Power distribution network grounding fault identification and early warning method
By collecting multi-dimensional data in real time on the distribution network lines and building an association judgment model, and dynamically adjusting the sampling frequency, the problem of the imbalance between accuracy and efficiency and the high false alarm rate in the traditional distribution network grounding fault identification method is solved. It achieves efficient and accurate grounding fault identification and early warning, adapts to complex working conditions, and reduces the risk of power outages.
Patent Information
- Application Number
- CN202511177697.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-21
- Publication Date
- 2025-12-09
AI Technical Summary
Traditional methods for identifying grounding faults in distribution networks suffer from problems such as an imbalance between accuracy and efficiency due to fixed sampling frequencies, high false alarm and false negative rates due to single characteristic quantities, and poor adaptability due to simple judgment logic. These issues make it difficult to meet the requirements of intelligent distribution networks for real-time performance, accuracy, and reliability.
By installing sensors on power distribution lines to collect multi-dimensional data in real time, including zero-sequence current, zero-sequence voltage, phase angle, harmonic components, and line temperature, the sampling frequency is dynamically adjusted, an association judgment model is constructed, and anomaly judgment logic of multiple characteristic quantities is combined to achieve accurate identification and early warning of grounding faults.
It effectively reduces false alarm and false alarm rates, improves the accuracy and reliability of fault identification, adapts to different topologies and environmental conditions, and can issue early warnings in the early stages of faults, reducing power outage losses.
Smart Images
Figure CN121090975A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power systems, in particular to a power distribution network grounding fault identification and early warning method. BACKGROUND
[0002] As a key link connecting the power transmission network and users in the power system, the safe and stable operation of the power distribution network is directly related to the reliability of power supply. However, the power distribution network has a wide distribution range and a complex topology, and is easily affected by natural environment (such as lightning, tree barriers, icing), equipment aging, human operation and other factors, and grounding faults occur from time to time.
[0003] If the grounding fault cannot be identified and warned in time, it may cause line tripping, equipment damage and other problems, and even cause large-area power outages in severe cases, causing great losses to industrial production and residents' life. The traditional power distribution network grounding fault identification method relies on a single electrical parameter (such as zero sequence current amplitude) or fixed threshold value judgment, which has the following limitations: first, the sampling frequency is fixed, it is difficult to balance the monitoring accuracy and data processing efficiency, and it is easy to produce data redundancy in normal operation, and the feature may be missed due to insufficient sampling in fault; second, the feature quantity is single, it is difficult to distinguish the real fault from the interference signal (such as harmonic pollution, instantaneous impact) only relying on the electrical parameter, and the false positive rate and the false negative rate are high; third, the judgment logic is simple, lacks multi-parameter correlation analysis, and has poor adaptability to fault identification under complex conditions.
[0004] With the improvement of the intelligent level of the power distribution network, higher requirements are put forward for the real-time, accuracy and reliability of fault identification, and an grounding fault identification and early warning method is urgently needed, which integrates multi-dimensional features, dynamically adjusts the sampling strategy and has correlation judgment ability. SUMMARY
[0005] In view of the deficiencies of the prior art, the present application provides a power distribution network grounding fault identification and early warning method, which solves the problems of imbalance between sampling efficiency and accuracy, single feature quantity leading to false and missed reports, simple judgment logic adaptability and lack of flexibility of fixed threshold value in traditional power distribution network grounding fault identification.
[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a power distribution network grounding fault identification and early warning method, comprising:
[0007] S1, data acquisition: through the sensors installed on the power distribution network line, real-time acquisition of zero sequence current, zero sequence voltage, phase angle, harmonic component and line temperature data;
[0008] S2, feature extraction: processing the collected data to extract the feature quantity for judging the grounding fault;
[0009] S3, variable judgment logic construction: set the judgment threshold of the characteristic quantity and the logic condition respectively, for judging whether each characteristic quantity is abnormal or not;
[0010] S4, correlation judgment model establishment: based on the judgment result of each characteristic quantity, the correlation judgment model is constructed, when the preset correlation condition is met, it is judged as grounding fault and the early warning is issued;
[0011] S5, early warning output: according to the judgment result of the correlation judgment model, the corresponding fault early warning information is output.
