Intelligent gateway-based electrical cabinet state comprehensive measurement and control method and system

Abstract

The application discloses a kind of based on intelligent gateway's electrical cabinet state comprehensive measurement and control method and system, the method includes: through intelligent gateway receives the message of electrical cabinet inside collection equipment, forms original sampling sequence;From original sampling sequence extraction candidate electrical event, and according to electrical relationship event pairing;Select reference electrical data source, estimate the time deviation of non-reference electrical data source, correct message arrival time to data effective time;Identify load step segment on the corrected time axis, and carry out data credibility determination;The contact heat response model is input to reliable fragment, and heat response residual is obtained;According to heat response residual, cabinet environmental temperature and fan operating state classification, and check data effective time, output grading control result.The method can reduce the misjudgment caused by time dislocation of multi-source data, improve electrical cabinet state measurement and control accuracy.

Description

Technical Field

[0001] This application relates to the field of electrical cabinet status detection technology, specifically to a comprehensive monitoring and control method and system for electrical cabinet status based on a smart gateway. Background Technology

[0002] Electrical cabinets are widely used in power distribution systems, and their operating status directly affects power supply reliability and electrical safety. Existing electrical cabinets are typically equipped with data acquisition devices for current, voltage, power, circuit breaker status, protection actions, contact temperature measurement, cabinet ambient temperature, and fan status, and upload data through gateways or monitoring terminals. Because different acquisition devices have different communication protocols, refresh cycles, polling sequences, and transmission delays, the arrival time of messages for the same field status change often varies across different data sources. If status is judged directly based on message arrival time, time misalignment can easily occur during rapid changes such as circuit breaker opening and closing, protection actions, and the addition or removal of large loads, leading to apparent contradictions between electrical quantities, switching quantities, and temperature quantities, and consequently causing false alarms or missed alarms.

[0003] In addition, the temperature rise of the contact is affected by the load change, ambient temperature and heat dissipation conditions inside the cabinet. Traditional monitoring methods based on fixed temperature thresholds or single temperature rise thresholds are difficult to distinguish between different situations such as normal load temperature rise, local poor contact heating and deterioration of heat dissipation conditions inside the cabinet. Especially in the early abnormal stage of the contact, the absolute temperature may not have reached the alarm threshold, but its thermal response has already deviated from the normal pattern, and existing methods are difficult to identify in time. Summary of the Invention

[0004] This application provides a method and system for comprehensive monitoring and control of electrical cabinet status based on a smart gateway, in order to at least solve some of the technical problems existing in the related technologies described above.

[0005] According to a first aspect of the embodiments of this application, a method for comprehensive measurement and control of electrical cabinet status based on a smart gateway is provided, comprising: the smart gateway receiving messages from the data acquisition equipment inside the electrical cabinet to form an original sampling sequence, the original sampling sequence including current, voltage, active power, circuit breaker open / closed position status, protection action signal, contact temperature, cabinet ambient temperature, fan operating status, data arrival time, and communication quality parameters; extracting candidate electrical events from the original sampling sequence and pairing the candidate electrical events based on electrical relationships; selecting a reference electrical data source from the paired event group and estimating the time deviation estimate of the non-reference electrical data source, and correcting the data arrival time of the corresponding message to the corrected data validity time; identifying load step segments on the corrected data validity time axis, and generating a data reliability judgment result based on electrical relationships, communication quality parameters, and temperature message arrival interval; inputting the load step segments that pass the data reliability judgment into the contact thermal response model to obtain thermal response residuals; classifying according to the thermal response residuals, cabinet ambient temperature, and fan operating status, verifying the data validity time based on the thermal response residuals, and outputting graded measurement and control results.

[0006] As an optional approach, extracting candidate electrical events from the original sampling sequence includes: establishing cumulative statistics for current and active power, updating the cumulative statistics based on the current sampled value, the average value of the most recent steady-state period, and the allowable offset; when the cumulative statistics exceed a preset judgment threshold, recording the corresponding sampling time as a candidate change point and clearing the cumulative statistics; for circuit breaker open / closed states and protection action signals, recording candidate change points according to the data arrival time of the state transition; when multiple candidate change points exist in the same circuit within the same merging time range, retaining the earliest candidate change point as a candidate electrical event, and merging other candidate change points into that candidate electrical event.

[0007] As an optional approach, pairing candidate electrical events based on electrical relationships includes: establishing a search window centered on the data arrival time of a candidate electrical event, with the radius of the search window determined according to the maximum refresh cycle of the data sources participating in the pairing; searching for candidate electrical events from other data sources within the search window; and when electrical relationships such as closing, opening, protection tripping, or large load input are met, grouping the found candidate electrical events and the candidate electrical event at the center into the same paired event group, and assigning an event identifier to the paired event group.

[0008] As an optional approach, estimating the time deviation of a non-reference electrical data source includes: selecting electrical data sources whose refresh cycles meet a preset reference cycle range and whose communication quality parameters are within a preset stable range as reference electrical data sources; for each non-reference electrical data source, collecting the differences between the data arrival times of candidate electrical events of that non-reference electrical data source and the data arrival times of candidate electrical events of the reference electrical data source in the most recent N paired event groups, forming a difference sequence, where N is a preset positive integer; taking the median of the difference sequence as the time deviation estimate of that non-reference electrical data source; and subtracting the time deviation estimate from the data arrival time of each message from that non-reference electrical data source to obtain the corrected data validity time.

[0009] As an optional approach, identifying load step segments on the corrected data validity time axis includes: thresholding the change in current or active power within a preset change window against a threshold value; the period when the change exceeds the threshold value and maintains a new steady-state level within a preset steady-state duration is recorded as a load step segment; generating data reliability judgment results includes: generating a failed data reliability judgment result if any of the following is true: circuit breaker open / closed position change, protection action signal, relationship between voltage change and current change does not conform to electrical relationship, communication quality parameters exceed a preset range, or temperature message arrival interval exceeds a preset multiple of the normal refresh cycle; otherwise, generating a passed data reliability judgment result.

