A power distribution network health state dynamic evaluation and early warning system

Abstract

The application discloses a power distribution network health state dynamic evaluation and early warning system and relates to the technical field of smart power grids, and aims to solve the technical problems that the existing power distribution network pole-mounted switch health state evaluation technology cannot adapt to the low-computing-power hardware conditions of smart fusion terminals and cannot realize the accurate online identification and on-site early warning of the early jamming of the tripping iron core, comprising a tripping coil current sampling module, a double-window feature extraction module, a floating-point fixed-point operation processing module, a same-source cluster collaborative correction module and an on-site protection closed-loop execution module which are sequentially connected by signals, and a complete processing link of time sequence cooperation is formed among the modules; the tripping coil current sampling module is used for collecting current time-domain waveform data of the whole energization process of the tripping coil of the pole-mounted switch. The application has the advantages of strong low-computing-power hardware adaptability, high accuracy of iron core early deterioration identification and fast early warning response speed.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and more specifically, to a dynamic assessment and early warning system for the health status of a distribution network. Background Technology

[0002] With the continuous advancement of the construction of new power systems, the large-scale integration of a high proportion of distributed renewable energy into the 10kV distribution network places extremely high demands on the operational reliability and health status management capabilities of the core switching equipment in the distribution network. As the core implementing equipment for distribution network line segmentation, fault isolation, and anti-islanding protection, the reliability of the 10kV outdoor pole-mounted switch's tripping action directly determines the power supply stability of the distribution network, the security of renewable energy absorption, and the operational safety of on-site maintenance personnel. Tripping failure caused by core jamming is a core contributing factor to cascading trips, large-scale power outages, and anti-islanding protection failures in the distribution network.

[0003] Current assessment schemes for the health status of pole-mounted switches cannot achieve accurate online identification and local early warning of early sticking of the core during tripping. Existing technologies generally adopt a cloud-based centralized full-waveform analysis mode, which not only cannot be adapted to the low computing power hardware conditions of outdoor pole-mounted switch intelligent fusion terminals, making it difficult to capture the weak characteristics of early core degradation in the tripping coil current, but also cannot eliminate the interference of electrical conditions and environmental factors on the identification results. At the same time, the analysis transmission delay cannot meet the stringent time limit requirements of new energy anti-islanding protection and FA rapid fault isolation, which can easily lead to the expansion of the distribution network fault range due to tripping failure, seriously restricting the safe and stable operation of new power systems. In view of this, we propose a dynamic assessment and early warning system for the health status of distribution networks. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic assessment and early warning system for the health status of a distribution network, in order to solve the technical problem that the existing health status assessment technology for pole-mounted switches in distribution networks cannot be adapted to the low computing power hardware conditions of intelligent fusion terminals, and it is difficult to achieve accurate online identification and local early warning of early jamming of the tripping iron core.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a dynamic assessment and early warning system for the health status of a distribution network, comprising a trip coil current sampling module, a dual-window feature extraction module, a floating-point-free fixed-point arithmetic processing module, a co-source cluster collaborative correction module, and a local protection closed-loop execution module connected in sequence, with each module forming a complete processing link with time-coordinated collaboration. The trip coil current sampling module is used to collect the current time-domain waveform data of the pole-mounted switch trip coil during the entire energization process; The dual-window feature extraction module is used to divide the current time domain waveform into two time windows based on the electrical characteristics of the trip coil and the timing characteristics of the mechanical action of the iron core, and extract the two-dimensional feature quantities that are directly related to the mechanical state of the trip iron core within the corresponding window. The non-floating-point fixed-point arithmetic processing module is used to adapt to the native low computing power hardware conditions of the intelligent fusion terminal and complete the real-time fixed-point arithmetic processing of feature quantities. The same-source cluster collaborative correction module is used to build a smart fusion terminal cluster with matching working conditions in the same scenario, and to complete the multi-dimensional interference decoupling and dynamic correction of feature quantities. The local protection closed-loop execution module is used to deeply link with the native anti-islanding protection and fault section isolation functions of the intelligent fusion terminal to realize hierarchical early warning and full-link protection closed loop of the circuit breaker health status.

[0006] Preferably, the dual-window feature extraction module includes a pre-start window processing unit and a main start window processing unit connected by signals; The pre-start window processing unit is used to divide the electrical dead zone time window in the initial stage of the trip coil energization, extract the reference characteristic quantity used to characterize the electrical condition of the coil, and complete the pre-decoupling processing of non-mechanical fault interference factors. The main start-up window processing unit is used to divide the core action time window of the mechanical displacement of the tripping iron core, extract the core feature quantity used to directly characterize the mechanical deterioration state of the tripping iron core, realize the accurate identification of the early deterioration state of the tripping iron core, and the output result of the pre-start-up window processing unit is used as the pre-judgment basis of the main start-up window processing unit. The time window division and baseline feature extraction processes of the pre-startup window processing unit, and the time window division and core feature extraction processes of the main startup window processing unit, are implemented through the following formulas: Define the current sampling sequence of the trip coil as follows: ,in The sampling point number corresponds to the sampling time. ; The time range for the pre-launch window is defined as follows: ; in, The energization start time of the trip coil. The duration of the pre-startup window; The formula for calculating the baseline characteristic is: ; in, This is the starting sampling point number corresponding to the pre-startup window. This is the sequence number of the termination sampling point corresponding to the pre-start window. The baseline feature quantities extracted for the pre-startup window; The time range for the main startup window is defined as follows: ; in, Duration of the main startup window; The formula for calculating the core feature quantity is: ; in, The inflection point at which the current rise slope first abruptly changes within the main startup window. This represents the baseline inflection point time corresponding to the same-origin cluster. The core feature quantities extracted from the main startup window.

