CN113036786A - Low-voltage distribution transformer user phase sequence identification and three-phase imbalance adjustment method - Google Patents
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Abstract
The application shows a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user, which comprises the following steps: extracting voltage sequence data of the distribution transformer and the user intelligent electric meter thereof and preprocessing the voltage sequence data; identifying the phase sequence of the user by using the preprocessed data through a correlation coefficient clustering algorithm; obtaining user phase sequence data; judging the accuracy of the obtained user phase sequence data, if the accuracy is high, carrying out the next step, and if the accuracy is low, carrying out preprocessing again until the user phase sequence data with high accuracy is obtained; acquiring three-phase voltage electric data of a user, calculating three-phase unbalance, and associating the three-phase voltage electric data of the user with phase sequence data of the user; and preprocessing the data; and obtaining the optimal phase sequence of the user by adopting a simulated annealing algorithm according to the calculated three-phase unbalance degree by the preprocessed data. The invention can effectively identify the phase sequence of the user, calculate the optimal access phase sequence of the user, guide power distribution operation and inspection personnel to accurately set the phase sequence of the user and reduce the three-phase imbalance of the distribution transformer in a distribution area.
Description
Technical Field
The invention belongs to the field of low-voltage distribution transformers, and particularly relates to a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user.
Background
The low-voltage distribution network in China adopts three-phase four-wire system wiring, the user side is mostly single-phase load, the electricity consumption has larger randomness, and the transformer in the transformer area is easy to have the phenomenon of three-phase imbalance. The three-phase imbalance can cause the available capacity of the transformer to be reduced, the loss to be increased and the service life to be shortened, the transformer can be burnt in serious conditions, and meanwhile, the problem of electric energy quality can be brought, and the safe power utilization of users is influenced. In a power distribution network, it is difficult to maintain the operation of the transformer in a three-phase balanced state, but a power grid company needs to reduce the three-phase unbalance degree as much as possible.
In the prior art, a plurality of methods for treating three-phase imbalance of a transformer in a transformer area are provided, and methods such as installing a three-phase imbalance adjusting and compensating device are generally adopted.
However, the prior art is high in manufacturing cost, high in power consumption and complex in daily maintenance, and needs to provide a technical scheme which can monitor the real-time operation condition of the distribution transformer, timely adjust some users with heavier loads to another phase sequence with lighter loads, enable the three-phase loads to reach natural balance as much as possible, accurately grasp the change rule of each phase load of the transformer, more accurately identify the phase sequence of the transformer connected by each user, and further reasonably make a deployment strategy of the user loads.
Disclosure of Invention
Based on the problems, the invention provides a low-voltage distribution transformer user phase sequence identification and three-phase imbalance adjustment method, which guides power distribution operation and inspection personnel to accurately set the user phase sequence and reduces the three-phase imbalance of distribution transformer in a transformer area.
The application shows a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user, which comprises the following steps:
voltage sequence data of the distribution transformer and the user intelligent electric meter are extracted through the power utilization information acquisition system;
preprocessing the voltage sequence data of the distribution transformer and the intelligent electric meters of the users to which the distribution transformer belongs;
identifying the phase sequence of the low-voltage distribution transformer user by using the voltage sequence data of the pre-processed distribution transformer and the intelligent electric meter of the user to which the distribution transformer belongs by adopting a correlation coefficient clustering algorithm; obtaining user phase sequence data;
judging the accuracy of the obtained user phase sequence data, if the accuracy is high, performing step S5, and if the accuracy is low, preprocessing the data again until the user phase sequence data with high accuracy is obtained;
acquiring three-phase voltage current data of a user, calculating three-phase unbalance of a distribution transformer in a period of time, and associating the three-phase voltage current data of the user with phase sequence data of the user;
preprocessing the associated data of the user three-phase voltage current data and the user phase sequence data;
and obtaining the optimal phase sequence of the user by adopting a simulated annealing algorithm according to the calculated three-phase unbalance degree through the preprocessed associated data.
