisition time deviation caused by the inconsistent response rates of different types of sensors, and ensure a unified standard time reference.

[0026] After completing the entire data preprocessing process described above, a standardized operational state vector is constructed. This state vector can be directly used as the standard basic input data source for subsequent electrical disturbance intensity assessment, thermal response feature extraction, and dynamic adjustment of memory weight parameters.

[0027] S2: Extract the operating current sequence from the state vector and construct the disturbance intensity index at the current time. Based on the disturbance intensity index and the thermal response characteristics formed by the change in net temperature rise of the contact at adjacent times, jointly analyze the instantaneous urgency index of the contact at the current time. The net temperature rise of the contact is the difference between the surface temperature of the contact and the ambient temperature of the enclosure at the corresponding time.

[0028] Extract real-time operating current time-series data of the main circuit of the compact cable branch box to construct a continuous operating current sequence; calculate the square value of the operating current at the current sampling moment and the square value of the operating current at the previous adjacent sampling moment, and calculate the absolute difference between the two sets of current square values. Ratio the absolute difference with the preset rated reference current square value of the equipment to obtain the electrical disturbance intensity index corresponding to the current sampling moment.

[0029] The square value of the operating current can intuitively reflect the real-time heating power level of the circuit. The absolute deviation of the square of the current between adjacent moments can accurately characterize the fluctuation range of the heating power of the circuit load in a short period of time. By using the square of the rated reference current as a normalized reference quantity for ratio conversion, the calculation deviation caused by the difference in equipment specifications and rated parameters can be eliminated, so that the final disturbance intensity index has a unified evaluation standard and can objectively quantify the degree of instantaneous fluctuation of the electrical load of the main circuit.

[0030] Specifically, the disturbance intensity index satisfies the following relationship: ; In the formula, Indicates the first The disturbance intensity index at a given moment is used to characterize the relative severity of the electrical thermal shock at that moment. It represents the current in the main circuit at the current moment, reflecting the real-time load scale and heat generation base; This indicates the current in the main circuit at the previous moment, used to compare and capture load change trends; It represents the rated reference current of the equipment, serving as a standardized reference for fluctuation range; Represents a minimal positive constant. This prevents the denominator from approaching zero under extreme conditions, thus avoiding computational interruption and ensuring the stability of the edge processing unit's values.

[0031] Using any sampling time as the target calculation time, the surface temperature of the contact and the ambient air temperature inside the enclosure are collected at the target time, and the temperature difference between the two is calculated to obtain the net temperature rise of the contact at the target time. Simultaneously, the surface temperature of the contact and the ambient air temperature of the sampled time adjacent to the target time are collected, and the net temperature rise of the contact at the previous time is obtained through difference calculation.

[0032] The absolute value of the difference between the net temperature rise of the contact at the target time and the net temperature rise of the contact at the previous time is calculated. Then, it is combined with a preset temperature rise baseline to complete the normalization and quantization process, and finally the instantaneous change amplitude of the net temperature rise of the contact is obtained. The instantaneous change amplitude of the net temperature rise of the contact is the thermal response characteristic to be constructed.

[0033] From a physical perspective, the net temperature rise of the contact can eliminate the interference caused by ambient temperature fluctuations on the actual heating degree of the contact, accurately reflecting the true temperature rise level generated by the contact itself due to power-on heating; the difference in net temperature rise between adjacent moments can intuitively reflect the real-time change rate of the contact heating state, and after normalization processing, a unified quantitative evaluation scale can be established to eliminate numerical differences caused by equipment structure and temperature measurement location. The obtained thermal response characteristics can objectively characterize the dynamic change trend of the heating state of the contacts inside the compact cable branch box.

[0034] The electrical disturbance intensity index and the contact thermal response characteristics are summed to construct the total electrothermal coupling disturbance intensity that can simultaneously take into account electrical load fluctuations and dynamic changes in contact temperature rise.

[0035] The ratio of the electrical disturbance intensity index to the total electrothermal coupling disturbance intensity is used as the instantaneous urgency index of the internal connection of the cable branch box at the corresponding target sampling time.

