Line active real-time monitoring and limit customizing method, system and device and medium

Abstract

The invention discloses a line active real-time monitoring and limit customizing method, system and device and a medium, and belongs to the technical field of power system operation monitoring, and the method comprises the steps: obtaining an active telemetering value sequence, a conductor temperature telemetering sequence, a circuit breaker SOE event sequence, meteorological parameters and a power grid load distribution vector of a target line; calculating an equivalent thermal time constant through reverse parameter identification; solving a reverse constraint relation according to the equivalent thermal time constant, and calculating a thermal capacity attenuation coefficient to obtain a time-varying thermal limit sequence; identifying a topology change event, and establishing a power flow transfer mapping model; training the active power prediction model, and performing weighted correction on the reference prediction sequence according to the topology transfer risk degree to obtain a risk weighted prediction sequence; and calculating an instantaneous out-of-limit amount and a weighted out-of-limit amount, performing accumulation to obtain a thermal damage equivalent value, and triggering graded early warning. According to the invention, accurate monitoring and early warning of the thermal capacity of the power transmission line are realized.

Description

Technical Field

[0001] This invention relates to the field of power system operation monitoring technology, specifically to a method, system, equipment, and medium for real-time monitoring and customizable limits of active power on power lines. Background Technology

[0002] The current-carrying capacity of transmission lines is a crucial constraint for the safe operation of the power grid. Traditional current-carrying capacity monitoring uses a fixed limit method, which sets a fixed upper limit for current or active power based on design standards, monitors the line load in real time, and alarms when the limit is exceeded. This method is simple and easy to implement, but it has significant shortcomings: fixed limits cannot reflect the actual thermal state of the conductor, and may be too conservative in limiting transmission capacity when the conductor temperature is low, or may allow a large load when the conductor has accumulated a high temperature rise, leading to the risk of thermal overruns.

[0003] In recent years, dynamic thermal setting technology has been developed to adjust current-carrying capacity limits in real time based on meteorological conditions such as ambient temperature and wind speed, which has improved the rationality of the limits to some extent. However, existing dynamic thermal setting methods still have the following limitations: the thermal time constant required for calculating conductor temperature rise usually uses design or empirical values, which fail to reflect the actual heat dissipation characteristics of the conductor; when determining thermal limits, only the environmental conditions and load status at the current moment are considered, ignoring the cumulative effect of temperature rise caused by historical loads; when assessing future thermal risks, the power flow shift that may be caused by changes in grid topology is not considered, and it is unable to cope with active power surges caused by topological changes such as line tripping; when judging the risk of thermal overruns, a single overrun criterion is used, and a cumulative assessment mechanism for thermal damage is not established.

[0004] The aforementioned problems make it difficult for existing monitoring methods to accurately assess the real-time thermal capacity status of the lines and to effectively warn of future thermal overrun risks, thus affecting the safety of power grid operation. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for real-time monitoring and custom limit of active power of a line.

[0006] Therefore, the technical problems solved by this invention are: the inaccurate temperature rise prediction caused by the fixed conductor thermal time constant in the prior art; the inability to reflect the actual thermal capacity state due to the failure to consider the cumulative effect of temperature rise of historical load in the calculation of thermal limit; the failure to quantify the impact of power flow transfer caused by changes in grid topology on line thermal risk; and the lack of a comprehensive evaluation mechanism for multi-time over-limit behavior when using a single over-limit criterion.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for real-time monitoring and customizable limits of active power on power lines, comprising, obtaining an active power telemetry value sequence, a conductor temperature telemetry sequence, a circuit breaker SOE event sequence, a meteorological parameter and a power grid load distribution vector of a target line; extracting active power-temperature sample pairs from the active power telemetry value sequence and the conductor temperature telemetry sequence, and calculating an equivalent thermal time constant of the target line by inverse parameter identification; solving an inverse constraint relationship between an active power value and a duration when a conductor temperature rise reaches a rated upper limit according to the equivalent thermal time constant, calculating a thermal capacity decay coefficient at each time in the future based on the inverse constraint relationship, and dynamically adjusting a rated current carrying capacity according to the thermal capacity decay coefficient to obtain a time-varying thermal limit sequence; identifying a topology change event from the circuit breaker SOE event sequence, counting an active power increment of the target line before and after the topology change event, and establishing a power flow transfer mapping model; training an active power prediction model based on the active power telemetry value sequence, inputting the latest active power value in the active power telemetry value sequence and the meteorological parameter into the active power prediction model to obtain a baseline prediction sequence, substituting a current power grid load distribution vector into the power flow transfer mapping model to calculate a topology transfer risk degree, and weighting and correcting the baseline prediction sequence according to the topology transfer risk degree to obtain a risk weighted prediction sequence; calculating a difference between the risk weighted prediction sequence and the time-varying thermal limit sequence at each time, taking a positive value as an instantaneous overrun amount, and accumulating a thermal damage equivalent value according to a coupling relationship between the instantaneous overrun amount and the thermal capacity decay coefficient at the corresponding time, and triggering a warning when the thermal damage equivalent value exceeds a conductor thermal capacity threshold.

[0008] As a preferred scheme of the line active real-time monitoring and self-defined limit method, the inverse parameter identification includes identifying active power rise jump events and active power drop jump events from the active power telemetry value sequence, extracting a steady-state active power value before a jump and a temperature rise response sequence for each rise jump event, and extracting a steady-state active power value before a jump and a temperature drop response sequence for each drop jump event. calculating rise thermal time constant sample values from the temperature rise response sequence and associating the rise thermal time constant sample values with the steady-state active power values before the jumps, calculating drop thermal time constant sample values from the temperature drop response sequence and associating the drop thermal time constant sample values with the steady-state active power values before the jumps, and fitting an upward parameter correction function and a downward parameter correction function, respectively. According to the current active power value and the predicted active power change trend of the target line, the upward parameter correction function or the downward parameter correction function is selected to calculate the equivalent thermal time constant.

[0009] As a preferred scheme of the line active real-time monitoring and self-defined limit method, the method comprises the following steps: establishing a dynamic response equation of conductor temperature rise varying with time and active value according to a conductor temperature rise differential equation and the equivalent thermal time constant; setting a conductor temperature rise constraint condition that the conductor temperature rise does not exceed the rated temperature rise upper limit, reversely solving the dynamic response equation under the conductor temperature rise constraint condition to obtain an active value upper limit allowed under different durations, and constructing a reverse constraint relationship between the active value upper limit and the duration; extracting a current conductor temperature rise state from the conductor temperature telemetry sequence, and determining a residual temperature rise space according to the current conductor temperature rise state and the rated temperature rise upper limit; for each predicted time, searching for an active value upper limit from the reverse constraint relationship according to the duration of the time, correcting the active value upper limit according to the residual temperature rise space, and taking a ratio of the corrected active value upper limit to the rated current-carrying capacity as a thermal capacity decay coefficient of the time; performing correlation operation on the thermal capacity decay coefficient and the rated current-carrying capacity to obtain a thermal limit value of each predicted time, and arranging the thermal limit values in time sequence to form the time-varying thermal limit sequence.

