Intelligent power grid real-time fault positioning system based on electric power big data

Abstract

The invention discloses an intelligent power grid real-time fault positioning system based on electric power big data, and relates to the technical field of electric power. The system comprises a power grid data acquisition unit used for acquiring current data and voltage data of each node in a power grid in real time; the power grid model construction unit is used for constructing a directed graph of the power grid based on the nodes of the power grid and the connection relationship among the nodes; the first fault positioning unit is used for judging whether a fault exists between the two nodes or not; and the second fault positioning unit is used for establishing a fault positioning equation based on the voltage wave equation, the transmission function of the frequency response and the load flow calculation of the two nodes, and adding the coordinate position of the fault prediction point and the correction value of the fault position to obtain the position of the actual fault under the condition that the first fault positioning unit judges that the fault occurs. The method can improve the accuracy and efficiency of fault positioning, achieves the fault prediction and correction, and improves the operation safety and reliability of a power system.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a smart grid real-time fault location system based on electric power big data. Background Art

[0002] The power system is an indispensable infrastructure in modern society, and its stable operation is crucial to the normal operation of all industries. However, power system failures occur from time to time, such as short circuits and grounding faults, which may cause power outages, equipment damage, and even safety accidents. Therefore, quickly and accurately locating and solving power system failures has become one of the important issues that the power industry needs to solve.

[0003] Traditional power system fault location methods mainly rely on manual inspections and single sensor detection, which have the following problems: Reliance on manual labor: Traditional methods usually require personnel to conduct on-site inspections and troubleshoot, which is time-consuming and labor-intensive, and is easily affected by environmental and human factors, resulting in inaccurate fault location. Single sensor limitation: Traditional methods can usually only collect limited data through a single sensor, which makes it difficult to fully and accurately reflect the operating status of the power system, thus limiting the accuracy and reliability of fault location.

[0004] With the development of big data and artificial intelligence technologies, some smart grid technologies based on big data have emerged, trying to solve the problems of traditional methods: Data collection and analysis: Smart grid technology deploys a large number of sensors and data acquisition equipment to monitor and collect current, voltage and other data at each node of the power system in real time, and uses big data analysis technology to achieve real-time monitoring and analysis of the operating status of the power system. Intelligent fault diagnosis: Based on big data analysis and artificial intelligence algorithms, smart grid technology can quickly diagnose and locate faults in the power system, improving the accuracy and efficiency of fault location.

[0005] Although smart grid technology has brought certain improvements, it still faces the following challenges: Data quality: The amount of data in the power system is huge, but it also faces problems such as low data quality and noise interference. How to effectively process and use big data has become a difficult problem that smart grid technology needs to solve. Fault location accuracy: Although smart grid technology can achieve real-time monitoring and analysis of the power system, in a complex power system environment, the accuracy and precision of fault location still have certain challenges, especially for the diagnosis and location of some hidden faults. Summary of the invention

[0006] In view of this, the present invention provides a smart grid real-time fault location system based on power big data.

[0007] The technical solution adopted by the present invention is as follows:

[0008] A real-time fault location system for a smart grid based on electric power big data, the system comprising: a grid data acquisition unit, used for real-time acquisition of current data and voltage data of each node in the grid; a grid model construction unit, used for constructing a directed graph of the grid based on each node of the grid and the connection relationship between each node, in which a voltage fluctuation equation between any two nodes is established based on the current data and voltage data of each node acquired in real time, and a frequency response transmission function between any two nodes is calculated; a first fault location unit, used for judging whether there is a fault between the two nodes based on the frequency response transmission function and the voltage fluctuation equation of any two nodes; a second fault location unit, used for establishing a fault location equation based on the voltage fluctuation equation, the frequency response transmission function and the power flow calculation of the two nodes, and when the first fault location unit determines that a fault occurs, the midpoint position of the two nodes is defined as the fault prediction point, and a correction value of the fault position is calculated based on the fault location equation, and the coordinate position of the fault prediction point and the correction value of the fault position are added to obtain the actual fault position.

[0009] Furthermore, after the power grid data acquisition unit acquires the current data and voltage data of each node in the power grid in real time, it performs data preprocessing on the acquired current data and voltage data, specifically including: performing data filtering, data interpolation, and data alignment processing on the acquired current data and voltage data in turn.

[0010] Furthermore, the data interpolation process specifically includes: the specific process of data filtering includes: passing the current data and voltage data through a high-pass filter; detecting missing data in the current data and voltage data, and filling the missing data by the mean interpolation method; the data alignment process specifically includes: temporally aligning the current data and voltage data collected from different nodes.

[0011] Furthermore, each node in the power grid is connected by a power transmission line. In a directed graph, the power transmission line is the edge of the directed graph, describing the connection relationship between each node. The voltage fluctuation equation between two nodes is expressed by the following formula:

[0012]

[0013] in, represents the influence of thermal effect on voltage fluctuation, where T(x, t) is the temperature at position x at time t, α is the thermal effect coefficient, which describes the influence of temperature change on voltage; β·|V(x, t)| 2V(x, t) represents the norm correction term, where |V(x, t)| is the norm of the voltage, β is the correction factor used to correct the amplitude effect of the voltage; V(x, t) represents the voltage at position x at time t; L is the inductance per unit length; C is the capacitance per unit length; R is the resistance per unit length; α is the thermal effect coefficient, which describes the effect of temperature change on the voltage; β is the correction factor.