The beneficial effect of this application does:
the application shows a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user, which comprises the following steps: extracting voltage sequence data of the distribution transformer and the user intelligent electric meter thereof and preprocessing the voltage sequence data; identifying the phase sequence of the user by using the preprocessed data through a correlation coefficient clustering algorithm; obtaining user phase sequence data; judging the accuracy of the obtained user phase sequence data, if the accuracy is high, carrying out the next step, and if the accuracy is low, carrying out preprocessing again until the user phase sequence data with high accuracy is obtained; acquiring three-phase voltage electric data of a user, calculating three-phase unbalance, and associating the three-phase voltage electric data of the user with phase sequence data of the user; and preprocessing the data; and obtaining the optimal phase sequence of the user by adopting a simulated annealing algorithm according to the calculated three-phase unbalance degree by the preprocessed data. The invention can effectively identify the phase sequence of the user, calculate the optimal access phase sequence of the user, guide power distribution operation and inspection personnel to accurately set the phase sequence of the user and reduce the three-phase imbalance of the distribution transformer in a distribution area.
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In order to more clearly explain the technical solution of the application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user according to the present application;
FIG. 2 is a flow chart of the DBSCAN algorithm shown in the present application;
fig. 3 is a flow chart of a simulated annealing algorithm shown in the present application.
Detailed Description
Referring to fig. 1, fig. 1 shows a method for identifying a phase sequence and adjusting three-phase imbalance of a low-voltage distribution transformer user, which includes the steps of:
s1: voltage sequence data of the distribution transformer and the user intelligent electric meter are extracted through the power utilization information acquisition system;
the voltage sequence data is a sufficient number of rated voltage fluctuation events recorded over time; the generation reasons are as follows: the distribution network is often influenced by various emergency time to cause the voltage to fluctuate frequently, generally, compared with the emergency caused by a distribution transformer or a high-voltage distribution network, the voltage fluctuation event caused by the power consumption behavior of a user (such as starting of a high-power household appliance) has larger influence on other users in the same phase, the longer the power supply radius is, the more obvious the influence is, after a synchronous clock, the instantaneous voltage data of the user intelligent electric meter are uninterruptedly collected at the same time point, enough voltage fluctuation events are recorded in voltage sequence data along with the time lapse, the voltage curve fluctuation of users in the same phase is similar, and the voltage curve fluctuation similarity of users in different phases is poorer.
S2: preprocessing the voltage sequence data of the distribution transformer and the intelligent electric meters of the users to which the distribution transformer belongs;
the pretreatment reason is as follows: missing values and abnormal values exist in sequence data of an early-stage distribution transformer and a user intelligent voltage electric meter belonging to the early-stage distribution transformer, the data are directly used for modeling, the generalization capability of a model can be influenced, the prediction accuracy is reduced, meanwhile, different characteristics in the modeling data often have different units and different variation degrees, and the accuracy of a prediction trend can be influenced by directly participating in modeling, so that the original data need to be normalized, and the quality of the modeling data is improved;
the pretreatment method comprises the following steps: missing value filling, abnormal value cleaning and data normalization.
