A flow path layout planning system based on power grid power transmission

Abstract

The present invention discloses a flow path layout planning system based on power transmission of a power grid, which relates to the field of power grid power technology. By determining the conductor material of power transmission lines in different regions, the conductive performance data and size parameters are obtained, and the current carrying capacity of the conductor is calculated; based on historical power load data and power demand data, the power load demand is predicted through a time series model; the predicted load is analyzed with the current carrying capacity of the conductor to determine whether it is overloaded and to formulate an emergency plan; the overloaded lines are managed and their load status and heat dissipation capacity are analyzed, and the fault risk is evaluated; the risk level is divided according to the risk assessment result and the load distribution is dynamically planned, so as to effectively prevent and respond to the overload risk of the transmission line, ensure the safety and stability of the power grid operation, and improve the reliability and efficiency of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a flow path layout planning system based on power grid transmission. Background Art

[0002] Grid power transmission flow path layout planning refers to the scientific and reasonable design and optimization of the transmission path of electricity from power stations to various users in the power grid system. This planning involves determining the best flow path of electricity in the transmission network to ensure that electricity can be transmitted efficiently and stably to where it is needed, while minimizing transmission losses and improving the reliability of the power grid.

[0003] Specifically, flow path layout planning needs to consider multiple factors such as the load distribution of the power grid, the capacity of each transmission line, the voltage level, and the real-time operating status. By using advanced algorithms and data processing technologies, the power transmission path can be dynamically adjusted and optimized to adapt to changes in power demand and the complexity of power grid operation, ensuring the safe, stable and efficient operation of the power system.

[0004] The shortcomings of the prior art:

[0005] In the planning of the flow path layout of power transmission in the power grid, when the current carried by the transmission line exceeds its designed maximum capacity, it may cause the transmission line to be overloaded. When the transmission line is overloaded, the line will heat up. Excessive temperature will increase the resistance of the wire, further increase the temperature, and form a vicious cycle. In severe cases, the wire will melt or break, causing power outages or fires, which will have a significant impact on the safety of the power grid and the reliability of power supply. Summary of the invention

[0006] The purpose of the present invention is to provide a flow path layout planning system based on power grid power transmission to solve the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a flow path layout planning system based on power grid power transmission, comprising a current carrying capacity calculation module, a power load demand prediction module, an overload line determination module, a risk assessment module and a planning module;

[0008] Current carrying capacity calculation module: determine the conductor materials used for transmission lines in different regions, obtain the conductivity data of the conductor materials of each transmission line, collect the size parameters of the conductors of each transmission line, and calculate the conductor current carrying capacity based on the conductivity data of the conductor materials and the size parameters of the conductors;

[0009] Power load demand forecasting module: Based on the historical power load data and power demand data of power transmission lines in different regions, the power load demand of power transmission lines in each region is predicted through a time series model;

[0010] Overload line determination module: compares and analyzes the predicted power load demand with the calculated conductor current carrying capacity, determines whether the transmission line is overloaded based on the analysis results, and formulates an emergency plan for conductor overload;

[0011] Risk assessment module: manage overloaded transmission lines according to the conductor overload emergency plan, analyze the load state changes of the conductors of the overloaded transmission lines and the heat dissipation capacity of the conductors, and assess the risk of failure of the conductors of the overloaded transmission lines;

[0012] Planning module: Based on the evaluation results, the risk of transmission line failure is divided into high risk level, medium risk level and low risk level, and the load distribution of the transmission line is dynamically planned according to different risk levels.

[0013] In a preferred embodiment, the power load demand of each regional power transmission line is predicted by an autoregressive integrated moving average model, specifically:

[0014] The historical power load data and power demand data of the transmission lines in each region are collected, the data are preprocessed, and the ARIMA model is selected for time series forecasting. The ARIMA model consists of three parameters: the autoregressive order p, the number of lag terms in the autoregressive part of the model; the number of differences d, the number of differences required to make the time series stable; the moving average order q, the number of lag terms in the moving average part of the model;

[0015] Determine p and q through the autocorrelation function and partial autocorrelation function graph;

[0016] The ARIMA model is fitted using historical power load data to make the time series data stable, and the trend and seasonality are eliminated through the difference operation. The difference expression is: ;in, is the actual load data at time t, is the predicted future load demand value, and the specific calculation expression is: Among them, c is a constant term, is the offset in the model, is the autoregressive coefficient, which is used to indicate the impact of load data at past time points on the current forecast value. It represents the influence of the load data at time t−1 on the predicted value at the current time, and the coefficient is ; They represent the impact of load data at time t-2 and time t-p on the current forecast value, and the coefficients are ; ec is the white noise term, that is, the error term, is the moving average coefficient, It represents the influence of the prediction error ec at time t−1 on the prediction value at the current time. The coefficient represents the impact of the prediction error at time t−2 and time t−p on the current prediction value, and the coefficients are , p is the autoregressive order of the ARIMA model, and q is the moving average order of the ARIMA model.