[0007] Preferably, the dual-window feature extraction module has a built-in association determination unit, which is signal-connected to the pre-start window processing unit and the main start window processing unit, respectively, and is used to establish a linkage association determination rule between the benchmark feature quantity and the core feature quantity. Only when the benchmark feature quantity is within the normal operating condition threshold range, the abnormal state of the core feature quantity is effectively identified and determined. The linkage correlation determination rule of the correlation determination unit is implemented through the following formula: Define the normal operating condition threshold range of the reference characteristic quantity as follows: The association determination logic is as follows: ; in, This represents the lower limit of the threshold range for the baseline characteristic quantity under normal operating conditions. This represents the upper limit of the threshold range for the baseline characteristic quantity under normal operating conditions. As a feature for effective determination, The baseline features extracted for the pre-startup window.

[0008] Preferably, the non-floating-point fixed-point arithmetic processing module has a built-in fixed-point algorithm processing unit, which adopts a full fixed-point arithmetic architecture and matches the instruction set architecture of the native fixed-point arithmetic processing unit of the intelligent fusion terminal.

[0009] Preferably, the fixed-point algorithm processing unit has a built-in segmented slope differential processing subunit and a dual-threshold anchoring subunit for signal connection; The segmented slope differential processing subunit is used to perform segmented slope differential calculation on the current waveform data within the corresponding window based on the timing segmentation characteristics of the dual time window. The dual-threshold anchoring subunit is used to set two levels of judgment thresholds that correspond one-to-one with the dual time windows, so as to complete the accurate anchoring and effective identification of the benchmark feature quantity and the core feature quantity respectively. The piecewise slope difference calculation process of the piecewise slope difference processing subunit and the two-level judgment threshold anchoring process of the dual threshold anchoring subunit are respectively implemented through the following progressive formulas: For the current sampling sequence within a dual time window, the formula for calculating the piecewise slope difference is: ; The first-level judgment threshold corresponding to the pre-startup window The second-level judgment threshold corresponding to the main startup window The anchoring logic is as follows: ; in, For the first The slope difference results corresponding to each sampling point is the piecewise sliding step size, and is the fixed sampling point interval for sliding difference calculation. The first-level judgment threshold corresponding to the pre-startup window, This is the second-level judgment threshold corresponding to the main startup window. This is the sampling sequence of the trip coil current. This is the serial number of the current sampling point.

[0010] Preferably, the co-source cluster collaborative correction module includes a cluster dynamic networking unit. The cluster dynamic networking unit is used to automatically select pole-mounted switch intelligent fusion terminals of the same model, batch, service life, and environmental conditions based on the working condition consistency matching rule, with the same busbar of the 10kV distribution network as the core networking unit, and to form a dynamic collaborative co-source terminal cluster. The terminals in the cluster realize point-to-point characteristic data interaction based on their own native short-range communication channels.

[0011] Preferably, the co-source cluster collaborative correction module further includes a multi-factor decoupling correction unit, which is signal-connected to the cluster dynamic networking unit. It is used to decouple multi-dimensional environmental and operating condition interference factors that affect the identification results of core feature quantities based on the synchronous operating condition benchmark feature data of multiple terminals in the co-source cluster, establish a correction coefficient matrix that dynamically adapts to the operating conditions, and dynamically correct the core feature quantities extracted in real time to eliminate the influence of non-fault interference factors on the mechanical state identification results of the tripping gate. The establishment of the correction coefficient matrix and the dynamic correction process of the core feature quantity of the multi-factor decoupling correction unit are achieved through the following formula: Define the vector composed of multi-dimensional interference factors as follows: ; The formula for calculating the dynamic correction coefficient matrix is: ; The formula for calculating the corrected core feature quantity is as follows: ; in, A column vector composed of multi-dimensional interference factors. This represents the number of interference factors after decoupling. These are the environmental and operating condition interference factors after decoupling. This is the interference factor weight matrix. For dynamic correction coefficient matrix, It is a column vector of all 1s. These are the core feature quantities before correction. These are the corrected core feature quantities.

[0012] Preferably, the multi-factor decoupling correction unit has a built-in reference value dynamic update subunit. The reference value dynamic update subunit is used to automatically synchronize the feature data after the terminal in the same cluster completes the opening action that meets the normal working condition judgment standard to all terminals in the cluster, and to continuously update the reference feature value and the corresponding correction coefficient matrix of the cluster to adapt to the natural performance degradation law of the pole-mounted switch throughout its entire life cycle. The rolling update process of the cluster baseline feature value of the dynamic update subunit of the baseline value is achieved through the following formula: The formula for calculating the inflection point time of the cluster baseline after rolling updates is as follows: ; The updated correction coefficient matrix is ​​as follows: ; in, This is the baseline inflection point time for the cluster after rolling updates. The inflection point time of the cluster before the update. To update the weight coefficients on a rolling basis, This refers to the number of terminals within the same cluster. The terminal serial number within the same cluster. For the first in the cluster The inflection point after the terminal's normal tripping action correction. This is the updated dynamic correction coefficient matrix. This is the dynamic correction coefficient matrix before the update. This is the matrix correction amount calculated based on the synchronous feature data.