[0012] Preferably, the S1 further includes a dynamic sampling frequency adjustment mechanism: the sampling frequency is dynamically adjusted according to the line operation state.
[0013] Preferably, the characteristic quantity extracted in the S2 includes: zero sequence current sudden change value ΔI0, zero sequence voltage distortion degree D0, phase angle offset Δθ, harmonic component proportion H, line temperature change rate K t and fault transient characteristic quantity T.
[0014] Preferably, the judgment condition of the zero sequence current sudden change value ΔI0 in the S3 is: when ΔI0> ΔI 0th , it is judged that the zero sequence current exists abnormal sudden change; wherein ΔI0=|I 0t -I 0t-1 |, I 0t is the zero sequence current value at the current time, I 0t-1 is the zero sequence current value at the previous time, and I 0th is the zero sequence current sudden change threshold.
[0015] Preferably, the judgment condition of the zero sequence voltage distortion degree D0 in the S3 is: when D0>D 0th , it is judged that the zero sequence voltage exists distortion abnormality; wherein U1 is the zero sequence voltage fundamental component, U2, U3,..., U n is each harmonic component of the zero sequence voltage, D 0th is the voltage distortion degree threshold.
[0016] Preferably, the judgment condition of the phase angle offset Δθ in the S3 is: when |Δθ|>θ th , it is judged that the phase angle exists offset abnormality; wherein Δθ is the absolute value of the difference between the phase angle at the current time and the reference phase angle at the normal operation, and θ th is the phase angle offset threshold.
[0017] Preferably, the judgment condition of the harmonic component proportion H in the S3 is: when H>H th , it is judged that the harmonic component proportion is abnormal; wherein H is the ratio of the sum of the effective values of each harmonic current to the total current effective value, and Hth is a harmonic proportion threshold.
[0018] Preferably, the line temperature change rate K in S3 t is a judgment condition: when K t > K tth , it is determined that the line temperature change is abnormal; wherein T t is the line temperature at the current time, T t-t0 is the line temperature before t0 time, t0 is a set time interval, K tth is a temperature change rate threshold.
[0019] Preferably, the judgment condition of the fault transient characteristic T in S3 is: when T > T th , it is determined that there is an abnormal fault transient characteristic; wherein ΔU0 is a zero sequence voltage mutation value, t t is the duration of the transient process, T th is a transient characteristic threshold.
[0020] Preferably, the preset correlation condition in S4 is one of the following:
[0021] a. Abnormal mutation of zero sequence current and distortion anomaly of zero sequence voltage exist, and phase angle offset anomaly exists;
[0022] b. Abnormal mutation of zero sequence current and abnormal proportion of harmonic component exist, and line temperature change anomaly exists;
[0023] c. Distortion anomaly of zero sequence voltage and abnormal proportion of harmonic component exist, and phase angle offset anomaly exists;
[0024] d. Abnormal fault transient characteristic exists and at least 2 variable anomalies of any condition in a, b and c are met.
[0025] The beneficial effects thereof are as follows:
[0026] 1. The power distribution network grounding fault identification and early warning method effectively distinguishes real faults from interference signals by multi-dimensional feature fusion analysis combined with logical combination of correlation judgment models, reduces false positive rate, and reduces false negative rate. The dynamic sampling frequency adjustment mechanism uses the basic frequency in normal operation to reduce data processing amount; it is increased to 2-3 times frequency in abnormality, and to 4-5 times frequency in fault, ensuring that key features are not missed, and efficiency and accuracy are considered.
[0027] 2、The power distribution network grounding fault identification and early warning method supports dynamic adjustment of threshold value and sampling frequency, can adapt to different power distribution network topologies, load levels and environmental conditions, and has strong versatility. Through coupling analysis of the amplitude and time of the initial stage of the fault by the transient characteristic quantity T, in combination with early abnormal monitoring of the temperature change rate, an early warning can be issued before the fault develops into tripping, time is gained for fault handling, and power loss is reduced. BRIEF DESCRIPTION OF DRAWINGS
[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.
