High-voltage fuse array operation monitoring method and system based on internet of things architecture

Abstract

The present application relates to the field of power distribution system equipment state monitoring, in particular to a high-voltage fuse array operation monitoring method and system based on an Internet of Things architecture, which pre-constructs an array topology graph containing thermal neighbor relationships and heat dissipation area division in the commissioning phase; in the operation phase, the current environmental reference temperature is estimated based on the low-load fuse set in the array in each sampling period, and the expected temperature of each fuse is calculated by combining the current-carrying self-heating temperature rise and the heat dissipation condition offset, then the group environmental disturbance and isolated point degradation are distinguished by neighbor deviation synchronization rate, and finally the alarm is output after multi-cycle continuity confirmation. The present application can absorb the group temperature rise caused by the change of cabinet ventilation and environmental drift, significantly reduce the false alarm rate, and at the same time improve the recognition sensitivity to early degradation of the melt. The present application uses the array collective behavior as a self-referencing benchmark instead of a fixed threshold, which improves the early degradation detection capability and suppresses false alarms.

Description

Technical Field

[0001] This invention relates to the field of power distribution system equipment condition monitoring, specifically to a method and system for monitoring the operation of high-voltage fuse arrays based on an Internet of Things (IoT) architecture. Background Technology

[0002] High-voltage fuses are overcurrent protection components in power distribution systems. They are typically installed in arrays within high-voltage switchgear in industrial substations and large distribution centers, simultaneously providing short-circuit and overload protection for several or even dozens of feeder circuits. The reliable operation of high-voltage fuses directly impacts the continuity of power supply to downstream equipment; therefore, real-time monitoring of the fuse array's operating status is an indispensable part of power distribution system operation and management.

[0003] Currently, the mainstream practice in the industry for online monitoring of high-voltage fuses is to deploy temperature sensors at each installation location. Using IoT communication technology, temperature data from each location is collected and sent to a remote monitoring platform. The temperature readings at each location are then compared one by one with a pre-set fixed alarm threshold. An alarm is triggered if the temperature at any location exceeds the threshold. This method is simple in structure and easy to deploy, and is a common practice in the industry. Its judgment logic uses the absolute temperature value of a single location as the sole criterion, and the monitoring of each location in the array is independent and uncorrelated.

[0004] However, in actual power distribution center operations, the downstream load of each feeder circuit fluctuates with production schedules, resulting in significantly different current carrying capacities for fuses at different locations and times. Simultaneously, the heat dissipation conditions within the switchgear are spatially uneven due to factors such as ventilation paths, installation height, and the heat generated by adjacent equipment. This dynamic difference in load and heat dissipation conditions leads to inherently inconsistent normal operating temperatures across the array. To avoid false alarms triggered by heavily loaded or poorly cooled locations, traditional methods often force the fixed alarm threshold to be set at a relatively high absolute level. This creates a blind spot: when a fuse element at a certain location in the array, especially in a lightly loaded or well-heated location, begins to show early deterioration, the additional temperature rise caused by increased resistance is often submerged within the normal temperature fluctuation range, failing to bring the absolute temperature at that location to the high fixed alarm threshold level. Therefore, maintenance personnel cannot identify the early deterioration state at that location from the absolute temperature data on the monitoring platform.

[0005] For example, Chinese patent document CN204884139U discloses a Zigbee-based remote high-voltage switchgear temperature monitoring system, which aggregates temperature data from multiple nodes to a remote backend for comparison. Although this type of centralized aggregation and absolute temperature value-based alarm method has been widely deployed in the industry, this simple method still exposes underlying logic bottlenecks and monitoring blind spots when dealing with the complex thermodynamic environment inside high-voltage switchgear. Summary of the Invention

[0006] To address the issue of early degradation of fuse components and the failure of the additional temperature rise caused by increased resistance to reach the fixed alarm threshold level, this invention proposes a high-voltage fuse array operation monitoring method based on an Internet of Things (IoT) architecture in its first aspect. The method includes: S1, constructing an array topology model that includes thermal neighbor relationships and heat dissipation area division; S2, periodically reading the temperature and current of all fuses in the array, recording the ratio of current value to rated current carrying capacity as the load ratio, and classifying fuses with load ratios below a preset threshold as a low-load fuse set, and collaboratively estimating the ambient reference temperature for the current period based on the temperature readings within the low-load fuse set; S3, according to steady-state thermal balance, combining the ambient reference temperature, the current steady-state self-heating temperature rise, and the heat dissipation strips of the corresponding heat dissipation area. The expected health temperature of the fuse is obtained by superimposing the component offsets. The measured temperature minus the expected health temperature of the fuse is used as the thermal deviation value. S4: Filter fuses whose thermal deviation values ​​exceed the attention threshold, and count the proportion of their thermal neighbors that also exceed the threshold as the neighbor deviation synchronization rate. When the neighbor deviation synchronization rate exceeds the preset synchronization rate threshold, mark the fuse and its thermal neighbors that also exceed the attention threshold as an environmental correlation deviation group, correct the heat dissipation condition offset of the heat dissipation area, and re-execute S3. Otherwise, mark it as an isolated thermal deviation fuse. S5: Maintain a continuous hit counter for each fuse. Increment the counter when it is marked as an isolated thermal deviation fuse, and clear it to zero otherwise. When the counter reaches the preset number of confirmations, output the fuse deterioration alarm information.

