A power distribution equipment fault early warning method and device based on multi-source data, a terminal device, and a storage medium

Abstract

The application discloses a power distribution equipment fault early warning method and device based on multi-source data, terminal equipment and storage medium, and belongs to the technical field of power distribution equipment operation state detection. The method is: obtaining a battery parameter set; calculating a power supply health score according to the battery parameter set; obtaining a closing and opening coil current waveform; calculating a primary equipment mechanical state score according to the closing and opening coil current waveform; obtaining a secondary equipment operation state parameter set; calculating a secondary equipment operation state score according to the secondary equipment operation state parameter set; obtaining a measurement loop parameter set; calculating a measurement loop integrity score according to the measurement loop parameter set; calculating an overall risk score according to the power supply health score, the primary equipment mechanical state score, the secondary equipment operation state score and the measurement loop integrity score; and generating a power distribution equipment fault early warning based on the overall risk score. Therefore, by implementing the application, the problem of insufficient reliability of the power distribution equipment in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution equipment operation status detection technology, and in particular to a power distribution equipment fault early warning method, device, terminal equipment and storage medium based on multi-source data. Background Technology

[0002] In modern power systems, with the deepening of smart distribution network construction, the number of distribution network automation equipment has surged, and the reliability of its operation directly affects the quality of power supply. The existing operation and maintenance model adopts regular inspections plus fault detection, repairing distribution equipment according to a preset inspection cycle and intervening only after a fault occurs in the distribution equipment.

[0003] However, the performance degradation of power distribution equipment is affected by the combined effects of multiple components. The equipment status cannot be judged based on individual parameters alone. The existing operation and maintenance model lacks a comprehensive assessment of the overall health status of the equipment, making it difficult to identify potential failure risks and provide early warnings for power distribution equipment with failure risks. Therefore, there is a problem of insufficient reliability of power distribution equipment. Summary of the Invention

[0004] This invention provides a method, device, terminal equipment, and storage medium for early warning of power distribution equipment faults based on multi-source data, which can solve the problem of insufficient reliability of power distribution equipment in the prior art.

[0005] The fault early warning method for power distribution equipment based on multi-source data provided by the present invention includes: acquiring the terminal voltage change, internal resistance, charging and discharging current and ambient temperature of the power distribution equipment, and generating a battery parameter set; Calculate the power health score of the power distribution equipment based on the battery parameter set; Acquire the current waveform of the opening and closing coils of the power distribution equipment, extract the peak current and the integral of the operating energy from the current waveform of the opening and closing coils, and determine the core contact time and the mechanism action time in the current waveform of the opening and closing coils. The mechanical condition score of the primary equipment of the power distribution equipment is calculated based on the peak current, operating energy integral, core contact time, and mechanism action time. The system acquires the CPU utilization rate, memory usage rate, process status flags, communication error rate, time synchronization deviation, and set value verification flags of the power distribution equipment, and generates a set of operating status parameters for the secondary equipment. Calculate the secondary equipment operating status score of the power distribution equipment based on the secondary equipment operating status parameter set; Acquire the three-phase voltage phasors of the PT secondary side of the power distribution equipment, the secondary current of each CT, and the variance of the induced voltage of the unloaded line to generate a set of measurement circuit parameters. Calculate the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set; The overall risk score of the power distribution equipment is calculated based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, and measurement circuit integrity score. Power distribution equipment fault warnings are generated based on the overall risk score of the power distribution equipment.

[0006] Furthermore, the calculation of the power health score of the power distribution equipment based on the battery parameter set includes: The current voltage characteristic degradation is calculated based on the deviation between the preset standard voltage change and the terminal voltage change in the battery parameter set. Calculate voltage stability health based on the current voltage characteristic degradation and the preset maximum voltage characteristic degradation; The internal resistance aging factor is calculated using an exponential function based on the preset initial internal resistance and the internal resistance of the battery parameter set. The temperature stress term is calculated using the Arrhenius equation based on the preset rated operating temperature and the ambient temperature of the battery parameter set. The current stress term is calculated based on Joule's law, using the preset rated operating current and the charging and discharging current of the battery parameter set. Calculate environmental operating condition losses based on temperature stress and current stress terms; The power health score of power distribution equipment is calculated by considering voltage stability health, internal resistance aging factor, and environmental operating condition losses.

[0007] Furthermore, the mechanical condition score of the primary equipment of the power distribution equipment is calculated based on the peak current, integrated operating energy, core contact time, and mechanism action time, including: The mechanical condition score of primary equipment is calculated using the following formula: ; ; ; In the formula, Assess the mechanical condition of the equipment. This is the transition time for the action; Preset standard action transition time; The transition time for preset fault alarm limits; This refers to the peak current in the current waveform of the opening and closing coils. This refers to the peak current in the preset standard opening and closing coil current waveform; The peak current for setting the preset fault alarm threshold; The integral of the operating energy in the current waveform of the opening and closing coils; The integral of the operating energy in the preset standard opening and closing coil current waveform; The operational energy integral for setting the fault alarm threshold; Weighting of action transition time; Peak current weighting; For operational energy integral weighting; The moment when the iron core touches in the current waveform of the opening and closing coil; The moment of mechanism action in the current waveform of the opening and closing coil; This represents the starting moment in the current waveform of the opening and closing coils. This refers to the steady-state holding time in the current waveform of the opening and closing coils. This refers to the coil current in the opening and closing coil current waveform.

[0008] Furthermore, the operating status score of the secondary equipment is calculated using the following formula: ; ; ; ; ; ; In the formula, Scoring the operating status of secondary equipment; For logical integrity factors; For communication availability factor; Penalty points for CPU utilization; Memory load penalty points; Penalty points for time deviation; As a weight for CPU utilization; Memory load weight; Weights for time deviation; For process status flags; This is a value verification flag; For communication bit error rate; A preset communication error rate safety threshold is set. CPU utilization; The default CPU utilization threshold is set. Preset CPU utilization penalty range; Memory usage; Set a default threshold for memory usage penalty. Set a preset memory usage penalty range; This is due to time deviation; This is the preset maximum allowable time deviation.

[0009] Furthermore, the calculation of the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set includes: The positive and negative sequence voltages of the PT secondary side and the negative sequence voltage of the PT secondary side are calculated using the symmetrical component method based on the phasors of the three-phase voltages on the secondary side of the PT from the parameter set of the measurement circuit. The voltage imbalance is calculated by the ratio of the negative sequence voltage on the secondary side of the PT to the positive sequence voltage on the secondary side of the PT. The voltage imbalance penalty term is calculated based on the preset unbalance fault threshold and the voltage unbalance. The offset of each CT secondary side current is calculated based on the preset CT secondary side reference current and the CT secondary side current of each CT secondary side current in the measurement circuit parameter set. Select the largest CT secondary side current offset among all CT secondary side current offsets; Calculate the CT measurement consistency penalty based on the maximum CT secondary current offset; The latent defect deduction factor is calculated based on the preset unloaded line induced voltage reference fluctuation variance and the unloaded line induced voltage variance. The explicit score for measurement circuit integrity is obtained by deducting the base score for the integrity of the preset measurement circuit from the voltage imbalance penalty and the CT measurement consistency penalty. The measurement loop integrity score is obtained by adjusting the explicit score of the measurement loop integrity based on the latent defect deduction factor.

[0010] Furthermore, the calculation of the overall risk score of the power distribution equipment based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, and measurement circuit integrity score includes: The overall risk score of the power distribution equipment is obtained by aggregating the failure probabilities based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, measurement circuit integrity score, and corresponding weights.