[0029] Figure 1 The figure is a schematic diagram of the core process framework of the present application.
[0030] Figure 2 The figure is a schematic diagram of the data acquisition stage framework of the present application.
[0031] Figure 3 The figure is a schematic diagram of the feature extraction stage framework of the present application.
[0032] Figure 4 The figure is a schematic diagram of the variable judgment logic construction stage framework of the present application.
[0033] Figure 5 The figure is a schematic diagram of the correlation judgment model establishment stage framework of the present application.
[0034] Figure 6 The figure is a schematic diagram of the early warning output stage framework of the present application. DETAILED DESCRIPTION
[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0036] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings in the specification and specific embodiments.
[0037] The embodiments of the present application disclose a power distribution network grounding fault identification and early warning method, according to the attached Figure 1 The figure shows that it includes:
[0038] S1, data acquisition: through the sensor installed on the distribution network line, real-time acquisition of zero sequence current, zero sequence voltage, phase angle, harmonic component and line temperature data;
[0039] S2, feature extraction: processing the collected data, extracting the characteristic quantity for judging the ground fault;
[0040] S3, variable judgment logic construction: setting the judgment threshold and logic condition of the characteristic quantity respectively, for determining whether each characteristic quantity is abnormal;
[0041] S4, correlation judgment model establishment: based on the judgment result of each characteristic quantity, the correlation judgment model is established, when the preset correlation condition is met, it is judged as ground fault and the early warning is issued;
[0042] S5, early warning output: according to the judgment result of the correlation judgment model, output the corresponding fault warning information.
[0043] The overall framework of distribution network ground fault identification and early warning is built, and the complete process from data acquisition to final warning is clarified. In practical application, according to the order of data acquisition, feature extraction, variable judgment logic construction, correlation judgment model establishment and early warning output, the fault identification and early warning work is carried out in order, which is suitable for ground fault monitoring scene of various distribution network lines.
[0044] Further, S1 also includes dynamic sampling frequency adjustment mechanism: according to the line operation state, the sampling frequency is adjusted dynamically.
[0045] Under different line operation states, different sampling frequencies are set. When the line is in normal operation state, that is, all characteristic quantities for judging ground fault (such as zero sequence current sudden change value ΔI0, zero sequence voltage distortion degree D0, phase angle offset Δθ, harmonic component proportion H, line temperature change rate K t and fault transient characteristic quantity T) do not trigger abnormal threshold, the sampling frequency is set as the basic frequency f0. The value range of basic frequency f0 is usually 50-100Hz, which can meet the demand of normal state monitoring of line operation parameters, maintain the basic monitoring of line state with lower data acquisition amount, and avoid unnecessary data redundancy.
[0046] When any variable triggers an abnormal threshold but the fault determination condition has not been met, it means that there is a potential risk of a fault on the line, but it has not developed into an explicit ground fault. At this time, in order to more accurately capture the possible fault characteristics, the sampling frequency is automatically increased to f1, which is 2-3 times f0. If f0 is set to 60Hz, then f1 is between 120-180Hz. Higher sampling frequency can obtain more intensive line operation data, making the monitoring of the trend of fault characteristics more detailed, which helps to find fault hazards in advance and prevent further deterioration of the fault.
[0047] When the ground fault is determined according to the correlation determination model, in order to comprehensively and accurately record various data during the fault process and provide sufficient data support for subsequent fault analysis and processing, the sampling frequency is further increased to f2, which is 4-5 times f0. If f0 is 80Hz, then f2 reaches 320-400Hz. And this high sampling frequency will be maintained until the fault is eliminated, ensuring that the changes of various electrical parameters and physical quantities can be completely collected during the entire fault period, providing detailed and reliable data basis for accurate positioning and cause analysis of the fault.
[0048] Through the mechanism of dynamically adjusting the sampling frequency, on the one hand, it can reduce the data processing burden during normal operation, and on the other hand, it can timely increase the sampling frequency to capture key fault information when a fault occurs or a potential fault occurs, greatly improving the performance and reliability of the distribution network ground fault identification and early warning system.