[0010] As a preferred scheme of the line active real-time monitoring and self-defined limit method, the method comprises the following steps: establishing a dynamic response equation of conductor temperature rise varying with time and active value according to a conductor temperature rise differential equation and the equivalent thermal time constant; for each thermal risk associated event, extracting a power grid load distribution vector before the event and an active value of the target line at the time of the event, estimating a thermal capacity decay coefficient at the time of the event according to a ratio of the active value to the rated current-carrying capacity, calculating a safe active increment upper limit according to the power grid load distribution vector and the estimated thermal capacity decay coefficient, and taking a ratio of an actual active increment of the thermal risk associated event to the safe active increment upper limit as a thermal shock strength; establishing a mapping function of the power grid load distribution vector and the thermal capacity decay coefficient to the thermal shock strength, and the mapping function constitutes the power flow transfer mapping model.

[0011] The beneficial effects of the preferred technical solution are: defining the thermal shock strength from the perspective of thermal capacity safety margin, and quantifying the topology risk and unifying the thermal management target. The traditional power flow transfer prediction focuses on the absolute value of the transfer, but the same transfer value has completely different threats to the line under different thermal capacity states. The risk degree is represented by the ratio of the actual active power increment to the upper limit of the safety increment, so that the risk degree can adapt to the current thermal capacity state of the line.

[0012] As a preferred scheme of the line active real-time monitoring and self-defined limit method, wherein: the risk weighted prediction sequence obtained by weighting and correcting the reference prediction sequence according to the topology transfer risk degree comprises: extracting the current power grid load distribution vector and the current thermal capacity attenuation coefficient, substituting the power grid load distribution vector and the thermal capacity attenuation coefficient into the power flow transfer mapping model, calculating the thermal shock strength according to the power grid load distribution vector and the thermal capacity attenuation coefficient, and taking the thermal shock strength as the topology transfer risk degree. extracting the reference active power value at each future prediction time from the reference prediction sequence, for each prediction time, determining the correction strength according to the comparison result of the topology transfer risk degree and the preset risk threshold, when the topology transfer risk degree exceeds the preset risk threshold, multiplying the reference active power value by a risk correction coefficient to obtain a corrected active power value, and the risk correction coefficient is determined by the topology transfer risk degree. arranging the corrected active power values at each future prediction time in time sequence to form the risk weighted prediction sequence.

[0013] As a preferred scheme of the line active real-time monitoring and self-defined limit method, wherein: the thermal damage equivalent value accumulated according to the coupling relationship between the instantaneous over-limit amount and the thermal capacity attenuation coefficient at the corresponding time comprises: for each future prediction time, calculating the difference between the predicted active power value in the risk weighted prediction sequence and the thermal limit value in the time-varying thermal limit sequence, and when the difference is positive, taking the difference as the instantaneous over-limit amount at the time. for the prediction time with the instantaneous over-limit amount, extracting the thermal capacity attenuation coefficient at the time, calculating the reciprocal of the thermal capacity attenuation coefficient as a thermal stress amplification factor, and taking the product of the instantaneous over-limit amount and the thermal stress amplification factor as the weighted over-limit amount at the time. cumulatively summing the weighted over-limit amounts at each future prediction time in the time dimension to obtain the thermal damage equivalent value.

[0014] The beneficial effects of the preferred technical solution are: the reciprocal of the thermal capacity attenuation coefficient is introduced as a thermal stress amplification factor, and a nonlinear coupling relationship between thermal damage and thermal capacity state is established.

[0015] As a preferred scheme of the line active real-time monitoring and self-defined limit method, when the thermal damage equivalent value exceeds the conductor thermal capacity threshold, the pre-alarm is triggered, including setting the conductor thermal capacity threshold as the upper limit of the cumulative thermal damage allowed by the conductor according to the conductor material characteristics and historical operation data of the target line; The thermal damage equivalent value is compared with the conductor thermal capacity threshold in real time, and a hierarchical pre-alarm is triggered when the thermal damage equivalent value reaches a preset proportion of the conductor thermal capacity threshold, and the preset proportion includes threshold proportions corresponding to multiple pre-alarm levels; According to the growth rate of the thermal damage equivalent value and the remaining margin of the conductor thermal capacity threshold, the time to reach the conductor thermal capacity threshold is calculated, and the time is output as a pre-alarm advance.

[0016] The application provides a line active real-time monitoring and self-defined limit system.

[0017] To solve the above technical problems, the application provides the following technical scheme: a line active real-time monitoring and self-defined limit system, comprising: a data acquisition module for acquiring an active telemetry value sequence, a conductor temperature telemetry sequence, a circuit breaker SOE event sequence, meteorological parameters and a power grid load distribution vector of a target line; A parameter identification module is used to extract active-temperature sample pairs from the active telemetry value sequence and the conductor temperature telemetry sequence, and calculate the equivalent thermal time constant of the target line through reverse parameter identification. A limit calculation module is used to solve the inverse constraint relationship between the active value and the duration when the conductor temperature rise reaches the rated upper limit according to the equivalent thermal time constant, calculate the thermal capacity attenuation coefficient at each time in the future based on the inverse constraint relationship, and dynamically adjust the rated current-carrying capacity according to the thermal capacity attenuation coefficient to obtain a time-varying thermal limit sequence. A topology modeling module is used to identify topology change events from the circuit breaker SOE event sequence, count the active increment of the target line before and after the topology change event, and establish a power flow transfer mapping model. The prediction correction module is used to train an active power prediction model based on the active power telemetry value sequence, extract the latest active power value from the active power telemetry value sequence and input the meteorological parameters into the active power prediction model to obtain a baseline prediction sequence, substitute the current power grid load distribution vector into the power flow transfer mapping model to calculate the topology transfer risk degree, and correct the baseline prediction sequence according to the topology transfer risk degree to obtain a risk-weighted prediction sequence. The risk assessment and early warning module is used to calculate the difference between the risk-weighted prediction sequence and the time-varying heat limit sequence at each time step. When the difference is positive, it is taken as the instantaneous limit exceedance. The equivalent value of thermal damage is accumulated based on the coupling relationship between the instantaneous limit exceedance and the corresponding heat capacity decay coefficient. When the equivalent value of thermal damage exceeds the conductor heat capacity threshold, an early warning is triggered.

[0018] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method for real-time monitoring of active power on a line and custom limit.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for real-time monitoring of active power of a line and custom limit.

[0020] The beneficial effects of this invention are as follows: This invention solves the problem of temperature rise prediction deviation caused by using fixed design parameters by adaptively calculating the equivalent thermal time constant from measured data through inverse parameter identification. It distinguishes the heat dissipation differences during the active power rise and fall processes, enabling the parameters to reflect the two different physical processes of enhanced radiative heat dissipation when the load increases and convective heat dissipation dominating when the load decreases, thus improving the accuracy of temperature rise prediction.

[0021] This invention solves the reverse constraint relationship starting from the current conductor temperature rise state, incorporating the cumulative temperature rise caused by historical loads into the limit calculation. This allows the limit to be dynamically adjusted according to the actual thermal state of the conductor: the limit is reduced after continuous high-load operation to ensure safety, and the limit is increased after low-load operation to release transmission capacity, achieving bidirectional adaptive adjustment.

[0022] This invention quantifies topological risk by measuring thermal shock intensity, linking the impact of power flow transfer to the current thermal capacity state of the line. The same amount of power flow transfer poses different threats to the line when thermal capacity is sufficient versus when it is depleted, and thermal shock intensity can distinguish these differences. A selective correction strategy based on a risk threshold avoids applying conservative corrections to all prediction moments, maintaining both safety and predictive economics.

