Current-carrying unbalance risk prediction method and device for same-direction parallel cables

Abstract

The invention relates to a current-carrying unbalance risk prediction method and device for cables connected in parallel in the same direction. The method comprises the following steps: acquiring historical current data of a circuit corresponding to an in-phase parallel cable, and identifying sending end data parameters of the in-phase parallel cable and receiving end data parameters of the in-phase parallel cable based on the historical current data; based on the sending end data parameters and the receiving end data parameters, constructing a gray scale prediction model of the same-direction parallel cables, and adjusting the gray scale prediction model through a model optimization strategy to obtain a target gray scale prediction model; and on the basis of the sending end data parameters and the receiving end data parameters, predicting the predicted current-carrying unbalance degree of the in-phase parallel cable through a target gray scale prediction model, and identifying the current-carrying unbalance degree risk information of the in-phase parallel cable on the basis of the predicted current-carrying unbalance degree. By adopting the method, the accuracy of analyzing the current-carrying unbalance degree risk of the same-direction parallel cables can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of parallel cables in the same direction, and in particular to a method and device for predicting the risk of current imbalance of parallel cables in the same direction. Background Art

[0002] With the development of power systems, transformer capacity is constantly changing and showing an increasing trend. In new substations, the incoming current of the main transformer is often very large. At this time, one cable cannot meet the circuit current requirements, and multiple cables are usually required to be used in parallel. But in practice, the same distribution is not uniform. There is often a current difference of more than tens of amperes between two cables in the same phase. As a result, the current carrying imbalance of the parallel cables in the same direction is high. Therefore, monitoring and early warning of unbalanced phase currents are crucial for the control and fault alarm rate of power grids, especially distribution networks.

[0003] The current detection and early warning method for unbalanced phase current is to target the influence mechanisms of voltage deviation and three-phase imbalance on cable core loss, and quantitatively analyze the cable line loss under the influence of voltage deviation and three-phase imbalance based on simulation data, so as to obtain unbalanced phase current data. However, this method is based on the historical cable laying data and the known cable load. Since the output current of the substation is not always balanced in actual operation, the accuracy of analyzing the current imbalance risk of parallel cables in the same direction is low. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for predicting the risk of current imbalance of parallel cables in the same direction in order to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting the risk of current imbalance of parallel cables in the same direction. The method comprises:

[0006] Acquire historical current data of the circuit corresponding to the same-phase parallel cables, and identify data parameters of the sending end of the same-direction parallel cables and data parameters of the receiving end of the same-direction parallel cables based on the historical current data;

[0007] Based on the sending-end data parameters and the receiving-end data parameters, a grayscale prediction model of the co-directional parallel cables is constructed, and the grayscale prediction model is adjusted through a model optimization strategy to obtain a target grayscale prediction model;

[0008] Based on the sending end data parameters and the receiving end data parameters, the predicted current imbalance of the in-phase parallel cables is predicted through the target grayscale prediction model, and based on the predicted current imbalance, the current imbalance risk information of the in-phase parallel cables is identified.

[0009] Optionally, the identifying, based on the historical current data, data parameters of the sending end of the co-directional parallel cables and data parameters of the receiving end of the co-directional parallel cables includes:

[0010] Acquire impedance information of the co-directional parallel cables, and construct a network equation of the co-directional parallel cables based on the impedance information;

[0011] The network equation is matrixed to obtain an impedance matrix of the co-directional parallel cables, and based on the historical current data, the data parameters of the sending end of the co-directional parallel cables and the data parameters of the receiving end of the co-directional parallel cables are calculated through the impedance matrix.

[0012] Optionally, constructing the grayscale prediction model of the co-directional parallel cables based on the sending-end data parameters and the receiving-end data parameters includes:

[0013] Arrange the sending end data parameters and the receiving end data parameters in chronological order to obtain a first data column of the sending end and a second data column of the receiving end;

[0014] Based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed through an exponential curve expression.

[0015] Optionally, adjusting the grayscale prediction model through a model optimization strategy to obtain a target grayscale prediction model includes:

[0016] The average relative residual value of the grayscale prediction model is calculated by a residual test strategy, and the average level ratio deviation value of the grayscale prediction model is calculated by a level ratio deviation test strategy;

[0017] When the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the co-directional parallel cable based on the first data column of the sending end and the second data column of the receiving end through an exponential curve expression, until the average relative residual value is less than the preset residual threshold and the average level ratio deviation value is less than the preset deviation threshold, and the grayscale prediction model obtained from the last iteration is used as the target grayscale prediction model.

