Unattended intelligent operation and maintenance method and system for power distribution room based on digital twinning

Abstract

The present application belongs to the technical field of power supply and distribution system operation and maintenance, and particularly relates to an unattended intelligent operation and maintenance method and system for a power distribution room based on digital twinning, which comprises the following steps: according to the load current effective value of a target monitoring device in the power distribution room and the real-time running state of a cooling fan, and in combination with calibrated thermal physical fingerprint parameters, the theoretical temperature rise value of the target monitoring device at the current time is deduced in a virtual space; according to the actual running temperature of the target monitoring device and the environmental reference temperature, the actual temperature rise value is calculated; according to the difference between the actual temperature rise value and the theoretical temperature rise value and the load current effective value, the impedance degradation thermal accumulation degree of the target monitoring device at the current time is calculated; and according to the numerical interval where the impedance degradation thermal accumulation degree is located, the corresponding level of operation and maintenance response strategy is executed. The present application can effectively avoid device thermal inertia lag and fan cooling interference, and improve the accuracy of power distribution room device contact state perception and power supply safety.

Description

Technical Field

[0001] This invention relates to the field of power supply and distribution system operation and maintenance technology. More specifically, this invention relates to a digital twin-based unmanned intelligent operation and maintenance method and system for substations. Background Technology

[0002] With the rapid advancement of smart grid technology, the operation and maintenance mode of substations, as the nerve endings of the power system, is accelerating its transformation from traditional manual inspection to unmanned and digital operation. During long-term high-load operation of substations, critical components such as busbar lap surfaces of high-voltage switchgear, circuit breaker contacts, and cable joints often experience a gradual increase in contact resistance due to mechanical vibration, electrochemical corrosion, or loose fasteners. This contact degradation is often difficult to detect in its early stages, but as load current flows, the Joule heat generated by abnormal contact resistance accumulates, easily leading to insulation aging, thermal breakdown short circuits, or even severe arcing explosions, posing a significant threat to power supply reliability. Therefore, real-time and accurate monitoring of the contact status of these critical electrical nodes is a core task for ensuring the safe and stable operation of unmanned substations.

[0003] Current power distribution room status monitoring solutions mainly rely on direct temperature physical quantity measurement, such as regular manual inspections using handheld infrared thermal imagers, or online monitoring by installing wireless passive temperature sensors and fiber optic grating sensors at key equipment nodes. These technologies typically employ a static threshold discrimination method, which triggers an alarm when the real-time temperature value or relative temperature rise at the monitoring point exceeds the fixed limit specified by national standards.

[0004] The existing monitoring methods have significant limitations under complex working conditions. On the one hand, power equipment has significant thermal inertia, and temperature changes often lag behind drastic fluctuations in load current. This phase difference makes simple temperature rise detection prone to misjudgment during sudden load changes. On the other hand, modern power distribution rooms are generally equipped with automatic temperature control and ventilation systems. When the cooling fan starts to dissipate heat, the strong convection air will forcefully reduce the temperature of the equipment surface, thereby masking the abnormal heat generated by poor contact. This causes the values ​​measured by the sensor to remain within the normal range even under fault conditions, creating a hidden danger of false health. Summary of the Invention

[0005] To address the technical problems of misjudgment and missed reporting in contact status monitoring caused by the thermal inertia lag of equipment and the masking of fault heat by fan heat dissipation in existing temperature measurement methods, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an unattended intelligent operation and maintenance method for power distribution rooms based on digital twins, including: The system acquires the effective value of the load current, actual operating temperature, ambient reference temperature, and real-time operating status of the cooling fan of the target monitoring equipment in the power distribution room. Based on the effective value of the load current and the real-time operating status of the cooling fan, and combined with the thermal inertia characteristics of the target monitoring equipment, it calculates the theoretical temperature rise of the target monitoring equipment at the current moment. The theoretical temperature rise represents the reference temperature rise that the target monitoring equipment should reach under healthy contact conditions and current heat dissipation conditions. Based on the actual operating temperature and the ambient reference temperature, it calculates the actual temperature rise. Based on the difference between the actual temperature rise and the theoretical temperature rise, and the effective value of the load current, it calculates the impedance degradation heat accumulation degree of the target monitoring equipment at the current moment. The impedance degradation heat accumulation degree represents the degree of accumulation of additional heat generated by abnormal contact resistance under load current excitation. Based on the numerical range of the impedance degradation heat accumulation degree, it executes the corresponding level of operation and maintenance response strategy. The operation and maintenance response strategy includes starting the cooling fan for thermal troubleshooting, generating a contact failure hazard warning work order, and disconnecting the faulty circuit.