[0023] The present application introduces the reciprocal of the thermal capacity attenuation coefficient as a thermal stress amplification factor, so that the thermal damage assessment reflects the physical mechanism of the over-limit hazard amplification when the thermal capacity is insufficient. The more serious the thermal capacity attenuation, the greater the weighted damage caused by the same over-limit amount, which conforms to the physical law of accelerated performance degradation of the conductor under high temperature stress. The combination of hierarchical early warning and dynamic early warning advance makes the early warning change from post-alarm to pre-judgment, providing sufficient time margin for operation adjustment. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and all other drawings obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0025] Figure 1 The present application provides a line active real-time monitoring and self-defined limit method. DETAILED DESCRIPTION

[0026] In order to make the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0027] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a line active real-time monitoring and self-defined limit method, which comprises: Step 1: obtaining the active telemetry value sequence of the target line, the conductor temperature telemetry sequence, the circuit breaker SOE event sequence, the meteorological parameter and the power grid load distribution vector; It should be noted that the power grid load distribution vector selects the load nodes having an electrical coupling relationship with the target line. Through power grid topology analysis, the load nodes having a significant influence on the target line flow distribution are identified, and the electrical distance is used as a criterion to optimally select the load nodes connected to the target line through no more than two levels of transformers, thereby forming the load distribution vector.

[0028] The circuit breaker SOE event sequence is collected from the circuit breakers electrically related to the target line, including the circuit breakers in the voltage level where the target line is located and the adjacent voltage levels, which can cause the target line flow to change.

[0029] Step 2: Extract active-temperature sample pairs from the active telemetry value sequence and the conductor temperature telemetry sequence, calculate the equivalent thermal time constant of the target line through inverse parameter identification; It should be noted that step 2 includes the following steps: identifying active rising jump events and active falling jump events from the active telemetry value sequence respectively, extracting pre-jump steady-state active values and temperature rising response sequences for each rising jump event, and extracting pre-jump steady-state active values and temperature falling response sequences for each falling jump event; Calculate rising thermal time constant sample values from the temperature rising response sequence and associate them with pre-jump steady-state active values, calculate falling thermal time constant sample values from the temperature falling response sequence and associate them with pre-jump steady-state active values, and fit rising parameter correction functions and falling parameter correction functions respectively; According to the current active value and the predicted active change trend of the target line, select the rising parameter correction function or the falling parameter correction function to calculate the equivalent thermal time constant.

[0030] Specifically, the method for identifying active rising jump events and active falling jump events is as follows: calculate the active difference value of adjacent time points in the active telemetry value sequence, when the active difference value of more than 5 consecutive sampling points is positive and the absolute value exceeds the rated active power , it is identified as an active rising jump event and the jump start time is recorded; when the active difference value of more than 5 consecutive sampling points is negative and the absolute value exceeds the rated active power , it is identified as an active falling jump event and the jump start time is recorded. For each rising jump event, the method for extracting pre-jump steady-state active values is as follows: select a stable operation period of more than 30 minutes before the jump start time, the standard deviation of the active power in this period is less than of the average value, calculate the average value of the active power in this period as the pre-jump steady-state active value, denoted as . The method for extracting the temperature rising response sequence is as follows: extract the conductor temperature telemetry value sequence from the jump start time, until the absolute value of the temperature change rate of 10 consecutive sampling points is less than , it is determined that the conductor temperature reaches a new steady state. The difference sequence between the conductor temperature sequence and the ambient temperature sequence in this period constitutes the temperature rising response sequence, denoted as , where is the conductor temperature rise at time , and is the time from the jump start time .

[0031] For each falling jump event, the method for extracting pre-jump steady-state active values is as follows: select a stable operation period of more than 30 minutes before the jump start time, the standard deviation of the active power in this period is less than The average value of the active power during this period is calculated as the steady-state active power before the jump, denoted as . The method for extracting the temperature drop response sequence is as follows: starting from the initial moment of the jump, extract the conductor temperature telemetry value sequence until the absolute value of the temperature change rate at 10 consecutive sampling points is less than 10. The conductor temperature is determined to have reached a new steady state. The sequence of differences between the conductor temperature and the ambient temperature during this period constitutes the temperature decrease response sequence, denoted as . ,in For a moment The temperature rise of the conductor, From the moment of the transition Start time.

[0032] Specifically, the method for calculating the sample value of the rising thermal time constant based on the temperature rise response sequence is as follows: The dynamic process of conductor temperature rise under constant load follows a first-order exponential response law, and the time-domain form of the temperature rise process is: in, For the moment during the ascent The temperature rise of the conductor, For the steady-state temperature rise during the upward process, The time starting from the moment of the transition; The rising heat time constant; Represents an exponential function. The nonlinear least squares method is used to analyze the temperature rise response sequence. Perform fitting and construct the objective function: in, This represents the sum of squared fitting errors during the upward process; This represents the number of sampling points in the temperature rise response sequence. By minimizing... Solve for parameters and , obtained The value is used as a sample value of the rising thermal time constant of this rising jump event.

[0033] The method for calculating the sample value of the decreasing thermal time constant based on the temperature decrease response sequence is as follows: The time-domain form of the temperature rise during the decreasing process is: in, For the time during the descent The temperature rise of the conductor, This represents the initial temperature rise during the descent process. The time starting from the moment of the transition; Let be the decreasing thermal time constant. The nonlinear least squares method is used to analyze the temperature decrease response sequence. The fitting is performed to construct an objective function: wherein, is the fitting error sum of squares of the falling process; is the number of sampling points of the temperature falling response sequence. By minimizing solving parameters and , the obtained value is taken as the falling thermal time constant sample value of the falling jump event.

[0034] The rising thermal time constant sample value of each rising jump event is associated with the steady-state active value before the jump to form a data pair set wherein, is the rising jump event serial number, is the total number of rising jump events. The falling thermal time constant sample value of each falling jump event is associated with the steady-state active value before the jump to form a data pair set wherein, is the falling jump event serial number, is the total number of falling jump events.

[0035] The method for fitting the rising parameter correction function and the falling parameter correction function respectively is as follows: regression analysis is performed on the rising data pair set to establish a functional relationship between the steady-state active value and the rising thermal time constant , the function being the rising parameter correction function, wherein represents the steady-state active value, represents the corresponding rising thermal time constant; regression analysis is performed on the falling data pair set to establish a functional relationship between the steady-state active value and the falling thermal time constant , the function being the falling parameter correction function, represents the corresponding falling thermal time constant.

[0036] Specifically, the method for selecting the parameter correction function according to the current active value of the target line and the predicted active change trend is as follows: the active value change rate of the last 10 sampling points is calculated, and the active change rate calculation formula is as follows: wherein, is the active change rate; is the current active value, is the active average value of the last 10 sampling points, is the current time; is the intermediate time corresponding to the last 10 sampling points. When , it is determined that the trend is rising, and the rising parameter correction function is selected the current active value Substitute the function, and the equivalent thermal time constant is calculated; when , it is determined that the downward trend is selected, and the downward parameter correction function the current active value Substitute the function, and the equivalent thermal time constant is calculated.

