Measurement data-based transformer area layering and phase-splitting identification method, system and equipment

Abstract

The invention belongs to the field of power equipment, and particularly relates to a transformer area layering and phase-splitting identification method, system and equipment based on measurement data. According to the scheme, the type of each meter is determined according to archive information of each meter; then, extracting time sequence characteristics of each meter according to the measurement data uploaded each time, and extracting corresponding statistical characteristics according to all the measurement data uploaded for multiple times in the whole day; then, according to the time sequence features and the statistical features, a depth-first search algorithm and a locality-sensitive hash algorithm are adopted to complete feature recognition, and a label relation table capable of representing the phase sequence and affiliation relation of each measurement switch and the electric energy meter is generated; and finally, determining the real attributes of each measurement switch and each electric energy meter by combining the two label relation tables determined by different methods so as to complete the topological relation identification task of the transformer area meter. According to the invention, the problems of low efficiency and insufficient reliability due to the fact that layering and phase splitting of an existing transformer area meter depend on manual work are solved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power equipment, and in particular relates to a method, system and equipment for identifying substation stratification and phase separation based on measurement data. Background Art

[0002] As a key component of the low-voltage power distribution network, the meter box shoulders the important responsibility of supplying power to users, and its stable operation has a vital impact on the stability of the entire power grid. However, with the growth of electricity demand, many users are connected to the power grid in an unorderly manner, resulting in the increasingly complex structure of the low-voltage distribution network. The complexity of the distribution network makes it difficult for operation and maintenance personnel to accurately grasp the user topology structure and power phase sequence of the substation, resulting in the increasingly prominent problem of three-phase imbalance in power consumption in the substation and frequent low-voltage events. In addition, the complexity of the distribution network will also make it difficult to quickly locate other faults when they occur, which seriously threatens the safety and stability of users' power use. Therefore, it is extremely important for power supply companies and users to quickly and accurately grasp the topology structure of the substation and the power phase of users.

[0003] Traditionally, the topological structure and phase identification of distribution substations mainly rely on manual surveying and statistics by operation and maintenance personnel, as well as on-site detection using substation identifiers. The manual surveying and statistical method requires operation and maintenance personnel to determine the correspondence between transformers and electricity users one by one along the low-voltage distribution lines, and record them in detail in the substation archives. The on-site detection method based on substation identifiers requires the installation of terminal identifiers on the transformer side of the substation, and the phase detection operation is performed on the electric energy meter of each power user, and then the relevant information is recorded in the substation archives. Both methods have the problems of huge workload, low verification efficiency and untimely information update. With the continuous increase in the number of users, these problems become more prominent. Summary of the invention

[0004] In order to solve the problem that it is difficult for the prior art to accurately distinguish the phase sequence and level of each power node in a power distribution network in a complex substation, the present invention provides a substation layered and phased identification method, system and device based on measurement data.

[0005] The solution provided by the present invention includes:

[0006] A method for identifying stratified phase separation in a substation based on measurement data is used to analyze and determine the phase sequence and subordinate relationship of each meter according to the measurement data generated by each meter in the substation. The stratified phase separation method includes the following steps:

[0007] S1: Obtain the archive information of all meters in the substation from the electricity consumption information collection system, and collect the load data of the day in real time.

[0008] The load data includes phase voltage, phase current and phase power. The file information includes the equipment identification code, power consumption address or management unit code.

[0009] S2: Divide each meter into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and generate a data sequence for each gateway meter, a measuring switch and an electric energy meter in combination with the corresponding load type data.

[0010] S3: Calculate the change of the phase current of each meter according to the data sequence of the meter collected at adjacent sampling moments, and use it as the time series feature of each phase of the meter.

[0011] S4: using a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch according to the timing characteristics of each phase of each meter, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining a corresponding first label relationship table Z.

[0012] S5: Based on the data sequences of the three meters collected throughout the day, the mean, variance, and extreme value of the phase voltage, phase current, and phase power of each meter are calculated; and the normalized data are used as the statistical features of each phase.

[0013] S6: Based on the statistical characteristics of each phase of each meter, a local sensitive hash algorithm is used to perform phase sequence division between the gateway meter and the measuring switch, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining the corresponding second label relationship table Q.

[0014] S7: taking the relevant attributes of the meters with the same phase sequence and subordinate relationship in the first label relationship table Z and the second label relationship table Q as the real attributes, and classifying the meters that do not contain complete attributes into the pending set.

[0015] S8: Continue to collect the load data of each meter in the substation for the next day, take the meters in the pending set as the classification object, repeat steps S2-S7 until the pending set is an empty set, and then determine the phase sequence and subordinate relationship of all meters.