[0007] This invention establishes a health benchmark that can be dynamically adjusted in real time according to operating conditions by acquiring the effective value of the load current and the real-time operating status of the cooling fan, combined with the thermal inertia characteristics of the equipment. This effectively avoids the detection blind spots caused by temperature lag due to sudden load changes and the masking of fault heat by fan cooling. Furthermore, it calculates the actual temperature rise by combining the actual operating temperature and the ambient benchmark temperature. By analyzing the difference between the actual temperature rise and the theoretical temperature rise and superimposing the excitation effect of the effective value of the load current, it constructs the impedance degradation heat accumulation degree, which isolates environmental interference and normal load heating, and realizes the assessment of the degree of additional heat accumulation caused by abnormal contact resistance. Based on the impedance degradation heat accumulation degree, it implements a hierarchical operation and maintenance response strategy, realizing closed-loop management from proactive thermal investigation and hidden danger warning to automatic fault disconnection, improving the accuracy of equipment status perception and the safety of the power supply system in unattended operation and maintenance scenarios of power distribution rooms.

[0008] Preferably, the acquisition of the effective value of the load current, actual operating temperature, ambient reference temperature, and real-time operating status of the cooling fan of the target monitoring equipment in the power distribution room includes: setting a system synchronization sampling period based on the controller timestamp of the edge computing gateway of the power distribution room; for the actual operating temperature and ambient reference temperature, if no new data is reported at the current moment, the values ​​at the previous moment are kept unchanged; for the effective value of the load current, the time interval of the previous synchronization sampling period at the current moment is set as the effective calculation window, and the root mean square value of all collected data within the effective calculation window is calculated as the effective value of the load current at the current moment.

[0009] Preferably, the theoretical temperature rise value satisfies the expression: In the formula, Indicates the target monitoring equipment at any time The theoretical temperature rise value; Indicates the previous moment The theoretical temperature rise value; This represents the thermal inertia memory factor; This represents the dynamic steady-state electrothermal conversion gain, reflecting the real-time operating status of the cooling fan. The theoretical maximum temperature rise of the target monitoring equipment relative to the ambient temperature when it operates continuously at rated current and reaches thermal stability. Indicates time The effective value of the load current; This indicates the rated reference current of the target monitoring equipment.

[0010] This invention, by constructing a recursive calculation model incorporating thermal inertia memory factors and dynamic steady-state electrothermal conversion gain, can simulate the dynamic heating process of target monitoring equipment under load current excitation. It fully considers the heat capacity effect of the equipment under large-mass metal components, ensuring that the calculated theoretical temperature rise is no longer simply a value following sudden current changes, but possesses physical hysteresis and buffering characteristics. Simultaneously, by introducing a dynamic steady-state electrothermal conversion gain linked to the real-time operating status of the cooling fan, this invention can adjust the model's thermal equilibrium point in real time based on whether the fan is on or off. This allows for accurate calculation of the baseline temperature rise that the equipment should have under healthy conditions under the current cooling conditions. This provides a high-precision comparison benchmark for subsequently identifying minute temperature rise deviations caused by abnormal contact resistance, effectively solving the problem that traditional static threshold methods cannot distinguish between normal load heating and fault heating.

[0011] Preferably, the thermal inertia memory factor and dynamic steady-state electrothermal conversion gain are calibrated as follows: a segment of stable operating data from the target monitoring equipment is selected, and the stable operating data is divided into self-cooled datasets and air-cooled datasets based on the real-time operating status of the cooling fan; for each dataset, a linear regression equation is constructed. ,in This represents the actual temperature rise. This represents the actual temperature rise at the previous moment. The load factor is the square of the current load factor; the regression coefficients are solved using the least squares method. and Let the thermal inertia memory factor equal Let the dynamic steady-state electrothermal conversion gain equal Divide by .

[0012] This invention achieves personalized calibration of the thermal inertia memory factor and dynamic steady-state electrothermal conversion gain by screening stable operating data and distinguishing between self-cooling and air-cooling heat dissipation scenarios through linear regression analysis. This adaptive parameter optimization mechanism based on historical data can accurately extract the unique thermophysical characteristics of the target monitoring equipment, eliminate model deviations caused by differences in equipment model, installation location, or manufacturing process, and ensure that the model parameters can truly reflect the thermal response law of the equipment under different heat dissipation modes. This allows the subsequent theoretical temperature rise calculation to closely match the actual physical properties of the equipment, improving the fitting accuracy and generalization ability of the digital twin model.

[0013] Preferably, the impedance degradation heat accumulation satisfies the expression: In the formula, Indicates time The heat accumulation degree of impedance degradation; Indicates the previous moment The heat accumulation degree of impedance degradation; This represents the historical risk attenuation coefficient; Indicates time The actual operating temperature of the target monitoring equipment; Indicates time The ambient reference temperature; Indicates time The actual temperature rise; Indicates time The theoretical temperature rise value; The one-way activation function is defined as follows: ; Indicates time The effective value of the load current; This indicates the rated reference current of the target monitoring equipment.