[0013] Preferably, the local protection closed-loop execution module includes a hierarchical early warning and control unit, which is used to set two-level hierarchical early warning and linkage control rules based on the degree of deterioration of the tripping iron core corresponding to the corrected core characteristic quantity. The first-level warning corresponds to the early deterioration state of the tripping iron core, triggering a local warning at the terminal and simultaneously blocking the reclosing function of the switch. The Level 2 warning corresponds to a severely deteriorated state of the tripping iron core, triggering an emergency maintenance warning. At the same time, it blocks the opening and closing operations of the switch and prohibits the switch from participating in the isolation of the distribution network fault section and anti-islanding protection actions.

[0014] Preferably, it also includes a degradation trend prediction module, which is signal-connected to the same-source cluster collaborative correction module. It is used to perform dynamic fitting and prediction of the degradation trend of the tripping iron core locally on the intelligent fusion terminal based on the historical feature data sequence after correction by the same-source cluster, calculate the remaining reliable operation number of the tripping iron core, and output the corresponding maintenance warning prompt in advance. The degradation trend prediction module's dynamic fitting of degradation trends and calculation of remaining reliable action counts are achieved through the following formula: Based on the core feature sequence after historical correction The degradation trend equation was obtained by linear fitting: ; Define the fault threshold of the core feature quantity as The formula for calculating the remaining number of reliable actions is: ; in, This represents the number of times the circuit breaker has been tripped in history. This is the sequence of core feature quantities after historical correction. The slope of the deterioration trend. The intercept of the linear fit. The fault threshold is the core feature quantity. The remaining number of reliable operations for the tripping core. This is for floor function.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention solves the problem of existing technologies being unable to achieve accurate online identification and local early warning of early jamming of pole-mounted switch cores by constructing a complete technical system of dual-time-window feature extraction, fully fixed-point lightweight computation, co-source cluster collaborative correction, and local protection closed-loop linkage. Based on the timing characteristics of the electrical characteristics of the trip coil and the mechanical action of the core, this invention divides a dedicated dual-time window to complete the progressive extraction of dual-dimensional feature quantities, realizing the decoupling and separation of the electrical operating condition of the trip coil and the mechanical deterioration state of the core. It can accurately capture the weak current characteristics that characterize early jamming of the core. At the same time, it adopts a fully fixed-point lightweight computation architecture adapted to the native hardware of the terminal, which can complete the real-time extraction and computation of feature quantities locally on the terminal without the need for hardware modification of the existing terminal. It eliminates the dependence on high-computing-power cloud platforms, significantly shortens the processing latency of health status assessment, and can adapt to the time limit requirements of new energy anti-islanding protection and FA fault isolation. It fundamentally avoids the problem of distribution network fault expansion caused by trip failure, and provides reliable technical support for the safe and stable operation of distribution networks in scenarios with a high proportion of new energy access.

[0016] 2. This invention uses the same busbar in the distribution network as the core unit and establishes a common terminal cluster based on the operating condition consistency matching rule. Relying on the synchronous operating condition reference data of multiple terminals within the cluster, it completes the decoupling and dynamic correction of multi-dimensional interference factors. This can eliminate the influence of non-fault factors such as ambient temperature, bus voltage fluctuations, and natural performance degradation of switches on the feature recognition results. At the same time, through the rolling update mechanism of the cluster reference value, it adapts to the performance change law of the pole-mounted switch throughout its entire life cycle. It can maintain high accuracy of recognition results for a long time without manual offline calibration, effectively avoiding the problem of early warning failure or false alarm caused by misjudgment of feature recognition. This further ensures the reliability of the health status management of pole-mounted switches and also makes this technical solution adaptable to various complex operating scenarios such as wide outdoor temperature range and strong electromagnetic interference.

[0017] 3. This invention deeply integrates health status assessment results with the terminal's native anti-islanding protection and fault section isolation functions. Based on the degree of degradation of the tripping iron core, it sets graded early warning and control rules, which can simultaneously complete switch operation interlocking and emergency control when degradation risk is identified, avoiding failure to operate caused by faulty operation. At the same time, it can perform dynamic fitting of iron core degradation trend and calculation of remaining reliable operation number locally on the terminal, achieving advanced prediction of degradation trend without relying on cloud big data analysis. This provides accurate quantitative basis for equipment condition maintenance, realizes the transformation from passive fault handling to proactive prevention and control, and further improves the proactive safety management and control capabilities of the distribution network. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall system framework of the present invention; Figure 2This is a schematic diagram illustrating the relationship between the dual-window feature extraction and lightweight computation unit of the present invention; Figure 3 This is a schematic diagram illustrating the association of the co-source cluster collaborative correction unit of the present invention; Figure 4 This is a schematic diagram of the framework for the local protection closed loop and full-link interaction of the present invention. Detailed Implementation

[0019] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0020] Example 1, such as Figures 1-4 As shown, the present invention provides a dynamic assessment and early warning system for the health status of a distribution network, including a trip coil current sampling module, a dual-window feature extraction module, a floating-point-free fixed-point arithmetic processing module, a co-source cluster collaborative correction module, and a local protection closed-loop execution module connected in sequence, with each module forming a complete processing link with time-coordinated collaboration. The trip coil current sampling module is used to collect the current time-domain waveform data of the pole-mounted switch trip coil during the entire energization process, providing a basic data source for subsequent status identification; The dual-window feature extraction module is used to divide the current time domain waveform into two time windows based on the electrical characteristics of the trip coil and the timing characteristics of the mechanical action of the iron core, and extract the two-dimensional feature quantities that are directly related to the mechanical state of the trip iron core within the corresponding window. The non-floating-point fixed-point arithmetic processing module is used to adapt to the native low computing power hardware conditions of intelligent fusion terminals and complete real-time fixed-point arithmetic processing of feature quantities. The same-source cluster collaborative correction module is used to build a cluster of intelligent fusion terminals in the same scenario that matches the working conditions, and to complete the multi-dimensional interference decoupling and dynamic correction of feature quantities. The local protection closed-loop execution module is used to deeply link with the native anti-islanding protection and fault section isolation functions of the intelligent fusion terminal to realize hierarchical early warning and full-link protection closed loop of the circuit breaker health status.