[0011] Furthermore, it also includes: The remaining battery capacity of the power distribution equipment is obtained through a preset battery remaining capacity table based on the power health score of the power distribution equipment. When the remaining battery capacity is less than a preset capacity threshold, a battery replacement prompt message is generated.

[0012] Another embodiment of the present invention provides a method and apparatus for fault early warning of power distribution equipment based on multi-source data, including: a battery parameter set generation module, a power health scoring module, a switching coil current waveform extraction module, a primary equipment mechanical status scoring module, a secondary equipment operating status parameter set generation module, a secondary equipment operating status scoring module, a measurement circuit parameter set module, a measurement circuit integrity scoring module, an overall risk score calculation module, and a fault early warning generation module; The battery parameter set generation module is used to obtain the terminal voltage change, internal resistance, charging and discharging current and ambient temperature of the power distribution equipment to generate a battery parameter set. The power health scoring module is used to calculate the power health score of the power distribution equipment based on the battery parameter set. The circuit breaker coil current waveform extraction module is used to acquire the circuit breaker coil current waveform of the power distribution equipment, extract the peak current and operating energy integral in the circuit breaker coil current waveform, and determine the core contact time and mechanism action time in the circuit breaker coil current waveform. The primary equipment mechanical condition scoring module is used to calculate the primary equipment mechanical condition score of the power distribution equipment based on peak current, operating energy integral, core contact time and mechanism action time. The secondary equipment operating status parameter set generation module is used to obtain the CPU utilization rate, memory utilization rate, process status flag, communication error rate, time synchronization deviation and set value verification flag of the power distribution equipment, and generate the secondary equipment operating status parameter set. The secondary equipment operation status scoring module is used to calculate the secondary equipment operation status score of the power distribution equipment based on the secondary equipment operation status parameter set. The measurement circuit parameter set module is used to acquire the three-phase voltage phasors of the PT secondary side of the power distribution equipment, the secondary side current of each CT, and the variance of the induced voltage of the unloaded line, and generate the measurement circuit parameter set. The measurement loop integrity scoring module is used to calculate the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set. The overall risk score calculation module is used to calculate the overall risk score of the power distribution equipment based on the power health score, the primary equipment mechanical condition score, the secondary equipment operating condition score, and the measurement circuit integrity score. The fault warning generation module is used to generate fault warnings for power distribution equipment based on the overall risk score of the power distribution equipment.

[0013] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the power distribution equipment fault early warning method based on multi-source data provided by the present invention.

[0014] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the power distribution equipment fault early warning method based on multi-source data provided by the present invention.

[0015] The following benefits can be obtained by implementing the present invention: This invention discloses a fault early warning method for power distribution equipment based on multi-source data. The method acquires the terminal voltage change, internal resistance, charging / discharging current, and ambient temperature of the power distribution equipment's battery. The terminal voltage change reflects the battery's voltage stability; internal resistance is one of the most sensitive indicators of battery aging; charging / discharging current characterizes the battery pack's operating conditions; and ambient temperature characterizes the operating temperature. Current and temperature have a coupling effect that amplifies the battery pack's operating losses, characterizing the cumulative damage to battery life caused by external environment and operating conditions. A power health score characterizing the battery's remaining capacity is calculated comprehensively. The method also acquires the current waveform of the power distribution equipment's opening and closing coils; key points in the waveform are extracted, which can be used to characterize mechanism friction, spring performance degradation, and component jamming. The invention assesses the following: the degree of deformation; a comprehensive calculation yields a mechanical condition score for the primary equipment; several secondary equipment operating status parameters are used for evaluation: redundancy of computing resources, overload risk of equipment, logical function integrity, communication link availability, and time synchronization accuracy; a comprehensive calculation yields an operating status score for the secondary equipment; the three-phase voltage phasor on the secondary side of the PT and the current on the secondary side of the CT respectively characterize the voltage imbalance and current anomaly; the variance of the induced voltage of the unloaded line is used to characterize the degree of induced voltage fluctuation, reflecting poor contact and aging defects in the line components; a comprehensive measurement circuit parameter calculation yields a measurement circuit integrity score; finally, the overall risk score of the power distribution equipment is obtained by combining all scores, and equipment fault warnings can be generated based on the quantifiable overall risk score. Compared with existing operation and maintenance models, this invention can obtain an overall risk score through comprehensive calculation of multiple indicators. The overall risk score can directly quantify the fault risk of the power distribution equipment, realizing fault warnings before faults occur, which is beneficial to improving the reliability of the power distribution equipment. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for early warning of power distribution equipment faults based on multi-source data, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a power distribution equipment fault early warning device based on multi-source data provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" 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, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] See Figure 1 To address the insufficient reliability of power distribution equipment in existing technologies, an embodiment of the present invention provides a power distribution equipment fault early warning method based on multi-source data, comprising: 101. Obtain the terminal voltage change, internal resistance, charging and discharging current, and ambient temperature of the power distribution equipment to generate a battery parameter set.

[0026] Among them, the change in terminal voltage of the power distribution equipment is obtained by periodically applying pulse discharge of the same amplitude and duration to the battery pack through the power distribution monitoring system, collecting the stable floating charge voltage before the pulse and the terminal voltage at the end of the pulse in real time, and calculating the difference between the stable floating charge voltage before the pulse and the terminal voltage at the end of the pulse, and using the difference as the change in terminal voltage of the power distribution equipment. The internal resistance of the power distribution equipment is measured using the AC injection method (AC internal resistance method). A small-amplitude, high-frequency AC signal is injected into the battery, and the AC voltage response and AC current response at both ends of the battery are measured simultaneously. The internal resistance is calculated using Ohm's law. The AC signal source can be generated by the battery management system or the battery monitoring instrument. Commonly used frequencies are 50Hz, 100Hz, and 1kHz. The amplitude is selected from 5mV to 20mV, with the premise of not affecting the battery float charging. The AC voltage response uses differential sampling, extracting only the AC component and filtering the DC float charging voltage. The AC current response is obtained by synchronously acquiring the AC current. The charging and discharging current of the power distribution equipment is measured by the shunt method; a DC shunt is connected in series in the main positive or main negative circuit of the battery, and the voltage across the shunt is collected by a differential amplifier. The charging and discharging current is calculated by Ohm's law based on the voltage across the shunt and the internal resistance of the shunt. The ambient temperature of the power distribution equipment is measured by the temperature sensor of the power distribution equipment; the temperature sensor can be realized by a negative temperature coefficient thermistor. The thermistor is placed as a probe in the battery casing or battery cabinet, and then the resistance change is converted into a voltage signal. After AD sampling, the ambient temperature value is obtained by looking up a table or fitting.

[0027] Specifically, after acquiring the terminal voltage change, internal resistance, charging and discharging current, and ambient temperature of the power distribution equipment, the data is marked according to the detection window and stored in a data set; the data set is then labeled as the battery parameter set.

[0028] To illustrate, the terminal voltage change of the power distribution equipment can also be obtained through the constant current discharge voltage curve method, the alternating impedance method (EIS), and the float charge voltage fluctuation analysis method; the internal resistance of the power distribution equipment can also be obtained through the DC pulse method (DCIR method) and the impedance identification method; the charging and discharging current of the power distribution equipment can also be obtained through the Hall current sensor method and the fluxgate giant magnetoresistive (GMR) sensor; the ambient temperature of the power distribution equipment can also be accurately measured by platinum resistance thermometer (PT100 or PT1000) and infrared non-contact temperature measurement.