[0014] This invention introduces a historical risk attenuation coefficient and a one-way activation function to construct the calculation logic for impedance degradation heat accumulation, realizing a cumulative assessment of the degree of contact state anomalies. Through the one-way activation function, the system accumulates risk only when the actual temperature rise exceeds the theoretical temperature rise, effectively shielding negative fluctuations caused by measurement noise or minor model errors. At the same time, by combining the effective value of the load current to weight the difference, it not only reflects the magnitude of the temperature difference itself, but also reveals the degree of danger of the temperature difference under the current load. That is, even a small contact defect under high load will be amplified into a high risk, while anomalies under low load are reasonably assessed. In addition, the historical risk attenuation mechanism enables the impedance degradation heat accumulation to reflect the thermal accumulation effect of the fault, avoiding false alarms caused by instantaneous interference, and ensuring the keen capture and stable tracking of early impedance degradation trends.

[0015] Preferably, the step of executing the corresponding level of operation and maintenance response strategy based on the numerical range of impedance degradation heat accumulation includes: setting a first-level response threshold, a second-level response threshold, and a third-level response threshold, wherein the first-level response threshold is less than the second-level response threshold, and the second-level response threshold is less than the third-level response threshold; triggering a first-level response in response to impedance degradation heat accumulation being greater than the first-level response threshold and less than or equal to the second-level response threshold; triggering a second-level response in response to impedance degradation heat accumulation being greater than the second-level response threshold and less than or equal to the third-level response threshold; and triggering a third-level response in response to impedance degradation heat accumulation being greater than the third-level response threshold.

[0016] Preferably, the triggering of the first-level response includes: forcibly starting the cooling fan and running it continuously for a preset time; if, within the preset time, the difference between the actual temperature rise and the theoretical temperature rise decreases to within a preset temperature difference range, then the impedance degradation heat accumulation is reset to the product of the current value of the impedance degradation heat accumulation and the preset chronic degradation baseline coefficient.

[0017] Preferably, triggering the secondary response includes: generating a contact failure hazard warning work order and pushing the contact failure hazard warning work order to the operation and maintenance platform; triggering the tertiary response includes: linking the trip circuit to disconnect the faulty circuit circuit breaker.

[0018] This invention triggers a graded response strategy based on different ranges of impedance degradation thermal accumulation, realizing closed-loop operation and maintenance management from early warning to protection. For the medium-risk level 2 response, an early warning work order containing detailed hidden danger information is generated and pushed, enabling operation and maintenance personnel to carry out targeted maintenance before the fault worsens, thus achieving preventive maintenance. For the high-risk level 3 response, the system can directly link the trip circuit to cut off the fault source, preventing serious accidents such as arc short circuits caused by overheating and melting of contact points. This graded handling mechanism avoids power outages caused by overreaction and eliminates safety accidents caused by delayed response, achieving a balance between ensuring power supply continuity and equipment safety.

[0019] Preferably, the method further includes: constructing a state vector at the current moment that includes the effective value of the load current, the actual operating temperature, the ambient reference temperature, and the real-time operating status of the cooling fan; constructing an aligned state vector sequence based on the state vectors at consecutive moments; performing sliding window outlier cleaning on the state vector sequence; and correcting the center point value using linear interpolation in response to the center point value deviating from the window mean by more than a preset multiple of the standard deviation.

[0020] Secondly, the present invention provides an unattended intelligent operation and maintenance system for power distribution rooms based on digital twins, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned unattended intelligent operation and maintenance method for power distribution rooms based on digital twins is implemented.

[0021] By adopting the above technical solution, the above-mentioned unattended intelligent operation and maintenance method for power distribution rooms based on digital twins is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0022] The beneficial effects of this invention are as follows: By acquiring the effective value of the load current of the target monitoring equipment in the power distribution room and the real-time operating status of the cooling fan, and combining the thermal inertia characteristics of the equipment to calculate the theoretical temperature rise, a health benchmark that can be dynamically adjusted in real time according to the operating conditions is established. This effectively avoids the detection blind spots caused by temperature lag due to sudden load changes and by the fan's heat dissipation masking the heat of the fault. Furthermore, by combining the actual operating temperature and the ambient reference temperature to calculate the actual temperature rise, and by analyzing the difference between the actual temperature rise and the theoretical temperature rise and superimposing the excitation effect of the effective value of the load current, an impedance degradation heat accumulation degree is constructed, thus separating the environmental interference from the normal load. The invention utilizes heat-carrying capacity to quantitatively assess the degree of additional heat accumulation caused by abnormal contact resistance. Simultaneously, it implements a tiered operation and maintenance response strategy based on the heat accumulation degree of impedance degradation, achieving closed-loop management from proactive thermal investigation and hazard warning to automatic fault disconnection. Furthermore, by using historical operating data to personalize and dynamically correct the thermal inertia memory factor and dynamic steady-state electrothermal conversion gain, the invention ensures that the monitoring model can adapt to the physical characteristics and heat dissipation environment of different devices, eliminating the risk of false alarms caused by individual device differences and improving the accuracy of equipment status perception and the safety of the power supply system in unattended operation and maintenance scenarios in power distribution rooms. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the unattended intelligent operation and maintenance method for power distribution rooms based on digital twins in this invention; Figure 2 This is a schematic diagram illustrating the changes in the effective value of the load current and the real-time operating status of the cooling fan in an embodiment of the present invention. Figure 3 This is a schematic diagram of the curves showing the actual temperature rise and the theoretical temperature rise in an embodiment of the present invention; Figure 4 This is a schematic diagram of the graded response of impedance degradation thermal accumulation in an embodiment of the present invention. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. 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.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses an unattended intelligent operation and maintenance method for power distribution rooms based on digital twins, referring to... Figure 1 This includes steps S1-S4: S1. Obtain the effective value of the load current, actual operating temperature, ambient reference temperature, and real-time operating status of the cooling fan of the target monitoring equipment in the power distribution room.

