Energy storage control method for distribution line of transformer area

Abstract

The application provides a kind of energy storage control method of transformer area distribution line. Wherein the above-mentioned method includes: according to the multimodal operation data of transformer area distribution line, the upper limit value of the charge-discharge power of energy storage device in transformer area distribution line and the charge-discharge power constraint value are determined;Using energy storage strategy prediction model, the remaining life trend prediction of line is carried out based on multimodal operation data and aging curve, and the life extension prediction value of transformer area distribution line is obtained;According to life extension prediction value and charge-discharge power constraint value, the energy storage protection strategy of energy storage device is determined.The scheme of the application realizes the real-time linkage of fault risk and energy storage power, actively reduces the charge-discharge power when the fault risk of line increases, and reduces the risk of fault triggering from the source.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to an energy storage control method for distribution lines in a transformer substation. Background Technology

[0002] Currently, the aging detection of power distribution lines in the substation mainly relies on manual inspections and periodic preventive tests. This outdated method makes it difficult to identify potential safety hazards such as insulation deterioration, overheating of joints, and abnormal line losses in advance. Because these hazards cannot be identified early, only reactive repairs can be implemented after a line fault occurs, resulting in widespread power outages and increased maintenance costs. Furthermore, existing energy storage devices in the substation are typically only used for peak-valley arbitrage and are not involved in line safety protection, further contributing to the overall low reliability of the power supply. Summary of the Invention

[0003] One objective of this invention is to enhance the proactive identification capability of aging potential hazards in distribution lines in transformer substations and the collaborative protection performance of energy storage devices.

[0004] A further objective of this invention is to reduce the incidence of power outages and maintenance costs in transformer substations, and to improve the overall reliability of power supply.

[0005] Specifically, the present invention provides an energy storage control method for distribution lines in a transformer substation, comprising: Based on the multi-modal operation data of the distribution lines in the transformer area, a health index curve for characterizing the insulation status of the distribution lines in the transformer area, an aging curve for predicting the decay trend of current carrying capacity, and a fault probability curve for triggering the graded energy storage protection strategy were determined. Based on the health index curve and the aging curve, determine the upper limit of the charging and discharging power of the energy storage device in the power distribution line of the transformer area; The candidate power discount coefficient is determined based on the fault probability curve, and the charge / discharge power constraint value of the energy storage device is determined based on the upper limit of charge / discharge power and the candidate power discount coefficient. The energy storage strategy prediction model is used to predict the trend of the remaining life of the line based on the multimodal operation data and the aging curve, so as to obtain the predicted value of the life extension of the distribution line in the transformer area; the energy storage strategy prediction model is obtained by deep reinforcement learning training on historical multimodal operation data and historical aging curves labeled with the remaining life of the line. The energy storage protection strategy of the energy storage device is determined based on the predicted lifespan extension value and the charge / discharge power constraint value.

[0006] Optionally, determining the health index curve used to characterize the insulation status of the distribution lines in the transformer substation includes: Leakage current, partial discharge, temperature and humidity, and conductor surface aging and deformation data are extracted from the multimodal operation data and used as the first feature set. The first feature set is weighted and fused using a self-attention mechanism to obtain the insulation health value; Using time sequence as the horizontal axis and the insulation health value as the vertical axis, a continuously changing insulation health index curve is generated by fitting.

[0007] Optionally, determining the aging curve used to predict the current-carrying capacity decay trend includes: Extract long-term load current, temperature rise cycle count, ultraviolet irradiation duration, and cable oxidation thickness time-series data from the multimodal operation data; The long-term load current and the number of temperature rise cycles are fitted to obtain the electro-thermal coupled aging loss mapping relationship, and the basic aging loss rate is determined based on the electro-thermal coupled aging loss mapping relationship. An environmental stress correction coefficient is determined based on the ultraviolet irradiation duration and the time series data of the cable oxidation thickness, wherein the environmental stress correction coefficient is positively correlated with the ultraviolet irradiation duration and the time series data of the cable oxidation thickness. The actual aging loss rate is obtained by weighting and fusing the basic aging loss rate with the environmental stress correction coefficient. Based on the actual aging loss rate, determine the remaining percentage of the current carrying capacity of the line relative to the rated current carrying capacity in a time sequence. An aging curve is generated by fitting the time sequence as the horizontal axis and the remaining percentage as the vertical axis to show the trend of current carrying capacity decay.

[0008] Optionally, determining the fault probability curve used to trigger the graded energy storage protection strategy includes: Short-circuit events, leakage events, open-circuit events, and insulation breakdown events are extracted from the multimodal operating data and used as a second feature set. The probability of occurrence of each type of event in the second feature set at different time nodes is determined by Bayes' theorem, and the overall failure probability of the second feature set is determined based on the probability of occurrence of each type of event. Using time sequence as the horizontal axis and the overall failure probability as the vertical axis, the failure probability curve is fitted to generate the curve. Based on the preset fault risk level, the numerical range of the fault probability curve is divided into intervals, and corresponding energy storage protection strategies are configured according to each fault probability value interval.

[0009] Optionally, determining the upper limit of the charging and discharging power of the energy storage device in the distribution line of the transformer substation based on the health index curve and the aging curve includes: Extract the instantaneous insulation health index value of the health index curve and the instantaneous residual percentage of the aging curve; The power regulation coefficient is obtained by multiplying the instantaneous insulation health index value and the instantaneous remaining percentage. Multiplying the power regulation coefficient by the upper limit of the rated power of the distribution line in the transformer substation yields the upper limit of the charging and discharging power of the energy storage device adapted to the distribution line in the transformer substation.

[0010] Optionally, determining the candidate power discount factor based on the fault probability curve, and determining the charge / discharge power constraint value of the energy storage device based on the upper limit of charge / discharge power and the candidate power discount factor, includes: Extract the instantaneous overall failure probability corresponding to the failure probability curve, and match the corresponding candidate failure probability value range based on the instantaneous overall failure probability; Based on a preset mapping table of fault probability value ranges and power discount coefficients, the candidate power discount coefficients corresponding to the candidate fault probability value ranges are determined, wherein the power discount coefficients are inversely related to the level corresponding to the fault probability value ranges. The candidate power discount factor is multiplied by the upper limit of the charge / discharge power to obtain the charge / discharge power constraint value.