[0029] During the generation of the aforementioned battery parameter set, multi-dimensional parameters of the batteries in the power distribution equipment were collected, providing a data foundation for subsequent power health scoring. Among them, the change in terminal voltage can reflect the differences in battery polarization characteristics and can intuitively reflect the degree of decay of the battery's electrochemical reaction capability; internal resistance can sensitively reflect the changes in the battery's internal impedance caused by factors such as plate aging, electrolyte deterioration, and poor connection contact, characterizing the deterioration trend of the battery's power output capability; charging and discharging current characterizes the operating conditions of the battery pack, and ambient temperature is used to characterize the operating temperature. Furthermore, current and temperature will have a coupling effect to amplify the battery's operating losses, which is used to characterize the cumulative damage to battery life caused by the external environment and operating conditions.

[0030] 102. Calculate the power health score of the power distribution equipment based on the battery parameter set.

[0031] In a preferred embodiment, calculating the power health score of the power distribution equipment based on the battery parameter set includes: The current voltage characteristic degradation is calculated based on the deviation between the preset standard voltage change and the terminal voltage change in the battery parameter set. Calculate voltage stability health based on the current voltage characteristic degradation and the preset maximum voltage characteristic degradation; The internal resistance aging factor is calculated using an exponential function based on the preset initial internal resistance and the internal resistance of the battery parameter set. The temperature stress term is calculated using the Arrhenius equation based on the preset rated operating temperature and the ambient temperature of the battery parameter set. The current stress term is calculated based on Joule's law, using the preset rated operating current and the charging and discharging current of the battery parameter set. Calculate environmental operating condition losses based on temperature stress and current stress terms; The power health score of power distribution equipment is calculated by considering voltage stability health, internal resistance aging factor, and environmental operating condition losses.

[0032] Specifically, the power supply health score is calculated as follows: ; ; ; ; In the formula, Rate the power supply health; For voltage stability and health; It is an internal resistance aging factor; This refers to the loss due to environmental operating conditions; Weighting for voltage characteristic degradation; Weights for internal resistance aging factors; Weighting of environmental operating condition losses; This refers to the change in terminal voltage of the battery parameter set. This is the preset standard voltage change amount; This is the preset limit voltage change amount; The internal resistance of the battery parameter set; The initial internal resistance is preset; The internal resistance sensitivity coefficient; The ambient temperature for the battery parameter set; Preset rated operating temperature; The overall activation energy constant; Temperature is a weighting factor for its influence. The charging and discharging current for the battery parameter set; Preset rated operating current; This is the current ratio influence factor.

[0033] The preset standard voltage change is measured by performing a test method that is completely consistent with the terminal voltage change of the battery parameter set on a brand new battery of the same model under standard ambient temperature and rated current. It serves as the voltage benchmark for the initial health state of the battery. The difference between the preset standard voltage change and the terminal voltage change of the battery parameter set is used as the current voltage characteristic degradation amount. The preset limit voltage change is measured by performing a cyclic aging test on the same model of battery until the battery capacity decays to the battery scrapping standard of the power distribution equipment. The voltage change is measured under a test method that is completely consistent with the terminal voltage change of the battery parameter set. It serves as the voltage threshold for the end of the battery life. The difference between the preset standard voltage change and the preset limit voltage change is used as the preset maximum voltage characteristic degradation amount. The preset initial internal resistance is the initial internal resistance of a brand new battery of the same model, measured under standard conditions using the same measurement method as the internal resistance in the battery parameter set, and is used as the internal resistance benchmark. The internal resistance sensitivity coefficient is calibrated through battery cycle aging tests, tracking the correspondence between internal resistance and actual battery capacity at different aging stages, and is obtained by fitting. It is used to adjust the sensitivity of internal resistance changes to battery capacity. A positive value indicates that the larger the internal resistance, the faster the internal resistance aging factor decays. The preset rated operating temperature and preset rated operating current are determined through relevant battery process specifications; the comprehensive activation energy constant is calibrated using the Arrhenius equation, obtained by testing the battery reaction rate at different temperatures and fitting the slope of the reaction rate versus temperature curve, which characterizes the sensitivity of the electrochemical reaction to temperature. For example, the comprehensive activation energy constant of lithium-ion batteries is typically in the range of 0.3 eV to 0.6 eV; the temperature influence weighting factor is calibrated through battery aging tests and is used to adjust the influence weight of temperature on SOH, balancing the health assessment deviation at different temperatures; the current rate influence factor is calibrated through charge-discharge tests at different rates and is used to characterize the accelerating effect of high current on battery aging.

[0034] Both the current voltage characteristic degradation and the preset maximum voltage characteristic degradation are calculated based on the principle that after battery aging, voltage polarization intensifies, leading to a significant increase in voltage change under the same charge and discharge conditions. The preset maximum voltage characteristic degradation is used to characterize the limit voltage change when the voltage dimension health reaches the scrap standard. Then, the ratio of the current voltage characteristic degradation to the preset maximum voltage characteristic degradation is calculated, and the voltage stability health is obtained through normalization calculation. The internal resistance aging factor is calculated using an exponential function based on the battery's parameter set and a preset initial internal resistance. Essentially, it reflects the aging evolution of the battery's internal impedance. Battery aging leads to the shedding of active materials from the positive and negative electrodes, thickening of the solid electrolyte interphase (SEI) film, and increased ohmic internal resistance. Simultaneously, electrolyte decomposition hinders lithium-ion diffusion, increasing polarization internal resistance. Ultimately, this manifests as a monotonically increasing total battery internal resistance with aging. When the battery is in its newest state... At that time, the internal resistance aging factor is 1, indicating that the health of the internal resistance dimension is at its maximum; however, when the battery begins to age, The relative increase in internal resistance will also increase accordingly, and it will decay rapidly through an exponential function. It can accurately characterize the non-linear degradation of battery performance caused by the increase in internal resistance. For example, when the internal resistance doubles, the battery power performance will drop sharply and non-linearly. The internal resistance sensitivity coefficient is used to adapt to the internal resistance aging characteristics of different battery systems. For example, the internal resistance growth rate of lithium iron phosphate batteries is less than that of ternary lithium batteries, and the corresponding internal resistance sensitivity coefficient of lithium iron phosphate batteries should also be smaller. Temperature stress term The Arrhenius equation characterizes the coupling effect between ambient temperature and current at high temperatures, which leads to accelerated battery aging; when the battery is at its rated operating temperature, i.e. When the temperature stress term is zero, it means that no additional temperature stress is generated, and the battery is in an ideal thermodynamic state. At this time, the aging effect of current on the battery is characterized by voltage stability health and internal resistance aging factor. However, when the ambient temperature of the battery is higher than the rated operating temperature, i.e. At that time, as the ambient temperature rises, the temperature stress term also increases, which is reflected in the formula as an amplified current stress term; Current stress term It simulates the thermal effects and polarization damage caused by high charge / discharge rates based on Joule's law; current rate. Used to characterize the strength of the impact of current changes on battery health; as the charging and discharging current increases, the current ratio... As the current term increases, it is amplified by the second term, resulting in a rapid increase in the value of the current term and a significant amplification of the overall stress. In actual battery operation, high-current charging and discharging will intensify the electrochemical polarization and concentration polarization inside the battery, increase the rate of heat generation inside the battery, accelerate the decay of active materials and the occurrence of side reactions. The current term grows in a nonlinear manner, and the current stress term truly reflects the deteriorating effect of high-current conditions on the health of the battery, which is consistent with the actual aging law of the battery. The product of the temperature stress term and the current stress term characterizes the coupling effect between current and temperature, which amplifies the operating losses of the battery pack; then, the environmental operating condition losses are calculated by deducting the base score. Finally, based on voltage stability health, internal resistance aging factor, and environmental operating condition losses, the power supply health score is obtained by weighting and summing the results using corresponding weighting coefficients.