[0010] As an optional approach, a contact thermal response model is established for each contact temperature measurement location. The contact thermal response model uses the contact temperature change, load change, cabinet ambient temperature change, and fan operating status at the current sampling time as regression vectors, and the contact temperature change at the next sampling time as the output observation. The parameter vector and covariance matrix are updated using recursive least squares with a forgetting factor. The parameter vector includes thermal inertia decay parameters, load influence parameters, ambient temperature influence parameters, and fan heat dissipation influence parameters. The updated parameter vector is used to calculate the thermal response residual, and the updated covariance matrix is ​​used to update the recursive least squares at the next sampling time.

[0011] As an optional approach, obtaining the thermal response residual includes: taking the occurrence time of the load step segment determined by data reliability on the corrected data effective time axis as the starting point of the thermal response observation window; calculating the time constant based on the thermal inertia decay parameter and sampling period in the junction thermal response model; and determining the end point of the thermal response observation window according to the time constant; at each temperature sampling time within the thermal response observation window, recursively predicting the junction temperature change using the updated parameter vector to obtain the predicted temperature change; and subtracting the predicted temperature change from the measured temperature change at the same temperature sampling time to obtain the thermal response residual.

[0012] As an optional solution, the classification based on thermal response residuals, cabinet ambient temperature, and fan operating status includes: matching in the order of local contact heating risk, normal load temperature rise, and deterioration of heat dissipation conditions. When the thermal response residual of a single contact is continuously positive within the thermal response observation window after the load increases, and the thermal response residual does not return to the historical residual fluctuation range of that contact after the load reaches a new steady-state level, while the thermal response residuals of other contacts of the same type and location in the same circuit are within the historical residual fluctuation range, a local contact heating risk is generated; when the thermal response residuals of the three corresponding contacts in the same circuit are all within the historical residual fluctuation range, and the three-phase contact temperatures rise and fall synchronously with the load changes, a normal load temperature rise is generated; when multiple temperature measurement points in different circuits within the same electrical cabinet show positive thermal response residuals with similar directions and amplitudes at the same time, accompanied by an increase in cabinet ambient temperature or a fan stop signal, a deterioration of heat dissipation conditions is generated.

[0013] As an optional approach, the validity time of the thermal response residual verification data includes the following: when the temperature rise start time of multiple similar temperature measurement points in the same circuit is earlier than the load increase time on the corrected data validity time axis, the corresponding load step segment is marked as a sampling time anomaly, and the local contact heating risk classification is not output based on the load step segment; when the three-phase current change is aligned on the corrected data validity time axis, and the temperature of a temperature measurement point drops after a step jump within one or two sampling cycles, the corresponding load step segment is marked as a temperature data update anomaly, and the local contact heating risk classification is not output based on the load step segment; when the same temperature module in multiple consecutive load step segments shows a time offset with consistent direction and stable amplitude, the data validity time estimate of the temperature module is updated based on the time offset.

[0014] According to a second aspect of the embodiments of this application, a comprehensive monitoring and control system for the status of an electrical cabinet based on a smart gateway is also provided, comprising: a message receiving module, used by the smart gateway to receive messages from the data acquisition equipment inside the electrical cabinet, forming an original sampling sequence, the original sampling sequence including current, voltage, active power, circuit breaker open / closed position status, protection action signal, contact temperature, ambient temperature inside the cabinet, fan operating status, data arrival time, and communication quality parameters; an event pairing module, used to extract candidate electrical events from the original sampling sequence and pair the candidate electrical events based on electrical relationships; and a time correction module, used to select a reference electrical data source from the paired event group and estimate... The system includes a time deviation estimate for non-reference electrical data sources, which corrects the data arrival time of corresponding messages to the corrected data validity time; a reliability determination module, which identifies load step segments on the corrected data validity time axis and generates data reliability determination results based on electrical relationships, communication quality parameters, and temperature message arrival intervals; a thermal response analysis module, which inputs the load step segments that pass the data reliability determination into the contact thermal response model to obtain thermal response residuals; and a measurement and control result output module, which classifies data according to thermal response residuals, cabinet ambient temperature, and fan operating status, and verifies the data validity time based on thermal response residuals to output graded measurement and control results.

[0015] This application utilizes an intelligent gateway to uniformly process multi-source data, including current, voltage, power, switching quantities, protection signals, contact temperature, cabinet ambient temperature, and fan status. Based on electrical event pairing relationships, it estimates the time deviation between different data sources and corrects message arrival times to more accurately reflect changes in the field status. This reduces misjudgments caused by asynchronous sampling during circuit breaker opening / closing, protection actions, and load surges. Furthermore, this invention uses only load step segments verified through electrical relationships, communication quality, and temperature message intervals for contact thermal response analysis. By combining the contact thermal response model and thermal response residuals, it distinguishes between normal load temperature rise, localized contact overheating risk, and deteriorating heat dissipation conditions, avoiding delays in early anomaly identification caused by relying solely on fixed temperature thresholds. Simultaneously, by using thermal response residuals to reverse-verify the effective time of data and the temperature data update status, it eliminates interference from sampling time anomalies on the judgment results, thereby improving the accuracy and reliability of electrical cabinet status monitoring and control results.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Furthermore, no embodiment in this disclosure is required to achieve all the effects described above. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] Figure 1 A flowchart of a comprehensive monitoring and control method for the status of an electrical cabinet based on a smart gateway, provided in this embodiment of the disclosure; Figure 2 A flowchart for candidate electrical event extraction and pairing provided in embodiments of this disclosure; Figure 3 A flowchart for correcting the validity period of data provided in this embodiment of the disclosure; Figure 4 A flowchart illustrating the data credibility determination result provided in this embodiment of the disclosure; Figure 5 A flowchart for calculating thermal response residuals provided in this embodiment of the disclosure; Figure 6 A schematic block diagram of an electrical cabinet status integrated monitoring and control system based on a smart gateway, provided in an embodiment of this disclosure; Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0020] This disclosure provides a comprehensive monitoring and control method for the status of electrical cabinets based on a smart gateway. This method is applicable to electrical cabinets in power distribution systems equipped with a smart gateway. The cabinets are connected to various data acquisition devices via communication interfaces, including at least current transformers, voltage transformers, active power measurement units, circuit breaker auxiliary contact modules, protection devices, contact temperature measurement modules, cabinet temperature and humidity sensors, and fan status monitoring modules. The communication protocols, refresh cycles, and polling sequences of these devices are not entirely the same, and there is a variable deviation between the time when each device's message arrives at the gateway and the time when it reflects the actual status of the cabinet. The processing is completed at the gateway side, and a uniform refresh cycle for all instruments is not required. Monitoring targets include locations prone to increased contact resistance, such as busbar connections, circuit breaker inlet and outlet terminals, drawer cabinet contacts, and cable joints.

