CN111654044A - Big data analysis-based distribution transformer three-phase load imbalance problem diagnosis and treatment method and system - Google Patents
Big data analysis-based distribution transformer three-phase load imbalance problem diagnosis and treatment method and system Download PDFInfo
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- H—ELECTRICITY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention relates to a distribution transformer three-phase load unbalance problem diagnosis and treatment method and system based on big data analysis.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a distribution transformer three-phase load unbalance problem diagnosis and treatment method and system based on big data analysis.
Background
The distribution network operation regulation stipulates that the unbalance degree of three phases in the operation of the distribution transformer cannot exceed 25 percent for a long time, otherwise, the distribution transformer and the line loss are obviously increased, the power output capability of the distribution transformer is reduced, and the hazards of influencing the safety of electric equipment, the efficiency of a motor and the like are caused.
Aiming at the problems, operation and maintenance personnel usually solve the problems by adjusting the load phase measures of low-voltage users on site in the current actual production, and the operation and maintenance personnel are often blind and have poor effects; or the problem of frequent load phase change and short-time power failure is often solved by a measure of automatic phase change of the load by installing a three-phase load adjusting device on site, and the operation and maintenance cost of new equipment is also obviously increased.
In conclusion, the prior art of distribution transformer low-voltage three-phase unbalance treatment generally has the problem of blind treatment or the problems of short-time power failure and new equipment operation and maintenance cost caused by adding an automatic regulating device.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for diagnosing and treating a three-phase load imbalance problem of a distribution transformer based on big data analysis, which can solve the blindness of phase modulation on site without adding new operation and maintenance equipment.
The invention is realized by adopting the following scheme: a distribution transformer three-phase load unbalance problem diagnosis and treatment method based on big data analysis specifically comprises the following steps:
selecting a time window before and after the distribution low-voltage three-phase unbalance changes remarkably to obtain the distribution transformer area in the time window and the time point distribution, duration and three-phase unbalance degree conditions of the low-voltage three-phase unbalance changes of the branch line;
performing cluster analysis on the electricity consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average electricity consumption, daily electricity fluctuation, peak electricity consumption period and valley electricity consumption period to obtain the electricity consumption habits of the low-voltage users;
judging whether the electricity utilization habits of the users in the selected time window are significantly changed or not, if so, further carrying out relevance judgment on the time point distribution of the significant change of the electricity utilization habits of the users, the duration and the time point distribution of the significant change of the three-phase imbalance and the duration dimension, and analyzing the strong relevance relationship between the significant change of the three-phase imbalance and the significant change of the electricity utilization habits of the users;
calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution, duration and three-phase unbalance degree conditions of the significant change of the low-voltage three-phase unbalance; and analyzing by combining the phase data received by the low-voltage user and the electricity utilization habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the optimized combination of the electricity utilization loads of the low-voltage users meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance management are met.
Further, the method also comprises the following steps: judging whether the line loss value of the distribution transformer area in the selected time window is changed remarkably, if so, further carrying out relevance judgment on the time point distribution and the duration of the line loss value change remarkably and the time point distribution and the duration dimension of the three-phase unbalance change remarkably; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
Further, the obtaining of the distribution of time points, the duration and the three-phase imbalance degree of the low-voltage three-phase imbalance significant changes of the distribution transformer area and the branch line in the time window specifically includes:
and the distribution transformer area and the branch line low-voltage three-phase unbalance significant change time point distribution, duration and three-phase unbalance conditions are obtained by processing, counting and analyzing the time window and adopting the system distribution transformer operating current data and the primary leakage protection current data.
Further, the data source for performing cluster analysis on the electricity consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average electricity consumption, daily electricity fluctuation, peak electricity consumption period and valley electricity consumption period is specifically as follows: and actively and periodically calling and measuring the electric quantity data according to the daily electricity consumption data of the low-voltage users of the intelligent electric meter in a period of time and the daily time distribution and time division points of the low-voltage users.
Further, the method also comprises the following steps: and (3) carrying out on-site targeted survey and measurement on the line loss of the distribution transformer area and the electricity utilization habit of the user so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the user have a strong correlation with the three-phase imbalance obvious change.
The invention also provides a system for diagnosing and treating the three-phase load imbalance problem of the distribution transformer based on big data analysis, which comprises the following components:
the data acquisition module is used for acquiring power consumption data;
a memory to store a computer program executable by the processor;
a processor capable of implementing the method steps as described above when the processor executes the computer program.