[0035] The above-mentioned power health score calculation, through the fusion of multiple physical quantities to calculate the battery health status, realizes the organic integration of the battery's aging characteristics and real-time operating conditions. It can accurately reflect the long-term irreversible health degradation law of the battery, and has the characteristics of stable evaluation, strong adaptability and easy engineering implementation. It can meet the actual needs of online health monitoring and status early warning of batteries in power distribution equipment.

[0036] 103. Obtain the current waveform of the opening and closing coils of the power distribution equipment, extract the peak current and operating energy integral from the current waveform of the opening and closing coils, and determine the core contact time and mechanism action time in the current waveform of the opening and closing coils.

[0037] Specifically, the current waveform of the opening and closing coils is acquired in real time through a current sampling circuit, and key moments are identified and extracted. The method for identifying key moments and their physical meaning are as follows: The start-up moment in the current waveform of the opening and closing coils The starting point of the current rise at the instant the opening and closing coil is energized; in the waveform, it is represented by the current jumping from 0, the starting inflection point of the waveform; it can be identified by threshold detection or slope change. The moment of core contact in the current waveform of the opening and closing coils The moment when the iron core overcomes the spring force and friction to start driving the mechanism to move; in the waveform, this is manifested as a sudden change in the rate of current increase, with the first plateau inflection point appearing; the characteristic point where the rate of change of current changes from positive to flat or decreasing can be identified by differentiating the current waveform, or the abrupt change point can be extracted by wavelet transform; The timing of mechanism operation in the current waveform of the opening and closing coils The moment the iron core drives the contacts to complete the opening and closing action, and the mechanism is in position, is represented in the waveform as: the current shows a second inflection point, and then enters the holding phase; this is obtained by identifying the second abrupt change in slope in the current waveform, which corresponds to the moment when the mechanism ends and the iron core is in position. Steady-state holding time in the current waveform of the opening and closing coils The moment when the coil enters steady-state holding after the opening and closing operation is completed; in the waveform, this is represented by the current stabilizing at the holding current value and the waveform entering a flat segment; the steady-state holding moment is determined by identifying the starting point where the current fluctuation is less than the threshold. The steps for extracting the peak current and the integral of the operating energy from the current waveform of the opening and closing coils are as follows: Peak current in the opening and closing coil current waveform : The maximum current value in the opening and closing coil current waveform; the maximum value is extracted from the real-time acquired current waveform, corresponding to the peak value during the core startup phase; Operational energy integral in the current waveform of the opening and closing coils The operating energy consumed by the coil during the opening and closing process reflects the load state of the mechanism. It is obtained by square integral calculation of the current waveform from the start-up time to the steady-state holding time.

[0038] In the process of extracting the current waveform of the opening and closing coils, key information is extracted from the current waveform and corresponding calculations are performed to provide a data basis for the subsequent calculation of the mechanical condition score of the primary equipment.

[0039] 104. Calculate the mechanical condition score of the primary equipment of the power distribution equipment based on the peak current, operating energy integral, core contact time, and mechanism action time.

[0040] In a preferred embodiment, the primary equipment mechanical condition score of the power distribution equipment is calculated based on the peak current, operating energy integral, core contact time, and mechanism action time, including: The mechanical condition score of primary equipment is calculated using the following formula: ; ; ; In the formula, Assess the mechanical condition of the equipment. This is the transition time for the action; Preset standard action transition time; The transition time for preset fault alarm limits; This refers to the peak current in the current waveform of the opening and closing coils. This refers to the peak current in the preset standard opening and closing coil current waveform; The peak current for setting the preset fault alarm threshold; The integral of the operating energy in the current waveform of the opening and closing coils; The integral of the operating energy in the preset standard opening and closing coil current waveform; The operational energy integral for setting the fault alarm threshold; Weighting of action transition time; Peak current weighting; For operational energy integral weighting; The moment when the iron core touches in the current waveform of the opening and closing coil; The moment of mechanism action in the current waveform of the opening and closing coil; This represents the starting moment in the current waveform of the opening and closing coils. This refers to the steady-state holding time in the current waveform of the opening and closing coils. This refers to the coil current in the opening and closing coil current waveform.

[0041] Among them, the action transition time The time interval between the core being triggered and the mechanism's movement reflects the smoothness of the mechanism's motion. This is obtained by extracting key points and calculating the difference between the mechanism's movement moment and the core's trigger moment in the waveform timeline. A preset standard action transition time is also included. : Standard operating transition time of the equipment under factory and healthy conditions, obtained by taking the statistical average of multiple measurements during opening and closing tests on brand new equipment of the same model; Operating transition time of preset fault alarm limits. The corresponding action transition time threshold when the equipment fails mechanically, jams, or refuses to move is determined through equipment aging tests, industry standards, or manufacturer's operation and maintenance specifications. Peak current in the preset standard opening and closing coil current waveform The standard peak current of the health device is obtained by statistically averaging the opening and closing tests of brand-new devices of the same model; the peak current of the preset fault alarm limit is... The peak current threshold corresponding to coil failure and mechanism jamming is calibrated through equipment aging tests or operation and maintenance procedures. Operational energy integral in the preset standard opening and closing coil current waveform The standard operating energy of the health equipment is the average value of the integral results from the opening and closing tests of brand-new equipment; the integral of the operating energy for preset fault alarm limits. : The energy threshold corresponding to the jamming of the mechanism or the abnormality of the coil, aging test, and operation and maintenance procedure calibration.

[0042] Specifically, action transition time Used to characterize the smoothness and mechanical condition of the primary equipment mechanism, it is the core indicator for judging jamming, failure to move, and spring fatigue; the time from the core being touched to the mechanism being in place directly reflects the resistance of the mechanism's movement. When the mechanism is jammed, poorly lubricated, or the spring is fatigued, the action transition time will increase significantly. It is a health benchmark. These are fault thresholds, both used for... Normalization is performed to eliminate differences between different equipment models; This is used to adjust the weight of time metrics on the score, such as increasing it for equipment with high reliability requirements; Peak current Characterizing the electrical state of the opening and closing coils and the starting resistance of the iron core, it helps determine coil short circuits, iron core jamming, and power supply abnormalities. Peak current is the maximum current at the moment of iron core starting and is directly related to coil resistance and iron core resistance. When there is an inter-turn short circuit in the coil, the peak current abnormally increases; when the iron core is jammed, the peak current... Abnormally large; and These are the baseline and the threshold, used for normalization evaluation; Used to adjust the weight of current indicators to adapt to equipment with different voltage levels; Operational energy integral The total energy consumption during the entire opening and closing process is characterized by a comprehensive reflection of the mechanism load, coil status, and operating efficiency. The integral of the square of the coil current over time is essentially the energy consumed by the coil during the opening and closing process, which is positively correlated with the work done and losses of the mechanism. It increases significantly when the mechanism jams or the load increases; and it will also deviate from the standard value when the coil is abnormal or the operation is slow. and Used as a baseline and threshold for normalization; The weights used to adjust energy metrics are suitable for assessing the decline in institutional efficiency caused by long-term aging.