[0016] As a further improvement of the present invention, in step S2, each meter in the substation uploads metering data every 15 minutes, and a total of 96 sets of data are generated on the day; then the data sequence D of the gateway meter, measuring switch or electric energy meter n The expression of (t) is as follows:

[0017]

[0018] Wherein, id represents the device identification code; U, P, I represent the phase voltage, phase power and phase current respectively, and the subscripts A, B, C are the corresponding phase marks; t represents the sampling time mark, t = 1, 2, ..., 96; n represents the meter type mark, and n = 1, 2, 3 correspond to the gateway meter, measuring switch and electric energy meter respectively.

[0019] As a further improvement of the present invention, in step S3, the calculation formula of the change amount of the phase current is as follows:

[0020]

[0021] In the above formula, represents the current change of the jth phase of the ith meter at time t; represents the phase current of the jth phase of the ith meter at time t; Represents the phase current of the jth phase of the ith meter at time t-1.

[0022] As a further improvement of the present invention, in step S4, the process of generating the first label relationship table Z by using the depth-first search algorithm is as follows:

[0023] S41: The connection relationship between the upper meter and the lower meter is represented as a directed graph, the nodes of the graph represent the meters, and the edges represent the subordinate relationship and phase sequence relationship between the meters; then the method for constructing the first label relationship table Z is as follows:

[0024] S42: Initialize an empty label relationship table Z to record the relationship between each pair of meters;

[0025] S43: traverse from a certain measurement switch node corresponding to the root node, and record the current change of the measurement switch;

[0026] S44: recursively traverse the adjacent electric energy meter nodes under the measurement switch node. If the sum of the power change values ​​of several lower-level meter nodes is equal to the current change of a certain upper-level meter, it means that the lower-level meter is subordinate to the corresponding upper-level meter. The corresponding edge weight is recorded and a first label relationship table Z is generated.

[0027] As a further improvement of the present invention, in step S5, the mean value μ of the phase voltage, phase current or phase power is x and variance σ x The calculation formula is as follows:

[0028]

[0029] In the above formula, x(t) represents the original value of the phase voltage, phase current or phase power on each phase at time t.

[0030] As a further improvement of the present invention, in step S5, the expression for normalization of statistical features is as follows:

[0031]

[0032] Among them, x represents the original feature data, including voltage U, power P and current I; D' n (x) represents the normalized eigenvector; σ x Represents the variance of the original feature, μ x Represents the mean of the original features; α x and β x are two custom parameters used to adjust the normalization effect of different features.

[0033] As a further improvement of the present invention, in step S6, the process of generating the second label relationship table Q according to the statistical features of each phase of each meter is as follows:

[0034] S61: Perform hash mapping on the feature vectors between the measurement switch and the electric energy meter and between the gateway meter and the measurement switch based on the locality sensitive hash algorithm to obtain different hash buckets.

[0035] S62: Calculate the Euclidean distance value between any two data points belonging to different types of meters corresponding to the same hash bucket.

[0036] S63: Set the Euclidean distance value to be less than the threshold The two data points are regarded as a similarity pair, and the two meters in each similarity pair are upper and lower and correspond in sequence, and then the second label relationship table Q is constructed according to all similarity pairs in each hash bucket.

[0037] As a further improvement of the present invention, in step S61, the hash mapping adopted by the local sensitive hashing algorithm is as follows:

[0038]

[0039] In the above formula, a k and b k They respectively represent feature vectors established based on data from two different types of meters; w represents the width of the hash bucket; x′ represents feature data including normalized phase-by-phase voltage, phase-by-phase current, phase-by-phase power, as well as the mean, variance and extreme value of the phase-by-phase voltage, the mean, variance and extreme value of the phase-by-phase current, and the mean, variance and extreme value of the phase-by-phase power.

[0040] As a further improvement of the present invention, in step S62, the Euclidean distance value between any two data points is calculated as follows:

[0041] Suppose there are two data points V and Q: V = (v1, v2, ..., vn ), Q=(q1,q2,…,q n ), their Euclidean distance is calculated as follows:

[0042]

[0043] Where n is the length of the vector of data points.

[0044] As a further improvement of the present invention, in step S8, assuming that after a specified period of time, there are still meters with incomplete attributes in the pending set, the substation management personnel will go to the site for field inspection to determine the phase sequence and subordinate relationship of the corresponding meters.