[0011] Optionally, determining the energy storage protection strategy of the energy storage device based on the predicted lifetime extension value and the charge / discharge power constraint value includes: The predicted life extension value is compared with the preset life extension threshold to obtain the life protection level; The charging and discharging power constraint value is compared with a preset power constraint threshold to obtain the power constraint level; The energy storage protection strategy of the energy storage device is determined by matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship.

[0012] Optionally, the predicted lifespan extension value is compared with a preset lifespan extension threshold to obtain the lifespan protection level, including: The preset lifespan extension threshold includes a first lifespan extension threshold and a second lifespan extension threshold, and the first lifespan extension threshold is greater than the second lifespan extension threshold. If the predicted life extension is greater than or equal to the first life extension threshold, a first life protection level is obtained. If the predicted life extension is greater than or equal to the second life extension threshold and less than the first life extension threshold, a second life protection level is obtained. If the predicted life extension is less than the second life extension threshold, a third life protection level is obtained.

[0013] Optionally, the charging / discharging power constraint value is compared with a preset power constraint threshold to obtain the power constraint level, including: The preset power constraint threshold includes a first power constraint threshold and a second power constraint threshold, and the first power constraint threshold is greater than the second power constraint threshold. If the charging / discharging power constraint value is greater than or equal to the first power constraint threshold, a first power constraint level is obtained; If the charging / discharging power constraint value is greater than or equal to the second power constraint threshold and less than the first power constraint threshold, a second power constraint level is obtained; If the charging and discharging power constraint value is less than the second power constraint threshold, a third power constraint level is obtained.

[0014] Optionally, matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship to determine the energy storage protection strategy of the energy storage device includes: If it is in the first life protection level and the first power constraint level, or if it is in the first life protection level and the second power constraint level, the energy storage device is controlled to charge during the low electricity price period and discharge during the high electricity price period according to the time-of-use electricity price signal. If it is in the first life protection level and in the third power constraint level, or if it is in the second life protection level and in the first power constraint level, control the energy storage device to discharge in order to reduce the peak line current. If it is in the second life protection level and the second power constraint level, control the energy storage device to actively output power to share the line load and limit the line current to within the preset safety threshold. If the device is in the second life protection level and the third power constraint level, or if it is in the third life protection level and the first power constraint level, reduce the upper limit of the charging and discharging power of the energy storage device. If it is in the third life protection level and the second power constraint level, control the energy storage device to discharge at the rated power. If it is in the third life protection level and the third power constraint level, control the energy storage device to discharge at the rated power and disconnect the preset disconnectable loads in the distribution line of the transformer area.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the energy storage control method for any of the above-described distribution lines.

[0016] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of the energy storage control method for any of the above-described distribution lines.

[0017] According to another aspect of the present invention, a computer device is also provided, which includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the energy storage control methods for substation power distribution lines.

[0018] The energy storage control method for distribution lines in this invention determines health index curves, aging curves, and fault probability curves based on multimodal operation data of the distribution lines, achieving real-time perception and dynamic prediction of line insulation status, current-carrying capacity decay trends, and fault probability. The health index curve and aging curve are used as constraints on the energy storage charging and discharging power, dynamically adjusting the upper limit of the energy storage device's charging and discharging power according to the aging degree of the line, avoiding accelerated degradation of aging lines due to high-power charging and discharging. A power discount coefficient is determined through the fault probability curve, achieving real-time linkage between fault risk and energy storage power. When the line fault risk increases, the charging and discharging power is proactively reduced, mitigating the risk of fault triggering from the source. A deep reinforcement learning-trained energy storage strategy prediction model quantifies the effect of energy storage protection actions on extending the remaining lifespan of the line, providing quantifiable optimization targets for protection strategy formulation. By simultaneously considering both the lifespan extension and power constraint values, a dual-dimensional synergistic energy storage protection strategy is achieved, improving the adaptability of energy storage protection in different scenarios.

[0019] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0020] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a schematic flowchart of an energy storage control method for a transformer substation power distribution line according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of determining the health index curve in an energy storage control method for a transformer substation according to an embodiment of the present invention. Figure 3 This is a schematic flowchart of determining the aging curve in an energy storage control method for a transformer substation according to an embodiment of the present invention. Figure 4 This is a schematic flowchart of the method for determining the fault probability curve in the energy storage control of a distribution line in a transformer substation according to an embodiment of the present invention. Figure 5This is a schematic flowchart illustrating the determination of the upper limit of charging and discharging power in an energy storage control method for a distribution line in a transformer substation according to an embodiment of the present invention. Figure 6 This is a schematic flowchart illustrating the determination of charging and discharging power constraint values ​​in an energy storage control method for a distribution line in a transformer substation according to an embodiment of the present invention. Figure 7 This is a schematic diagram of a computer program product according to an embodiment of the present invention; Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; Figure 9 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0021] The aging detection of distribution lines in transformer substations mainly relies on periodic manual inspections and regular preventive tests. Manual inspections are usually conducted at fixed intervals, making it difficult to detect in real time progressive safety hazards that occur between two inspections, such as insulation deterioration, increasing contact resistance and heating at joints, and abnormal fluctuations in line loss. Although preventive tests can obtain some insulation parameters, the test cycle is long and the operating conditions are limited, making it difficult to capture sudden deterioration processes caused by dynamic factors such as load fluctuations and environmental stress.

[0022] Because safety hazards cannot be identified in advance, the only option after a line fault occurs is to adopt a "passive repair after the fault occurs" approach. This may result in the fault affecting the entire transformer area, making the power outage range uncontrollable. The time required from fault location to on-site repair is long, significantly reducing power supply reliability. Emergency repairs involve overtime work, rapid allocation of spare parts, and potential safety risks, which greatly increases operation and maintenance costs.