[0043] The above calculation of the mechanical condition score of primary equipment normalizes the deviations of the actual values ​​of the three dimensions of time, current, and energy from the standard values ​​by dividing them by the fault threshold, eliminating dimensional differences and mapping the deviations uniformly to the 0 to 1 range. The weighted sum of squares of the three normalized deviations is then applied, and the effect of severe deviations is amplified by squaring. For example, when the mechanism is severely jammed, the square of the action transition time deviation will increase significantly, which is in line with the principle of severe abnormality in fault warning. Finally, the square root of the weighted sum of squares is taken to obtain the comprehensive deviation value. The deviation is then converted into a health score by subtracting the comprehensive deviation from 1, and finally multiplied by 100 to convert it into an intuitive 0 to 100 score.

[0044] 105. Obtain the CPU utilization rate, memory utilization rate, process status flags, communication error rate, time synchronization deviation, and set value verification flags of the power distribution equipment, and generate a set of secondary equipment operating status parameters.

[0045] Among them, CPU utilization The real-time CPU utilization rate of the secondary equipment's main control unit reflects the current resource usage of the equipment; it is read in real time from the system monitoring interface of the equipment's operating system, and the unit is percentage; when the CPU utilization rate continuously exceeds 70% to 85%, it indicates that the protection logic needs to be enabled or disabled appropriately. Memory usage Real-time memory usage of the secondary equipment's main control unit; read in real-time from the operating system's system monitoring interface, in percentage form; considering practicality, reliability, and economy, the equipment's file storage system is generally limited. When there are too many system files, if the memory usage consistently exceeds 80% to 85%, it should raise a red flag, indicating that the system cache space is running low. Frequent page swapping will lead to performance degradation. This indicator can remind maintenance personnel to perform file cleanup. Process status flags : The running status flag of the core business process of the secondary equipment; read the process status in real time from the secondary equipment operating system or monitoring system, set it to 1 when the process is running normally (when there is no AD sampling abnormality or input abnormality pop-up window), and set it to 0 when the process exits abnormally, freezes, or restarts. Communication error rate Bit error rate of secondary equipment communication links such as Ethernet, serial port and fiber optic, reflects communication quality and measures the accuracy of secondary equipment data transmission; it is obtained in real time from the statistical data of communication interface chip or switch port, and the ratio of the number of erroneous packets to the total number of packets per unit time is calculated. Time deviation Time deviation between the local clock of the secondary equipment and the reference clock such as Beidou, GPS and substation time synchronization server; read in real time from the equipment time synchronization module, in microseconds or milliseconds; Fixed value verification flag : Integrity verification flag for secondary equipment protection settings and configuration parameters; periodically perform CRC checks on the equipment setting area and configuration file, setting it to 1 when the check passes and 0 when the check fails; Cyclic Redundancy Check (CRC check) is widely used in power secondary equipment. The CRC check method is used every time the device is powered on for initialization and setting area switching. By calculating the CRC of the setting data and comparing it with the previously stored CRC value, a discrepancy is detected, indicating a setting storage error and triggering an alarm; when setting modifications are performed, the CRC value is updated synchronously and an update flag is set. When an abnormal setting occurs or an invalid download process occurs, the CRC checksums will be inconsistent before and after, ensuring the accuracy of the secondary equipment protection logic settings.

[0046] Specifically, an embedded agent program is deployed within the power distribution equipment to collect the above parameters in real time and generate a set of secondary equipment operating status parameters for subsequent secondary equipment operating status scoring calculations.

[0047] During the generation of the above-mentioned secondary equipment operating status parameter set, multi-dimensional parameters of the secondary equipment are collected, providing a data foundation for subsequent secondary equipment operating status scoring; it can also be used to evaluate the redundancy of computing resources, whether the equipment is at risk of overload, the integrity of logical functions, the availability of communication links, and the accuracy of time synchronization.

[0048] 106. Calculate the secondary equipment operating status score of the power distribution equipment based on the secondary equipment operating status parameter set.

[0049] In a preferred embodiment, the secondary equipment operating status score is calculated using the following formula: ; ; ; ; ; ; In the formula, Scoring the operating status of secondary equipment; For logical integrity factors; For communication availability factor; Penalty points for CPU utilization; Memory load penalty points; Penalty points for time deviation; As a weight for CPU utilization; Memory load weight; Weights for time deviation; For process status flags; This is a value verification flag; For communication bit error rate; A preset communication error rate safety threshold is set. CPU utilization; The default CPU utilization threshold is set. Preset CPU utilization penalty range; Memory usage; Set a default threshold for memory usage penalty. Set a preset memory usage penalty range; This is due to time deviation; This is the preset maximum allowable time deviation.

[0050] Among them, logical integrity factor : Characterizes the overall health status of the core logic and configuration of secondary equipment; by and Multiplying them together, we get the result only when both flags are 1. Otherwise, it is 0; as a veto-type basic factor, verifying the validity of the core logic and configuration of the secondary equipment is a prerequisite for the normal operation of the equipment. Communication availability factor : Characterizes the availability of the communication link of the secondary equipment; based on the comparison between the bit error rate and the threshold, it is set to 1 when the bit error rate is lower than the threshold, and set to 0 when it is higher than or equal to the threshold; the threshold is a preset communication bit error rate safety threshold. This represents the maximum permissible bit error rate for normal operation of the communication link. It is calibrated based on the communication standards of secondary power distribution equipment and on-site operation and maintenance experience, and is usually taken as 10. −6 Up to 10 −9 Magnitude; CPU usage penalty points The penalty score corresponds to the CPU utilization exceeding the threshold. A positive penalty score is only generated when the CPU utilization exceeds the threshold; otherwise, it is 0. The threshold is the starting point for CPU utilization penalty. The CPU utilization rate triggers a penalty threshold, typically set at 70% to 80% depending on the device hardware configuration and workload. A preset CPU utilization penalty range is also provided. The length of the CPU utilization range from the penalty threshold to full load, used to normalize the excess portion of CPU utilization; Memory load penalty score : The penalty score corresponding to exceeding the memory usage threshold. A positive penalty score is only generated when the memory usage exceeds the threshold; otherwise, it is 0. The threshold is the preset memory usage threshold for initiating the penalty. The threshold at which memory usage begins to trigger penalties is determined based on the device's memory capacity and business requirements, typically ranging from 80% to 85%; a preset memory usage penalty range is also included. For: the length of the interval between the penalty threshold and full load of memory usage, used to normalize the excess portion of memory usage; Time deviation penalty score The penalty score corresponding to the time deviation is only generated when the time deviation is negative, i.e., when there is a transmission delay; otherwise, it is 0. A preset maximum allowable time deviation is specified. The maximum permissible time deviation for normal operation of secondary equipment is determined according to the power system time standard. For example, protection equipment is usually set to 1ms, and measurement and control equipment is usually set to 10ms.

