Power distribution network fault monitoring method and monitoring equipment

Abstract

The invention relates to a power distribution network fault monitoring method and monitoring equipment, and the method comprises the following steps: data collection: collecting data information in a power distribution network line, and achieving the automatic fault recording; receiving data: receiving various data and fault recording information, and uploading the data for analysis and processing; data preprocessing: carrying out standardization processing on related data by adopting a similar maximum standardization method; current data clustering calculation is carried out; a C mean value fuzzy clustering algorithm is adopted to realize power distribution network line load prediction; current-day load mode matching: determining a current-day load affiliation category by adopting a maximum membership degree method; mismatch degree monitoring: defining a mismatch degree index considering membership degree and Euclidean distance factors as a fault evaluation criterion; fault type research and judgment: determining the fault type of the line which is judged to be faulty; fault positioning: accurately positioning a fault position by adopting a single-end distance measurement algorithm; the method has the advantages of fault monitoring analysis based on big data, fault evaluation criterion definition and accurate positioning.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network fault monitoring technology, and specifically relates to a power distribution network fault monitoring method and monitoring equipment. Background Technology

[0002] Against the backdrop of accelerating urban-rural integration and rural power grid transformation, my country's electricity demand is increasing daily, and the requirements for power supply security and efficiency are becoming increasingly stringent. While vigorously developing the urban and rural economy, the transformation of urban and rural distribution networks has been further deepened with the upgrading of rural and urban power grids, the widespread investment in power cables, and the use of new components and equipment. The construction of 10kV and above high-voltage levels is now basically complete, improving automation levels and making dispatch and operation management more standardized, significantly reducing the distribution network failure rate. However, the power grid currently has a large variety and quantity of distribution equipment, and with varying levels of economic development in different regions, a considerable number of non-automated devices still exist in the distribution network, resulting in many branch lines being unmonitored. Real-time monitoring and fault diagnosis of the distribution network are crucial for ensuring its reliable and safe operation and understanding its real-time operation. The status of power distribution networks is of great importance. However, the current real-time monitoring and fault diagnosis devices for power distribution networks are not yet fully functional, directly affecting the safe and reliable operation of the power distribution network system. This is especially true in rural power grids, which are numerous, widely distributed, and have complex operating conditions. When a fault occurs, it is difficult to accurately locate the fault area, and the processing time is long. This is particularly true in remote mountainous areas. With rapid economic development, the load on 10kV distribution lines is constantly increasing, and the power supply radius is also constantly expanding, making the power distribution network increasingly complex. Especially during summer thunderstorms, there are many 10kV distribution line trips, and troubleshooting is time-consuming and labor-intensive, increasing the difficulty of restoring power supply and directly affecting the reliability and quality of service of power supply. Therefore, it is very necessary to provide a power distribution network fault monitoring method and monitoring equipment based on big data analysis, defining fault evaluation criteria, and accurate location. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and equipment for monitoring power distribution network faults based on big data analysis, defining fault evaluation criteria, and accurately locating faults.

[0004] The objective of this invention is achieved as follows: a method for monitoring faults in a power distribution network, the method comprising the following steps:

[0005] Step 1: Data Acquisition: Collect data on equipment status, load current / voltage, sudden action current / voltage, duration, etc., at multiple measurement points in the distribution network line, and realize automatic fault recording;

[0006] Step 2: Data Reception: Receive various types of collected data and fault waveform information, and upload the data for analysis and processing;

[0007] Step 3: Data preprocessing: Use a method similar to maximum normalization to standardize the relevant data information so that the data have similar orders of magnitude and appropriate amplitude;

[0008] Step 4: Current data clustering calculation: The C-means fuzzy clustering algorithm is used to calculate the predicted value of the line current, so as to realize the load prediction of the distribution network line;

[0009] Step 5: Daily load pattern matching: The C-means fuzzy clustering algorithm is used to identify the load pattern of the distribution line, and the maximum membership method is used to determine the category of the daily load.