[0023] Energy storage devices deployed in distribution substations often focus their control strategies on peak-valley electricity price arbitrage or reactive power compensation, failing to actively participate in the safety protection of the substation's distribution lines. For example, they may not actively participate in supporting the output power to share the peak load current, slow down the aging rate of the lines, or prevent overload-induced faults. As a result, the substation's distribution lines lack proactive intervention measures when facing the risks of line aging and overload.

[0024] Figure 1 This is a schematic flowchart of an energy storage control method for a transformer substation distribution line according to an embodiment of the present invention. Figure 1 As shown, the energy storage control method for the distribution lines in this area can generally include: Step S101: Based on the multi-mode operation data of the distribution lines in the transformer area, determine the health index curve used to characterize the insulation status of the distribution lines in the transformer area, the aging curve used to predict the current carrying capacity decay trend, and the fault probability curve used to trigger the graded energy storage protection strategy. Step S102: Determine the upper limit of charging and discharging power of the energy storage device in the power distribution line of the transformer substation based on the health index curve and aging curve. Step S103: Determine the candidate power discount coefficient based on the fault probability curve, and determine the charge and discharge power constraint value of the energy storage device based on the upper limit of charge and discharge power and the candidate power discount coefficient. Step S104: The energy storage strategy prediction model is used to predict the trend of the remaining life of the line based on multimodal operation data and aging curves, so as to obtain the predicted value of the life extension of the distribution line in the transformer area; the energy storage strategy prediction model is obtained by deep reinforcement learning training on historical multimodal operation data and historical aging curves labeled with the remaining life of the line. Step S105: Determine the energy storage protection strategy of the energy storage device based on the predicted lifespan extension value and the charge / discharge power constraint value.

[0025] In this embodiment, multimodal operation data of the distribution lines in the transformer substation are collected through a sensor network, including three main categories: electrical data, environmental data, and equipment status data. Based on insulation-related data (including but not limited to leakage current, partial discharge, ambient temperature and humidity, and conductor surface deformation) from the multimodal operation data, an insulation health index is calculated time-series. A weighted fusion method (such as self-attention mechanism, principal component analysis, or entropy weight method) can be used to fuse the multi-dimensional insulation health index into a single-dimensional insulation health index. The single-dimensional insulation health index is then plotted as a continuously changing insulation state health index curve. This health index curve is used to characterize the insulation state of the distribution lines in the transformer substation at the current moment, and the preferred value range is 0 to 1, with higher values ​​indicating better insulation state.

[0026] Based on aging-related data (including but not limited to long-term load current, temperature rise cycle count, UV irradiation duration, and cable oxidation thickness) from multimodal operation data, the decay trend of the line's current-carrying capacity is determined time-series. According to the aging-related data, the remaining percentage of the line's current-carrying capacity relative to its rated current-carrying capacity is calculated time-series. This remaining percentage is plotted as a current-carrying capacity decay aging curve that decreases over time. This aging curve is used to predict the future trend of the line's current-carrying capacity, with a preferred value range of 0 to 100%. A higher value indicates that the current-carrying capacity is closer to its initial state.

[0027] Based on fault-related data (including but not limited to characteristic parameters associated with short circuits, leakage current, open circuits, and insulation breakdown) from multimodal operation data, the comprehensive fault probability of the line is calculated time-by-time. Probabilistic assessment methods (such as Bayesian formulas, logistic regression, or decision trees) can be used to first determine the probability of occurrence of various faults, and then a probability fusion method (such as taking the maximum value, weighted summation, or union calculation) is used to determine the overall fault probability. The overall fault probability is plotted as a dynamically fluctuating fault probability curve. This fault probability curve is used to trigger a tiered energy storage protection strategy, with a preferred value range of 0 to 100%, where a higher value indicates a greater risk of line fault occurrence.

[0028] In this embodiment, the current health index value and aging curve value are obtained. The health index value characterizes the current insulation state of the line, preferably ranging from 0 to 1, with higher values ​​indicating better insulation. The aging curve value characterizes the remaining percentage of the current carrying capacity relative to the rated current carrying capacity, preferably ranging from 0 to 100%, with higher values ​​indicating less current carrying capacity decay. When both the health index value and the aging curve value are high, it indicates that the line is in a healthy state, and the upper limit of charging and discharging power is set to a higher value, allowing the energy storage device to operate at or near full power. When the health index value or the aging curve value is low, it indicates that the line has undergone significant aging or its current carrying capacity has decreased significantly, and the upper limit of charging and discharging power is correspondingly reduced to limit the high-power charging and discharging behavior of the energy storage device and avoid causing additional load impact on aging lines. The upper limit of charging and discharging power has a monotonically increasing relationship with both the health index value and the aging curve value; that is, the lower the health index value or the aging curve value, the smaller the upper limit of charging and discharging power.

[0029] In this embodiment, a candidate power discount coefficient is determined based on the risk level associated with the current fault probability value. The candidate power discount coefficient has a monotonically decreasing relationship with the fault risk level: the lower the fault probability, the higher the candidate power discount coefficient; the higher the fault probability, the lower the candidate power discount coefficient. The upper limit of charging and discharging power is calculated (e.g., multiplied or weighted) with the candidate power discount coefficient to obtain the charging and discharging power constraint value. The charging and discharging power constraint value includes a charging power constraint value and a discharging power constraint value, which are used as the upper limit of the actual operating power of the energy storage device in the charging and discharging states, respectively. When the fault probability curve indicates that the line is in a low-risk state, the charging and discharging power constraint value is close to the upper limit of the charging and discharging power, and the energy storage device can fully utilize its regulation capability; when the fault probability curve indicates that the line is in a high-risk state, the charging and discharging power constraint value is significantly reduced, and the output of the energy storage device is limited to avoid the risk of line faults being aggravated by high-power charging and discharging.

