Power distribution network fault detection method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of power grid power, and discloses a power distribution network fault detection method and device, computer equipment and a storage medium, and the method comprises the steps: collecting the power grid data of each target node in a distributed power distribution network system in real time through a pre-deployed sensor, and carrying out the preprocessing of the power grid data, and obtaining the preprocessed power grid data; through a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, calculating to obtain a corresponding voltage fluctuation characteristic value; calculating a net power balance value of the distributed power distribution network system through a pre-constructed generalized steady-state interaction model according to the preprocessed power grid data of each target node and the voltage fluctuation characteristic value of each access node; judging whether the net power balance value is within a preset normal threshold range or not; if not, determining that the distributed power distribution network system has a fault; therefore, intelligent fault detection of the distributed power distribution network system is realized, and the accuracy and reliability of fault detection are improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a distribution network fault detection method, device, computer equipment and storage medium. Background Art

[0002] Driven by the sharp increase in global energy demand and the increasing popularity of renewable energy, the complexity and scale of modern distribution network systems continue to expand, prompting traditional power systems to accelerate their transformation towards intelligence and automation. In this context, fault detection technology, as a core element to ensure the reliable operation and stable supply of power systems, has attracted widespread attention in the industry.

[0003] Fault detection in power systems mainly relies on traditional means such as current protection and voltage protection devices. However, when faced with large-scale and complex fault scenarios, these traditional methods have limitations such as insufficient sensitivity and delayed response, making it difficult to meet the needs of modern power grids for efficient and accurate fault detection. Summary of the invention

[0004] Based on this, it is necessary to propose a distribution network fault detection method, device, computer equipment and storage medium to address the problems of inefficiency and delayed response of existing traditional distribution network fault detection methods.

[0005] A first aspect of the present invention provides a distribution network fault detection method, the method comprising:

[0006] The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals;

[0007] By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated;

[0008] The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0009] Determining whether the net power balance value is within a preset normal threshold range;

[0010] If not, it is determined that the distributed power distribution network system fails.

[0011] Furthermore, after the step of determining that a fault occurs in the distributed power distribution network system, the step further includes:

[0012] Inputting the voltage fluctuation signals of each of the load nodes into the voltage fluctuation characteristic model respectively, and outputting voltage fluctuation characteristic values ​​corresponding to each of the load nodes one by one;

[0013] Determine whether the voltage fluctuation characteristic value of each target node exceeds a preset voltage fluctuation threshold value;

[0014] If yes, the target node exceeding the preset voltage fluctuation threshold is regarded as a potential fault node;

[0015] Injecting a traveling wave signal into the potential fault node at the current measurement point by a traveling wave injection method;

[0016] Measuring the propagation speed of the traveling wave signal and the time difference between the sending and returning of the traveling wave signal;

[0017] Calculating the distance between the potential fault node and the current measurement point according to the propagation speed and the time difference;

[0018] The location of the potential fault point is determined according to the distance.

[0019] Furthermore, after the step of determining the location of the potential fault point according to the distance, the method further includes:

[0020] An operation report is generated, wherein the operation report includes the pre-processed power grid data, voltage fluctuation characteristic values, and the location of the potential fault point.

[0021] Furthermore, the step of calculating the voltage fluctuation characteristic value corresponding to each access node one by one according to the voltage fluctuation signal of each access node by using the pre-constructed voltage fluctuation characteristic model comprises:

[0022] Extracting selected voltage fluctuation signals within a preset frequency band from each of the voltage fluctuation signals;

[0023] Analyzing and extracting spectrum parameters of the selected voltage fluctuation signal, wherein the spectrum parameters include voltage peak position, voltage amplitude, voltage phase and corresponding time point;

[0024] Determining whether a change in at least one of the spectrum parameters exceeds a preset change threshold;

[0025] If so, the corresponding voltage fluctuation characteristic value is calculated according to the voltage amplitude of the selected voltage fluctuation signal by using a pre-constructed voltage fluctuation characteristic model.

[0026] Furthermore, the pre-built voltage fluctuation characteristic model is expressed as:

[0027]

[0028] Among them, V s,i (t) is the voltage amplitude of the voltage fluctuation signal of the ith access node at time point t, α i and β i They are the parameters for adjusting the response sensitivity, F(V s,i (t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t.

