A method and system for monitoring and early warning of temperature inside a converter valve

Abstract

This invention discloses a method and system for monitoring and warning the internal temperature of a converter valve, specifically relating to the field of converter valve temperature monitoring technology. By deploying temperature sensors in multiple key heat-generating units, local temperature data is collected in real time. A time series is constructed by combining the inlet and outlet temperatures of the cooling water. A temperature trend prediction model is built based on the temperature rise rate and temperature gradient changes, accurately identifying the risk of local thermal runaway. By comparing the prediction results with tiered thresholds, multi-level warning signals are output, and corresponding processing strategies are linked. In extreme cases where the local temperature continues to rise abnormally and the cooling system response is lagging, the system can trigger an emergency shutdown protection step-by-step to prevent device failure. This method effectively improves the perception and response speed of local thermal faults, significantly enhancing the operational safety and intelligence level of the converter valve system.

Description

Technical Field

[0001] This invention relates to the field of converter valve temperature monitoring technology, specifically to a method and system for monitoring and early warning of the internal temperature of a converter valve. Background Technology

[0002] Converter valves are critical components in high-voltage direct current (HVDC) transmission systems, and their operational status directly impacts the safety and stability of the entire system. Because converter valves contain numerous power devices (such as thyristors or IGBTs), capacitors, and other electronic components, these devices generate significant heat during operation, requiring forced cooling systems for heat dissipation. Currently, the commonly used method involves monitoring the inlet and outlet temperatures of the cooling water and triggering an early warning signal after a certain delay once the temperature exceeds a set threshold.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, delayed alarm mechanisms based on inlet valve cooling water temperature work effectively in most operating scenarios. However, in special cases such as sudden high load on the converter valve or partial failure of the cooling system, local thermal runaway delayed alarms may occur. Specifically, some internal power devices or contact areas may experience rapid temperature rise due to blockage of cooling pipes, scaling, or poor local ventilation. However, because the overall temperature of the cooling water rises slowly, the system only detects a slight exceedance and enters a delayed waiting state, failing to issue an alarm immediately. This leads to heat accumulation in localized areas, ultimately causing device burnout or insulation breakdown. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for monitoring and early warning of the internal temperature of a converter valve, so as to overcome the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring and early warning of internal temperature of a converter valve, comprising:

[0007] Multiple temperature sensors are installed in several key heating units inside the converter valve to collect local temperature data of each area in real time.

[0008] The local temperature data and the cooling water inlet and outlet temperature data are used to construct a time series, and a temperature trend prediction model is constructed based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway.

[0009] The judgment results are compared with the gradient risk threshold to generate early warning signals of different levels and to perform corresponding processing.

[0010] If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

[0011] Preferably, the key heat-generating unit includes: the thyristor or IGBT module body and its heat dissipation base, the power capacitor and resistor connection terminals, the power device bus interface or copper bus joint, and the insulating structure surface of the strong electric field concentration area.

[0012] Preferably, the local temperature data T collected by temperature sensors installed on each key heating unit inside the converter valve is used. i (t) and cooling water inlet temperature T in (t), water outlet temperature T out (t) is recorded synchronously with a fixed sampling period to form a multidimensional temperature time series T(t) = {T1(t), T2(t), ..., T...} under a unified time reference. m (t),T in (t),T out (t)}, where m is the total number of temperature data.

[0013] Preferably, the rate of temperature change per unit time is calculated for each sensor point, and the temperature sampling period of the system is set to Δt. For any temperature sensor point i located at the converter valve, the rate of temperature change per unit time is calculated at two consecutive sampling times t. k and t k+1 Obtain its temperature value and calculate the rate of temperature rise per unit time: In the formula, T i (t k T represents the current temperature value. i (t k+1 S represents the temperature value at the next moment. i (t k () represents the rate of temperature change.

