An abnormal point identification method and system based on power distribution network voltage data cleaning

Abstract

The application discloses an abnormal point identification method and system based on power distribution network voltage data cleaning, which comprises the following steps: obtaining three-phase voltage data of each measurement point in the power distribution network according to a preset sampling frequency, and marking data within 30% of the upper and lower limits of voltage as valid; calculating the average voltage of each phase of each measurement point, and inferring the rated voltage grade of each voltage; inferring the rated voltage grade of the measurement point according to the rated voltage grade of each phase, and marking data within 30% of the upper and lower limits of the rated voltage of the measurement point as valid; calculating the voltage qualification rate of each phase according to the valid data, and calculating the voltage qualification rate of the measurement point; and finding out the measurement point with abnormal data and marking it. The application can improve the accuracy of the voltage qualification rate, provide accurate and effective judgment basis for voltage monitoring management, and identify abnormal measurement points in the power distribution network, thereby providing reliable judgment basis for dispatchers.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power monitoring, in particular to an abnormal point identification method and system based on power distribution network voltage data cleaning. BACKGROUND

[0002] As an important part of the power system, the power distribution network undertakes the function of distributing electric energy to various users. The power distribution network is composed of overhead lines, cables, towers, distribution transformers, disconnectors, reactive compensation capacitors, metering devices and some auxiliary facilities, etc., and generally adopts closed-loop design and open-loop operation, with a radial structure. The power distribution network is large in scale, has many operation points and wide operation surfaces, and has relatively poor safety environment, so there are relatively many safety risk factors. How to accurately and quickly identify abnormal conditions in the power distribution network and develop appropriate maintenance or maintenance plans based on abnormal conditions to ensure that suspected fault locations can be quickly and accurately located and isolated has become a problem we need to solve.

[0003] The voltage monitoring management system can realize data acquisition and reporting of voltage monitoring points, but the massive data accessed by the system may have data quality problems, affecting the correctness of data statistics such as voltage qualification rate. Before data calculation, these problem data need to be preprocessed, otherwise it is difficult to provide accurate and effective judgment basis for voltage monitoring management.

[0004] Therefore, the present application provides an abnormal point identification method based on voltage data cleaning to improve the accuracy of voltage qualification rate, identify abnormal voltage data in the power distribution network, mark abnormal points, and provide reliable judgment basis for dispatchers. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that there are a large number of metering points in the power distribution network, and the voltage data will be disturbed by various factors, including power grid load fluctuation, equipment failure or interference, etc., resulting in noise and outliers in the collected data. If these data are not effectively cleaned, it will directly affect the accuracy of voltage monitoring, and then lead to incorrect voltage qualification rate evaluation, and cannot provide reliable power grid state judgment basis. Since the voltage fluctuation range of each metering point in the power distribution network is large, and the nominal voltage levels of different equipment are also different, the traditional method is difficult to accurately infer the rated voltage level of the metering point through simple sampling and calculation, which is easy to lead to inaccurate voltage state judgment, especially in the face of complex, multi-phase power grid environment, the risk of misjudgment will be higher. The existing voltage monitoring system usually relies on manual experience or simple algorithm to process the voltage data, and it is difficult to automatically identify and mark abnormal data points, which leads to potential power grid failure or abnormal situation cannot be found in time, increasing the risk of power grid operation.

[0007] To solve the above technical problems, the present application provides the following technical solutions: an abnormal point identification method based on power distribution network voltage data cleaning, comprising:

[0008] Obtain three-phase voltage data of each metering point in the power distribution network according to the preset sampling frequency, and mark the data within 30% of the upper and lower limits of the voltage as valid;

[0009] Calculate the average voltage of each phase of each metering point, and infer the rated voltage level of each voltage;

[0010] According to the rated voltage level of each phase, infer the rated voltage level of the metering point, and mark the data within 30% of the upper and lower limits of the rated voltage of the metering point as valid;

[0011] Calculate the voltage qualification rate of each phase according to the valid data, and calculate the voltage qualification rate of the metering point;

[0012] Find out the metering point with abnormal data and mark it.

