Feeder automation terminal fault monitoring system and method based on big data analysis

Abstract

The invention discloses a feeder automation terminal fault monitoring system and method based on big data analysis, and relates to the technical field of feeder automation terminal fault monitoring, the feeder automation terminal fault monitoring system comprises the following modules: an acquisition module, a data processing module and a regulation and control module, and the specific method comprises the following steps: collecting terminal data at the rear end of a trip switch; performing data quantitative analysis in combination with historical first data of the feeder automation equipment; establishing an analysis model by using a mathematical method according to the terminal data of the rear end of the trip switch and the historical first data of the feeder automation equipment, and generating a quantized value fault evaluation coefficient; and according to the quantitative fault evaluation coefficient, fault condition analysis and comparison are carried out on the feeder automation terminal equipment, the working state of a power grid system controlled by the feeder automation terminal equipment is evaluated, and corresponding configuration optimization and structure adjustment are executed. The system can help the power industry to realize equipment intelligentization, operation and maintenance high efficiency and cost reduction, and improves the safety, reliability and sustainable development level of a power supply system.

Description

Technical Field

[0001] The present invention relates to the technical field of automated terminal fault monitoring, and more specifically, to a feeder automation terminal fault monitoring system and method based on big data analysis. Background Art

[0002] Big data analysis refers to the use of advanced computing technology and algorithms to collect, store, process and analyze massive and diverse data in order to discover patterns, associations and trends hidden behind the data and extract valuable information and insights from it.

[0003] Feeder automation terminal refers to the equipment or system used for feeder automation control and protection in the power system. Feeder automation terminal can be an intelligent device that can monitor the feeder status in the power system, detect faults, and perform corresponding control actions according to preset logic and algorithms, such as circuit breaker operation, fault isolation, etc. These terminals usually communicate with other power system equipment, such as protection devices, telecontrol devices, etc., as well as monitoring and control centers to achieve automated management and control of feeders and improve the reliability, safety and efficiency of the power system.

[0004] However, as the equipment is used for a long time, the feeder automation terminal may have hardware failures, such as sensor failure, controller failure, communication module failure, and power failure; as well as software problems, such as control algorithm errors, program errors, or data processing problems; some potential environmental factors may also cause the feeder automation terminal to fail, such as damage caused by extreme temperature, humidity, and harsh working environment. At present, in the application of distribution automation, due to signal problems and aging of terminal equipment, the following problems may occur: the fault is located in the back section of the switch, and the over-level tripping is caused by excessive current, but due to the failure of the terminal equipment or being offline, the switch trips without sending protection action and opening signals. Maintenance personnel need to pay attention to and monitor these potential sources of failure, and take appropriate preventive measures and maintenance measures to ensure the stable operation of the system.

[0005] The existing feeder automation system realizes load transfer based on the load size of the transfer line and the maximum recovery range. However, the above system may have some defects when working, resulting in action errors, incorrect fault interval judgment, or load transfer to the fault point. In order to prevent this phenomenon and ensure the reliability of residential electricity use, an improved feeder automation strategy is proposed.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a feeder automation terminal fault monitoring system and method based on big data analysis, which generates a quantitative fault assessment coefficient by analyzing the terminal data at the back end of the trip switch and the historical first data of the feeder automation equipment, and performs fault condition analysis on the feeder automation terminal equipment, optimizes the feeder terminal equipment configuration and performs structural adjustment, and obtains the optimal power grid configuration decision model to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: A feeder automation terminal fault monitoring method based on big data analysis, characterized in that it includes the following steps: Collect terminal data from the back end of the trip switch and conduct quantitative data analysis based on the historical first data of the feeder automation equipment; According to the terminal data at the rear end of the trip switch and the historical first data of the feeder automation equipment, a mathematical method is used to establish an analysis model to generate a quantitative value fault assessment coefficient; According to the quantitative fault assessment coefficient, the fault conditions of the feeder automation terminal equipment are analyzed and compared, the working status of the power grid system controlled by the feeder automation terminal is evaluated, and the corresponding configuration optimization and structural adjustment are performed.

[0009] In a preferred embodiment, it is characterized in that the terminal data includes a current state coefficient and a voltage waveform coefficient, and the historical first data includes a tripping action coefficient.