[0018] Optionally, the predicting the predicted current unbalance of the in-phase parallel cables based on the sending-end data parameter and the receiving-end data parameter through the target grayscale prediction model includes:

[0019] Based on the sending-end data parameters and the receiving-end data parameters, predicting the prediction data of the sending-end and the prediction data of the receiving-end through the target grayscale prediction model;

[0020] Based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance degree of each phase of the in-phase parallel cables, the average imbalance degree of the in-direction parallel cables, and the imbalance degree of the receiving end of the in-phase parallel cables are calculated by an imbalance degree algorithm;

[0021] The phase unbalance, the average unbalance, and the receiving end unbalance are used as the predicted current-carrying unbalance of the in-phase parallel cables.

[0022] Optionally, the identifying the current imbalance risk information of the co-directional parallel cables based on the predicted current imbalance includes:

[0023] Obtaining setting values ​​of various types of imbalance of the parallel cables in the same direction, and determining a deviation index value corresponding to each setting value based on historical imbalance information of the parallel cables in the same phase;

[0024] Based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value;

[0025] The abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type are used as the current-carrying imbalance risk information of the parallel cables connected in the same direction.

[0026] In a second aspect, the present application also provides a device for predicting the risk of current imbalance of cables connected in parallel in the same direction. The device comprises:

[0027] An acquisition module, used to acquire historical current data of the circuit corresponding to the same-phase parallel cable, and identify the sending end data parameters of the same-direction parallel cable and the receiving end data parameters of the same-direction parallel cable based on the historical current data;

[0028] An adjustment module, for constructing a grayscale prediction model of the co-directional parallel cables based on the sending-end data parameters and the receiving-end data parameters, and adjusting the grayscale prediction model through a model optimization strategy to obtain a target grayscale prediction model;

[0029] A prediction module is used to predict the predicted current imbalance of the in-phase parallel cables based on the sending end data parameters and the receiving end data parameters through the target grayscale prediction model, and to identify the current imbalance risk information of the in-phase parallel cables based on the predicted current imbalance.

[0030] Optionally, the acquisition module is specifically used to:

[0031] Acquire impedance information of the co-directional parallel cables, and construct a network equation of the co-directional parallel cables based on the impedance information;

[0032] The network equation is matrixed to obtain an impedance matrix of the co-directional parallel cables, and based on the historical current data, the data parameters of the sending end of the co-directional parallel cables and the data parameters of the receiving end of the co-directional parallel cables are calculated through the impedance matrix.

[0033] Optionally, the adjustment module is specifically used to:

[0034] Arrange the sending end data parameters and the receiving end data parameters in chronological order to obtain a first data column of the sending end and a second data column of the receiving end;

[0035] Based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed through an exponential curve expression.

[0036] Optionally, the adjustment module is specifically used to:

[0037] The average relative residual value of the grayscale prediction model is calculated by a residual test strategy, and the average level ratio deviation value of the grayscale prediction model is calculated by a level ratio deviation test strategy;

[0038] When the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the co-directional parallel cable based on the first data column of the sending end and the second data column of the receiving end through an exponential curve expression, until the average relative residual value is less than the preset residual threshold and the average level ratio deviation value is less than the preset deviation threshold, and the grayscale prediction model obtained from the last iteration is used as the target grayscale prediction model.

[0039] Optionally, the prediction module is specifically used to:

[0040] Based on the sending-end data parameters and the receiving-end data parameters, predicting the prediction data of the sending-end and the prediction data of the receiving-end through the target grayscale prediction model;

[0041] Based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance degree of each phase of the in-phase parallel cables, the average imbalance degree of the in-direction parallel cables, and the imbalance degree of the receiving end of the in-phase parallel cables are calculated by an imbalance degree algorithm;

[0042] The phase unbalance, the average unbalance, and the receiving end unbalance are used as the predicted current-carrying unbalance of the in-phase parallel cables.

[0043] Optionally, the prediction module is specifically used to:

[0044] Obtaining setting values ​​of various types of imbalance of the parallel cables in the same direction, and determining a deviation index value corresponding to each setting value based on historical imbalance information of the parallel cables in the same phase;

[0045] Based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value;

[0046] The abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type are used as the current-carrying imbalance risk information of the parallel cables connected in the same direction.