[0027] It should be noted that, because the thermal field and heat dissipation conditions in the power distribution room are constantly changing, a single temperature data point cannot isolate the influence of the environmental substrate. Furthermore, the sampling frequencies of different types of sensors vary by orders of magnitude; current transformers typically sample at the millisecond level, while wireless temperature sensors, to reduce power consumption, usually report at the minute level. This results in multi-source data being discrete and misaligned in the time dimension, and directly performing logical operations will lead to the curse of dimensionality and timing misalignment. Therefore, this embodiment establishes a unified clock reference and resampling mechanism to construct a synchronization vector space that includes excitation, response, and environmental disturbances.

[0028] Specifically, based on the controller timestamp of the edge computing gateway in the power distribution room, a state vector is constructed for each moment. This state vector is defined as a four-dimensional column vector containing load current, actual temperature, ambient temperature, and fan status.

[0029] in, Indicates time The state vector; Indicates time The effective value of the load current is collected by a current transformer and used as the excitation input of the system's heat source; Indicates time The actual operating temperature of the target monitoring equipment is collected by a wireless temperature sensor and used as the system's thermal response output. Indicates time The ambient reference temperature is collected by the temperature and humidity transmitter inside the cabinet. Indicates time The real-time operating status of the cooling fan is read through the PLC digital input / output port and used as an environmental disturbance variable. A value of 1 indicates that the fan is on, and a value of 0 indicates that the fan is off.

[0030] Furthermore, to address the issue of inconsistent data frequencies, this embodiment sets the system synchronization sampling period to be [missing information]. The original data is time-domain normalized using a zero-order hold and root mean square resampling strategy. For low-frequency temperature data, if time... If no new data is reported, the value from the previous time step remains unchanged, i.e., let equal ,make equal For high-frequency current data, the time interval is... Set as the effective calculation window, and calculate the root mean square value of all load current RMS values ​​within the effective calculation window as the current time. The effective value of the load current is used to accurately reflect the Joule heat accumulation effect within the synchronization cycle. After the above resampling process, the discrete data of each dimension are synchronized on the time axis, forming an aligned state vector sequence. Sliding window outlier cleaning is performed on the state vector sequence. The sliding window length is set to 5 synchronization sampling points. If the value of the center point deviates from the mean of the window by more than 3 times the standard deviation, linear interpolation is used for correction to remove impulse noise.

[0031] For example, Figure 2 This is a schematic diagram showing the changes in the effective value of the load current and the real-time operating status of the cooling fan.

[0032] S2. Based on the effective value of the load current and the real-time operating status of the cooling fan, and combined with the thermal inertia characteristics of the target monitoring equipment, calculate the theoretical temperature rise of the target monitoring equipment at the current moment. The theoretical temperature rise represents the reference temperature rise that the target monitoring equipment should reach under healthy contact conditions and current heat dissipation conditions.

[0033] It should be noted that since the temperature rise of electrical equipment is essentially a dynamic equilibrium process of Joule heat generation and convective-radiative heat dissipation, the thermal resistance and thermal capacity characteristics of equipment with different materials, shapes, and air-cooled conditions are completely different. Furthermore, the thermal inertia of the equipment causes the temperature response to lag behind the current excitation. If the temperature is directly calculated using the square of the current, the time effect of heat storage and release will be ignored. Therefore, this embodiment, based on the concept of digital twin technology, constructs a recursive calculation model that can sense the real-time operating status of the cooling fan and has thermal inertia memory. This model is used to deduce the theoretical temperature rise trajectory of the target monitoring equipment under ideal healthy conditions. Through recursive iteration, the theoretical temperature rise value simultaneously reflects the current Joule heat input and historical heat storage, thereby faithfully reproducing the thermal hysteresis characteristics and dynamic thermal equilibrium process of the target monitoring equipment in digital space, achieving an isomorphic mapping from the physical entity to the virtual twin.