[0010] Step 6: Mismatch monitoring: Based on the clustering and matching results, calculate the predicted value of the next data point, and define a mismatch index that considers membership degree and Euclidean distance factors as a fault evaluation criterion;

[0011] Step 7: Fault type analysis: For lines identified as faulty, further logical analysis is performed on the current telemetry data to determine the type of fault.

[0012] Step 8: Fault location: For faulty lines whose fault type is determined, a single-end ranging algorithm is used to calculate and analyze the fault current using the characteristics of traveling wave transmission, so as to accurately locate the fault location.

[0013] The data preprocessing in step 3 specifically involves: using a maximum value normalization method to normalize the data based on the line current limit, i.e.: In the formula, x ij The data before processing; x′ ij The processed data is shown; d represents the line current limit.

[0014] The current data clustering calculation in step 4 is specifically as follows: Since the daily load curve exhibits different load patterns on weekdays and holidays, the cluster number c is set to 2. The line load patterns are clustered according to weekdays and holidays, resulting in two clusters: weekday cluster A1, with cluster center Z1 = (z...). 11 ,z 12 ,...,z 1m ); Non-working day class A2, cluster center Z2=(z 21 ,z 22 ,...,z 2m ).

[0015] The daily load pattern matching in step 5 specifically involves: reading the daily load data X0 of the line up to the current time t = (x 01 ,x 02,...,x 0n Let n be the length of the daily load data at time t, where n ≤ m. Using the subsequence matching method, the categories are determined, and subsequences Z1 and Z2 are selected. 1s =(z 11 ,z 12 ,...,z 1n ),Z 2s =(z 21 ,z 22 ,...,z 2n ), calculate X0 with respect to Z respectively 1s Z 2s membership degree u 01 u 02 The category to which the load belongs on a given day is determined by the maximum membership method.

[0016] The mismatch monitoring in step 6 specifically involves calculating the predicted value for the next data point based on the clustering and matching results. Read the standardized sample value x0(t) of the line at time t; define the mismatch index: In the formula, u0 = max(u 01 ,u 02 Set the mismatch threshold ε, K e If (t)≥ε, then the line is considered to be faulty.

[0017] The fault type assessment in step 7 is as follows: branch line tripping: current decreases significantly but is not zero; branch line closing: current increases significantly; main line tripping: current decreases to zero; main line closing: current increases from zero to a certain value; connecting line loop closing: branch line tripping and closing events occur on two lines respectively, and the current changes have equal amplitudes and opposite directions.

[0018] A power distribution network fault monitoring device, based on the power distribution network fault monitoring method described above, realizes power distribution network fault monitoring and location, including a data acquisition unit, a data aggregation unit, a data processing unit, and a power supply module; the data processing unit integrates a human-machine interface unit; the data acquisition unit is connected to the data aggregation unit, the data aggregation unit is connected to the data processing unit, and the data processing unit is connected to a remote monitoring backend.

[0019] The acquisition unit is configured to acquire data such as equipment status, load current / voltage, sudden action current / voltage, and duration at multiple measurement points in the distribution network line, and to automatically record fault waveforms. The collection unit is configured to receive the acquired data and fault waveform information, and send the data information up for analysis and processing. The data processing unit is configured to use a method similar to maximum normalization to standardize the relevant data information so that the data have similar orders of magnitude and appropriate amplitude.

[0020] The acquisition unit includes a power distribution terminal and a DAS master station. The power distribution terminal includes a DTU, an FTU, and a TTU. The power distribution terminal is connected to the DAS master station through a terminal access network and a backbone communication network. The acquisition unit also includes an EMS acquisition terminal. The EMS acquisition terminal adopts an RTU. The EMS acquisition terminal is connected to the EMS master station through serial communication and Ethernet.

[0021] The acquisition unit uses an LTC1608 chip; the data processing unit uses a TMS320VC33 chip; and the power supply module uses an ADD-155B UPS switching power supply and a WRA2412YMD-6W module power supply.