The invention also provides another diagnosis and treatment system for the three-phase load unbalance problem of the distribution transformer based on big data analysis, which comprises the following steps:
the data acquisition module is used for acquiring power consumption data;
the three-phase imbalance analysis module is used for selecting a time window before and after the distribution low-voltage three-phase imbalance is remarkably changed to obtain the distribution and transformation area in the time window and the distribution and transformation area and the time point distribution, duration and three-phase imbalance conditions of the low-voltage three-phase imbalance remarkable change of the branch line;
the power consumption habit analysis module is used for carrying out cluster analysis on the power consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average power consumption, daily power fluctuation, power consumption peak periods and power consumption valley periods to obtain the power consumption habits of the low-voltage users;
the strong association analysis module is used for judging whether the electricity utilization habits of the users in the selected time window are obviously changed or not, if so, further carrying out association judgment on the time point distribution of the obvious change of the electricity utilization habits of the users, the time point distribution of the obvious change of the duration and the three-phase imbalance and the dimension of the duration, and analyzing the strong association relationship between the three-phase imbalance and the electricity utilization habits of the users;
the three-phase unbalance treatment module is used for calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution, duration and three-phase unbalance degree conditions of the significant change of the low-voltage three-phase unbalance; and analyzing by combining the phase data received by the low-voltage user and the electricity utilization habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the optimized combination of the electricity utilization loads of the low-voltage users meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance management are met.
Further, still include:
the line loss analysis module is used for judging whether the line loss value of the distribution transformer area in the selected time window is changed remarkably or not, and if yes, further carrying out relevance judgment on the time point distribution and the duration of the line loss value which are changed remarkably and the time point distribution and the duration dimension of the three-phase unbalance remarkable change; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
Further, still include:
and the field survey verification module is used for carrying out field targeted survey on the line loss of the distribution transformer area and the electricity utilization habit conditions of the users so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the users have a strong correlation with the three-phase imbalance obvious change.
Furthermore, the data acquisition module acquires data including acquisition system distribution transformation operating current data, primary leakage protection current data, daily electricity consumption data of low-voltage users of the intelligent electric meter in a period of time and electric quantity data actively and periodically summoned and measured by time points distributed and divided according to daily time.
Compared with the prior art, the invention has the following beneficial effects: the invention fully utilizes various existing data resources of the power company, diagnoses and analyzes the distribution transformer low-voltage three-phase imbalance by a big data analysis means, provides a targeted scheme for field load phase modulation, and effectively solves the problem of the distribution transformer low-voltage three-phase imbalance. The invention can solve the blindness of on-site phase modulation without adding new operation and maintenance equipment.
Drawings
FIG. 1 is a schematic block diagram of an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for diagnosing and treating a three-phase load imbalance problem of a distribution transformer based on big data analysis, which specifically includes the following steps:
selecting a time window before and after the distribution low-voltage three-phase unbalance changes remarkably to obtain the distribution transformer area in the time window and the time point distribution, duration and three-phase unbalance degree conditions of the low-voltage three-phase unbalance changes of the branch line; meanwhile, whether the periodic regular change exists or not can be analyzed. In this embodiment, the time of day distribution may be simply divided into several periods: early morning (00: 00-6: 00), morning (06: 00-11: 00), noon (11: 00-13: 00), morning (13: 00-16: 00), morning (16: 00-18: 00), morning (18: 00-24: 00);
the method can actively call and measure the electric quantity data periodically according to the electric quantity category and the access mode data of the low-voltage users registered by the user file, the selected time window daily electric quantity data of the low-voltage users in the transformer area and the time points divided according to the daily time distribution for analysis; and performing cluster analysis on the electricity utilization habits of all low-voltage users under the distribution transformer from a plurality of dimensions including daily average electricity consumption, daily electricity consumption fluctuation and electricity consumption peak-valley distribution to obtain the electricity utilization habits of the low-voltage users. The method is characterized in that a time window is selected for statistical analysis, and the electricity utilization habits of low-voltage users are analyzed and described in several dimensions such as electricity utilization type, daily average electricity consumption, daily electricity consumption fluctuation, peak-valley time period distribution and the like (for example, the electricity utilization habits of common residential users and the electricity utilization habits of production factories are statistically analyzed by using a monthly time window, and generally show regular changes by using a week as a period, and the average electricity consumption, daily electricity consumption fluctuation, peak-valley time period distribution of working days, weekends and days also have a certain rule). The power utilization habits of all low-voltage users in the distribution room are reasonably classified, and the power utilization habits of the low-voltage users in a period of time window before and after the three-phase load imbalance is remarkably changed are further analyzed.