[0007] Compared to existing high-voltage switchgear temperature monitoring schemes that compare single-point absolute temperature values ​​with fixed thresholds one by one, this invention constructs an array topology model that includes thermal neighbor relationships and heat dissipation area divisions during the commissioning phase. In each sampling cycle, it collaboratively estimates the ambient reference temperature based on the set of low-load fuses within the array, and then calculates the expected healthy temperature of each fuse by superimposing steady-state self-heating temperature rise and heat dissipation area offset. This allows the alarm threshold to dynamically fluctuate with load conditions, seasonal changes, and cabinet ventilation conditions, fundamentally eliminating the blind spot where a fixed threshold must be raised under heavy load, but this results in the degradation signal being submerged under light load. Furthermore, it utilizes the neighbor deviation synchronization rate to detect over-threshold fuses. The device distinguishes between group and isolated thermal deviations, and performs offset correction and backtracking recalculation on the heat dissipation area where the environmentally related deviation group is located. This allows group temperature rises caused by external factors such as local ventilation deterioration and heat radiation from adjacent equipment to be automatically absorbed and not misjudged as deterioration. Only truly spatially isolated abnormal temperature rises are retained as suspected deterioration. Finally, a multi-cycle continuous confirmation mechanism filters out sensor noise and transient disturbances before outputting an alarm. This invention significantly reduces the false alarm rate and greatly improves the detection sensitivity of early fuse deterioration under the actual complex working conditions of power distribution centers. It can substantially improve the monitoring capability of existing array-type high-voltage fuses without replacing the main control hardware.

[0008] Furthermore, the rules for marking any two fuses as hot neighbors in step S1 include: two fuses located in the same cabinet, with a spatial Euclidean distance not exceeding a preset distance threshold, and without any enclosed metal partition blocking air convection between them, are marked as hot neighbors; two fuses located upstream and downstream of the same air inlet-outlet ventilation channel and connected by ventilation along the channel direction are still marked as hot neighbors even if the spatial distance exceeds the preset distance threshold; the array topology model is stored in the non-volatile storage of the edge computing gateway and is regenerated only when the array physical layout changes.

[0009] By establishing the marking rules for thermal neighbor relationships based on both the spatial Euclidean distance criterion and the ventilation channel connectivity criterion, even if two fuses are physically far apart, as long as they are upstream and downstream of the same air intake-exhaust ventilation channel, they can still be correctly identified as having a thermal coupling relationship. This avoids the problem of missing fuses that are actually affected downstream of the channel due to the division of neighbors solely based on Euclidean distance. At the same time, the topology model is stored in the non-volatile storage of the edge computing gateway and rebuilt only when the array layout changes. This eliminates the need to repeatedly build the topology map in each sampling period, saving computing power at the edge and ensuring the real-time performance of subsequent steps.

[0010] Furthermore, when the low-load fuse set contains two or more fuses, the median of their temperature readings is taken as the ambient reference temperature; when the low-load fuse set contains only one fuse, the temperature reading of that fuse is directly used as the ambient reference temperature; when there are no low-load fuses in the array, for each fuse, the theoretical temperature rise caused by its current-carrying self-heating is first deducted according to steady-state Joule's law, and then the median of the residual value after deduction is taken as the ambient reference temperature.

[0011] By differentiating between three scenarios—multiple, single, or zero fuses in the low-load fuse set—and employing strategies of median value, single value, or median value minus self-heating residual value respectively, this invention can still stably output the ambient reference temperature when the overall load pattern of the array changes significantly. This avoids the situation where relying solely on a single estimation formula fails when there are insufficient low-load samples. The introduction of median value statistics can also effectively suppress the bias of the overall estimate caused by zero drift or aging deviation of individual sensors, making the ambient reference temperature well adaptable to sensor anomalies.

[0012] Furthermore, step S2 also includes an environmental reference temperature rollback strategy: when the number of readable fuses is less than a preset proportion of the total number due to communication packet loss or sensor disconnection within three consecutive sampling periods, the arithmetic mean of the last successful estimate and the reading of the independent ambient thermometer in the power distribution center control room is used as the environmental reference temperature for the current period. At the same time, the communication anomaly event is recorded and reported to the operation and maintenance monitoring platform until the number of readable fuses is restored before returning to the normal estimation process.

[0013] When communication packet loss or sensor disconnection leads to an insufficient number of readable fuses, this invention initiates a fallback strategy under the condition that insufficient samples cannot be obtained in multiple consecutive sampling cycles. The average of historical successful estimates and the reading of an independent ambient thermometer in the control room is used as a fallback benchmark, and communication anomalies are reported simultaneously. This ensures that the monitoring logic will not stop or report false alarms due to insufficient samples when a partial failure occurs in the IoT link, guaranteeing the long-term continuous availability of the system in engineering practice. It also provides maintenance personnel with traceable clues for link failures.