[0010] In a preferred embodiment, it is characterized in that the specific method of evaluating the working state of the power grid system controlled by the feeder automation terminal and optimizing the configuration and structural adjustment is as follows: Compare and analyze the quantitative value fault assessment coefficient with the standard fault assessment threshold. If the quantitative value fault assessment coefficient is greater than the warning threshold, the feeder automation terminal power grid equipment is marked as a dangerous working state. If the quantitative value fault assessment coefficient is less than or equal to the warning threshold and greater than the safety threshold, it is calibrated as a medium stable state, and the quantitative value fault assessment coefficient is further compared with the given resident power supply value. When it is greater than the given resident power supply value, the terminal is judged to be faulty and the power supply of the feeder automation terminal equipment is suspended. When it is less than the given resident power supply value, the terminal is judged to be reliable. If the quantized value fault assessment coefficient is less than the safety threshold, it is calibrated to a high stability state.

[0011] In a preferred embodiment, it is characterized in that the specific method for obtaining the current state coefficient is as follows: Install current sensors on feeder automation terminals, collect real-time current data within a certain period of time and fit it into a current curve, calibrate the current data and remove abnormal current values. Integrate the calibrated current values ​​into a current set and extract current characteristic values ​​from the current set, and use the extracted characteristic values ​​to calculate the current state coefficient.

[0012] In a preferred embodiment, it is characterized in that the specific method for obtaining the voltage waveform coefficient is as follows: Install voltage sensors on the feeder automation terminal equipment to obtain real-time voltage waveform data, display the collected voltage waveform data in the time domain, obtain the voltage time domain waveform, filter out the part with the lowest fundamental frequency in the voltage time domain waveform, reduce the interference of external noise frequency on the voltage time domain waveform, perform harmonic analysis on the filtered voltage time domain waveform, combine the voltage harmonic amplitude, voltage harmonic period, and harmonic frequency, calculate the harmonic effective value, and then combine the voltage fundamental frequency and voltage harmonic phase to obtain the voltage waveform coefficient.

[0013] In a preferred embodiment, it is characterized in that the specific method for obtaining the tripping action coefficient is as follows: First, the protection devices in the power grid system controlled by the feeder automation terminal are observed. When a terminal fault is detected, the current value is measured and recorded, and the time interval from the detection of the fault to the tripping of the terminal device is recorded. The tripping fault coefficient is calculated, and the tripping action coefficient is further calculated in combination with the fault current value.

[0014] In a preferred embodiment, it is characterized by comprising the following modules: Acquisition module: collects terminal data from the back end of the trip switch and conducts comprehensive analysis in combination with the historical first data of the feeder automation equipment. The terminal data includes the current state coefficient and the voltage waveform coefficient, and the historical first data includes the trip action coefficient; Data processing module: Based on the terminal data at the back end of the trip switch and the historical first data of the feeder automation equipment, the analysis model is established using mathematical methods to generate a quantitative value fault assessment coefficient; Control module: According to the quantitative fault assessment coefficient, the fault conditions of the feeder automation terminal equipment are analyzed and compared, the working status of the power grid system controlled by the feeder automation terminal is evaluated and the configuration and structural adjustment are optimized to obtain the optimal power grid configuration decision model.

[0015] Technical effects and advantages of a feeder automation terminal fault monitoring system and method based on big data analysis of the present invention: 1. The present invention installs a current sensor on the feeder automation terminal, collects real-time current data within a certain period of time and fits it into a current curve, calibrates the current data and removes abnormal current values. The calibrated current values ​​are integrated into a current set and the current characteristic values ​​are extracted from the current set. The current state coefficient is calculated using the extracted characteristic values ​​to reflect the current state monitored by the automation terminal, so as to facilitate the fault diagnosis and operation status evaluation of the feeder automation terminal; by installing a voltage sensor on the feeder automation terminal equipment, real-time voltage waveform data is obtained, the collected voltage waveform data is displayed in the time domain, the voltage time domain waveform is obtained, the part with the lowest fundamental frequency in the voltage time domain waveform is filtered out, the interference of external noise frequency on the voltage time domain waveform is reduced, and the filtered voltage time domain waveform is harmonic analyzed, combining the voltage harmonic amplitude and voltage harmonic The harmonic effective value is calculated by combining the voltage fundamental frequency and the voltage harmonic phase to obtain the voltage waveform coefficient, so as to evaluate the quality and stability of the feeder automation terminal equipment; by observing the protection devices in the power grid system controlled by the feeder automation terminal, when a terminal fault is detected, the current value is measured and recorded, and the time interval from the detection of the fault to the tripping of the terminal equipment is recorded to calculate the tripping fault coefficient, and combined with the fault current value, the tripping action coefficient is further calculated, which can help evaluate the response speed and accuracy of the protection device in the event of a fault, thereby ensuring the safe and reliable operation of the power system.