[0037] Step 3: According to the equivalent thermal time constant, the inverse constraint relationship between the active value and the duration when the conductor temperature rise reaches the rated upper limit is solved, the thermal capacity decay coefficient of each time in the future is calculated based on the inverse constraint relationship, and the time-varying thermal limit sequence is obtained by dynamically adjusting the rated current carrying capacity according to the thermal capacity decay coefficient; It should be noted that step 3 includes the following steps: according to the conductor temperature rise differential equation and the equivalent thermal time constant, a dynamic response equation of conductor temperature rise changing with time and active value is established; The conductor temperature rise constraint condition is set as the conductor temperature rise not exceeding the rated temperature rise upper limit, the dynamic response equation is inversely solved under the conductor temperature rise constraint condition, the active value upper limit allowed under different durations is obtained, and the corresponding relationship between the active value upper limit and the duration constitutes the inverse constraint relationship; The current conductor temperature rise state is extracted from the conductor temperature telemetry sequence, and the remaining temperature rise space is determined according to the current conductor temperature rise state and the rated temperature rise upper limit; For each predicted time in the future, the active value upper limit is found from the inverse constraint relationship according to the duration of the time, the active value upper limit is modified according to the remaining temperature rise space, and the ratio of the modified active value upper limit to the rated current carrying capacity is taken as the thermal capacity decay coefficient at the time; The thermal capacity decay coefficient is used as an adjustment factor to associate with the rated current carrying capacity, and the thermal limit value of each predicted time in the future is obtained, and the time-varying thermal limit sequence is formed in time sequence.

[0038] Specifically, the method for establishing the dynamic response equation of conductor temperature rise changing with time and active value is: based on the equivalent thermal time constant obtained in step 2 , the dynamic process of conductor temperature rise changing with time and active value can be represented as: Wherein, is the equivalent thermal time constant; is the conductor temperature rise, is the time variable; is the derivative of temperature rise with respect to time; is the active value corresponding to the steady-state temperature rise; Active power value. Steady-state temperature rise. The relationship with the active power is determined through the conductor's thermal equilibrium. Under given environmental conditions, the steady-state temperature rise is proportional to the conductor's heating power. The above differential equation is the dynamic response equation for the conductor's temperature rise as a function of time and active power.

[0039] Specifically, the method for obtaining the inverse constraint relationship by solving the active power value in reverse is as follows: Suppose that the conductor temperature rises at a certain moment as... This moment is recorded as the start time. The conductor temperature rise constraint is set to ensure that the conductor temperature rise does not exceed the rated upper limit. Assume that from the initial moment, the target line has a constant active power value. Continuous runtime Solving for runtime At the end, the conductor temperature rise just reached the rated upper limit. active power .

[0040] According to the dynamic response equation, under constant active power... Under the action, the conductor temperature rises from the initial value Change to The process satisfies: in, This is the upper limit of the rated temperature rise; Active value The corresponding steady-state temperature rise; This represents the conductor's temperature rise at the initial moment; Runtime; This represents an exponential function. Solving the above equation in reverse yields results for different runtimes. Maximum allowed active power The correspondence between the upper limit of the active power value and the runtime constitutes a reverse constraint relationship.

[0041] Specifically, the method for extracting the current conductor temperature rise state from the conductor temperature telemetry sequence is as follows: read the conductor temperature at the current moment from the conductor temperature telemetry sequence. Read the ambient temperature at the current moment from meteorological parameters. Calculate the current conductor temperature rise: in, This represents the current temperature rise of the conductor. The current conductor temperature; The current ambient temperature. Based on the current conductor temperature rise. and the upper limit of rated temperature rise Calculate the remaining temperature rise space: wherein, is the remaining temperature rise space.

[0042] Specifically, the method for correcting the active value upper limit according to the remaining temperature rise space is: for each prediction time in the future, the prediction time sequence number is wherein, is the total number of prediction times. The running time from the current time to the prediction time is calculated: wherein, is the running time to the prediction time; is the prediction time step. According to the running time , the corresponding active value upper limit from the reverse constraint relationship is found.

[0043] When the remaining temperature rise space is large, it indicates that the current conductor temperature rise is low, and a higher active value is allowed; when the remaining temperature rise space is small, it indicates that the current conductor temperature rise is close to the upper limit, and the allowed active value needs to be reduced. The calculation method of the corrected active value upper limit is: wherein, is the corrected active value upper limit of the prediction time; is the running time corresponding to the reverse constraint active value upper limit; is the steady-state temperature rise at zero active.

[0044] The ratio of the corrected active value upper limit to the rated current carrying capacity is taken as the thermal capacity attenuation coefficient of the prediction time: wherein, is the thermal capacity attenuation coefficient of the prediction time; is the rated current carrying capacity.

[0045] Specifically, the method for calculating the thermal limit value of each prediction time in the future according to the thermal capacity attenuation coefficient is: taking the thermal capacity attenuation coefficient as an adjustment factor, and multiplying it with the rated current carrying capacity to obtain the thermal limit value of the prediction time: in, For the first The heat limit values ​​for each predicted time point are then arranged into a sequence according to the chronological order of the predicted times. This sequence constitutes a time-varying heat limit sequence.

[0046] It should be noted that steady-state temperature rise With active value The relationship is determined through the thermal equilibrium of the conductor. Under steady-state conditions, the heating power of the conductor equals the heat dissipation power. The heating power of the conductor is... ,in For current, This represents the conductor's resistance. (Active power value) With current They are directly proportional, therefore the heating power is directly proportional to... Under given environmental conditions, heat dissipation power mainly includes convective heat dissipation and radiative heat dissipation. In engineering applications, the steady-state temperature rise can be approximated as being proportional to the heat dissipation power. The proportional relationship is determined based on rated operating conditions: when the active power is equal to the rated current-carrying capacity... When the steady-state temperature rise reaches the upper limit of the rated temperature rise. Therefore, the steady-state temperature rise relationship is: Differentiating the active power, we obtain the derivative of the steady-state temperature rise with respect to the active power: This derivative represents the steady-state temperature rise caused by a unit increase in active power, and is used in step 4 to calculate the upper limit of safe active power increment.

[0047] It should be noted that the specific method for inverse solution is as follows: substituting the steady-state temperature rise relationship into the dynamic response equation, we obtain the following about... Nonlinear equations: After sorting, we get: The maximum allowable active power value is obtained by solving the problem. For different predicted future times, substitute the corresponding runtime. The upper limit of active power at that moment is calculated, and the correspondence between the upper limit of active power and the running time constitutes a reverse constraint relationship.

[0048] Step 4: Identify topology change events from the circuit breaker SOE event sequence, statistically analyze the power grid load distribution vector before the topology change event and the active power increment of the target line after the event, and establish a power flow transfer mapping model; It should be noted that step 4 includes the following steps: identifying topology change events from the circuit breaker SOE event sequence that cause the active power increment of the target line to exceed a preset thermal risk threshold, and marking the topology change events as thermal risk associated events; For each thermal risk associated event, the power grid load distribution vector before the event and the active power value of the target line at the time of the event are extracted. The thermal capacity attenuation coefficient at the time of the event is estimated based on the ratio of the active power value to the rated current carrying capacity. The upper limit of safe active power increment is calculated based on the power grid load distribution vector and the estimated thermal capacity attenuation coefficient. The ratio of the actual active power increment of the thermal risk associated event to the upper limit of safe active power increment is taken as the thermal shock intensity. A mapping function is established from the power grid load distribution vector and the heat capacity attenuation coefficient to the thermal shock intensity. This mapping function constitutes the power flow transfer mapping model.