[0036] From a physical perspective, the electrical disturbance intensity index only reflects the fluctuation of the load current on the circuit side, and the thermal response characteristics only reflect the rate of change of the heating state of the contact side itself. Neither of them can comprehensively characterize the overall operational risk of the contact when used alone. By combining and summing the two to obtain the total electrothermal coupling disturbance intensity, an integrated characterization of electrical operating characteristics and contact heating characteristics can be achieved. By constructing an instantaneous urgency index in the form of a ratio, the weight of load disturbance and temperature rise disturbance in the overall abnormal situation can be quantitatively distinguished, accurately determining the cause and urgency of the contact anomaly at the current moment. This provides a standardized and quantifiable core evaluation basis for subsequent time-based risk accumulation assessment and adaptive adjustment of operating conditions.

[0037] Specifically, the instantaneous urgency index satisfies the following relationship: ; In the formula, Indicates the first The instantaneous urgency indicator of a moment. Indicates the first The intensity index of the disturbance at any given time. Indicates the first The difference between the surface temperature of the contact point and the ambient temperature of the chamber at any given time. Indicates the first The difference between the surface temperature at time node and the ambient temperature. This represents the rated temperature difference between the contact surface temperature and the air temperature inside the enclosure. The rated temperature difference is determined by running the equipment continuously under standard test conditions of rated operating current and ambient temperature of 25°C until the contact temperature stabilizes and no longer changes. The measured steady-state surface temperature of the contact and the ambient temperature is the rated operating condition reference temperature rise. Alternatively, it can be calculated based on the equipment structural design parameters and the thermophysical properties of the conductor and insulation materials. Represents a minimal positive constant. .

[0038] S3: Set a fixed window length, calculate the sum of all instantaneous urgency indicators within the window length, and obtain the cumulative urgency of the window corresponding to the window length.

[0039] Determine the preset sliding statistics window length Define the range of historical data to be included in this weighted calculation, starting with the current number of... Based on the time reference, select forward a total of Data from consecutive historical sampling times are used in the calculation.

[0040] Set timing offset variable ,make Take values ​​sequentially from 0 to It iterates through all historical sampling times within the sliding window. It then retrieves the original instantaneous urgency index corresponding to each offset time. Simultaneously, match the exponential time decay weighting coefficient under the corresponding offset. The exponential time-decay weighting coefficient at the same time position is multiplied group by group with the original instantaneous urgency index at the corresponding time to obtain the weighted urgency index values ​​for each group. All weighted urgency index values ​​within the window are then summed to obtain the total weighted sum in the numerator of the formula.

[0041] The weights of all exponential time-decaying coefficients within the sliding window are summed individually to obtain a total weight, and then a minimal positive constant of 0.001 is added to this sum. Adding a minimal positive constant as a denominator term effectively prevents division-by-zero errors caused by the total weighted sum approaching zero, ensuring the stability of the calculation process. Dividing the weighted sum of the numerator by the total value of the denominator yields the final result. Instantaneous urgency indicators after time-series weighted smoothing .

[0042] Specifically, the cumulative urgency of the window satisfies the following relationship: ; In the formula, Indicates the first The instantaneous urgency indicator of a moment. This indicates the preset length of the sliding statistics window. For example, the preset window length is 5-8 data points. Indicates the first The instantaneous urgency indicator of a moment. This represents the exponential time-decaying weighting coefficient. Represents a minimal positive constant. .

[0043] This formula adopts an exponential decay weighting rule with larger weights for near-term and smaller weights for distant-term historical data. The closer the instantaneous urgency index is to the current time, the greater the weight is assigned, while the farther the historical data is from the current time, the smaller the weight is assigned. This not only fully reflects the lag characteristics of the continuous evolution of the electrical and thermal state of the compact cable branch box contacts and weakens the interference of invalid historical data, but also smooths and filters short-term operating fluctuations, making the final output instantaneous urgency index more closely reflect the actual continuous operating status of the equipment.