[0021] In an embodiment of the present invention, the dual-window feature extraction module includes a pre-start window processing unit and a main start window processing unit connected by signals; The pre-start window processing unit is used to divide the electrical dead time window in the initial stage of the trip coil energization, extract the reference characteristic quantity to characterize the electrical condition of the coil, and complete the pre-decoupling processing of non-mechanical fault interference factors. The main start-up window processing unit is used to divide the core action time window of mechanical displacement of the tripping iron core, extract the core feature quantity used to directly characterize the mechanical deterioration state of the tripping iron core, and realize the accurate identification of the early deterioration state of the tripping iron core. The output result of the pre-start-up window processing unit serves as the pre-judgment basis of the main start-up window processing unit. The time window division and baseline feature extraction processes of the pre-startup window processing unit, and the time window division and core feature extraction processes of the main startup window processing unit, are implemented through the following formulas: Define the current sampling sequence of the trip coil as follows: ,in The sampling point number corresponds to the sampling time. ; The time range for the pre-launch window is defined as follows: ; in, The starting moment of energizing the trip coil is the timing reference point for the division of the dual time windows, which strictly corresponds to the moment when the trip control command is issued; The duration of the pre-start window corresponds to the electrical dead zone during the initial stage of energizing the trip coil. During this interval, the iron core has not yet undergone mechanical displacement, and the current change only reflects the electrical characteristics of the coil. The formula for calculating the baseline characteristic is: ; in, The starting sampling point number corresponding to the pre-start window is strictly in line with the start time of the energization of the trip coil. This is the sequence number of the termination sampling point corresponding to the pre-start window, which strictly corresponds to the end time of the electrical dead zone; The reference feature quantity extracted for the pre-start window characterizes the average rising slope of the trip coil current within the pre-start window, and is used to quantitatively reflect the real-time electrical condition of the trip coil. The time range for the main startup window is defined as follows: ; in, The duration of the main start-up window corresponds to the core action range in which the tripping iron core undergoes mechanical displacement, and is the effective extraction range for the mechanical state characteristics of the iron core. The formula for calculating the core feature quantity is: ; in, The inflection point at which the current rise slope first abruptly changes within the main start-up window corresponds precisely to the moment when the tripping core begins mechanical displacement. The reference inflection point time corresponding to the same source cluster is the standard inflection point time of normal tripping action under the same working conditions, providing a benchmark for mechanical state determination; The core feature quantity extracted from the main start-up window represents the delay between the measured inflection point and the reference inflection point, and can directly quantify the mechanical deterioration state of the tripping iron core. By precisely dividing the time sequence with dual time windows and progressively extracting dual-dimensional features, the electrical characteristics of the trip coil and the mechanical characteristics of the iron core are decoupled and separated, fundamentally distinguishing the impact of electrical fluctuations and mechanical degradation on the current waveform. At the same time, the lightweight computational design based on discrete sampling sequences is fully adapted to the low computing power hardware conditions of intelligent fusion terminals, and can complete the accurate extraction of core features without relying on high computing power cloud platforms, providing a reliable feature foundation for subsequent status recognition, interference correction and early warning protection.

[0022] In an embodiment of the present invention, the dual-window feature extraction module has a built-in association determination unit. The association determination unit is connected to the pre-start window processing unit and the main start window processing unit respectively. It is used to establish the linkage association determination rules between the reference feature quantity and the core feature quantity. Only when the reference feature quantity is within the normal operating condition threshold range, the abnormal state of the core feature quantity is effectively identified and determined, so as to completely eliminate the interference of electrical operating condition fluctuations on the mechanical state identification result of the circuit breaker. The linkage correlation determination rule of the correlation determination unit is implemented through the following formula: Define the normal operating condition threshold range of the reference characteristic quantity as follows: The association determination logic is as follows: ; in, It is the lower limit of the threshold range of the reference characteristic quantity under normal operating conditions, determined based on the reference data of normal tripping action under the same operating conditions; It is the upper limit of the threshold range of the reference characteristic quantity under normal operating conditions, determined based on the reference data of normal tripping action under the same operating conditions; This is a valid feature determination identifier, a binary variable used to identify whether the extracted feature quantity is under a valid electrical condition. The baseline feature quantities extracted for the pre-startup window; By using effective binarization identification, a pre-verification mechanism is established for the core feature quantity based on the baseline feature quantity: the core feature quantity extracted is considered valid and can be used for subsequent mechanical state identification only when the baseline feature quantity is within the normal operating condition threshold range; if the baseline feature quantity exceeds the threshold range, the electrical operating condition is considered abnormal and the identification result of the core feature quantity is invalid, thus eliminating the interference of electrical operating condition fluctuations on mechanical state identification.

[0023] In the embodiments of the present invention, the non-floating-point fixed-point arithmetic processing module has a built-in fixed-point algorithm processing unit. The fixed-point algorithm processing unit adopts a full fixed-point arithmetic architecture, which is fully matched with the instruction set architecture of the native fixed-point arithmetic processing unit of the intelligent fusion terminal. Without any modification to the terminal hardware or the addition of computing power acceleration units, it can complete the real-time extraction and processing of two-dimensional feature quantities. All data processing, feature extraction, and threshold determination links in the entire calculation process are implemented using fixed-point arithmetic.