[0030] In this embodiment, the energy storage strategy prediction model is used to simulate the evolution trend of the remaining lifespan of the distribution lines in the transformer area under the current energy storage action decision. The model's input includes at least: current multimodal operating data (such as electrical data, status data, and environmental data) and aging curve values. The output of the energy storage strategy prediction model is a predicted lifespan extension value, which characterizes the increase in the remaining lifespan of the line obtained by adopting the current energy storage protection strategy compared to not adopting any protection strategy (i.e., natural aging). The predicted lifespan extension value is expressed in days; a larger value indicates a more significant effect of the current energy storage action on delaying line aging.

[0031] It should be noted that the training process of the energy storage strategy prediction model includes: constructing an agent that takes historical multimodal operating data and historical aging curves as state inputs and energy storage charging and discharging as output decisions. The remaining lifespan extension of the line is used as a reward signal. The agent learns the appropriate energy storage actions under different states through interaction with the simulated environment to maximize the cumulative reward. Historical data includes multimodal operating data and aging curve data of the distribution lines in the area during historical operating cycles, as well as corresponding remaining lifespan labels. The remaining lifespan labels can be obtained through one of the following methods: retrospective labeling based on historical fault time points, estimation based on accelerated aging tests, or estimation based on statistical data of similar lines.

[0032] In this embodiment, the following conditions are met when determining the energy storage protection strategy: the energy storage output power does not exceed the charge / discharge power constraint value; the operating state of the energy storage device does not exceed its own constraints, which include one or more of the following: upper and lower limits of state of charge, mutual exclusion constraints of charge / discharge states, and daily charge / discharge frequency limits. The energy storage protection strategy is determined with the goal of maximizing the predicted lifetime extension. This energy storage protection strategy includes: the charging or discharging power value that the energy storage device should output at each moment; the selection of charging or discharging for each time period; and the range or target value of the state of charge that the energy storage device should maintain.

[0033] Based on the above steps, and using multimodal operation data of the distribution lines in the transformer substation area, health index curves, aging curves, and fault probability curves are determined, enabling real-time perception and dynamic prediction of line insulation status, current-carrying capacity decay trends, and fault probability. The health index curves and aging curves are used as constraints on the charging and discharging power of energy storage devices, allowing the upper limit of the charging and discharging power of the energy storage device to be dynamically adjusted according to the degree of line aging, avoiding accelerated degradation of aging lines due to high-power charging and discharging. A power discount coefficient is determined through the fault probability curve, achieving real-time linkage between fault risk and energy storage power. When the line fault risk increases, the charging and discharging power is proactively reduced, mitigating the risk of fault triggering from the source. A deep reinforcement learning-trained energy storage strategy prediction model quantifies the effect of energy storage protection actions on extending the remaining lifespan of the lines, providing quantifiable optimization targets for protection strategy formulation. By simultaneously considering both the lifespan extension and power constraint values, a dual-dimensional synergistic energy storage protection strategy is achieved, improving the adaptability of energy storage protection in different scenarios.

[0034] Figure 2 This is a schematic flowchart illustrating the determination of a health index curve in an energy storage control method for a transformer substation according to an embodiment of the present invention. Figure 2 As shown, the health index curves used to characterize the insulation status of distribution lines in a transformer substation include: Step S201: Extract leakage current, partial discharge, temperature and humidity, and conductor surface aging deformation data from the multi-mode operation data as the first feature set; Step S202: Perform weighted fusion of the first feature set under the self-attention mechanism to obtain the insulation health value; Step S203: With time sequence as the horizontal axis and insulation health value as the vertical axis, a continuously changing insulation health index curve is generated by fitting.

[0035] In this embodiment, leakage current is used to reflect the degree of leakage from the line insulation layer to ground; an increase in leakage current usually indicates insulation degradation. Partial discharge quantity is used to characterize whether there is local breakdown or air gap discharge inside the insulation, and is an early sign of insulation degradation. Temperature and humidity, including ambient temperature and relative humidity, affect the electrical properties and aging rate of insulation materials. Surface aging deformation data of the conductor is used to reflect the degree of physical aging of the conductor insulation layer or sheath, such as cracking, hardening, and discoloration.

[0036] At each sampling time, the feature values ​​in the first feature set are used to construct a feature vector X=[x1,x2,x3,x4], corresponding to leakage current, partial discharge, temperature and humidity, and conductor surface aging deformation data, respectively. The self-attention mechanism calculates the fused insulation health value through the following steps: Calculate the importance score for each feature: , where W and b are the learnable weight matrix and bias term; The scores are converted into attention weights using a normalized exponential function: ; The insulation health value is obtained by summing the eigenvalues ​​according to the attention weights: .

[0037] The self-attention mechanism can automatically learn the degree of influence of different features on the insulation state, and give higher weights to key features, thereby improving the accuracy of insulation health value representation.

[0038] The insulation health values ​​calculated at each sampling time are arranged in chronological order, and a continuously changing insulation health index curve is plotted with the running time as the horizontal axis and the insulation health value as the vertical axis.

[0039] Based on the above steps, by fusing multi-dimensional features such as leakage current, partial discharge, temperature and humidity, and conductor surface aging and deformation data, the insulation status of the line can be comprehensively reflected, avoiding the limitations of single-parameter detection and improving the accuracy of insulation status characterization. A self-attention mechanism is used to weight and fuse the first feature set, avoiding subjective bias from manually setting weights. The trend of the insulation health index over time is presented as a continuous curve, supporting historical trajectory tracing, degradation rate analysis, and early warning, providing an intuitive basis for operation and maintenance decisions.