[0021] Before proceeding with each step, a brief explanation of several terms used in the embodiments of this disclosure is provided. Data arrival time refers to the local time recorded when the gateway receives a message; data validity time refers to the estimated time at which the actual state of the cabinet corresponding to the message content occurs on-site; load step segment refers to a process in which the current or active power of a circuit changes significantly within a short period and then remains stable; thermal response residual refers to the deviation between the measured temperature change at a contact temperature measurement location and the predicted temperature change from the contact thermal response model.

[0022] The implementation process of the method described in this application will be described in detail below with reference to specific embodiments. It should be noted that this embodiment is only used to explain this application and is not intended to limit the scope of protection of this application. Conventional adjustments or substitutions of each step by those skilled in the art without departing from the concept of this application should be included in the scope of protection of this application.

[0023] Please see Figure 1 , Figure 1 This is a flowchart of an electrical cabinet status integrated measurement and control method based on a smart gateway according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes steps S1-S6: In step S1, the intelligent gateway receives the data acquisition device message in the electrical cabinet and forms an original sampling sequence. The original sampling sequence includes current, voltage, active power, circuit breaker open / closed position status, protection action signal, contact temperature, cabinet ambient temperature, fan operating status, data arrival time, and communication quality parameters.

[0024] Specifically, the intelligent gateway continuously receives messages from each data acquisition device in the cabinet, and writes the content of each message, along with the corresponding data arrival time and communication quality parameters, into local storage to form an original sampling sequence. Each record in the original sampling sequence includes the following fields: RMS current value, RMS voltage value, active power, circuit breaker open / closed position status, protection action signal, temperature measurement value of each contact, ambient temperature and humidity inside the cabinet, fan operating status, data arrival time of the message, and communication quality parameters. Among them, the communication quality parameters may include message round-trip delay, packet loss marker, and continuous packet loss count, which are statistically obtained by the gateway during the communication process.

[0025] In step S2, candidate electrical events are extracted from the original sampling sequence, and these candidate electrical events are paired based on electrical relationships. For details, please refer to... Figure 2 , Figure 2 A flowchart illustrating the candidate electrical event extraction and pairing process provided in an embodiment of this disclosure is shown, such as... Figure 2 As shown in box 201, the gateway extracts candidate electrical events from the original sampling sequence.

[0026] Candidate electrical events refer to moments in the data sequence that exhibit abrupt changes and may correspond to state transitions of equipment within a cabinet. Typical scenarios include circuit breaker closing or opening, bus tie switching, protection tripping, and large load connection or disconnection. For continuous quantities such as current and active power, the gateway uses accumulation and change point detection methods to locate abrupt changes. Specifically, during normal and stable operation, each sampled value fluctuates around the mean, and the cumulative statistic remains near zero. Once the mean undergoes a step change, continuous deviations accumulate rapidly, the cumulative statistic increases accordingly, and quickly exceeds the judgment threshold, thus identifying the moment the change occurred.

[0027] Specifically, the gateway maintains a positive cumulative statistic for each loop. and negative cumulative statistics In the Each sampling time can be updated using the following formula: ; ;in This is the current sampled value. This is the average value of the continuous quantity over the most recent period of steady-state operation. The allowable offset is used to suppress false triggering caused by normal load fluctuations. Its value is related to the rated load level of the circuit and can be configured as a certain percentage of the rated current or rated active power. The gateway automatically re-counts and updates the data each time a new steady-state phase is entered; when... or Exceeding the preset judgment threshold At that time, the gateway records the sampling moment as a candidate change point, and marks it as an upward or downward candidate change point based on the cumulative statistics exceeding the threshold. and Zero; Determine threshold There is a coordination relationship between it and the allowable offset. The larger the value, the lower the sensitivity but the stronger the anti-interference ability. Optionally, [the value can be...]. Configured as Several times that.

[0028] For switching quantities such as circuit breaker open / closed position status and protection action signals, the gateway directly records candidate change points based on the arrival time of the data for the status transition.

[0029] In one embodiment, multiple candidate change points may be generated in the same circuit within a short period of time. For example, the current may show two consecutive step changes at the moment the circuit breaker is closed. For multiple candidate change points that appear within the same merging time range, the gateway retains the earliest candidate change point as the time marker of the candidate electrical event and retains the rising or falling change direction corresponding to each candidate change point as the attribute of the candidate electrical event. The remaining change points are merged into the candidate electrical event and are no longer processed as independent events. The merging time range can be set according to the maximum refresh cycle in the system.