[0047] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0049] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0050] The above-mentioned method and device for predicting the risk of current imbalance of the same-direction parallel cables obtain the historical current data of the circuit corresponding to the same-phase parallel cables, and identify the sending end data parameters of the same-direction parallel cables and the receiving end data parameters of the same-direction parallel cables based on the current data; construct a grayscale prediction model of the same-direction parallel cables based on the sending end data parameters and the receiving end data parameters, and adjust the grayscale prediction model through a model optimization strategy to obtain a target grayscale prediction model; predict the predicted current imbalance of the same-phase parallel cables through the target grayscale prediction model based on the sending end data parameters and the receiving end data parameters, and identify the current imbalance risk information of the same-direction parallel cables based on the predicted current imbalance. This scheme uses historical current data to identify the sending end data parameters and receiving end data parameters of the same-direction parallel cable, thereby constructing a grayscale prediction model for the same-direction parallel cable, and optimizing the grayscale prediction model to ensure the accuracy of the predicted current imbalance predicted by the prediction model. Then, through the predicted current imbalance of the same-phase parallel cable, the current imbalance risk information of the same-direction parallel cable is identified, avoiding the problem that the current carrying capacity difference between the phases caused by the imbalance of the sending and receiving end parameters requires the current carrying capacity of the cable to reduce the current load, thereby reducing the utilization rate of the power transmission of the cable. This scheme analyzes the current imbalance of the cable based on the imbalance of the sending and receiving end parameters, and can ensure the accuracy of the current imbalance risk information of the same-direction parallel cable, so that the current carrying capacity of each cable in the same phase reaches the maximum utilization value, thereby improving the current carrying capacity utilization rate of the cable, thereby comprehensively improving the accuracy of analyzing the current imbalance risk of the same-direction parallel cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flow chart of a method for predicting the risk of current imbalance of cables connected in parallel in the same direction in one embodiment;

[0052] Figure 2 A schematic diagram of a flow chart of a circuit model of in-phase parallel cables in one embodiment;

[0053] Figure 3 A schematic diagram of a flow chart of an example of current imbalance risk prediction for cables connected in parallel in the same direction in one embodiment;

[0054] Figure 4 It is a structural block diagram of a device for predicting the risk of current imbalance of cables connected in parallel in the same direction in one embodiment;

[0055] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The method for predicting the risk of current imbalance of parallel cables in the same direction provided in the embodiment of the present application can be applied to the application environment of the risk of current imbalance of parallel cables in the same direction. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. Among them, the terminal identifies the sending end data parameters and the receiving end data parameters of the parallel cables in the same direction through historical current data, thereby constructing a grayscale prediction model of the parallel cables in the same direction, and optimizing the grayscale prediction model to ensure the accuracy of the predicted current imbalance predicted by the prediction model, and then, through the predicted current imbalance of the parallel cables in the same phase, the current imbalance risk information of the parallel cables in the same direction is identified, avoiding the difference in the current carrying capacity between the phases caused by the imbalance of the sending end and the receiving end parameters, and requiring the current carrying capacity of the cable to reduce the current load, thereby reducing the utilization rate of the power transmission of the cable. This solution analyzes the current imbalance of the cable based on the imbalance of the sending and receiving end parameters. It can ensure the accuracy of the current imbalance risk information of the same-direction parallel cables, so that the current carrying capacity of each cable in the same phase reaches the maximum utilization value, thereby improving the current carrying capacity utilization rate of the cable, thereby comprehensively improving the accuracy of analyzing the current imbalance risk of the same-direction parallel cables.

[0058] In one embodiment, Figure 1 As shown, a method for predicting the risk of current imbalance of cables connected in parallel in the same direction is provided, and the method is described by taking the application of the method to a terminal as an example, and the method includes the following steps:

[0059] Step S101, obtaining historical current data of the circuit corresponding to the same-phase parallel cables, and identifying the sending-end data parameters of the same-direction parallel cables and the receiving-end data parameters of the same-direction parallel cables based on the historical current data.

[0060] In this embodiment, the terminal identifies the current data of each phase cable in the same-direction parallel cable circuit and the voltage data of each cable through the data collected in the historical period by the current detection device set in the same-direction parallel cable, and obtains the historical current data, wherein, Figure 2As shown in the figure, it is a circuit model of a parallel cable in the same direction, where a, b, and c are the corresponding phase identifiers of each of the three phases in the parallel cable in the same direction. Then, based on the historical current data, the terminal identifies the sending end data parameters of the parallel cable in the same direction and the receiving end data parameters of the parallel cable in the same direction. The sending end data parameters are the sending end voltage parameters, and the receiving end data parameters are the receiving end load information parameters. The specific identification process will be described in detail later.

[0061] Step S102, based on the sending end data parameters and the receiving end data parameters, a grayscale prediction model of the co-directional parallel cables is constructed, and the grayscale prediction model is adjusted through a model optimization strategy to obtain a target grayscale prediction model.

[0062] In this embodiment, the terminal constructs a grayscale prediction model of the co-directional parallel cable based on the data parameters of the sending end and the receiving end, and adjusts the grayscale prediction model through the model optimization strategy to obtain the target grayscale prediction model. The specific construction process will be described in detail later. Among them, the grayscale prediction model is a function model based on an exponential fitting curve.

[0063] Step S103, based on the sending end data parameters and the receiving end data parameters, the predicted current imbalance of the same-phase parallel cables is predicted through the target grayscale prediction model, and based on the predicted current imbalance, the current imbalance risk information of the same-direction parallel cables is identified.