[0040] Figure 3 This is a schematic flowchart illustrating the determination of the aging curve in an energy storage control method for a transformer substation according to an embodiment of the present invention. Figure 3 As shown, the aging curve used to predict the current-carrying capacity decay trend includes: Step S301: Extract long-term load current, temperature rise cycle count, ultraviolet irradiation duration, and cable oxidation thickness time series data from the multi-modal operation data; Step S302: Fit the long-term load current and the number of temperature rise cycles to obtain the electro-thermal coupling aging loss mapping relationship, and determine the basic aging loss rate based on the electro-thermal coupling aging loss mapping relationship. Step S303: Determine the environmental stress correction coefficient based on the time series data of ultraviolet irradiation duration and cable oxidation thickness, wherein the environmental stress correction coefficient is positively correlated with the time series data of ultraviolet irradiation duration and cable oxidation thickness. Step S304: The basic aging loss rate is weighted and fused with the environmental stress correction coefficient to obtain the actual aging loss rate. Step S305: Based on the actual aging loss rate, determine the remaining percentage of the current carrying capacity of the line relative to the rated current carrying capacity in time sequence. Step S306: Using time sequence as the horizontal axis and remaining percentage as the vertical axis, an aging curve is fitted to generate the current carrying capacity decay trend.

[0041] In this embodiment, the long-term load current reflects the electrical load level that the line bears over a long period and is the main factor leading to thermal aging. The temperature rise cycle count records the number of temperature rise and fall cycles experienced by the line during operation; temperature rise cycles accelerate the thermomechanical fatigue of the insulation material. Ultraviolet irradiation duration reflects the cumulative duration of ultraviolet radiation exposure to the line in the outdoor environment; ultraviolet radiation accelerates the aging of organic insulation materials. Cable oxidation thickness characterizes the degree of oxidation of the conductor's metallic parts (such as copper core, aluminum core, or joints); increased oxidation thickness leads to increased contact resistance and intensified localized heating.

[0042] By fitting the extracted long-term load current and temperature rise cycle number, an electro-thermal coupling aging loss mapping relationship was established, and the basic aging loss rate was determined based on this mapping relationship. The electro-thermal coupling aging loss mapping relationship is used to characterize the quantitative correlation between the degree of degradation of the line's current-carrying capacity and the magnitude of the load current and the frequency of temperature rise cycles under the combined effects of electricity and heat. The larger the long-term load current and the more temperature rise cycles, the more severe the aging loss caused by electro-thermal coupling, and the higher the basic aging loss rate.

[0043] It should be noted that the fitting can employ regression analysis methods (such as linear regression, polynomial regression, or exponential fitting), using long-term load current and the number of temperature rise cycles as input variables and the basic aging loss rate as the output variable to establish a mapping function. The basic aging loss rate is expressed in units of / day, characterizing the degree of current-carrying capacity decay caused by electro-thermal coupling per unit time.

[0044] The environmental stress correction factor is used to characterize the effect of environmental factors on accelerating or slowing down the aging process of cables. Specifically, the environmental stress correction factor is positively correlated with the duration of ultraviolet (UV) irradiation and the thickness of cable oxidation; that is, the longer the UV irradiation duration, the larger the environmental stress correction factor; and the greater the cable oxidation thickness, the larger the environmental stress correction factor.

[0045] The actual aging loss rate is obtained by weighting and fusing the basic aging loss rate with the environmental stress correction factor. The weights can be set according to the actual operating conditions.

[0046] Based on the actual aging loss rate, the remaining percentage of the current carrying capacity of the line relative to the rated current carrying capacity is determined time-series. This remaining percentage is used to quantify the effective current carrying capacity remaining on the line at the current moment. Let the current carrying capacity at the initial moment (when the line is in operation or not yet aging) be the rated current carrying capacity C. rated After time-series cumulative losses, the current carrying capacity C(t) at the current moment satisfies: C(t) = C rated ×η(t), where η(t) is the remaining proportion. The recursive relationship of the remaining proportion over time is: η(t) = η(t-Δt) × (1- r actual (t)×Δt), where ractual (t) represents the actual aging loss rate at time t, and Δt represents the time step.

[0047] The remaining percentages calculated at each sampling time are arranged in chronological order, and an aging curve showing the decay trend of current carrying capacity is generated by fitting the running time as the horizontal axis and the remaining percentage as the vertical axis.

[0048] Based on the above steps, four characteristic parameters—long-term load current, temperature rise cycle count, UV irradiation duration, and cable oxidation thickness—are extracted to correspond to four aging factors: electrical, thermal, optical, and chemical, respectively, thus improving the accuracy of aging prediction. By fitting the long-term load current and temperature rise cycle count, an electro-thermal coupled aging loss mapping relationship is obtained, quantifying the fundamental aging loss caused by long-term energization and repeated temperature rises, and reconstructing the degradation process of the line body under the coupled effects of current and temperature. Compared with single-index calculation methods, this effectively improves the accuracy of calculating the fundamental aging loss rate. Considering the characteristics of outdoor power distribution lines being subjected to long-term UV irradiation and oxidation of the insulation layer and conductors, an environmental stress correction coefficient positively correlated with irradiation duration and oxidation thickness is set to correct the fundamental aging loss rate. The fundamental aging loss rate and the environmental stress correction coefficient are weighted and fused to obtain the actual aging loss rate, achieving a comprehensive quantification of both physical aging and environmental aging.

[0049] Figure 4 This is a schematic flowchart illustrating the determination of the fault probability curve in an energy storage control method for a transformer substation according to an embodiment of the present invention. Figure 4 As shown, the fault probability curves used to trigger the tiered energy storage protection strategy include: Step S401: Extract short-circuit events, leakage events, open-circuit events, and insulation breakdown events from the multimodal operation data as the second feature set; Step S402: Use Bayes' theorem to determine the occurrence probability of each type of event in the second feature set under different time nodes, and determine the overall failure probability of the second feature set based on the occurrence probability of each type of event. Step S403: With time sequence as the horizontal axis and overall failure probability as the vertical axis, a failure probability curve is generated by fitting the curve. Step S404: Based on the preset fault risk level, divide the numerical range of the fault probability curve into intervals, and configure the corresponding energy storage protection strategy according to each fault probability value interval.