[0030] In box 202, candidate electrical events are paired based on electrical relationships. Candidate electrical events are distributed across the timelines of different data sources. Due to differences in refresh cycles and communication delays among the data sources, the arrival times of candidate electrical events generated from the same cabinet state change on different data sources often do not overlap. This embodiment groups candidate electrical events from different data sources belonging to the same field event into the same paired event group based on the basic electrical relationships within the cabinet.

[0031] Specifically, the gateway establishes a search window centered on the arrival time of a candidate electrical event from a certain data source. The radius of the search window is determined based on the maximum refresh cycle among the data sources participating in the pairing, and can optionally be a multiple of the maximum refresh cycle. Within this window, the gateway searches for candidate electrical events in other data sources and determines the electrical relationship between them.

[0032] The electrical relationships used for pairing include: For closing events, the pairing condition is: the circuit breaker changes from open to closed, and simultaneously, a candidate change point of voltage establishment or current rise appears in the corresponding circuit within the window; for opening events, the pairing condition is: the circuit breaker changes from closed to open, and the current in the corresponding circuit decreases within the window; for protection tripping events, the pairing condition is: the protection action signal is set, the circuit breaker state changes to open, and the current decreases within the window; for high load input events, the pairing condition is: current or active power shows a step increase, and there are no circuit breaker state changes or protection actions during the same time period. When any of the above electrical relationships are met, the gateway will group the candidate electrical events matched in the search window and the central candidate electrical event into the same paired event group and assign an event identifier to this group.

[0033] If a candidate electrical event cannot be matched with other candidate electrical events from other data sources within the search window, the gateway records it separately. This event is not included in subsequent time deviation estimation, but is still retained for anomaly analysis reference.

[0034] In step S3, a reference electrical data source is selected from the paired event group, and the time deviation estimate of the non-reference electrical data source is estimated. The data arrival time of the corresponding message is then corrected to the corrected data validity time.

[0035] Since the arrival time of messages from different instruments at the gateway is not equal to the actual time when the field status reflected by the message occurs, if multi-source data is directly arranged according to arrival time, false contradictions such as current still existing after opening or voltage not being established after closing may easily occur during periods of rapid change such as circuit breaker opening and closing or protection action. Therefore, according to the embodiments of this disclosure, the arrival time difference between candidate electrical events of each data source in the paired event group is used to estimate the time deviation of each data source relative to the reference data source; thus eliminating or reducing such time misalignment.

[0036] Please see Figure 3 , Figure 3 A flowchart illustrating the data validity time correction process provided in this disclosure embodiment is shown, such as... Figure 3 As shown in box 301, select a reference electrical data source from the paired event group.

[0037] The gateway selects electrical data sources from the paired data sources whose refresh cycles meet the preset reference cycle range and whose communication quality parameters are within the preset stable range as reference electrical data sources. The refresh cycle of the switch quantity acquisition module is usually much shorter than that of the ammeter and temperature module, so it is suitable as a reference in most cabinet types. In some embodiments, if the protection device has its own event time stamp, and the time relationship between the time stamp and the switch quantity change point remains stable in multiple consecutive events, the protection device can also be used as a reference data source.

[0038] In box 302, the time skew estimates for non-reference electrical data sources are estimated. For each non-reference electrical data source, the gateway collects its most recent... The difference between the arrival time in the paired event group and the arrival time of the reference electrical data source within the same group. The preset positive integer can be optionally configured to represent a series of differences from the most recent several to dozens of paired events. The gateway takes the median of this difference series as the time deviation estimate for the non-reference electrical data source. The median is chosen because individual paired events may have larger differences due to communication congestion, and the median is better at resisting such discrete outliers than the arithmetic mean. The time deviation estimate is updated as new paired events are continuously included, and optionally only the differences from the most recent few events are retained for calculation, so that the estimation results can adapt to changes in communication conditions over time.

[0039] In box 303, the data arrival time of the corresponding message is corrected to the corrected data validity time. For each message from the non-reference electrical data source, the gateway subtracts the corresponding time deviation estimate from its data arrival time, and the result is the corrected data validity time for that message.

[0040] For the temperature module, since its refresh cycle is typically longer than that of electrical quantities, the slow change in temperature value is mainly reflected in thermal inertia response. Therefore, the gateway does not use the temperature module as a non-reference electrical data source for time deviation estimation based on electrical event pairing. When a temperature message enters the contact thermal response model, its data arrival time is initially used as the initial data validity time. If subsequent steps identify a consistent and stable time offset for the temperature module based on multiple consecutive load step segments, the estimated data validity time of the temperature module is then updated. Details are described below and will not be repeated here.

[0041] In step S4, load step segments are identified on the corrected data validity time axis, and a data reliability determination result is generated based on electrical relationships, communication quality parameters, and temperature message arrival intervals.

[0042] Please see Figure 4 , Figure 4 A flowchart illustrating the data credibility determination result provided in this embodiment of the disclosure is shown, such as... Figure 4 As shown in box 401, the gateway identifies load step segments from the current or active power sequence on the corrected data validity time axis.

[0043] Specifically, the determination method is to compare the change amplitude of current or active power within a preset change window with a threshold value. The period when the change amplitude exceeds the threshold value and maintains a new steady-state level within a preset steady-state duration is recorded as a load step segment. The threshold value is related to the rated load level of the circuit and can be configured as a certain percentage of the rated current. The steady-state duration requirement is used to eliminate instantaneous disturbances such as motor starting impact and can be configured as several times the start-up transition time of typical load equipment in the circuit.

[0044] In box 402, for each identified load step segment, a data reliability judgment result is generated based on electrical relationships, communication quality parameters, and temperature message arrival intervals.