[0016] 2. The present invention establishes an analysis model using mathematical methods based on the terminal data at the back end of the trip switch and the historical first data of the feeder automation equipment to generate a quantitative value fault assessment coefficient; and compares and analyzes the quantitative value fault assessment coefficient with a preset threshold value, regulates and optimizes equipment under different stabilities, and manually intervenes in the faults that occur, so as to protect the stability of electricity consumption to the greatest extent, so as to obtain the optimal power grid configuration decision model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of a feeder automation terminal fault monitoring method based on big data analysis of the present invention.

[0018] Figure 2 This is a structural schematic diagram of a feeder automation terminal fault monitoring system based on big data analysis in the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] Embodiment 1, Figure 1 The present invention provides a feeder automation terminal fault monitoring system and method based on big data analysis, comprising the following steps: S10, acquisition module: collect terminal data at the rear end of the trip switch, and conduct comprehensive analysis in combination with the historical first data of the feeder automation equipment. The terminal data includes current state coefficient and voltage waveform coefficient, and the historical first data includes trip action coefficient.

[0021] The current state coefficient is an indicator used for power system monitoring and fault diagnosis. It can evaluate the operating status of the system by monitoring the changes in the current waveform. In the feeder automation terminal fault monitoring system based on big data analysis, the current state coefficient has the following functions: Fault detection and diagnosis: By monitoring the current waveform on the feeder and calculating the current state coefficient, the system can detect faults on the feeder in real time, such as short circuit, overload, etc., and quickly diagnose the fault type and location. Abnormal values ​​of the current state coefficient may indicate a fault or abnormal condition in the system.

[0022] Fault warning and prevention: Based on historical data and model prediction analysis, the system can identify the change pattern of the current state coefficient and issue an alarm in advance so that preventive measures can be taken to prevent the occurrence or expansion of faults.

[0023] Operation status assessment: By analyzing the changing trends and fluctuations of the current state coefficient, the system can assess the operation status of the feeder system, including load conditions, line health, and potential risks.

[0024] Optimize operation strategy: By real-time monitoring and analysis of current state coefficients, the system can optimize the operation strategy of the feeder automation terminal, such as adjusting line load distribution, implementing dynamic voltage regulation, etc., to improve the efficiency and reliability of the system.

[0025] Data-driven maintenance decisions: Based on big data analysis of the current state coefficient, the system can make more reasonable maintenance plans and decisions, such as optimizing equipment maintenance cycles and improving equipment utilization, thereby reducing maintenance costs and improving system availability.

[0026] The specific method for obtaining the current state coefficient is as follows: Install current sensors on feeder automation terminals to collect time The real-time current data in the system is fitted into a current curve based on the current data. Compared with the standard current value, the data is calibrated to remove abnormal current values. The calibrated current values ​​are integrated into a current set. ,in , is a positive integer. The average current value, the current standard value and the peak current are extracted from the current set as the current characteristic values, and the average current value and the current standard value are recorded as and , and the peak current is recorded as , the current state coefficient is calculated using the extracted eigenvalues, and the specific calculation formula of the current state coefficient is as follows:

[0027] It can be seen from the above formula that when the current standard value and the current average value are larger, the performance value of the current state coefficient is larger, indicating that the current working state of the feeder automation terminal is more stable; when the current standard value and the current average value are smaller, the performance value of the current state coefficient is smaller, indicating that the current working state of the feeder automation terminal is worse; by monitoring the current state coefficient, the current state monitored by the feeder automation terminal can be reflected in real time, so as to facilitate fault diagnosis and operation status evaluation of the feeder automation terminal.

[0028] The voltage waveform factor is an indicator used to evaluate the voltage waveform change of the power system. It has the following functions in the feeder automation terminal fault monitoring system: Fault diagnosis: By monitoring the voltage waveform on the feeder and calculating the waveform factor, the system can detect faults on the feeder in real time, such as voltage anomalies, voltage fluctuations, etc., and quickly diagnose the fault type and location. Abnormal voltage waveform factors may indicate a fault or abnormality in the system.

[0029] Fault prevention: Based on historical data and model prediction analysis, the system can identify the change pattern of the voltage waveform factor and issue an alarm in advance so that preventive measures can be taken to prevent the occurrence or expansion of faults.

[0030] Evaluate system operating status: By analyzing the changing trends and fluctuations of the voltage waveform factor, the system can evaluate the operating status of the feeder system, including voltage quality, line health, and potential risks.