[0064] In this embodiment, the terminal predicts the predicted current unbalance of the same-phase parallel cables based on the data parameters of the sending end and the receiving end through the target grayscale prediction model, and identifies the current unbalance risk information of the same-direction parallel cables based on the predicted current unbalance. The predicted current unbalance includes the unbalance of each phase, the average unbalance, and the unbalance of the receiving end. The unbalance of the receiving end is used to characterize the unbalance between the two currents of a certain phase compared to the other two phases. The specific identification process will be described in detail later.

[0065] Based on the above scheme, the sending end data parameters and the receiving end data parameters of the same-direction parallel cable are identified through historical current data, so as to construct a grayscale prediction model of the same-direction parallel cable, and optimize the grayscale prediction model to ensure the accuracy of the predicted current imbalance predicted by the prediction model. Then, the current imbalance risk information of the same-direction parallel cable is identified through the predicted current imbalance of the same-phase parallel cable, avoiding the difference in phase current carrying capacity caused by the imbalance of the sending and receiving end parameters, and requiring the current carrying capacity of the cable to reduce the current load, thereby reducing the utilization rate of the power transmission of the cable. This scheme analyzes the current imbalance of the cable based on the imbalance of the sending and receiving end parameters, and can ensure the accuracy of the current imbalance risk information of the same-direction parallel cable, so that the current carrying capacity of each cable in the same phase reaches the maximum utilization value, thereby improving the current carrying capacity utilization rate of the cable, thereby comprehensively improving the accuracy of analyzing the current imbalance risk of the same-direction parallel cable.

[0066] Optionally, based on historical current data, the data parameters of the sending end of the unidirectional parallel cables and the data parameters of the receiving end of the unidirectional parallel cables are identified, including: obtaining the impedance information of the unidirectional parallel cables, and constructing the network equation of the unidirectional parallel cables based on the impedance information; matrixing the network equation to obtain the impedance matrix of the unidirectional parallel cables, and calculating the data parameters of the sending end of the unidirectional parallel cables and the data parameters of the receiving end of the unidirectional parallel cables through the impedance matrix based on the historical current data.

[0067] In this embodiment, the terminal obtains the impedance information of the parallel cables in the same direction, and constructs the network equation of the parallel cables in the same direction based on the impedance information. Then, the terminal performs matrix processing on the network equation to obtain the impedance matrix of the parallel cables in the same direction, and calculates the data parameters of the sending end of the parallel cables in the same direction and the data parameters of the receiving end of the parallel cables in the same direction through the impedance matrix based on the historical current data.

[0068] Specifically, Figure 2 As shown, if only the series impedance of the cable is considered, the following network equation can be obtained:

[0069] E a =Z C1C1 I C1 +Z C1C2 I C2 +Z C1C3 I C3 +Z C1C4 I C4 +Z C1C5 I C5 +Z C1C6 I C6

[0070] +Z C1S1 IS1 +Z C1S2 I S2 +z C1S3 I S3 +Z C1S4 I S4 +Z C1S5 I S5 +Z C1S6 I S6 +Z a I C1 +U N

[0071] In the above formula, E a is the voltage at the sending end of cable core No. 1, U C1 and U' C1 are the voltage phasors of the sending and receiving ends respectively; I C1 , I C2 ,…,I C6 is the current of the 6-cable core, I S1 , I S2 ,…,I S6 is the corresponding sub-cable sheath current; Z C1C1 is the self-impedance of cable core No. 1; Z C1S2 is the mutual impedance between the No. 1 cable core and the No. 2 cable sheath, and so on. a A phase load, Z N is the neutral point resistance. Similarly, similar equations exist for the No. 1 cable sheath, as well as the cores and sheaths of the remaining sub-cables.

[0072] Expressed in matrix form, we can get a 12×12 impedance matrix, which can be divided into four 6×6 block matrices:

[0073]

[0074] In the above formula, By analogy, we can get ΔU S , Z CS , Z SC , Z SS , and I S The expression is:

[0075] U N =(I C1 +I C2 +I C3 +I C4 +I C5 +I C6 )Z N

[0076] The terminal can obtain the sending-end voltage and receiving-end load information parameters by measuring the current and neutral point voltage of the three-phase six-circuit line.

[0077] Based on the above scheme, the sending-end voltage and receiving-end load information parameters can be analyzed by acquiring historical current data, which improves the efficiency and accuracy of obtaining the sending-end voltage and receiving-end load information parameters.

[0078] Optionally, based on the data parameters of the sending end and the data parameters of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed, including: arranging the data parameters of the sending end and the data parameters of the receiving end in chronological order to obtain a first data column of the sending end and a second data column of the receiving end; based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed through an exponential curve expression.