[0044] S4: Based on the cumulative urgency of the window, the preset memory adjustment coefficient is corrected, and the internal operation parameters are adaptively updated through a recursive algorithm. According to the updated operation parameters, the calculation rules of the cumulative urgency of the window are corrected in real time, the time sequence weight and statistical interval adaptation relationship are dynamically calibrated synchronously, and the cumulative urgency within the window is iteratively refreshed by combining the instantaneous urgency index obtained at the latest sampling time, and the optimized cumulative urgency result that conforms to the real-time electrothermal evolution law of the compact cable branch box is output.

[0045] Using the cumulative urgency of the window obtained at the current sampling time as a benchmark, its corresponding numerical complement is calculated. This complement quantifies the degree of operational redundancy of the compact cable branch box without abnormal electrothermal disturbance. This redundancy degree is then multiplied by a pre-set adjustment span coefficient to obtain the adaptive memory dynamic compensation amount for the real-time operating conditions of the equipment. Based on this, the adaptive memory dynamic compensation amount is summed with a preset benchmark memory coefficient, and the result is the memory adjustment coefficient corresponding to the current sampling time.

[0046] From a physical perspective, the cumulative urgency of the window directly reflects the severity of current abnormal heating and electrical disturbances at the branch box contacts. The complement of the cumulative urgency reflects the remaining safety margin for stable equipment operation. The adjustment span coefficient limits the overall adjustable range of the memory parameters, determining the adjustment range of memory capacity as operating conditions change. By solving for the dynamic compensation amount through the degree of redundancy, the compensation intensity can be accurately matched to the actual stable operating state of the equipment. Combined with the fixed benchmark memory coefficient, numerical superposition is completed. This ensures that the algorithm has the ability to collect basic historical data and can autonomously adjust the memory weight based on real-time operating conditions. The final memory adjustment coefficient can accurately replace the fixed forgetting parameters in the recursive algorithm, realizing the adaptive adjustment of the algorithm's memory according to the electrothermal operating state of the branch box.

[0047] Specifically, the memory regulation coefficient satisfies the following relationship: ; In the formula, Indicates the first The memory adjustment coefficient at each moment is used to replace the fixed forgetting parameter in the recursive algorithm; The baseline memory coefficient represents the reliability of the algorithm based on historical data under stable operating conditions. The adjustment span coefficient represents the elastic range of weight adjustment in response to continuous disturbances; and it satisfies the following constraints: This ensures that the mapping process possesses inherent numerical self-limiting properties; Indicates the first The urgency of the window at any given moment. In this embodiment, Set to 0.95. Setting the value to 0.05 allows for adjustments to the baseline memory coefficient for scenarios with poor heat dissipation and frequent load fluctuations. Simultaneously, the adjustment span coefficient can be increased to improve the algorithm's adaptability to dynamic and changing operating conditions. Both sets of parameters can remain constant within a single control cycle or be slightly adjusted according to a preset long-term maintenance cycle, ensuring that the memory weight adjustment baseline always precisely matches the actual physical operating state of the cable branch box, on-site heat dissipation conditions, and load operating characteristics.

[0048] During the adaptive update of internal operational parameters in the recursive algorithm, the internal operational parameters to be updated mainly include three categories: recursive gain vector, thermal model parameter vector, and state confidence matrix. The thermal model parameter vector uses the temperature prediction error between the actual measured value and the model prediction value of the contact temperature as the driving basis for iteration, autonomously completing iterative updates based on the error change pattern, without being directly controlled by the memory adjustment coefficient. The recursive gain vector is used to control the correction strength of the measured operational data on the model parameters, and the state confidence matrix is ​​used to characterize the reliability of the estimation results of each operational parameter. Both of these parameters incorporate a memory adjustment coefficient obtained through real-time operating condition tuning during the iterative calculation process. This coefficient enables adaptive dynamic adjustment of the update amplitude and correction weight, allowing the iteration rhythm of the two types of parameters to adapt to the actual electrothermal operating conditions of the cable branch box.