[0014] Furthermore, the method for calculating the expected temperature in step S3 is as follows: ; In the formula, For fuse The expected healthy temperature of the fuse; The ambient reference temperature; For fuse The current current value; For fuse The rated resistance value indicated in the product's manufacturer's datasheet; For fuse The nominal heat dissipation coefficient indicated in the product's factory data sheet; For fuse Heat dissipation area The offset of the heat dissipation conditions.

[0015] By superimposing the ambient reference temperature, the steady-state self-heating temperature rise driven by the square of the current, and the offset of the heat dissipation area, the expected temperature is obtained. This allows the healthy expected temperature under heavy load conditions to automatically rise with the square of the current, and the alarm threshold to move upward accordingly, avoiding false alarms caused by normal load fluctuations. At the same time, after considering the offset of the heat dissipation area, the group temperature rise caused by local changes in cabinet ventilation can also be taken into account in the expected temperature, so that the thermal deviation value truly reflects only abnormal heating that exceeds the healthy expectation, providing a physically meaningful input for subsequent judgment.

[0016] Further, the calculation method of the heat dissipation condition offset in step S3 within each sampling period is as follows: within each heat dissipation area, fuses with load ratios within a preset medium load range are selected as medium load samples; for each medium load sample, the difference between the measured temperature and the theoretical temperature obtained based on the ambient reference temperature and the theoretical temperature rise of current self-heating is calculated; the arithmetic mean of all the differences in the heat dissipation area is taken as the heat dissipation condition offset of the heat dissipation area; when there is no medium load sample in a heat dissipation area, the heat dissipation condition offset of the heat dissipation area adopts the value of the previous successful calculation period.

[0017] Further, the processing of the environmental correlation deviation group in step S4 specifically includes: for each environmental correlation deviation group, calculating the average thermal deviation value of all fuses in the group as a correction amount and adding it to the heat dissipation condition offset of the heat dissipation area to which the group belongs; based on the corrected heat dissipation condition offset, re-performing the expected temperature estimation and thermal deviation value calculation of step S3 for all fuses in the heat dissipation area, performing only one round and without iterative loops; for fuses that still maintain a thermal deviation value exceeding the attention threshold and a neighbor deviation synchronization rate that does not exceed the preset synchronization rate threshold after recalculation, they are finally identified as the isolated thermal deviation fuses in this sampling period.

[0018] Furthermore, the preset number of confirmations in step S5 ranges from 3 to 8 times; the fuse deterioration alarm information is uploaded to the remote operation and maintenance monitoring platform via Ethernet by the edge computing gateway, and each fuse deterioration alarm information includes at least the following fields: the number of the alarm fuse and its cabinet identifier, the number of the heat dissipation area, the most recent measured temperature value and the thermal deviation value, the protected feeder circuit number, and the alarm timestamp.

[0019] Furthermore, in step S2, each fuse is configured with a set of sensing units. The sensing unit includes a wireless temperature sensor fixed to the middle of the outer tube wall of the fuse in a surface mount manner and a through-type current transformer fitted on the feeder outlet. The temperature sensor and the current transformer sample synchronously. The sampling data of the sensing unit is sent to the top aggregation gateway node through a low-power wireless module. The aggregation gateway node is cascaded to the edge computing gateway via RS-485 bus using the Modbus-RTU protocol.

[0020] In a second aspect, the present invention provides a high-voltage fuse array operation monitoring system based on an Internet of Things (IoT) architecture, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the high-voltage fuse array operation monitoring method based on an IoT architecture of the present invention is implemented.

[0021] The technical effects of this invention are as follows: This invention addresses the shortcomings of existing online monitoring systems for high-voltage fuses, which typically rely on comparing the absolute temperature value of a single point with a fixed threshold, failing to account for load differences and uneven heat dissipation within the cabinet. It proposes a novel monitoring paradigm using the collective behavior of an array as a self-reference benchmark: First, the thermal coupling structure between fuses is characterized by thermal neighbor relationships and heat dissipation area topology. Then, the dynamic environmental reference temperature is estimated collaboratively by a group of low-load fuses, and the expected healthy temperature of each fuse is calculated by overlaying self-heating temperature rise and heat dissipation area offset. Furthermore, the neighbor deviation synchronization rate is used to distinguish between group environmental disturbances and isolated point degradation, and offset backtracking recalculation is performed on environmentally related areas. Finally, an alarm is output after multi-cycle continuous confirmation. This invention can reduce false alarm rates, improve the sensitivity of early degradation detection, and can be deployed in software on existing edge computing devices. Attached Figure Description

[0022] Figure 1 This is a flowchart of the high-voltage fuse array operation monitoring method based on the Internet of Things architecture in this embodiment of the invention; Figure 2 This is a schematic diagram of the thermal neighbor relationship and heat dissipation area division of the high-voltage fuse array in an embodiment of the present invention. Detailed Implementation

[0023] This embodiment is applied in the switch room of a medium-voltage power distribution center. Multiple metal-clad high-voltage switchgear cabinets are arranged side-by-side along both sides of the aisle inside the switchgear room. Each switchgear cabinet contains high-voltage current-limiting fuses corresponding to multiple outgoing feeder circuits. All fuses together form a spatially centralized and electrically parallel fuse array. In this embodiment, the fuse array contains 24 fuses, distributed in three adjacent high-voltage switchgear cabinets, with each cabinet accommodating 8 fuses corresponding to 8 feeder circuits. The number of fuses can be configured between 8 and 128 in actual deployment, depending on the number of outgoing circuits.