[0079] In this embodiment, the terminal arranges the data parameters of the sending end and the data parameters of the receiving end in chronological order to obtain the first data column of the sending end and the second data column of the receiving end. Then, based on the first data column of the sending end and the second data column of the receiving end, the terminal constructs a grayscale prediction model of the co-directional parallel cables through an exponential curve expression.

[0080] Specifically, the terminal is based on the sending end data parameters and the receiving end data parameters, such as Figure 2 As shown, taking the voltage of phase A at the sending end as an example, its observed data on sequence k is recorded as x i (k), where k = 1, 2, 3, ..., n. It can be denoted as X i =(x i (1),x i (2),…,x i (n)). Add them together to get the new data column x (1) (t), use the expression of the exponential curve to approximate the sequence x (1) (t) Correspondingly, a first-order ordinary differential equation can be constructed to solve the functional expression of the fitting exponential curve:

[0081]

[0082] In order to eliminate the randomness of data, define:

[0083] z i =(z i (1),z i (2),…,z i (n))

[0084] in:

[0085] z i (m) = δx (1) (m)+(1-δ)x(1) (m-1)

[0086] The differential equation is changed to:

[0087] x (0) (t) = -az(t) + u

[0088] That is the grayscale prediction model.

[0089] Based on the above scheme, the first data column corresponding to the sending end data parameters and the second data column corresponding to the receiving end data parameters are fitted by fitting an exponential curve to construct a grayscale prediction model, thereby improving the accuracy and simplicity of the constructed grayscale prediction model.

[0090] Optionally, the grayscale prediction model is adjusted through a model optimization strategy to obtain a target grayscale prediction model, including: calculating the average relative residual value of the grayscale prediction model through a residual test strategy, and calculating the average level ratio deviation value of the grayscale prediction model through a level ratio deviation test strategy; when the average relative residual value is not less than a preset residual threshold, or the average level ratio deviation value is not less than a preset deviation threshold, returning to execute the step of constructing a grayscale prediction model of the same-direction parallel cable based on the first data column of the sending end and the second data column of the receiving end through an exponential curve expression, until the average relative residual value is less than the preset residual threshold, and the average level ratio deviation value is less than the preset deviation threshold, the grayscale prediction model obtained from the last iteration is used as the target grayscale prediction model.

[0091] In this embodiment, the terminal calculates the average relative residual value of the grayscale prediction model through a residual test strategy, and calculates the average level ratio deviation value of the grayscale prediction model through a level ratio deviation test strategy.

[0092] Specifically, the terminal residual test strategy needs to calculate

[0093] 1. Absolute residual

[0094] 2. Relative residual

[0095] 3. Average relative residual

[0096] when It is believed that the grayscale prediction model meets the residual requirements.

[0097] The level ratio deviation test strategy needs to calculate

[0098] Level ratio deviation

[0099] Average level deviation

[0100] When the average level ratio deviation It is believed that the grayscale prediction model meets the residual requirements.

[0101] Then, the terminal determines the size relationship between the average relative residual value and the preset residual threshold, and the size relationship between the average level ratio deviation value and the preset deviation threshold. When the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the same-direction parallel cable based on the first data column of the sending end and the second data column of the receiving end through the exponential curve expression, until the average relative residual value is less than the preset residual threshold, and the average level ratio deviation value is less than the preset deviation threshold, the grayscale prediction model obtained by the last iteration is used as the target grayscale prediction model.

[0102] Based on the above scheme, when the prediction effect of the model is not good, the grayscale prediction model is adjusted and the model prediction effect test is re-performed to ensure that the prediction effect of the target grayscale prediction model is better, thereby improving the prediction effect of the target grayscale prediction model.

[0103] Optionally, based on the data parameters of the sending end and the data parameters of the receiving end, the predicted current-carrying imbalance of the in-phase parallel cables is predicted through a target grayscale prediction model, including: based on the data parameters of the sending end and the data parameters of the receiving end, the predicted data of the sending end and the predicted data of the receiving end are predicted through a target grayscale prediction model; based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance algorithm is used to calculate the imbalance of each phase of the in-phase parallel cables, the average imbalance of the in-direction parallel cables, and the imbalance of the receiving end of the in-phase parallel cables; the imbalance of each phase, the average imbalance, and the imbalance of the receiving end are used as the predicted current-carrying imbalance of the in-phase parallel cables.

[0104] In this embodiment, the terminal predicts the prediction data of the sending end and the prediction data of the receiving end based on the data parameters of the sending end and the data parameters of the receiving end through the target grayscale prediction model.

[0105] Specifically, the terminal solves the target grayscale prediction model by the least squares method. The least squares method solves the problem by minimizing the sum of squares of the observed value and the estimated value of the dependent variable, and obtains the relevant parameters. Substituting the differential equation back to solve the problem, we can obtain:

[0106]

[0107] Based on solving the equation, the predicted value of the equation can be obtained. That is, the transmission voltage (i.e. the predicted data of the transmission end) and the head-end load prediction value (i.e. the predicted data of the receiving end).