[0014] Furthermore, the frequency response transfer function H(s) is expressed using the following formula:

[0015]

[0016] Among them, K is the transfer function gain; T1 is the time constant of the transfer function; T2 is the damping time constant of the transfer function; A is the thermal effect correction gain; ω0 is the thermal effect correction natural frequency; B is the paradigm correction gain; s is the internal parameter of the transfer function; V(x, s) is the frequency domain representation of the voltage data; I(x, s) is the frequency domain representation of the current data.

[0017] Furthermore, the first fault location unit determines whether there is a fault between two nodes based on the transmission function of the frequency response of any two nodes and the voltage fluctuation equation. The method specifically includes: calculating the pole position of the transmission function, which corresponds to the resonant frequency; converting the voltage fluctuation equation from the time domain to the frequency domain by Fourier transform to obtain the frequency response of the voltage fluctuation equation, and obtaining its frequency characteristics based on the frequency response of the voltage fluctuation equation; if the resonant frequency corresponding to the extreme point of the transmission function is consistent with the frequency characteristics, then calculating the difference between the amplitude and phase at time t and the amplitude and phase at time t-1 in the voltage fluctuation equation; if the difference in amplitude and the difference in phase both exceed their respective set thresholds, it is determined that a fault has occurred between the two nodes.

[0018] Furthermore, the fault location equation established by the second fault location unit is expressed using the following formula:

[0019]

[0020] Among them, Δx is the fault location correction value; ΔV is the voltage flow calculation correction value; Δω is the phase angle flow calculation correction value.

[0021] Furthermore, the voltage flow calculation correction value ΔV is calculated using the following formula:

[0022]

[0023] Among them, X dij is the admittance difference between node i and node j among the two nodes; ΔP ij is the difference in active power between node i and node j; z is the imaginary sign; ΔQij is the difference in reactive power between node i and node j; θ ij is the phase angle difference between node i and node j; Y ij is the mean admittance of node i and node j.

[0024] Furthermore, the phase angle power flow calculation correction value Δω is calculated using the following formula:

[0025] Δω=|ω0(Y ij sin(θ ij )-Y ij cos(θ ij ))|;

[0026] Among them, || is the modulo operation.

[0027] By adopting the above technical scheme, the present invention produces the following beneficial effects: the traditional power system fault location method relies on manual inspection and single sensor detection, which is easily affected by human factors and the environment, resulting in inaccurate positioning results. The smart grid real-time fault location system of the present invention uses power big data and advanced analysis algorithms to achieve rapid and accurate positioning of power system faults. By real-time acquisition of current data and voltage data of each node in the power grid, combined with power grid model construction and fault location algorithm, the system can accurately locate the fault point, greatly improving the accuracy and efficiency of fault location. The system of the present invention can not only realize real-time positioning of faults, but also predict and correct faults. By establishing a fault location equation and a correction value for predicting the fault point, the system can quickly predict possible fault points after the fault occurs, and make corrections, thereby improving the timeliness and accuracy of fault handling. This fault prediction and correction function can help power system operators take timely measures to reduce the impact of faults on the system and ensure the stable operation of the power system. The power system is an indispensable infrastructure in modern society, and its stable operation is crucial to the normal operation of all walks of life. The smart grid real-time fault location system of the present invention can help power system operators to discover and handle faults in a timely manner, thereby improving the operational safety and reliability of the power system. By monitoring the operating status of the power system in real time, promptly discovering potential faults and quickly locating and correcting them, we can effectively reduce the impact of faults on the system, ensure the stable operation of the power system, and ensure power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a method flow chart of the 3D printing modeling and design method of the Chinese bone defect filling body in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0030] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other equivalent or alternative features having similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0031] Example 1: Reference Figure 1 , a real-time fault location system for a smart grid based on electric power big data, the system comprising: a grid data acquisition unit, used for real-time acquisition of current data and voltage data of each node in the grid; a grid model construction unit, used for constructing a directed graph of the grid based on the connection relationship between each node of the grid and each node, in which a voltage fluctuation equation between any two nodes is established based on the current data and voltage data of each node acquired in real time, and a frequency response transmission function between any two nodes is calculated; a first fault location unit, used for judging whether there is a fault between the two nodes based on the frequency response transmission function and the voltage fluctuation equation of any two nodes; a second fault location unit, used for establishing a fault location equation based on the voltage fluctuation equation, the frequency response transmission function and the power flow calculation of the two nodes, and when the first fault location unit determines that a fault occurs, the midpoint position of the two nodes is defined as the fault prediction point, and a correction value of the fault position is calculated based on the fault location equation, and the coordinate position of the fault prediction point and the correction value of the fault position are added to obtain the actual fault position.