[0051] Furthermore, logical integrity factor As a fundamental factor that serves as a veto, verifying the validity of the core logic and configuration of secondary equipment is a prerequisite for the normal operation of the equipment. Ensure the normal operation of core business processes and prevent protection failures and measurement and control failures caused by process anomalies; Ensure the integrity of protection settings and configuration parameters to prevent false or failed protection actions caused by setting tampering or file corruption; output the secondary equipment operation status score only when both flags are normal; otherwise, directly set the secondary equipment operation status score to 0 to reflect the severity of the logical fault. Communication availability factor Similarly, as a veto-type communication status factor, verifying the availability of the secondary device's communication link is fundamental for data upload and remote control; the communication error rate directly reflects the quality of the communication link, and an excessively high error rate can lead to data packet loss or control command failure; when At that time, communication was normal. ;when At that time, communication failed. This directly sets the secondary equipment's operating status score to 0, reflecting the fatal impact of communication failures on the operation of secondary equipment. CPU usage penalty points This characterizes the CPU load status of the secondary equipment's main control unit, reflecting the sufficiency of the equipment's computing resources. At that time, the CPU load was normal. No penalty; otherwise, CPU load is too high. Follow The score increases linearly, reaching a maximum of 100 points, reflecting the deteriorating impact of high load on equipment operational stability. Memory load penalty score This characterizes the memory load status of the secondary equipment's main control unit, reflecting the adequacy of the equipment's storage resources. At that time, the memory load was normal. No penalty; otherwise, memory load is too high. Follow The score increases linearly, reaching a maximum of 100 points, reflecting the risk of program crashes or data loss caused by insufficient memory. Time deviation penalty score It characterizes the clock synchronization status of secondary equipment and reflects the time synchronization accuracy of the equipment; When the time deviation is positive, i.e., when it is ahead, Since the lead deviation has little impact on the protection action, there is no penalty; the timing deviation... When it is negative, i.e. when it lags, The score decreases linearly as the deviation increases (negative score), and is used to correct the score, reflecting the negative impact of clock lag on fault recording and event sequence recording.

[0052] In the calculation of the above-mentioned secondary equipment operation status score, two 0-1 type factors, the logical integrity factor and the communication availability factor, are used to pre-check the logical integrity and communication availability of the secondary equipment. If any factor is abnormal, the score is directly set to 0, which meets the safe operation requirements of the secondary equipment. Under the premise that the basic state is normal, the three performance indicators of CPU, memory and time synchronization are normalized and penalized. The total penalty score is obtained by weighted summation. Then, the total penalty score is subtracted from 100 to obtain the final performance score. This not only ensures the security of the core state, but also quantifies the degree of performance degradation, realizing the evaluation logic of prioritizing security while taking performance into account.

[0053] 107. Obtain the three-phase voltage phasors of the PT secondary side, the current of each CT secondary side, and the variance of the induced voltage of the unloaded line from the power distribution equipment, and generate a set of measurement circuit parameters.

[0054] Among them, the three-phase voltage phasor on the secondary side of the PT of the power distribution equipment is the three-phase voltage phasor output from the secondary side of the voltage transformer (PT), which includes amplitude and phase information; the instantaneous value of the three-phase voltage is collected in real time through the voltage sampling circuit of the power distribution secondary equipment, and the three-phase voltage phasor is obtained through Fourier transform and phasor calculation; The secondary current of each CT is the effective value of the current output from the secondary side of the current transformer (CT) in the measurement circuit; the instantaneous value of the secondary current of each CT is collected in real time through the current sampling circuit of the secondary equipment, and the effective value is calculated. The variance of induced voltage on unloaded lines is the real-time fluctuation variance of induced voltage on unloaded lines, reflecting abnormal fluctuations in circuit insulation and contact conditions. The time series of induced voltage on unloaded lines is collected in real time, and the variance of voltage fluctuation is obtained through statistical calculation, which characterizes the dispersion of the data.

[0055] Specifically, the three-phase voltage phasors of the PT secondary side, the current of each CT secondary side, and the variance of the induced voltage of the unloaded line are collected and stored in the measurement loop parameter set; this provides a data basis for the subsequent calculation of the measurement loop integrity score.

[0056] 108. Calculate the measurement circuit integrity score of the power distribution equipment based on the measurement circuit parameter set.

[0057] In a preferred embodiment, calculating the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set includes: The positive and negative sequence voltages of the PT secondary side and the negative sequence voltage of the PT secondary side are calculated using the symmetrical component method based on the phasors of the three-phase voltages on the secondary side of the PT from the parameter set of the measurement circuit. The voltage imbalance is calculated by the ratio of the negative sequence voltage on the secondary side of the PT to the positive sequence voltage on the secondary side of the PT. The voltage imbalance penalty term is calculated based on the preset unbalance fault threshold and the voltage unbalance. The offset of each CT secondary side current is calculated based on the preset CT secondary side reference current and the CT secondary side current of each CT secondary side current in the measurement circuit parameter set. Select the largest CT secondary side current offset among all CT secondary side current offsets; Calculate the CT measurement consistency penalty based on the maximum CT secondary current offset; The latent defect deduction factor is calculated based on the preset unloaded line induced voltage reference fluctuation variance and the unloaded line induced voltage variance. The explicit score for measurement circuit integrity is obtained by deducting the base score for the integrity of the preset measurement circuit from the voltage imbalance penalty and the CT measurement consistency penalty. The measurement loop integrity score is obtained by adjusting the explicit score of the measurement loop integrity based on the latent defect deduction factor.

[0058] Specifically, the calculation method for the measurement loop integrity score is as follows: ; ; ; ; In the formula, Score the loop integrity. This is a penalty term for voltage imbalance; This is a penalty for consistency in CT measurements. This is a deduction factor for latent defects; As a weight for voltage imbalance; CT consistency weight; This represents the weighting coefficient for latent defects. Voltage imbalance; Set the preset imbalance alarm threshold; Let i be the secondary current of each CT. The preset reference current value; The variance of induced voltage fluctuation on unloaded lines; The pre-set unloaded line induced voltage reference fluctuation variance; Voltage imbalance penalty The penalty score corresponding to voltage imbalance characterizes the integrity and health status of the PT measurement circuit and the three-phase voltage circuit, and is used to identify faults such as PT open circuit, poor circuit contact, or three-phase load imbalance; ideally, the three-phase voltage is balanced. , No penalty; however, when the PT secondary circuit is disconnected, has poor contact, or the fuse blows, a severe imbalance in the three-phase voltage occurs. Significantly increased, The value increases accordingly, with the highest clamping score reaching 100, demonstrating the severe impact of circuit faults on measurement integrity; voltage imbalance. First, the positive and negative sequence voltages on the secondary side of the PT are calculated using the symmetrical component method based on the three-phase voltage phasors. Then, the degree of imbalance of the three-phase voltages is obtained by calculating the ratio of the negative sequence voltage to the positive sequence voltage on the secondary side of the PT. A preset imbalance alarm threshold is set. The maximum permissible alarm value for voltage imbalance is determined based on power distribution system operation standards and on-site operation and maintenance experience, and is usually taken as 2% or 4%. CT measurement consistency penalty The penalty score corresponding to CT measurement consistency characterizes the consistency of multiple CT measurement circuits and is used to identify faults such as open circuits, short circuits, poor contacts, and sampling abnormalities in the CT secondary circuit. Ideally, each CT measurement value is highly consistent with the reference current, with minimal relative deviation. No penalty; however, when the CT secondary circuit is open-circuited, short-circuited, or the contact resistance increases, the corresponding CT measurement value will show a significant deviation, with the maximum relative deviation increasing rapidly. As the current increases, it accurately locates the most severe loop anomaly and outputs a penalty score; a preset reference current value is also provided. The standard current sampled by the main meter or protection device, or the arithmetic mean of the secondary currents of each CT, is usually taken as the reference for the actual current. Latent defect deduction factor The latent defect factor, which deducts from the score, is used to identify latent defects in the measurement circuit, such as insulation aging, loose connections, and intermittent faults. These defects do not cause obvious deviations in voltage and current, but they can cause abnormal fluctuations in induced voltage. Under healthy conditions, the induced voltage of the unloaded line is stable. , There is no significant deduction in the score; however, when there is a hidden defect in the circuit, the induced voltage will fluctuate abnormally. Significantly increased, As it rises, through explicit score for measurement loop integrity Deductions are made to provide early warning of latent defects; the variance of the reference fluctuation of the induced voltage on unloaded lines. By extracting induced voltage data from the equipment's historical healthy operating phases, the statistical variance is calculated as a benchmark. Under healthy conditions, this value is extremely small, close to 0.