[0017] In a preferred embodiment, the predicted power load demand is compared with the calculated current carrying capacity of the conductor: the predicted load yt′ is the predicted load demand value at the future time t, and the current carrying capacity of the conductor Iemax is the maximum current carrying capacity of the conductor;

[0018] If at time t, the predicted load yt′ is greater than or equal to the conductor current carrying capacity Iemax, it is considered that the transmission line is at risk of overload and a conductor overload emergency plan is formulated; if at time t, the predicted load yt′ is less than the conductor current carrying capacity Iemax, it is considered that there is no overload risk for the transmission line and no adjustment is required.

[0019] In a preferred embodiment, a load fluctuation rate abnormality index is generated according to the load state change of the overloaded transmission line conductor, and the method for obtaining the load fluctuation rate abnormality index is:

[0020] Collect the load data Ps at each time interval and calculate the real-time load fluctuation rate. The specific calculation expression is: ; Where LFs is the load data at time s;

[0021] Convert the load fluctuation rate data into windowed time series data and set the window length to ; Where Xi is time series data;

[0022] Design and build an LSTM network, use the training data set to train the LSTM model, input the test data into the trained LSTM model, obtain the predicted load fluctuation rate LFRs, and calculate the prediction error: ; Where xs is the prediction error at the sth moment, the mean μs and standard deviation σs of the prediction error are calculated, the mean and standard deviation of the prediction error of the load fluctuation rate are analyzed, and the load fluctuation rate abnormality index is calculated. The specific calculation expression is: ; Where WD is the load fluctuation rate abnormality index.

[0023] In a preferred embodiment, the overloaded transmission line is managed according to the conductor overload emergency plan, and the heat dissipation capacity of the conductor is analyzed to generate the thermal fatigue loss index of the conductor. The method for obtaining the thermal fatigue loss index is:

[0024] Real-time acquisition of temperature change data experienced by the conductor during operation, sorting the collected temperature data in chronological order, and calculating the thermal stress caused by temperature change: ; Where A is the thermal stress, α is the thermal expansion coefficient of the wire material, E is the Young's modulus of the wire material, and ΔQ is the temperature change, i.e., Qmax−Qmin;

[0025] Determine the fatigue life Nh corresponding to each stress level through the material's SN curve; calculate the fatigue loss for each temperature cycle: ; Where Dh is the fatigue loss of the h-th temperature cycle, nh is the actual number of cycles of the h-th temperature cycle, and Nh is the fatigue life under the h-th temperature cycle. The thermal fatigue loss index is calculated. The specific calculation expression is: ; Where SX is the thermal fatigue loss index and f is the total number of temperature cycles.

[0026] In a preferred embodiment, the load fluctuation rate anomaly index and the thermal fatigue loss index are normalized, and the risk coefficient of overloaded transmission line conductor failure is calculated based on the normalized load fluctuation rate anomaly index and thermal fatigue loss index.

[0027] In a preferred embodiment, the acquired risk coefficient of the overloaded transmission line conductor failure is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the risk coefficient of the overloaded transmission line conductor failure is compared with the first standard threshold and the second standard threshold respectively;

[0028] If the risk factor of overloaded transmission line conductor failure is greater than the second standard threshold, the risk of transmission line failure is classified as high risk, and load reduction, enhanced monitoring and emergency maintenance are immediately carried out;

[0029] If the risk factor of a failure of an overloaded transmission line conductor is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk of a failure of the transmission line is classified as a medium risk level, and prevention is carried out through load balancing and regular inspections, and the probability of risk accidents occurring in medium risk level transmission lines is predicted and warned in advance;

[0030] If the risk factor of failure of an overloaded transmission line conductor is less than the first standard threshold, the risk of failure of the transmission line is classified as a low risk level, and routine monitoring and preventive maintenance are maintained, and the risk level is evaluated and updated regularly.

[0031] In a preferred embodiment, when the risk of a transmission line failure is classified as a medium risk level, that is, the risk coefficient generated within a period of time is greater than or equal to a first standard threshold and less than or equal to a second standard threshold, the risk coefficients generated within a subsequent period of time that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, the mean and standard deviation of the data set are analyzed, and the probability of risk accidents occurring in medium risk level transmission lines is predicted and warned in advance.