[0014] Preferably, for multiple physically adjacent sensor points, the spatial temperature distribution gradient is calculated: a graph structure G = (V, E, W) is constructed; V represents the set of all temperature sensor points in the graph, denoted as V = {v1, v2, ..., v...}. n}; E is the set of edges, representing the spatial relationship between adjacent sensors; W is the set of edge weights; construct the temperature vector T, T = [T1, T2, ..., T n ] S S is the vector transpose, and the graph Laplacian matrix L is calculated as: L = DW; where D is the degree matrix; for each node v i The spatial temperature distribution gradient is defined as: Let G be the set of neighboring nodes of node i, and let G be the spatial temperature distribution gradient.

[0015] Preferably, the temperature change rate and spatial temperature distribution gradient are converted into a comprehensive feature vector. The comprehensive feature vector is used as the input to the machine learning model. The machine learning model uses the prediction of local thermal runaway risk value labels for each set of comprehensive feature vectors as the prediction objective and minimizes the sum of prediction errors for all local thermal runaway risk value labels as the training objective. The machine learning model is trained until the sum of prediction errors converges, at which point the model training stops. The local thermal runaway risk value is determined based on the model output. The machine learning model is a multinomial regression model.

[0016] Preferably, the obtained local thermal runaway risk value is compared with the gradient risk threshold, which includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. The local thermal runaway risk value is compared with the first risk threshold and the second risk threshold respectively.

[0017] If the risk value is greater than the second risk threshold, it is judged as a serious risk of thermal runaway, and a level 2 emergency alarm or protection action is immediately triggered; if the risk value is between the first and second risk thresholds, it is judged as a moderate risk state, and a level 1 warning is triggered, prompting operation and maintenance intervention; if the risk value is less than the first risk threshold, the current state is judged as safe and stable, and no warning is triggered.

[0018] Preferably, when the temperature at a local sensor point or adjacent sensor area shows a continuous upward trend, and the upward trend meets the following set of combined conditions, the system will be considered to have experienced potential thermal runaway:

[0019] First judgment condition: The temperature of a certain sensor shows a monotonically increasing trend for multiple consecutive sampling cycles; and its temperature change rate continues to exceed the set threshold. At this time, it is determined that the local area has entered an abnormal thermal accumulation state.

[0020] Second judgment condition: The current temperature exceeds the device's allowable safe temperature limit, and the inlet and outlet temperatures T of the surrounding cooling water system are also above the limit. in (t),T out The change in the difference (t) is less than the set response threshold ΔT min That is: |T out (t)-T in (t)|<ΔT min This indicates that the overall temperature of the cooling system has not yet reflected the local temperature anomaly, meaning that the cooling system may not be responding effectively or there may be a local failure.

[0021] The third judgment condition is that the flow rate or pressure monitoring value of the cooling channel corresponding to the current area is normal, which further confirms that it is a local thermal anomaly rather than a system false alarm.

[0022] The present invention also provides a temperature monitoring and early warning system for the internal temperature of a converter valve, including a temperature data acquisition module, a thermal runaway prediction module, an early warning module, and an anomaly handling module;

[0023] Temperature data acquisition module: Multiple temperature sensors are set in several key heating units inside the converter valve to collect local temperature data of each area in real time;

[0024] Thermal runaway prediction module: Constructs a time series by combining the local temperature data with the cooling water inlet and outlet temperature data, and builds a temperature trend prediction model based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway.

[0025] Early warning module: compares the judgment result with the gradient risk threshold, generates early warning signals of different levels, and performs corresponding processing;

[0026] Anomaly handling module: If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

[0027] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0028] 1. This invention deploys multiple temperature sensors at various key heat-generating units inside the converter valve to collect local temperature data in real time. It also constructs a temperature time series based on a unified time reference by combining the inlet and outlet temperatures of the cooling water. Furthermore, it extracts a comprehensive feature vector based on the rate of temperature change and the spatial temperature distribution gradient, and uses a multinomial regression model to intelligently predict the risk of local thermal runaway. This effectively overcomes the response lag problem caused by traditional alarms that rely on the average temperature of the cooling water and fixed delay. It can identify local overheating trends earlier and realize the transformation from temperature over-limit alarms to thermal risk trend prediction.