[0022] The beneficial effects of this invention are as follows: This invention provides a method and equipment for monitoring faults in power distribution networks. Addressing the limitations of current power distribution network monitoring methods and low data utilization, this invention employs a C-means fuzzy clustering algorithm to identify the load patterns of power distribution lines and perform load prediction. It defines a mismatch index considering membership degree and Euclidean distance factors as a criterion for evaluating line faults, effectively identifying the load patterns of power distribution lines, reliably determining line faults, and to some extent avoiding false alarms caused by normal load fluctuations. Finally, a single-end ranging algorithm is used to calculate and analyze the fault current using the characteristics of traveling wave transmission, accurately locating the fault. This invention has the advantages of fault monitoring based on big data analysis, defining fault evaluation criteria, and accurate fault location. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention.

[0024] Figure 2 This is a block diagram of the overall structure of the monitoring device of the present invention.

[0025] Figure 3 This is a circuit diagram of the switching power supply of the present invention.

[0026] Figure 4 This is a circuit diagram of the power module of the present invention. Detailed Implementation

[0027] The present invention will now be further described with reference to the accompanying drawings.

[0028] Example 1

[0029] like Figure 1-4 As shown, a method for monitoring faults in a power distribution network includes the following steps:

[0030] Step 1: Data Acquisition: Collect data on equipment status, load current / voltage, sudden action current / voltage, duration, etc., at multiple measurement points in the distribution network line, and realize automatic fault recording;

[0031] Step 2: Data Reception: Receive various types of collected data and fault waveform information, and upload the data for analysis and processing;

[0032] Step 3: Data preprocessing: Use a method similar to maximum normalization to standardize the relevant data information so that the data have similar orders of magnitude and appropriate amplitude;

[0033] In this invention, before performing fuzzy clustering, it is usually necessary to standardize the data to ensure that the data have similar orders of magnitude and appropriate amplitude. For the clustering objects of this invention, for a given line, its historical current level remains at a similar order of magnitude, and the load differences between weekdays and holidays should not be erased by the preprocessing steps. This invention selects a method similar to maximum standardization, using the line current limit number for standardization, that is: In the formula, x ij The data before processing; x′ ij The data is the processed data; d is the line current limit, which is a constant for all distribution lines based on the actual production situation of the power supply company.

[0034] Step 4: Current data clustering calculation: The C-means fuzzy clustering algorithm is used to calculate the predicted value of the line current, so as to realize the load prediction of the distribution network line;

[0035] In this invention, for a given power line, without large-scale modifications, its daily load curve exhibits different load patterns on weekdays and holidays. The specific differences depend on the type and proportion of the load it carries. Therefore, the cluster number c is set to 2, and the power line load patterns are clustered according to weekdays and holidays, resulting in two clusters: weekday cluster A1, with cluster center Z1 = (z 11 ,z 12 ,...,z 1m ); Non-working day class A2, cluster center Z2=(z 21 ,z 22 ,...,z 2m ).

[0036] The principle of C-means fuzzy clustering algorithm: Given a sample A = {X1, X2, ..., X...} n}, where X k ={x k1 ,x k2 ,...,x km Let the number of clusters be c, then the objective function is: In the formula, d kiFor the k-th sample to the i-th class center z i The distance is calculated using the following formula: u ki The membership degree of the k-th sample in the i-th class is calculated using the following formula: Given the number of clusters c, calculate the initial cluster centers; calculate the membership degree of the samples and correct the cluster centers; finally, determine the category to which the samples belong by using the membership degree.

[0037] Step 5: Daily load pattern matching: The C-means fuzzy clustering algorithm is used to identify the load pattern of the distribution line, and the maximum membership method is used to determine the category of the daily load.