[0108] Then, based on the predicted data of the sending end and the predicted data of the receiving end, the terminal calculates the imbalance of each phase of the in-phase parallel cable, the average imbalance of the in-direction parallel cable, and the imbalance of the receiving end of the in-phase parallel cable through the imbalance algorithm. Finally, the terminal uses the imbalance of each phase, the average imbalance, and the imbalance of the receiving end as the predicted current imbalance of the in-phase parallel cable.

[0109] Specifically, Figure 2 As shown, the terminal calculates the cable imbalance in the next time period based on the predicted data of the sending end and the predicted data of the receiving end. The calculation formula is as follows:

[0110]

[0111] In the above formula, the predicted value Predicted value By analogy, we can get ΔU S , Z CS , Z SC , Z SS , and I S The expression is:

[0112] U N =(I C1 +I C2 +I C3 +I C4 +I C5 +I C6 )Z N

[0113] Calculate the cable core current

[0114] Further calculate the unbalance of each phase and the average unbalance. Taking phase A as an example, the unbalance of the two phases of phase A is:

[0115] UF a =|I C1 / I C2 -1|

[0116] The in-phase imbalance is:

[0117]

[0118] When the receiving end is unbalanced, that is, when the currents of two phases in one phase are too large compared with the other two phases, an early warning is also required. Therefore, the unbalance degree of the receiving end is defined as:

[0119]

[0120] Based on the above scheme, the predicted current imbalance is determined by respectively calculating the imbalance of each phase, the average imbalance, and the receiving end imbalance, thereby improving the comprehensiveness of the determined predicted current imbalance.

[0121] Optionally, based on the predicted current imbalance, the current imbalance risk information of the same-direction parallel cables is identified, including: obtaining the setting values ​​of each imbalance type of the same-direction parallel cables, and determining the deviation index value corresponding to each setting value based on the historical imbalance information of the same-phase parallel cables; based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value; using the abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type as the current imbalance risk information of the same-direction parallel cables.

[0122] In this embodiment, the terminal obtains the setting value of each type of imbalance of the parallel cables in the same direction, and determines the deviation index value corresponding to each setting value based on the historical imbalance information of the parallel cables in the same phase. Then, the terminal identifies the imbalance of each type of imbalance based on the predicted cable imbalance, and screens the abnormal imbalance type based on the imbalance of each type of imbalance, the setting value of each type of imbalance, and the deviation index value corresponding to each setting value. Among them, the imbalance type includes phase imbalance type, average imbalance type, and receiving end imbalance type.

[0123] Among them, based on the historical imbalance information of the same-phase parallel cables, the process of determining the deviation index value corresponding to each set value is that the terminal identifies the corresponding current-carrying imbalance of each imbalance type in the historical imbalance information, identifies the normal current-carrying imbalance range of each imbalance type, and then, the terminal calculates the deviation index value corresponding to each set value through the set value of each imbalance type and the normal current-carrying imbalance range of each imbalance type.

[0124] Finally, the terminal uses the abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type as the current-carrying imbalance risk information of the co-directional parallel cables.

[0125] Based on the above scheme, by analyzing the current imbalance of different imbalance types of parallel cables in the same direction, the abnormal compensation balance type and the compensation balance corresponding to the abnormal compensation balance type are judged, thereby improving the accuracy and comprehensiveness of identifying the current imbalance risk information of parallel cables in the same direction.

[0126] This application also provides an example of risk prediction of current imbalance of cables connected in parallel in the same direction, such as Figure 3As shown, the specific processing process includes the following steps:

[0127] Step S301, obtaining historical current data of the circuit corresponding to the in-phase parallel cables.

[0128] Step S302, obtaining impedance information of the co-directional parallel cables, and constructing a network equation of the co-directional parallel cables based on the impedance information.

[0129] Step S303, matrixing the network equation to obtain the impedance matrix of the co-directional parallel cables, and calculating the sending end data parameters of the co-directional parallel cables and the receiving end data parameters of the co-directional parallel cables through the impedance matrix based on the historical current data.

[0130] Step S304, arranging the data parameters of the sending end and the data parameters of the receiving end in chronological order to obtain a first data column of the sending end and a second data column of the receiving end.

[0131] Step S305, based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the parallel cables in the same direction is constructed through an exponential curve expression.

[0132] Step S306, calculating the average relative residual value of the grayscale prediction model through the residual test strategy, and calculating the average level ratio deviation value of the grayscale prediction model through the level ratio deviation test strategy.

[0133] Step S307, when the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the same-direction parallel cable based on the first data column of the sending end and the second data column of the receiving end through the exponential curve expression, until the average relative residual value is less than the preset residual threshold, and the average level ratio deviation value is less than the preset deviation threshold, the grayscale prediction model obtained by the last iteration is used as the target grayscale prediction model.