[0059] Specifically, the measurement circuit integrity score is obtained by first deducting 100 points from the preset measurement circuit integrity base score using two explicit indicators: voltage imbalance and CT measurement consistency. Then, the implicit defect deduction factor is introduced by the variance of induced voltage fluctuation in the unloaded line to adjust the measurement circuit integrity score, and finally, the measurement circuit integrity score is obtained.

[0060] The calculation of the above measurement circuit integrity score can evaluate the integrity of the measurement circuit online in real time, accurately identify obvious faults such as open circuit, short circuit and disconnection of PT / CT circuit, as well as hidden defects such as insulation aging and poor contact, and provide quantitative basis for condition inspection and fault early warning of power distribution equipment measurement circuit.

[0061] 109. Calculate the overall risk score of the power distribution equipment based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, and measurement circuit integrity score.

[0062] In a preferred embodiment, the step of calculating the overall risk score of the power distribution equipment based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, and measurement circuit integrity score includes: The overall risk score of the power distribution equipment is obtained by aggregating the failure probabilities based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, measurement circuit integrity score, and corresponding weights.

[0063] Specifically, the overall risk score for power distribution equipment is calculated as follows: ; SOH; ; ; ; In the formula, The overall risk score for power distribution equipment; Let be the normalized value of the i-th score; Let be the weight coefficient of the i-th score, satisfying The weights can be determined by using the Analytic Hierarchy Process (AHP), the entropy weight method, or the experience of power industry experts, combined with the operating scenario, voltage level, and importance of the power distribution equipment.

[0064] In calculating the overall risk score of the aforementioned power distribution equipment, the health status of the four dimensions is uniformly normalized and converted into the failure probability of the corresponding components. The power distribution equipment is regarded as a system composed of batteries, primary equipment, secondary equipment, and measurement circuits connected in series. Failure in any link will lead to the overall failure of the equipment. Following the multiplicative form of the failure probability aggregation of series systems in reliability engineering, the multiplicative structure strictly matches the failure logic of series systems. Compared with the traditional linear weighted model, it is more in line with the actual operating characteristics of power equipment, and the risk calculation results are more engineering reasonable and theoretically rigorous. It realizes the full-dimensional risk assessment of the four core links of power supply system, primary equipment, secondary equipment, and measurement circuit, and fully covers the health status of the power distribution equipment throughout its entire life cycle.

[0065] 110. Generate early warning of power distribution equipment faults based on the overall risk score of the power distribution equipment.

[0066] Specifically, based on the overall risk score of the power distribution equipment, fault warning information is retrieved from the preset power distribution equipment risk level table, and the fault warning information is then visualized. The preset power distribution equipment risk level table is shown in Table 1. Table 1 In another preferred embodiment, the power distribution equipment fault early warning method based on multi-source data further includes: The remaining battery capacity of the power distribution equipment is obtained through a preset battery remaining capacity table based on the power health score of the power distribution equipment. When the remaining battery capacity is less than a preset capacity threshold, a battery replacement prompt message is generated.

[0067] Specifically, the remaining battery capacity is shown in Table 2. When the remaining battery capacity is less than 70%, a battery replacement prompt message is generated so that maintenance personnel can replace the batteries in the power distribution equipment in a timely manner to prevent malfunctions.

[0068] Table 2 By implementing the above embodiments, the following effects are achieved: the overall risk score of power distribution equipment can be calculated based on multi-source data, and a fault warning for power distribution equipment can be generated based on the overall risk score of power distribution equipment; the overall risk score can directly quantify the fault risk of power distribution equipment, realizing fault warning for equipment before the fault occurs, which is conducive to improving the reliability of power distribution equipment.

[0069] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides a method and apparatus for fault early warning of power distribution equipment based on multi-source data, including: a battery parameter set generation module, a power health scoring module, a switching coil current waveform extraction module, a primary equipment mechanical status scoring module, a secondary equipment operating status parameter set generation module, a secondary equipment operating status scoring module, a measurement circuit parameter set module, a measurement circuit integrity scoring module, an overall risk score calculation module, and a fault early warning generation module; The battery parameter set generation module is used to obtain the terminal voltage change, internal resistance, charging and discharging current and ambient temperature of the power distribution equipment to generate a battery parameter set. The power health scoring module is used to calculate the power health score of the power distribution equipment based on the battery parameter set. The circuit breaker coil current waveform extraction module is used to acquire the circuit breaker coil current waveform of the power distribution equipment, extract the peak current and operating energy integral in the circuit breaker coil current waveform, and determine the core contact time and mechanism action time in the circuit breaker coil current waveform. The primary equipment mechanical condition scoring module is used to calculate the primary equipment mechanical condition score of the power distribution equipment based on peak current, operating energy integral, core contact time and mechanism action time. The secondary equipment operating status parameter set generation module is used to obtain the CPU utilization rate, memory utilization rate, process status flag, communication error rate, time synchronization deviation and set value verification flag of the power distribution equipment, and generate the secondary equipment operating status parameter set. The secondary equipment operation status scoring module is used to calculate the secondary equipment operation status score of the power distribution equipment based on the secondary equipment operation status parameter set. The measurement circuit parameter set module is used to acquire the three-phase voltage phasors of the PT secondary side of the power distribution equipment, the secondary side current of each CT, and the variance of the induced voltage of the unloaded line, and generate the measurement circuit parameter set. The measurement loop integrity scoring module is used to calculate the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set. The overall risk score calculation module is used to calculate the overall risk score of the power distribution equipment based on the power health score, the primary equipment mechanical condition score, the secondary equipment operating condition score, and the measurement circuit integrity score. The fault warning generation module is used to generate fault warnings for power distribution equipment based on the overall risk score of the power distribution equipment.

[0070] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the power distribution equipment fault early warning method based on multi-source data provided by any of the above-described method embodiments of the present invention.

[0071] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0072] Based on the above embodiments of the power distribution equipment fault early warning method based on multi-source data, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power distribution equipment fault early warning method based on multi-source data of any embodiment of the present invention.

[0073] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0074] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0075] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0076] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution equipment fault early warning method based on multi-source data as described in any of the above-described method embodiments of the present invention.

[0077] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0078] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power distribution equipment failure early warning method based on multi-source data, characterized in that, include: The terminal voltage change, internal resistance, charging and discharging current and ambient temperature of the power distribution equipment are obtained to generate a battery parameter set. Calculate the power health score of the power distribution equipment based on the battery parameter set; Acquire the current waveform of the opening and closing coils of the power distribution equipment, extract the peak current and the integral of the operating energy from the current waveform of the opening and closing coils, and determine the core contact time and the mechanism action time in the current waveform of the opening and closing coils. The mechanical condition score of the primary equipment of the power distribution equipment is calculated based on the peak current, operating energy integral, core contact time, and mechanism action time. The system acquires the CPU utilization rate, memory usage rate, process status flags, communication error rate, time synchronization deviation, and set value verification flags of the power distribution equipment, and generates a set of operating status parameters for the secondary equipment. Calculate the secondary equipment operating status score of the power distribution equipment based on the secondary equipment operating status parameter set; Acquire the three-phase voltage phasors of the PT secondary side of the power distribution equipment, the secondary current of each CT, and the variance of the induced voltage of the unloaded line to generate a set of measurement circuit parameters. Calculate the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set; The overall risk score of the power distribution equipment is calculated based on the power health score, the primary equipment mechanical condition score, the secondary equipment operating condition score, and the measurement circuit integrity score. Power distribution equipment fault warnings are generated based on the overall risk score of the power distribution equipment.