[0024] In the data acquisition layer, each fuse is equipped with a set of sensing units, which include a wireless temperature sensor and a through-hole current transformer. The temperature sensor is surface-mounted to the middle of the outer tube wall of the fuse, with a nominal temperature measurement range of [missing information]. Nominal measurement uncertainty Sampling period The current transformer is mounted on the feeder output terminal, and its range covers the rated current. to Times, accuracy class is The sampling period is synchronized with the temperature sensor. The sampling period is determined based on the thermal time constant of the melt, and can be adjusted in applications where the thermal inertia of the monitored fuse changes. to Adjustments can be made within the specified range.

[0025] At the communication layer, each group of sensing units operates by... The low-power wireless module in the frequency band transmits the sampled data to the aggregation gateway node arranged on the top of the same cabinet, with one aggregation gateway node configured on each cabinet. The three aggregation gateway nodes are cascaded via RS-485 bus using the Modbus-RTU protocol and connected to the edge computing gateway located in the power distribution center control room. It should be noted that when the on-site electromagnetic environment is complex... When frequency band interference is significant, the wireless transmission link can be replaced with one more suitable for the conditions. Short-range wireless protocols or the CAN bus protocol with better shielding.

[0026] At the processing layer, the edge computing gateway serves as the runtime environment for all data processing logic in this embodiment, and is configured with a quad-core processor based on the ARM Cortex-A53 architecture, with a main frequency of... RAM Non-volatile storage .

[0027] Example 1: like Figure 1 As shown, the high-voltage fuse array operation monitoring method based on the Internet of Things architecture of the present invention includes: S1. A topology model of the fuse array, including thermal neighbor relationships and heat dissipation area division, is pre-constructed for the heat dissipation and conduction relationship of the fuse array.

[0028] During the system commissioning phase, this embodiment completes the modeling of the thermal neighbor relationships and heat dissipation areas within the array in one go.

[0029] Specifically, the edge computing gateway first reads the cabinet number, internal installation coordinates, feeder circuit number, and ventilation structure diagram of each fuse from the existing equipment ledger system in the power distribution center. Then, it marks the thermal neighbor relationship between any two fuses in the topology diagram according to the following rules: First, they are located within the same cabinet, and their spatial Euclidean distance does not exceed [a certain value]. Two fuses that are not separated by a closed metal partition to block air convection are marked as thermal neighbors; Second, two fuses located upstream and downstream of the same air intake-exhaust ventilation duct and connected along the duct direction, even if the spatial distance exceeds [a certain value], will still be valid. It is still marked as a hot neighbor. Distance threshold This is an optimal value determined with reference to the width of a typical high-voltage switchgear functional unit. For different cabinet types, the spatial distance can be adjusted to the width of a single functional unit. Within times.

[0030] Furthermore, the edge computing gateway divides heat dissipation areas according to the cabinet ventilation structure: internal chambers of the cabinet sharing the same air inlet and outlet constitute a heat dissipation area. In this embodiment, each cabinet is divided into upper and lower chambers by a central metal partition, forming a total of 6 heat dissipation areas across the three cabinets, with each heat dissipation area containing 4 fuses. The heat dissipation areas and their associated thermal neighbors are organized into an array topology map and stored in the non-volatile storage of the edge computing gateway. The array topology map is only regenerated by maintenance personnel when the array physical layout changes, remaining unchanged during periods without changes, thus avoiding repeated construction every cycle.

[0031] like Figure 2 The diagram illustrates the thermal proximity and heat dissipation areas of a high-voltage fuse array. The array comprises 24 high-voltage fuses, numbered F01 to F24, distributed across three adjacent high-voltage switchgear cabinets. Switchgear cabinets #1, #2, and #3 each house eight high-voltage fuses. Each switchgear cabinet is divided into upper and lower heat dissipation areas based on its internal ventilation structure and chamber isolation. The three switchgear cabinets together form heat dissipation areas 1 to 6, with each area containing four high-voltage fuses.

[0032] Figure 2The dashed boxes in the diagram represent the boundaries of heat dissipation areas, used to characterize internal chambers within the cabinet that share the same air inlet and outlet. Solid lines represent the thermal proximity of high-voltage fuses located in the same heat dissipation area or chamber, spatially close, and without any enclosed metal partitions blocking air convection. Dotted lines represent the thermal proximity of high-voltage fuses located upstream and downstream of the same ventilation channel, capable of generating thermal effects through ventilation connections. Both solid and dotted lines are used to represent heat conduction or thermal effect relationships between high-voltage fuses, not electrical connections.