Judging whether the electricity utilization habits of the users in the selected time window are significantly changed or not, if so, further carrying out relevance judgment on the time point distribution of the significant change of the electricity utilization habits of the users, the duration and the time point distribution of the significant change of the three-phase imbalance and the duration dimension, and analyzing the strong relevance relationship between the significant change of the three-phase imbalance and the significant change of the electricity utilization habits of the users;
calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution, duration and three-phase unbalance degree conditions of the significant change of the low-voltage three-phase unbalance; and combining the phase data received by the low-voltage users and the analysis of the electricity consumption habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the electricity consumption load optimized combination of the low-voltage users (the users with the electricity consumption habits which are obviously changed and have strong correlation with the current three-phase imbalance) meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance treatment are met. The phase sequence adjustment is carried out by selecting the optimized combination of the low-voltage user electrical loads meeting the conditions: according to the low-voltage load quantity and the time distribution requirement of the phase to be adjusted, the electricity utilization habit is selected as the user of the corresponding phase in the corresponding time period at the electricity utilization peak time, and the partial load of the user is adjusted to the other phase. If a certain area generates periodic three-phase load unbalance problem in the evening (16: 00-18: 00) of working day, wherein the A phase load is heavier and the C phase load is lighter. The result of the power utilization habits of the low-voltage users in the transformer area is obtained by analyzing according to the method, the A-phase low-voltage user loads in the transformer area with the power utilization habits characterized in that the power utilization peak time periods are distributed in the evening (16: 00-18: 00) of the working day are found, and meanwhile, partial combinations of the A-phase low-voltage user loads can be adjusted to the C-phase in the transformer area according to the magnitude of the unbalance degree of the three-phase loads. The phase sequence of the transformer area accessed by the low-voltage user can be verified and detected by a low-voltage phase sequence detection tool commonly used in the daily production of the power grid.
In this embodiment, the method further comprises the steps of: according to the selected time window distribution transformer daily electricity consumption and low-voltage user electricity consumption data, calculating distribution transformer area line loss, judging whether the distribution transformer area line loss value in the selected time window is changed remarkably or not, and if so, further carrying out relevance judgment on time point distribution with remarkably changed line loss value, time point distribution with remarkably changed duration and three-phase imbalance and duration dimensionality; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
In this embodiment, the obtaining of the distribution of time points, the duration of time points, and the three-phase imbalance degree of the low-voltage three-phase imbalance of the distribution transformer area and the branch line in the time window, which are significantly changed, is specifically:
and the distribution transformer area and the branch line low-voltage three-phase unbalance significant change time point distribution, duration and three-phase unbalance conditions are obtained by processing, counting and analyzing the time window and adopting the system distribution transformer operating current data and the primary leakage protection current data.
In this embodiment, the data sources for performing cluster analysis on the electricity consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average electricity consumption, daily electricity fluctuation, peak electricity consumption period, and valley electricity consumption period are specifically: and actively and periodically calling and measuring the electric quantity data according to the daily electricity consumption data of the low-voltage users of the intelligent electric meter in a period of time and the daily time distribution and time division points of the low-voltage users.
In this embodiment, the method further comprises the steps of: and (3) carrying out on-site targeted survey and measurement on the line loss of the distribution transformer area and the electricity utilization habit of the user so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the user have a strong correlation with the three-phase imbalance obvious change.
Preferably, the embodiment further comprises the steps of implementing the load phase modulation scheme and subsequent tracking. After a low-voltage load phase modulation scheme is arranged and implemented on site, a system is used for tracking and monitoring whether the low-voltage three-phase imbalance of the distribution transformer obtains a better treatment effect. And if the treatment effect is not expected, reselecting the optimized combination of the low-voltage user electrical loads meeting the conditions to perform phase sequence adjustment.