[0034] Specifically, based on the discretized derivation of the law of conservation of energy and Newton's law of cooling, the theoretical temperature rise of the target monitoring point at the current moment is calculated:

[0035] In the formula, Indicates the target monitoring equipment at any time The theoretical temperature rise value, in degrees Celsius, reflects the temperature rise level that the target monitoring equipment should achieve under healthy contact resistance and current heat dissipation conditions. At system initialization, let... The value is equal to 0, assuming the target monitoring device is initially in a state of thermal equilibrium with the environment; Indicates the previous moment The theoretical temperature rise value; This represents the thermal inertia memory factor, reflecting the physical inertia of a device's material in maintaining its original thermal state under current convection conditions. Its value ranges from 0 to 1. The larger the value, the stronger the ability of the target monitoring equipment to maintain its current thermal state, and the longer the buffer period for the equipment to cool down or heat up; This is a legacy of historical thermal energy. This represents the dynamic steady-state electrothermal conversion gain, expressed in degrees Celsius. Its physical meaning is the gain under the real-time operating conditions of the cooling fan. The theoretical maximum temperature rise of the target monitoring equipment relative to the ambient temperature when it operates continuously at rated current and reaches thermal stability. Indicates time The effective value of the load current, This refers to the rated reference current of the target monitoring equipment, which is obtained by reading the nameplate parameters of the target monitoring equipment or retrieving them from the power distribution room equipment asset database. Considering that the temperature rise of the equipment is essentially driven by the heat generated by the current passing through the contact resistance, and follows the electrothermal conversion mechanism revealed by Joule's law (i.e., the conductor's heating power is proportional to the square of the effective value of the current), this embodiment introduces a rated reference current. As the denominator, the absolute heating power is converted into a relative heating power multiple relative to the rated state, that is, the square of the current load rate is used. As a dimensionless thermal power excitation signal, it not only cancels out the microscopic resistance variables that are difficult to measure online, but also increases the dynamic steady-state electrothermal conversion gain. It can always maintain a pure temperature dimension, defining the specific operating state of the cooling fan. Below, the ceiling of the maximum temperature rise that the equipment can generate when running at full load continuously; This represents the weight of the injected new heat, corresponding to the energy conservation distribution relationship between the equipment absorbing new heat and maintaining the old heat. This embodiment uses recursive iteration to achieve the theoretical temperature rise value. It simultaneously reflects the current Joule heat input and historical thermal energy storage, thus mathematically simulating the thermal hysteresis characteristics of electrical equipment.

[0036] In the formula, thermal inertia memory factor With dynamic steady-state electrothermal conversion gain This is a key parameter determining the model's accuracy. In this embodiment, it is obtained through the following calibration process: A period of stable operating data is selected during the initial operation of the target monitoring equipment, based on the real-time operating status of the cooling fan. The data is divided into self-cooled datasets and air-cooled datasets. The self-cooled datasets refer to the real-time operating status of the cooling fans from the selected stable operating data. The subset of data corresponding to all sampling moments with a value of 0 indicates that, in this state, the heat dissipation of the target monitoring equipment mainly relies on natural convection and radiation, following the thermophysical laws of natural cooling. The air-cooled dataset refers to the real-time operating status of the cooling fans. The subset of data corresponding to all sampling moments with a value of 1 represents the state where the forced airflow generated by the fan significantly alters the convective heat transfer coefficient on the surface of the target monitoring equipment. The target monitoring equipment follows the thermophysical laws of forced cooling, and the two datasets correspond to drastically different steady-state electrothermal conversion gain parameters. For each dataset, the recursive expression for the theoretical temperature rise is reconstructed into a linear regression form. Among them, equal That is, the actual temperature rise; let equal That is, the actual temperature rise at the previous moment; let equal This represents the squared load factor at the current moment. The least squares method is used to construct the observation matrix and solve for the regression coefficients. and Ultimately, the physical parameters are restored: thermal inertia memory factor. equal Dynamic steady-state electrothermal conversion gain equal Divide by Through this process, the dynamic steady-state electrothermal conversion gain when the fan is off can be obtained. Dynamic steady-state electrothermal conversion gain when the fan is turned on The aforementioned thermal inertia memory factor With dynamic steady-state electrothermal conversion gain Together, these elements constitute the thermophysical fingerprint of the target monitoring equipment, accurately anchoring the attribute parameters of the digital twin to the personalized characteristics of the physical entity. This ensures that the evolution mechanism of the twin model remains consistent with the physical entity under different heat dissipation boundary conditions. In this embodiment, the time window length for screening stable operation data is set to 168 hours, which covers a typical weekly load fluctuation cycle. In other embodiments, implementers can adjust the data screening window length according to the actual commissioning cycle and load fluctuation characteristics of the target monitoring equipment.