[0045] The present invention also includes a stratified phase separation system for meters in a substation based on measurement data, which is used to determine the phase sequence and subordinate relationship of each meter in the substation using the above-mentioned stratified phase separation identification method based on measurement data. The stratified phase separation system includes: an object library, a data acquisition unit, a time series feature generation unit, a statistical feature generation unit, a first classification module, a second classification module and an attribute update unit.

[0046] The object library stores the archive information of all meters with unconfirmed phase sequence and subordinate relationship. The data acquisition unit is used to obtain the archive information of all meters and collect the load data of the day in real time; then, each meter is divided into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and the data sequence of each gateway meter, measuring switch and electric energy meter is generated in combination with the corresponding load data.

[0047] The time series feature generation unit is used to calculate the change of the phase current of each meter after the data sequence of each meter completes the update, and use it as the time series feature of each phase. The statistical feature generation unit is used to calculate the mean, variance, and extreme value of the phase voltage, phase current, and phase power in the data sequence of each meter on that day after the data sequence of each meter completes the daily update; and normalize them as the statistical features of each phase.

[0048] The first classification module uses a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch according to the timing characteristics of each phase of each meter in the object library, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining the corresponding first label relationship table.

[0049] The second classification module is used to perform phase sequence division between the gateway meter and the measuring switch, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter based on the statistical characteristics of each phase of each meter in the object library using a local sensitive hashing algorithm; thereby obtaining the corresponding second label relationship table.

[0050] The attribute updating unit is used to obtain the first label relationship table and the second label relationship table generated by the first classification module and the second classification module; then the overlapping phase relationship and subordinate relationship in the first label relationship table and the second label relationship table are used as the real attributes of the corresponding meter, and the meter is deleted from the object library.

[0051] The present invention also includes a stratified phase separation device for meters in a substation based on measured data, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the aforementioned stratified phase separation identification method for the substation based on measured data is implemented, thereby determining the phase sequence and subordinate relationship of each meter in the substation.

[0052] The technical solution provided by the present invention has the following beneficial effects:

[0053] The present invention utilizes real-time acquisition of load data such as current, voltage, and power to extract the timing characteristics and statistical characteristics of each meter, and then combines the depth-first search algorithm and the local sensitive hash algorithm to implement stratification and phase separation of the meters based on the two characteristics. The classification results of the two methods are then compared and mutually verified, thereby significantly improving the work efficiency and accuracy of the stratification and phase separation tasks. Compared to the current method that relies on manual mapping and statistics by operation and maintenance personnel and the use of substation identification instruments for household detection, the present invention realizes online automated substation topology and phase identification, effectively reducing overall costs and improving work efficiency and timeliness;

[0054] The present invention can efficiently carry out "three-phase" line loss calculation in the substation by identifying the layers and phases of the substation, quickly lock the three-phase unbalanced users, and meet the refined management of the smart grid. In addition, the method provided by the present invention is not only applicable to existing gateway meters, measuring switches, and electric energy meters, but also to the same type of equipment added in the future, and has strong practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the topological structure of each meter in a typical power substation.

[0056] Figure 2 This is a flow chart of the steps of the method for identifying stratification and phase separation in a station area based on measurement data provided in Example 1 of the present invention.

[0057] Figure 3 This is a system architecture diagram of a hierarchical and phase-separated system of meters within a substation based on measurement data provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] Example 1

[0060] In a typical power substation, the meters of the main station system that regularly generates and uploads measurement data include three types, namely the power meter of the substation (or called: household power meter), measurement switch, and gateway meter. Among them, the power meter is installed at the power consumption node corresponding to each power user, and measures the power load generated by the user in real time. Multiple power meters located in an area are usually installed in a digital metering box, and the measurement switch in the metering box measures the total load of the power users corresponding to all the power meters. The gateway meter is responsible for measuring the load data of the entire substation. Therefore, the topological structure of the power substation should be as follows: Figure 1 The pyramid structure shown in Figure 1. However, in actual power distribution scenarios, the additional power demand brought by electric vehicle charging piles, charging stations, and sporadic businesses often appears in disordered access, and for power users, household electricity meters are also divided into different types such as single-phase meters and three-phase meters. Therefore, the actual power topology structure of the substation is often more complicated. This will greatly increase the difficulty of traditional manual meter phase and layer separation.

[0061] In response to the above problems, this embodiment provides a substation layered phase identification method based on measurement data, which is used to analyze and determine the phase sequence and subordinate relationship of each meter according to the measurement data generated by each meter in the substation. The overall idea of ​​the technical solution provided by this embodiment is to first determine the type of the meter, whether it is an electric energy meter, a measuring switch or a gateway meter, based on the archive information of each meter, and add a type mark to it. Then, the measurement data uploaded daily by each meter is used to perform phase sequence classification tasks in the gateway meter and multiple measurement switches, as well as phase sequence classification and subordinate relationship distinction tasks between multiple measurement switches and multiple electric energy meters.