[0013] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, wherein: the three-phase voltage data of each metering point in the power distribution network is obtained according to the preset sampling frequency, which includes obtaining the A, B and C three-phase voltage data U s (t), U A (t), U B (t), U C (t) of each metering point at the preset sampling frequency f

[0014] For each collected voltage data, if its value is within 30% of the upper and lower limits of the current measurement point rated voltage, it is marked as valid data; otherwise, it is marked as invalid data;

[0015] All marked data is stored in the database in time sequence.

[0016] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, the calculation of the average voltage of each phase of each measurement point comprises time averaging the three-phase voltage data U A (t), U B (t), U C (t) of each measurement point to obtain the average voltage of each phase

[0017] The estimation of the rated voltage level of each voltage comprises estimating the rated voltage level V of each phase according to the average voltage of each phase nominal,X , if falls within the upper and lower limits of the rated voltage level, the rated voltage of the current phase is estimated to be the current level, and the estimation basis is:

[0018] If the average voltage of one phase falls within the range of [46.192V, 69.288V], the rated voltage level of the phase is estimated to be 57.74V;

[0019] If the average voltage of one phase falls within the range of [80V, 120V], the rated voltage level of the phase is estimated to be 100V;

[0020] If the average voltage of one phase falls within the range of [176V, 264V], the rated voltage level of the phase is estimated to be 220V;

[0021] If the average voltage of one phase falls within the range of [304V, 456V], the rated voltage level of the phase is estimated to be 380V;

[0022] If the average voltage of one phase is not within any range, the voltage data of the phase is marked as invalid.

[0023] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, wherein: the step of inferring the rated voltage level of the metering point according to the rated voltage level of each phase comprises: if the metering point has rated voltages of three phases and the rated voltages of the three phases are the same, setting the rated voltage level of the metering point as the rated voltage level; otherwise, setting the rated voltage level of the metering point as unknown.

[0024] if the metering point has rated voltages of two phases and the rated voltages of the two phases are the same, setting the rated voltage level of the metering point as the rated voltage level; otherwise, setting the rated voltage level of the metering point as unknown.

[0025] if the metering point has rated voltage of only one phase, setting the rated voltage level of the metering point as the rated voltage level of the phase;

[0026] if the metering point has no effective rated voltage, setting the rated voltage level of the metering point as unknown.

[0027] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, wherein: the step of marking the data within 30% of the upper and lower limits of the rated voltage of the metering point as effective comprises: calculating the effective voltage data range of the metering point according to the inferred rated voltage of each metering point , which is defined as:

[0028]

[0029] , wherein, is the inferred rated voltage of the metering point;

[0030] for the voltage data of each metering point, if the value falls within the effective voltage data range , the data is marked as effective data; otherwise, it is marked as invalid data;

[0031] all the data marked as effective are stored in the database in time sequence.

[0032] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, wherein: the step of calculating the voltage qualification rate of each phase according to the effective data comprises: calculating the voltage qualification rate P 合格,X of each phase X according to the effective voltage data, which is defined as:

[0033]

[0034] , wherein, 合格,X is the number of qualified voltage data within the effective voltage data range , and N 总,X is the number of all effective voltage data of the phase.

[0035] For the phase set to invalid voltage, no voltage eligibility rate calculation is performed, and the data of the phase is directly excluded;

[0036] The calculating the voltage eligibility rate of the metering point comprises: aggregating the voltage eligibility rates of all the effective phases to calculate a comprehensive voltage eligibility rate P of the metering point 合格，计量点 which is defined as:

[0037]

[0038] wherein, N 有效相 is the number of effective phases participating in the calculation, and ∑ 有效相 P 合格,X is the sum of the phase voltage eligibility rates participating in the calculation.

[0039] The eligibility rate calculation method in the application provides a more detailed voltage quality evaluation means by separately calculating single-phase and overall voltage data. Especially in the case of possibly existing invalid voltage phases, the application can flexibly adjust the calculation strategy to avoid overall misjudgment caused by invalid data in the traditional method. In addition, by calculating the average eligibility rate of the effective phases, the real situation of the voltage quality can be more accurately reflected. This method can effectively improve the analysis ability of the power grid monitoring system on voltage data in practical application, thereby providing more reliable support for power grid operation and maintenance.