[0045] The gateway checks whether the relationship between the circuit breaker's open / closed state changes, protection action signals, voltage changes, and current changes during the time period conforms to the electrical relationship defined in the aforementioned steps. If there are inconsistencies such as the circuit breaker not operating but the current direction changes abruptly, or the protection signal being set but the current not decreasing accordingly, and the known time deviation estimate cannot explain the contradiction, then the condition is met.

[0046] The gateway checks whether the communication quality parameters of each data source exceed the preset range during the period, i.e., whether there is a concentrated increase in packet loss or a sudden increase in delay. If the communication quality parameters deteriorate beyond the preset range, then this condition is met.

[0047] The gateway checks whether the arrival interval of temperature messages within this period exceeds a preset multiple of the normal refresh cycle of the temperature module. If it does, it indicates that there may be old temperature values ​​mixed into the current sequence, and the condition is met.

[0048] If any one of the above three conditions is met, a data reliability judgment result that fails is generated, and the corresponding load step segment is excluded from the contact thermal response model update and subsequent classification judgment, and is only recorded as a communication or sampling anomaly. If none of the three conditions are met, a data reliability judgment result that passes is generated, and the segment enters the subsequent thermal response analysis. Thus, if the timing information of the load change itself is unreliable, the start and end times of the subsequent thermal response observation window will be offset, and the residuals will also be distorted. Therefore, it is necessary to filter out unreliable segments before thermal response analysis.

[0049] In step S5, the load step segment determined by data reliability is input into the junction thermal response model to obtain the thermal response residual.

[0050] The gateway establishes a contact thermal response model for each contact temperature measurement location. This model expresses the dynamic change process of the contact temperature as a linear combination of the previous temperature state, circuit load change, cabinet ambient temperature change, and heat dissipation conditions. It automatically matches the thermal characteristics of each temperature measurement location using online parameter estimation methods, eliminating the need for manual pre-entry of heat dissipation parameters for each cabinet type.

[0051] Specifically, the model uses the contact temperature change, load change, cabinet ambient temperature change, and fan operating status at the current sampling time as the four components of the regression vector, and the contact temperature change at the next sampling time as the output observation; its discrete time can be expressed as follows: ;in For the first The change in temperature at a given sampling point relative to a reference temperature, where the reference temperature is the temperature value of the most recent steady-state low-load phase at that sampling point. The load change of the corresponding circuit can be represented by the square change of current or the change of active power. This represents the change in ambient temperature inside the cabinet. This is a variable representing the operating status of the fan; it is set to 1 when the fan is on and 0 when it is off.

[0052] Wherein, parameter vector It contains four components; thermal inertia decay parameter This reflects the attenuation rate of temperature change in each sampling period when there is no new power input; its value range is... Load Influence Parameters Parameters reflecting the intensity of the effect of load changes on the temperature at this measuring point; influence parameters of ambient temperature. Reflects the extent to which changes in ambient temperature are transmitted to the temperature measurement point; parameters affecting fan heat dissipation. This reflects the contribution of the fan to the heat dissipation capacity of the temperature measuring point during operation. Under normal circumstances, it is a negative value, indicating that the fan helps to reduce the temperature change. In some embodiments, if the cabinet door open / closed status signal is available, the gateway uses it as a data filtering condition before parameter updates; for example, when the cabinet door is open for a long time, the corresponding data is not used for parameter updates, and the regression vector and parameter vector composed of the above four components are not changed.

[0053] In one embodiment, the gateway updates the parameter vector and covariance matrix using a recursive least squares method with a forgetting factor. Specifically, for each new set of valid observation data received, only one matrix operation is needed to complete the parameter correction, resulting in low computational complexity and suitability for long-term gateway operation. At each valid sampling time, the gateway constructs a regression vector. Read the measured temperature change at the next moment. As the output observations, the following updates are then performed sequentially: ; ; ;in For the gain vector, Let covariance matrix be the variance matrix. The forgetting factor; the range of values ​​for the forgetting factor is... The closer the value is to 1, the longer the historical data is remembered and the smoother the parameter estimation; the smaller the value is, the faster the tracking of recent data is. It can be optionally configured to a value between 0.95 and 0.99. The initial value can be set to a large positive number multiplied by the identity matrix, and the initial value of the parameter vector can be set to the zero vector or given according to the empirical value of similar cabinet positions to speed up convergence; the updated parameter vector is used for subsequent thermal response residual calculation, and the updated covariance matrix is ​​used for the recursive least squares update process at the next sampling time.

[0054] In one embodiment, to ensure that the model parameters reflect the thermal response characteristics under normal operating conditions, data during power outage and restoration transitions, data indicating prolonged cabinet door opening as indicated by the cabinet door opening / closing status, data with missing or contradictory fan status signals, and load step segments deemed unreliable in the aforementioned steps are all excluded from parameter updates. Optionally, when the thermal inertia decay parameter The estimated value exceeds When defining a range, the gateway constrains it to the range boundary, for example, by constraining it to... ,and ,in When the covariance matrix When the trace value drops below a preset lower limit, for Reset the parameters to maintain their ability to track new changes.

[0055] In one embodiment, when the aforementioned steps output a load step segment that passes the data reliability determination, the gateway initiates residual calculation for each contact temperature measurement location in the loop containing that segment. See also... Figure 5 , Figure 5 A flowchart for calculating the thermal response residuals provided in the embodiments of this disclosure is shown below. Figure 5 As shown in box 501, the gateway uses the moment when the load step segment occurs on the corrected data valid time axis as the starting point of the thermal response observation window.

[0056] The endpoint of the window is determined based on the thermal inertia decay parameter in the thermal response model of the temperature measurement point. and sampling period Calculated time constant Confirmed, among which ,in The constrained thermal inertia decay parameter satisfies , The window length can be optionally set to... Three to five times; the window start point is selected from the corrected effective time rather than the temperature message arrival time. The purpose is to ensure that the window truly covers the main process of temperature response after load change, and to avoid window offset due to message delay.