[0032] In a preferred embodiment, if the mean value of the risk coefficient in the data set is greater than or equal to the reference threshold value of the risk coefficient mean value, and the standard deviation of the risk coefficient is less than the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is high, and a first-level warning signal is generated at this time;

[0033] If the mean value of the risk coefficient is greater than or equal to the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is average, and a secondary warning signal is generated at this time;

[0034] If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is low, and a third-level warning signal is generated at this time;

[0035] If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is less than the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is extremely low, and no warning signal is generated at this time.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] 1. The present invention calculates the current carrying capacity of the conductor by determining the conductor material, conductive performance data and size parameters used in the transmission line; uses a time series model to predict the power load demand, and compares and analyzes the prediction results with the current carrying capacity of the conductor to determine whether there is an overload risk and formulate an emergency plan. According to the emergency plan, the overloaded line is managed, the load state and heat dissipation capacity of the conductor are analyzed, the fault risk is evaluated, and the load distribution is dynamically planned to ensure the safety and stability of the power grid operation.

[0038] 2. The present invention compares the risk coefficient of overloaded transmission lines with the gradient standard threshold, divides the risk level, and takes corresponding load management and maintenance measures to improve the reliability and efficiency of the power grid. When the risk coefficient is high, immediately reduce the load, strengthen monitoring and emergency maintenance; when the risk coefficient is medium, prevent through load balancing and regular inspections, and predict and warn in advance; when the risk coefficient is low, maintain regular monitoring and preventive maintenance, and regularly evaluate and update the risk level. It can effectively prevent and respond to the risk of overload of transmission lines and prevent major failures caused by vicious cycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 The present invention is a flow chart of the method.

[0041] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] For examples, see Figure 1 , Figure 2 As shown, the flow path layout planning system based on power grid power transmission described in this embodiment includes a current carrying capacity calculation module, a power load demand prediction module, an overload line determination module, a risk assessment module and a planning module;

[0044] Current carrying capacity calculation module: determine the conductor materials used for transmission lines in different regions, obtain the conductivity data of the conductor materials of each transmission line, collect the size parameters of the conductors of each transmission line, and calculate the conductor current carrying capacity based on the conductivity data of the conductor materials and the size parameters of the conductors;

[0045] Power load demand forecasting module: Based on the historical power load data and power demand data of power transmission lines in different regions, the power load demand of power transmission lines in each region is predicted through a time series model;

[0046] Overload line determination module: compares and analyzes the predicted power load demand with the calculated conductor current carrying capacity, determines whether the transmission line is overloaded based on the analysis results, and formulates an emergency plan for conductor overload;

[0047] Risk assessment module: manage overloaded transmission lines according to the conductor overload emergency plan, analyze the load state changes of the conductors of the overloaded transmission lines and the heat dissipation capacity of the conductors, and assess the risk of failure of the conductors of the overloaded transmission lines;

[0048] Planning module: Based on the evaluation results, the risk of transmission line failure is divided into high risk level, medium risk level and low risk level, and the load distribution of the transmission line is dynamically planned according to different risk levels.

[0049] Among them, in the current carrying capacity calculation module, the conductor materials used for transmission lines in different regions are determined, and the conductive performance data of the conductor materials of each transmission line are obtained, and the size parameters of the conductors of each transmission line are collected. The conductor current carrying capacity is calculated based on the conductive performance data of the conductor materials and the size parameters of the conductors, specifically:

[0050] Collect and confirm the types of conductor materials used for transmission lines in different regions. For example, the conductor material can be aluminum, copper, or aluminum alloy, etc. Understand what specific types of conductor materials are used for transmission lines in each region.

[0051] Collect the conductive properties data of each wire material, such as resistivity, conductivity, etc. This data can usually be obtained from the material supplier, technical manual or relevant standards. For example, the resistivity of copper is about , the resistivity of aluminum is about .

[0052] Obtain the size parameters of each transmission line conductor, such as the conductor diameter, cross-sectional area, length, etc. These parameters are usually obtained through design drawings, technical specifications or on-site measurements.

[0053] The current carrying capacity of the wire is calculated using the collected conductivity data and the wire size parameters. The current carrying capacity (current carrying capacity) refers to the maximum current that the wire can carry under safe working conditions. The calculation formula involves the wire's resistance, thermal conductivity, ambient temperature, heat dissipation conditions, etc.

[0054] The resistance R can be calculated from the resistivity ρ, length L and cross-sectional area A of the wire: ; The calculation expression of the conductor current carrying capacity is: ; Where Ie is the current carrying capacity of the conductor, ΔT is the allowable temperature rise of the conductor, and β is a coefficient related to the heat dissipation of the conductor.

[0055] Power load demand forecasting module: Based on the historical power load data and power demand data of power transmission lines in different regions, the power load demand of power transmission lines in each region is predicted through a time series model;

[0056] Commonly used time series models include: ARIMA (Autoregressive Integrated Moving Average model);

[0057] SARIMA (Seasonal Autoregressive Integrated Moving Average Model); LSTM (Long Short-Term Memory Neural Network), etc.

[0058] The power load demand of each regional transmission line is predicted by the autoregressive integrated moving average model, specifically:

[0059] Collect historical power load data of transmission lines in each region, and record them at time intervals such as hours, days, and months. Collect power demand data in each region, including factors that affect power load, such as population growth, seasonal changes, and economic activities.