[0040] As a preferred scheme of the abnormal point identification method based on power distribution network voltage data cleaning, the method comprises the following steps: finding out a metering point with data anomaly, wherein, if the three-phase voltage of the metering point cannot determine the rated voltage level, the metering point is an abnormal metering point, and is marked as a metering point with unknown rated voltage;

[0041] If the metering point has no voltage data within the statistical period, the metering point is an abnormal metering point, and is marked as a metering point without data;

[0042] If the proportion of the number of effective voltage data points of the metering point in the total number of data points within the statistical period is less than 50%, the metering point is an abnormal metering point, and is marked as a metering point with insufficient data;

[0043] For the three-phase voltage of the metering point, if the three-phase variance is greater than a set threshold value m, the metering point is an abnormal metering point, and is marked as a metering point with large three-phase voltage difference;

[0044] If only two-phase voltage exists, the voltage difference ΔU between the two phases is calculated, and if ΔU exceeds a set threshold value n, the metering point is an abnormal metering point, and is marked as a metering point with large two-phase voltage difference.

[0045] The abnormal metering point identification method can not only identify the abnormal points (such as no data or insufficient data metering points) easily ignored in traditional methods, but also find potential power grid problems through detailed analysis of the differences between three-phase voltage and two-phase voltage. This detailed abnormal point marking strategy significantly improves the sensitivity and accuracy of the power grid monitoring system, effectively reducing the risk of power grid failure caused by the failure to identify abnormal points in time. Especially in the processing of multi-phase voltage data, by introducing the variance and voltage difference quantization standard, the present application can maintain high judgment accuracy in complex power grid environments.

[0046] An abnormal point identification system based on power distribution network voltage data cleaning, characterized by comprising,

[0047] The data acquisition and preprocessing module acquires data at the nodes and devices of the power distribution network and performs preprocessing;

[0048] The dynamic simulation and modeling module establishes a photovoltaic power generation model, a new energy power generation model, a special load model, and a distributed energy storage model, and constructs a simulation environment for simulating the dynamic interaction between the new energy power generation system and the power distribution network;

[0049] The fault identification and processing module divides the power distribution network into multiple regions, constructs a hierarchical positioning model, identifies and locates different types of faults, and processes and repairs faults through an intelligent dispatching system;

[0050] The access point optimization and control module optimizes the access point position of the new energy power generation system in real time according to the fault processing results and the energy storage system adjustment.

[0051] The present application adopts clear upper and lower limit rules, i.e. defines the range of valid data according to the 30% upper and lower limit range of the rated voltage. This standard ensures the rationality and consistency of the data, and effectively filters out abnormal data that deviates from the normal range, preventing these abnormal data from affecting the judgment of the power grid state. This strategy is particularly suitable for complex power grid environments and helps to improve the sensitivity of the system to abnormal conditions, thereby more effectively monitoring the voltage state.

[0052] The present application has the beneficial effects that: the present application can effectively filter out invalid data that does not meet the 30% range of the upper and lower limits of the voltage, thereby ensuring the effectiveness of the collected data. This method greatly improves the quality of the voltage data and prevents noise data from affecting the judgment of the power grid state. Compared with traditional technologies, the present application can automatically process massive voltage data and filter out abnormal values in time, and this cleaning method significantly improves the accuracy and effectiveness of voltage monitoring.

[0053] The present application can accurately predict the rated voltage level of the metering point by calculating the average value of each phase voltage and combining the preset voltage level interval. During prediction, not only single-phase voltage is considered, but also three-phase voltage is comprehensively analyzed to ensure the consistency of different phase voltages and avoid misjudgment caused by single-phase fluctuation. Compared with the traditional single-phase prediction method, the multi-phase joint prediction method of the present application is more robust, especially suitable for multi-phase power grid environment, and can more accurately reflect the real situation of power grid operation.

[0054] The present application can automatically identify abnormal data points and classify them (such as data insufficient points, three-phase voltage large difference points, etc.) by calculating three-phase voltage variance and inter-phase voltage difference quantitative standards. This refined abnormal point identification method can timely discover potential problems in the power grid and take early warning and processing. Compared with the traditional manual identification or simple voltage difference judgment method, the present application can realize automatic processing in the large-scale data environment of the power grid, greatly improving the efficiency and accuracy of abnormal point identification. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the figures needed in the embodiment description will be briefly introduced. Obviously, the figures in the following description are only some embodiments of the present application, and those skilled in the art can obtain other figures according to these figures without creative labor.