[0057] In box 502, at each temperature sampling moment within the observation window, the thermal response residual is recursively obtained through the thermal response model. The gateway takes the load change, ambient temperature change, and fan operating status on the corrected data effective time axis as input, and uses the parameter vector updated in the aforementioned steps to recursively calculate the predicted temperature change according to the model. Simultaneously, the measured temperature change at that moment is read. The thermal response residual at that moment is obtained by subtracting the predicted value from the measured value. Positive residuals indicate that the actual temperature rise is faster than the model expects, while negative residuals indicate that the actual temperature rise is slower than expected or the actual temperature drop is faster than expected.

[0058] In step S6, the classification is based on thermal response residual, cabinet ambient temperature and fan operating status, and the graded measurement and control results are output according to the effective time of thermal response residual verification data.

[0059] The gateway categorizes data according to the following order: risk of local contact overheating, normal load temperature rise, and deterioration of heat dissipation conditions. Once a rule is matched, subsequent matching stops. The first rule is risk of local contact overheating: after the load increases, the thermal response residual of a certain contact is continuously positive within the observation window, and after the load reaches a new steady-state level, the residual still does not return to the historical residual fluctuation range of that contact. At the same time, the thermal response residuals of other contacts of the same type and location in the same circuit are within the historical residual fluctuation range. The historical residual fluctuation range is obtained by statistically analyzing the distribution range of the residual at that temperature measurement point during normal operation of the gateway.

[0060] The statistical objects of historical residual fluctuation range are residual samples that have passed the data reliability judgment and have not been subsequently marked as having abnormal sampling time or abnormal temperature data update. The gateway forms the historical residual fluctuation range of the corresponding temperature measurement point based on the upper and lower limits of the residual sample, and updates it with new normal samples. When matching this rule, a local contact heating risk classification is generated. The temperature anomaly detected here is often manifested as a rapid temperature rise, but the absolute temperature has not yet reached the alarm threshold. Traditional threshold methods are difficult to issue warnings at this stage, but based on residuals, the additional heating caused by increased contact resistance can be identified.

[0061] The second rule is for normal load temperature rise: the thermal response residuals of the three phases corresponding to the same circuit are all within their respective historical residual fluctuation ranges, and the temperature of the three phases at the junctions shows a synchronous rising and falling trend with the load change; meeting this condition indicates that the thermal response of each junction is consistent with the model expectation, and the temperature rise is caused by normal load, so no alarm is generated; the historical residual fluctuation range in this rule uses the same statistical caliber as the historical residual fluctuation range described in the first rule.

[0062] The third rule, deteriorating heat dissipation conditions: If the risk of local contact overheating and the normal load temperature rise rule are not met, multiple temperature measurement points of different circuits in the same electrical cabinet will show positive residuals with similar direction and amplitude at the same time. Similar direction means that the thermal response residuals of the multiple temperature measurement points are all positive, and similar amplitude means that the difference between the thermal response residuals of the multiple temperature measurement points does not exceed the preset amplitude difference threshold obtained by statistically analyzing the differences in residuals of multiple temperature measurement points during normal operation, and is accompanied by an increase in the ambient temperature inside the cabinet or a fan stop signal. The simultaneous appearance of similar positive residuals in multiple circuits and multiple temperature measurement points indicates that the high temperature is not caused by an increase in the contact resistance of a single contact, but is more likely due to a decrease in the heat dissipation capacity of the cabinet. In this case, the alarm content points to the heat dissipation system rather than a specific contact.

[0063] In one embodiment, the gateway uses the analysis results of the thermal response residuals to reverse verify whether the data validity time correction in the aforementioned steps is reliable. If the time deviation estimate itself has a large deviation, the thermal response observation window will be misaligned, and the residuals may exhibit characteristics that violate physical laws, misjudging data-level problems as device malfunctions.

[0064] Specifically, the onset time of temperature rise at multiple similar temperature measurement points in the same circuit is earlier than the time of load increase on the corrected data effective time axis; temperature change cannot physically precede the power input that causes the change. Based on this, the gateway marks the corresponding load step segment as an abnormal sampling time, and does not classify the risk of local contact heating based on the segment output.

[0065] In this case, the three-phase current changes are aligned on the corrected data time axis, but the temperature at a certain measurement point experiences a step jump within one or two sampling cycles followed by a rapid drop, with the amplitude and rate of the jump unable to be explained by a first-order thermal inertia process. This usually indicates an anomaly in the message queuing or data refresh timing of the temperature module. The gateway marks the corresponding segment as an abnormal temperature data update and does not output a local contact heating risk classification based on this.

[0066] In multiple consecutive load step segments, the same temperature module exhibits a consistent and stable time offset, indicating that the temperature module has a fixed additional delay or lead. The gateway updates the data validity time estimate of the temperature module based on this time offset. The updated data validity time estimate is used as the data validity time correction for subsequent temperature messages of the temperature module and takes effect in the subsequent thermal response residual calculation.

[0067] After completing the above verification and eliminating abnormal sampling time and abnormal temperature data update, if the residual of a certain phase connection point is still high, and the relationship between the phase current change, circuit breaker status and adjacent temperature measurement points can reasonably explain the residual, the gateway confirms that there is a risk of local contact heating at the temperature measurement location.

[0068] In one embodiment, the gateway integrates the above processing results and outputs hierarchical measurement and control results. Specifically: Level 1, electrical anomaly: After the data validity time is arranged after correction, there are still situations where there are contradictory electrical states such as the current not decreasing after the circuit breaker is opened or the voltage not being established after the circuit breaker is closed. The gateway directly outputs electrical anomaly.