[0038] In this invention, load pattern matching is achieved by reading the daily load data (x0) up to the current time (t) of the line. 01 ,x 02 ,...,x 0n Let n be the length of the daily load data at time t, where n ≤ m. Using the subsequence matching method, the categories are determined, and subsequences Z1 and Z2 are selected. 1s =(z 11 ,z 12 ,...,z 1n ),Z 2s =(z 21 ,z 22 ,...,z 2n ), calculate X0 with respect to Z respectively 1s Z 2s membership degree u 01 u 02 The category to which the load belongs on a given day is determined by the maximum membership method.

[0039] Step 6: Mismatch monitoring: Based on the clustering and matching results, calculate the predicted value of the next data point, and define a mismatch index that considers membership degree and Euclidean distance factors as a fault evaluation criterion;

[0040] In this invention, mismatch determination involves calculating the predicted value of the next data point based on clustering and matching results. Read the standardized sample value x0(t) of the line at time t. Define the mismatch index: In the formula, u0 = max(u 01 ,u 02 ).

[0041] The mismatch index considers both the deviation between real-time and predicted values ​​and the membership degree of subsequences. The clearer the historical load pattern of the line and the higher the membership degree, the more sensitive the mismatch index is to load fluctuations. Conversely, even if the load fluctuations are relatively large, they are not obvious in the mismatch index.

[0042] Set the mismatch threshold ε, K e If (t)≥ε, then the line is considered to be faulty.

[0043] Step 7: Fault type analysis: For lines identified as faulty, further logical analysis is performed on the current telemetry data to determine the type of fault.

[0044] In this invention, fault type assessment involves further logical analysis of the current telemetry data for lines identified as faulty to determine the type of fault.

[0045] Branch line tripping: Current decreases significantly but is not zero;

[0046] Branch line closing: current increases significantly;

[0047] Main line tripping: Current drops to zero;

[0048] Main line closing: increasing from zero to a certain value;

[0049] Linkage loop closing: Two lines experience branch line opening and closing events respectively, and the current changes have equal amplitudes and opposite directions.

[0050] The detected faulty lines and automatically identified fault types are pushed to the control workstation for confirmation and handling by control personnel.

[0051] Step 8: Fault location: For faulty lines whose fault type is determined, a single-end ranging algorithm is used to calculate and analyze the fault current using the characteristics of traveling wave transmission, so as to accurately locate the fault location.

[0052] In this invention, after a fault occurs in a transmission line, the voltage and current signals undergo significant distortion during transient changes due to the presence of attenuated DC components and variable harmonics. Digital filters and algorithms are needed to eliminate these distortions, thereby improving the accuracy and precision of fault location. This invention employs the full-wave Fourier algorithm, assuming the mathematical model is a periodic time function of the sampled signal, expressed as follows: In the formula, A is the DC component in the sampled signal; a n b n The amplitudes of the sine and cosine terms of each harmonic are represented, where: In the formula, N represents the number of sampling points (within one period); X k represents the k-th sample value (1 cycle); n represents the nth harmonic.

[0053] In a 10kV distribution network system application, assuming a single-phase ground fault occurs at point K of a certain phase of the line at time t-1, the bus fault phase voltage is equal to the short-circuit fault phase voltage U. Kφ Combined with the voltage drop across the line, we have: Uφ =U Kφ +I1Z1+I2Z2+I0Z0, where, Let A, B, and C represent the fault phases A, B, and C, respectively. Therefore, U... φ =U 1φ +Z1(I φ +3KI0),

[0054] Assuming a phase-to-phase fault occurs, based on the above principle, we can obtain: U φφ =U kφ +Z1I φ In the formula, φφ represents AB, BC, and AC, respectively, indicating the two faulty phases; when U Kφ When = 0, we have: This allows us to calculate the fault impedance from the busbar or protection installation point to the fault location; analyzing the uniformly distributed impedance of the line, assuming the line length is L, the fault distance is calculated as follows: In the formula, Z1 represents the total impedance of the distribution line; z represents the impedance value per unit length of the line. Therefore, the formula for calculating the location of a single-phase ground fault is:

[0055] For phase-to-phase short circuits, the calculation formula is: Errors are inevitable in the measurement process. The error calculation formula is usually as follows: In the formula, L represents the length of the power distribution line; x calculate Indicates the calculated fault distance; x actual Indicates the actual distance to the fault.