[0029] Furthermore, the step of calculating the net power balance value of the distributed distribution network system by using the pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node includes:

[0030] The actual output power A(t) of the access node at time point t is calculated by the output power calculation formula, and the output power calculation formula is:

[0031]

[0032] Among them, F(Vs,i(t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t, represents the natural attenuation of the output power of the i-th access node at time t, λ i is the attenuation coefficient of the i-th distributed generation access node, P s,i (t) is the output power of the i-th access node at time t, and m is the total number of access nodes;

[0033] The actual power consumption B(t) of the load node at time point t is calculated by the power consumption calculation formula, and the power consumption calculation formula is:

[0034]

[0035] Among them, I l,j (t) is the current value of the jth load node at time point t, k j is the current response sensitivity of the jth load node, is the hyperbolic tangent function in the nonlinear function, τ is the integral variable, V l,j (τ) is the voltage value of the load node in the time integral variable τ, dτ represents the small change of the integral variable, γ j is the hysteresis effect coefficient of the load node to the voltage fluctuation, P l,j (t) is the power consumption of the jth load node at time t, and n is the total number of load nodes;

[0036] The net power balance value P of the distributed distribution network system at time point t is calculated by using a pre-built generalized steady-state interaction model. s,l (t), expressed as:

[0037]

[0038] Among them, T is the entire time interval and dt is the time increment.

[0039] Further, in the step of determining whether the net power balance value is within a preset normal threshold range, the preset normal threshold range is greater than or equal to 0.

[0040] A second aspect of the present invention provides a distribution network fault detection device, the device comprising:

[0041] A power grid data acquisition module is used to collect power grid data of each target node in the distributed power distribution network system in real time through pre-deployed sensors, and pre-process the power grid data to obtain pre-processed power grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed power grid data includes voltage fluctuation signals and current fluctuation signals;

[0042] A voltage fluctuation characteristic calculation module, configured to calculate a voltage fluctuation characteristic value corresponding to each of the access nodes based on a voltage fluctuation signal of each of the access nodes using a pre-constructed voltage fluctuation characteristic model;

[0043] A net power balance value calculation module, used to calculate the net power balance value of the distributed distribution network system according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node through a pre-built generalized steady-state interaction model;

[0044] A first judging module, configured to judge whether the net power balance value is within a preset normal threshold range;

[0045] The fault determination module is used to determine that a fault occurs in the distributed distribution network system if the fault is not within a preset normal threshold range.

[0046] A third aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0047] The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals;

[0048] By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated;

[0049] The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0050] Determining whether the net power balance value is within a preset normal threshold range;

[0051] If not, it is determined that the distributed power distribution network system fails.

[0052] A fourth aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0053] The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals;

[0054] By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated;

[0055] The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0056] Determining whether the net power balance value is within a preset normal threshold range;

[0057] If not, it is determined that the distributed power distribution network system fails.

[0058] The present invention collects the grid data of each target node in the distributed distribution network system in real time through pre-deployed sensors, and pre-processes the grid data to obtain pre-processed grid data; then, through a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, the voltage fluctuation characteristic value corresponding to each access node is calculated; and through a pre-constructed generalized steady-state interaction model, according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node, the net power balance value of the distributed distribution network system is calculated; if the net power balance value is not within a preset normal threshold range, it is determined that the distributed distribution network system has a fault; thereby, intelligent detection of faults in the distributed distribution network system is realized, the accuracy and reliability of fault detection are improved, the dependence on manual intervention is reduced, and the detection efficiency is greatly improved; at the same time, through real-time monitoring and analysis of grid data, potential faults can be discovered and handled in a timely manner, effectively ensuring the safe and stable operation of the distribution network system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] in:

[0061] Figure 1 An application environment diagram of a distribution network fault detection method in an embodiment;

[0062] Figure 2 is a flow chart of a method for detecting a power distribution network fault in one embodiment;

[0063] Figure 3 is a structural block diagram of a distribution network fault detection device in one embodiment;

[0064] Figure 4 FIG. 4 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0066] Figure 1 FIG. 1 is an application environment diagram of a distribution network fault detection method in an embodiment. Figure 1 , the distribution network fault detection method is applied to the distribution network fault detection system. The distribution network fault detection system includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network, and the terminal 110 can be a desktop terminal or a mobile terminal, and the mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The server 120 can be implemented as an independent server or a server cluster composed of multiple servers. The terminal 110 is used to collect the power grid data of each target node in the distributed distribution network system, and the server 120 is used to analyze the power grid data according to the distribution network fault detection method.

[0067] like Figure 2 As shown, in one embodiment, a distribution network fault detection method is provided. The method can be applied to both a terminal and a server. This embodiment is illustrated by applying to a terminal. The distribution network fault detection method specifically includes the following steps:

[0068] S1: collecting grid data of each target node in the distributed distribution network system in real time through pre-deployed sensors, and pre-processing the grid data to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data include voltage fluctuation signals and current fluctuation signals;

[0069] S2: using a pre-built voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, calculating a voltage fluctuation characteristic value corresponding to each access node one by one;

[0070] S3: calculating the net power balance value of the distributed distribution network system through a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0071] S4: Determine whether the net power balance value is within a preset normal threshold range;

[0072] S5: If not, it is determined that the distributed power distribution network system fails.