[0050] In this embodiment, short-circuit events include associated characteristics of fault types such as phase-to-phase short circuits and single-phase-to-ground short circuits, such as sudden changes in current, voltage sags, and harmonic content changes. Leakage events include associated characteristics of leakage faults caused by insulation damage, such as zero-sequence current, leakage current amplitude, and their trends. Open-circuit events include associated characteristics of faults such as conductor breakage and connector detachment, such as current interruption and abnormal voltage fluctuations. Insulation breakdown events include associated characteristics of faults caused by insulation breakdown, such as a surge in partial discharge and transient overvoltage.

[0051] The probability of occurrence of each type of event in the second feature set at different time points is determined by Bayes' theorem, and the overall failure probability of the second feature set is determined based on the probability of occurrence of each type of event.

[0052] For each type of fault event, the probability of its occurrence at the current time node is calculated using Bayes' theorem: in, Given that feature data X is observed, the posterior probability of the occurrence of the i-th type of fault event; Let X be the likelihood probability of observing feature data X when the i-th type of fault event occurs. The prior probability of the occurrence of the i-th type of fault event can be determined based on historical statistical data; The marginal probability of feature data X is used as a normalization factor.

[0053] Based on the probability of occurrence of various fault events, the overall fault probability of the second feature set is determined. The overall fault probability is used to comprehensively characterize the total risk of any fault occurring on the line at the current moment. The overall fault probability can be determined using the maximum value method, the weighted summation method, or the union probability method.

[0054] The overall failure probability calculated at each time point is arranged in time sequence, and a dynamically fluctuating failure probability curve is generated by fitting the running time as the horizontal axis and the overall failure probability as the vertical axis.

[0055] Based on the importance and operational requirements of the line, multiple fault risk levels are preset. Each risk level corresponds to a numerical range on the fault probability curve. The fault risk levels include low-risk, medium-risk, and high-risk ranges. The overall fault probability in the low-risk range is low, the line is in normal operation, and the possibility of a fault is small; the overall fault probability in the medium-risk range is moderate, the line shows certain abnormal signs, requiring attention and preventive measures; the overall fault probability in the high-risk range is high, the line shows obvious signs of impending fault, and immediate protective actions are required.

[0056] For different fault probability ranges, corresponding energy storage protection strategies are configured. The energy storage protection strategy is matched with the risk level; the higher the risk, the more proactive the energy storage protection action. In the low-risk range, the energy storage device executes the normal operation strategy (such as peak-valley arbitrage) without initiating additional protective actions. In the medium-risk range, the energy storage device initiates light-load support or actively shares the load to reduce the peak current of the line and delay the development of the fault. In the high-risk range, the energy storage device executes full-power support and coordinates the disconnection of non-critical loads to maximize line safety.

[0057] Based on the above steps, the correlation features of four types of fault events—short circuit, leakage current, open circuit, and insulation breakdown—are extracted from multimodal operation data as a second feature set, covering the main fault types of distribution lines in the transformer area. The probability of each event is calculated using Bayes' theorem, which fully utilizes prior knowledge and real-time observation data to achieve accurate probability estimation under limited sample conditions, exhibiting good statistical reliability and computational efficiency. By fusing the probabilities of individual events into an overall fault probability, the overall risk of any fault occurring on the line is comprehensively characterized, avoiding the one-sidedness of single-type probability assessments.

[0058] Figure 5 This is a schematic flowchart illustrating the determination of the upper limit of charging and discharging power in an energy storage control method for a distribution line in a transformer substation according to an embodiment of the present invention. Figure 5 As shown, based on the health index curve and aging curve, the upper limit values ​​of charging and discharging power of energy storage devices in the distribution lines of the transformer substation are determined as follows: Step S501: Extract the instantaneous insulation health index value of the health index curve and the instantaneous residual percentage of the aging curve. Step S502: Multiply the instantaneous insulation health index value and the instantaneous residual ratio to obtain the power regulation coefficient; Step S503: Multiply the power regulation coefficient by the upper limit of the rated power of the distribution line in the transformer substation to obtain the upper limit of the charging and discharging power of the energy storage device in the distribution line in the transformer substation.

[0059] Based on the above steps, the health level of the line is comprehensively assessed from two dimensions—the current insulation condition and the degree of current-carrying capacity decay—by extracting the instantaneous insulation health index value from the health index curve and the instantaneous remaining percentage from the aging curve, thus avoiding the limitations of single-dimensional assessment. The power regulation coefficient is obtained by multiplying the instantaneous insulation health index value and the instantaneous remaining percentage, achieving joint constraints from multiple health factors. The power regulation coefficient is then multiplied by the rated power upper limit benchmark value to obtain the charging and discharging power upper limit value, allowing the power upper limit to adjust linearly and smoothly with the line health level, avoiding control jitter caused by threshold jumps.

[0060] Figure 6This is a schematic flowchart illustrating the determination of charging and discharging power constraint values ​​in an energy storage control method for a distribution line in a transformer substation according to an embodiment of the present invention. Figure 6 As shown, the candidate power discount factor is determined based on the fault probability curve, and the charge / discharge power constraint value of the energy storage device is determined based on the upper limit of charge / discharge power and the candidate power discount factor, including: Step S601: Extract the instantaneous overall failure probability corresponding to the failure probability curve, and match the corresponding candidate failure probability value range based on the instantaneous overall failure probability. Step S602: Based on the preset mapping table of fault probability value range and power discount coefficient, determine the candidate power discount coefficient corresponding to the candidate fault probability value range, wherein the power discount coefficient is inversely related to the level corresponding to the fault probability value range. Step S603: Multiply the candidate power discount factor with the upper limit of charge / discharge power to obtain the charge / discharge power constraint value.