[0032] Specifically, the power grid data acquisition unit uses advanced sensor technology and is installed at each key node of the power grid. These sensors can accurately and quickly measure the current and voltage data at the node. For example, for the collection of current data, the sensor can measure based on the Hall effect or current transformer principle, and convert the real-time current value into a digital signal output. For the collection of voltage data, the sensor can measure through the voltage division principle or the capacitive coupling principle, and convert the real-time voltage value into a digital signal output. After signal processing and digital conversion, the collected current data and voltage data are transmitted to the data processing unit of the system. In the data processing unit, these data are integrated, stored, and analyzed and processed by algorithms. Among them, the real-time requirements for current data and voltage data are extremely high. Therefore, the delay and accuracy of data transmission need to be considered in the signal processing process to ensure the system's real-time monitoring of the power grid status. The power grid data acquisition unit also needs to consider the security and reliability of the data. Because the accuracy of the power grid data directly affects the performance and reliability of the system. Therefore, in the process of data collection and transmission, a series of security measures, such as data encryption, identity authentication, etc., need to be taken to prevent data from being maliciously tampered with or stolen, and ensure the system's accurate monitoring and analysis of the power grid status. The design of the power grid data acquisition unit should take into account its flexibility and scalability in practical applications. Because the topology and operating environment of the power grid may change, the acquisition unit needs to have a certain degree of adaptability and be able to be deployed and adjusted as needed to meet the data acquisition needs in different power grid scenarios. The power grid data acquisition unit provides key data support for the real-time fault location system of the smart grid by collecting current data and voltage data in real time. It is based on advanced sensor technology and signal processing technology, as well as the emphasis on data security and reliability, to ensure the system's accurate monitoring of the power grid status and fault location capabilities. At the same time, its flexibility and scalability also facilitate the application and deployment of the system.

[0033] The power grid model building unit needs to obtain the topological structure information of the power grid. The topological structure of the power grid describes the connection relationship between each node in the power grid, including the electrical connection between nodes and the layout of the wires. This information can be obtained through power grid design drawings, operation records or on-site surveys. By integrating and analyzing this information, a topological structure model of the power grid can be constructed to form a directed graph, in which the nodes represent each device or node in the power grid and the edges represent the connection relationship between the nodes. The power grid model building unit needs to obtain the current and voltage data of the power grid nodes. This data can be collected in real time by the power grid data acquisition unit or obtained through historical records. By analyzing this data, the real-time status of the current and voltage of each node in the power grid can be determined, thereby establishing a state model of the power grid. Based on the topological structure model of the power grid and the current and voltage status information of the nodes, the power grid model building unit can use the basic theories and equations of the power system for modeling and analysis. For example, according to Ohm's law and Kirchhoff's current law, the voltage and current relationship between nodes can be established; according to the power flow equation and the power balance equation, the power distribution and power flow of each node in the power grid can be analyzed. Through these equations and models, the operation status of the power grid can be accurately described and analyzed. The power grid model building unit can also use advanced mathematical methods and computing techniques, such as numerical calculation methods, optimization algorithms, etc., to simulate and analyze the dynamic behavior and stability of the power grid. By simulating the operation of the power grid under different working conditions, the stability and security of the power grid can be evaluated, and a reference can be provided for the optimization and adjustment of the system. The power grid model building unit needs to continuously update and optimize the power grid model to adapt to the changes in the power grid operation status and the needs of the system. This includes regularly updating and correcting the power grid topology and node parameters, as well as optimizing and improving the power grid model and algorithm to improve the accuracy and reliability of the system. The power grid model building unit provides key support for the smart grid real-time fault location system by modeling and analyzing the power grid structure and operating parameters. Its principle is based on the basic theory and equations of the power system, as well as advanced mathematical methods and computing techniques, which ensure the system's accurate monitoring of the power grid status and fault location capabilities. At the same time, its continuous updating and optimization characteristics also provide guarantees for the stable operation and performance improvement of the system.

[0034] The first fault location unit calculates the frequency response transfer function between any two nodes by collecting the current and voltage data of the grid nodes. The frequency response transfer function is a mathematical model that describes the voltage and current relationship between the grid nodes. It can reflect the response characteristics of the voltage and current between the nodes at different frequencies. By analyzing these transfer functions, the electrical connection and power transmission characteristics between the nodes can be understood, so as to determine whether there is a fault. The first fault location unit uses the voltage fluctuation equation to analyze the voltage fluctuation between the nodes. The voltage fluctuation equation describes the change law of the node voltage in the grid over time, and is an important indicator for evaluating the stability and fault conditions of the grid. By monitoring the amplitude and frequency of the voltage fluctuation, it can be determined whether there is a fault in the grid, such as short circuit, grounding and other fault types. The first fault location unit comprehensively analyzes the data of the frequency response transfer function and the voltage fluctuation equation, and compares and determines the status between any two nodes in the grid. By comparing the actual frequency response transfer function and the theoretical model, it can be determined whether the electrical connection between the nodes is normal; by comparing the measured voltage fluctuation and the expected range, it can be determined whether there is an abnormality between the nodes. Through these determinations, it can be preliminarily determined whether there is a fault in the grid. If the first fault location unit determines the possibility of a fault, it will further analyze the specific location of the fault. In this case, the first fault location unit will use the midpoint position between the fault nodes as the fault prediction point, and calculate the correction value of the fault position based on the frequency response transfer function and the voltage fluctuation equation. This correction value reflects the offset of the actual fault position relative to the prediction point, and can be used to locate the fault position more accurately. Finally, the first fault location unit adds the coordinate position of the fault prediction point and the correction value of the fault position to obtain the actual fault position. This position information can reflect the specific location of the fault and provide an important reference for subsequent fault handling and repair.