[0073] In this embodiment, the distribution network fault refers to the physical state that causes the distribution equipment (components) to fail to work normally as required. Distribution network faults are generally divided into line faults, equipment faults, faults caused by natural disasters, and faults caused by human factors. In addition, distribution line faults are generally divided into three forms: single-phase grounding, phase-to-phase short circuit, and grounded phase-to-phase short circuit. Specifically, it includes phase-to-phase short circuit caused by foreign objects, phase-to-phase short circuit caused by bird damage, and phase-to-phase short circuit caused by lightning strike. These fault types have their own characteristics. For example, single-phase grounding faults are more difficult to find, while phase-to-phase short circuit faults may be caused by foreign objects, lightning strikes, or reduced equipment insulation.

[0074] In the above step S1, the distributed distribution network system includes multiple target nodes, wherein the target nodes are further divided into two categories: access nodes and load nodes. Access nodes are nodes in the distributed distribution network system that are connected to distributed power sources, thereby providing power to the distributed distribution network system. Load nodes are nodes in the power grid system that are connected to loads. Each power-consuming device can be regarded as a load node, and load nodes are used to consume electrical energy in the power grid system. High-frequency sensors are deployed at each access node and load node of the distributed distribution network system to collect power grid data, such as voltage, current, etc. in real time, and pre-process the power grid data to remove noise, fill in missing values, etc., so as to obtain pre-processed power grid data. High-frequency sensors can collect and process voltage fluctuation signals and current fluctuation signals in real time, ensuring that transient waveforms and harmonic interference with short-term, high-frequency characteristics are captured. Through high-frequency sampling, common fault types in the power grid system can be accurately detected, which is conducive to accurate judgment of fault types and severity, greatly improving the sensitivity and accuracy of fault detection.

[0075] In the above step S2, the voltage fluctuation signal of the access node of the distributed distribution network system is spectrally analyzed by using discrete Fourier transform, and the calculated voltage fluctuation characteristic value is used to intuitively understand the voltage change of the access node in the distributed distribution network system, and is used for judging the existence of faults in subsequent steps.

[0076] In the above steps S3-S5, the net power balance value is used to evaluate the overall operating status of the distributed distribution network system. When a fault occurs in the distributed distribution network system, the net power balance value can quickly reflect the existence of power imbalance, thereby being used to quickly identify a fault in the distribution network system.

[0077] Through the above steps, this embodiment realizes intelligent detection of distributed distribution network system faults, improves the accuracy and reliability of fault detection, reduces the reliance on manual intervention, and greatly improves detection efficiency. At the same time, through real-time monitoring and analysis of power grid data, potential faults can be discovered and handled in a timely manner, effectively ensuring the safe and stable operation of the distribution network system.

[0078] In a specific embodiment, after the step S5 of determining that a fault occurs in the distributed power distribution network system, the method further includes:

[0079] S6: inputting the voltage fluctuation signal of each load node into the voltage fluctuation characteristic model respectively, and outputting the voltage fluctuation characteristic value corresponding to each load node one by one;

[0080] S7: Determine whether the voltage fluctuation characteristic value of each target node exceeds a preset voltage fluctuation threshold;

[0081] S8: If yes, treating the target node that exceeds the preset voltage fluctuation threshold as a potential fault node;

[0082] S9: injecting a traveling wave signal into the potential fault node at the current measurement point by a traveling wave injection method;

[0083] S10: measuring the propagation speed of the traveling wave signal and the time difference between the sending and returning of the traveling wave signal;

[0084] S11: Calculate the distance between the potential fault node and the current measurement point according to the propagation speed and the time difference;

[0085] S12: Determine the location of the potential fault point according to the distance.

[0086] In this embodiment, in case of a fault in the distributed power distribution network system, the voltage fluctuations of all nodes are further checked to determine the fault location.

[0087] In the above step S6, the same method as step S2 is adopted, and the frequency spectrum analysis is performed on the voltage fluctuation signal of each load node of the distributed distribution network system by using discrete Fourier transform.

[0088] In the above steps S7-S8, the above preset voltage fluctuation threshold can be set by those skilled in the art based on the normal operation experience of the distribution network and the voltage fluctuation standard. By comparing the voltage fluctuation characteristics of each target node with the preset voltage fluctuation threshold, potential fault nodes are preliminarily screened out.