[0049] It should be noted that the parameter vector of the thermal model satisfies the following relationship: ; In the formula, Indicates the first The parameter vector of the hot model after each iteration. Indicates the first The historical thermal model parameter vector completed at each iteration time. Indicates the first The recursive gain vector at time step, Indicates the first The actual relative temperature rise at the contact point at any given moment. Indicates the first The transposed regression vector is constructed from electrical operation data at any given time.

[0050] Specifically, the corrected recursive gain vector satisfies the following relationship: ; In the formula, Indicates the first The recursive gain vector at each time step is used to determine the magnitude of the impact of the new measurement on the parameter correction. Indicates the first The state trust matrix at time step Indicates the first The regression vector at time step, Indicates the first The memory regulation coefficient at any given moment. This represents the standard variance of temperature measurement.

[0051] Specifically, the corrected state trust matrix satisfies the following relationship: ; In the formula, Indicates the first The state trust matrix at time step Indicates the first The memory regulation coefficient at any given moment. Indicates the first The state trust matrix at time step Indicates the first The recursive gain vector at time step, Indicates the first The regression vector at time step is in the form of ,in To determine the transpose sign, the square of the current and the constant term are used as input features. Represents a minimal positive constant. , This represents the identity matrix, used to inject an equal amount of baseline trust into each diagonal of the state trust matrix.

[0052] It should be noted that during the startup and full operation of this adaptive control algorithm, only the initial thermal model parameter vector required for the initial startup of the algorithm needs to be preset. With the initial state trust matrix Two types of initial baseline parameters are used, and all other operational variables do not require manual pre-setting of values.

[0053] Among them, the initial thermal model parameter vector The values ​​are determined through calibration tests before the equipment is officially put into operation. Specifically, a known rated load current is passed into the circuit of the compact cable branch box. After the equipment operation status tends to be stable, the steady-state temperature rise data of the corresponding contact is collected. Based on the correspondence between measured current and temperature rise, the initial value of the heat conversion coefficient is solved. Then, the initial value of the temperature offset is determined by combining the actual environmental reference temperature on site. The two types of parameters are integrated and arranged to form a complete initial thermal model parameter vector, which provides an accurate initial calculation reference for subsequent algorithm iteration and correction.

[0054] For example, when a stable current of 400A is applied, the measured steady-state temperature rise of the contact is 20℃. The initial value of the heat transfer coefficient can then be calculated. The initial value of the temperature offset is set based on the on-site reference ambient temperature. The final combination yields the initial parameter vector. .

[0055] The initial state trust matrix is ​​uniformly followed By pre-configuring and using a large numerical initial matrix, the system can highly rely on real-time field measurement data during the device power-on and initial algorithm iteration phases, ensuring rapid response in initial state identification.

[0056] Apart from the two types of initial parameters mentioned above, all other variables participating in the algorithm operation do not need to be manually set in advance: recursive gain vector. The solution is obtained by directly calculating the operational data collected in real time at each sampling moment, combined with the state confidence matrix completed in the previous iteration; the memory adjustment coefficient is also used. It is generated dynamically in real time by combining the pre-condition assessment process with the cumulative urgency of the window; the temperature measurement reference variance, minimum regularity constant and identity matrix are all... The sensor's inherent calibration parameters and general fixed mathematical constants; regression vector Relative temperature rise of contacts It is generated synchronously based on the real-time data collected at each sampling time, including the circuit operating current, contact surface temperature, and the difference between the ambient temperature and the sampled data.

[0057] This algorithm establishes a computational framework by pre-calibrating initial reference parameters. It relies on real-time data acquisition from the field to drive the autonomous calculation of various intermediate variables and iteratively updates parameters at each sampling time. This enables the logical and orderly linkage and coordination of various operational variables, allowing the entire control system to operate independently of manual parameter tuning, autonomously and accurately track the electric heating status of the branch box, and autonomously output adaptive control commands that match the actual working conditions.