[0035] The second fault location unit uses the voltage fluctuation equation to analyze the node voltage in the power grid. The voltage fluctuation equation describes the change law of the node voltage in the power grid over time and is an important indicator for evaluating the stability and fault conditions of the power grid. By monitoring the amplitude and frequency of the voltage fluctuation, it is possible to preliminarily determine whether there is a fault in the power grid. The second fault location unit uses the frequency response transfer function to analyze the electrical connection between nodes in the power grid. The frequency response transfer function can reflect the response characteristics of the voltage and current between nodes at different frequencies, so as to understand the electrical connection status of the nodes in the power grid. By analyzing these transfer functions, it is possible to determine whether the electrical connection between nodes is normal. The second fault location unit evaluates the flow of electric energy at each node in the power grid based on the power flow calculation of the node. Power flow calculation is a common method in power system analysis, which is used to determine parameters such as voltage, power and current of each node in the power grid. By calculating the power flow of the power grid, the load conditions and power distribution of each node in the power grid can be understood, so as to further determine whether there is a fault. If the second fault location unit determines that there is a fault, a fault location equation will be established. The fault location equation accurately locates the fault position by comprehensively considering the voltage fluctuation equation, the frequency response transfer function and the power flow calculation results. In this equation, the correction value of the fault prediction point and the fault location is defined, and the actual fault location is obtained by calculation. The second fault location unit adds the coordinate position of the fault prediction point and the correction value of the fault location to obtain the actual fault location. This location information can reflect the specific location of the fault and provide an important reference for subsequent fault handling and repair.

[0036] Embodiment 2: After the power grid data acquisition unit acquires the current data and voltage data of each node in the power grid in real time, it performs data preprocessing on the acquired current data and voltage data, specifically including: performing data filtering, data interpolation, and data alignment processing on the acquired current data and voltage data in turn.

[0037] Specifically, data filtering is to remove noise and interference in the data and retain the processing steps of effective signals. In the power grid, various factors such as electromagnetic interference and equipment operation fluctuations will cause fluctuations and outliers in the collected data, affecting the accuracy and reliability of the data. Therefore, the goal of data filtering is to smooth the data curve and remove noise by applying technologies such as digital filters to make the data more stable and reliable. Common filters include moving average filtering, median filtering, bandpass filtering, etc., and appropriate filtering methods are selected for processing according to the characteristics of the data. Data interpolation is to fill in the missing parts of the data so that the data has continuity and integrity. In the actual collection process, due to equipment failure, communication interruption and other reasons, data collection of some nodes may fail or be missing. In order to ensure the integrity and continuity of the data, the missing data needs to be interpolated. The interpolation method can be selected according to the characteristics of the data. Common interpolation methods include linear interpolation, polynomial interpolation, Lagrange interpolation, etc. Through these interpolation methods, the possible values ​​of the missing data can be inferred based on the laws and trends of the existing data to fill the gaps in the data. Data alignment processing is to ensure that the data collected by different nodes have the same time standard, which is convenient for subsequent data analysis and comparison. In the actual collection process, the data of different nodes may have time deviations or inconsistencies, and need to be aligned. Data alignment methods include timestamp calibration, timing alignment, etc. Through these methods, the data collected by different nodes can be unified on the same time axis to ensure the timing consistency of the data, which is convenient for subsequent data analysis and processing.

[0038] Embodiment 3: The current data and voltage data are processed through a high-pass filter. The high-pass filter can remove the low-frequency components in the data and retain the high-frequency components in the data, thereby filtering out noise and interference and improving the quality and reliability of the data. Next, the missing parts in the current data and voltage data, that is, the missing values ​​in the data, are detected. Once the missing values ​​are detected, the mean interpolation method can be used to fill them. The mean interpolation method refers to estimating the missing values ​​based on the mean of the existing data or the mean of the adjacent data, so that the data remains smooth and continuous at the missing point. For example, the mean of the data within a certain time range before and after the missing data can be calculated, and then the mean is used to fill the missing values. The purpose of data alignment is to ensure that the current data and voltage data collected by different nodes have the same time standard to facilitate subsequent data analysis and comparison. The specific steps are as follows: The current data and voltage data collected from different nodes are aligned in time: First, determine the timestamp or sampling time point of the data. Then, the data collected by different nodes are sorted according to the timestamp or sampling time point. Finally, the data collected by different nodes are unified onto the same time axis through methods such as interpolation or truncation to ensure the temporal consistency of the data.

[0039] Embodiment 4: Each node in the power grid is connected by a power transmission line; in a directed graph, the power transmission line is used as an edge of the directed graph to describe the connection relationship between each node; the voltage fluctuation equation between two nodes is expressed by the following formula:

[0040]

[0041] in, represents the influence of thermal effect on voltage fluctuation, where T(x, t) is the temperature at position x at time t, α is the thermal effect coefficient, which describes the influence of temperature change on voltage; β·|V(x, t)| 2 V(x, t) represents the norm correction term, where |V(x, t)| is the norm of the voltage, β is the correction factor used to correct the amplitude effect of the voltage; V(x, t) represents the voltage at position x at time t; L is the inductance per unit length; C is the capacitance per unit length; R is the resistance per unit length; α is the thermal effect coefficient, which describes the effect of temperature change on the voltage; β is the correction factor.