[0024] In an embodiment of the present invention, the fixed-point algorithm processing unit has a built-in segmented slope differential processing subunit and a dual-threshold anchoring subunit for signal connection. The segmented slope differential processing subunit is used to perform segmented slope differential calculation on the current waveform data within the corresponding window based on the time segmentation characteristics of the dual time window. It does not require full window traversal processing of the full waveform data, which greatly reduces the computational power required in the calculation process. The dual-threshold anchoring subunit is used to set two levels of judgment thresholds that correspond one-to-one with the dual time windows, so as to accurately anchor and effectively identify the benchmark feature quantity and the core feature quantity respectively. The piecewise slope difference calculation process of the piecewise slope difference processing subunit and the two-level decision threshold anchoring process of the dual threshold anchoring subunit are implemented through the following progressive formulas: For the current sampling sequence within a dual time window, the formula for calculating the piecewise slope difference is: ; The first-level judgment threshold corresponding to the pre-startup window The second-level judgment threshold corresponding to the main startup window The anchoring logic is as follows: ; in, For the first The slope difference results corresponding to each sampling point characterize the current change amplitude within a fixed interval before and after the sampling point, which is used to accurately identify the abrupt change point of the current slope. The segmented sliding step size is the fixed sampling point interval for sliding difference calculation, which can be adapted to the sampling rate and computing power of the terminal. The first-level judgment threshold corresponding to the pre-startup window is used to anchor the effective range of the benchmark feature quantity within the pre-startup window; The second-level judgment threshold corresponding to the main startup window is used to anchor the inflection point of the sudden change in the current slope within the main startup window. This is the sampling sequence of the trip coil current. The serial number of the current sampling point; First, by using segmented sliding differential calculation with a fixed step size, the high-computing-power full waveform spectrum transformation in the traditional scheme is replaced, and lightweight calculation of the slope change of the current waveform is completed without the need to traverse the entire window of the full current data. Then, by using two-level judgment thresholds that correspond one-to-one with the two time windows, the effective anchoring of the reference feature quantity in the pre-start window and the accurate identification of the core inflection point position in the main start window are completed respectively, which greatly reduces the computing power consumption in the calculation process.

[0025] In an embodiment of the present invention, the same-source cluster collaborative correction module includes a cluster dynamic networking unit. The cluster dynamic networking unit is used to automatically select pole-mounted switch intelligent fusion terminals of the same model, batch, service life, and environmental conditions based on the same busbar of the 10kV distribution network as the core networking unit and the same operating condition consistency matching rule, to form a dynamic collaborative same-source terminal cluster. The terminals in the cluster realize point-to-point characteristic data interaction based on their own native short-range communication channels, without the need for additional communication hardware.

[0026] In an embodiment of the present invention, the co-source cluster collaborative correction module further includes a multi-factor decoupling correction unit. The multi-factor decoupling correction unit is signal-connected to the cluster dynamic networking unit and is used to decouple multi-dimensional environmental and operating condition interference factors that affect the identification results of core feature quantities based on the synchronous operating condition benchmark feature data of multiple terminals in the co-source cluster, establish a correction coefficient matrix that dynamically adapts to the operating conditions, and dynamically correct the core feature quantities extracted in real time to eliminate the influence of non-fault interference factors on the identification results of the mechanical state of the tripping gate. The establishment of the correction coefficient matrix and the dynamic correction process of the core feature quantity of the multi-factor decoupling correction unit are achieved through the following formula: Define the vector composed of multi-dimensional interference factors as follows: ; The formula for calculating the dynamic correction coefficient matrix is: ; The formula for calculating the corrected core feature quantity is as follows: ; in, It is a column vector composed of multi-dimensional interference factors, where each element corresponds to a decoupled environmental or working condition interference factor, covering all non-fault factors that affect the identification of core feature quantities; is the number of interference factors after decoupling, and is the dimension of the interference factor vector; The decoupled environmental and operating condition interference factors correspond to different types of non-fault interference factors. The interference factor weight matrix is ​​obtained by fitting the baseline working condition data of the same cluster, and it represents the influence weight of each dimension of interference factor on the core feature quantity. This is a dynamic correction coefficient matrix used to quantify the comprehensive influence of multi-dimensional interference factors on core feature quantities; It is a column vector of all 1s, used to convert the multi-dimensional correction coefficient matrix into a comprehensive correction coefficient, thus completing the comprehensive correction of multiple interference factors; , where is the core feature quantity before correction, and is the measured inflection point delay. The corrected core characteristic quantity eliminates the influence of multi-dimensional non-fault interference factors and can truly reflect the mechanical deterioration state of the tripping iron core. First, the multi-dimensional environmental and operating condition interference factors affecting the core feature quantity are integrated into a vector form. Then, through matrix operations between the interference factor vector and the weight matrix, a dynamic correction coefficient matrix is ​​constructed to quantify the degree of influence of each dimension interference factor on the core feature quantity. Finally, the measured core feature quantity is comprehensively corrected through the correction coefficient matrix to eliminate the influence of non-fault interference factors on the feature quantity, and the corrected feature quantity that only reflects the mechanical deterioration state of the iron core is obtained.