[0049] Specifically, the method for identifying topology change events that cause the active power increment of the target line to exceed a preset thermal risk threshold is as follows: Extract the time tag of each circuit breaker action event from the circuit breaker SOE event sequence, denoted as... Within the time window before and after the circuit breaker tripping event, extract the active power telemetry values ​​of the target line and calculate the active power increment: in, This refers to the active power increment caused by the topology change event; This is the average active power of the target line during the stable period following the event. This is the average active power of the target line during the stable period before the event. When the active power increment... The absolute value exceeds the preset thermal risk threshold. At that time, the topology change event is marked as a thermal risk associated event, where The active power increment threshold is set according to the thermal management requirements of the target line.

[0050] Specifically, the method for extracting the power grid load distribution vector for each thermal risk-related event is as follows: During the stable operating period before the event occurs, the active power load values ​​of the relevant load nodes determined in step 1 are extracted to form the power grid load distribution vector before the event occurs. ,in For the first Active load of each load node This represents the total number of load nodes. Simultaneously, the active power value of the target line at the time the event occurred is extracted and denoted as... .

[0051] According to the active power value With rated current carrying capacity The ratio of the estimated thermal capacity decay coefficient to the actual active power increment of the thermal risk related event is taken as the thermal shock intensity: wherein, is the estimated thermal capacity decay coefficient at the event occurrence; is the active power value of the target line at the event occurrence; is the rated current carrying capacity. The estimation method is based on the assumption that the target line has been running stably before the event occurrence, at which time the conductor temperature rise is close to the steady state temperature rise corresponding to the active power value and the thermal capacity decay coefficient is approximately equal to the ratio of the current active power to the rated capacity.

[0052] According to the power grid load distribution vector and the estimated thermal capacity decay coefficient the method for calculating the upper limit of the safe active power increment is: according to the method of step 3, in the case of knowing the current thermal capacity decay coefficient , the maximum active power value allowed by the target line is . The upper limit of the safe active power increment is defined as the difference between the maximum active power value allowed and the current active power value: After simplification: The above formula shows that under steady state operating conditions, the upper limit of the safe active power increment is theoretically zero. However, in actual operation, due to fluctuations in environmental conditions and dynamic changes in heat dissipation capacity, the conductor can withstand a certain active power increment. Therefore, the upper limit of the safe active power increment is determined according to the remaining temperature rise space: wherein, is the upper limit of the safe active power increment; is the upper limit of the rated temperature rise; is the active power value corresponding to the steady state temperature rise; is the derivative of the steady state temperature rise with respect to the active power value, indicating the temperature rise change caused by unit active power increment.

[0053] The ratio of the actual active power increment of the thermal risk related event to the upper limit of the safe active power increment is taken as the thermal shock intensity: wherein, is the thermal shock intensity of the thermal risk related event.

[0054] Specifically, the method for establishing a mapping function from the power grid load distribution vector and the thermal capacity decay coefficient to the thermal shock intensity is: collecting data of all thermal risk related events to form a training data set wherein is an event number, is a total number of thermal risk related events. By using a regression analysis method, a mapping function from a power grid load distribution vector and a thermal capacity decay coefficient to a thermal shock intensity is established: wherein, is a thermal shock intensity; is a mapping function; is a power grid load distribution vector; is a thermal capacity decay coefficient. The mapping function constitutes the power flow transfer mapping model, which is used to predict potential topology transfer risks according to the current power grid load distribution and thermal capacity state.

[0055] It should be noted that the specific method for establishing the mapping function is to arrange the training data set into a matrix form, and let the input feature matrix be wherein the first row is the input feature vector, and the output vector is wherein the first element is . By using a multiple linear regression method, a mapping relationship is established: The regression coefficient vector is solved by a least square method, i.e., minimizing the sum of squares of errors between the predicted value and the actual value. The solving formula is: wherein, is a transpose matrix of . After the regression coefficient is obtained, for the current power grid load distribution and the thermal capacity decay coefficient , the thermal shock intensity is calculated by substituting the above linear model. The linear regression model constitutes the mapping function .

[0056] Step 5: training an active power prediction model based on the active power telemetry value sequence, extracting the latest active power value in the active power telemetry value sequence and the meteorological parameter inputting the active power prediction model to obtain a benchmark prediction sequence; substituting the current power grid load distribution vector into the power flow transfer mapping model to calculate a topology transfer risk degree, and weighting and correcting the benchmark prediction sequence according to the topology transfer risk degree to obtain a risk weighted prediction sequence; It should be noted that step 5 comprises the following steps: extracting the current power grid load distribution vector and the current thermal capacity attenuation coefficient, substituting the power flow transfer mapping model, calculating the output thermal shock intensity according to the power grid load distribution vector and the thermal capacity attenuation coefficient, and taking the thermal shock intensity as the topology transfer risk degree; extracting the benchmark active power value at each future prediction time from the benchmark prediction sequence, for each prediction time, determining the correction intensity according to the comparison result of the topology transfer risk degree and the preset risk threshold, when the topology transfer risk degree exceeds the preset risk threshold, multiplying the benchmark active power value by a risk correction coefficient to obtain a corrected active power value, and the risk correction coefficient is determined by the topology transfer risk degree; arranging the corrected active power values at each future prediction time in chronological order to form the risk-weighted prediction sequence.

[0057] Specifically, the method for training the active power prediction model based on the active power telemetry value sequence is: extracting the active power telemetry value sequence obtained in step 1, combining the weather parameters at the corresponding time, and constructing a training data set. A time series prediction method is used to establish an active power prediction model, which takes historical active power values and weather parameters as input features and outputs active power prediction values in future time periods.

[0058] The method for extracting the latest active power value in the active power telemetry value sequence and the weather parameter into the active power prediction model to obtain a benchmark prediction sequence is: extracting the active power value in the latest time period from the active power telemetry value sequence, denoted as , wherein represents the current time, represents the time corresponding to the sampling point before the current time, represents the historical active power value. At the same time, the weather parameters at the corresponding time are extracted. These historical active power values and weather parameters are input into the trained active power prediction model to output benchmark active power values at each future prediction time, forming a benchmark prediction sequence , wherein is the benchmark active power value at the th prediction time, is the total number of prediction times.

[0059] Specifically, the method for extracting the current power grid load distribution vector and the current thermal capacity attenuation coefficient into the power flow transfer mapping model to calculate the output thermal shock intensity is: extracting the load distribution at the current time from the power grid load distribution vector obtained in step 1, denoted as , wherein is the current active load of the th load node, is the total number of load nodes. The thermal capacity attenuation coefficient corresponding to the current time is extracted from the thermal capacity attenuation coefficient sequence calculated in step 3, denoted as The current power grid load distribution vector and current heat capacity decay coefficient Substitute the power flow transfer mapping model established in step 4: in, To calculate the output thermal shock intensity; The mapping function established in step 4. The thermal shock intensity... This serves as the risk level for the aforementioned topology transition.

[0060] Specifically, the method for extracting the baseline active power values ​​for each future prediction time from the baseline prediction sequence, and determining the correction strength for each prediction time based on the topology shift risk level, is as follows: For the _____, At each prediction time, extract the baseline active power value from the baseline prediction sequence. Based on topology transfer risk level With preset risk threshold The comparison results determine whether correction is needed, among which The risk threshold is set based on operational experience.