[0042] Specifically, the first term in the formula It represents the second-order spatial partial derivative of the voltage V(x, t) with respect to the position x. This term describes the rate of change of the voltage in space, that is, the lateral propagation characteristics of the voltage on the transmission line. Through this term, we can understand the spatial distribution of the voltage on the transmission line. The second term in the formula is Describes the effect of inductance L and capacitance C on the second-order time partial derivative of voltage V(x, t) at time t. This term reflects the speed at which voltage changes over time, that is, the dynamic response characteristics of voltage on the transmission line. Through this term, we can understand the rate of change of voltage over time, that is, the frequency and amplitude of voltage fluctuations. The third term in the formula is Describes the effect of resistance R and capacitance C on the first-order time partial derivative of voltage V(x, t) at time t. This term represents the damping effect of voltage changing over time, that is, the process in which voltage fluctuations are gradually attenuated by the influence of resistance. Through this term, we can understand the damping characteristics of voltage fluctuations, that is, the attenuation rate and stability of voltage fluctuations. The fourth term in the formula is Describes the impact of thermal effects on voltage fluctuations. This term takes into account the Joule heating effect generated by the current passing through the transmission line, causing the temperature T(x, t) of the transmission line to change with time t, thereby affecting the voltage fluctuation. Among them, α is the thermal effect coefficient, which describes the impact of temperature changes on voltage. Through this term, we can understand how the temperature change of the transmission line affects the voltage fluctuation, and then evaluate the thermal stability and power transmission capacity of the power grid. The last term in the formula β·|V(x, t)| 2·V(x, t) represents the amplitude correction term. This term takes into account the effect of the voltage itself on its fluctuation, that is, the nonlinear characteristics of the voltage. Among them, |V(x, t)| is the modulus of the voltage, and β is the correction coefficient used to correct the amplitude effect of the voltage. Through this term, we can understand the impact of the nonlinear characteristics of the voltage on the voltage fluctuation, and then evaluate the stability and power transmission capacity of the power grid. By solving this wave equation, we can evaluate the characteristics of the voltage fluctuation in the power grid, including frequency, amplitude and stability. This is of great significance for evaluating the stability and reliability of the power grid, helping to predict the stability of the power grid under different working conditions, and providing a theoretical basis for the operation and dispatch of the power grid. The voltage fluctuation equation describes the propagation and response characteristics of the voltage between nodes in the power grid, and can be used to locate the fault location in the power grid. By analyzing the propagation law of voltage fluctuations, the voltage changes around the fault point can be determined, so as to accurately locate the fault location and provide guidance for fault handling and repair of the power grid. The wave equation can help engineers understand the laws and characteristics of voltage fluctuations in the power grid, which helps to optimize the design and planning of the power grid. By analyzing the impact of voltage fluctuations on various components of the power grid, a more stable and reliable power grid structure can be designed to improve the operation efficiency and power transmission capacity of the power grid. The wave equation can be used as a basic model for power grid simulation and simulation to simulate the voltage fluctuation of the power grid under different working conditions. By solving and simulating the equation, the voltage response of the power grid under different fault conditions can be simulated, the stability and reliability of the power grid can be evaluated, and decision support can be provided for power grid operation and dispatching. By analyzing the voltage fluctuation equation, the types and causes of faults in the power grid can be identified. By analyzing the characteristics and changing laws of voltage fluctuations, the possible types of faults in the power grid can be diagnosed, providing guidance for fault diagnosis and troubleshooting of the power grid.

[0043] Embodiment 5: The frequency response transfer function H(s) is expressed using the following formula:

[0044]

[0045] Among them, K is the transfer function gain; T1 is the time constant of the transfer function; T2 is the damping time constant of the transfer function; A is the thermal effect correction gain; ω0 is the thermal effect correction natural frequency; B is the paradigm correction gain; s is the internal parameter of the transfer function; V(x, s) is the frequency domain representation of the voltage data; I(x, s) is the frequency domain representation of the current data.