[0056] Figure 1 A flowchart of an abnormal point identification method based on power distribution network voltage data cleaning is provided for the first embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0058] Embodiment 1

[0059] REFERENCE Figure 1 For an embodiment of the present application, an abnormal point identification method based on power distribution network voltage data cleaning is provided, comprising:

[0060] S1: Obtain three-phase voltage data of each metering point in the power distribution network according to the preset sampling frequency, and mark the data within 30% of the upper and lower limits of voltage as valid.

[0061] At a preset sampling frequency f s Collect A, B, C three-phase voltage data U of each metering point A (t), U B (t), U C (t), and store these data in the database in real time.

[0062] Sampling frequency f s According to the dynamic adjustment of the power grid load change and voltage fluctuation, the specific adjustment strategy is:

[0063]

[0064] Wherein, f0 is the reference sampling frequency, V nominal is the rated voltage, L(t) is the current load, L0 is the reference load value; β and γ are adjustable parameters, used to adjust the different influence weights of voltage and load on sampling frequency.

[0065] For each collected voltage data, if its value is within the upper and lower limits of 30% of the rated voltage of the current metering point, it is marked as valid data; otherwise, it is marked as invalid data.

[0066] The range of valid data is defined as:

[0067] V effective =[0.7×V nominal ,1.3×V nominal ]

[0068] All marked data are stored in the database according to time sequence.

[0069] It should be noted that the reference sampling frequency f0 is the initial setting of the system in the stable state, which ensures the basic accuracy of the voltage data. The present application realizes the dynamic adjustment of the sampling frequency, which can respond to the power grid load change and voltage fluctuation in real time. This adjustment strategy increases the sampling frequency when the power grid load increases or the voltage fluctuation intensifies, so as to capture more subtle voltage changes. In the traditional technology, fixed sampling frequency is usually used, which is difficult to quickly respond to the dynamic changes of the power grid. Therefore, the present application has significant advantages in adaptability and precision.

[0070] The character V nominal (rated voltage) and L(t) (current load) are the core parameters for dynamic adjustment of sampling frequency. The rated voltage V nominalThe reference point of the voltage data is determined, and the current load L(t) reflects the real-time operating condition of the power grid. By combining these two parameters, the sampling frequency can not only be adjusted in time when the voltage fluctuates abnormally, but also be adaptively adjusted according to the load change, thereby ensuring the accuracy and integrity of the data. This double adjustment mechanism is rare in the prior art, and generally, the power grid monitoring system only considers the adjustment of a single factor. However, the present application considers the joint influence of voltage and load, thereby improving the representativeness and reliability of the collected data.

[0071] Further, the present application adopts explicit upper and lower limit rules, i.e., the range of effective data is defined according to the 30% upper and lower limit range of the rated voltage. This standard ensures the rationality and consistency of the data, and effectively filters abnormal data deviating from the normal range, preventing these abnormal data from affecting the judgment of the power grid state. This strategy is particularly suitable for complex power grid environments and helps to improve the sensitivity of the system to abnormal conditions, thereby more effectively monitoring the voltage state.

[0072] S2: Calculate the average voltage of each phase of each measurement point, and infer the rated voltage level of each voltage.

[0073] The three-phase voltage data U A (t), U B (t), U C (t) of each measurement point are time-averaged to obtain the average voltage of each phase. The calculation formula is:

[0074]

[0075] where N is the number of sampling points, t i is the time point of the i-th sampling time; X is the voltage phase, X=A, B, C.

[0076] Infer the rated voltage level of each voltage, which includes: in the preset voltage level interval, and infer its rated voltage level V nominal,X , if falls within the upper and lower limit range of the rated voltage level, then the rated voltage of the current phase is inferred to be the current level, and the inference basis is:

[0077] If the average voltage of one phase falls within the range of [46.192V, 69.288V], it is presumed that the rated voltage level of the phase is 57.74V; if the average voltage of one phase falls within the range of [80V, 120V], it is presumed that the rated voltage level of the phase is 100V; if the average voltage of one phase falls within the range of [176V, 264V], it is presumed that the rated voltage level of the phase is 220V; if the average voltage of one phase falls within the range of [304V, 456V], it is presumed that the rated voltage level of the phase is 380V; if the average voltage of one phase does not fall within any of the above ranges, the voltage data of the phase is marked as invalid.