[0062] In the phase sequence classification and subordinate relationship distinction task of this embodiment, the time series characteristics of each meter are first generated according to the measurement data uploaded each time, and the statistical characteristics of each meter are generated according to all the measurement data uploaded multiple times throughout the day. Then, the depth-first search algorithm and the local sensitive hashing (LSH) algorithm are used to complete the feature recognition according to the time series characteristics and statistical characteristics, and a label relationship table that can characterize the phase sequence and subordinate relationship of each measurement switch and electric energy meter is generated. Finally, the two label relationship tables determined by different methods are combined to determine the true attributes of each measurement switch and electric energy meter, thereby realizing the identification of the topological relationship of the meter in the substation.

[0063] In detail, Figure 2 As shown, the method for identifying the stratification and phase separation of the substation based on the measurement data provided in this embodiment includes the following steps:

[0064] S1: Obtain the archive information of all meters in the area from the electricity consumption information collection system, denoted as D info , and collect the load data D0 of the day in real time.

[0065] The load data include phase voltage, phase current and phase power. A , U B , U C ; Phase current includes I A ,I B ,I C ; Phase power includes P A , P B , P C The file information includes the equipment identification code ID, electricity address or management unit code.

[0066] S2: Divide each meter into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and generate a data sequence for each gateway meter, a measuring switch and an electric energy meter in combination with the corresponding load type data.

[0067] In the existing power system, automatic power data collection has basically been realized. In this system, the substation management personnel do not need to go to the site to read the meter. Instead, the meter in the substation automatically reports its own measurement data to the concentrator at a preset time interval, and the concentrator sends it to the main station. At present, under the existing system, each meter in the substation uploads measurement data every 15 minutes, and a total of 96 sets of data are generated on the day; then the data sequence D of the gateway meter, measurement switch or electric energy meter n The expression of (t) is as follows:

[0068]

[0069] Wherein, id represents the device identification code; U, P, I represent the phase voltage, phase power and phase current respectively, and the subscripts A, B, C are the corresponding phase marks; t represents the sampling time mark, t = 1, 2, ..., 96; n represents the meter type mark, and n = 1, 2, 3 correspond to the gateway meter, measuring switch and electric energy meter respectively.

[0070] For example, It can represent the data sequence generated according to the 25th group of load data reported by the measuring switch with ID 1 at 06:15 on the same day. Correspondingly, D1 represents the data sequence of the gateway table of the substation area, and D3 represents the data sequence of the electric energy meter.

[0071] S3: Calculate the change of the phase current of each meter according to the data sequence of the meter collected at adjacent sampling moments, and use it as the time series feature of each phase of the meter.

[0072] In this embodiment, the calculation formula of the change in the phase current of any meter (including a gateway meter, a measuring switch, and an electric energy meter) is as follows:

[0073]

[0074] In the above formula, represents the current change of the jth phase of the ith meter at time t; represents the phase current of the jth phase of the ith meter at time t; Represents the phase current of the jth phase of the ith meter at time t-1.

[0075] S4: using a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch according to the timing characteristics of each phase of each meter, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining a corresponding first label relationship table Z.

[0076] In this embodiment, the gateway meter is located at the top of the entire substation, and there is only one gateway meter in one substation, and all measuring switches are subordinate devices of the gateway meter. Therefore, only the phase sequence division is required between the gateway meter and the measuring switch. The number of measuring switches and the number of electric energy meters in the substation are more than one. Any two electric energy meters may be subordinate devices of the same measuring switch, or they may be subordinate devices of two different measuring switches. Therefore, corresponding to any measuring switch and electric energy meter, it is necessary not only to distinguish the phase sequence relationship between them, but also to divide the subordinate relationship between the two.

[0077] For this special hierarchical phase separation task, this embodiment proposes a label relationship table to store the corresponding information. In the label relationship table, a phase sequence label for distinguishing the phase sequence is added to each gateway table, and a phase sequence label for distinguishing the phase sequence and a subordinate relationship label for characterizing the corresponding upper device are added to each measuring switch and electric energy meter.

[0078] In the solution of this embodiment, the technicians extracted the time series characteristics to reflect the increment of the metering results of each meter. Generally speaking, the increment of the metering results of the same upper device should be after the increment of the metering results of all subordinate devices. Based on this feature, this embodiment first uses the depth-first search algorithm to generate the first label relationship table according to the time series characteristics of each phase of each meter.