[0027] In an embodiment of the present invention, the multi-factor decoupling correction unit has a built-in reference value dynamic update subunit. The reference value dynamic update subunit is used to automatically synchronize the feature data after the terminal in the same cluster completes the opening action that meets the normal working condition judgment standard to all terminals in the cluster, and to continuously update the reference feature value and the corresponding correction coefficient matrix of the cluster, adapting to the natural performance degradation law of the pole-mounted switch throughout its entire life cycle, without the need for manual offline calibration. The rolling update process of the cluster baseline characteristic value of the dynamic update sub-unit is achieved through the following formula: The formula for calculating the inflection point time of the cluster baseline after rolling updates is as follows: ; The updated correction coefficient matrix is ​​as follows: ; in, The baseline inflection point time of the cluster after rolling updates is the standard inflection point time of normal tripping action within the same source cluster after the update. The baseline inflection point of the cluster before the update is , and the standard inflection point of the cluster obtained from historical iterations is . The weighting coefficients are updated on a rolling basis to balance the weight ratio between historical benchmark data and the current measured data; The number of terminals within the same source cluster is , and the total number of valid terminals participating in the baseline update is . This is the terminal sequence number within the same cluster, used to identify different terminals within the cluster; For the first in the cluster The inflection point after the corrected normal tripping action of the terminal; The updated dynamic correction coefficient matrix is ​​adapted to the updated cluster baseline eigenvalues. is the dynamic correction coefficient matrix before the update, and is the correction coefficient matrix obtained from the historical iterations; This is the matrix correction amount calculated based on the feature data of this synchronization, used to adapt to the correction requirements after the benchmark value is updated; By using an iterative weighted moving average method, the historical benchmark inflection point time is integrated with the measured correction data of normal tripping actions within the current cluster, thus completing the rolling update of the cluster benchmark inflection point time and balancing the stability of historical data with the adaptability of new data to operating conditions. At the same time, based on the feature data synchronized this time, the correction coefficient matrix is ​​updated synchronously to adapt to the updated benchmark feature values ​​and the natural performance degradation law of the switch throughout its entire life cycle.

[0028] In an embodiment of the present invention, the local protection closed-loop execution module includes a hierarchical early warning and control unit, which is used to set two levels of hierarchical early warning and linkage control rules based on the degree of deterioration of the tripping iron core corresponding to the corrected core characteristic quantity. The first-level warning corresponds to the early deterioration state of the tripped iron core, triggering a local warning at the terminal and simultaneously blocking the reclosing function of the switch to prevent reclosing with a fault from aggravating the deterioration of the iron core. The Level 2 warning corresponds to a severely deteriorated state of the tripping iron core, triggering an emergency maintenance warning. At the same time, it blocks the opening and closing operations of the switch, prohibiting the switch from participating in the isolation of the distribution network fault section and anti-islanding protection actions, so as to avoid the occurrence of tripping failure faults.

[0029] In an embodiment of the present invention, the local protection closed-loop execution module further includes a tripping action pre-verification unit. The tripping action pre-verification unit is signal-connected to the hierarchical early warning and control unit. After the intelligent fusion terminal receives the tripping command for anti-islanding protection or fault section isolation issued by the distribution network master station, it first applies a short-time pre-excitation pulse to the tripping coil, collects the reference characteristic data within the pre-start window, and predicts the reliability of this tripping action based on the reference characteristic data. If it is predicted that there is a risk of tripping failure, it immediately triggers the backup tripping control circuit and simultaneously reports the prediction result to the distribution network master station.

[0030] In an embodiment of the present invention, the local protection closed-loop execution module further includes an action closed-loop emergency control unit. The action closed-loop emergency control unit is signal-connected to the tripping action pre-verification unit and is used to track the dynamic change status of the core characteristic quantity in the main start window in real time during the execution of the tripping action. If it is detected that the characteristic quantity of the tripping action exceeds the normal threshold range, or that there is an excessive tripping delay and a risk of failure to operate, the strong excitation control logic of the tripping coil is immediately triggered to increase the excitation driving force of the coil and forcefully complete the tripping action, thereby avoiding the expansion of the distribution network fault range caused by the failure to operate the tripping action.

[0031] In an embodiment of the present invention, the action closed-loop emergency control unit has a built-in action self-calibration subunit. The action self-calibration subunit is used to automatically collect the feature data of the entire process of this action after the tripping action is completed, and synchronize the data to the same source terminal cluster after data correction, triggering the rolling update of the reference feature value and correction coefficient matrix in the cluster, so as to realize the dynamic self-calibration of the system in the entire operation cycle without manual intervention.

[0032] In an embodiment of the present invention, the system further includes a degradation trend prediction module, which is signal-connected to the same-source cluster collaborative correction module. It is used to perform dynamic fitting and prediction of the degradation trend of the tripping iron core locally on the intelligent fusion terminal based on the historical feature data sequence after correction by the same-source cluster, calculate the remaining reliable operation number of the tripping iron core, and output the corresponding maintenance warning prompt in advance. The entire prediction process does not rely on big data analysis and processing in the cloud and is completed entirely on the terminal. The degradation trend prediction module's dynamic fitting of degradation trends and calculation of remaining reliable action counts are achieved through the following formula: Based on the core feature sequence after historical correction The degradation trend equation was obtained by linear fitting: ; Define the fault threshold of the core feature quantity as The formula for calculating the remaining number of reliable actions is: ; in, The number of historical tripping actions is denoted by , and the length of the historical corrected core feature sequence is denoted by . This is a sequence of core feature quantities after historical correction, where each element corresponds to the inflection point delay after a tripping action correction; The slope represents the rate of degradation of the tripping core. The larger the absolute value of the slope, the faster the core deteriorates. is the linear fitting intercept, and is the constant term of the degradation trend equation; The fault threshold is the core characteristic quantity, corresponding to the critical inflection point delay when the tripping iron core fails to operate. The remaining reliable number of tripping actions of the tripping core represents the number of reliable tripping actions that the core can still perform before a fault occurs. This is a floor function used to convert the calculated number of remaining actions into an integer, usable result for the project. Based on the historically corrected core feature sequence, the dynamic fitting of the deterioration trend of the tripping core is completed locally on the terminal through linear fitting, and a trend equation characterizing the deterioration rate is obtained. Then, based on the fault threshold of the core feature and the current deterioration state, the remaining number of reliable actions of the tripping core is calculated, so as to realize the advanced prediction of the deterioration trend of the tripping core. The entire calculation process is completed locally on the terminal without relying on cloud big data analysis.