[0078] It should be noted that the setting of different voltage levels (such as 57.74V, 100V, 220V, 380V) is determined according to the actual operation standards of the power grid and the design requirements of electrical equipment. These levels provide a reliable basis for inferring the rated voltage of each phase. By comparing the average voltage with these intervals, the level to which the current voltage belongs can be reasonably inferred.

[0079] The voltage level inference step in the present application not only provides clear technical guidance, but also has good adaptability. In particular, in a complex power grid environment, by using pre-set voltage intervals, it can be ensured that even in the case of large voltage fluctuations, the voltage level can still be accurately determined. In addition, for voltage data that does not fall within any pre-set interval, the present application clearly marks it as invalid, thereby avoiding the influence of abnormal data on the entire system. This processing method can significantly improve the accuracy and effectiveness of data analysis in practical applications, avoid misjudgment caused by abnormal data interference, and ultimately improve the stability of power grid monitoring and control.

[0080] S3: According to the rated voltage level of each phase, the rated voltage level of the metering point is inferred, and the data within 30% of the upper and lower limits of the metering point rated voltage is marked as valid.

[0081] If the metering point has three-phase rated voltages, the three-phase rated voltages must be the same, otherwise the rated voltage level of the metering point is set to unknown; if the metering point has two-phase rated voltages, the two-phase rated voltages must be the same, otherwise the rated voltage level of the metering point is set to unknown; if the metering point has only one-phase rated voltage, the rated voltage level of the metering point is set to the rated voltage level of the phase; if the metering point has no valid rated voltage, the rated voltage level of the metering point is set to unknown.

[0082] According to the inferred rated voltage of each metering point, the valid voltage data range of the metering point is calculated It is defined as:

[0083]

[0084] wherein, The estimated rated voltage of the metering point.

[0085] For the voltage data of each metering point, if its value falls within the valid voltage data range , the data is marked as valid data; otherwise, it is marked as invalid data.

[0086] All data marked as valid is stored in the database in chronological order.

[0087] It should be noted that the voltage level estimation method in the present application has significant advantages in handling cases of large voltage fluctuations or inconsistent multi-phase. Through consistency checking of three-phase voltage levels, the system can quickly and accurately identify abnormal situations and mark invalid voltage data that does not meet the conditions. This method effectively reduces false positives caused by inconsistent phase voltages, thereby improving data accuracy in power grid monitoring. In addition, by setting the valid voltage data range, the present application can better filter out voltage data within a reasonable fluctuation range, allowing the system to remain efficient and accurate when dealing with complex power grid environments.

[0088] Further, after estimating the rated voltage level of each phase, the voltage data is compared with the newly determined upper and lower limit 30% range according to the estimated rated voltage level. This process is to verify the reasonableness of the voltage data more accurately, especially for cases of inconsistency between different phases. Further improve the accuracy of the data, ensure that in the case of inconsistent rated voltages between different phases, only those valid data within the new range are retained. This helps to discover potential abnormalities that may not have been identified in the first round of screening, especially when there are differences or uncertainties between phases.

[0089] S4: Calculate the voltage qualification rate of each phase according to the valid data, and calculate the voltage qualification rate of the metering point.

[0090] According to the valid voltage data, the voltage qualification rate P 合格,X of each phase X is calculated, which is defined as:

[0091]

[0092] where N 合格,X is the number of qualified voltage data within the valid data range , and N 总,X is the number of all valid voltage data of the phase.

[0093] For the phase set as invalid voltage, the voltage qualification rate is not calculated, and the data of the phase is directly excluded.

[0094] The voltage qualification rate of the metering point is calculated by aggregating the voltage qualification rates of all the effective phases to obtain a comprehensive voltage qualification rate P of the metering point 合格，计量点 which is defined as:

[0095]

[0096] wherein N 有效相 is the number of effective phases participating in the calculation, and ∑ 有效相 P 合格,X is the sum of the phase voltage qualification rates participating in the calculation.