[0029] 2. This invention achieves a hierarchical control mechanism for risk response by setting graded risk thresholds. When a significant thermal runaway trend is detected but the cooling system shows no obvious response, an emergency protection command is triggered, thereby significantly improving the safety and robustness of the converter valve system under complex conditions such as sudden high loads and partial cooling failures. This method has strong real-time performance, predictability, and adaptability, and can be widely applied in high-voltage direct current transmission systems, significantly improving the intelligence level and reliability of equipment operation. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0031] Figure 1 This is a flowchart of the early warning method of the present invention.

[0032] Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1, please refer to Figure 1 As shown in this embodiment, a method for monitoring and early warning of the internal temperature of a converter valve includes:

[0035] Multiple temperature sensors are installed in several key heating units inside the converter valve to collect local temperature data of each area in real time.

[0036] The local temperature data and the cooling water inlet and outlet temperature data are used to construct a time series, and a temperature trend prediction model is constructed based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway.

[0037] The judgment results are compared with the gradient risk threshold to generate early warning signals of different levels and to perform corresponding processing.

[0038] If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

[0039] In this invention, to achieve precise monitoring of the internal temperature of the converter valve, high-sensitivity temperature sensors are deployed on multiple key heat-generating units within the converter valve structure, forming a multi-point temperature acquisition network. The specific implementation method is as follows:

[0040] Sensor deployment targets: Select key components inside the converter valve that are prone to hot spots or have the risk of failure as deployment points, including: the thyristor or IGBT module body and its heat sink, the connection terminals of power capacitors and resistors, the bus interface or copper bus joint of power devices, and the surface of the insulation structure in areas with concentrated strong electric fields.

[0041] Sensor type selection: Thermistors (NTC / PTC) can be used for medium and low temperature regions; thermocouples or fiber optic temperature sensors can be used for high-pressure or strong electromagnetic interference environments; for areas with high accuracy requirements, digital miniature temperature chips can be used, supporting fast response and remote data transmission.

[0042] Sensor placement strategy: Avoid single-point judgment by only placing sensors at the cooling water inlet or outlet. Adopt a "point-area combination" placement method, that is, set multiple measurement points on a single element, and compare and analyze the lateral and longitudinal temperature distributions. Add a "thermal distribution reference point" to compare the heat diffusion rate of different parts.

[0043] Data Acquisition and Processing: All sensors transmit real-time temperature data to the monitoring control unit via a high-speed data bus (such as CAN, RS485, or fiber optic communication). The control unit has an embedded temperature analysis module that performs data comparison and trend identification according to the sampling frequency (such as 1Hz~10Hz) and generates a heat distribution map. It dynamically calculates the temperature rise rate (ΔT / Δt) and temperature gradient (ΔT / Δx) to implement anomaly detection logic.

[0044] The advantages of this invention are: enabling rapid location of local abnormal temperature rise inside the converter valve; reducing the early warning lag caused by traditional average temperature judgment; improving the safety response capability of the equipment in the event of sudden load or local cooling failure; and providing data support for subsequent AI-based big data diagnosis and thermal failure prediction.

[0045] The local temperature data T collected by temperature sensors installed on key heating units inside the converter valve i (t) and cooling water inlet temperature T in (t), water outlet temperature T out (t) is recorded synchronously with a fixed sampling period (e.g., 1 second) to form a multidimensional temperature time series under a unified time base. This data structure can be represented as: T(t)={T1(t),T2(t),...,T m (t),T in (t),T out (t)};m represents the total number of temperature data.

[0046] Calculate the rate of temperature change per unit time for each sensor point: Set the system's temperature sampling period to Δt (unit: seconds), such as Δt = 1s. All temperature sensors sample synchronously according to this period to ensure data timing consistency.

[0047] For any temperature sensor point i located at a critical part of the converter valve, at two consecutive sampling times t k and t k+1 Obtain its temperature value and calculate the rate of temperature rise per unit time: In the formula, T i (t k T represents the current temperature value. i (t k+1 S represents the temperature value at the next moment. i (t k () represents the rate of temperature change.