[0089] In the above steps S9-S12, the traveling wave injection method is a high-precision technology for fault location in the distribution network system. It uses the electromagnetic wave propagation characteristics in the power line to determine the location of the fault point by injecting a traveling wave signal after the fault occurs and measuring the propagation time and speed of the signal.

[0090] The traveling wave injection method is used to inject the traveling wave signal into the potential fault node at the current measurement point, measure the propagation speed and time difference of the traveling wave signal, calculate the distance between the potential fault point and the measurement point, and locate the position of the potential fault point. The specific formula is:

[0091] D=v·s

[0092] Where D is the distance between the potential fault point and the current measurement point, v is the propagation speed of the traveling wave signal, and s is the time difference of the traveling wave signal propagation.

[0093] According to the distance between the potential fault point and the current measurement point, combined with the topological structure of the distribution network system and the known distance information, the specific location of the potential fault point can be determined.

[0094] In a specific embodiment, after the step S12 of determining the location of the potential fault point according to the distance, the method further includes:

[0095] S13: Generate an operation report, wherein the operation report includes the pre-processed power grid data, voltage fluctuation characteristic values, and the location of the potential fault point.

[0096] In this embodiment, data is recorded and backed up regularly to generate an operation report. Specifically, after the potential fault point is located, all data in the detection process are recorded in a database. The recorded data includes pre-processed power grid data, voltage fluctuation characteristic values ​​and the location of the potential fault point. The data is backed up regularly, and a periodic operation report is generated based on the operation status of the distribution network, which helps to continuously monitor and improve the operation status of the power grid and improve the efficiency and reliability of power grid management.

[0097] In a specific embodiment, the step S2 of calculating the voltage fluctuation characteristic value corresponding to each access node one by one according to the voltage fluctuation signal of each access node by using the pre-constructed voltage fluctuation characteristic model includes:

[0098] S201, extracting selected voltage fluctuation signals within a preset frequency band from each of the voltage fluctuation signals;

[0099] S202, analyzing and extracting spectrum parameters of the selected voltage fluctuation signal, wherein the spectrum parameters include a voltage peak position, a voltage amplitude, a voltage phase, and a corresponding time point;

[0100] S203, determining whether a change in at least one of the spectrum parameters exceeds a preset change threshold;

[0101] S204: If yes, then using a pre-built voltage fluctuation characteristic model, according to the voltage amplitude of the selected voltage fluctuation signal, calculate the corresponding voltage fluctuation characteristic value.

[0102] In this embodiment, in the above steps S201-S202, the preset frequency range is set according to the actual situation of the distribution network and the characteristics of voltage fluctuations. Since the voltage fluctuations are abnormally concentrated in the low frequency band, specifically, especially around 50 Hz or 60 Hz, illustratively, in a specific implementation, the preset frequency range is a frequency range of 0 to 100 Hz. The extracted selected voltage fluctuation signal is subjected to spectrum analysis to extract its spectrum parameters.

[0103] In the above step S203, it is determined whether the change of one or more spectrum parameters exceeds the preset change threshold, for example, it is determined whether the offset of the voltage peak position exceeds the preset offset change threshold, it is determined whether the voltage phase change exceeds the preset phase change threshold, etc. The preset change threshold is set by those skilled in the art based on the normal operation experience of the distribution network and the allowable range of voltage fluctuations. By determining whether there is an obvious change in the spectrum (such as an obvious peak offset and / or an obvious phase change), the voltage disturbance is determined, and the voltage fluctuation signal that may be abnormal is preliminarily screened out.

[0104] In the above step S204, if the change of the spectrum parameter exceeds the preset change threshold, the corresponding voltage fluctuation characteristic value is further calculated according to the voltage amplitude of the selected voltage fluctuation signal through the pre-built voltage fluctuation characteristic model. The voltage fluctuation characteristic value is used to more intuitively represent the voltage fluctuation characteristics of the access node.

[0105] Specifically, the pre-built voltage fluctuation characteristic model is expressed as:

[0106]

[0107] Among them, V s,i (t) is the voltage amplitude of the voltage fluctuation signal of the ith access node at time point t, α i and β i They are the parameters for adjusting the response sensitivity, F(V s,i (t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the ith access node at time point t, and log is a logarithmic function.

[0108] When α i When the voltage increases, the distribution network system is more sensitive to voltage fluctuations, and the voltage change causes F(V s,i (t)) is suitable for detecting voltage fluctuations in sensitive equipment (such as electronic components). i When it increases, the amplitude change of the voltage fluctuation signal will be significantly amplified, and the distribution network system will be easier to judge faults when there is a large voltage fluctuation.