[0058] Synchronous dynamic calibration of the time-series weights and statistical interval adaptation relationship, specific implementation steps: Based on the recursive calculation parameters that have been adaptively updated, the memory adjustment coefficient is mapped to the time-series weight allocation ratio: when the equipment is operating stably and the risk of overheating is low, the weight of historical time-series data is increased and the weight of the latest sampled data is decreased to suppress instantaneous fluctuation interference; when the equipment is under strong disturbance and the temperature rise is abnormally significant, the weight of historical time-series data is decreased and the weight of the latest recent data is increased to accelerate the speed of following the operating condition and complete the dynamic allocation of data weights throughout the entire time period.

[0059] Combining the real-time thermal inertia of the branch box with the current level of electrothermal disturbance, the data acceptance bias of the statistical window interval is adaptively adjusted: during steady-state operation, the range of historical interval data acceptance is broadened, and the evaluation stability is improved by relying on long-term operating data; during sudden load fluctuations and abnormal contact temperature rise, the range of long-term historical data acceptance is narrowed, and the effective data of the recent short interval is given priority, so that the statistical evaluation range matches the actual heat evolution rhythm of the equipment.

[0060] The time sequence weights and statistical intervals are no longer fixed at the factory, but are uniformly adjusted based on the updated recursive internal parameters. The two are adjusted in tandem, so that when calculating the cumulative urgency of the subsequent integrated instantaneous urgency indicators, the data acceptance rules are fully consistent with the current actual electrothermal operation status of the compact cable branch box, eliminating the evaluation bias caused by the fixed statistical mode.

[0061] The time-series weights of data at each sampling time are adjusted by using the updated recursive calculation parameters. At the same time, the data acceptance range within the statistical window is adjusted in combination with the real-time operating disturbance of the equipment, so as to achieve the coordinated adaptation of time-series weights and statistical acceptance intervals to the electrothermal change law of the equipment.

[0062] It should be noted that the cumulative urgency of the window is a comprehensive quantitative representation of the electrothermal state of the equipment over a period of time, including electrical disturbances and thermal response. The magnitude and trend of the cumulative urgency directly reflect the current operating condition of the equipment, such as: stable operation, slight disturbance, strong impact, etc. This is the core basis for realizing the adaptive update of the recursive algorithm parameters. The specific logic is as follows: Traditional recursive algorithms use fixed computational parameters, such as fixed forgetting factors and fixed weights, which have the drawback of being unable to adapt to fluctuations in operating conditions. When operating smoothly, they are prone to amplifying noise interference and cannot accurately track slow changes in contact resistance. When operating conditions change abruptly, they have a delayed response and cannot adjust parameters in time to adapt to thermal changes, which can easily lead to parameter estimation errors and affect the accuracy of monitoring and control.

[0063] The cumulative urgency of the window is obtained by accumulating instantaneous urgency indicators. It includes both the intensity of electrical load disturbances and the continuous characteristics of thermal response, which can accurately reflect the overall operating status of the equipment in the current period. It provides a direct and reliable basis for the adjustment of recursive algorithm parameters and avoids parameter updates from deviating from actual working conditions.

[0064] Based on the cumulative urgency of the window, and combined with the construction logic of the memory adjustment system, it is linked to the parameter adjustment of the recursive algorithm. When the cumulative urgency of the window is low and the equipment is running smoothly, the algorithm parameters such as gain and weight tend to be stable, giving priority to ensuring the stability of data and anti-interference ability. When the cumulative urgency of the window is high and the equipment has continuous disturbances or anomalies, the algorithm parameters are dynamically adjusted to increase the weight of new data, speed up the parameter update speed, and ensure that the recursive algorithm can accurately track the changes in the operating status of the equipment.

[0065] By using a logic design that accumulates urgency through a window and then adaptively updates the recursive parameters, the problem of fixed parameters and poor adaptability in traditional recursive algorithms is solved. This ensures that the recursive algorithm is always synchronized with the actual operating conditions of the equipment, providing accurate parameter support for subsequent optimization of accumulated urgency and risk assessment, and guaranteeing the reliability and practicality of the entire monitoring and control system.