[0031] Optimize maintenance decisions: Based on big data analysis of voltage waveform coefficients, the system can formulate more reasonable maintenance plans and decisions, such as optimizing equipment maintenance cycles and improving equipment utilization, thereby reducing maintenance costs and improving system availability.

[0032] The specific method for obtaining the voltage waveform coefficient is as follows: Install voltage sensors on feeder automation terminal equipment to obtain real-time voltage waveform data. Display the collected voltage waveform data in the time domain to obtain the voltage time domain waveform. Filter out the lowest fundamental frequency part of the voltage time domain waveform to reduce the interference of external noise frequency on the voltage time domain waveform. Perform harmonic analysis on the filtered voltage time domain waveform. Record the frequency in the harmonic component of the voltage waveform as the voltage harmonic frequency and calibrate it as , the amplitude of the harmonic component of the voltage waveform is recorded as the voltage harmonic amplitude, calibrated as , the phase of the harmonic component of the voltage waveform is recorded as the voltage harmonic phase, calibrated as , calculate the effective value of the harmonic amplitude.

[0033] The calculation formula of the effective value of harmonics is:

[0034] In the formula, is the voltage harmonic period, is the harmonic frequency.

[0035] The specific calculation formula of the voltage waveform coefficient is as follows:

[0036] It can be seen from the above formula that when the phase in the harmonic component of the voltage waveform is larger, that is, the current value of the feeder automation terminal lags behind the voltage value, the voltage waveform coefficient is smaller; when the fundamental frequency and amplitude in the harmonic component of the voltage waveform are larger, the voltage waveform coefficient is larger, and the working stability performance of the feeder automation terminal equipment is better. By obtaining the voltage waveform coefficient of the feeder automation terminal equipment, the quality and stability of the feeder automation terminal equipment can be evaluated, reflecting the current operating status of the power system.

[0037] The tripping action coefficient is an indicator used to evaluate the tripping action of circuit breakers or protection devices in power systems. In the feeder automation terminal fault monitoring system based on big data analysis, the functions of the tripping action coefficient are as follows: Fault detection and diagnosis: The tripping action coefficient can monitor the tripping action of the circuit breaker or protection device and record the time, location and cause of each trip. By analyzing the frequency and pattern of tripping actions, the system can quickly detect faults on the feeder and diagnose the fault type and location.

[0038] Fault location and isolation: Based on the information of tripping action, the system can accurately locate the fault point and implement corresponding isolation measures to minimize the impact of the fault on the system and improve power supply reliability.

[0039] Fault cause analysis: By analyzing the cause and mode of the tripping action, the system can identify the type of fault that may cause the tripping, such as short circuit, overload, ground fault, etc., providing a basis for further fault cause analysis.

[0040] Fault prevention and optimization: By monitoring and analyzing the tripping action, the system can identify potential problems and hidden dangers in the feeder system and take preventive measures to prevent future faults and optimize the system's operating strategy.

[0041] Data-driven maintenance decisions: Based on big data analysis of tripping actions, the system can make more reasonable maintenance plans and decisions, such as optimizing the maintenance cycle of circuit breakers and improving the setting parameters of protection devices, thereby improving the availability and reliability of the system.

[0042] The specific method for obtaining the tripping action coefficient is as follows: First, the protection devices in the power grid system controlled by the feeder automation terminal are observed. When a terminal fault is detected, the current value is measured and recorded and calibrated as , records the time interval from when the protection device detects a fault to when the terminal equipment trips, calibrated as ; Calculate the tripping fault coefficient.

[0043] The specific calculation formula of the tripping fault coefficient is as follows:

[0044] In the formula, is the terminal adjustment factor, and its value is generally between 0 and 1. Representative Measurements, is the total number of measurements.

[0045] Combined with the relationship between the tripping fault coefficient and the fault current, the calculation formula of the tripping action coefficient is as follows:

[0046] It can be seen from the above formula that the larger the ratio between the fault current and the tripping fault coefficient, the larger the performance value of the tripping action coefficient, indicating that the probability of tripping due to excessive current in the feeder automation terminal is greater; the smaller the ratio between the fault current and the tripping fault coefficient, the smaller the performance value of the tripping action coefficient, indicating that the probability of tripping is smaller when the current fluctuation in the feeder automation terminal is smaller or the passing current is smaller; through trend analysis and abnormal value detection of the tripping action coefficient, it can help evaluate the response speed and accuracy of the protection device in fault conditions, thereby ensuring the safe and reliable operation of the power system.