[0109] This embodiment uses discrete Fourier transform to perform spectrum analysis on voltage fluctuation signals of access nodes of the distributed distribution network system, extracts selected voltage fluctuation signals corresponding to main frequency components, and establishes a voltage fluctuation characteristic model, so as to accurately identify abnormal voltage fluctuations. By adjusting sensitivity parameters, the response to voltage fluctuations and fault judgment are more sensitive, which is suitable for voltage fluctuation detection of sensitive equipment and can significantly improve the accuracy of fault identification when there is a large voltage fluctuation, thereby enhancing the fault detection capability of the distributed distribution network system.

[0110] In a specific embodiment, the step S3 of calculating the net power balance value of the distributed distribution network system by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node includes:

[0111] S301. Calculate the actual output power A(t) of the access node at time point t using an output power calculation formula. The output power calculation formula is:

[0112]

[0113] Among them, F(V s,i (t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t, represents the natural attenuation of the output power of the ith access node at time t, λ i is the attenuation coefficient of the i-th distributed generation access node, P s,i (t) is the output power of the i-th access node at time t, and m is the total number of access nodes;

[0114] S302, calculating the actual power consumption B(t) of the load node at time point t by using a power consumption calculation formula, wherein the power consumption calculation formula is:

[0115]

[0116] Among them, I l,j (t) is the current value of the jth load node at time point t, k j is the current response sensitivity of the jth load node, is the hyperbolic tangent function in the nonlinear function, τ is the integral variable, V l,j (τ) is the voltage value of the load node in the time integral variable τ, dτ represents the small change of the integral variable, γ j is the hysteresis effect coefficient of the load node to the voltage fluctuation, P l,j (t) is the power consumption of the jth load node at time t, and n is the total number of load nodes;

[0117] S303, calculating the net power balance value P of the distributed distribution network system at time point t by using a pre-built generalized steady-state interaction model s,l (t), expressed as:

[0118]

[0119] Among them, T is the entire time interval and dt is the time increment.

[0120] In this embodiment, in the above step S301, in actual scenarios, the power output of distributed access nodes (including solar energy and wind power generation) is not constant, and their output power often decays over time and is affected by the natural environment. An exponential decay function is introduced to describe the decay of the power of distributed power access nodes over time, and the expression is:

[0121] in, represents the natural attenuation of the output power of the ith access node at time t, λ i is the attenuation coefficient of the i-th access node.

[0122] In the above step S302, the power consumption of the load node depends not only on the voltage but also on the current fluctuation. A nonlinear function is introduced to represent the relationship between the power consumption of the load node and the current fluctuation. The expression is:

[0123] Among them, I l,j (t) is the current value of the jth load node at time point t, k j is the current response sensitivity of the jth load node, which is obtained by statistical analysis of historical power grid data. It is the hyperbolic tangent function among nonlinear functions, which is used to describe how the power consumption of the load node changes with current fluctuations.

[0124] By introducing the exponential decay function, the natural decay characteristics of distributed power sources over time are accurately described, the adaptability and accuracy of the model are enhanced, and the output changes of distributed power sources are reflected in real time.

[0125] In some load nodes, power consumption is not only affected by the current voltage, but also by the cumulative effect of long-term voltage fluctuations. The cumulative effect of voltage fluctuations is described by an integral function, and the expression is:

[0126] Among them, τ is the integral variable, which is used to describe the voltage change of the load node from the initial time to the time point t, V l,j(τ) is the voltage value of the load node in the time integral variable τ. The integral term represents the cumulative effect from the initial time to the time point t. dτ represents the small change in the integral variable, which is used to accumulate the voltage fluctuation effect within the calculation time period. j It is the hysteresis effect coefficient of the load node to voltage fluctuations. It is used to describe the sensitivity of the load node to long-term voltage fluctuations. The hysteresis effect coefficient is obtained by experimental measurement at the actual load node.

[0127] The power consumption of load nodes is not only related to voltage fluctuations, but also closely related to current fluctuations. The relationship between the power consumption of load nodes and current fluctuations is described by nonlinear functions, and the nonlinear change characteristics of load consumption are captured by hyperbolic tangent function, so as to more accurately reflect the load response dynamics. Especially in complex power grid environments, the dynamic characteristics of loads are particularly important. The long-term cumulative effect of load nodes on voltage fluctuations is also considered. The integral function is used to describe the cumulative effect of load nodes on voltage fluctuations in the time dimension, which can effectively capture the impact of long-term voltage fluctuations on load power consumption, and quantify the sensitivity of loads to voltage fluctuations through the hysteresis effect coefficient, and comprehensively calculate the distributed power supply output and load consumption.