[0061] When topology transfer risk Exceeding the preset risk threshold At that time, that is A significant topology shift risk has been identified, necessitating a correction to the baseline active power value. The correction method involves adjusting the baseline active power value... Multiply by risk adjustment factor The corrected active power value is obtained as follows: in, For the first The corrected active power value at each predicted time; This is a risk adjustment factor. The risk adjustment factor... The risk degree of topology transfer Confirmed, the calculation method is as follows: in, The correction intensity parameter represents the contribution of the portion of the risk level exceeding the threshold to the correction coefficient.

[0062] It should be noted that the strength parameters are corrected. Determined based on the degree of impact of topology transfer risk on active power prediction. From the steps The established power flow transfer mapping model shows that the thermal shock intensity This reflects the upper limit of active power increment caused by topology change events and the safe active power increment. The ratio of thermal shock intensity. for When, it means that the potential increase in active power equals the safety limit; when for At this time, it indicates that the potential increase in active power is twice the safe upper limit. Correction strength parameter. Set as to , making when Exceed Risk adjustment factor per unit Increase to That is, the baseline active power value Corresponding increase to This reflects the potential impact of topology shift risk on future active power. The specific value can be determined based on the topology shift frequency and historical active power increment statistical characteristics of the power grid where the target line is located.

[0063] When topology transfer risk The risk threshold was not exceeded. At that time, that is The topology shift risk is determined to be low, requiring no correction; the corrected active power value is equal to the baseline active power value. Specifically, the method for arranging the corrected active power values ​​at each future prediction time in chronological order to form the risk-weighted prediction sequence is as follows: For all prediction times... The corrected active power values ​​at each time point are obtained using the method described above. Arranged in chronological order as a sequence This sequence constitutes the risk-weighted prediction sequence.

[0064] Step 6: Calculate the difference between the risk-weighted prediction sequence and the time-varying thermal limit sequence at each time step. When the difference is positive, it is taken as the instantaneous limit exceedance. The equivalent value of thermal damage is accumulated based on the coupling relationship between the instantaneous limit exceedance and the thermal capacity decay coefficient at the corresponding time step. When the equivalent value of thermal damage exceeds the conductor thermal capacity threshold, an early warning is triggered.

[0065] It should be noted that step 6 includes the following steps: for each future prediction time, calculate the difference between the predicted active power value in the risk-weighted prediction sequence and the heat limit value in the time-varying heat limit sequence; when the difference is positive, use the difference as the instantaneous over-limit amount at that time. For the predicted moment where there is an instantaneous limit exceedance, the heat capacity decay coefficient at that moment is extracted, the reciprocal of the heat capacity decay coefficient is calculated as the thermal stress amplification factor, and the product of the instantaneous limit exceedance and the thermal stress amplification factor is taken as the weighted limit exceedance at that moment. The weighted over-limit quantity of each predicted time in the future is accumulated and summed in the time dimension to obtain the thermal damage equivalent value.

[0066] According to the conductor material characteristics and historical operation data of the target line, the conductor heat capacity threshold is set as an upper limit of the cumulative thermal damage allowed by the conductor; The thermal damage equivalent value is compared with the conductor heat capacity threshold in real time, and a graded early warning is triggered when the thermal damage equivalent value reaches a preset proportion of the conductor heat capacity threshold, and the preset proportion includes threshold proportions corresponding to a plurality of early warning levels; According to the growth rate of the thermal damage equivalent value and the remaining margin of the conductor heat capacity threshold, the time of reaching the conductor heat capacity threshold is calculated, and the time is output as an early warning advance.

[0067] Specifically, the method for calculating the instantaneous over-limit quantity for each predicted time in the future is as follows: for the i-th predicted time, the predicted active value is extracted from the risk-weighted prediction sequence obtained in step 5 , the thermal limit value at the corresponding time is extracted from the time-varying thermal limit sequence obtained in step 3 , and the difference between the two is calculated: Among them, is the active difference value at the i-th predicted time. When the difference value is positive, that is , it indicates that the predicted active power exceeds the thermal limit, and the difference value is taken as the instantaneous over-limit quantity at this time, denoted as ; when the difference value is zero or negative, that is , it indicates that the predicted active power does not exceed the thermal limit, and there is no instantaneous over-limit quantity at this time, denoted as .

[0068] Specifically, the method for calculating the weighted over-limit quantity for the predicted time with the instantaneous over-limit quantity is as follows: for the predicted time with the instantaneous over-limit quantity , the thermal capacity attenuation coefficient at this time is extracted from the thermal capacity attenuation coefficient sequence obtained in step 3 . The reciprocal of the thermal capacity attenuation coefficient is calculated as the thermal stress amplification factor: Among them, is the thermal stress amplification factor at the i-th predicted time. The physical meaning of the thermal stress amplification factor is: when the thermal capacity attenuation coefficient is small, it indicates that the conductor thermal capacity has been significantly attenuated, and the same over-limit quantity will cause more serious thermal stress to the conductor, so is larger; when is close to 1, it indicates that the conductor thermal capacity is sufficient, is smaller.​​ Approaching 1.

[0069] The transient over-limit quantity is multiplied by the thermal stress amplification factor as the weighted over-limit quantity at this moment: wherein, is the weighted over-limit quantity at the th prediction moment.

[0070] Specifically, the method for accumulating and summing the weighted over-limit quantities of each prediction moment in the time dimension to obtain the thermal damage equivalent value is as follows: the weighted over-limit quantities of all prediction moments are accumulated and summed as follows: wherein, is the thermal damage equivalent value; is the weighted over-limit quantity at the th prediction moment; is the prediction time step; is the total number of prediction moments. The accumulation and summation process considers the time step of each prediction moment, converts the discrete weighted over-limit quantity into a time integral form, and reflects the cumulative thermal damage effect.

[0071] Specifically, the method for setting the conductor heat capacity threshold according to the conductor material characteristics and historical operation data of the target line is as follows: the conductor heat capacity threshold reflects the upper limit of the cumulative thermal damage that the conductor can withstand in the operation cycle. The threshold is determined according to the thermal aging characteristics of the conductor material. The annealing and creep of aluminum conductors under high temperature conditions will cause the mechanical strength to decrease, and the thermal expansion of steel cores will cause the sag to increase. According to the thermal aging life curve provided by the conductor material manual and the conductor temperature rise data recorded in the historical operation, combined with the operation life of the target line and the maintenance record, the conductor heat capacity threshold is set.

[0072] The specific determination method of the conductor heat capacity threshold is as follows: according to the corresponding relationship between the allowable operating temperature and the cumulative high-temperature exposure time provided by the conductor material manual, combined with the design life and maintenance cycle of the target line, the upper limit of the cumulative thermal damage allowed is calculated. For example, for ACSR conductors, the long-term allowable operating temperature is usually to , and the short-term allowable temperature can reach to . According to the rated temperature rise upper limit of the conductor, the corresponding active value in the steady-state operation can be calculated, and then according to the cumulative length of time allowed to exceed the rated temperature rise within the operation cycle (such as one year), the cumulative over-limit quantity allowed is estimated. The conductor heat capacity threshold​ The equivalent value set for the cumulative over-limit amount is usually taken as the rated current-carrying capacity The equivalent value set for the cumulative over-limit amount is usually taken as the rated current-carrying capacity The equivalent value set for the cumulative over-limit amount is usually taken as the rated current-carrying capacity MW·s. For lines with a long service life, the conductor state (such as the annealing degree and sag change) found through historical inspections should be appropriately reduced The value.