[0046] Specifically, the first part of the formula It is a first-order inertia link that describes the basic response characteristics of the system to frequency changes. K represents the gain of the transfer function, and T1 represents the time constant of the transfer function. This part mainly reflects how fast the system responds to frequency changes, that is, the higher the frequency, the smaller the response of the transfer function, and the lower the frequency, the greater the response of the transfer function. This is of great significance for evaluating the frequency response characteristics of the system and can help understand the sensitivity of the system to frequency changes. The second part of the formula is an exponential decay term that describes the damping characteristics of the system. T2 represents the damping time constant of the transfer function. This part mainly reflects the system's attenuation rate to frequency changes, that is, the higher the frequency, the faster the attenuation of the transfer function, and the lower the frequency, the slower the attenuation of the transfer function. This is of great significance for evaluating the damping characteristics and stability of the system, and can help understand the system's attenuation rate to frequency changes, and then evaluate the stability and reliability of the system. The third part of the formula represents the frequency domain representation of voltage data, where V(x, s) is the frequency domain representation of voltage data, is the gain part of thermal effect correction. This part describes the response characteristics of voltage data in the frequency domain, taking into account the influence of thermal effect on voltage fluctuation. Among them, A represents the thermal effect correction gain, and ω0 represents the thermal effect correction natural frequency. This part can help understand the response of the system to voltage fluctuation at different frequencies and evaluate the influence of thermal effect on voltage fluctuation. The last part of the formula represents the frequency domain representation of the current data, where I(x, s) is the frequency domain representation of the current data, is the gain part of the paradigm correction. This part describes the response characteristics of the current data in the frequency domain, taking into account the impact of the paradigm correction on the current fluctuation. Among them, B represents the paradigm correction gain. This part can help understand the response of the system to current fluctuations at different frequencies and evaluate the impact of the paradigm correction on the current fluctuation.

[0047] Embodiment 6: A method for determining whether there is a fault between two nodes based on the transmission function and the voltage fluctuation equation of the frequency response of any two nodes by a first fault location unit specifically includes: calculating the pole position of the transmission function, which corresponds to the resonant frequency; converting the voltage fluctuation equation from the time domain to the frequency domain by Fourier transform to obtain the frequency response of the voltage fluctuation equation, and obtaining its frequency characteristics based on the frequency response of the voltage fluctuation equation; if the resonant frequency corresponding to the extreme point of the transmission function is consistent with the frequency characteristics, then calculating the difference between the amplitude and phase at time t and the amplitude and phase at time t-1 in the voltage fluctuation equation; if the difference in amplitude and the difference in phase both exceed their respective set thresholds, then determining that a fault has occurred between the two nodes.

[0048] Specifically, by calculating the pole position of the transfer function, that is, the resonant frequency, and analyzing the characteristics of the frequency response, the response of the system to different frequencies can be understood. Faults usually cause the system to have abnormal responses at a specific frequency, such as resonance or attenuation abnormalities. Therefore, if the resonant frequency of the transfer function matches the frequency response characteristics, there may be a fault. At the frequency at which the fault may be confirmed, by comparing the amplitude and phase difference of the voltage fluctuation equation at different time points, it can be further verified whether there is a fault. Faults usually cause changes in the amplitude and phase of the voltage fluctuation, so if the difference in amplitude and phase exceeds the set threshold, there may be a fault. Frequency response refers to the response of the system to input signals of different frequencies. By analyzing the characteristics of the frequency response, the sensitivity of the system to frequency changes, phase changes, and amplitude changes can be understood. Faults usually cause the system to have abnormal responses at a specific frequency, such as resonance or attenuation abnormalities. Resonance abnormality means that the amplitude of the system at a certain frequency increases abnormally, which may cause the system to become unstable; while attenuation abnormality means that the amplitude of the system at a certain frequency decreases abnormally, which may cause the signal transmission quality to deteriorate. The poles of the transfer function refer to the solutions that make the transfer function zero, that is, the roots of the transfer function with a denominator of zero. In frequency domain analysis, these poles correspond to the resonant frequency of the system. The resonant frequency is the frequency at which the system produces the maximum amplitude in the frequency response, that is, the system has the maximum response to external excitation at this frequency. Therefore, by calculating the pole position of the transfer function, that is, the resonant frequency, we can understand the response of the system to different frequencies. The voltage fluctuation equation describes the change of voltage over time in the power system. In fault detection, the focus is on the amplitude and phase of the voltage fluctuation. The amplitude indicates the magnitude of the voltage fluctuation, and the phase indicates the phase angle of the voltage fluctuation. This information can reflect the characteristics and changes of the voltage fluctuation. At the frequency where the fault may be confirmed, the amplitude and phase of the voltage fluctuation equation at different time points are compared to verify whether there is a fault. Normally, normal voltage fluctuations should have stable amplitudes and phases. However, when a fault occurs in the system, it may cause the amplitude and phase of the voltage fluctuation to change. Therefore, if the difference between the amplitude and phase of the voltage fluctuation exceeds the set threshold at the fault frequency, there may be a fault. Faults usually cause changes in system parameters, such as resistance and inductance, which affect the amplitude and phase of voltage fluctuations. When a system fault occurs, it may cause abnormal conditions such as an increase or decrease in the amplitude of voltage fluctuations and a phase shift. Therefore, by comparing the amplitude and phase differences of the voltage fluctuation equation at different time points, it is possible to indirectly detect whether the system has a fault.

[0049] Embodiment 7: The fault location equation established by the second fault location unit is expressed using the following formula:

[0050]

[0051] Among them, Δx is the fault location correction value; ΔV is the voltage flow calculation correction value; Δω is the phase angle flow calculation correction value.