[0060] Preprocess the data, including handling missing values ​​and outliers, to ensure the integrity and accuracy of the data. Use methods such as moving average to smooth the data and reduce noise.

[0061] The ARIMA model is selected for time series forecasting. The ARIMA model consists of three parameters: the autoregressive order p, which is the number of lagged terms in the autoregressive part of the model; the number of differences d, which is the number of differences required to make the time series stable; and the moving average order q, which is the number of lagged terms in the moving average part of the model.

[0062] Determine p and q through ACF (autocorrelation function) and PACF (partial autocorrelation function) plots;

[0063] The ARIMA model is fitted using historical power load data to make the time series data stable, and the trend and seasonality are eliminated through the difference operation. The difference expression is: ;in, is the actual load data at time t, is the predicted future load demand value, and the specific calculation expression is: , where c is a constant term, is the offset in the model, is the autoregressive coefficient, which is used to indicate the impact of load data at past time points on the current forecast value. It represents the influence of the load data at time t−1 on the predicted value at the current time, and the coefficient is ; They represent the impact of load data at time t-2 and time t-p on the current forecast value, and the coefficients are ; ec is the white noise term, that is, the error term, is the moving average coefficient, It represents the influence of the prediction error ec at time t−1 on the prediction value at the current time. The coefficient represents the impact of the prediction error at time t−2 and time t−p on the current prediction value, and the coefficients are , p is the autoregressive order of the ARIMA model, and q is the moving average order of the ARIMA model. The accuracy of the forecast results is verified, and the model parameters are adjusted in real time to improve the accuracy.

[0064] The moving average coefficient in the formula and The specific value of the autoregressive coefficient is generally determined by those skilled in the art according to the actual situation. The technicians collect multiple sets of sample data (multiple sets of actual load data and multiple sets of predicted load data) and set corresponding preset proportional coefficients for each set of sample data; substitute the preset proportional coefficients and the collected sample data into the formula, screen the calculated coefficients and take the average, and obtain and The value of .

[0065] In the invention, historical power load data and power demand data are collected and preprocessed, and the ARIMA model is selected and fitted to finally predict power load demand. The accuracy and reliability of the prediction results are ensured by verifying and adjusting the model parameters. It is necessary to use time series analysis methods and power system expertise in combination to provide scientific predictions of future load demand.

[0066] Overload line determination module: Compare and analyze the predicted power load demand with the calculated conductor current carrying capacity, determine whether the transmission line is overloaded based on the analysis results, and formulate an emergency plan for conductor overload.

[0067] Use the time series model to predict the power load demand in the future and obtain the predicted load data at each moment. Calculate the current carrying capacity of the wire based on the conductive performance data of the wire material and the size parameters of the wire.

[0068] The predicted power load demand is compared with the calculated conductor current carrying capacity: the predicted load yt′ is the predicted load demand value at the future time t, and the conductor current carrying capacity Iemax is the maximum current carrying capacity of the conductor.

[0069] If at time t, the forecast load If the load is greater than or equal to the conductor current carrying capacity Iemax, the transmission line is considered to be at risk of overload and an emergency plan for conductor overload is formulated; if at time t, the predicted load If it is less than the conductor current carrying capacity Iemax, it is considered that there is no overload risk for the transmission line and no adjustment is required;

[0070] If an overload is found, an emergency plan needs to be formulated to ensure the safe operation of the transmission line. The emergency plan specifically includes:

[0071] Load distribution adjustment: transfer part of the load to other lines to reduce the load on overloaded lines.

[0072] Temporary additional equipment: Add temporary substations or backup lines near overloaded lines to share the load.

[0073] Power restriction measures: In extreme cases, orderly power consumption will be implemented to restrict the power consumption of some users to ensure the stability of the overall power grid.

[0074] Real-time monitoring and alarm: Strengthen real-time monitoring of overloaded lines, set up an alarm system, and issue an alarm in time when the load approaches or exceeds the carrying capacity.

[0075] Rapid response team: Establish an emergency response team to be on call at all times to handle emergencies and ensure rapid response and handling in overload situations.

[0076] In the present invention, by predicting the power load demand and calculating the current carrying capacity of the conductor, the overload risk of the transmission line can be effectively evaluated. According to the analysis results, a detailed emergency plan is formulated to ensure that measures can be taken quickly in the event of an overload to ensure the safe and stable operation of the transmission system.

[0077] Risk assessment module: manage overloaded transmission lines according to the conductor overload emergency plan, analyze the load state changes of the conductors of the overloaded transmission lines and the heat dissipation capacity of the conductors, and assess the risk of failure of the overloaded transmission line conductors.