[0049] According to the accompanying drawings Figure 2 Further, the feature quantities extracted in S2 include: zero sequence current mutation value ΔI0, zero sequence voltage distortion degree D0, phase angle offset Δθ, harmonic component proportion H, line temperature change rate K t and fault transient characteristic quantity T.
[0050] After determining the key feature dimensions for judging the ground fault, a multi-dimensional feature system is formed by fusing electrical parameters and line temperature parameters, which can more comprehensively reflect the characteristics of the ground fault, lay a foundation for subsequent accurate fault judgment, and effectively avoid the limitations of single feature quantity judgment.
[0051] In the feature extraction stage, the corresponding calculation method is used to extract the six feature quantities from the collected original data to form a feature set for fault judgment, providing data support for variable judgment logic construction.
[0052] According to the accompanying drawings Figure 3 The judgment condition of zero sequence current mutation value ΔI0 in S3 is: when ΔI0> ΔI 0th , it is determined that there is an abnormal mutation of zero sequence current; wherein ΔI0= |I 0t -I0t-1 |I 0t is the zero-sequence current value at the current moment, I 0t-1 is the zero-sequence current value at the previous moment, I 0th is the zero-sequence current mutation threshold.
[0053] By monitoring the mutation of the zero-sequence current, the abnormal change of the zero-sequence current when the ground fault occurs can be quickly captured, which is one of the important electrical characteristic indicators for judging the ground fault, and provides a key basis for fault identification. The current value ΔI0 and the previous moment value I0t-1 of the zero-sequence current are obtained in real time, and the zero-sequence current mutation value is calculated according to the formula ΔI0 = |I 0t -I 0t-1 , which is compared with the preset zero-sequence current mutation threshold I0th. If ΔI0 > ΔI 0th , it is determined that the zero-sequence current has an abnormal mutation.
[0054] The judgment condition of the zero-sequence voltage distortion degree D0 in S3 is: when D0 > D 0th , it is determined that the zero-sequence voltage has a distortion anomaly; wherein U1 is the zero-sequence voltage fundamental component, U2, U3,..., U n is the zero-sequence voltage harmonic component, and D 0th is the voltage distortion degree threshold.
[0055] By analyzing the distortion degree of the zero-sequence voltage, the influence of the ground fault on the zero-sequence voltage waveform can be reflected, which is another important electrical characteristic for identifying the ground fault, and is helpful for distinguishing normal voltage fluctuation from voltage anomaly caused by fault. The collected zero-sequence voltage is subjected to harmonic decomposition to obtain the fundamental component U1 and the harmonic components U2, U3,..., U n , and the zero-sequence voltage distortion degree is calculated according to the formula D0 = |U | / U1, which is compared with the voltage distortion degree threshold D0th. If D0 > D 0th , it is determined that the zero-sequence voltage has a distortion anomaly.
[0056] The judgment condition of the phase angle offset Δθ in S3 is: when |Δθ| > θ th , it is determined that the phase angle has an offset anomaly; wherein Δθ is the absolute value of the difference between the current moment phase angle and the reference phase angle in normal operation, and θ th is the phase angle offset threshold.
[0057] By monitoring the offset of the phase angle, the phase imbalance phenomenon caused by the ground fault can be captured, which provides additional characteristic support for fault judgment and improves the accuracy of fault identification. The reference phase angle in normal operation of the line is determined in advance, the absolute value Δθ of the difference between the current moment phase angle and the reference phase angle is calculated in real time, and it is compared with the phase angle offset threshold θ thComparing, if |Δθ|>θ th , it is determined that there is a phase angle deviation anomaly.
[0058] The judgment condition of the harmonic component proportion H in S3 is: when H>H th , it is determined that the harmonic component proportion is abnormal; wherein H is the ratio of the sum of the effective values of the harmonic currents to the effective value of the total current, and H th is the harmonic proportion threshold.