The missing value types include: complete random deletion, complete non-random deletion;
according to different missing value types, different missing value processing methods are adopted;
the deletion value is generated because: failure of power consumption data acquisition;
the missing value filling method comprises the following steps: inquiring the current-day active indication value and the previous-day active indication value, calculating and filling a missing value by using an averaging method and the like, and if the daily frozen electric energy indication value is missing, calculating and filling the current-day frozen electric quantity by using a Lagrange interpolation method, wherein the Lagrange interpolation method comprises the following steps of: the daily power consumption data is filled from the product of the base function 1 and the power consumption on the first date, the product of the base function 2 and the power consumption on the 2 nd date, and the product of the base function 3 and the power consumption data on the third date, and the calculation formula is as follows:
the abnormal value is an individual value in the sample, and the value of the abnormal value is obviously deviated from the rest observed values of the sample to which the abnormal value belongs;
the abnormal value is determined by the following method:
testing the sample data by using a normality check model, and if P is more than 0.05, judging that the index obeys normal distribution under the current sample data;
if P is less than 0.05, judging that the index does not obey normal distribution under the current sample data; removing outliers, and testing sample data by reusing the normality check model until the sample data obeys normal distribution; when the sample data all obeys normal distribution, further analyzing the sample data;
and (4) ensuring that 95% of sample values fall into a domain value range as an abnormal value judgment requirement on the basis of a normal distribution confidence interval and a distribution coverage ratio comparison table. And calculating the mean value and the standard deviation of the sample data by using a normal distribution statistical analysis model. And 2 standard deviation fluctuations are made around the central point according to the upper limit and the lower limit to form an index threshold value. Upper limit: mean +2 standard deviation; lower limit: mean-2 standard deviation. And carrying out coverage rate test on the current index data according to the threshold value. Data outside the threshold range is determined to be abnormal data. The comparison table of the confidence interval of the normal distribution model and the sample value coverage rate is as follows:
the selected data has different dimensions and dimension units, which affect the subsequent model result, so the data needs to be normalized in order to eliminate the dimension influence among the features. See the following formula:
in the formula, x and x' are data before and after normalization, μ is the mean of all sample data, and σ is the standard deviation of all data.
S3: identifying the phase sequence of the low-voltage distribution transformer user by using the voltage sequence data of the pre-processed distribution transformer and the intelligent electric meter of the user to which the distribution transformer belongs by adopting a correlation coefficient clustering algorithm; obtaining user phase sequence data;
the method of the correlation coefficient clustering algorithm comprises the following steps:
s31: calculating a correlation coefficient between each user through the complex correlation coefficient and the biased correlation coefficient;
the complex correlation coefficient is as follows:
the multiple correlation coefficient is an index for measuring the multiple correlation degree, and describes the correlation between one variable and several variables, and the larger the multiple correlation coefficient is, the more closely the linear correlation degree between the elements or variables is
The essence of the multiple correlation is the correlation of the actual observed value of Y with the value predicted by the p arguments.
In one possible embodiment, assume that user y is associated with multiple other users X1，X2，…，XKThe complex correlation coefficient between the two can be considered to construct a correlation coefficient related to X1，X2，…，XKBy calculating a simple correlation coefficient between the linear combination and y as the variables y and X1，X2，…，XKA complex correlation coefficient between; the specific calculation process is as follows:
(1) by y to X1，X2，…，XKAnd (4) performing regression to obtain:
(2) simple correlation coefficient is calculated as y and X1，X2，…，XKThe complex correlation coefficient between the two is calculated according to the following formula:
the deviation correlation coefficient is as follows:
the partial correlation coefficient, also called partial correlation coefficient, reflects the degree of net correlation between two variables.
Assuming that there is a correlation between users X, Y, Z, when the linear action of variable Z is controlled, the first order partial correlation coefficient between X and Y is defined as:
s32: clustering the users into different categories by adopting a DBSCAN clustering algorithm according to the correlation coefficient between each user;
the DBSCAN clustering algorithm is also called a noise density-based clustering method, and is a density-based spatial clustering algorithm. The algorithm divides the area with sufficient density into clusters and finds arbitrarily shaped clusters in a noisy spatial database, which defines clusters as the largest set of densely connected points. The flow of the DBSCAN algorithm is detailed in FIG. 2.
S33: and calculating a correlation coefficient of the three-phase voltage sequence data of each type of users and the distribution transformer to obtain a phase with the maximum correlation number of each type of users in the three-phase voltage data, and identifying the phase sequence of the users.
S4: judging the accuracy of the obtained user phase sequence data, if the accuracy is high, performing the step S5, if the accuracy is low, returning to the step S2, and preprocessing the data again until the user phase sequence data with high accuracy is obtained;
the method for judging the accuracy of the obtained user phase sequence data comprises the following steps:
and comparing the obtained user phase sequence data with the user real phase sequence to obtain the accuracy, considering that the accuracy is high if the accuracy is in a first numerical range, and considering that the accuracy is low if the accuracy is not in the first numerical range, wherein the first numerical range is set according to the actual situation.