[0128] In the above step S303, by constructing a generalized steady-state interaction model and calculating the net power balance value of the distribution network system, an effective method for dynamically and real-time evaluation of the operating status of the distribution network is provided, and the power of the access node and the load node of the distributed distribution network system are calculated respectively, so that the power balance calculation is no longer limited to the evaluation under static conditions, but can reflect the dynamic changes of the distribution network in real time. The calculation formula of the generalized steady-state interaction model can calculate the net power balance value of the system in real time, which is crucial for the safe operation, fault detection and emergency response of the power grid, and also takes into account the actual situation that the power output of the distributed power source decays over time. It not only overcomes the limitation that the traditional steady-state model cannot reflect dynamic interaction, but also provides a more refined evaluation method for the operation of complex power grids. Through this method, the power supply and demand balance can be monitored in real time, and faults or abnormal conditions can be quickly identified, thereby improving the safety and stability of the power grid operation.

[0129] In a specific embodiment, in the step S4 of determining whether the net power balance value is within a preset normal threshold range, the preset normal threshold range is greater than or equal to 0.

[0130] In this embodiment, if P s,l (t)<0, it means that the distributed distribution network system has insufficient power supply, which includes at least one of the load exceeding the power supply capacity and partial power failure. s,l (t)≥0, it means that the distribution network system operates normally.

[0131] like Figure 3 As shown, in one embodiment, a distribution network fault detection device is provided. The device includes:

[0132] The power grid data acquisition module 10 is used to collect power grid data of each target node in the distributed power distribution network system in real time through pre-deployed sensors, and pre-process the power grid data to obtain pre-processed power grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed power grid data includes voltage fluctuation signals and current fluctuation signals;

[0133] A voltage fluctuation characteristic calculation module 20, configured to calculate a voltage fluctuation characteristic value corresponding to each of the access nodes according to the voltage fluctuation signal of each of the access nodes by using a pre-constructed voltage fluctuation characteristic model;

[0134] A net power balance value calculation module 30 is used to calculate the net power balance value of the distributed distribution network system according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node through a pre-constructed generalized steady-state interaction model;

[0135] A first judging module 40 is used to judge whether the net power balance value is within a preset normal threshold range;

[0136] The fault determination module 50 is used to determine that a fault occurs in the distributed distribution network system if the fault is not within a preset normal threshold range.

[0137] In a specific embodiment, the distribution network fault detection device further includes:

[0138] A load voltage fluctuation characteristic calculation module, used to input the voltage fluctuation signal of each load node into the voltage fluctuation characteristic model, and output a voltage fluctuation characteristic value corresponding to each load node;

[0139] A second judgment module is used to judge whether the voltage fluctuation characteristic value of each target node exceeds a preset voltage fluctuation threshold;

[0140] A potential fault node determination module, used for, if a preset voltage fluctuation threshold is exceeded, treating a target node exceeding the preset voltage fluctuation threshold as a potential fault node;

[0141] A traveling wave signal injection module, used for injecting a traveling wave signal into the potential fault node at a current measurement point by a traveling wave injection method;

[0142] A measuring module, used to measure the propagation speed of the traveling wave signal and the time difference between the sending and returning of the traveling wave signal;

[0143] A calculation module, configured to calculate the distance between the potential fault node and the current measurement point according to the propagation speed and the time difference;

[0144] A location determination module is used to determine the location of the potential fault point according to the distance.

[0145] In a specific embodiment, the distribution network fault detection device further includes:

[0146] The report generation module is used to generate an operation report, wherein the operation report includes the pre-processed power grid data, voltage fluctuation characteristic values, and the location of the potential fault point.

[0147] In a specific embodiment, the voltage fluctuation characteristic calculation module 20 includes:

[0148] A selected frequency band unit, used to extract selected voltage fluctuation signals within a preset frequency band from each of the voltage fluctuation signals;

[0149] A parameter extraction unit, used for analyzing and extracting spectrum parameters of the selected voltage fluctuation signal, wherein the spectrum parameters include a voltage peak position, a voltage amplitude, a voltage phase and a corresponding time point;

[0150] A first judging unit, configured to judge whether a change in at least one of the spectrum parameters exceeds a preset change threshold;

[0151] The first calculation unit is used to calculate the corresponding voltage fluctuation characteristic value according to the voltage amplitude of the selected voltage fluctuation signal through a pre-constructed voltage fluctuation characteristic model if the voltage exceeds a preset change threshold.