[0048] For multiple physically adjacent sensor points, calculate the spatial temperature distribution gradient: Construct a graph structure G = (V, E, W); V represents the set of all temperature sensor points in the graph, denoted as V = {v1, v2, ..., v...} n}; E is the set of edges, representing the spatial relationship between adjacent sensors (e.g., distance less than a threshold); W is the set of edge weights, representing the heat conduction relationship or distance attenuation between nodes, which can be defined as: Where d ij Let σ be the physical distance between sensors i and j, and σ be the scale parameter for adjusting the thermally affected range.

[0049] Construct a temperature vector T, T = [T1, T2, ..., T n ] S T represents the column vector of temperatures collected by each sensor at a certain moment, in degrees Celsius, and S is the vector transpose. Calculate the graph Laplacian matrix L: L = DW; where D is the degree matrix, and the diagonal elements are the node degrees. ii =∑ j w ij .

[0050] Calculate the thermal gradient energy term (intensity of temperature change) E(T): E(T) = ∑ i,j w ij (T i -T j ) 2 ; represents the sum of squares of temperature differences between all adjacent points in the figure, reflecting the spatial variation energy of the temperature field, and can be used to determine whether there are abrupt change regions.

[0051] For each node v i The spatial temperature distribution gradient is defined as: Let G be the set of neighboring nodes of node i, and let G be the spatial temperature distribution gradient.

[0052] When G is greater than a certain preset threshold G th If the temperature difference is significant, it can be determined that there is a significant local temperature difference near that point, which may be a source of thermal runaway.

[0053] The rate of temperature change and the gradient of spatial temperature distribution are converted into a comprehensive feature vector. This comprehensive feature vector is used as the input to a machine learning model. The machine learning model uses the prediction of local thermal runaway risk value labels for each set of comprehensive feature vectors as the prediction objective and minimizes the sum of prediction errors for all local thermal runaway risk value labels as the training objective. The machine learning model is trained until the sum of prediction errors converges, at which point the model training stops. The local thermal runaway risk value is determined based on the model output. The machine learning model is a multinomial regression model.

[0054] The obtained local thermal runaway risk value is compared with the gradient risk threshold, which includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. The local thermal runaway risk value is compared with the first risk threshold and the second risk threshold respectively.

[0055] If the risk value is greater than the second risk threshold, it is judged as a serious risk of thermal runaway, and a level two emergency alarm or protection action is immediately triggered.

[0056] If the risk value is between the first and second risk thresholds, it is judged as a medium risk state, triggering a level one warning and prompting operation and maintenance intervention;

[0057] If the risk value is less than the first risk threshold, the current state is considered safe and stable, and no warning is triggered.

[0058] The system determines that the monitoring point or area has entered a state of severe overheating, which is highly likely to cause device failure or insulation breakdown. The handling measures include: immediately triggering a secondary alarm signal and pushing a high-priority alarm to the operation and maintenance system and control center; initiating emergency protection actions, including: forcibly switching the cooling mode (such as boosting and increasing the flow); reducing the load; triggering the corresponding converter valve module to automatically exit service (de-parallel connection); and linking and activating the bypass protection system; recording event data, including the current feature vector, heat map, response measure timestamp, etc., for later analysis and model retraining.

[0059] The system determines that there is a tendency or potential for thermal runaway in the current area, but it has not yet reached an emergency state. The handling measures include: triggering a level 1 early warning signal and sending a warning alarm to the operation and maintenance platform; dynamically adjusting the operating parameters of the cooling system, such as increasing the cooling power of the area; activating the backup cooling channel; activating the heat distribution trend tracking mechanism to continuously monitor the temperature changes in the area at high frequency in the next few minutes; marking the area as a thermal anomaly area and prioritizing its inclusion in the scheduling and inspection plan.

[0060] The system determines that the temperature at the current monitoring point is stable and the operation is normal. The handling methods include: maintaining the normal monitoring frequency and not triggering any alarms; optionally enabling a dynamic threshold adaptive mechanism: if the system is in a low-risk state for a long time, the risk threshold can be appropriately tightened to enhance sensitivity; and recording stable operating data for continuous model optimization and training.