[0073] Specifically, the method for triggering a graded early warning by comparing the thermal damage equivalent value with the conductor thermal capacity threshold in real time is to calculate the ratio of the thermal damage equivalent value to the conductor thermal capacity threshold . Among them, is the thermal damage proportion. According to the comparison result of the thermal damage proportion and the preset proportion, different early warning levels are triggered. The preset proportion includes threshold proportions corresponding to multiple early warning levels, for example: when a first-level early warning is triggered, prompting attention; when 0.8, a second-level early warning is triggered, suggesting adjusting the operation mode; when , a third-level early warning is triggered, requiring immediate load reduction measures; and when , a fourth-level early warning is triggered, indicating that the line is at risk of thermal damage over-limit.

[0074] It should be noted that the threshold proportion of the graded early warning is determined according to the response time of the dispatch operation and the urgency of the measures taken. The first-level early warning threshold is set to , at this time, the thermal damage proportion reaches , and the remaining margin is still . According to the calculation of the early warning lead time , there is usually several hours of response time, and at this stage, only the operation personnel need to be prompted to pay attention. The second-level early warning threshold is set to , and the remaining margin is reduced to . The response time is shortened to to hours, and the dispatch personnel need to start developing a plan to adjust the operation mode. The third-level early warning threshold is set to , and the remaining margin is only . The response time is usually less than hours, requiring immediate load reduction measures. The fourth-level early warning threshold is set to , that is, the thermal damage equivalent value reaches the conductor thermal capacity threshold , the line faces the risk of thermal damage over-limit and needs to be urgently offloaded. The setting of the threshold proportion follows the principle that the closer to the threshold, the higher the warning level and the shorter the response time.

[0075] Specifically, the method for calculating the warning advance according to the growth rate of the thermal damage equivalent value and the remaining margin of the conductor heat capacity threshold is as follows: the growth rate of the thermal damage equivalent value is calculated, and the thermal damage increment of the recent several time steps is used for estimation: wherein, is the growth rate of the thermal damage equivalent value; is the current thermal damage equivalent value; is the thermal damage equivalent value at the previous moment; is the time interval between two calculations.

[0076] The remaining margin of the conductor heat capacity threshold is calculated as follows: wherein, is the remaining margin.

[0077] According to the growth rate and the remaining margin , the time when the conductor heat capacity threshold is reached is calculated as follows: wherein, is the warning advance. When , represents how long the thermal damage equivalent value will reach the conductor heat capacity threshold at the current growth rate; when , it is indicated that the thermal damage equivalent value is decreasing or remaining stable, and the warning advance does not need to be calculated. The calculated warning advance is output to the dispatching system to provide a decision basis for the operation personnel.

[0078] Embodiment 2 is an embodiment of the present application, which provides a line active real-time monitoring and self-defined limit system, comprising: a data acquisition module, configured to acquire an active telemetry value sequence of a target line, a conductor temperature telemetry sequence, a circuit breaker SOE event sequence, meteorological parameters and a power grid load distribution vector; a parameter identification module, configured to extract active-temperature sample pairs from the active telemetry value sequence and the conductor temperature telemetry sequence, and calculate the equivalent thermal time constant of the target line through reverse parameter identification; The limit calculation module is configured to solve a reverse constraint relationship between an active value and a duration when a conductor temperature rise reaches a rated upper limit according to the equivalent thermal time constant, calculate a thermal capacity decay coefficient at each time in the future based on the reverse constraint relationship, and dynamically adjust a rated current carrying capacity according to the thermal capacity decay coefficient to obtain a time-varying thermal limit sequence; The topology modeling module is configured to identify a topology change event from the circuit breaker SOE event sequence, count an active power increment of the target line before and after the topology change event, and establish a power flow transfer mapping model; The prediction correction module is configured to train an active power prediction model based on the active power telemetry value sequence, extract a latest active power value in the active power telemetry value sequence and input the meteorological parameter into the active power prediction model to obtain a baseline prediction sequence, substitute a current power grid load distribution vector into the power flow transfer mapping model to calculate a topology transfer risk degree, and weight and correct the baseline prediction sequence according to the topology transfer risk degree to obtain a risk weighted prediction sequence. The risk assessment and early warning module is configured to calculate a difference between the risk weighted prediction sequence and the time-varying thermal limit sequence at each time, take a positive value as an instantaneous overrun amount when the difference is positive, accumulate a thermal damage equivalent value according to a coupling relationship between the instantaneous overrun amount and the thermal capacity decay coefficient at the corresponding time, and trigger a warning when the thermal damage equivalent value exceeds a conductor thermal capacity threshold.

[0079] The embodiment also provides an electronic device suitable for the line active real-time monitoring and self-defined limit method, which comprises a memory and a processor.

[0080] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the line active real-time monitoring and self-defined limit method.

[0081] The storage medium provided by the embodiment and the line active real-time monitoring and self-defined limit method provided by the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0082] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary universal hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for real-time monitoring and customizable limits of active power on power lines, characterized in that: include: Acquire the active power telemetry sequence, conductor temperature telemetry sequence, circuit breaker SOE event sequence, meteorological parameters, and power grid load distribution vector of the target line; Active power-temperature sample pairs are extracted from the active power telemetry sequence and the conductor temperature telemetry sequence, and the equivalent thermal time constant of the target line is calculated by inverse parameter identification. The inverse constraint relationship between the active power and the duration when the conductor temperature rise reaches the rated upper limit is solved based on the equivalent thermal time constant. The heat capacity decay coefficient at each future moment is calculated based on the inverse constraint relationship. The rated current carrying capacity is dynamically adjusted according to the heat capacity decay coefficient to obtain the time-varying heat limit sequence. Identify topology change events from the circuit breaker SOE event sequence, statistically analyze the power grid load distribution vector before the topology change event and the active power increment of the target line after the event, and establish a power flow transfer mapping model. The active power prediction model is trained based on the active power telemetry value sequence, and the latest active power value in the active power telemetry value sequence and the meteorological parameters are extracted and input into the active power prediction model to obtain the baseline prediction sequence. Substitute the current power grid load distribution vector into the power flow transfer mapping model to calculate the topology transfer risk degree, and then weight and correct the benchmark prediction sequence according to the topology transfer risk degree to obtain the risk-weighted prediction sequence. The difference between the risk-weighted prediction sequence and the time-varying thermal limit sequence is calculated at each time step. When the difference is positive, it is taken as the instantaneous limit exceedance. The equivalent value of thermal damage is accumulated based on the coupling relationship between the instantaneous limit exceedance and the thermal capacity decay coefficient at the corresponding time step. When the equivalent value of thermal damage exceeds the conductor thermal capacity threshold, an early warning is triggered.

2. The method for real-time monitoring and customized limit of active power on a power line as described in claim 1, characterized in that: The step of identifying and calculating the equivalent thermal time constant of the target line by reverse parameter identification includes: identifying active power rise jump events and active power fall jump events from the active power telemetry value sequence; extracting the steady-state active power value before the jump and the temperature rise response sequence for each rise jump event; and extracting the steady-state active power value before the jump and the temperature fall response sequence for each fall jump event. The sample values ​​of the rising thermal time constant are calculated based on the temperature rise response sequence and correlated with the steady-state active power value before the jump. The sample values ​​of the falling thermal time constant are calculated based on the temperature fall response sequence and correlated with the steady-state active power value before the jump. The rising parameter correction function and the falling parameter correction function are fitted respectively. Based on the current active power value of the target line and the predicted active power change trend, the equivalent thermal time constant is calculated by selecting either the rising parameter correction function or the falling parameter correction function.