[0033] Among them, heat dissipation area 1 includes high-voltage fuses F01, F02, F07 and F08; heat dissipation area 2 includes high-voltage fuses F03, F04, F09 and F10; heat dissipation area 3 includes high-voltage fuses F05, F06, F11 and F12; heat dissipation area 4 includes high-voltage fuses F13, F14, F19 and F20; heat dissipation area 5 includes high-voltage fuses F15, F16, F21 and F22; and heat dissipation area 6 includes high-voltage fuses F17, F18, F23 and F24.

[0034] S2. The ambient reference temperature for the current period is obtained by coordinating the temperature readings of the low-load fuse set within the array.

[0035] At the start of each sampling period, the edge computing gateway first polls the three aggregation gateway nodes sequentially according to the Modbus-RTU protocol to read the current temperature and current values ​​of all fuses in the array. In traditional monitoring methods, the ambient temperature is usually assumed to be a fixed constant (e.g., ...). Using this as a baseline in the thermal model leads to numerous collective false alarms under actual environmental conditions that deviate from the assumptions, such as seasonal changes and air conditioning start-up and shutdown. Under the premise that different fuses within the array share the same environmental conditions at the same time, the environmental baseline temperature is estimated in real time from the collective data.

[0036] Specifically, the edge computing gateway reads the rated current carrying capacity from the product datasheet for each fuse and defines the ratio of the fuse's current value to its rated current carrying capacity as the load ratio; it then filters out fuses with load ratios lower than [a certain value]. The fuses are classified as low-load fuses. When the load ratio is lower than... At that time, the Joule heating power of the melt is only the rated value. The magnitude of the temperature rise caused by its own resistance relative to the temperature sensor. The nominal measurement uncertainty is negligible, therefore the temperature reading of the fuse housing under low load is approximately equal to the ambient temperature.

[0037] The ambient reference temperature was then determined as follows: When the set of low-load fuses contains two or more fuses, the median value of their temperature readings is taken as the ambient reference temperature. The purpose of using the median value for statistics is to ensure that the zero drift or aging deviation of individual sensors does not skew the overall estimate. When the low-load fuse set contains only one fuse, the temperature reading of that fuse shall be used directly. When there are no low-load fuses in the array, for each fuse, the theoretical temperature rise caused by its current-carrying self-heating is first deducted according to steady-state Joule's law. Then, the median of the residual value after deduction is used as the ambient reference temperature. The current-carrying self-heating temperature rise is calculated based on the current value, the rated resistance value indicated in the product manual, and the nominal heat dissipation coefficient. The load ratio screening threshold is mentioned above. This is the preferred value under the sensor accuracy conditions in this embodiment. In scenarios where sensor accuracy conditions change, the threshold can be adjusted. to Adjustments can be made within the specified range.

[0038] Specifically, if the number of readable fuses in the array is less than the total number due to communication packet loss, sensor disconnection, or other reasons within three consecutive sampling periods... When the edge computing gateway initiates an environmental reference temperature rollback strategy, it uses the arithmetic mean of the previous successful estimate and the reading of the independent environmental thermometer in the power distribution center control room as the environmental reference temperature for the current cycle. At the same time, it records communication anomalies and reports them to the operation and maintenance monitoring platform until the number of readable fuses is restored before returning to the normal estimation process.

[0039] S3. Based on the steady-state thermal equilibrium relationship, calculate the expected temperature of each fuse under healthy conditions and extract the thermal deviation value between the measured temperature and the expected temperature.

[0040] The ambient reference temperature output in step S2 only describes the common external conditions of the array, but the temperature that each fuse should exhibit under the current current due to its own Joule heating is not yet statistically analyzed. If the difference between the measured temperature and the ambient reference temperature is directly used as the alarm signal, the heavily loaded fuses will continuously generate false alarms. Therefore, this embodiment calculates the expected temperature that each fuse should exhibit under a healthy fusible state, and uses the difference between the measured temperature and the expected temperature as the thermal deviation value.

[0041] In this embodiment, the expected temperature is calculated by superimposing the ambient reference temperature, the steady-state self-heating temperature rise caused by the current current, and the difference between the actual heat dissipation conditions of the heat dissipation area and the nominal conditions. This superposition reflects the power balance of the melt under healthy conditions, from heat absorption by the environment to steady-state heat dissipation. Based on this, this embodiment uses the following steady-state thermal balance relationship to calculate the expected temperature: ; In the formula, For fuse The expected temperature, in units of ; The current periodic environmental reference temperature is estimated in step S2, in units of ; For fuse The current value read from the current transformer, in units of ; For fuse The rated resistance value specified in the product's manufacturer's datasheet is in units of... ; For fuse The nominal heat dissipation coefficient, as indicated in the product's manufacturer's datasheet, is measured in units of... ; For fuse Heat dissipation area The offset of heat dissipation conditions, in units of .

[0042] From the above relationship, it can be seen that when the current When the value increases, the steady-state self-heating temperature rise represented by the second term of the formula increases quadratically, meaning that the expected healthy temperature of the same fuse under heavy load conditions is already at a higher level, and the corresponding alarm threshold will also rise, thereby avoiding false alarms triggered by load fluctuations; when the heat dissipation condition offset... When the cabinet ventilation changes from zero to a positive value, the overall expected temperature rises, thus absorbing the collective temperature rise caused by localized ventilation deterioration without misjudging it as deterioration.