[0037] S3. Calculate the actual temperature rise value based on the actual operating temperature and the ambient reference temperature. Based on the difference between the actual temperature rise value and the theoretical temperature rise value, as well as the effective value of the load current, calculate the impedance degradation heat accumulation degree of the target monitoring equipment at the current moment. The impedance degradation heat accumulation degree characterizes the degree of accumulation of the additional heat generated by abnormal contact resistance under the excitation of the load current.

[0038] It should be noted that, due to the small Joule heat under low load conditions, minor temperature fluctuations often originate from sensor measurement noise or environmental disturbances. Directly determining faults under these conditions can easily lead to false alarms. However, under high load conditions, the same temperature difference is highly likely to correspond to an actual increase in contact resistance. Therefore, this embodiment constructs an impedance degradation heat accumulation degree with signal-to-noise ratio identification capability. By reducing the risk index weight in the low load range and enhancing the sensitivity of abnormal temperature rise in the high load range, the identification of contact condition degradation can be achieved.

[0039] Specifically, the heat accumulation degree of impedance degradation is calculated based on the deviation between the actual temperature rise and the theoretical temperature rise:

[0040] In the formula, Indicates time The impedance degradation thermal accumulation is used to measure the degree of abnormality in the contact condition; Indicates the previous moment The heat accumulation degree of impedance degradation; This represents the historical risk decay coefficient, used to control the rate at which historical risk values ​​are forgotten; Indicates time The actual operating temperature of the target monitoring equipment; Indicates time The ambient reference temperature; Indicates time The actual temperature rise; Indicates time The theoretical temperature rise value; This represents the residual between the actual temperature rise and the theoretical temperature rise; a positive value indicates abnormal heating. The one-way activation function is defined as follows: That is, when the input is greater than 0, the output is equal to the input, and when the input is less than or equal to 0, the output is 0. The function is to directly filter out negative deviation data where the actual temperature rise is lower than the theoretical temperature rise. Indicates time The effective value of the load current; Indicates the rated reference current of the target monitoring equipment; For load factor, this embodiment squares the load factor. As a load confidence weight, it corresponds to the nonlinear Joule thermal amplification effect generated by abnormal conductor contact resistance under overload current. Only when the actual temperature rise is significantly higher than the theoretical temperature rise and the system is under high load conditions does the impedance degradation thermal accumulation rate [become more significant]. This allows for rapid accumulation and increases, significantly enhancing the system's sensitivity to full-load thermal runaway.

[0041] It should be noted that the historical risk decay coefficient The settings are intended to balance the system's alarm sensitivity and anti-interference capability. In this embodiment... The value is set to 0.98, which is based on the discrete-time constant logic setting. This ensures that the impedance degradation heat accumulation has a half-life of approximately 50 sampling cycles after the anomaly disappears. This avoids false alarms caused by instantaneous glitches while retaining the ability to remember persistent overheating trends. In other embodiments, the implementer can adjust the value according to the intensity of electromagnetic interference and the sampling frequency at the site. The value of can be appropriately increased in environments with strong interference. The value can be set to enhance the smoothing effect, for example, 0.99. However, in critical loops where response speed is extremely important, the value can be appropriately reduced. Values, for example, 0.95.

[0042] For example, Figure 3 This is a schematic diagram of the curves showing the actual temperature rise versus the theoretical temperature rise in an embodiment of the present invention, as shown below. Figure 3 As shown, in the first half of the monitoring interval, the theoretical temperature rise closely follows the fluctuation of the actual temperature rise. Even in the local interval where the temperature drops sharply due to the start of the cooling fan, the two still maintain a highly fitted synchronous trend, indicating that the established twin model accurately isolates environmental disturbance variables and has the ability to resist interference in active cooling conditions. In the second half of the monitoring interval, the actual temperature rise gradually separates and is significantly higher than the theoretical temperature rise, forming a continuously expanding positive residual between the two. This reveals the existence of an unexpected additional heat source inside the target monitoring equipment, thereby capturing the abnormal Joule heat accumulation characteristics caused by the deterioration of contact resistance.

[0043] S4. Based on the numerical range of impedance degradation heat accumulation, execute the corresponding level of operation and maintenance response strategy. The operation and maintenance response strategy includes starting the cooling fan for thermal troubleshooting, generating a work order for early warning of poor contact, and disconnecting the faulty circuit.

[0044] It should be noted that, since sensors themselves may experience zero-point drift faults, a single data analysis result cannot completely rule out the possibility of hardware failure. By actively changing the physical heat dissipation conditions and observing the system's thermal response behavior, the physical authenticity of the temperature rise data can be effectively verified. Therefore, this embodiment constructs a closed-loop logic that integrates sensing and control, triggering a graded response strategy based on different intervals of impedance degradation heat accumulation.