[0052] Specifically, Δx in the formula represents the correction value of the fault location, that is, the correction value obtained by calculation after the first fault location unit determines the fault. ΔV and Δω represent the correction values ​​of the voltage flow calculation and the phase angle flow calculation, respectively. These values ​​will be used to correct the voltage and phase angle to improve the accuracy of fault location. The rest of the formula includes the derivatives of the voltage fluctuation equation and the transfer function, as well as the description of the changes in the voltage fluctuation equation and the phase angle, taking into account the influence of inductance, capacitance and resistance. When understanding the principle of the formula, we must first understand the fault location process in the power system. In the power system, fault location refers to determining the specific location of the fault in the system. This is a crucial task because it directly affects the safety and reliability of the system. Traditional fault location methods are usually based on analysis of measurement data and models, but this method often takes a lot of time and resources. Therefore, it is particularly important to use mathematical models and calculation methods to achieve automated fault location. Next, analyze each part of the formula. The first part of the formula The derivatives of the voltage fluctuation equation and the transfer function are described. This part reflects the changes in the power system in the frequency domain, which is related to the frequency characteristics of the system response. By calculating these derivatives, a deeper understanding of the system response can be obtained. The second part of the formula The voltage fluctuation equation and the change of phase angle are described, and the influence of inductance, capacitance and resistance are considered. This part is associated with the correction value of voltage and phase angle, which is used to calculate the correction value of fault location.

[0053] Embodiment 8: The voltage flow calculation correction value ΔV is calculated using the following formula:

[0054]

[0055] Among them, X dij is the admittance difference between node i and node j among the two nodes; ΔP ij is the difference in active power between node i and node j; z is the imaginary sign; ΔQ ij is the difference in reactive power between node i and node j; θ ij is the phase angle difference between node i and node j; Y ij is the mean admittance of node i and node j.

[0056] Specifically, ΔV in the formula represents the correction value of the voltage flow calculation. In the power system, the stability and accuracy of voltage is one of the important conditions to ensure the operation of the system. The flow calculation is used to calculate the relationship between the voltage and power of each node in the power system in order to evaluate the stability and operation status of the system. However, due to the complexity and uncertainty of the power system, there may be certain errors in the flow calculation results. Therefore, it is necessary to adjust the calculation results through the correction value to obtain a more accurate voltage value. The various items in the formula represent the factors that affect the voltage flow calculation. The first is X dij , which represents the admittance difference between node i and node j. Admittance is a physical quantity that describes the relationship between voltage and current in a power system. The admittance difference reflects the difference in voltage and current between nodes. The influence of the admittance difference reflects the voltage fluctuation between different nodes in the system, thus affecting the calculation results of the voltage flow. Next, ΔP ij and ΔQ ij Represents the difference in active power and reactive power between node i and node j respectively. Active power and reactive power are important indicators for transmitting electric energy in power systems, and their difference can reflect the power changes in the system. These power differences will affect the voltage stability between nodes, so they need to be considered in the correction value. z is the imaginary symbol, which is used to represent the imaginary part of a complex number. In power systems, complex numbers are often used to describe the phase relationship between voltage and current. The impact of the imaginary part reflects the impact of frequency changes on voltage in power systems, which requires correction in voltage flow calculations. θ ij represents the phase angle difference between node i and node j. The phase angle is the phase difference that describes the voltage fluctuation in the power system. It reflects the change of the voltage phase between nodes. The change of phase angle will directly affect the voltage stability between nodes, so it also needs to be considered in the correction value. Finally, Y ij Represents the mean admittance between node i and node j. The mean admittance is the average value of the admittance between nodes, which reflects the overall characteristics of the voltage and current between nodes. The influence of the mean admittance reflects the overall situation of the voltage fluctuation between nodes in the system, so it needs to be corrected in the voltage flow calculation.

[0057] Embodiment 9: The phase angle power flow calculation correction value Δω is calculated using the following formula:

[0058] Δω=|ω0(Y ij sin(θ ij )-Y ij cos(θ ij ))|;

[0059] Among them, || is the modulo operation.

[0060] Specifically, understand each symbol and term in the formula. Δω in the formula represents the correction value of the phase angle power flow calculation, that is, when performing the phase angle power flow calculation, the correction value of the phase angle difference between nodes is considered. This correction value is used to adjust the calculation result of the phase angle to obtain a more accurate phase angle value. ω0 represents the natural frequency of the phase angle, which reflects the frequency characteristics of the phase change in the system. Y ij It represents the mean value of the admittance between nodes i and j, reflecting the overall characteristics of the voltage and current between nodes. ij It represents the phase angle difference between node i and node j, and describes the physical quantity of the voltage phase difference between nodes in the power system. In the power system, the phase angle is the phase difference that describes the voltage fluctuation. The change of phase angle will directly affect the voltage stability and power transmission in the system. Therefore, when performing power flow calculation, it is necessary to consider the difference in phase angle between nodes to ensure the accuracy and reliability of the calculation results. The calculation process of the formula involves two main steps. First, calculate Y ij sin(θ ij ) and Y ij cos(θ ij ), which represents the sine and cosine of the product of the mean admittance between nodes and the phase angle difference. This difference reflects the relationship between the mean admittance between nodes and the phase angle difference, which further affects the phase change in the system. Next, multiply the difference by the natural frequency ω0 of the phase angle. This step is to calculate the correction value Δω for the phase angle power flow calculation. The natural frequency reflects the frequency characteristics of the phase change in the system. Multiplying it by the difference can obtain the correction value, which is used to adjust the calculation result of the phase angle. Finally, the calculated correction value is modulo operated, which means taking the absolute value of the correction value. This step ensures that the correction value is a non-negative number, thereby ensuring the accuracy and rationality of the correction process.