[0134] Step S308, based on the data parameters of the sending end and the data parameters of the receiving end, the prediction data of the sending end and the prediction data of the receiving end are predicted through the target grayscale prediction model.

[0135] Step S309, based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance algorithm is used to calculate the imbalance of each phase of the in-phase parallel cables, the average imbalance of the in-direction parallel cables, and the imbalance of the receiving end of the in-phase parallel cables.

[0136] Step S310: taking the unbalance of each phase, the average unbalance, and the receiving end unbalance as the predicted current carrying unbalance of the same-phase parallel cables.

[0137] Step S311, obtaining setting values ​​of each type of imbalance of the same-direction parallel cables, and determining the deviation index value corresponding to each setting value based on the historical imbalance information of the same-phase parallel cables.

[0138] Step S312, based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value.

[0139] Step S313: taking the abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type as the current-carrying imbalance risk information of the parallel cables in the same direction.

[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a device for predicting the risk of current imbalance of parallel cables in the same direction for implementing the method for predicting the risk of current imbalance of parallel cables in the same direction. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for predicting the risk of current imbalance of parallel cables in the same direction provided below can refer to the limitations of the method for predicting the risk of current imbalance of parallel cables in the above text, and will not be repeated here.

[0142] In one embodiment, Figure 4 As shown, a device for predicting the risk of current imbalance of parallel cables in the same direction is provided, comprising: an acquisition module 410, an adjustment module 420 and a prediction module 430, wherein:

[0143] The acquisition module 410 is used to acquire the historical current data of the circuit corresponding to the same-phase parallel cable, and identify the sending end data parameters of the same-direction parallel cable and the receiving end data parameters of the same-direction parallel cable based on the historical current data;

[0144] An adjustment module 420 is used to construct a grayscale prediction model of the co-directional parallel cables based on the sending end data parameters and the receiving end data parameters, and adjust the grayscale prediction model through a model optimization strategy to obtain a target grayscale prediction model;

[0145] The prediction module 430 is used to predict the predicted current imbalance of the in-phase parallel cables based on the sending end data parameters and the receiving end data parameters through the target grayscale prediction model, and identify the current imbalance risk information of the in-phase parallel cables based on the predicted current imbalance.

[0146] Optionally, the acquisition module 410 is specifically configured to:

[0147] Acquire impedance information of the co-directional parallel cables, and construct a network equation of the co-directional parallel cables based on the impedance information;

[0148] The network equation is matrixed to obtain an impedance matrix of the co-directional parallel cables, and based on the historical current data, the data parameters of the sending end of the co-directional parallel cables and the data parameters of the receiving end of the co-directional parallel cables are calculated through the impedance matrix.

[0149] Optionally, the adjustment module 420 is specifically configured to:

[0150] Arrange the sending end data parameters and the receiving end data parameters in chronological order to obtain a first data column of the sending end and a second data column of the receiving end;

[0151] Based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed through an exponential curve expression.

[0152] Optionally, the adjustment module 420 is specifically configured to:

[0153] The average relative residual value of the grayscale prediction model is calculated by a residual test strategy, and the average level ratio deviation value of the grayscale prediction model is calculated by a level ratio deviation test strategy;

[0154] When the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the co-directional parallel cable based on the first data column of the sending end and the second data column of the receiving end through an exponential curve expression, until the average relative residual value is less than the preset residual threshold and the average level ratio deviation value is less than the preset deviation threshold, and the grayscale prediction model obtained from the last iteration is used as the target grayscale prediction model.

[0155] Optionally, the prediction module 430 is specifically used for:

[0156] Based on the sending-end data parameters and the receiving-end data parameters, predicting the prediction data of the sending-end and the prediction data of the receiving-end through the target grayscale prediction model;

[0157] Based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance degree of each phase of the in-phase parallel cables, the average imbalance degree of the in-direction parallel cables, and the imbalance degree of the receiving end of the in-phase parallel cables are calculated by an imbalance degree algorithm;

[0158] The phase unbalance, the average unbalance, and the receiving end unbalance are used as the predicted current-carrying unbalance of the in-phase parallel cables.

[0159] Optionally, the prediction module 430 is specifically used for:

[0160] Obtaining setting values ​​of various types of imbalance of the parallel cables in the same direction, and determining a deviation index value corresponding to each setting value based on historical imbalance information of the parallel cables in the same phase;

[0161] Based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value;

[0162] The abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type are used as the current-carrying imbalance risk information of the parallel cables connected in the same direction.