[0061] If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

[0062] Specifically, when the system detects a continuous upward trend in temperature at a local sensor point or in an adjacent sensor area, and this upward trend meets the following set of combined conditions, the system will be considered to have experienced potential thermal runaway:

[0063] The first judgment condition is that the temperature of a certain sensor increases monotonically for multiple consecutive sampling periods (e.g., N consecutive samplings) and its temperature change rate continuously exceeds the set threshold. At this time, it is determined that the local area has entered an abnormal thermal accumulation state.

[0064] Second judgment condition: The current temperature exceeds the device's allowable safe temperature limit, and the inlet and outlet temperatures T of the surrounding cooling water system are also above the limit. in (t),T out The change in the difference (t) is less than the set response threshold ΔT min That is: |T out (t)-T in (t)|<ΔT min This indicates that the overall temperature of the cooling system has not yet reflected the local temperature anomaly, meaning that the cooling system may not be responding effectively or there may be a local failure.

[0065] The third judgment condition is that the flow rate or pressure monitoring value of the cooling channel corresponding to the current area is normal (not due to cooling interruption), which further confirms that it is a local thermal anomaly rather than a system false alarm.

[0066] After meeting the above conditions, the system will comprehensively determine that the area exhibits a typical localized rapid thermal runaway with ineffective cooling response. Since such anomalies are often masked in existing technologies due to delay mechanisms or average temperature assessments, potentially leading to severe component failure or fire risks, this invention sets a priority triggering path for emergency shutdown protection commands in this scenario: skipping conventional delay and alarm procedures; directly triggering the control system to enter emergency response mode, executing: disconnecting the faulty converter valve module from the grid; shutting down the entire system for protection or switching to a bypass channel; sending a red alarm signal to the monitoring center and locking the fault data.

[0067] Example 2, please refer to Figure 2As shown in the figure, the internal temperature monitoring and early warning system of the converter valve described in this embodiment includes a temperature data acquisition module, a thermal runaway prediction module, an early warning module, and an anomaly handling module.

[0068] Temperature data acquisition module: Multiple temperature sensors are set in several key heating units inside the converter valve to collect local temperature data of each area in real time;

[0069] Thermal runaway prediction module: Constructs a time series by combining the local temperature data with the cooling water inlet and outlet temperature data, and builds a temperature trend prediction model based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway.

[0070] Early warning module: compares the judgment result with the gradient risk threshold, generates early warning signals of different levels, and performs corresponding processing;

[0071] Anomaly handling module: If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

[0072] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0073] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring and early warning of internal temperature of a converter valve, characterized in that: include: Multiple temperature sensors are installed in several key heating units inside the converter valve to collect local temperature data of each area in real time. The local temperature data and the cooling water inlet and outlet temperature data are used to construct a time series, and a temperature trend prediction model is constructed based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway. Specifically, this includes: converting the rate of temperature change and the spatial temperature distribution gradient into a comprehensive feature vector; using the comprehensive feature vector as input to a machine learning model; the machine learning model uses the prediction of local thermal runaway risk value labels for each set of comprehensive feature vectors as the prediction objective; minimizing the sum of prediction errors for all local thermal runaway risk value labels as the training objective; training the machine learning model until the sum of prediction errors converges; and determining the local thermal runaway risk value based on the model output. The machine learning model is a multinomial regression model. The spatial temperature distribution gradient is calculated as follows: Construct a graph structure G = (V, E, W); V represents the set of all temperature sensor points in the graph, denoted as V = {v1, v2, ..., v...} n }; E is the set of edges, representing the spatial relationship between adjacent sensors; W is the set of edge weights; construct the temperature vector T, T = [T1, T2, ..., T n ] S S is the vector transpose, and the graph Laplacian matrix L is calculated as: L = DW; where D is the degree matrix; for each node v i The spatial temperature distribution gradient is defined as: Let G be the set of neighboring nodes of node i, and let G be the spatial temperature distribution gradient. The judgment results are compared with the gradient risk threshold to generate early warning signals of different levels and to perform corresponding processing. If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

2. The method for monitoring and early warning of internal temperature of a converter valve according to claim 1, characterized in that: The key heat-generating unit includes: the thyristor or IGBT module body and its heat dissipation base, the power capacitor and resistor connection terminals, the power device bus interface or copper bus joint, and the insulating structure surface of the strong electric field concentration area.