[0078] Managing overloaded transmission lines according to the conductor overload emergency plan means that when an overload occurs on a transmission line, it is handled systematically according to the pre-established emergency measures. This includes real-time monitoring of the load status and conductor temperature of the transmission line, identifying and predicting potential overload risks, and taking corresponding load adjustment, load transfer, temporary addition of equipment or power restriction measures to reduce the load pressure on the conductor and ensure the safe operation of the transmission line.

[0079] In addition, it is necessary to regularly inspect and maintain overloaded lines, analyze the heat dissipation capacity and load state changes of the conductors, evaluate the failure risk of the conductors under overload conditions, and develop further prevention and treatment plans. This series of management measures aims to minimize the impact of overload on the transmission system, prevent conductors from overheating, damage or failure, and ensure the reliability and stability of power supply.

[0080] According to the conductor overload emergency plan, the overloaded transmission line is managed, and the load fluctuation rate abnormality index is generated according to the load state change of the conductor of the overloaded transmission line. The method for obtaining the load fluctuation rate abnormality index is as follows:

[0081] Collect the load data Ps at each time interval and calculate the real-time load fluctuation rate. The specific calculation expression is: ; Where LFs is the load data at time s;

[0082] Convert the load fluctuation rate data into windowed time series data and set the window length to ; Where Xi is time series data;

[0083] Design and build an LSTM network, including input layer, LSTM layer, fully connected layer and output layer. Use the training data set to train the LSTM model and adjust the model parameters so that it can accurately predict the load fluctuation rate.

[0084] Input the test data into the trained LSTM model to obtain the predicted load fluctuation rate LFRs and calculate the prediction error: ; Where xs is the prediction error at the sth moment, the mean μs and standard deviation σs of the prediction error are calculated, the mean and standard deviation of the prediction error of the load fluctuation rate are analyzed, and the load fluctuation rate abnormality index is calculated. The specific calculation expression is: ; Where WD is the load fluctuation rate abnormality index.

[0085] The load fluctuation rate abnormality index reflects the degree to which the load fluctuation rate deviates from the normal value. A higher load fluctuation rate abnormality index indicates that the current load fluctuation is significantly abnormal and exceeds the range of historical fluctuations. Abnormal load fluctuations often indicate that the transmission line may be subjected to sudden high loads or rapidly changing loads. In this case, the conductors and related equipment may be in abnormal working conditions and face greater stress and heat loads.

[0086] When the load fluctuation rate abnormality index is high, the wire will experience frequent load changes and high current, which will accelerate the aging of the wire material and the degradation of the insulation layer. At the same time, frequent load fluctuations will cause rapid changes in wire temperature, increase thermal stress, and may cause uneven thermal expansion and contraction of the wire, further increasing mechanical stress. Under the combined effect of these factors, the mechanical strength and conductivity of the wire will decrease, and the risk of failure will increase significantly.

[0087] Given the high risk of failure corresponding to the abnormal index of high load fluctuation rate, the management and emergency plan of overloaded transmission lines are particularly important. Real-time monitoring and timely response measures, such as load adjustment, temperature monitoring and regular inspection, can effectively reduce the impact of load fluctuations on conductors. In addition, the formulation and implementation of scientific overload emergency plans ensure that measures can be taken quickly when abnormal fluctuations occur, reduce the probability of failures, and ensure the safe and stable operation of transmission lines.

[0088] According to the conductor overload emergency plan, the overloaded transmission line is managed, and the heat dissipation capacity of the conductor is analyzed to generate the thermal fatigue loss index of the conductor. The method for obtaining the thermal fatigue loss index is:

[0089] Real-time acquisition of temperature change data experienced by the conductor during operation, arranging the collected temperature data in chronological order to ensure the continuity and accuracy of the data, and calculating the thermal stress caused by temperature change: ; Where A is the thermal stress, α is the thermal expansion coefficient of the wire material, E is the Young's modulus of the wire material, and ΔQ is the temperature change, i.e., Qmax−Qmin;

[0090] Determine the fatigue life Nh corresponding to each stress level through the material's SN curve (stress-life curve); calculate the fatigue loss for each temperature cycle: ; Where Dh is the fatigue loss of the h-th temperature cycle, nh is the actual number of cycles of the h-th temperature cycle, and Nh is the fatigue life under the h-th temperature cycle. The thermal fatigue loss index is calculated. The specific calculation expression is: ; Where SX is the thermal fatigue loss index and f is the total number of temperature cycles.

[0091] The larger the thermal fatigue loss index, the more temperature cycles and stress changes the overloaded transmission line conductor has experienced during operation. Each temperature cycle causes the conductor material to expand and contract, generating thermal stress, which accumulates in the conductor material and gradually causes fatigue loss of the material. A higher fatigue loss index indicates that the conductor has been subjected to more thermal fatigue cycles, and the microstructure of the material may have changed, with microcracks or other damage.