[0033] In embodiments of the present invention, a master station interaction module is also included. The master station interaction module is connected to the local protection closed-loop execution module and the degradation trend prediction module respectively. It is used to upload only the graded early warning results, the correction data of the core characteristic quantities, the degradation trend prediction results and the maintenance prompt information to the distribution network master station, without uploading the full current waveform data of the trip coil, which greatly reduces the bandwidth occupation of the distribution network communication channel and improves the real-time performance and reliability of data transmission.

[0034] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A dynamic assessment and early warning system for the health status of a power distribution network, characterized in that, It includes a trip coil current sampling module, a dual-window feature extraction module, a floating-point-free fixed-point arithmetic processing module, a co-source cluster collaborative correction module, and a local protection closed-loop execution module, which are connected in sequence. The modules form a complete processing link with time-coordinated collaboration. The trip coil current sampling module is used to collect the current time-domain waveform data of the pole-mounted switch trip coil during the entire energization process; The dual-window feature extraction module is used to divide the current time domain waveform into two time windows based on the electrical characteristics of the trip coil and the timing characteristics of the mechanical action of the iron core, and extract the two-dimensional feature quantities that are directly related to the mechanical state of the trip iron core within the corresponding window. The non-floating-point fixed-point arithmetic processing module is used to adapt to the native low computing power hardware conditions of the intelligent fusion terminal and complete the real-time fixed-point arithmetic processing of feature quantities. The same-source cluster collaborative correction module is used to build a smart fusion terminal cluster with matching working conditions in the same scenario, and to complete the multi-dimensional interference decoupling and dynamic correction of feature quantities. The local protection closed-loop execution module is used to deeply link with the native anti-islanding protection and fault section isolation functions of the intelligent fusion terminal to realize hierarchical early warning and full-link protection closed loop of the circuit breaker health status.

2. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 1, characterized in that, The dual-window feature extraction module includes a pre-start window processing unit and a main start window processing unit connected by signals; The pre-start window processing unit is used to divide the electrical dead zone time window in the initial stage of the trip coil energization, extract the reference characteristic quantity used to characterize the electrical condition of the coil, and complete the pre-decoupling processing of non-mechanical fault interference factors. The main start-up window processing unit is used to divide the core action time window of the mechanical displacement of the tripping iron core, extract the core feature quantity used to directly characterize the mechanical deterioration state of the tripping iron core, realize the accurate identification of the early deterioration state of the tripping iron core, and the output result of the pre-start-up window processing unit is used as the pre-judgment basis of the main start-up window processing unit. The time window division and baseline feature extraction processes of the pre-startup window processing unit, and the time window division and core feature extraction processes of the main startup window processing unit, are implemented through the following formulas: Define the current sampling sequence of the trip coil as follows: ,in The sampling point number corresponds to the sampling time. ; The time range for the pre-launch window is defined as follows: ； in, The energization start time of the trip coil. The duration of the pre-startup window; The formula for calculating the baseline characteristic is: ； in, This is the starting sampling point number corresponding to the pre-startup window. This is the sequence number of the termination sampling point corresponding to the pre-start window. The baseline feature quantities extracted for the pre-startup window; The time range for the main startup window is defined as follows: ； in, Duration of the main startup window; The formula for calculating the core feature quantity is: ； in, The inflection point at which the current rise slope first abruptly changes within the main startup window. This represents the baseline inflection point time corresponding to the same-origin cluster. The core feature quantities extracted from the main startup window.

3. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 2, characterized in that, The dual-window feature extraction module has a built-in association determination unit, which is connected to the pre-start window processing unit and the main start window processing unit respectively. It is used to establish the linkage association determination rules between the benchmark feature quantity and the core feature quantity. Only when the benchmark feature quantity is within the normal operating condition threshold range, the abnormal state of the core feature quantity is effectively identified and determined. The linkage correlation determination rule of the correlation determination unit is implemented through the following formula: Define the normal operating condition threshold range of the reference characteristic quantity as follows: The logic for determining association is as follows: ； in, This represents the lower limit of the threshold range for the baseline characteristic quantity under normal operating conditions. This represents the upper limit of the threshold range for the baseline characteristic quantity under normal operating conditions. As a feature for effective determination, The baseline features extracted for the pre-startup window.

4. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 1, characterized in that, The non-floating-point fixed-point arithmetic processing module has a built-in fixed-point algorithm processing unit. The fixed-point algorithm processing unit adopts a full fixed-point arithmetic architecture, which matches the instruction set architecture of the native fixed-point arithmetic processing unit of the intelligent fusion terminal.

5. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 4, characterized in that, The fixed-point algorithm processing unit has a built-in segmented slope differential processing subunit and a dual-threshold anchoring subunit for signal connection; The segmented slope differential processing subunit is used to perform segmented slope differential calculation on the current waveform data within the corresponding window based on the timing segmentation characteristics of the dual time window. The dual-threshold anchoring subunit is used to set two levels of judgment thresholds that correspond one-to-one with the dual time windows, so as to complete the accurate anchoring and effective identification of the benchmark feature quantity and the core feature quantity respectively. The piecewise slope difference calculation process of the piecewise slope difference processing subunit and the two-level judgment threshold anchoring process of the dual threshold anchoring subunit are respectively implemented through the following progressive formulas: For the current sampling sequence within a dual time window, the formula for calculating the piecewise slope difference is: ； The first-level judgment threshold corresponding to the pre-startup window The second-level judgment threshold corresponding to the main startup window The anchoring logic is as follows: ； in, For the first The slope difference results corresponding to each sampling point is the piecewise sliding step size, and is the fixed sampling point interval for sliding difference calculation. The first-level judgment threshold corresponding to the pre-startup window, This is the second-level judgment threshold corresponding to the main startup window. This is the sampling sequence of the trip coil current. This is the serial number of the current sampling point.

6. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 1, characterized in that, The co-source cluster collaborative correction module includes a cluster dynamic networking unit. The cluster dynamic networking unit is used to automatically select pole-mounted switch intelligent fusion terminals of the same model, batch, service life, and environmental conditions based on the working condition consistency matching rule, with the same busbar of the 10kV distribution network as the core networking unit, and to form a dynamic collaborative co-source terminal cluster. The terminals in the cluster realize point-to-point characteristic data interaction based on their own native short-range communication channels.

7. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 6, characterized in that, The same-source cluster collaborative correction module also includes a multi-factor decoupling correction unit. The multi-factor decoupling correction unit is signal-connected to the cluster dynamic networking unit. It is used to decouple multi-dimensional environmental and operating condition interference factors that affect the identification results of core feature quantities based on the synchronous operating condition benchmark feature data of multiple terminals in the same-source cluster, establish a correction coefficient matrix that dynamically adapts to the operating conditions, and dynamically correct the core feature quantities extracted in real time to eliminate the influence of non-fault interference factors on the mechanical state identification results of the tripping gate. The establishment of the correction coefficient matrix and the dynamic correction process of the core feature quantity of the multi-factor decoupling correction unit are achieved through the following formula: Define the vector composed of multi-dimensional interference factors as follows: ; The formula for calculating the dynamic correction coefficient matrix is: ； The formula for calculating the corrected core feature quantity is as follows: ； in, A column vector composed of multi-dimensional interference factors. This represents the number of interference factors after decoupling. These are the environmental and operating condition interference factors after decoupling. This is the interference factor weight matrix. For dynamic correction coefficient matrix, It is a column vector of all 1s. These are the core feature quantities before correction. These are the corrected core feature quantities.

8. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 7, characterized in that, The multi-factor decoupling correction unit has a built-in benchmark value dynamic update subunit. The benchmark value dynamic update subunit is used to automatically synchronize the feature data after the terminal in the same cluster completes the opening action that meets the normal working condition judgment standard to all terminals in the cluster, and to continuously update the benchmark feature value and the corresponding correction coefficient matrix of the cluster to adapt to the natural performance degradation law of the pole-mounted switch throughout its entire life cycle. The rolling update process of the cluster baseline feature value of the dynamic update subunit of the baseline value is achieved through the following formula: The formula for calculating the inflection point time of the cluster baseline after rolling updates is as follows: ； The updated correction coefficient matrix is ​​as follows: ； in, This is the baseline inflection point time for the cluster after rolling updates. The inflection point time of the cluster before the update. To update the weight coefficients on a rolling basis, This refers to the number of terminals within the same cluster. The terminal serial number within the same cluster. For the first in the cluster The inflection point after the terminal's normal tripping action correction. This is the updated dynamic correction coefficient matrix. This is the dynamic correction coefficient matrix before the update. This is the matrix correction amount calculated based on the synchronous feature data.

9. The dynamic assessment and early warning system for the health status of a power distribution network according to claim 1, characterized in that, The local protection closed-loop execution module includes a hierarchical early warning and control unit, which is used to set two-level hierarchical early warning and linkage control rules based on the degree of deterioration of the tripping iron core corresponding to the corrected core characteristic quantity. The first-level warning corresponds to the early deterioration state of the tripping iron core, triggering a local warning at the terminal and simultaneously blocking the reclosing function of the switch. Level 2 warning corresponds to a severely deteriorated state of the tripping iron core, triggering an emergency maintenance warning. At the same time, it blocks the opening and closing operations of the switch and prohibits the switch from participating in the isolation of the distribution network fault section and anti-islanding protection actions.

10. A dynamic assessment and early warning system for the health status of a power distribution network according to claim 1, characterized in that, It also includes a degradation trend prediction module, which is signal-connected to the same-source cluster collaborative correction module. It is used to perform dynamic fitting and prediction of the degradation trend of the tripping iron core locally on the intelligent fusion terminal based on the historical feature data sequence after correction by the same-source cluster, calculate the remaining reliable operation number of the tripping iron core, and output the corresponding maintenance warning prompt in advance. The degradation trend prediction module's dynamic fitting of degradation trends and calculation of remaining reliable action counts are achieved through the following formula: Based on the core feature sequence after historical correction The degradation trend equation was obtained by linear fitting: ； Define the fault threshold of the core feature quantity as The formula for calculating the remaining number of reliable actions is: ； in, This represents the number of times the circuit breaker has been tripped in history. This is the sequence of core feature quantities after historical correction. The slope of the deterioration trend. The intercept of the linear fit. The fault threshold is the core feature quantity. The remaining number of reliable operations for the tripping core. This is for floor function.

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