[0056] This invention discloses a method for monitoring distribution network faults. Addressing the limitations of current distribution network monitoring methods and low data utilization, this method employs a C-means fuzzy clustering algorithm to identify distribution line load patterns and predict load. It defines a mismatch index considering membership degree and Euclidean distance as a criterion for line fault evaluation. This effectively identifies distribution line load patterns, reliably determines line faults, and to some extent avoids false alarms caused by normal load fluctuations. Specifically, when the distribution network is operating normally, the main line current conforms to its load pattern, and the current curve closely matches historical data. When a fault occurs in the distribution network, the real-time sampled value changes abruptly, losing its match with the predicted value calculated based on historical data. Therefore, simply detecting the presence of mismatch points in the real-time current of the distribution line is sufficient for distribution network fault monitoring. Further logical judgment determines the specific fault type. This method effectively identifies distribution network faults that cannot be monitored by existing methods. Finally, a single-end ranging algorithm is used to calculate and analyze the fault current using traveling wave transmission characteristics, accurately locating the fault. This invention has the advantages of fault monitoring based on big data analysis, defining fault evaluation criteria, and accurate fault location.

[0057] Example 2

[0058] like Figure 1-4 As shown, a power distribution network fault monitoring device realizes power distribution network fault monitoring and location based on the power distribution network fault monitoring method described above, including a data acquisition unit, a data aggregation unit, a data processing unit, and a power supply module; the data processing unit integrates a human-machine interface unit; the data acquisition unit is connected to the data aggregation unit, the data aggregation unit is connected to the data processing unit, and the data processing unit is connected to a remote monitoring backend.

[0059] In this invention, the acquisition unit and the aggregation unit are installed on the distribution line to monitor the line's operating load parameters, detect short circuits and ground faults, and send monitoring information and fault detection data to the remote monitoring backend. The acquisition unit, installed on the distribution line, captures the line load current and sudden operating current, and uses the amount and duration of the sudden load current change as criteria to identify transient and permanent short circuit faults. When a ground fault occurs, the acquisition unit sends the transient characteristic information of the ground fault and realizes automatic fault waveform recording, which is then sent to the aggregation unit. It can not only monitor historical data curves such as the maximum value of the ground operating current and the line-to-ground electric field, but also the waveform recording range includes no less than 3 cycles before the fault to 5 cycles after the fault, with no less than 64 points per cycle. The acquisition unit indicates the fault alarm locally by flipping a card and flashing an indicator light, and the short circuit fault and ground fault indicator lights can indicate with different flashing frequencies.

[0060] The acquisition unit is configured to acquire data such as equipment status, load current / voltage, sudden action current / voltage, and duration at multiple measurement points in the distribution network line, and to automatically record fault waveforms. The collection unit is configured to receive the acquired data and fault waveform information, and send the data information up for analysis and processing. The data processing unit is configured to use a method similar to maximum normalization to standardize the relevant data information so that the data have similar orders of magnitude and appropriate amplitude.

[0061] The acquisition unit includes a power distribution terminal and a DAS master station. The power distribution terminal includes a DTU, an FTU, and a TTU. The power distribution terminal is connected to the DAS master station through a terminal access network and a backbone communication network. The acquisition unit also includes an EMS acquisition terminal. The EMS acquisition terminal adopts an RTU. The EMS acquisition terminal is connected to the EMS master station through serial communication and Ethernet.

[0062] The acquisition unit uses an LTC1608 chip; the data processing unit uses a TMS320VC33 chip; and the power supply module uses an ADD-155B UPS switching power supply and a WRA2412YMD-6W module power supply.