3. The method for monitoring and early warning of internal temperature of a converter valve according to claim 1, characterized in that: The local temperature data T collected by temperature sensors installed on key heating units inside the converter valve i (t) and cooling water inlet temperature T in (t), water outlet temperature T out (t) is recorded synchronously with a fixed sampling period to form a multidimensional temperature time series T(t) = {T1(t), T2(t), ..., T...} under a unified time reference. m (t),T in (t),T out (t)}, where m is the total number of temperature data.

4. The method for monitoring and early warning of internal temperature of a converter valve according to claim 3, characterized in that: For each sensor point, calculate its temperature change rate per unit time. Set the system temperature sampling period to Δt. For any temperature sensor point i located at the converter valve, at two consecutive sampling times t... k and t k+1 Obtain its temperature value and calculate the rate of temperature rise per unit time: In the formula, T i (t k T represents the current temperature value. i (t k+1 S represents the temperature value at the next moment. i (t k () represents the rate of temperature change.

5. The method for monitoring and early warning of internal temperature of a converter valve according to claim 1, characterized in that: The obtained local thermal runaway risk value is compared with the gradient risk threshold, which includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. The local thermal runaway risk value is compared with the first risk threshold and the second risk threshold respectively. If the risk value is greater than the second risk threshold, it is judged as a serious risk of thermal runaway, and a level two emergency alarm or protection action is immediately triggered. If the risk value is between the first and second risk thresholds, it is judged as a medium risk state, triggering a level one warning and prompting operation and maintenance intervention; If the risk value is less than the first risk threshold, the current state is considered safe and stable, and no warning is triggered.

6. The method for monitoring and early warning of internal temperature of a converter valve according to claim 1, characterized in that: When the temperature at a local sensor point or in an adjacent sensor area shows a continuous upward trend, and this upward trend meets the following set of combined conditions, the system will be considered to have experienced potential thermal runaway: First judgment condition: The temperature of a certain sensor shows a monotonically increasing trend for multiple consecutive sampling cycles; and its temperature change rate continues to exceed the set threshold. At this time, it is determined that the local area has entered an abnormal thermal accumulation state. Second judgment condition: The current temperature exceeds the device's allowable safe temperature limit, and the inlet and outlet temperatures T of the surrounding cooling water system are also above the limit. in (t),T out The change in the difference (t) is less than the set response threshold ΔT min That is: |T out (t)-T in (t)|<ΔT min This indicates that the overall temperature of the cooling system has not yet reflected the local temperature anomaly, meaning that the cooling system may not be responding effectively or there may be a local failure. The third judgment condition is that the flow rate or pressure monitoring value of the cooling channel corresponding to the current area is normal, which further confirms that it is a local thermal anomaly rather than a system false alarm.

7. A converter valve internal temperature monitoring and early warning system, used to implement the converter valve internal temperature monitoring and early warning method according to any one of claims 1-6, characterized in that: It includes a temperature data acquisition module, a thermal runaway prediction module, an early warning module, and an anomaly handling module; Temperature data acquisition module: Multiple temperature sensors are set in several key heating units inside the converter valve to collect local temperature data of each area in real time; Thermal runaway prediction module: Constructs a time series by combining the local temperature data with the cooling water inlet and outlet temperature data, and builds a temperature trend prediction model based on the local temperature rise rate and temperature gradient changes to determine whether there is a risk of local thermal runaway. Early warning module: compares the judgment result with the gradient risk threshold, generates early warning signals of different levels, and performs corresponding processing; Anomaly handling module: If the local temperature continues to rise abnormally and exceeds the safe temperature limit, and the overall temperature of the cooling system does not reflect the abnormal situation in sync, an emergency shutdown protection command will be triggered.

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