[0043] Heat dissipation offset This reflects the systematic difference between the actual heat dissipation capacity of a certain heat dissipation area and the ideal conditions corresponding to the product's nominal parameters. Its calculation method within each sampling period is as follows: Within each heat dissipation area, the screening load ratio is between... to The fuses in between are used as intermediate load samples. For each intermediate load sample, the difference between its measured temperature and the theoretical temperature obtained based on the ambient reference temperature and the theoretical temperature rise due to current-carrying self-heating is calculated. The arithmetic mean of all these differences within the region is taken as the heat dissipation condition offset for that region. A positive offset indicates that the actual heat dissipation in that region is worse than the nominal conditions, while a negative offset indicates that it is better than the nominal conditions. When there are no intermediate load samples in a region, the offset for that region uses the value from the previous successful calculation cycle.

[0044] The thermal deviation value is defined as the measured temperature of the fuse minus its expected temperature. A thermal deviation value close to zero indicates that the thermal behavior of the fuse is in line with the expected health; a positive and large thermal deviation value indicates the presence of an additional heat source beyond the expected health, which is a suspected signal of fuse deterioration.

[0045] S4. Use the thermal neighbor relationship to distinguish between group and isolated spatial distribution characteristics of positive thermal deviation values ​​in order to identify fuses suspected of actual deterioration.

[0046] The positive thermal deviation output in step S3 may physically originate from two types of causes: one is localized abnormal heating caused by the deterioration of the fuse element itself and increased contact resistance; the other is a collective temperature rise caused by short-term changes in environmental factors affecting multiple adjacent fuses, such as temporary obstruction of ventilation openings by maintenance equipment or additional heat radiation from short-term operation of adjacent equipment being conducted to a local area of ​​the switchgear. This step uses the thermal neighbor relationship established in step S1 to distinguish between these two types of causes, avoiding misjudging collective environmental disturbances as single-point deterioration.

[0047] Within each sampling period, this step is performed according to the following procedure: First, the edge computing gateway filters fuses whose thermal deviation values ​​exceed an attention threshold to form a set to be judged. In this embodiment, the attention threshold is set to... Approximately 1 / 3 of the measurement uncertainty of the temperature sensor Times, can be used to measure uncertainty. to Adjust the value between [number] and [number] based on the on-site noise level.

[0048] Furthermore, for each fuse in the discrimination set, its hot neighbor list is read from the array topology diagram, the number of these hot neighbors whose hot deviation values ​​also exceed the attention threshold is counted, and the proportion of these hot neighbors to the total number of hot neighbors of that fuse is calculated, which is called the neighbor deviation synchronization rate.

[0049] Subsequently, classification is performed based on the neighbor deviation synchronization rate: When the neighbor deviation synchronization rate exceeds When this occurs, it means that most of the fuse’s neighbors simultaneously exhibit positive deviations of similar magnitude, which is consistent with the group characteristics caused by regional environmental factors. The fuse and its neighbors that simultaneously exceed the attention threshold are collectively marked as the environmentally related deviation group. When the neighbor deviation synchronization rate does not exceed At that time, the deviation of the fuse is isolated in space, which is consistent with the local temperature rise characteristics caused by the deterioration of a single melt element, and is marked as a candidate for an isolated thermal deviation fuse.

[0050] Next, for each environmental correlation deviation group, the edge computing gateway calculates the average thermal deviation of all fuses within the group and adds it as a correction to the heat dissipation condition offset of the heat dissipation area to which the group belongs. Then, based on the corrected heat dissipation condition offset, the expected temperature estimation and thermal deviation calculation described in step S3 are re-executed for all fuses in the heat dissipation area. The recalculation is performed only once and without iterative loops to ensure that the total calculation delay of this cycle does not exceed half of the sampling cycle.

[0051] Finally, for those cases where the thermal deviation value still exceeds the attention threshold after recalculation, and the neighbor deviation synchronization rate still does not exceed [the threshold value], [these cases are considered as examples of cases where the thermal deviation value still exceeds the attention threshold]. The fuse was ultimately identified as the isolated thermal deviation fuse for this cycle.

[0052] The The synchronization rate threshold is approximately [missing information] per fuse. to The preferred value selected under the conditions of this embodiment for the number of hot neighbors. In this embodiment, the neighbor deviation synchronization rate is only one specific way to distinguish between groups and isolated states. Those skilled in the art will understand that when the number of hot neighbors for some fuses is small, the threshold should be increased to... to The range is biased towards the higher side to avoid a few coupling biases being incorrectly attributed to environmental associations.

[0053] S5. Perform multi-cycle continuous verification on isolated thermal deviation fuses to filter out occasional disturbances and output the final fuse deterioration alarm information.

[0054] After spatial correlation analysis in step S4, the isolated thermal deviation fuse is highly suspected of degradation. However, sporadic factors such as sensor random noise, current transformer transient deviation, and transient environmental disturbances may still exist within a single sampling period. If an alarm is directly output without time-dimensional filtering, it will bring unnecessary maintenance responses to the operation and maintenance team. Therefore, this step outputs the final alarm through a multi-cycle continuous confirmation mechanism.