[0047] S20, data processing module: according to the terminal data at the rear end of the trip switch and the historical first data of the feeder automation equipment, an analysis model is established by using a mathematical method to generate a quantitative value fault assessment coefficient.

[0048] The specific calculation formula of the quantitative value fault assessment coefficient is as follows:

[0049] In the formula, is the current state coefficient, is the voltage waveform factor, is the tripping action coefficient, is the weight factor of the current state coefficient, is the weight factor of the voltage waveform factor, is the weight factor of the tripping action coefficient. It can be seen from the above formula that the larger the current state coefficient, the larger the voltage waveform coefficient, the larger the tripping action coefficient, the larger the performance value of the quantitative value fault assessment coefficient; indicating that the possibility of failure of the feeder automation terminal equipment is greater; conversely, the smaller the current state coefficient, the smaller the voltage waveform coefficient, the smaller the tripping action coefficient, the smaller the performance value of the quantitative value fault assessment coefficient; indicating that the possibility of failure of the feeder automation terminal equipment is smaller, and the operating state of the power grid equipment is stable when working.

[0050] Embodiment 2, S30, control module: according to the quantitative fault assessment coefficient, analyze and compare the fault conditions of the feeder automation terminal equipment, evaluate the working state of the power grid system controlled by the feeder automation terminal, and perform corresponding configuration optimization and structural adjustment.

[0051] The specific method of evaluating the working state of the power grid system controlled by the feeder automation terminal and optimizing the configuration and structural adjustment to obtain the optimal power grid configuration decision model is as follows: The quantitative value fault assessment coefficient is compared and analyzed with the standard fault assessment threshold. If the quantitative value fault assessment coefficient is greater than the warning threshold, the feeder automation terminal power grid equipment is calibrated as a dangerous working state.

[0052] In dangerous working conditions, the power grid will give priority to ensuring the reliability of power supply to residents, transfer power to backup power sources or backup lines in residential areas, open and close distribution stations for power reverse transmission, and send available power from backup lines or backup generators to affected residential areas to restore power supply to residential users as soon as possible. At the same time, in order to minimize the impact of the fault on other users, the faulty line is separated from the system, the circuit breaker of the faulty line is opened, and other section switches are closed to ensure that the rest of the system can still be powered normally.

[0053] If the quantitative value fault assessment coefficient is less than or equal to the warning threshold and greater than the safety threshold, the feeder automation terminal power grid equipment is calibrated as a medium stable state. After ensuring the power supply to residents, the quantitative value fault assessment coefficient is compared with the given resident power supply value. When it is greater than the given resident power supply value, the terminal fault is judged. To prevent the scope of the fault from expanding, the feeder automation terminal equipment power supply is suspended and human intervention is performed; when it is less than the given resident power supply value, the terminal is judged to be reliable, and the load is continued to be transferred to the front section by the incoming line of the community switch.

[0054] If the quantitative value fault assessment coefficient is less than the standard fault safety threshold, the feeder automation terminal power grid equipment is calibrated to a high stability state. After ensuring the reliability of power supply to residents, if there are multiple line contact points for load transfer, the most suitable line for load transfer is selected based on the historical data of the contact point, the probability of failure after transfer, the load size, and the number of distribution transformers that can be transferred under full load. This can maximize the stability and reliability of the system, while minimizing the risks and impacts that may be caused by the transfer and optimizing the grid configuration.

[0055] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0056] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0057] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0058] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0059] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0060] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A feeder automation terminal fault monitoring method based on big data analysis, characterized in that: The following steps are involved: Collect terminal data from the back end of the trip switch and conduct quantitative data analysis based on the historical first data of the feeder automation equipment; According to the terminal data at the rear end of the trip switch and the historical first data of the feeder automation equipment, a mathematical method is used to establish an analysis model to generate a quantitative value fault assessment coefficient; According to the quantitative fault assessment coefficient, the fault conditions of the feeder automation terminal equipment are analyzed and compared, the working status of the power grid system controlled by the feeder automation terminal is evaluated, and the corresponding configuration optimization and structural adjustment are performed.

2. According to the feeder automation terminal fault monitoring system and method based on big data analysis according to claim 1, it is characterized in that: The terminal data includes a current state coefficient and a voltage waveform coefficient, and the historical first data includes a tripping action coefficient.