[0061] The present invention is not limited to the above-mentioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A real-time fault location system for smart grid based on power big data, characterized by: The system comprises: a power grid data acquisition unit, which is used to acquire the current data and voltage data of each node in the power grid in real time; a power grid model construction unit, which is used to construct a directed graph of the power grid based on the connection relationship between each node of the power grid and each node, in which a voltage fluctuation equation between any two nodes is established based on the current data and voltage data of each node acquired in real time, and a transmission function of the frequency response between any two nodes is calculated; a first fault location unit, which is used to determine whether there is a fault between two nodes based on the transmission function of the frequency response and the voltage fluctuation equation of any two nodes; a second fault location unit, which is used to establish a fault location equation based on the voltage fluctuation equation, the transmission function of the frequency response and the power flow calculation of the two nodes, and when the first fault location unit determines that a fault occurs, the midpoint position of the two nodes is defined as the fault prediction point, and a correction value of the fault position is calculated based on the fault location equation, and the coordinate position of the fault prediction point and the correction value of the fault position are added to obtain the actual fault position.

2. The smart grid real-time fault location system based on power big data according to claim 1 is characterized in that: The power grid data acquisition unit collects the current data and voltage data of each node in the power grid in real time, and then performs data preprocessing on the collected current data and voltage data, specifically including: performing data filtering, data interpolation, and data alignment processing on the collected current data and voltage data in turn.

3. The smart grid real-time fault location system based on power big data as claimed in claim 2, characterized in that: The data interpolation process specifically includes: the specific process of data filtering includes: passing the current data and voltage data through a high-pass filter; detecting missing data in the current data and voltage data, and filling the missing data by the mean interpolation method; the data alignment process specifically includes: temporally aligning the current data and voltage data collected from different nodes.

4. The smart grid real-time fault location system based on power big data as claimed in claim 3, characterized in that: Each node in the power grid is connected by a power transmission line. In a directed graph, the power transmission line is the edge of the directed graph, describing the connection relationship between each node. The voltage fluctuation equation between two nodes is expressed by the following formula: in, represents the influence of thermal effect on voltage fluctuation, where T(x,t) is the temperature at position x at time t, α is the thermal effect coefficient, which describes the influence of temperature change on voltage; β·|V(x,t)| 2 V(x,t) is the norm correction term, where |V(x,t)| is the norm of the voltage, β is the correction factor used to correct the voltage amplitude effect; V(x,t) is the voltage at position x at time t; L is the inductance per unit length; C is the capacitance per unit length; R is the resistance per unit length; α is the thermal effect coefficient, which describes the effect of temperature change on the voltage; β is the correction factor.

5. The smart grid real-time fault location system based on power big data as claimed in claim 4, characterized in that: The frequency response transfer function H(s) is expressed as follows: Among them, K is the transfer function gain; T1 is the time constant of the transfer function; T2 is the damping time constant of the transfer function; A is the thermal effect correction gain; ω0 is the thermal effect correction natural frequency; B is the paradigm correction gain; s is the internal parameter of the transfer function; V(x,s) is the frequency domain representation of the voltage data; I(x,s) is the frequency domain representation of the current data.

6. The smart grid real-time fault location system based on power big data according to claim 5, characterized in that: The first fault location unit, based on the transmission function of the frequency response of any two nodes and the voltage fluctuation equation, determines whether there is a fault between the two nodes. The method specifically includes: calculating the pole position of the transmission function, which corresponds to the resonant frequency; converting the voltage fluctuation equation from the time domain to the frequency domain by Fourier transform to obtain the frequency response of the voltage fluctuation equation, and obtaining its frequency characteristics based on the frequency response of the voltage fluctuation equation; if the resonant frequency corresponding to the extreme point of the transmission function is consistent with the frequency characteristics, then calculating the difference between the amplitude and phase at time t and the amplitude and phase at time t-1 in the voltage fluctuation equation; if the difference in amplitude and the difference in phase both exceed their respective set thresholds, it is determined that a fault has occurred between the two nodes.

7. The smart grid real-time fault location system based on power big data according to claim 6, characterized in that: The fault location equation established by the second fault location unit is expressed using the following formula: Among them, Δx is the fault location correction value; ΔV is the voltage flow calculation correction value; Δω is the phase angle flow calculation correction value.

8. The smart grid real-time fault location system based on power big data according to claim 7, characterized in that: The voltage flow calculation correction value ΔV is calculated using the following formula: Among them, X dij is the admittance difference between node i and node j among the two nodes; ΔP ij is the difference in active power between node i and node j; z is the imaginary sign; ΔQ ij is the difference in reactive power between node i and node j; θ ij is the phase angle difference between node i and node j; Y ij is the mean admittance of node i and node j.

9. The smart grid real-time fault location system based on power big data according to claim 8, characterized in that: The phase angle power flow calculation correction value Δω is calculated using the following formula: Dω=|ω0(Y ij sin(θ ij )-Y ij cos(θ ij ))|; Among them, || is the modulo operation.

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