[0069] Level 2, Localized Thermal Anomaly: When a localized contact heating risk classification is generated, and the corresponding load step segment is not marked as an abnormal sampling time or an abnormal temperature data update, the gateway outputs a localized thermal anomaly, along with the loop identifier, phase identifier, temperature measurement location identifier, load step segment information, thermal response residual change process, and data validity time verification status, for maintenance personnel and the upper-level system to review.

[0070] Level 3, Heat dissipation or environmental anomaly: When generating a classification indicating deteriorating heat dissipation conditions, the gateway outputs "heat dissipation or environmental anomaly".

[0071] Level 4, Data Sampling Anomaly: When a sampling time anomaly or temperature data update anomaly flag is generated, the gateway outputs a data sampling anomaly, along with information about the abnormal module and the affected period.

[0072] Optionally, for safety-related emergency events such as severe over-temperature or protection tripping that can be directly determined from the contact temperature, cabinet ambient temperature, or protection action signal in the original sampling sequence, the gateway will immediately report to the upper-level system without going through the above complete analysis process.

[0073] Therefore, the intelligent gateway of this invention first uses multi-source electrical event pairing to estimate the time deviation of non-reference electrical data sources and correct the data validity time. During the thermal response residual verification process, it updates the data validity time estimate for temperature modules with stable time offsets. Based on this, it identifies reliable load step segments as input for thermal response analysis. Then, it calculates the thermal response residuals at each contact temperature measurement location using a first-order thermal inertia model and recursive least squares online parameter estimation. This distinguishes between three types of situations: normal load temperature rise, local contact heating, and deterioration of heat dissipation conditions. It also reverse-verifies the reliability of the data validity time to eliminate sampling time anomalies and temperature data update anomalies. This improves the ability to identify early heating at contact points, reduces false alarms caused by data time misalignment during circuit breaker opening and closing and protection actions, and also takes into account the actual working conditions of mixed access of equipment from different manufacturers and with different refresh cycles in existing electrical cabinets.

[0074] Please see Figure 6 , Figure 6 This is a structural block diagram of an electrical cabinet status integrated monitoring and control system based on a smart gateway, provided in an embodiment of this application. (See diagram below.) Figure 6As shown, the system includes: a message receiving module 601, used by the intelligent gateway to receive messages from the data acquisition equipment inside the electrical cabinet, forming an original sampling sequence. The original sampling sequence includes current, voltage, active power, circuit breaker open / closed position status, protection action signals, contact temperature, cabinet ambient temperature, fan operating status, data arrival time, and communication quality parameters; an event pairing module 602, used to extract candidate electrical events from the original sampling sequence and pair the candidate electrical events based on electrical relationships; and a time correction module 603, used to select a reference electrical data source from the paired event groups and estimate the time deviation of non-reference electrical data sources. The system corrects the arrival time of the corresponding message to the corrected data validity time; the reliability judgment module 604 is used to identify load step segments on the corrected data validity time axis and generate data reliability judgment results based on electrical relationships, communication quality parameters and temperature message arrival intervals; the thermal response analysis module 605 is used to input the load step segments that have passed the data reliability judgment into the contact thermal response model to obtain thermal response residuals; the measurement and control result output module 606 is used to classify according to thermal response residuals, cabinet ambient temperature and fan operating status, and output graded measurement and control results based on the thermal response residuals to verify the data validity time.

[0075] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0076] Based on the same inventive concept, this application also provides an electronic device, the method corresponding to which can be the method in the foregoing embodiments, and its problem-solving principle is similar to that method. For example... Figure 7 As shown, Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the methods and / or technical solutions of the foregoing embodiments of the present application.

[0077] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by a processor, it performs the functions defined in the methods of this application.

[0078] Another embodiment of this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application.

[0079] In the above embodiments, the descriptions of each embodiment have different focuses. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The above descriptions are merely preferred embodiments of this application and explanations of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by specific combinations of the above technical features, but should also cover other technical solutions formed by arbitrary combinations of the above technical features or their equivalent features without departing from the inventive concept.

Claims

1. A method for comprehensive monitoring and control of the status of electrical cabinets based on a smart gateway, characterized in that, include: The intelligent gateway receives messages from the data acquisition devices inside the electrical cabinet and forms an original sampling sequence. The original sampling sequence includes current, voltage, active power, circuit breaker open / closed position status, protection action signals, contact temperature, ambient temperature inside the cabinet, fan operating status, data arrival time, and communication quality parameters. Candidate electrical events are extracted from the original sampling sequence and paired based on electrical relationships. Select a reference electrical data source from the paired event group, estimate the time deviation of the non-reference electrical data source, and correct the data arrival time of the corresponding message to the corrected data validity time. The load step segments are identified on the corrected data validity time axis, and data reliability judgment results are generated based on electrical relationships, communication quality parameters, and temperature message arrival intervals. The load step segments that pass the data reliability judgment are input into the contact thermal response model to obtain the thermal response residuals. Based on the classification of thermal response residuals, cabinet ambient temperature, and fan operating status, and according to the effective time of thermal response residual verification data, the graded measurement and control results are output.

2. The method according to claim 1, characterized in that, Extracting candidate electrical events from the original sampling sequence includes: establishing cumulative statistics for current and active power, updating the cumulative statistics based on the current sampled value, the average value of the most recent steady-state period, and the allowable offset; when the cumulative statistics exceed a preset judgment threshold, the corresponding sampling time is recorded as a candidate change point and the cumulative statistics are cleared; for circuit breaker open / closed status and protection action signals, candidate change points are recorded according to the arrival time of the data for the state transition; when there are multiple candidate change points in the same circuit within the same merging time range, the earliest candidate change point is retained as a candidate electrical event, and other candidate change points are merged into this candidate electrical event.