3. According to the method for monitoring feeder automation terminal faults based on big data analysis in claim 1, it is characterized in that: The specific method for evaluating the working state of the power grid system controlled by the feeder automation terminal and optimizing the configuration and structural adjustment is as follows: Compare and analyze the quantitative value fault assessment coefficient with the standard fault assessment threshold. If the quantitative value fault assessment coefficient is greater than the warning threshold, the feeder automation terminal power grid equipment is marked as a dangerous working state. If the quantized value fault assessment coefficient is less than or equal to the warning threshold and greater than the safety threshold, it is calibrated as a medium stable state, and the quantized value fault assessment coefficient is further compared with the given resident power supply value. When it is greater than the given resident power supply value, the terminal is judged to be faulty and the power supply of the feeder automation terminal equipment is suspended. When it is less than the given resident power supply value, the terminal is judged to be reliable. If the quantized value fault assessment coefficient is less than the safety threshold, it is calibrated to a high stability state.

4. According to the method for monitoring feeder automation terminal faults based on big data analysis in claim 2, it is characterized in that: The specific method for obtaining the current state coefficient is as follows: Install current sensors on feeder automation terminals, collect real-time current data within a certain period of time and fit it into a current curve, calibrate the current data and remove abnormal current values; The calibrated current values ​​are integrated into a current set, current characteristic values ​​are extracted from the current set, and the current state coefficients are calculated according to the current characteristic values.

5. The method for monitoring feeder automation terminal faults based on big data analysis according to claim 2 is characterized in that: The specific method for obtaining the voltage waveform coefficient is as follows: Acquire real-time voltage waveform data and display the collected voltage waveform data in the time domain; Obtain the voltage time domain waveform, and filter out the part with the lowest fundamental frequency in the voltage time domain waveform; Perform harmonic analysis on the filtered voltage time domain waveform to obtain the voltage harmonic amplitude and voltage harmonic period; The voltage harmonic amplitude, voltage harmonic period and harmonic frequency are combined to calculate the harmonic effective value, and the voltage waveform coefficient is obtained by combining the voltage fundamental frequency and voltage harmonic phase.

6. A feeder automation terminal fault monitoring method based on big data analysis according to claim 2, characterized in that: The specific method for obtaining the tripping action coefficient is as follows: Observe the protection devices in the power grid system controlled by the feeder automation terminal. When a terminal fault is detected, measure and record the current value, record the time interval from the detection of the fault by the protection device to the tripping of the terminal device, and calculate the tripping fault coefficient; The tripping action coefficient is further calculated by combining the fault current value with the tripping fault coefficient.

7. A feeder automation terminal fault monitoring system and method based on big data analysis according to claim 1, characterized in that: The specific calculation formula of the quantized value fault assessment coefficient is as follows: In the formula, is the current state coefficient, is the voltage waveform factor, is the tripping action coefficient, is the weight factor of the current state coefficient, is the weight factor of the voltage waveform factor, is the weight factor of the tripping action coefficient, is the quantitative value fault assessment coefficient.

8. The method for monitoring feeder automation terminal faults based on big data analysis according to claim 5 is characterized in that: The calculation formula of the effective value of harmonics is: The specific calculation formula of the voltage waveform coefficient is as follows: In the formula, is the voltage harmonic period, is the harmonic frequency, is the voltage harmonic amplitude, is the voltage harmonic frequency, is the voltage harmonic phase, is the effective value of harmonics, is the voltage waveform factor.

9. A feeder automation terminal fault monitoring system and method based on big data analysis according to claim 6, characterized in that: The specific calculation formula of the tripping fault coefficient is as follows: The calculation formula of the tripping action coefficient is as follows: In the formula, is the terminal adjustment factor, ranging from 0 to 1. Representative Measurements, is the total number of measurements, is the time interval from the failure of the protection device to the tripping of the terminal equipment, is the current value at the terminal fault, is the tripping fault coefficient, is the tripping action coefficient.

10. The feeder automation terminal fault monitoring system based on big data analysis according to claim 7, characterized in that: Includes the following modules: Acquisition module: collects terminal data from the back end of the trip switch and conducts comprehensive analysis based on the historical first data of the feeder automation equipment; Data processing module: Based on the terminal data at the back end of the trip switch and the historical first data of the feeder automation equipment, the analysis model is established using mathematical methods to generate a quantitative value fault assessment coefficient; Control module: Based on the quantitative fault assessment coefficient, analyze and compare the fault conditions of the feeder automation terminal equipment, evaluate the working status of the power grid system controlled by the feeder automation terminal, and perform corresponding configuration optimization and structural adjustment.

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