S5: acquiring three-phase voltage current data of a user, calculating three-phase unbalance of a distribution transformer in a period of time, and associating the three-phase voltage current data of the user with phase sequence data of the user;
the method for acquiring the three-phase voltage and current data of the user comprises the following steps:
obtaining a set U of three-phase users according to the identification of the phase sequence of the users, wherein the set of the three-phase users is respectively as follows: u shapeA、UB、UC(ii) a Obtaining three-phase voltage data according to the three-phase user set and the voltage sequence data of the user intelligent electric meter;
when the three-phase user set is determined, each phase of load is equal to the superposition of the phase of user load, and the three-phase current data obtained according to the three-phase user set are respectively as follows: i isA、IB、IC。
Wherein:
wherein nA is the number of A-phase users, and the users: u. ofIA(1≤IAnA) and nB is the number of B-phase users and uIB(1≤IBnB) and nC is the number of C-phase users, and the users: : u. ofIC(1≤IC≤Nc)
When the set of users in each phase sequence is determined, the load of the phase is equal to the superposition of the loads of the users, I represents the current, IA，IB，ICRepresenting the three-phase current of distribution transformer A, B, C.
Thus, the user's selection of the optimal phase sequence strategy translates into: how to distribute the users over the three phases so that the three-phase loads are as balanced as possible.
The three-phase unbalance calculation method comprises the following steps:
acquiring three-phase current data of j time point, wherein the three-phase current data are IAj、IBj、ICj；
Calculating the jth time period of the distribution transformer in a period of time according to the three-phase current data of the jth time pointThree-phase unbalance degree UBj(ii) a The U isBjThe calculation formula of (2) is as follows:
the average three-phase imbalance of the transformer over a period of time is therefore:
s6: preprocessing the associated data of the user three-phase voltage current data and the user phase sequence data;
the pretreatment method comprises the following steps: missing value filling, abnormal value cleaning and data normalization.
S7: and obtaining the optimal phase sequence of the user by adopting a simulated annealing algorithm according to the calculated three-phase unbalance degree through the preprocessed associated data.
The simulated annealing algorithm is as follows:
the simulated annealing algorithm can be decomposed into three parts of a solution space, an objective function and an initial solution, and referring to fig. 3, fig. 3 shows a flowchart of the simulated annealing algorithm.
Basic idea of simulated annealing:
(1) initialization: initial temperature T (sufficiently large), initial solution state S (being the starting point of the algorithm iteration), number of iterations L for each value of T;
(2) carrying out the steps (3) to (6) on k which is 1, … and L;
(3) generating a new solution S';
(4) calculating an increment Δ T ═ C (S') -C (S), where C (S) is a cost function;
(5) if the delta T is less than 0, S 'is accepted as a new current solution, otherwise, S' is accepted as a new current solution according to the probability exp (-delta T/T);
(6) and if the termination condition is met, outputting the current solution as the optimal solution, and ending the program. The termination condition is usually taken to terminate the algorithm when several successive new solutions are not accepted;
(7) t is gradually reduced, and T- > 0, and then the step 2 is carried out.
The present application has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to limit the application. Those skilled in the art will appreciate that various equivalent substitutions, modifications or improvements may be made to the embodiments and implementations of the disclosure without departing from the spirit and scope of the disclosure, which is within the scope of the disclosure as defined by the appended claims.