[0163] Each module in the above-mentioned current unbalance risk prediction device for the same-direction parallel cables can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0164] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for predicting the risk of current imbalance of parallel cables in the same direction is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0165] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0166] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0168] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting the risk of current imbalance of parallel cables in the same direction, characterized in that: The method comprises: Acquire historical current data of the circuit corresponding to the same-phase parallel cables, and identify data parameters of the sending end of the same-direction parallel cables and data parameters of the receiving end of the same-direction parallel cables based on the historical current data; Based on the sending-end data parameters and the receiving-end data parameters, a grayscale prediction model of the co-directional parallel cables is constructed, and the grayscale prediction model is adjusted through a model optimization strategy to obtain a target grayscale prediction model; Based on the sending end data parameters and the receiving end data parameters, the predicted current imbalance of the in-phase parallel cables is predicted through the target grayscale prediction model, and based on the predicted current imbalance, the current imbalance risk information of the in-phase parallel cables is identified.

2. The method according to claim 1, characterized in that The identifying, based on the historical current data, data parameters of the sending end of the parallel cables in the same direction and data parameters of the receiving end of the parallel cables in the same direction includes: Acquire impedance information of the co-directional parallel cables, and construct a network equation of the co-directional parallel cables based on the impedance information; The network equation is matrixed to obtain an impedance matrix of the co-directional parallel cables, and based on the historical current data, the data parameters of the sending end of the co-directional parallel cables and the data parameters of the receiving end of the co-directional parallel cables are calculated through the impedance matrix.

3. The method according to claim 1, characterized in that The grayscale prediction model of the co-directional parallel cables is constructed based on the sending end data parameters and the receiving end data parameters, including: Arrange the sending end data parameters and the receiving end data parameters in chronological order to obtain a first data column of the sending end and a second data column of the receiving end; Based on the first data column of the sending end and the second data column of the receiving end, a grayscale prediction model of the co-directional parallel cables is constructed through an exponential curve expression.

4. The method according to claim 3, characterized in that The grayscale prediction model is adjusted through a model optimization strategy to obtain a target grayscale prediction model, including: The average relative residual value of the grayscale prediction model is calculated by a residual test strategy, and the average level ratio deviation value of the grayscale prediction model is calculated by a level ratio deviation test strategy; When the average relative residual value is not less than the preset residual threshold, or the average level ratio deviation value is not less than the preset deviation threshold, return to execute the step of constructing the grayscale prediction model of the co-directional parallel cable based on the first data column of the sending end and the second data column of the receiving end through an exponential curve expression, until the average relative residual value is less than the preset residual threshold and the average level ratio deviation value is less than the preset deviation threshold, and the grayscale prediction model obtained from the last iteration is used as the target grayscale prediction model.

5. The method according to claim 1, characterized in that The predicting of the current unbalance of the in-phase parallel cables based on the sending-end data parameter and the receiving-end data parameter through the target grayscale prediction model includes: Based on the sending-end data parameters and the receiving-end data parameters, predicting the prediction data of the sending-end and the prediction data of the receiving-end through the target grayscale prediction model; Based on the predicted data of the sending end and the predicted data of the receiving end, the imbalance degree of each phase of the in-phase parallel cables, the average imbalance degree of the in-direction parallel cables, and the imbalance degree of the receiving end of the in-phase parallel cables are calculated by an imbalance degree algorithm; The phase unbalance, the average unbalance, and the receiving end unbalance are used as the predicted current-carrying unbalance of the in-phase parallel cables.

6. The method according to claim 1, characterized in that The step of identifying the risk information of the current imbalance of the cables connected in parallel in the same direction based on the predicted current imbalance includes: Obtaining setting values ​​of various types of imbalance of the parallel cables in the same direction, and determining a deviation index value corresponding to each setting value based on historical imbalance information of the parallel cables in the same phase; Based on the predicted cable imbalance, identifying the imbalance of each imbalance type, and screening abnormal imbalance types based on the imbalance of each imbalance type, the setting value of each imbalance type, and the deviation index value corresponding to each setting value; The abnormal imbalance type and the imbalance corresponding to the abnormal imbalance type are used as the current-carrying imbalance risk information of the parallel cables connected in the same direction.

7. A device for predicting the risk of current imbalance of cables connected in parallel in the same direction, characterized in that: The device comprises: An acquisition module, used to acquire historical current data of the circuit corresponding to the same-phase parallel cable, and identify the sending end data parameters of the same-direction parallel cable and the receiving end data parameters of the same-direction parallel cable based on the historical current data; An adjustment module, for constructing a grayscale prediction model of the co-directional parallel cables based on the sending-end data parameters and the receiving-end data parameters, and adjusting the grayscale prediction model through a model optimization strategy to obtain a target grayscale prediction model; A prediction module is used to predict the predicted current imbalance of the in-phase parallel cables based on the sending end data parameters and the receiving end data parameters through the target grayscale prediction model, and to identify the current imbalance risk information of the in-phase parallel cables based on the predicted current imbalance.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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