[0055] Specifically, the edge computing gateway maintains a continuous hit counter for each fuse in the array. When a sample period is marked as an isolated thermal deviation fuse in step S4, the counter increments. If a fuse is not included in the isolated thermal deviation fuse set in step S4 during a certain sampling period, the counter is reset to zero. When the consecutive hit count of a fuse reaches a preset number of confirmations, it is determined that the fuse has fuse element deterioration, and a deterioration alarm message is output.

[0056] In this embodiment, the preset number of confirmations is set to... Next, combined The sampling period corresponds to The continuous observation duration. This value is a preferred value under conditions where the sensor noise level is similar to that of this embodiment, which can cover the decay time of most occasional noise and transient disturbances without significantly delaying the alarm timing of the actual degradation signal. In applications with different sensor noise levels, the number of confirmations can be... to Adjustments within this range.

[0057] The degradation alarm information is uploaded to the remote operation and maintenance monitoring platform via Ethernet by the edge computing gateway. Each alarm message includes at least the following fields: the fuse number and its cabinet identifier, the heat dissipation area number, the most recent measured temperature value and thermal deviation value, the protected feeder circuit number, and the alarm timestamp. Maintenance personnel use this information to arrange the inspection and replacement of the fuse.

[0058] Example 2: To address application scenarios where fuses from different manufacturers or batches are installed within some power distribution center arrays, resulting in inconsistent rated parameters and significant errors in physical model calculations based on product rated parameters, this invention further provides the following embodiments. In Embodiment 2, steps S1, S2, and S5 are executed in the same manner as the corresponding steps in Embodiment 1. The difference lies in the expected benchmark and discrimination mechanism used in steps S3 and S4: Embodiment 1 calculates the expected temperature for each fuse based on a physical model with rated electrical parameters, while this embodiment uses the collective behavior of a group under the same operating conditions as the expected benchmark, thereby avoiding reliance on the accuracy of rated parameters.

[0059] Specifically, within each sampling period, the edge computing gateway divides all fuses in the array into several load level groups based on the load ratio of each fuse, such as load ratio. Classified as ultra-light load group Classified as light load group Classified as medium load group, higher than Classify fuses into the heavy-load group. For a subset of fuses belonging to the same load level group within the same heat dissipation area, calculate the median and median absolute deviation of their temperature readings. When the difference between the temperature reading of a fuse and the median of its group exceeds a certain percentage of the median absolute deviation of that group... When the value is doubled, the fuse is marked as a group out-of-group fuse, which is equivalent to the isolated thermal deviation fuse in Example 1, and directly proceeds to step S5 to perform multi-cycle continuous confirmation and alarm output.

[0060] Specifically, when a load level group contains only a single fuse in a certain heat dissipation area and lacks comparable group partners, the edge computing gateway uses the physical model method described in Example 1 as a supplementary discrimination path to fall back to the fuse, so as to avoid the fuse being in the discrimination blind zone in this cycle.

[0061] This embodiment differs structurally from Embodiment 1 in its decision-making mechanism: Embodiment 1 uses a bias discrimination based on a physical model, while this embodiment uses an outlier discrimination based on population statistics. In applications where product models within the array are highly heterogeneous and rated parameters are unreliable, this embodiment, compared to Embodiment 1, can avoid the bias caused by... , The systematic bias introduced by inaccurate rated parameters; however, under the application conditions where the product models are uniform and the rated parameters are reliable within the array, Example 1 shows more stable performance in a small sample heat dissipation area compared to this example.

[0062] Example 3: This invention also provides a high-voltage fuse array operation monitoring system based on an Internet of Things (IoT) architecture. The system includes at least a processor, a memory, and a communication interface. The memory stores computer program instructions, which, when executed by the processor, implement all the steps of the monitoring method described in Embodiment 1 or Embodiment 2 above. The processor, memory, and communication interface are connected via a bus structure well-known in the art; their arrangement and function are known in the art and will not be described further.

[0063] This system is designed to be adapted to the direct deployment of monitoring methods into existing edge computing devices in power distribution centers, facilitating the intelligent upgrade of existing power distribution stations without replacing the main control hardware.