Claims (6)
1. A low-voltage distribution transformer user phase sequence identification and three-phase unbalance adjustment method is characterized by comprising the following steps:
s1: voltage sequence data of the distribution transformer and the user intelligent electric meter are extracted through the power utilization information acquisition system;
s2: preprocessing the voltage sequence data of the distribution transformer and the intelligent electric meters of the users to which the distribution transformer belongs;
s3: identifying the phase sequence of the low-voltage distribution transformer user by using the voltage sequence data of the pre-processed distribution transformer and the intelligent electric meter of the user to which the distribution transformer belongs by adopting a correlation coefficient clustering algorithm; obtaining user phase sequence data;
s4: judging the accuracy of the obtained user phase sequence data, if the accuracy is high, performing the step S5, if the accuracy is low, returning to the step S2, and preprocessing the data again until the user phase sequence data with high accuracy is obtained;
s5: acquiring three-phase voltage current data of a user, calculating three-phase unbalance of a distribution transformer in a period of time, and associating the three-phase voltage current data of the user with phase sequence data of the user;
s6: preprocessing the associated data of the user three-phase voltage current data and the user phase sequence data;
s7: and obtaining the optimal phase sequence of the user by adopting a simulated annealing algorithm according to the calculated three-phase unbalance degree through the preprocessed associated data.
2. The method for identifying the phase sequence and adjusting the three-phase imbalance of the low-voltage distribution transformer user according to claim 1, wherein the method for preprocessing the voltage sequence data of the distribution transformer and the user smart meters thereof comprises the following steps: missing value filling, abnormal value cleaning and data normalization.
3. The method for identifying the phase sequence and adjusting the three-phase imbalance of the low-voltage distribution transformer user according to claim 1, wherein the method of the correlation coefficient clustering algorithm comprises the following steps:
calculating a correlation coefficient between each user through the complex correlation coefficient and the biased correlation coefficient;
clustering the users into different categories by adopting a DBSCAN clustering algorithm according to the correlation coefficient between each user;
and calculating a correlation coefficient of the three-phase voltage sequence data of each type of users and the distribution transformer to obtain a phase with the maximum correlation number of each type of users in the three-phase voltage data, and identifying the phase sequence of the users.
4. The method for identifying the phase sequence and adjusting the three-phase imbalance of the low-voltage distribution transformer user according to claim 1, wherein the method for judging the accuracy of the obtained user phase sequence data comprises the following steps:
and comparing the obtained user phase sequence data with the user real phase sequence to obtain the accuracy, considering that the accuracy is high if the accuracy is in a first numerical range, and considering that the accuracy is low if the accuracy is not in the first numerical range, wherein the first numerical range is set according to the actual situation.
5. The method for identifying the phase sequence and adjusting the three-phase imbalance of the low-voltage distribution transformer user according to claim 1, wherein the method for acquiring the three-phase voltage and current data of the user comprises the following steps:
obtaining a set U of three-phase users according to the identification of the phase sequence of the users, wherein the set of the three-phase users is respectively as follows: u shapeA、UB、UC(ii) a According to the three-phase user set and the voltage sequence data of the user intelligent electric meterThree-phase voltage data can be obtained;
when the three-phase user set is determined, each phase of load is equal to the superposition of the phase of user load, and the three-phase current data obtained according to the three-phase user set are respectively as follows: i isA、IB、IC。