[0066] S5: Compare and determine the optimized cumulative urgency results with the preset multi-level risk thresholds. Based on the comparison results, classify the electric heating operation risk level of the compact cable branch box connection point, and simultaneously output the corresponding status warning signal and control and disposal instructions to complete the graded early warning and active operation condition control of the abnormal heating situation at the connection point.

[0067] When the optimized cumulative urgency value is less than or equal to the preset low-risk threshold, it is determined that the overall electrothermal operation of the compact cable branch box is stable, and there is no abnormal heating or load disturbance risk at the connection. At this time, the system outputs a normal operating status signal of the equipment, without issuing any operating condition control commands, and maintains the original operating mode of the equipment unchanged.

[0068] When the optimized cumulative urgency value is greater than the low-risk threshold but does not exceed the preset medium-risk threshold, it is determined that the equipment operation has shown a slight abnormal state, and the contact heating fluctuation and load disturbance have begun to appear. The system then outputs a first-level warning signal and simultaneously executes monitoring and control actions, actively increasing the frequency of real-time acquisition and monitoring of contact temperature data, and encrypting the state sensing frequency to achieve early detection of abnormal trends.

[0069] When the optimized cumulative urgency value exceeds the medium-risk threshold but does not exceed the preset high-risk threshold, it is determined that the abnormal heating of the equipment contacts and the degree of electrical load disturbance have been further aggravated, forming a significant operational safety hazard. The system outputs a level-two early warning signal and simultaneously issues a load adaptation adjustment control command. By reasonably adjusting the circuit operating load, the continuous rise in contact temperature is suppressed, and the abnormal operating situation is gradually calmed down.

[0070] When the cumulative urgency value after optimization exceeds the preset high-risk threshold, it is determined that the risk of equipment contact overheating and electrical disturbance has reached a high-risk level. Continued operation may easily lead to equipment failure and safety accidents. The system immediately outputs a level three high-risk warning signal and promptly issues emergency shutdown control commands or power supply circuit switching control commands to quickly cut off high-risk operating conditions and ensure the overall safe operation of equipment and power supply lines.

[0071] In this embodiment, a tiered judgment threshold is preset with a low-risk threshold of 0.35, a medium-risk threshold of 0.60, and a high-risk threshold of 0.85. It should be noted that setting the low-risk threshold to 0.35 is based on the range of electrical thermal disturbance fluctuations under normal and stable operation of the cable branch box. This value corresponds to the normal operating range where the equipment has no significant load fluctuations and the contact temperature rise is stable, which can accurately distinguish between normal operating conditions and slightly abnormal operating conditions. The medium-risk threshold of 0.60 is defined in combination with the critical abnormal operating condition of slowly rising contact temperature and small fluctuations in circuit load, which is adapted to the operating characteristics of initial heat deterioration and load disturbance of the equipment, and serves as the basis for determining the activation of enhanced monitoring. The high-risk threshold of 0.85 is set with reference to the safety operation specifications of power distribution equipment and the early warning standard for contact overheating faults, matching the dangerous operating state of increased contact resistance and rapid temperature rise, and serves as the critical basis for triggering emergency safety control actions.

[0072] This invention also provides a compact adaptive control system for cable branch boxes. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an adaptive control method for a compact cable branch box according to the first aspect of the present invention. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, the setup and functions of which are known in the art and will not be described further here.