[0063] In this invention, ① the data acquisition AD chip of the data acquisition unit adopts the LTC1608 chip, which has an on-chip sample / hold circuit, a resolution of 16 bits, and a conversion rate of 500KSPS; during data sampling, the analog signal first passes through a first-order RC low-pass filter and a sample-hold circuit, and then passes through multiple analog switches and operational amplifiers for proportional attenuation before being transmitted to the data buffer; the data conversion adopts a simultaneous sampling and time-division conversion method, using a timer to trigger a signal to start simultaneous sampling of multiple channels, and the software scans the set number of loops at the highest rate to perform time-division conversion (i.e., polling method) or uses an interrupt method to respond to the interrupt when the AD conversion ends, and samples the multiple analog values ​​one by one through the set channel selection register.

[0064] ② Data Processing Unit: The data processing unit uses the TMS320VC33 chip, with external expansion including 128K of 8-bit EPROM, 64K of 32-bit high-speed RAM, 1M of 8-bit NVRAM, and 16K of 8-bit E2PROM. The TMS320VC33 chip operates at a frequency of 60MHz and has an instruction cycle of 17ns. It can perform parallel multiplication and ALU operations on data in a single cycle, and has a high-speed floating-point operation capability of 120-150MFLOPS and 60-75MIPS. It has 34Kx32-bit RAM on-chip. The program is stored in the external EPROM and is automatically loaded into the fast on-chip RAM after power-on. Fault waveform data is stored in the external NVRAM and can continuously record the four cycles before the fault and the 20 cycles after the fault.

[0065] ③ Power supply module: The structural diagram of the ADD-155B UPS switching power supply is as follows. Figure 3 As shown in the diagram, the structural diagram of the WRA2412YMD-6W module power supply is as follows: Figure 4 As shown.

[0066] This invention relates to a power distribution network fault monitoring device, enabling real-time monitoring and fault diagnosis of the power distribution network. In use, the device can measure basic electrical parameters such as three-phase voltage, current, power, power factor, and harmonics in the power distribution network in real time. It can also monitor the system status in real time. When a fault occurs, it reliably records the dynamic process of the fault and accurately calculates the faulty line (fault selection) based on the aforementioned power distribution network fault monitoring method. Furthermore, under normal conditions, the fault location detection terminal is powered by a feeder converter; in fault conditions, it is powered by a battery. The battery voltage determines the stability and reliability of the device under fault conditions. Therefore, this invention uses an ADD-155BUPS switching power supply and a WRA2412YMD-6W modular power supply to ensure reliability and stable, reliable operation of the device. This invention has the advantages of fault monitoring based on big data analysis, defining fault evaluation criteria, and accurate fault location.

Claims

1. A method for monitoring faults in a power distribution network, characterized in that: The method includes the following steps: Step 1: Data Acquisition: Collect data on equipment status, load current / voltage, sudden action current / voltage, duration, etc., at multiple measurement points in the distribution network line, and realize automatic fault recording; Step 2: Data Reception: Receive various types of collected data and fault waveform information, and upload the data for analysis and processing; Step 3: Data preprocessing: The relevant data information is standardized using the maximum value standardization method to make the data have similar orders of magnitude and appropriate amplitude; Step 4: Current data clustering calculation: The C-means fuzzy clustering algorithm is used to calculate the predicted value of the line current, so as to realize the load prediction of the distribution network line; Step 5: Daily load pattern matching: The C-means fuzzy clustering algorithm is used to identify the load pattern of the distribution line, and the maximum membership method is used to determine the category of the daily load. Step 6: Mismatch monitoring: Based on the clustering and matching results, calculate the predicted value of the next data point, and define a mismatch index that considers membership degree and Euclidean distance factors as a fault evaluation criterion; Step 7: Fault type analysis: For lines identified as faulty, further logical analysis is performed on the current telemetry data to determine the type of fault. Step 8: Fault location: For faulty lines whose fault type is determined, a single-end ranging algorithm is used to calculate and analyze the fault current using the characteristics of traveling wave transmission, so as to accurately locate the fault location.