[0097] It should be noted that the qualification rate calculation method in the present application provides a more detailed voltage quality evaluation means by separately calculating the single-phase and overall voltage data. Especially in the case of possible invalid voltage phases, the present application can flexibly adjust the calculation strategy to avoid the overall misjudgment caused by invalid data in the traditional method. In addition, by calculating the average qualification rate of the effective phases, the real situation of the voltage quality can be more accurately reflected. This method can effectively improve the analysis ability of the power grid monitoring system for voltage data in practical application, thereby providing more reliable support for power grid operation and maintenance.

[0098] S5: Find the metering point with data anomaly and mark it.

[0099] If the three-phase voltage of the metering point cannot determine its rated voltage level, the metering point is an abnormal metering point, which is marked as a metering point with unknown rated voltage; if the metering point has no voltage data within the statistical period, the metering point is an abnormal metering point, which is marked as a metering point with no data; if the proportion of the number of effective voltage data points of the metering point in the total number of data points within the statistical period is less than 50%, the metering point is an abnormal metering point, which is marked as a metering point with insufficient data; for the three-phase voltage of the metering point, if the three-phase variance is greater than a set threshold m (generally 16), the metering point is an abnormal metering point, which is marked as a metering point with large three-phase voltage difference; if only two-phase voltage is available, the voltage difference ΔU between the two phases is calculated, and if ΔU exceeds a set threshold n (it is recommended to set the maximum value of the two-phase voltage difference in the historical data plus a suitable margin, and the specific value can be adjusted according to the device specification and power grid operation characteristics), the metering point is an abnormal metering point, which is marked as a metering point with large two-phase voltage difference.

[0100] It should be noted that the abnormal metering point identification method not only can identify the easily neglected abnormal points in traditional methods (such as no data or insufficient data metering points), but also can find potential power grid problems through detailed analysis of the differences between three-phase voltage and two-phase voltage. This detailed abnormal point marking strategy significantly improves the sensitivity and accuracy of the power grid monitoring system, effectively reduces the risk of power grid failure caused by untimely identification of abnormal points. Especially in the processing of multi-phase voltage data, by introducing the variance and voltage difference quantization standard, the present application can maintain high judgment accuracy in complex power grid environment.

[0101] Embodiment 2 is the second embodiment of the present application, which is different from the previous embodiment:

[0102] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the prior art that make essential contributions or parts of the current technical solutions can be embodied in the form of software products, and the current computer software product is stored in a storage medium, including a number of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and various program code storage media.

[0103] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, which can be specifically embodied in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from an instruction execution system, device or apparatus. For the purpose of this specification, "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with an instruction execution system, device or apparatus, or in conjunction with these instruction execution systems, devices or apparatus.

[0104] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, as necessary, to create electronic data representing the program, which data can then be stored in computer memory.

[0105] Embodiment 3, as an embodiment of the present application, provides an abnormal point identification system based on power distribution network voltage data cleaning, specifically comprising a data acquisition module, a voltage average calculation and rated voltage estimation module, a metering point rated voltage estimation module and a voltage qualification rate calculation module.

[0106] The data acquisition module: obtains three-phase voltage data of each metering point in the power distribution network according to a preset sampling frequency, and marks data within 30% of the upper and lower limits of voltage as valid.

[0107] The voltage average calculation and rated voltage estimation module: calculates the average voltage of each phase of each metering point, and estimates the rated voltage grade of each voltage.

[0108] The metering point rated voltage estimation module: estimates the rated voltage grade of the metering point according to the rated voltage grade of each phase, and marks data within 30% of the upper and lower limits of the metering point rated voltage as valid.

[0109] The voltage qualification rate calculation module: calculates the voltage qualification rate of each phase according to the valid data, and calculates the voltage qualification rate of the metering point.

[0110] The abnormal point identification and marking module: finds out the metering point with abnormal data and marks it.

[0111] Embodiment 4, as an embodiment of the present application, provides an abnormal point identification method based on power distribution network voltage data cleaning, in order to verify the beneficial effects of the present application, economic benefit calculation and simulation / contrast experiments are carried out for scientific demonstration.

[0112] In order to verify the effectiveness and innovation of the invention, a systematic experiment was conducted, and six power distribution network measurement points (named A, B, C, D, E, and F respectively) were selected for the experiment. The voltage data of each measurement point was collected by presetting the sampling frequency, which was dynamically adjusted according to the load change and voltage fluctuation of the power grid to ensure the real-time and accuracy of the data. The voltage data was collected as A-phase, B-phase, and C-phase voltage and stored in the database.