3. The method according to claim 2, characterized in that, Pairing candidate electrical events based on electrical relationships includes: establishing a search window centered on the data arrival time of a candidate electrical event, with the radius of the search window determined by the maximum refresh cycle of the data sources participating in the pairing; searching for candidate electrical events from other data sources within the search window; and when electrical relationships such as closing, opening, protection tripping, or large load input are met, grouping the found candidate electrical events and the candidate electrical event at the center into the same paired event group, and assigning an event identifier to the paired event group.

4. The method according to claim 3, characterized in that, The estimation of the time deviation of non-reference electrical data sources includes: selecting electrical data sources whose refresh cycles meet the preset reference cycle range and whose communication quality parameters are within the preset stable range as reference electrical data sources; for each non-reference electrical data source, collecting the differences between the data arrival times of candidate electrical events of the non-reference electrical data source and the data arrival times of candidate electrical events of the reference electrical data source in the most recent N paired event groups, forming a difference sequence, where N is a preset positive integer; taking the median of the difference sequence as the time deviation estimate of the non-reference electrical data source; and subtracting the time deviation estimate from the data arrival time of each message from the non-reference electrical data source to obtain the corrected data validity time.

5. The method according to claim 4, characterized in that, Identifying load step segments on the corrected data validity time axis includes: thresholding the change in current or active power within a preset change window against a threshold value; the period when the change exceeds the threshold value and maintains a new steady-state level within a preset steady-state duration is recorded as a load step segment; generating data reliability judgment results includes: if any of the following conditions are met, a failed data reliability judgment result is generated; otherwise, a passed data reliability judgment result is generated.

6. The method according to claim 5, characterized in that, A contact thermal response model is established for each contact temperature measurement location. The contact thermal response model uses the contact temperature change, load change, cabinet ambient temperature change, and fan operating status at the current sampling time as regression vectors, and the contact temperature change at the next sampling time as the output observation. The parameter vector and covariance matrix are updated by recursive least squares with a forgetting factor. The parameter vector includes thermal inertia decay parameters, load influence parameters, ambient temperature influence parameters, and fan heat dissipation influence parameters. The updated parameter vector is used to calculate the thermal response residual, and the updated covariance matrix is ​​used to update the recursive least squares at the next sampling time.

7. The method according to claim 6, characterized in that, The process of obtaining the thermal response residuals includes: taking the occurrence time of the load step segment determined by data reliability on the effective time axis of the corrected data as the starting point of the thermal response observation window; calculating the time constant based on the thermal inertia decay parameter and sampling period in the junction thermal response model; determining the end point of the thermal response observation window based on the time constant; at each temperature sampling time within the thermal response observation window, recursively predicting the junction temperature change using the updated parameter vector to obtain the predicted temperature change; and subtracting the predicted temperature change from the measured temperature change at the same temperature sampling time to obtain the thermal response residuals.

8. The method according to claim 7, characterized in that, Based on the classification according to thermal response residual, cabinet ambient temperature, and fan operating status, the following are included: matching in the order of local contact heating risk, normal load temperature rise, and deterioration of heat dissipation conditions. When the thermal response residual of a single contact is continuously positive within the thermal response observation window after the load increases, and the thermal response residual does not return to the historical residual fluctuation range of that contact after the load reaches a new steady-state level, while the thermal response residuals of other contacts of the same type and location in the same circuit are within the historical residual fluctuation range, a local contact heating risk is generated; when the thermal response residuals of the three corresponding contacts in the same circuit are all within the historical residual fluctuation range, and the three-phase contact temperatures rise and fall synchronously with the load change, a normal load temperature rise is generated; when multiple temperature measurement points of different circuits in the same electrical cabinet show positive thermal response residuals with similar direction and amplitude at the same time, accompanied by a rise in cabinet ambient temperature or a fan stop signal, a deterioration of heat dissipation conditions is generated.

9. The method according to claim 8, characterized in that, The validity time of thermal response residual verification data includes: when the temperature rise start time of multiple similar temperature measurement points in the same circuit is earlier than the load increase time on the corrected data validity time axis, the corresponding load step segment is marked as a sampling time abnormality, and the local contact heating risk classification is not output based on the load step segment; when the three-phase current change is aligned on the corrected data validity time axis, and the temperature of a temperature measurement point drops after a step jump within one or two sampling cycles, the corresponding load step segment is marked as a temperature data update abnormality, and the local contact heating risk classification is not output based on the load step segment; when the same temperature module in multiple consecutive load step segments shows a time offset with consistent direction and stable amplitude, the data validity time estimate of the temperature module is updated based on the time offset.

10. An integrated monitoring and control system for the status of electrical cabinets based on a smart gateway, characterized in that, include: The message receiving module is used by the smart gateway to receive messages from the data acquisition equipment in the electrical cabinet and form an original sampling sequence. The original sampling sequence includes current, voltage, active power, circuit breaker open / closed position status, protection action signal, contact temperature, cabinet ambient temperature, fan operating status, data arrival time, and communication quality parameters. The event pairing module is used to extract candidate electrical events from the original sampling sequence and pair the candidate electrical events based on electrical relationships. The time correction module is used to select a reference electrical data source from the paired event group, estimate the time deviation of non-reference electrical data sources, and correct the data arrival time of the corresponding message to the corrected data validity time. The reliability assessment module is used to identify load step segments on the corrected data validity time axis and generate data reliability assessment results based on electrical relationships, communication quality parameters, and temperature message arrival intervals. The thermal response analysis module is used to input the load step segment determined by data reliability into the contact thermal response model to obtain the thermal response residual; The measurement and control result output module is used to classify the results according to the thermal response residual, the ambient temperature inside the cabinet, and the fan operating status, and output the graded measurement and control results based on the effective time of the thermal response residual verification data.

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