6. The method for identifying the phase sequence and adjusting the three-phase imbalance of the low-voltage distribution transformer user according to claim 1, wherein the method for calculating the three-phase imbalance of the distribution transformer in a period of time comprises the following steps:
acquiring three-phase current data of j time point, wherein the three-phase current data are IAj、IBj、ICj；
Calculating the three-phase unbalance UB of the distribution transformer in the jth time period within a period of time according to the three-phase current data of the jth time pointj(ii) a The UBjThe calculation formula of (2) is as follows:
the average three-phase imbalance of the transformer over a period of time is therefore:
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113922395A (en) * | 2021-12-15 | 2022-01-11 | 泰豪软件股份有限公司 | Three-phase unbalance treatment method and system |
| CN114935697A (en) * | 2022-07-25 | 2022-08-23 | 广东电网有限责任公司佛山供电局 | Three-phase load unbalance identification method, system, equipment and medium |
| CN115541993A (en) * | 2022-12-02 | 2022-12-30 | 北京志翔科技股份有限公司 | Phase-splitting electric quantity determination method, device, equipment and storage medium of three-phase electric quantity |
| CN116316712A (en) * | 2023-04-07 | 2023-06-23 | 国网江苏省电力有限公司南通市通州区供电分公司 | Three-phase load intelligent cooperative regulation and control method based on electricity utilization characteristics |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106505593A (en) * | 2016-10-14 | 2017-03-15 | 国网信通亿力科技有限责任公司 | A kind of method of the analysis of distribution transforming three-phase imbalance and load adjustment based on big data |
| CN108535599A (en) * | 2018-04-16 | 2018-09-14 | 国网河南省电力公司电力科学研究院 | Low-voltage platform area user's phase recognition methods based on voltage curve clustering |
| CN108776869A (en) * | 2018-06-07 | 2018-11-09 | 国网河南省电力公司电力科学研究院 | Taiwan area Three-Phase Transformer imbalance administering method based on intelligent electric meter big data analysis |
| CN109888800A (en) * | 2019-03-07 | 2019-06-14 | 南京工程学院 | A kind of power distribution station three-phase imbalance load method of adjustment based on load prediction and commutation strategy |
| CN111654044A (en) * | 2020-05-29 | 2020-09-11 | 国网福建省电力有限公司 | Big data analysis-based distribution transformer three-phase load imbalance problem diagnosis and treatment method and system |
-
2021
- 2021-03-05 CN CN202110253312.3A patent/CN113036786A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106505593A (en) * | 2016-10-14 | 2017-03-15 | 国网信通亿力科技有限责任公司 | A kind of method of the analysis of distribution transforming three-phase imbalance and load adjustment based on big data |
| CN108535599A (en) * | 2018-04-16 | 2018-09-14 | 国网河南省电力公司电力科学研究院 | Low-voltage platform area user's phase recognition methods based on voltage curve clustering |
| CN108776869A (en) * | 2018-06-07 | 2018-11-09 | 国网河南省电力公司电力科学研究院 | Taiwan area Three-Phase Transformer imbalance administering method based on intelligent electric meter big data analysis |
| CN109888800A (en) * | 2019-03-07 | 2019-06-14 | 南京工程学院 | A kind of power distribution station three-phase imbalance load method of adjustment based on load prediction and commutation strategy |
| CN111654044A (en) * | 2020-05-29 | 2020-09-11 | 国网福建省电力有限公司 | Big data analysis-based distribution transformer three-phase load imbalance problem diagnosis and treatment method and system |
Non-Patent Citations (2)
| Title |
|---|
| 宋万清等: "Python数据分析", 中国铁道出版社有限公司, pages: 138 - 141 * |
| 耿俊成等: "基于电压曲线聚类分析的低压用户相序自动识别", 《电力大数据》, vol. 22, no. 12, 31 December 2019 (2019-12-31), pages 1 - 8 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113922395A (en) * | 2021-12-15 | 2022-01-11 | 泰豪软件股份有限公司 | Three-phase unbalance treatment method and system |
| CN113922395B (en) * | 2021-12-15 | 2022-03-15 | 泰豪软件股份有限公司 | Three-phase unbalance treatment method and system |
| CN114935697A (en) * | 2022-07-25 | 2022-08-23 | 广东电网有限责任公司佛山供电局 | Three-phase load unbalance identification method, system, equipment and medium |
| CN115541993A (en) * | 2022-12-02 | 2022-12-30 | 北京志翔科技股份有限公司 | Phase-splitting electric quantity determination method, device, equipment and storage medium of three-phase electric quantity |
| CN116316712A (en) * | 2023-04-07 | 2023-06-23 | 国网江苏省电力有限公司南通市通州区供电分公司 | Three-phase load intelligent cooperative regulation and control method based on electricity utilization characteristics |
| CN116316712B (en) * | 2023-04-07 | 2024-02-09 | 国网江苏省电力有限公司南通市通州区供电分公司 | Three-phase load intelligent cooperative regulation and control method based on electricity utilization characteristics |
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