Claims

1. A method for monitoring the operation of a high-voltage fuse array based on an Internet of Things (IoT) architecture, characterized in that, The method includes: S1, constructing an array topology model that includes thermal neighbor relationships and heat dissipation area division; S2. Read the temperature and current of all fuses in the array periodically, record the ratio of current value to rated current carrying capacity as the load ratio, and take the fuses with load ratios lower than a preset threshold as the low-load fuse set. Based on the temperature readings in the low-load fuse set, collaboratively estimate the ambient reference temperature for this period. S3. The ambient reference temperature, the current steady-state self-heating temperature rise and the heat dissipation condition offset of the heat dissipation area are superimposed according to the steady-state thermal balance to obtain the expected healthy temperature of the fuse. The measured temperature minus the expected healthy temperature of the fuse is used as the thermal deviation value. S4. Filter fuses whose thermal deviation values ​​exceed the attention threshold, and count the proportion of their thermal neighbors that also exceed the threshold as the neighbor deviation synchronization rate. When the neighbor deviation synchronization rate exceeds the preset synchronization rate threshold, mark the fuse and its thermal neighbors that also exceed the attention threshold as an environmental correlation deviation group, correct the heat dissipation condition offset of the heat dissipation area, and then re-execute S3. Otherwise, mark it as an isolated thermal deviation fuse. S5. Maintain a continuous hit counter for each fuse. Increment the counter when the fuse is marked as an isolated thermal deviation fuse, and reset it to zero otherwise. Output a fuse deterioration alarm message when the counter reaches the preset number of confirmations. The rules for marking any two fuses as thermal neighbors in step S1 include: Two fuses located in the same cabinet, with a spatial Euclidean distance not exceeding a preset distance threshold, and without a closed metal partition between them blocking air convection, are marked as thermal neighbors; Two fuses located upstream and downstream of the same air inlet-outlet ventilation channel and connected to each other along the channel direction are still marked as thermal neighbors even if the spatial distance exceeds the preset distance threshold. The array topology model is stored in the non-volatile storage of the edge computing gateway and is regenerated only when the array physical layout changes.

2. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, The methods for collaboratively estimating the environmental reference temperature in step S2 include: When the set of low-load fuses contains two or more fuses, the median value of their temperature readings shall be taken as the ambient reference temperature. When the low-load fuse set contains only one fuse, the temperature reading of that fuse is directly used as the ambient reference temperature. When there are no low-load fuses in the array, the theoretical temperature rise caused by the self-heating of its current-carrying component is first deducted for each fuse according to the steady-state Joule law, and then the median of the residual value after deduction is used as the ambient reference temperature.

3. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, Step S2 also includes an environmental reference temperature back-off strategy: If the number of readable fuses is less than the preset proportion of the total number due to communication packet loss or sensor disconnection within three consecutive sampling periods, the arithmetic mean of the previous successful estimate and the reading of the independent ambient temperature meter in the power distribution center control room is used as the ambient reference temperature for the current period. At the same time, the communication anomaly event is recorded and reported to the operation and maintenance monitoring platform until the number of readable fuses is restored, and then the normal estimation process is resumed.

4. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, The method for calculating the expected temperature in step S3 is as follows: ； In the formula, For fuse The expected healthy temperature of the fuse; The ambient reference temperature; For fuse The current current value; For fuse The rated resistance value indicated in the product's manufacturer's datasheet; For fuse The nominal heat dissipation coefficient indicated in the product's factory data sheet; For fuse Heat dissipation area The offset of the heat dissipation conditions.

5. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, The calculation method for the heat dissipation condition offset in step S3 within each sampling period is as follows: Within each heat dissipation area, fuses with load ratios within a preset medium-load range are selected as medium-load samples. For each of the aforementioned medium-load samples, calculate the difference between the measured temperature and the theoretical temperature obtained based on the environmental reference temperature and the theoretical temperature rise due to current self-heating. Take the arithmetic mean of all the differences within the heat dissipation area as the heat dissipation condition offset of the heat dissipation area; When there is no intermediate sample in a certain heat dissipation area, the heat dissipation condition offset of that heat dissipation area adopts the value of the previous successful calculation cycle.

6. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, The processing of the environmental correlation deviation group in step S4 specifically includes: For each environmentally related deviation group, the average thermal deviation value of all fuses in the group is calculated and superimposed on the heat dissipation condition offset of the heat dissipation area to which the group belongs as a correction amount; Based on the corrected heat dissipation condition offset, the expected temperature calculation and thermal deviation value calculation of step S3 are re-executed for all fuses in the heat dissipation area, only once and without iterative loops. The fuses that, after recalculation, still have thermal deviation values ​​exceeding the attention threshold and whose neighbor deviation synchronization rate still does not exceed the preset synchronization rate threshold, are ultimately identified as the isolated thermal deviation fuses of this sampling period.

7. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, The preset number of confirmations mentioned in step S5 ranges from 3 to 8 times; The fuse degradation alarm information is uploaded to the remote operation and maintenance monitoring platform via Ethernet by the edge computing gateway. Each fuse degradation alarm information includes at least the following fields: the number of the alarm fuse and its cabinet identifier, the number of the heat dissipation area, the most recent measured temperature value and the thermal deviation value, the number of the protected feeder circuit, and the alarm timestamp.

8. The high-voltage fuse array operation monitoring method based on IoT architecture according to claim 1, characterized in that, In step S2, each fuse is configured with a set of sensing units. The sensing unit includes a wireless temperature sensor fixed to the middle of the outer tube wall of the fuse in a surface mount manner and a through-type current transformer fitted on the feeder outlet. The temperature sensor and the current transformer sample synchronously. The sampling data from the sensing unit is transmitted to the top-of-cabinet aggregation gateway node via a low-power wireless module. The aggregation gateway node is then cascaded to the edge computing gateway via an RS-485 bus using the Modbus-RTU protocol.

9. A high-voltage fuse array operation monitoring system based on an Internet of Things (IoT) architecture, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the high-voltage fuse array operation monitoring method based on the Internet of Things architecture as described in any one of claims 1 to 8 is implemented.

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