[0079] The depth-first algorithm is an algorithm used to traverse or search a tree or graph. Its basic idea is to start from the root node, traverse the nodes of the tree along the depth of the tree, and search the branches of the tree as deeply as possible. When all the edges of node v have been explored, or it is impossible to go forward, the search will go back to the starting node of the edge where node v was found and continue traversing. This process is repeated until all nodes have been visited.

[0080] In this embodiment, the process of generating the first label relationship table Z using the depth-first search algorithm is as follows:

[0081] S41: The connection relationship between the upper meter and the lower meter is represented as a directed graph, the nodes of the graph represent the meters, and the edges represent the subordinate relationship and phase sequence relationship between the meters; then the method for constructing the first label relationship table Z is as follows:

[0082] S42: Initialize an empty label relationship table Z to record the relationship between each pair of meters;

[0083] S43: traverse from a certain measurement switch node corresponding to the root node, and record the current change of the measurement switch;

[0084] S44: recursively traverse the adjacent electric energy meter nodes under the measurement switch node. If the sum of the power change values ​​of several lower-level meter nodes is equal to the current change of a certain upper-level meter, it means that the lower-level meter is subordinate to the corresponding upper-level meter. The corresponding edge weight is recorded and a first label relationship table Z is generated.

[0085] It can be seen that the deep learning algorithm can complete a search every time all meters generate new measurement data, and then assign multiple child nodes whose increments are equal to the total increments of the parent node to the corresponding parent node. Every day, the deep learning algorithm can be executed 96 times based on the 96 uploaded data, and the previous search results can be corrected and updated respectively. In this way, a more accurate first label relationship table Z can be obtained.

[0086] S5: Based on the data sequences of the three meters collected throughout the day, the mean, variance, and extreme value of the phase voltage, phase current, and phase power of each meter are calculated; and the normalized data are used as the statistical features of each phase.

[0087] In this embodiment, each meter uploads 96 sets of measurement data in a whole day. According to the data sequence corresponding to these measurement data, the mean, variance and extreme value of the phase current, voltage and power data of each meter can be generated, and then these data are used to obtain the statistical characteristics of the meter's data throughout the day. Specifically,

[0088] The mean value μ of the phase voltage, phase current or phase powerx and variance σ x The calculation formula is as follows:

[0089]

[0090] In the above formula, x(t) represents the original value of the phase voltage, phase current or phase power on each phase at time t.

[0091] The expression of normalization of statistical features is as follows:

[0092]

[0093] Among them, x represents the original feature data, including voltage U, power P and current I; D' n (x) represents the normalized eigenvector; σ x Represents the variance of the original feature, μ x Represents the mean of the original features; α x and β x These are two custom parameters used to adjust the normalization effect of different features. Tanh and weighted smoothing are used in the normalization process to ensure that the data has a consistent scale and eliminates noise after normalization.

[0094] S6: Based on the statistical characteristics of each phase of each meter, a local sensitive hash algorithm is used to perform phase sequence division between the gateway meter and the measuring switch, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining the corresponding second label relationship table Q.

[0095] In this embodiment, the process of generating the second label relationship table Q according to the statistical features of each phase of each meter is as follows:

[0096] S61: Based on the local sensitive hashing algorithm, hash mapping is performed on the feature vectors between the measurement switch and the electric energy meter, and between the gateway meter and the measurement switch to obtain different hash buckets; wherein the hash mapping adopted by the local sensitive hashing algorithm is as follows:

[0097]

[0098] In the above formula, a k and b k They represent feature vectors established based on data of two different types of meters; w represents the width of the hash bucket; x′ represents feature data including normalized split-phase voltage, split-phase current, split-phase power, and the mean, variance and extreme value of the split-phase voltage, the mean, variance and extreme value of the split-phase current, and the mean, variance and extreme value of the split-phase power;

[0099] S62: Calculate the Euclidean distance between any two data points belonging to different types of meters corresponding to the same hash bucket; the calculation method is as follows:

[0100] Suppose there are two data points V and Q: V = (v1, v2, ..., v n ), Q=(q1,q2,…,q n ), their Euclidean distance is calculated as follows:

[0101]

[0102] Where n is the length of the vector of data points.

[0103] S63: Set the Euclidean distance value to be less than the threshold The two data points are regarded as a similarity pair, and the two meters in each similarity pair are upper and lower and correspond in sequence, and then the second label relationship table Q is constructed according to all similarity pairs in each hash bucket.

[0104] S7: taking the relevant attributes of the meters with the same phase sequence and subordinate relationship in the first label relationship table Z and the second label relationship table Q as the real attributes, and classifying the meters that do not contain complete attributes into the pending set.