[0059] By analyzing the proportion of the harmonic component in the total current, the harmonic anomaly caused by the ground fault can be identified, which helps to distinguish the harmonic interference caused by the fault and the normal load, and enhances the specificity of fault judgment. Usage: Calculate the sum of the effective values of the harmonic currents, and then compare it with the effective value of the total current to get the harmonic component proportion H, and compare it with the harmonic proportion threshold H th . If H>H th , it is determined that the harmonic component proportion is abnormal.
[0060] The judgment condition of the line temperature change rate K t in S3 is: when K t >K tth , it is determined that the line temperature change is abnormal; wherein T t is the line temperature at the current time, T t-t0 is the line temperature before t0 time, t0 is the set time interval, and K tth is the temperature change rate threshold.
[0061] By introducing the line temperature as a physical parameter and monitoring the temperature change rate, the local overheating phenomenon that may be accompanied by the ground fault can be captured, which makes up for the shortcomings of relying only on electrical parameters to judge the fault, and improves the comprehensiveness of fault identification. Set the time interval t0 to obtain the line temperature T t at the current time and the line temperature T t-t0 before t0 time, calculate the line temperature change rate according to the formula , and compare it with the temperature change rate threshold K tth . If K t >K tth , it is determined that the line temperature change is abnormal.
[0062] The judgment condition of the fault transient characteristic T in S3 is: when T>T th , it is determined that there is a fault transient characteristic anomaly; wherein ΔU0 is the zero sequence voltage mutation value, t t is the duration of the transient process, and T th is the transient characteristic threshold.
[0063] The transient characteristics of the initial fault can be effectively captured by comprehensively considering the sudden change values of the zero sequence current and voltage and the duration of the transient process, the identification ability of the early fault is improved, and the misjudgment caused by instantaneous interference is reduced. t , according to the formula of , the fault transient characteristic quantity T is obtained, which is compared with the transient characteristic threshold T th , if T>T th , it is determined that there is an abnormal fault transient characteristic.
[0064] According to the accompanying Figure 5 , the preset correlation condition in S4 is one of the following:
[0065] a. Abnormal sudden change of zero sequence current and distortion anomaly of zero sequence voltage, and phase angle offset anomaly;
[0066] b. Abnormal sudden change of zero sequence current and abnormal proportion of harmonic component, and abnormal change of line temperature;
[0067] c. Distortion anomaly of zero sequence voltage and abnormal proportion of harmonic component, and phase angle offset anomaly;
[0068] d. Abnormal fault transient characteristic quantity and at least 2 variable anomalies satisfying any of the above a, b and c.
[0069] The correlation judgment model is a model based on the judgment results of multiple variables for identifying the grounding fault of the distribution network. According to the provided distribution network grounding fault identification and early warning method, its specific content is: when one of the following conditions is met, it is determined that there is a grounding fault and an early warning is issued:
[0070] Abnormal sudden change of zero sequence current and distortion anomaly of zero sequence voltage, and phase angle offset anomaly. That is, when ΔI0>ΔI 0th , D0>D 0th and |Δθ|>θ th , it is determined that there is a grounding fault, wherein ΔI0 is the sudden change value of the zero sequence current, I 0th is the judgment threshold thereof; D0 is the distortion degree of the zero sequence voltage, D0th is the judgment threshold thereof; Δθ is the phase angle offset, θ th is the judgment threshold thereof.
[0071] Abnormal sudden change of zero sequence current and abnormal proportion of harmonic component, and abnormal change of line temperature. That is, when ΔI0>ΔI 0th , H>H th and K t >K tth , it is determined that there is a grounding fault, wherein H is the proportion of the harmonic component, H this a judgment threshold of the same; t is a line temperature change rate, K tth is a judgment threshold of the same.
[0072] The zero sequence voltage is abnormal in distortion and harmonic component proportion, and the phase angle is abnormal in offset. That is, when D0>D 0th , H>H th , and |Δθ|>θ th , it is determined that there is a ground fault.
[0073] The fault transient characteristic quantity is abnormal and at least meets two variable abnormalities in any of the above conditions. That is, when T>T th , and at the same time meets two variable abnormalities in any of the above three conditions, it is determined that there is a ground fault, where T is a fault transient characteristic quantity, T th is a judgment threshold of the same.