[0073] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An adaptive control method for a compact cable branch box, characterized in that, include: The operating current, contact surface temperature, and ambient temperature of the main circuit of the compact cable branch box are obtained and preprocessed to obtain the preprocessed state vector. The operating current sequence is extracted from the state vector to construct the disturbance intensity index at the current time. Based on the disturbance intensity index and combined with the thermal response characteristics formed by the change in net temperature rise of the contact at adjacent time points, the instantaneous urgency index of the contact at the current time is jointly analyzed. The net temperature rise of the contact is the difference between the surface temperature of the contact and the ambient temperature of the enclosure at the corresponding time. Set a fixed window length, calculate the sum of all instantaneous urgency indicators within the window length, and obtain the cumulative urgency of the window corresponding to the window length; Based on the window cumulative urgency, the preset memory adjustment coefficient is corrected, and the internal operation parameters are adaptively updated through a recursive algorithm. According to the updated operation parameters, the window cumulative urgency calculation rule is corrected in real time, the time sequence weight and statistical interval adaptation relationship are dynamically calibrated synchronously, and the cumulative urgency in the window is iteratively refreshed by combining the instantaneous urgency index obtained at the latest sampling time, and the optimized cumulative urgency result that conforms to the real-time electrothermal evolution law of the compact cable branch box is output. The optimized cumulative urgency result is compared with the preset multi-level risk threshold. Based on the comparison result, the risk level of electric heating operation of the compact cable branch box connection is divided, and the corresponding status warning signal and control and disposal command are output simultaneously to complete the graded early warning and active operation control of the abnormal heating situation of the connection point.

2. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The method for constructing the disturbance intensity index is as follows: Extract the operating current sequence, calculate the absolute deviation between the square of the current operating current at the current moment and the square of the operating current at the previous moment, and use the ratio of the absolute deviation to the square of the rated reference current as the disturbance intensity index at the current moment.

3. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The method for obtaining the thermal response characteristics is as follows: Taking any given moment as the target moment, calculate the difference between the contact surface temperature and the air temperature at the target moment to obtain the net temperature rise of the contact at the target moment; and calculate the difference between the contact surface temperature and the air temperature at the moment before the target moment to obtain the net temperature rise of the contact at the moment before the target moment. Calculate the absolute value of the difference between the net temperature rise of the contact at the target time and the net temperature rise of the contact at the previous time, and perform normalization to obtain the instantaneous change amplitude of the net temperature rise of the contact. The thermal response characteristic is the instantaneous change amplitude.

4. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The instantaneous urgency index is calculated as follows: The disturbance intensity index is added to the thermal response characteristics to obtain the total electrothermal coupling disturbance intensity, and the ratio of the disturbance intensity to the total electrothermal coupling disturbance intensity is used as the instantaneous urgency index of the junction at the target time.

5. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The preset memory adjustment coefficient is corrected based on the cumulative urgency of the window, including: The complement of the current window cumulative urgency is used as the degree of redundancy of the cable branch box without abnormal disturbance. The product of the degree of redundancy and the adjustment span coefficient is used as the dynamic compensation amount of the working condition adaptive memory. The sum of the working condition adaptive memory dynamic compensation amount and the reference memory coefficient is used as the memory adjustment coefficient at the current moment.

6. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The internal computational parameters are adaptively updated using a recursive algorithm, including: The internal operating parameters include: recursive gain vector, thermal model parameter vector, and state confidence matrix. The thermal model parameter vector is iteratively updated based on the temperature prediction error, and the recursive gain vector and state confidence matrix respectively introduce memory adjustment coefficients to participate in adaptive correction.

7. The adaptive control method for a compact cable branch box according to claim 1, characterized in that, The classification of the electrical heating operation risk level of compact cable branch box contacts includes: When the optimized cumulative urgency is less than or equal to the low-risk threshold, a normal operation signal is output and no control operation is performed. When the cumulative urgency after optimization is greater than the low-risk threshold but less than or equal to the medium-risk threshold, a first-level warning signal is output and the operation of increasing the frequency of real-time monitoring of contact temperature is initiated. When the cumulative urgency after optimization is greater than the medium-risk threshold but less than or equal to the high-risk threshold, a level-two early warning signal is output and a load adjustment command is initiated. When the risk level exceeds the high-risk threshold, a level 3 warning signal is output, and an emergency shutdown or circuit switching command is initiated.

8. A compact adaptive control system for a cable branch box, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the adaptive control method for a compact cable branch box according to any one of claims 1-7.

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