3. The method for real-time monitoring and customized limit of active power on a power line as described in claim 2, characterized in that: The step of solving the inverse constraint relationship between the active power and the duration when the conductor temperature rise reaches the rated upper limit based on the equivalent thermal time constant, and calculating the heat capacity decay coefficient at each future moment based on the inverse constraint relationship, includes establishing a dynamic response equation for the change of conductor temperature rise with time and active power based on the conductor temperature rise differential equation and the equivalent thermal time constant. The conductor temperature rise constraint condition is set as follows: the conductor temperature rise does not exceed the rated temperature rise upper limit. The dynamic response equation is solved in reverse under the conductor temperature rise constraint condition to obtain the upper limit of the allowed active power value under different durations. The correspondence between the upper limit of the active power value and the duration constitutes the inverse constraint relationship. Extract the current conductor temperature rise status from the conductor temperature telemetry sequence, and determine the remaining temperature rise space based on the current conductor temperature rise status and the rated temperature rise upper limit; For each predicted time in the future, the upper limit of active power is found from the reverse constraint relationship based on the duration of the time. The upper limit of active power is corrected based on the remaining temperature rise space. The ratio of the corrected upper limit of active power to the rated current carrying capacity is used as the heat capacity decay coefficient for the time. The heat capacity decay coefficient is used as an adjustment factor and correlated with the rated current carrying capacity to obtain the heat limit value for each predicted time in the future, and arranged in chronological order to form the time-varying heat limit sequence.

4. The method for real-time monitoring and customized limit of active power on a power line as described in claim 3, characterized in that: The establishment of the power flow transfer mapping model includes identifying topology change events from the circuit breaker SOE event sequence that cause the active power increment of the target line to exceed a preset thermal risk threshold, and marking the topology change events as thermal risk associated events. For each thermal risk associated event, the power grid load distribution vector before the event and the active power value of the target line at the time of the event are extracted. The thermal capacity attenuation coefficient at the time of the event is estimated based on the ratio of the active power value to the rated current carrying capacity. The upper limit of safe active power increment is calculated based on the power grid load distribution vector and the estimated thermal capacity attenuation coefficient. The ratio of the actual active power increment of the thermal risk associated event to the upper limit of safe active power increment is taken as the thermal shock intensity. A mapping function is established from the power grid load distribution vector and the heat capacity attenuation coefficient to the thermal shock intensity. This mapping function constitutes the power flow transfer mapping model.

5. The method for real-time monitoring and customized limit of active power on a power line as described in claim 4, characterized in that: The step of obtaining a risk-weighted prediction sequence by weighting and correcting the baseline prediction sequence based on the topology shift risk degree includes: extracting the current grid load distribution vector and the current heat capacity decay coefficient, substituting them into the power flow shift mapping model, calculating and outputting the thermal shock intensity based on the grid load distribution vector and the heat capacity decay coefficient, and using the thermal shock intensity as the topology shift risk degree; The baseline active power value for each future prediction time is extracted from the baseline prediction sequence. For each prediction time, the correction intensity is determined based on the comparison between the topology transfer risk degree and the preset risk threshold. When the topology transfer risk degree exceeds the preset risk threshold, the baseline active power value is multiplied by the risk correction coefficient to obtain the corrected active power value. The risk correction coefficient is determined by the topology transfer risk degree. The corrected active power values ​​at each future prediction time are arranged in chronological order to form the risk-weighted prediction sequence.

6. The method for real-time monitoring and customized limit of active power on a power line as described in claim 5, characterized in that: The step of accumulating the equivalent value of thermal damage based on the coupling relationship between the instantaneous over-limit and the corresponding heat capacity decay coefficient includes: for each predicted time in the future, calculating the difference between the predicted active power value in the risk-weighted prediction sequence and the heat limit value in the time-varying heat limit sequence; when the difference is positive, the difference is used as the instantaneous over-limit at that time. For the predicted moment where there is an instantaneous limit exceedance, the heat capacity decay coefficient at that moment is extracted, the reciprocal of the heat capacity decay coefficient is calculated as the thermal stress amplification factor, and the product of the instantaneous limit exceedance and the thermal stress amplification factor is taken as the weighted limit exceedance at that moment. The weighted excess values ​​at each predicted future time are accumulated and summed over time to obtain the equivalent value of thermal damage.

7. The method for real-time monitoring and customized limit of active power on a power line as described in claim 6, characterized in that: The step of triggering an early warning when the equivalent value of thermal damage exceeds the conductor thermal capacity threshold includes setting the conductor thermal capacity threshold as the upper limit of the allowable cumulative thermal damage to the conductor based on the conductor material characteristics and historical operating data of the target line. The equivalent value of thermal damage is compared with the conductor thermal capacity threshold in real time. When the equivalent value of thermal damage reaches a preset ratio of the conductor thermal capacity threshold, a graded warning is triggered. The preset ratio includes threshold ratios corresponding to multiple warning levels. Based on the growth rate of the equivalent value of thermal damage and the remaining margin of the conductor thermal capacity threshold, the estimated time to reach the conductor thermal capacity threshold is calculated, and the time is output as the early warning lead time.

8. A real-time monitoring and customizable limit system for active power transmission lines, employing the real-time monitoring and customizable limit method for active power transmission lines as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire the active power telemetry sequence, conductor temperature telemetry sequence, circuit breaker SOE event sequence, meteorological parameters, and power grid load distribution vector of the target line; The parameter identification module is used to extract active-temperature sample pairs from the active telemetry value sequence and the conductor temperature telemetry sequence, and calculate the equivalent thermal time constant of the target line through reverse parameter identification. The limit calculation module is used to solve the inverse constraint relationship between the active power value and the duration when the conductor temperature rise reaches the rated upper limit based on the equivalent thermal time constant, calculate the heat capacity decay coefficient at each future moment based on the inverse constraint relationship, and dynamically adjust the rated current carrying capacity according to the heat capacity decay coefficient to obtain the time-varying heat limit sequence. The topology modeling module is used to identify topology change events from the circuit breaker SOE event sequence, statistically analyze the power grid load distribution vector before the topology change event and the active power increment of the target line after the event, and establish a power flow transfer mapping model. The prediction correction module is used to train an active power prediction model based on the active power telemetry value sequence, extract the latest active power value from the active power telemetry value sequence and input the meteorological parameters into the active power prediction model to obtain a baseline prediction sequence, substitute the current power grid load distribution vector into the power flow transfer mapping model to calculate the topology transfer risk degree, and correct the baseline prediction sequence according to the topology transfer risk degree to obtain a risk-weighted prediction sequence. The risk assessment and early warning module is used to calculate the difference between the risk-weighted prediction sequence and the time-varying heat limit sequence at each time step. When the difference is positive, it is taken as the instantaneous limit exceedance. The equivalent value of thermal damage is accumulated based on the coupling relationship between the instantaneous limit exceedance and the corresponding heat capacity decay coefficient. When the equivalent value of thermal damage exceeds the conductor heat capacity threshold, an early warning is triggered.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for real-time monitoring and custom limit of line active power as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for real-time monitoring and custom limit of line active power as described in any one of claims 1 to 7.

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