[0152] In a specific embodiment, the pre-built voltage fluctuation characteristic model is expressed as:

[0153]

[0154] Among them, V s,i (t) is the voltage amplitude of the voltage fluctuation signal of the ith access node at time point t, α i and β i They are the parameters for adjusting the response sensitivity, F(V s,i (t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t.

[0155] In a specific embodiment, the net power balance value calculation module 30 includes:

[0156] The second calculation unit is used to calculate the actual output power A(t) of the access node at time point t by using an output power calculation formula, wherein the output power calculation formula is:

[0157]

[0158] Among them, F(Vs,i(t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t, represents the natural attenuation of the output power of the i-th access node at time t, λ i is the attenuation coefficient of the i-th distributed generation access node, P s,i (t) is the output power of the i-th access node at time t, and m is the total number of access nodes;

[0159] The third calculation unit is used to calculate the actual power consumption B(t) of the load node at time point t by using a power consumption calculation formula, and the power consumption calculation formula is:

[0160]

[0161] Among them, I l,j (t) is the current value of the jth load node at time point t, k j is the current response sensitivity of the jth load node, is the hyperbolic tangent function in the nonlinear function, τ is the integral variable, V l,j (τ) is the voltage value of the load node in the time integral variable τ, dτ represents the small change of the integral variable, γ j is the hysteresis effect coefficient of the load node to the voltage fluctuation, P l,j (t) is the power consumption of the jth load node at time t, and n is the total number of load nodes;

[0162] The fourth calculation unit is used to calculate the net power balance value P of the distributed distribution network system at time point t through a pre-built generalized steady-state interaction model. s,l (t), expressed as:

[0163]

[0164] Among them, T is the entire time interval and dt is the time increment.

[0165] In a specific embodiment, the preset normal threshold range in the first judgment module 40 is greater than or equal to 0.

[0166] This embodiment realizes intelligent detection of faults in the distributed distribution network system, improves the accuracy and reliability of fault detection, reduces the reliance on manual intervention, and greatly improves detection efficiency. At the same time, through real-time monitoring and analysis of power grid data, potential faults can be discovered and handled in a timely manner, effectively ensuring the safe and stable operation of the distribution network system.

[0167] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in an embodiment. The computer device may be a terminal or a server. Figure 4 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement the distribution network fault detection method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement the distribution network fault detection method. Those skilled in the art will understand that Figure 4 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.

[0168] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0169] The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals;

[0170] By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated;

[0171] The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0172] Determining whether the net power balance value is within a preset normal threshold range;

[0173] If not, it is determined that the distributed power distribution network system fails.

[0174] This embodiment realizes intelligent detection of faults in the distributed distribution network system, improves the accuracy and reliability of fault detection, reduces the reliance on manual intervention, and greatly improves detection efficiency. At the same time, through real-time monitoring and analysis of power grid data, potential faults can be discovered and handled in a timely manner, effectively ensuring the safe and stable operation of the distribution network system.

[0175] In one embodiment, a computer-readable storage medium is provided, storing a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0176] The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals;

[0177] By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated;

[0178] The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node;

[0179] Determining whether the net power balance value is within a preset normal threshold range;

[0180] If not, it is determined that the distributed power distribution network system fails.

[0181] This embodiment realizes intelligent detection of faults in the distributed distribution network system, improves the accuracy and reliability of fault detection, reduces the reliance on manual intervention, and greatly improves detection efficiency. At the same time, through real-time monitoring and analysis of power grid data, potential faults can be discovered and handled in a timely manner, effectively ensuring the safe and stable operation of the distribution network system.

[0182] 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 program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0183] 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.

[0184] The above-mentioned 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 distribution network fault detection method, characterized in that: The method comprises: The grid data of each target node in the distributed power distribution network system is collected in real time by using pre-deployed sensors, and the grid data is pre-processed to obtain pre-processed grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed grid data includes voltage fluctuation signals and current fluctuation signals; By using a pre-constructed voltage fluctuation characteristic model, according to the voltage fluctuation signal of each access node, a voltage fluctuation characteristic value corresponding to each access node is calculated; The net power balance value of the distributed distribution network system is calculated by using a pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node; Determining whether the net power balance value is within a preset normal threshold range; If not, it is determined that the distributed power distribution network system fails.