[0105] According to the 96 pieces of measurement data reported daily by each meter, this embodiment extracts the time series features of each reported data, and uses the deep search algorithm to iteratively search 96 times, generating and updating the first label relationship table Z that can characterize the phase relationship and subordinate relationship between each meter. The statistical features of all reported data of each meter are extracted from the measurement data of the whole day, and the second label relationship table Q that can characterize the phase relationship and subordinate relationship between each meter is generated and updated using the local sensitive hashing algorithm.

[0106] On this basis, in order to make the final stratification and phase separation results more confident, this embodiment uses the relevant attributes of the meters with the same phase sequence and subordinate relationship in the two label relationship tables as the real attributes.

[0107] For example, for a certain electric energy meter, if it has complete attributes (both phase sequence labels and subordinate labels) in the first tag relationship table Z, but the attribute content of the electric energy meter in the second tag relationship table Q is incomplete, then the attribute information of the electric energy meter in the first tag relationship table Z is considered to be unreliable. For a certain electric energy meter, if the attribute information in the first tag relationship table Z and the second tag relationship table Q is complete, but one of the phase sequence labels and subordinate labels in the two tag relationship tables does not match, then the attribute information of the electric energy meter in the first tag relationship table Z and the second tag relationship table Q is considered to be unreliable. For a certain electric energy meter, if the attribute information in the first tag relationship table Z and the second tag relationship table Q is complete, and the phase sequence labels and subordinate labels in the two tag relationship tables are exactly the same, then the attribute information of the electric energy meter in the first tag relationship table Z and the second tag relationship table Q is considered to be credible. This implementation regards it as the true attribute of the meter and completes the layering and phase separation tasks of the electric energy meter.

[0108] S8: Continue to collect the load data of each meter in the substation for the next day, take the meters in the pending set as the classification object, repeat steps S2-S7 until the pending set is an empty set, and then determine the phase sequence and subordinate relationship of all meters.

[0109] In the scheme of the present embodiment, the metering data of a certain day can usually complete the stratification and phase separation of the metering meters in a substation with fewer nodes and simple topological relationships, but for a complex large substation. The measurement data of a single day may not be sufficient to achieve stratification and phase separation of all meters. In view of this situation, the present embodiment will put the metering meters that have not completed stratification and phase separation into a pending set separately, and then continue to collect the load data uploaded by all meters, repeat the above classification process, and then complete the phase sequence division and subordinate relationship classification tasks of the remaining measurement switches and electric energy meters.

[0110] Generally speaking, using data from multiple consecutive days, the stratification and phase separation tasks of more than 90% of the meters can basically be completed. On this basis, it is assumed that there are some special nodes whose generated power loads are difficult to distinguish through timing characteristics and statistical characteristics. The solution provided in this embodiment also provides a relief plan, in which relevant technical personnel go to the site to check the table and complete the corresponding stratification and phase separation tasks. That is: assuming that after a period of a specified length of time, there are still meters with incomplete attributes in the pending set, the substation management personnel will go to the site for field inspection to determine the phase sequence and subordination of the corresponding meters.

[0111] Combining the above content, it can be seen that the technical solution provided by this embodiment can use real-time acquisition of load data such as current, voltage, and power to determine the topological structure and phase of the distribution station area in real time, and has the advantages of high precision, low cost, and high efficiency. It effectively overcomes the problems of large workload, low efficiency, and delayed information update of traditional methods, and provides solid technical support for the construction and progress of smart grids.

[0112] Example 2

[0113] Based on the scheme of Example 1, this embodiment further provides a stratified phase separation system for meters in a substation based on measurement data, which is used to determine the phase sequence and subordinate relationship of each meter in the substation using the stratified phase separation identification method based on measurement data as in Example 1. Figure 3 As shown, the hierarchical and phase-separated system includes: an object library, a data acquisition unit, a time series feature generation unit, a statistical feature generation unit, a first classification module, a second classification module and an attribute update unit.

[0114] The object library stores the archive information of all meters with unconfirmed phase sequence and subordinate relationship. The data acquisition unit is used to obtain the archive information of all meters and collect the load data of the day in real time; then, each meter is divided into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and the data sequence of each gateway meter, measuring switch and electric energy meter is generated in combination with the corresponding load data.

[0115] The time series feature generation unit is used to calculate the change of the phase current of each meter after the data sequence of each meter completes the update, and use it as the time series feature of each phase. The statistical feature generation unit is used to calculate the mean, variance, and extreme value of the phase voltage, phase current, and phase power in the data sequence of each meter on that day after the data sequence of each meter completes the daily update; and normalize them as the statistical features of each phase.