[0113] For each collected voltage data, it was marked according to whether it was within the upper and lower limits of 30% of the rated voltage. All the data marked as valid will continue to be used for subsequent analysis, while the data marked as invalid is excluded.

[0114] The three-phase voltage data of each measurement point was time-averaged, and the average voltage of each phase was calculated, and the rated voltage level was estimated according to the preset voltage level interval. If the average value of a certain phase voltage falls within the specified level range, its rated voltage level is determined, otherwise the phase data is marked as invalid.

[0115] Based on the valid data, the voltage qualification rate of each phase was calculated, and the comprehensive voltage qualification rate of the measurement point was calculated. If a certain phase data is marked as invalid, the phase does not participate in the qualification rate calculation.

[0116] According to the abnormal data points identified in the previous steps, including no data, insufficient data, three-phase voltage difference too large, two-phase voltage difference too large, etc., the abnormal measurement points were marked one by one and stored in the database for further analysis. The specific data is shown in Table 1.

[0117] Table 1 Implementation data table

[0118]

[0119]

[0120] From the data in the above table, it can be seen that the application effect of the invention in the power distribution network voltage monitoring has obvious advantages. First, based on the dynamically adjusted sampling frequency and reasonable voltage data marking strategy, the voltage data of each measurement point is accurately collected and effectively marked. This can be proved by the voltage qualification rate data of each measurement point: for example, the comprehensive voltage qualification rate of measurement point A reaches 96.87%, indicating that in a complex power grid environment, this method can effectively ensure the accuracy and stability of voltage monitoring.

[0121] Secondly, by inferring and calculating the rated voltage level and the pass rate of each phase, the system can comprehensively evaluate the voltage quality of different measurement points. As shown in the table, although there are differences in the three-phase voltage data of each measurement point, through reasonable pass rate calculation, the overall voltage situation of each measurement point can be objectively reflected. Especially when there is a large difference between two phases of some measurement points (such as the difference between phase B and phase C of measurement point B), the system can quickly identify and mark the abnormality, thereby avoiding potential power grid operation risks.

[0122] Finally, the identification and marking of data anomalies further improve the monitoring capability of the system. For example, if the data of a certain measurement point is insufficient or the three-phase voltage variance is too large in some cases, the system can timely mark the abnormal situation and store this information in the database for further processing by power grid maintenance personnel. Compared with the prior art, the present application not only achieves a technological breakthrough in data acquisition and processing, but also significantly improves the accuracy and reliability of power grid monitoring through innovative data marking and analysis methods.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. An anomaly identification method based on distribution network voltage data cleaning, characterized in that, include: Three-phase voltage data of each metering point in the distribution network are obtained according to the preset sampling frequency, and data within 30% of the upper and lower limits of voltage are marked as valid. Calculate the average voltage of each phase at each metering point and estimate the rated voltage level of each phase. Based on the rated voltage level of each phase, the rated voltage level of the metering point is inferred, and data within 30% of the upper and lower limits of the rated voltage of the metering point are marked as valid. Calculate the voltage qualification rate for each phase based on the valid data, and calculate the voltage qualification rate for the metering points. Identify and mark measurement points with abnormal data; The process of acquiring three-phase voltage data at each metering point in the distribution network according to a preset sampling frequency includes using a preset sampling frequency f s Collect three-phase voltage data U of phases A, B, and C at each metering point. A (t), U B (t), U C (t), and store these data in the database in real time; For each voltage data collected, if its value is within 30% of the upper or lower limit of the rated voltage at the current metering point, it is marked as valid data; otherwise, it is marked as invalid data. All tagged data is stored in the database in time series order; The calculation of the average voltage of each phase at each metering point includes processing the three-phase voltage data U at each metering point. A (t),U B (t),U C (t) Perform time averaging to obtain the average voltage of each phase. The estimated rated voltage level for each voltage term includes, based on the average voltage of each phase. Estimate its rated voltage level V within a preset voltage level range. nominal,X ,like If the voltage falls within the upper or lower limit of the rated voltage level, then the rated voltage of the current phase is presumed to be the current level. The basis for this prediction is: If the average voltage of one phase If the voltage falls within the range of [46.192V, 69.288V], then the rated voltage level of this phase is estimated to be 57.74V. If the average voltage of one phase If the voltage falls within the range of [80V, 120V], then the rated voltage level of that phase is presumed to be 100V. If the average voltage of one phase If the voltage falls within the range of [176V, 264V], then the rated voltage level of this phase is presumed to be 220V; If the average voltage of one phase If the voltage falls within the range of [304V, 456V], then the rated voltage level of this phase is presumed to be 380V; If the average voltage of one phase If the voltage data for a phase is outside any range, it is marked as invalid. The method of inferring the rated voltage level of the metering point based on the rated voltage level of each phase includes the following: if all three phases of the metering point have rated voltages, then the rated voltages of the three phases must be the same; otherwise, the rated voltage level of the metering point is set to unknown. If a metering point has two phase rated voltages, the two phase rated voltages must be the same; otherwise, the rated voltage level of the metering point should be set to unknown. If the metering point has only one phase rated voltage, then the rated voltage level of the metering point is set to the rated voltage level of that phase; If the metering point has no effective rated voltage, then set the rated voltage level of the metering point to unknown.