[0061] Based on the above steps, by extracting the instantaneous overall fault probability corresponding to the fault probability curve and matching it based on a preset fault probability value range, rapid perception and classification of real-time line fault risks are achieved. Candidate power discount coefficients are determined based on a preset fault probability value range-power discount coefficient mapping table. The power discount coefficient and the level corresponding to the fault probability value range are inversely related; the higher the fault risk, the smaller the power discount coefficient, and vice versa, conforming to the safety control logic of strict constraints for high-risk and broad constraints for low-risk. Power constraints are used to limit the energy storage device. When the fault probability increases, the actual allowable power of the energy storage is automatically reduced. Risk constraints are superimposed on the upper limit of charging and discharging power, which can prevent overload risks caused by line aging and avoid high-power charging and discharging behaviors before sudden faults, significantly improving the safety margin of energy storage participating in line protection.

[0062] In the implementation of step S105, determining the energy storage protection strategy of the energy storage device based on the predicted lifetime extension value and the charge / discharge power constraint value includes: comparing the predicted lifetime extension value with a preset lifetime extension threshold to obtain a lifetime protection level; comparing the charge / discharge power constraint value with a preset power constraint threshold to obtain a power constraint level; and matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship to determine the energy storage protection strategy of the energy storage device.

[0063] In this embodiment, comparing the predicted lifespan extension with a preset lifespan extension threshold to obtain the lifespan protection level includes: the preset lifespan extension threshold includes a first lifespan extension threshold and a second lifespan extension threshold, and the first lifespan extension threshold is greater than the second lifespan extension threshold; if the predicted lifespan extension is greater than or equal to the first lifespan extension threshold, a first lifespan protection level is obtained; if the predicted lifespan extension is greater than or equal to the second lifespan extension threshold and less than the first lifespan extension threshold, a second lifespan protection level is obtained; if the predicted lifespan extension is less than the second lifespan extension threshold, a third lifespan protection level is obtained.

[0064] In this embodiment, comparing the charge / discharge power constraint value with a preset power constraint threshold to obtain the power constraint level includes: the preset power constraint threshold includes a first power constraint threshold and a second power constraint threshold, and the first power constraint threshold is greater than the second power constraint threshold; if the charge / discharge power constraint value is greater than or equal to the first power constraint threshold, a first power constraint level is obtained; if the charge / discharge power constraint value is greater than or equal to the second power constraint threshold and less than the first power constraint threshold, a second power constraint level is obtained; if the charge / discharge power constraint value is less than the second power constraint threshold, a third power constraint level is obtained.

[0065] In this embodiment, the energy storage protection strategy of the energy storage device is determined by matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship: If it is in the first life protection level and the first power constraint level, or if it is in the first life protection level and the second power constraint level, the energy storage device is controlled to charge during the low electricity price period and discharge during the high electricity price period according to the time-of-use electricity price signal. If it is in the first life protection level and in the third power constraint level, or if it is in the second life protection level and in the first power constraint level, control the energy storage device to discharge in order to reduce the peak line current. If it is in the second life protection level and the second power constraint level, control the energy storage device to actively output power to share the line load and limit the line current to within the preset safety threshold. If the device is in the second life protection level and the third power constraint level, or if it is in the third life protection level and the first power constraint level, reduce the upper limit of the charging and discharging power of the energy storage device. If it is in the third life protection level and the second power constraint level, control the energy storage device to discharge at the rated power. If it is in the third life protection level and the third power constraint level, control the energy storage device to discharge at the rated power and disconnect the preset disconnectable loads in the distribution line of the transformer area.

[0066] Based on the above implementation methods, by comparing the predicted lifetime extension value with a preset lifetime extension threshold, a lifetime protection level is obtained, enabling a graded and quantitative assessment of the effect of energy storage in delaying line aging. By comparing the charging and discharging power constraint value with a preset power constraint threshold, a power constraint level is obtained, enabling a graded division of the current allowable output range of energy storage, reducing the complexity of decision-making. By matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship, the one-sidedness of single-dimensional decision-making is avoided.

[0067] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0068] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0069] This embodiment also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 7 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 8 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 9 This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. Figure 7 As shown, the computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of any of the above-described energy storage control methods for distribution lines in a transformer substation. For example... Figure 8 As shown, a computer-readable storage medium 20 stores the aforementioned computer program 11, which, when executed by the processor 32, implements the steps of the energy storage control method for the distribution line of any of the above embodiments. Figure 9 As shown, the computer device 30 may include a memory 31, a processor 32, and a computer program 11 stored on the memory 31 and running on the processor 32.

[0070] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer. In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing status information of the computer-readable program instructions.

[0071] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0072] For the purposes of this embodiment, computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0073] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0074] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of said instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0075] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.

[0076] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for energy storage control of a distribution line in a transformer substation, characterized in that, include: Based on the multi-modal operation data of the distribution lines in the transformer area, a health index curve for characterizing the insulation status of the distribution lines in the transformer area, an aging curve for predicting the decay trend of current carrying capacity, and a fault probability curve for triggering the graded energy storage protection strategy were determined. Based on the health index curve and the aging curve, determine the upper limit of the charging and discharging power of the energy storage device in the power distribution line of the transformer area; The candidate power discount coefficient is determined based on the fault probability curve, and the charge / discharge power constraint value of the energy storage device is determined based on the upper limit of charge / discharge power and the candidate power discount coefficient. The energy storage strategy prediction model is used to predict the trend of the remaining life of the line based on the multimodal operation data and the aging curve, so as to obtain the predicted value of the life extension of the distribution line in the transformer area; the energy storage strategy prediction model is obtained by deep reinforcement learning training on historical multimodal operation data and historical aging curves labeled with the remaining life of the line. The energy storage protection strategy of the energy storage device is determined based on the predicted lifespan extension value and the charge / discharge power constraint value.

2. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, The health index curves used to characterize the insulation status of distribution lines in a transformer substation include: Leakage current, partial discharge, temperature and humidity, and conductor surface aging and deformation data are extracted from the multimodal operation data and used as the first feature set. The first feature set is weighted and fused using a self-attention mechanism to obtain the insulation health value; Using time sequence as the horizontal axis and the insulation health value as the vertical axis, a continuously changing insulation health index curve is generated by fitting.

3. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, The aging curve used to predict the current-carrying capacity decay trend includes: Extract long-term load current, temperature rise cycle count, ultraviolet irradiation duration, and cable oxidation thickness time-series data from the multimodal operation data; The long-term load current and the number of temperature rise cycles are fitted to obtain the electro-thermal coupled aging loss mapping relationship, and the basic aging loss rate is determined based on the electro-thermal coupled aging loss mapping relationship. An environmental stress correction coefficient is determined based on the ultraviolet irradiation duration and the time series data of the cable oxidation thickness, wherein the environmental stress correction coefficient is positively correlated with the ultraviolet irradiation duration and the time series data of the cable oxidation thickness. The actual aging loss rate is obtained by weighting and fusing the basic aging loss rate with the environmental stress correction coefficient. Based on the actual aging loss rate, determine the remaining percentage of the current carrying capacity of the line relative to the rated current carrying capacity in a time sequence. An aging curve is generated by fitting the time sequence as the horizontal axis and the remaining percentage as the vertical axis to show the trend of current carrying capacity decay.

4. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, The fault probability curves used to trigger the graded energy storage protection strategy include: Short-circuit events, leakage events, open-circuit events, and insulation breakdown events are extracted from the multimodal operation data and used as a second feature set. The probability of occurrence of each type of event in the second feature set at different time nodes is determined by Bayes' theorem, and the overall failure probability of the second feature set is determined based on the probability of occurrence of each type of event. Using time sequence as the horizontal axis and the overall failure probability as the vertical axis, the failure probability curve is fitted to generate the curve. Based on the preset fault risk level, the numerical range of the fault probability curve is divided into intervals, and corresponding energy storage protection strategies are configured according to each fault probability value interval.

5. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, Based on the health index curve and the aging curve, the upper limit of the charging and discharging power of the energy storage device in the distribution line of the transformer substation is determined as follows: Extract the instantaneous insulation health index value of the health index curve and the instantaneous residual percentage of the aging curve; The power regulation coefficient is obtained by multiplying the instantaneous insulation health index value and the instantaneous remaining percentage. Multiplying the power regulation coefficient by the upper limit of the rated power of the distribution line in the transformer substation yields the upper limit of the charging and discharging power of the energy storage device adapted to the distribution line in the transformer substation.

6. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, Determining the candidate power discount factor based on the fault probability curve, and determining the charge / discharge power constraint value of the energy storage device based on the upper limit of charge / discharge power and the candidate power discount factor, includes: Extract the instantaneous overall failure probability corresponding to the failure probability curve, and match the corresponding candidate failure probability value range based on the instantaneous overall failure probability; Based on a preset mapping table of fault probability value ranges and power discount coefficients, the candidate power discount coefficients corresponding to the candidate fault probability value ranges are determined, wherein the power discount coefficients are inversely related to the level corresponding to the fault probability value ranges. The candidate power discount factor is multiplied by the upper limit of the charge / discharge power to obtain the charge / discharge power constraint value.

7. The energy storage control method for distribution lines in a transformer substation according to claim 1, characterized in that, Determining the energy storage protection strategy of the energy storage device based on the predicted lifetime extension value and the charge / discharge power constraint value includes: The predicted life extension value is compared with the preset life extension threshold to obtain the life protection level; The charging and discharging power constraint value is compared with a preset power constraint threshold to obtain the power constraint level; The energy storage protection strategy of the energy storage device is determined by matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship.

8. The energy storage control method for distribution lines in a transformer substation according to claim 7, characterized in that, The predicted lifespan extension is compared with a preset lifespan extension threshold to obtain the lifespan protection level, which includes: The preset lifespan extension threshold includes a first lifespan extension threshold and a second lifespan extension threshold, and the first lifespan extension threshold is greater than the second lifespan extension threshold. If the predicted life extension is greater than or equal to the first life extension threshold, a first life protection level is obtained. If the predicted life extension is greater than or equal to the second life extension threshold and less than the first life extension threshold, a second life protection level is obtained. If the predicted life extension is less than the second life extension threshold, a third life protection level is obtained.

9. The energy storage control method for distribution lines in a transformer substation according to claim 7, characterized in that, The charging and discharging power constraint value is compared with a preset power constraint threshold to obtain the power constraint level, which includes: The preset power constraint threshold includes a first power constraint threshold and a second power constraint threshold, and the first power constraint threshold is greater than the second power constraint threshold. If the charging / discharging power constraint value is greater than or equal to the first power constraint threshold, a first power constraint level is obtained; If the charging / discharging power constraint value is greater than or equal to the second power constraint threshold and less than the first power constraint threshold, a second power constraint level is obtained; If the charging and discharging power constraint value is less than the second power constraint threshold, a third power constraint level is obtained.

10. The energy storage control method for distribution lines in a transformer substation according to claim 7, characterized in that, Matching the lifetime protection level and the power constraint level with a preset energy storage protection mapping relationship to determine the energy storage protection strategy of the energy storage device includes: If it is in the first life protection level and the first power constraint level, or if it is in the first life protection level and the second power constraint level, the energy storage device is controlled to charge during the low electricity price period and discharge during the high electricity price period according to the time-of-use electricity price signal. If it is in the first life protection level and in the third power constraint level, or if it is in the second life protection level and in the first power constraint level, control the energy storage device to discharge in order to reduce the peak line current. If it is in the second life protection level and the second power constraint level, control the energy storage device to actively output power to share the line load and limit the line current to within the preset safety threshold. If the device is in the second life protection level and the third power constraint level, or if it is in the third life protection level and the first power constraint level, reduce the upper limit of the charging and discharging power of the energy storage device. If it is in the third life protection level and the second power constraint level, control the energy storage device to discharge at the rated power. If it is in the third life protection level and the third power constraint level, control the energy storage device to discharge at the rated power and disconnect the preset disconnectable loads in the distribution line of the transformer area.

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