[0116] The first classification module uses a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch according to the timing characteristics of each phase of each meter in the object library, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining the corresponding first label relationship table.

[0117] The second classification module is used to perform phase sequence division between the gateway meter and the measuring switch, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter based on the statistical characteristics of each phase of each meter in the object library using a local sensitive hashing algorithm; thereby obtaining the corresponding second label relationship table.

[0118] The attribute updating unit is used to obtain the first label relationship table and the second label relationship table generated by the first classification module and the second classification module; then the overlapping phase relationship and subordinate relationship in the first label relationship table and the second label relationship table are used as the real attributes of the corresponding meter, and the meter is deleted from the object library.

[0119] Example 3

[0120] Based on the scheme of Example 1, this embodiment further provides a stratified phase separation device for meters in a substation based on measured data, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the stratified phase separation identification method for the substation based on measured data as in Example 1 is implemented, thereby determining the phase sequence and subordinate relationship of each meter in the substation.

[0121] The hierarchical and phase-splitting device for the meter in the substation area based on the measured data provided in this embodiment is essentially a computer device for implementing the solution in Embodiment 1. The computer device can be an intelligent terminal capable of executing a program, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server, or a server cluster composed of multiple servers), etc.

[0122] The computer device indicated in this embodiment includes at least but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0123] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0124] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for identifying the stratification and phase separation of a substation based on measurement data, which is used to analyze and determine the phase sequence and subordinate relationship of each meter according to the measurement data generated by each meter in the substation; characterized in that: The stratified phase separation method comprises the following steps: S1: Obtain the archive information of all meters in the substation from the electricity consumption information collection system, and collect the load data of the day in real time; The load data includes phase voltage, phase current and phase power; the file information includes equipment identification code, electricity address or management unit code; S2: Divide each meter into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and generate a data sequence for each gateway meter, a measuring switch and an electric energy meter in combination with the corresponding load type data; S3: Calculate the change of the phase current of each meter according to the data sequence of the meter collected at adjacent sampling moments, and use it as the time series feature of each phase of the meter; S4: using a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch according to the timing characteristics of each phase of each meter, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining a corresponding first label relationship table Z; S5: Based on the data sequences of the three meters collected throughout the day, the mean, variance, and extreme value of the phase voltage, phase current, and phase power of each meter are calculated; and the normalized data are used as the statistical features of each phase; S6: Based on the statistical characteristics of each phase of each meter, a local sensitive hash algorithm is used to perform phase sequence division between the gateway meter and the measuring switch, and to perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter; thereby obtaining a corresponding second label relationship table Q; S7: taking the relevant attributes of the meters with the same phase sequence and subordinate relationship in the first label relationship table Z and the second label relationship table Q as the real attributes, and classifying the meters that do not contain complete attributes into the pending set; S8: Continue to collect the load data of each meter in the substation for the next day, take the meters in the pending set as the classification object, repeat steps S2-S7 until the pending set is an empty set, and then determine the phase sequence and subordinate relationship of all meters.

2. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 1, characterized in that: In step S2, each meter in the area uploads metering data every 15 minutes, and a total of 96 sets of data are generated on the day; then the data sequence D of the gateway meter, measuring switch or energy meter n The expression of (t) is as follows: Wherein, id represents the device identification code; U, P, I represent the phase voltage, phase power and phase current respectively, and the subscripts A, B, C are the corresponding phase marks; t represents the sampling time mark, t = 1, 2, ..., 96; n represents the meter type mark, and n = 1, 2, 3 correspond to the gateway meter, measuring switch and electric energy meter respectively.

3. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 2, characterized in that: In step S3, the calculation formula of the change amount of the phase current is as follows: In the above formula, represents the current change of the jth phase of the ith meter at time t; represents the phase current of the jth phase of the ith meter at time t; Represents the phase current of the jth phase of the ith meter at time t-1.

4. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 1, characterized in that: In step S4, the process of generating the first label relationship table Z using the depth-first search algorithm is as follows: S41: The connection relationship between the upper meter and the lower meter is represented as a directed graph, the nodes of the graph represent the meters, and the edges represent the subordinate relationship and phase sequence relationship between the meters; then the method for constructing the first label relationship table Z is as follows: S42: Initialize an empty label relationship table Z to record the relationship between each pair of meters; S43: traverse from a certain measurement switch node corresponding to the root node, and record the current change of the measurement switch; S44: recursively traverse the adjacent electric energy meter nodes under the measurement switch node. If the sum of the power change values ​​of several lower-level meter nodes is equal to the current change of a certain upper-level meter, it means that the lower-level meter is subordinate to the corresponding upper-level meter. The corresponding edge weight is recorded and a first label relationship table Z is generated.

5. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 1, characterized in that: In step S5, the mean value μ of the phase voltage, phase current or phase power is x and variance σ x The calculation formula is as follows: In the above formula, x(t) represents the original value of the phase voltage, phase current or phase power on each phase at time t.

6. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 2, characterized in that: In step S5, the expression for normalization of statistical features is as follows: Where x represents the original feature data, including voltage U, power P and current I; D' n (x) represents the normalized eigenvector; σ x Represents the variance of the original feature, μ x Represents the mean of the original features; α x and β x are two custom parameters used to adjust the normalization effect of different features.

7. The method for identifying the stratification and phase separation of a station based on measurement data according to claim 6, characterized in that: In step S6, the process of generating the second label relationship table Q according to the statistical features of each phase of each meter is as follows: S61: Based on the local sensitive hashing algorithm, hash mapping is performed on the feature vectors between the measurement switch and the electric energy meter, and between the gateway meter and the measurement switch to obtain different hash buckets; wherein the hash mapping adopted by the local sensitive hashing algorithm is as follows: In the above formula, a k and b k They represent feature vectors established based on data of two different types of meters; w represents the width of the hash bucket; x′ represents feature data including normalized split-phase voltage, split-phase current, split-phase power, and the mean, variance and extreme value of the split-phase voltage, the mean, variance and extreme value of the split-phase current, and the mean, variance and extreme value of the split-phase power; S62: Calculate the Euclidean distance between any two data points belonging to different types of meters corresponding to the same hash bucket; the calculation method is as follows: Suppose there are two data points V and Q: V = (v1, v2, ..., v n ), Q=(q1,q2,…,q n ), their Euclidean distance is calculated as follows: Where n is the length of the vector of data points; S63: Set the Euclidean distance value to be less than the threshold The two data points are regarded as a similarity pair, and the two meters in each similarity pair are upper and lower and correspond in sequence, and then the second label relationship table Q is constructed according to all similarity pairs in each hash bucket.

8. A method for identifying the stratification and phase separation of a station based on measurement data, characterized in that: In step S8, assuming that after a specified period of time, there are still meters with incomplete attributes in the pending set, the substation management personnel will go to the site for field inspection to determine the subordinate relationship and phase relationship of the corresponding meters.

9. A layered and phased system for meters in a substation based on measurement data, characterized in that: It is used to determine the phase sequence and subordinate relationship of each meter in the substation by using the substation layered phase identification method based on measurement data as described in any one of claims 1 to 8; The stratified phase separation system comprises: The object library stores the archive information of all meters with unconfirmed phase sequence and subordinate relationship; A data acquisition unit is used to obtain the archive information of all meters and collect the load data of the day in real time; then, each meter is divided into a gateway meter, a measuring switch or an electric energy meter according to the archive information, and a data sequence of each gateway meter, measuring switch and electric energy meter is generated in combination with the corresponding load data; A time series feature generating unit, which is used to calculate the change of the phase current of each meter after the data sequence of each meter is updated, and use it as the time series feature of each phase; A statistical feature generation unit is used to calculate the mean, variance, and extreme value of the phase voltage, phase current, and phase power in the data sequence of each meter on that day after the data sequence of each meter is updated daily; and perform normalization processing on the calculated values ​​as the statistical features of each phase; A first classification module, which uses a depth-first search algorithm to perform phase sequence division between the gateway meter and the measuring switch, and performs subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter according to the timing characteristics of each phase of each meter in the object library; thereby obtaining a corresponding first label relationship table; A second classification module, which is used to perform phase sequence division between the gateway meter and the measuring switch, and perform subordinate relationship classification and phase sequence division between the measuring switch and the electric energy meter according to the statistical characteristics of each phase of each meter in the object library by using a local sensitive hashing algorithm; thereby obtaining a corresponding second label relationship table; and An attribute updating unit is used to obtain the first label relationship table and the second label relationship table generated by the first classification module and the second classification module; then the overlapping phase relationship and subordinate relationship in the first label relationship table and the second label relationship table are used as the real attributes of the corresponding meter, and the meter is deleted from the object library.

10. A stratified and phased device for meters in a substation based on measured data, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and running in the processor, and is characterized in that: when the processor executes the computer program, it implements the substation stratification and phase identification method based on measurement data as described in any one of claims 1-8, and then determines the phase sequence and subordinate relationship of each meter in the substation.

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