[0092] As the thermal fatigue loss index increases, the mechanical strength and electrical conductivity of the wire will gradually decrease. Cracks and damage inside the material will gradually expand, which may eventually lead to the breakage of the wire or the destruction of the insulation layer. In addition, the accumulated thermal stress may also cause the deformation of the wire, increasing the risk of wire breakage. High loads and frequent temperature fluctuations will further aggravate this process and increase the possibility of failure.

[0093] Therefore, the greater the thermal fatigue loss index, the higher the risk of overloaded transmission line conductor failure. In order to avoid this situation, it is necessary to regularly inspect and maintain the conductors with high fatigue loss to promptly discover and repair potential fault points. At the same time, through reasonable load management and temperature control measures, the thermal fatigue loss of the conductors can be reduced, their service life can be extended, and the safe and stable operation of the transmission lines can be ensured.

[0094] The load fluctuation rate anomaly index and the thermal fatigue loss index are normalized, and the risk coefficient of overloaded transmission line conductor failure is calculated based on the normalized load fluctuation rate anomaly index and the thermal fatigue loss index.

[0095] For example, the present invention can use the following formula to calculate the risk coefficient of overloaded transmission line conductor failure, and the calculation expression is: ; In the formula, KW is the risk coefficient, WD is the load fluctuation rate abnormality index, SX is the thermal fatigue loss index, is the proportionality coefficient of the load fluctuation rate abnormality index and the thermal fatigue loss index, and .

[0096] Planning module: Based on the evaluation results, the risk of transmission line failure is divided into high risk level, medium risk level and low risk level, and the load distribution of the transmission line is dynamically planned according to different risk levels.

[0097] Compare the acquired risk coefficient of the overloaded transmission line conductor failure with the gradient standard threshold, the gradient standard threshold in the present application includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and compare the risk coefficient of the overloaded transmission line conductor failure with the first standard threshold and the second standard threshold respectively;

[0098] If the risk factor of overloaded transmission line conductor failure is greater than the second standard threshold, the risk of transmission line failure is classified as high risk; immediately transfer part of the load to other lines with lighter loads to reduce the load pressure on high-risk lines. Strengthen real-time monitoring of high-risk lines and pay close attention to their load changes and temperature conditions. Arrange emergency inspections and maintenance, and promptly deal with possible hidden dangers to ensure safe operation of the lines.

[0099] If the risk coefficient of overloaded transmission line conductor failure is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk of transmission line failure is classified as medium risk level; through load balancing measures, try to distribute the load to other lines of medium risk level to avoid overloading of a single line. Arrange regular inspections and maintenance to promptly discover and repair possible fault points to prevent risk escalation. Forecast and warn the probability of risk accidents in medium risk level transmission lines in advance, and take corresponding measures.

[0100] If the risk factor of overloaded transmission line conductor failure is less than the first standard threshold, the risk of transmission line failure is classified as low risk. Continue routine monitoring to ensure that the line operates within the safe load range. Perform preventive maintenance regularly to keep the line in good condition and extend its service life. Regularly reassess the risk of the line and update the risk level and corresponding response measures in a timely manner.

[0101] In the present invention, by comparing the risk coefficient of overloaded transmission line conductor failure with the gradient standard threshold, it is divided into high risk, medium risk and low risk levels, and corresponding load management and maintenance measures are taken: at the high risk level, immediately reduce the load, strengthen monitoring and emergency maintenance; at the medium risk level, prevent through load balancing and regular inspection, and predict and warn in advance; at the low risk level, maintain regular monitoring and preventive maintenance, and regularly evaluate and update the risk level. This method effectively prevents and responds to the risk of overload of transmission lines, improves the safety, stability and reliability of the power grid, and prolongs the service life of transmission lines.

[0102] When the risk of a transmission line failure is classified as a medium risk level, that is, the risk coefficient generated within a period of time is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk coefficients generated in a subsequent period of time that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, the mean and standard deviation of the data set are analyzed, and the probability of risk accidents occurring in medium risk level transmission lines is predicted and warned in advance.

[0103] If the mean value of the risk coefficient in the data set is greater than or equal to the reference threshold of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is less than the reference threshold of the standard deviation of the risk coefficient, the probability of a risk accident in the transmission line is high. At this time, a first-level warning signal is generated to monitor the line load and temperature in real time and pay close attention to risk changes. Arrange more frequent preventive maintenance to detect and deal with potential problems in a timely manner. When possible, reduce the load of the line as much as possible and distribute it to other low-load lines.

[0104] If the mean value of the risk coefficient is greater than or equal to the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is average, and a secondary warning signal is generated at this time; real-time monitoring of line load and temperature, dynamic adjustment of load distribution according to risk fluctuations, to prevent load shocks in a short period of time. Conduct a detailed fault cause analysis to find out the root cause of the risk fluctuation and deal with it.