2. The method for monitoring faults in a power distribution network as described in claim 1, characterized in that: The data preprocessing in step 3 specifically involves: using a maximum value normalization method to normalize the data based on the line current limit, i.e.: In the formula, x ij The data before processing; x′ ij The processed data is shown; d represents the line current limit.

3. The method for monitoring power distribution network faults as described in claim 1, characterized in that: The current data clustering calculation in step 4 is specifically as follows: Since the daily load curve exhibits different load patterns on weekdays and holidays, the cluster number c is set to 2. The line load patterns are clustered according to weekdays and holidays, resulting in two clusters: weekday cluster A1, with cluster center Z1 = (z...). 11 ,z 12 ,...,z 1m ); Non-working day class A2, cluster center Z2=(z 21 ,z 22 ,...,z 2m ).

4. The method for monitoring faults in a power distribution network as described in claim 1, characterized in that: The daily load pattern matching in step 5 specifically involves: reading the daily load data X0 of the line up to the current time t = (x 01 ,x 02 ,...,x 0n Let n be the length of the daily load data at time t, where n ≤ m. Using the subsequence matching method, the categories are determined, and subsequences Z1 and Z2 are selected. 1s =(z 11 ,z 12 ,...,z 1n ),Z 2s =(z 21 ,z 22 ,...,z 2n ), calculate X0 with respect to Z respectively 1s Z 2s membership degree u 01 u 02 The category to which the load belongs on a given day is determined by the maximum membership method.

5. The method for monitoring faults in a power distribution network as described in claim 1, characterized in that: The mismatch monitoring in step 6 specifically involves calculating the predicted value for the next data point based on the clustering and matching results. Read the standardized sample value x0(t) of the line at time t; define the mismatch index: In the formula, u0 = max(u 01 ,u 02 Set the mismatch threshold ε, K e If (t)≥ε, then the line is considered to be faulty.

6. The method for monitoring faults in a power distribution network as described in claim 1, characterized in that: The fault type assessment in step 7 is as follows: branch line tripping: current decreases significantly but is not zero; branch line closing: current increases significantly; main line tripping: current decreases to zero; main line closing: current increases from zero to a certain value; connecting line loop closing: branch line tripping and closing events occur on two lines respectively, and the current changes have equal amplitudes and opposite directions.

7. A power distribution network fault monitoring device, which realizes power distribution network fault monitoring and location based on the power distribution network fault monitoring method as described in any one of claims 1-6, characterized in that: It includes a data acquisition unit, a data aggregation unit, a data processing unit, and a power supply module; the data processing unit integrates a human-machine interface unit; the data acquisition unit is connected to the data aggregation unit, the data aggregation unit is connected to the data processing unit, and the data processing unit is connected to a remote monitoring backend.

8. The power distribution network fault monitoring device as described in claim 7, characterized in that: The acquisition unit is configured to acquire data such as equipment status, load current / voltage, sudden action current / voltage, and duration at multiple measurement points in the distribution network line, and to automatically record fault waveforms. The collection unit is configured to receive the acquired data and fault waveform information, and send the data information up for analysis and processing. The data processing unit is configured to use a method similar to maximum normalization to standardize the relevant data information so that the data have similar orders of magnitude and appropriate amplitude.

9. The power distribution network fault monitoring device as described in claim 8, characterized in that: The acquisition unit includes a power distribution terminal and a DAS master station. The power distribution terminal includes a DTU, an FTU, and a TTU. The power distribution terminal is connected to the DAS master station through a terminal access network and a backbone communication network. The acquisition unit also includes an EMS acquisition terminal. The EMS acquisition terminal adopts an RTU. The EMS acquisition terminal is connected to the EMS master station through serial communication and Ethernet.

10. The power distribution network fault monitoring device as described in claim 7, characterized in that: The acquisition unit uses an LTC1608 chip; the data processing unit uses a TMS320VC33 chip; and the power supply module uses an ADD-155B UPS switching power supply and a WRA2412YMD-6W module power supply.

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