[0074] The fault judgment logic associated with multiple characteristic quantities is constructed, the accuracy and reliability of fault determination are improved by combining different characteristic quantity abnormal conditions, the complex fault scenarios can be effectively dealt with, and the probability of false positives and false negatives is reduced. In the associated judgment stage, the system comprehensively analyzes the abnormal conditions of each characteristic quantity, and if any of the above four conditions is met, it is determined that there is a ground fault, and the early warning mechanism is triggered.
[0075] Through the fusion analysis of multi-dimensional characteristic quantities and the logical combination of the associated judgment model, the real fault and the interference signal can be effectively distinguished, the false positive rate is reduced, and the false negative rate is reduced. The dynamic sampling frequency adjustment mechanism uses the basic frequency in normal operation to reduce the data processing amount; it is increased to 2-3 times the frequency in abnormality, and it is increased to 4-5 times the frequency in fault, so as to ensure that key characteristics are not missed, and efficiency and accuracy are considered.
[0076] The dynamic adjustment of the threshold value and the sampling frequency can adapt to different power distribution network topologies, load levels and environmental conditions, and has strong universality. Through the amplitude and time coupling analysis of the transient characteristic quantity T in the initial stage of the fault, combined with the early abnormal monitoring of the temperature change rate, an early warning can be given before the fault develops into tripping, time for fault handling is gained, and power loss is reduced.
[0077] According to the accompanying Figures 1-6 , the overall use process is as follows:
[0078] Data acquisition stage: install sensors at key nodes of the power distribution network, and real-time collect zero sequence current, zero sequence voltage, phase angle, harmonic component and line temperature data. At the same time, start the dynamic sampling frequency adjustment mechanism:
[0079] The initial state is normal operation mode, and the sampling frequency is set to the basic frequency f0(50-100Hz);
[0080] If any characteristic quantity triggers the abnormal threshold (the fault condition is not met), automatically switch to f1 (2-3 times f0);
[0081] If it is determined that there is a fault, switch to f2 (4-5 times f0) and keep until the fault is eliminated.
[0082] Feature extraction stage: pre-process the collected data and calculate key characteristic quantities:
[0083] Zero sequence current sudden change value ΔI0=|I 0t -I 0t-1 |;
[0084] Zero sequence voltage distortion degree
[0085] Phase angle offset Δθ is the absolute value of the difference between the phase angle at the current moment and the reference phase angle when operating normally;
[0086] Harmonic component proportion H is the ratio of the sum of the effective values of each harmonic current to the total current effective value;
[0087] Line temperature change rate
[0088] Fault transient characteristic quantity ΔU0 is the zero sequence voltage sudden change value, t t is the duration of the transient process.
[0089] Variable judgment stage: compare each characteristic quantity with the preset threshold (I 0th , D 0th , θ th , H th , K tth , T th ) to determine whether it is abnormal (e.g. ΔI0> ΔI 0th , zero sequence current is abnormal).
[0090] Correlation judgment stage: based on the variable judgment result, apply the correlation model to determine whether it is a ground fault: trigger the warning when one of the following conditions is met:
[0091] There is an abnormal sudden change in the zero sequence current and an abnormal distortion in the zero sequence voltage, and there is an offset anomaly in the phase angle;
[0092] There is an abnormal sudden change in the zero sequence current and an abnormal proportion of harmonic components, and there is an abnormal change in the line temperature;
[0093] There is an abnormal distortion in the zero sequence voltage and an abnormal proportion of harmonic components, and there is an offset anomaly in the phase angle;
[0094] There is an abnormality in the fault transient characteristic quantity and at least two variables meet any of the above conditions.
[0095] Early warning output stage: according to the association judgment result, the early warning information containing fault position, characteristic quantity abnormality details and fault severity is generated, and is pushed to the operation and maintenance terminal through the power distribution automation system to guide fault troubleshooting and processing.
[0096] It should be noted that in this document, relational terms such as first and second and the like can only be used to distinguish one entity or action from another entity or action, without necessarily requiring or implying that there is any such relationship or order between or among the entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.
[0097] The basic principles and main features of the present application and the advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.