2. The anomaly identification method based on distribution network voltage data cleaning as described in claim 1, characterized in that: The step of marking data within 30% of the upper and lower limits of the rated voltage of the metering point as valid includes calculating the effective voltage data range of the metering point based on the estimated rated voltage of each metering point. Its definition is: in, The estimated rated voltage at the metering point; For the voltage data at each metering point, if its value falls within the effective voltage data range If the data is valid, mark it as valid; otherwise, mark it as invalid. All data marked as valid is stored in the database in time series order.

3. The anomaly identification method based on distribution network voltage data cleaning as described in claim 2, characterized in that: The calculation of the voltage qualification rate for each phase based on valid data includes calculating the voltage qualification rate P for each phase X based on the valid voltage data. 合格,X Its definition is: Where, N 合格,X To be within the valid data range The number of qualified voltage data within, N 总,X This represents the total number of effective voltage data for this phase. For phases that are set to invalid voltage, the voltage qualification rate is not calculated, and the data of that phase is directly excluded. The calculation of the voltage qualification rate at the metering point includes summing the voltage qualification rates of all valid phases and calculating the overall voltage qualification rate P of the metering point. 合格，计量点 Its definition is: Where, N 有效相 Σ represents the number of effective phases involved in the calculation. 有效相 P 合格,X This represents the sum of the phase voltage qualification rates used in the calculation.

4. The anomaly identification method based on distribution network voltage data cleaning as described in claim 3, characterized in that: The process of identifying metering points with abnormal data includes: if the rated voltage level of the three-phase voltage of a metering point cannot be determined, then the metering point is an abnormal metering point and is marked as a metering point with unknown rated voltage. If a metering point has no voltage data within the statistical period, then the metering point is an abnormal metering point and is marked as a metering point with no data. If the number of effective voltage data points of a metering point during the statistical period accounts for less than 50% of the total number of data points, then the metering point is an abnormal metering point and is marked as a metering point with insufficient data. If the three-phase voltage of a metering point has a three-phase variance greater than the set threshold m, then the metering point is an abnormal metering point and is marked as a metering point with large three-phase voltage difference. If there are only two phase voltages, calculate the voltage difference ΔU between the two phases. If ΔU exceeds the set threshold n, the metering point is an abnormal metering point and is marked as a metering point with a large voltage difference between the two phases.

5. An anomaly identification system based on distribution network voltage data cleaning, employing the method described in any one of claims 1-4, characterized in that: Data acquisition module: Acquires three-phase voltage data of each metering point in the distribution network according to the preset sampling frequency, and marks data within 30% of the upper and lower limits of voltage as valid; Voltage Average Calculation and Rated Voltage Estimation Module: Calculates the average voltage of each phase at each metering point and estimates the rated voltage level of each phase. Metering point rated voltage estimation module: Based on the rated voltage level of each phase, the rated voltage level of the metering point is estimated, and data within 30% of the upper and lower limits of the rated voltage of the metering point are marked as valid; Voltage qualification rate calculation module: Calculates the voltage qualification rate of each phase based on valid data, and calculates the voltage qualification rate of the metering point; Anomaly identification and marking module: Identifies and marks measurement points with data anomalies.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

Images