2. The power distribution equipment failure warning method based on multi-source data of claim 1, wherein, The calculation of the power health score of the power distribution equipment based on the battery parameter set includes: The current voltage characteristic degradation is calculated based on the deviation between the preset standard voltage change and the terminal voltage change in the battery parameter set. Calculate voltage stability health based on the current voltage characteristic degradation and the preset maximum voltage characteristic degradation; The internal resistance aging factor is calculated using an exponential function based on the preset initial internal resistance and the internal resistance of the battery parameter set. The temperature stress term is calculated using the Arrhenius equation based on the preset rated operating temperature and the ambient temperature of the battery parameter set. The current stress term is calculated based on Joule's law, using the preset rated operating current and the charging and discharging current of the battery parameter set. Calculate environmental operating condition losses based on temperature stress and current stress terms; The power health score of power distribution equipment is calculated by considering voltage stability health, internal resistance aging factor, and environmental operating condition losses.

3. The power distribution equipment failure warning method based on multi-source data of claim 2, wherein, The mechanical condition score of the primary equipment of the power distribution equipment is calculated based on the peak current, integrated operating energy, core contact time, and mechanism action time, including: The mechanical condition score of primary equipment is calculated using the following formula: ； ； ； In the formula, Assess the mechanical condition of the equipment. This is the transition time for the action; Preset standard action transition time; The transition time for the preset fault alarm threshold; This refers to the peak current in the current waveform of the opening and closing coils. This refers to the peak current in the preset standard opening and closing coil current waveform; The peak current for setting the preset fault alarm threshold; The integral of the operating energy in the current waveform of the opening and closing coils; The integral of the operating energy in the preset standard opening and closing coil current waveform; The operational energy integral for setting the fault alarm threshold; Weighting of action transition time; Peak current weighting; For operational energy integral weighting; The moment when the iron core touches in the current waveform of the opening and closing coil; The moment of mechanism action in the current waveform of the opening and closing coil; is the start-up time in the closing coil current waveform; is the steady-state holding time in the closing coil current waveform; is the coil current in the closing coil current waveform.

4. The power distribution equipment failure warning method based on multi-source data of claim 3, wherein, The operating status score of secondary equipment is calculated using the following formula: ； ； ； ； ； ； In the formula, Scoring the operating status of secondary equipment; For logical integrity factors; For communication availability factor; Penalty points for CPU utilization; Memory load penalty points; Penalty points for time deviation; As a weight for CPU utilization; Memory load weight; Weights for time deviation; For process status flags; This is a value verification flag; a communication error rate; a preset communication error rate safety threshold; is a CPU occupancy rate; is a preset CPU occupancy rate penalty threshold; is a preset CPU occupancy rate penalty interval; is a memory usage rate; is a preset memory usage rate penalty threshold; is a preset memory usage rate penalty interval; is a preset maximum allowed time deviation; is a preset maximum allowed time deviation.

5. The power distribution equipment failure warning method based on multi-source data of claim 4, wherein, The calculation of the measurement circuit integrity score of the power distribution equipment based on the measurement circuit parameter set includes: The positive and negative sequence voltages of the PT secondary side and the negative sequence voltage of the PT secondary side are calculated using the symmetrical component method based on the phasors of the three-phase voltages on the secondary side of the PT from the parameter set of the measurement circuit. The voltage imbalance is calculated by the ratio of the negative sequence voltage on the secondary side of the PT to the positive sequence voltage on the secondary side of the PT. The voltage imbalance penalty term is calculated based on the preset unbalance fault threshold and the voltage unbalance. The offset of each CT secondary side current is calculated based on the preset CT secondary side reference current and the CT secondary side current of each CT secondary side current in the measurement circuit parameter set. Select the largest CT secondary side current offset among all CT secondary side current offsets; Calculate the CT measurement consistency penalty based on the maximum CT secondary current offset; The latent defect deduction factor is calculated based on the preset unloaded line induced voltage reference fluctuation variance and the unloaded line induced voltage variance. The explicit score for measurement circuit integrity is obtained by deducting the base score for the integrity of the preset measurement circuit from the voltage imbalance penalty and the CT measurement consistency penalty. The measurement loop integrity score is obtained by adjusting the explicit score of the measurement loop integrity based on the latent defect deduction factor.

6. The power distribution equipment failure warning method based on multi-source data of claim 5, wherein, The calculation of the overall risk score for power distribution equipment based on power health score, primary equipment mechanical condition score, secondary equipment operating condition score, and measurement circuit integrity score includes: The overall risk score of the power distribution equipment is obtained by aggregating the failure probabilities based on the power health score, primary equipment mechanical condition score, secondary equipment operating condition score, measurement circuit integrity score, and corresponding weights.

7. The power distribution equipment failure warning method based on multi-source data of claim 6, wherein, Also includes: The remaining battery capacity of the power distribution equipment is obtained through a preset battery remaining capacity table based on the power health score of the power distribution equipment. When the remaining battery capacity is less than a preset capacity threshold, a battery replacement prompt message is generated.

8. A power distribution equipment failure early warning method device based on multi-source data, characterized in that, include: The system includes a battery parameter set generation module, a power health scoring module, a circuit breaker coil current waveform extraction module, a primary equipment mechanical status scoring module, a secondary equipment operating status parameter set generation module, a secondary equipment operating status scoring module, a measurement circuit parameter set module, a measurement circuit integrity scoring module, an overall risk score calculation module, and a fault early warning generation module. The battery parameter set generation module is used to obtain the terminal voltage change, internal resistance, charging and discharging current and ambient temperature of the power distribution equipment to generate a battery parameter set. The power health scoring module is used to calculate the power health score of the power distribution equipment based on the battery parameter set. The circuit breaker coil current waveform extraction module is used to acquire the circuit breaker coil current waveform of the power distribution equipment, extract the peak current and operating energy integral in the circuit breaker coil current waveform, and determine the core contact time and mechanism action time in the circuit breaker coil current waveform. The primary equipment mechanical condition scoring module is used to calculate the primary equipment mechanical condition score of the power distribution equipment based on peak current, operating energy integral, core contact time and mechanism action time. The secondary equipment operating status parameter set generation module is used to obtain the CPU utilization rate, memory utilization rate, process status flag, communication error rate, time synchronization deviation and set value verification flag of the power distribution equipment, and generate the secondary equipment operating status parameter set. The secondary equipment operation status scoring module is used to calculate the secondary equipment operation status score of the power distribution equipment based on the secondary equipment operation status parameter set. The measurement circuit parameter set module is used to acquire the three-phase voltage phasors of the PT secondary side of the power distribution equipment, the secondary side current of each CT, and the variance of the induced voltage of the unloaded line, and generate the measurement circuit parameter set. The measurement loop integrity scoring module is used to calculate the measurement loop integrity score of the power distribution equipment based on the measurement loop parameter set. The overall risk score calculation module is used to calculate the overall risk score of the power distribution equipment based on the power health score, the primary equipment mechanical condition score, the secondary equipment operating condition score, and the measurement circuit integrity score. The fault warning generation module is used to generate fault warnings for power distribution equipment based on the overall risk score of the power distribution equipment.

9. A terminal device, comprising: The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power distribution equipment fault early warning method based on multi-source data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power distribution equipment fault early warning method based on multi-source data as described in any one of claims 1-7.

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