Claims
1. A power distribution network ground fault identification and early warning method, characterized in that, The method comprises the following steps: S1, data acquisition: real-time acquisition of zero sequence current, zero sequence voltage, phase angle, harmonic component and line temperature data through sensors installed on the power distribution network line; S2, feature extraction: processing the collected data to extract characteristic quantities for judging ground faults; S3, variable judgment logic construction: setting the judgment threshold and logic condition of the characteristic quantities respectively, for determining whether each characteristic quantity is abnormal; S4, correlation judgment model establishment: based on the judgment results of each characteristic quantity, a correlation judgment model is established, and when the preset correlation condition is met, it is determined as a ground fault and a warning is issued; S5, warning output: according to the judgment result of the correlation judgment model, output the corresponding fault warning information.
2. The power distribution network ground fault identification and early warning method of claim 1, wherein, The S1 also includes a dynamic sampling frequency adjustment mechanism: dynamically adjusting the sampling frequency according to the line operating state.
3. The power distribution network ground fault identification and early warning method of claim 1, wherein, The feature quantity extracted in S2 includes: zero sequence current sudden change value ΔI0, zero sequence voltage distortion degree D0, phase angle offset Δθ, harmonic component proportion H, line temperature change rate K t and fault transient state feature quantity T.
4. The power distribution network ground fault identification and early warning method of claim 3, wherein, The judgment condition of the zero sequence current mutation value ΔI0 in the S3 is: when ΔI0 > ΔI 0th , it is determined that the zero sequence current exists abnormal mutation; wherein ΔI0 = |I 0t - 0t-1 |, I 0t is the zero sequence current value at the current moment, I 0t-1 is the zero sequence current value at the previous moment, and I 0th is the zero sequence current mutation threshold value.
5. The power distribution network ground fault identification and early warning method of claim 3, wherein, The judgment condition of the zero sequence voltage distortion degree D0 in the S3 is: when D0>D 0th , it is determined that the zero sequence voltage has distortion abnormality; wherein U1 is a zero sequence voltage fundamental component, U2, U3,..., U n is each harmonic component of the zero sequence voltage, and D 0th is a voltage distortion degree threshold value.
6. The power distribution network ground fault identification and early warning method of claim 3, wherein, The judgment condition of the phase angle offset Δθ in the S3 is that when |Δθ| > θ th , it is determined that there is an offset anomaly in the phase angle; wherein Δθ is the absolute value of the difference between the phase angle at the current time and the reference phase angle at the normal operation, and θ th is the phase angle offset threshold value.
7. The power distribution network ground fault identification and early warning method of claim 3, wherein, The judgment condition of the harmonic component proportion H in the S3 is: when H>H th , it is determined that the harmonic component proportion is abnormal; wherein H is the ratio of the sum of the effective values of each harmonic current to the total current effective value, H th is the harmonic proportion threshold value.
8. The power distribution network ground fault identification and early warning method of claim 3, wherein, The line temperature change rate K in the S3 t The judgment condition is: when K t > K tth , the line temperature change is abnormal; wherein T t is the line temperature at the current time, T t-t0 is the line temperature before t0 time, t0 is a set time interval, K tth is a temperature change rate threshold.
9. The power distribution network ground fault identification and early warning method of claim 3, wherein, The judgment condition of the fault transient state characteristic T in S3 is: when T > T th , it is determined that there is an abnormal fault transient state characteristic; wherein ΔU0 is the zero sequence voltage jump value, t t is the transient process duration, T th is the transient characteristic threshold value.
10. The power distribution network ground fault identification and warning method of claim 1, wherein, The preset correlation condition in S4 is one of the following: a. Abnormal mutation of zero sequence current and distortion anomaly of zero sequence voltage, and phase angle offset anomaly; b. Abnormal mutation of zero sequence current and abnormal proportion of harmonic component, and abnormal change of line temperature; c. Distortion anomaly of zero sequence voltage and abnormal proportion of harmonic component, and phase angle offset anomaly; d. Abnormality of fault transient characteristic quantity and at least 2 variable abnormalities satisfying any of the above a, b and c conditions.