[0045] Specifically, the value of the heat accumulation due to impedance degradation is monitored in real time, and a three-level response is executed: Response to impedance degradation heat accumulation When the threshold is greater than the Level 1 response threshold but less than or equal to the Level 2 response threshold, a Level 1 response is triggered, and the edge gateway forcibly issues a command to start the cooling fan. If, after the fan is turned on, the absolute value of the difference between the actual temperature rise and the theoretical temperature rise drops to within 2 degrees Celsius within 10 minutes, it indicates that the current situation is a localized transient heat accumulation. The system will then reset the impedance degradation heat accumulation to the product of the current value and a preset chronic degradation baseline coefficient, which is 0.05. This non-absolute zeroing mechanism ensures that while clearing short-term false alarms, the system can still remember and track the long-term irreversible slow degradation trend caused by contact oxidation, preventing the underreporting of hidden faults. Response to impedance degradation heat accumulation When the threshold is greater than the level 2 response threshold and less than or equal to the level 3 response threshold, the level 2 response is triggered. The system automatically generates a contact failure hazard warning work order and pushes it to the operation and maintenance platform. The contact failure hazard warning work order includes the thermophysical fingerprint of the target monitoring equipment and the change curves of the actual temperature rise value, theoretical temperature rise value and impedance degradation heat accumulation degree. Response to impedance degradation heat accumulation When the threshold for a Level 3 response is exceeded, a Level 3 response is triggered, and the system trips the circuit breaker to disconnect the faulty circuit breaker, preventing arc short circuit accidents caused by contact point melting.

[0046] It should be further explained that the threshold values ​​for each level are set based on the statistical distribution characteristics of the impedance degradation heat accumulation of the target monitoring equipment in a healthy state. For the first-level response threshold, the impedance degradation heat accumulation of the target monitoring equipment during the healthy operating cycle is statistically analyzed. Based on the distribution characteristics, the peak value under healthy conditions is selected as the benchmark. A safety redundancy coefficient of 2 is introduced to cover 99.9% of random fluctuations and measurement noise. Twice the peak value of impedance degradation heat accumulation under healthy conditions is used as the first-level response threshold. In this embodiment, the empirical value of the first-level response threshold is 5. For the third-level response threshold, the critical state of irreversible annealing and softening of the contact surface metal is determined by material thermal aging experiments. This critical state is the safety limit at which the system must be shut down. The impedance degradation heat accumulation corresponding to the critical state is used as the third-level response threshold. In this embodiment, the empirical value of the third-level response threshold is 30. For the second-level response threshold, it is set as the geometric midpoint between the first-level and third-level response thresholds. This aims to define the buffer zone between confirmed faults that do not yet pose an immediate danger, in order to balance the system's early warning lead time and maintenance response window. In this embodiment, the empirical value of the second-level response threshold is 15. In other embodiments, implementers can dynamically calibrate the thresholds according to the aging degree of the target monitoring equipment and the seasonal characteristics of the environment. For example, the first-level response threshold can be recalibrated quarterly based on historical health data to adapt to the characteristic evolution of the equipment throughout its entire life cycle.

[0047] For example, Figure 4 This is a schematic diagram of the graded response of impedance degradation thermal accumulation in an embodiment of the present invention, as shown below. Figure 4 As shown, during the middle period of the monitoring process, although there was a slight deviation in the temperature rise data, the impedance degradation heat accumulation only maintained a small fluctuation at a low level because the corresponding time interval was under low load conditions. There was no false triggering of the threshold, which verified the effectiveness of the algorithm in suppressing measurement noise under low signal-to-noise ratio conditions through load confidence weight. In the subsequent high load time interval, the impedance degradation heat accumulation showed a steep upward trend, rapidly and continuously breaking through the threshold defense lines set at each level. This verified the system's keen perception capability under high load and high risk conditions, as well as the closed-loop control logic that sequentially triggers thermal investigation, early warning work orders, and emergency protection based on the degree of increase in the risk index. This achieved full-process management of contact faults, from initial perception to early warning and final protection.

[0048] The present invention also discloses an unattended intelligent operation and maintenance system for power distribution rooms based on digital twins, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the unattended intelligent operation and maintenance method for power distribution rooms based on digital twins according to the present invention is implemented.