2. The method for detecting distribution network faults according to claim 1, characterized in that: After the step of determining that a fault occurs in the distributed power distribution network system, the method further includes: Inputting the voltage fluctuation signals of each of the load nodes into the voltage fluctuation characteristic model respectively, and outputting voltage fluctuation characteristic values ​​corresponding to each of the load nodes one by one; Determine whether the voltage fluctuation characteristic value of each target node exceeds a preset voltage fluctuation threshold value; If yes, the target node exceeding the preset voltage fluctuation threshold is regarded as a potential fault node; Injecting a traveling wave signal into the potential fault node at the current measurement point by a traveling wave injection method; Measuring the propagation speed of the traveling wave signal and the time difference between the sending and returning of the traveling wave signal; Calculating the distance between the potential fault node and the current measurement point according to the propagation speed and the time difference; The location of the potential fault point is determined according to the distance.

3. The method for detecting distribution network faults according to claim 2, characterized in that: After the step of determining the location of the potential fault point according to the distance, the method further includes: An operation report is generated, wherein the operation report includes the pre-processed power grid data, voltage fluctuation characteristic values, and the location of the potential fault point.

4. The method for detecting a distribution network fault according to claim 1, characterized in that: The step of calculating the voltage fluctuation characteristic value corresponding to each access node one by one according to the voltage fluctuation signal of each access node by using the pre-constructed voltage fluctuation characteristic model comprises: Extracting selected voltage fluctuation signals within a preset frequency band from each of the voltage fluctuation signals; Analyzing and extracting spectrum parameters of the selected voltage fluctuation signal, wherein the spectrum parameters include voltage peak position, voltage amplitude, voltage phase and corresponding time point; Determining whether a change in at least one of the spectrum parameters exceeds a preset change threshold; If so, the corresponding voltage fluctuation characteristic value is calculated according to the voltage amplitude of the selected voltage fluctuation signal by using a pre-constructed voltage fluctuation characteristic model.

5. The method for detecting distribution network faults according to claim 4, characterized in that: The pre-built voltage fluctuation characteristic model is expressed as: Among them, V s,i (t) is the voltage amplitude of the voltage fluctuation signal of the ith access node at time point t, α i and β i They are the parameters for adjusting the response sensitivity, F(V s,i (t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t.

6. The method for detecting distribution network faults according to claim 1, characterized in that: The step of calculating the net power balance value of the distributed distribution network system by using the pre-constructed generalized steady-state interaction model according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node comprises: The actual output power A(t) of the access node at time point t is calculated by the output power calculation formula, and the output power calculation formula is: Among them, F(Vs,i(t)) represents the voltage fluctuation characteristic value of the voltage fluctuation signal of the i-th access node at time point t, represents the natural attenuation of the output power of the i-th access node at time t, λ i is the attenuation coefficient of the i-th distributed generation access node, P s,i (t) is the output power of the i-th access node at time t, and m is the total number of access nodes; The actual power consumption B(t) of the load node at time point t is calculated by the power consumption calculation formula, and the power consumption calculation formula is: Among them, I l,j (t) is the current value of the jth load node at time point t, k j is the current response sensitivity of the jth load node, is the hyperbolic tangent function in the nonlinear function, τ is the integral variable, V l,j (τ) is the voltage value of the load node in the time integral variable τ, dτ represents the small change of the integral variable, γ j is the hysteresis effect coefficient of the load node to the voltage fluctuation, P l,j (t) is the power consumption of the jth load node at time t, and n is the total number of load nodes; The net power balance value P of the distributed distribution network system at time point t is calculated by using a pre-built generalized steady-state interaction model. s,l (t), expressed as: Among them, T is the entire time interval and dt is the time increment.

7. The method for detecting distribution network faults according to claim 6, characterized in that: In the step of determining whether the net power balance value is within a preset normal threshold range, the preset normal threshold range is greater than or equal to 0.

8. A distribution network fault detection device, characterized in that: The device comprises: A power grid data acquisition module is used to collect power grid data of each target node in the distributed power distribution network system in real time through pre-deployed sensors, and pre-process the power grid data to obtain pre-processed power grid data; the target nodes include multiple access nodes and multiple load nodes; the pre-processed power grid data includes voltage fluctuation signals and current fluctuation signals; A voltage fluctuation characteristic calculation module, configured to calculate a voltage fluctuation characteristic value corresponding to each of the access nodes based on a voltage fluctuation signal of each of the access nodes using a pre-constructed voltage fluctuation characteristic model; A net power balance value calculation module, used to calculate the net power balance value of the distributed distribution network system according to the pre-processed grid data of each target node and the voltage fluctuation characteristic value of each access node through a pre-built generalized steady-state interaction model; A first judging module, configured to judge whether the net power balance value is within a preset normal threshold range; The fault determination module is used to determine that a fault occurs in the distributed distribution network system if the fault is not within a preset normal threshold range.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes the steps of the distribution network fault detection method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distribution network fault detection method according to any one of claims 1 to 7.

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