[0105] If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is low. At this time, a third-level warning signal is generated, and regular monitoring of the line is maintained, paying attention to risk fluctuations. Measures to deal with risk fluctuations are formulated, such as taking preventive load adjustments when the risk coefficient suddenly increases. Regularly inspect and maintain the line to ensure that risk fluctuations do not cause actual failures.

[0106] If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is less than the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is extremely low. At this time, no warning signal is generated, and routine monitoring continues to ensure that the line operates within the safe load range. Regular preventive maintenance is carried out to ensure the safety and stability of the line.

[0107] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the first-level warning signal is greater than that of the third-level warning signal. Relevant personnel can carry out corresponding management according to the level of the warning signal.

[0108] In this embodiment, by determining the conductor materials used in the transmission lines in different regions and obtaining the conductive performance data of the conductor materials of each transmission line, the size parameters of the conductors of each transmission line are collected, and the current carrying capacity of the conductors is calculated according to the conductive performance data of the conductor materials and the size parameters of the conductors; based on the historical power load data and power demand data of the transmission lines in different regions, the power load demand of the transmission lines in each region is predicted through a time series model; the predicted power load demand is compared and analyzed with the calculated conductor current carrying capacity, and whether the transmission line is overloaded is determined according to the analysis results, and a conductor overload emergency plan is formulated; the transmission lines with overload conditions are managed according to the conductor overload emergency plan, and the load state changes of the conductors of the overloaded transmission lines and the heat dissipation capacity of the conductors are analyzed to evaluate the risk of failure of the conductors of the overloaded transmission lines; according to the evaluation results, the risk of failure of the transmission lines is divided into high risk level, medium risk level and low risk level, and the load distribution of the transmission lines is dynamically planned according to different risk levels. It can effectively prevent and respond to the risk of overload of transmission lines, ensure the safety and stability of power grid operation, and improve the reliability and efficiency of the power system.

[0109] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0110] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0111] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0115] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0116] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A flow path layout planning system based on power grid power transmission, characterized in that: It includes current carrying capacity calculation module, power load demand prediction module, overload line determination module, risk assessment module and planning module; Current carrying capacity calculation module: determine the conductor materials used for transmission lines in different regions, obtain the conductivity data of the conductor materials of each transmission line, collect the size parameters of the conductors of each transmission line, and calculate the conductor current carrying capacity based on the conductivity data of the conductor materials and the size parameters of the conductors; Power load demand forecasting module: Based on the historical power load data and power demand data of power transmission lines in different regions, the power load demand of power transmission lines in each region is predicted through a time series model; Overload line determination module: compares and analyzes the predicted power load demand with the calculated conductor current carrying capacity, determines whether the transmission line is overloaded based on the analysis results, and formulates an emergency plan for conductor overload; Risk assessment module: manage overloaded transmission lines according to the conductor overload emergency plan, analyze the load state changes of the conductors of the overloaded transmission lines and the heat dissipation capacity of the conductors, and assess the risk of failure of the conductors of the overloaded transmission lines; The risk assessment module specifically includes: The load fluctuation rate abnormality index is generated according to the load state change of the overloaded transmission line conductor. The method for obtaining the load fluctuation rate abnormality index is: collect the load data Ps at each time interval and calculate the real-time load fluctuation rate. The specific calculation expression is: ; In the formula, is the load data at time s; the load fluctuation rate data is converted into windowed time series data, and the window length is set to n: Xi=(LFi,LF ,…,LF ), where Xi is the time series data; design and construct the LSTM network, use the training data set to train the LSTM model, input the test data into the trained LSTM model, and obtain the predicted load fluctuation rate , calculate the prediction error: = ;in, is the prediction error at the sth moment, calculate the mean μs and standard deviation σs of the prediction error, analyze the mean and standard deviation of the prediction error of the load fluctuation rate, and calculate the load fluctuation rate abnormality index. The specific calculation expression is: ; In the formula, is the load fluctuation rate abnormality index; According to the conductor overload emergency plan, the overloaded transmission line is managed, and the heat dissipation capacity of the conductor is analyzed to generate the conductor's thermal fatigue loss index. The thermal fatigue loss index is obtained by real-time acquisition of the temperature change data experienced by the conductor during operation, arranging the collected temperature data in chronological order, and calculating the thermal stress caused by the temperature change: ;in, is the thermal stress, α is the thermal expansion coefficient of the conductor material, E is the Young's modulus of the conductor material, and ΔQ is the temperature change, that is, Qmax−Qmin; the fatigue life Nh corresponding to each stress level is determined by the SN curve of the material; the fatigue loss of each temperature cycle is calculated: Dh= ; Where Dh is the fatigue loss of the hth temperature cycle, For the The actual number of cycles for a temperature cycle, For the The fatigue life under a temperature cycle is calculated to calculate the thermal fatigue loss index. The specific calculation expression is: ;in, is the thermal fatigue loss index, is the total number of temperature cycles; Planning module: Based on the evaluation results, the risk of transmission line failure is divided into high risk level, medium risk level and low risk level, and the load distribution of the transmission line is dynamically planned according to different risk levels.