[0049] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. An unattended intelligent operation and maintenance method for a power distribution room based on digital twinning, characterized in that, include: Acquire the effective value of load current, actual operating temperature, ambient reference temperature, and real-time operating status of cooling fans of the target monitoring equipment in the power distribution room; Based on the effective value of the load current and the real-time operating status of the cooling fan, combined with the thermal inertia characteristics of the target monitoring equipment, the theoretical temperature rise of the target monitoring equipment at the current moment is calculated. The theoretical temperature rise represents the reference temperature rise that the target monitoring equipment should reach under healthy contact conditions and current heat dissipation conditions. The actual temperature rise is calculated based on the actual operating temperature and the ambient reference temperature. Based on the difference between the actual and theoretical temperature rise values ​​and the effective value of the load current, the impedance degradation heat accumulation of the target monitoring equipment at the current moment is calculated, satisfying the expression: , Indicates time The thermal accumulation of impedance degradation Indicates the previous moment The thermal accumulation of impedance degradation This represents the historical risk decay coefficient. Indicates time The actual operating temperature of the target monitoring equipment Indicates time The ambient reference temperature Indicates time The actual temperature rise value, Indicates time The theoretical temperature rise value; The one-way activation function is defined as follows: ; Indicates time The effective value of the load current, The rated reference current of the target monitoring equipment is indicated; the impedance degradation heat accumulation degree characterizes the degree of accumulation of additional heat generated by abnormal contact resistance under load current excitation; Based on the numerical range of impedance degradation heat accumulation, the corresponding level of operation and maintenance response strategy is executed. The operation and maintenance response strategy includes starting the cooling fan for thermal troubleshooting, generating a work order for early warning of poor contact, and disconnecting the faulty circuit.

2. The unmanned intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 1, characterized in that, The acquisition of the effective value of the load current, actual operating temperature, ambient reference temperature, and real-time operating status of the cooling fan of the target monitoring equipment in the power distribution room includes: Based on the controller timestamp of the power distribution room edge computing gateway, the system synchronization sampling period is set; for the actual operating temperature and the ambient reference temperature, if no new data is reported at the current moment, the value of the previous moment remains unchanged; for the effective value of the load current, the time interval of the previous synchronization sampling period is set as the effective calculation window, and the root mean square value of all collected data within the effective calculation window is calculated as the effective value of the load current at the current moment.

3. The unmanned intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 1, characterized in that, The theoretical temperature rise value satisfies the expression: ； In the formula, Indicates the target monitoring equipment at any time The theoretical temperature rise value; Indicates the previous moment The theoretical temperature rise value; This represents the thermal inertia memory factor; This represents the dynamic steady-state electrothermal conversion gain, reflecting the real-time operating status of the cooling fan. The theoretical maximum temperature rise of the target monitoring equipment relative to the ambient temperature when it operates continuously at the rated current and reaches thermal stability. Indicates time The effective value of the load current; This indicates the rated reference current of the target monitoring equipment.

4. The unattended intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 3, characterized in that, The thermal inertia memory factor and dynamic steady-state electrothermal conversion gain are calibrated in the following ways: A segment of stable operating data from the target monitoring equipment was selected, and the stable operating data was divided into self-cooling dataset and air-cooling dataset based on the real-time operating status of the cooling fans; for each dataset, a linear regression equation was constructed. ,in This represents the actual temperature rise. This represents the actual temperature rise at the previous moment. The load factor is the square of the current load factor; the regression coefficients are solved using the least squares method. and Let the thermal inertia memory factor equal Let the dynamic steady-state electrothermal conversion gain equal Divide by .

5. The unmanned intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 1, characterized in that, The step of implementing a corresponding level of operation and maintenance response strategy based on the numerical range of impedance degradation heat accumulation includes: Set a first-level response threshold, a second-level response threshold, and a third-level response threshold, where the first-level response threshold is less than the second-level response threshold, and the second-level response threshold is less than the third-level response threshold; The first-level response is triggered when the accumulated heat of impedance degradation is greater than the first-level response threshold and less than or equal to the second-level response threshold; the second-level response is triggered when the accumulated heat of impedance degradation is greater than the second-level response threshold and less than or equal to the third-level response threshold; and the third-level response is triggered when the accumulated heat of impedance degradation is greater than the third-level response threshold.

6. The unmanned intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 5, characterized in that, The triggering of the first-level response includes: The cooling fan is forcibly started and runs continuously for a preset time. If the difference between the actual temperature rise and the theoretical temperature rise decreases to within the preset temperature difference range within the preset time, the impedance degradation heat accumulation is reset to the product of the current impedance degradation heat accumulation and the preset chronic degradation baseline coefficient.

7. The unattended intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 5, characterized in that, The triggering of the secondary response includes: generating a contact failure hazard warning work order and pushing the contact failure hazard warning work order to the operation and maintenance platform; The triggering of the three-level response includes: the linkage tripping circuit disconnecting the faulty circuit breaker.

8. The unattended intelligent operation and maintenance method for power distribution rooms based on digital twins according to claim 2, characterized in that, Also includes: A state vector is constructed that includes the effective value of the load current, the actual operating temperature, the ambient reference temperature, and the real-time operating status of the cooling fan. An aligned state vector sequence is formed based on the state vectors of consecutive time moments. Sliding window outlier cleaning is performed on the state vector sequence. In response to the standard deviation of the center point value from the window mean exceeding a preset multiple, the center point value is corrected using linear interpolation.

9. A digital twin-based unmanned intelligent operation and maintenance system for power distribution rooms, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the unattended intelligent operation and maintenance method for a power distribution room based on digital twins according to any one of claims 1-8.

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