2. The flow path layout planning system based on power grid power transmission according to claim 1, characterized in that: The power load demand of each regional transmission line is predicted by the autoregressive integrated moving average model, specifically: The historical power load data and power demand data of the transmission lines in each region are collected, the data are preprocessed, and the ARIMA model is selected for time series forecasting. The ARIMA model consists of three parameters: the autoregressive order p, the number of lag terms in the autoregressive part of the model; The number of differences d is the number of differences required to make the time series stationary; The moving average order q is the number of lag terms in the moving average part of the model; Determine p and q through the autocorrelation function and partial autocorrelation function graph; The ARIMA model is fitted using historical power load data to make the time series data stable, and the trend and seasonality are eliminated through the difference operation. The difference expression is: in, is the actual load data at time t, is the predicted future load demand value, and the specific calculation expression is: Among them, c is a constant term, is the offset in the model, is the autoregressive coefficient, which is used to indicate the impact of load data at past time points on the current forecast value. It represents the influence of the load data at time t−1 on the predicted value at the current time, and the coefficient is ; They represent the impact of load data at time t-2 and time t-p on the current forecast value, and the coefficients are , ; is the white noise term, i.e. the error term, is the moving average coefficient, It represents the influence of the prediction error ec at time t−1 on the prediction value at the current time. The coefficient , represents the impact of the prediction error at time t−2 and time t−p on the current prediction value, and the coefficients are , , p is the autoregressive order of the ARIMA model, and q is the moving average order of the ARIMA model.

3. The flow path layout planning system based on power grid power transmission according to claim 2 is characterized in that: Compare the predicted electrical load demand with the calculated conductor current carrying capacity: Predicted Load is the predicted load demand value at the future time t, and the conductor current carrying capacity Iemax is the maximum current carrying capacity of the conductor; If at time t, the forecast load If the load is greater than or equal to the conductor current carrying capacity Iemax, the transmission line is considered to be at risk of overload and an emergency plan for conductor overload is formulated; if at time t, the predicted load If it is less than the conductor current carrying capacity Iemax, it is considered that there is no overload risk for the transmission line and no adjustment is required.

4. The flow path layout planning system based on power grid power transmission according to claim 1, characterized in that: The load fluctuation rate anomaly index and the thermal fatigue loss index are normalized, and the risk coefficient of overloaded transmission line conductor failure is calculated based on the normalized load fluctuation rate anomaly index and the thermal fatigue loss index.

5. The flow path layout planning system based on power grid power transmission according to claim 4 is characterized in that: The obtained risk coefficient of the overloaded transmission line conductor failure is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the risk coefficient of the overloaded transmission line conductor failure is compared with the first standard threshold and the second standard threshold respectively; If the risk factor of overloaded transmission line conductor failure is greater than the second standard threshold, the risk of transmission line failure is classified as high risk, and load reduction, enhanced monitoring and emergency maintenance are immediately carried out; If the risk factor of a failure of an overloaded transmission line conductor is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk of a failure of the transmission line is classified as a medium risk level, and prevention is carried out through load balancing and regular inspections, and the probability of risk accidents occurring in medium risk level transmission lines is predicted and warned in advance; If the risk factor of failure of an overloaded transmission line conductor is less than the first standard threshold, the risk of failure of the transmission line is classified as a low risk level, and routine monitoring and preventive maintenance are maintained, and the risk level is evaluated and updated regularly.

6. The flow path layout planning system based on power grid power transmission according to claim 5, characterized in that: When the risk of a transmission line failure is classified as a medium risk level, that is, the risk coefficient generated within a period of time is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk coefficients generated in a subsequent period of time that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, the mean and standard deviation of the data set are analyzed, and the probability of risk accidents occurring in medium risk level transmission lines is predicted and warned in advance.

7. The flow path layout planning system based on power grid power transmission according to claim 6 is characterized by: If the mean value of the risk coefficient in the data set is greater than or equal to the reference threshold value of the risk coefficient mean value, and the standard deviation of the risk coefficient is less than the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is high, and a first-level warning signal is generated at this time; If the mean value of the risk coefficient is greater than or equal to the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is average, and a secondary warning signal is generated at this time; If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is greater than or equal to the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is low, and a third-level warning signal is generated at this time; If the mean value of the risk coefficient is less than the reference threshold value of the mean value of the risk coefficient, and the standard deviation of the risk coefficient is less than the reference threshold value of the standard deviation of the risk coefficient, the probability of a risk accident occurring in the transmission line is extremely low, and no warning signal is generated at this time.

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