This embodiment also provides a distribution transformation three-phase unbalanced load problem diagnosis treatment system based on big data analysis, includes:
the data acquisition module is used for acquiring power consumption data;
a memory to store a computer program executable by the processor;
a processor capable of implementing the method steps as described above when the processor executes the computer program.
The embodiment also provides another distribution transformer three-phase load unbalance problem diagnosis and treatment system based on big data analysis, which comprises:
the data acquisition module is used for acquiring power consumption data;
the three-phase imbalance analysis module is used for selecting a time window before and after the distribution low-voltage three-phase imbalance is remarkably changed to obtain the distribution and transformation area in the time window and the distribution and transformation area and the time point distribution, duration and three-phase imbalance conditions of the low-voltage three-phase imbalance remarkable change of the branch line;
the power consumption habit analysis module is used for carrying out cluster analysis on the power consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average power consumption, daily power fluctuation, power consumption peak periods and power consumption valley periods to obtain the power consumption habits of the low-voltage users;
the strong association analysis module is used for judging whether the electricity utilization habits of the users in the selected time window are obviously changed or not, if so, further carrying out association judgment on the time point distribution of the obvious change of the electricity utilization habits of the users, the time point distribution of the obvious change of the duration and the three-phase imbalance and the dimension of the duration, and analyzing the strong association relationship between the three-phase imbalance and the electricity utilization habits of the users;
the three-phase unbalance treatment module is used for calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution, duration and three-phase unbalance degree conditions of the significant change of the low-voltage three-phase unbalance; and analyzing by combining the phase data received by the low-voltage user and the electricity utilization habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the optimized combination of the electricity utilization loads of the low-voltage users meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance management are met.
In this embodiment, the method further includes:
the line loss analysis module is used for judging whether the line loss value of the distribution transformer area in the selected time window is changed remarkably or not, and if yes, further carrying out relevance judgment on the time point distribution and the duration of the line loss value which are changed remarkably and the time point distribution and the duration dimension of the three-phase unbalance remarkable change; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
In this embodiment, the method further includes:
and the field survey verification module is used for carrying out field targeted survey on the line loss of the distribution transformer area and the electricity utilization habit conditions of the users so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the users have a strong correlation with the three-phase imbalance obvious change.
In this embodiment, the data acquisition module acquires data including data of operating current of the distribution transformer of the acquisition system, data of primary leakage protection current, daily power consumption data of low-voltage users of the smart meter within a period of time, and data of electric quantity actively and periodically summoned and measured by time distribution and time division points of the daily power consumption data according to the daily time distribution.
According to the embodiment, the method for analyzing the large data by utilizing various data and user file data collected by the existing distribution transformer terminal device and the user low-voltage intelligent meter, performing cluster analysis on the power utilization habits of the low-voltage users in the distribution transformer area, monitoring and analyzing sudden changes of the power utilization habits of the users, performing correlation analysis on the three-phase imbalance of the distribution transformer and the power utilization habits of the low-voltage users in a period window, and the like is used for diagnosing which changes of the power utilization habits of the users have obvious influences, and the three-phase imbalance of the distribution transformer is adjusted in a targeted manner by partial low-voltage load phase modulation measures according to the diagnosis result, so that the blindness of field phase modulation can be solved, and new operation and.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (10)
1. A distribution transformer three-phase load unbalance problem diagnosis and treatment method based on big data analysis is characterized by comprising the following steps:
selecting a time window before and after the distribution low-voltage three-phase unbalance changes remarkably to obtain the distribution transformer area in the time window and the time point distribution, duration and three-phase unbalance degree conditions of the low-voltage three-phase unbalance changes of the branch line;
performing cluster analysis on the electricity consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average electricity consumption, daily electricity fluctuation, peak electricity consumption period and valley electricity consumption period to obtain the electricity consumption habits of the low-voltage users;
judging whether the electricity utilization habits of the users in the selected time window are significantly changed or not, if so, further carrying out relevance judgment on the time point distribution of the significant change of the electricity utilization habits of the users, the duration and the time point distribution of the significant change of the three-phase imbalance and the duration dimension, and analyzing the strong relevance relationship between the significant change of the three-phase imbalance and the significant change of the electricity utilization habits of the users;
calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution, duration and three-phase unbalance degree conditions of the significant change of the low-voltage three-phase unbalance; and analyzing by combining the phase data received by the low-voltage user and the electricity utilization habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the optimized combination of the electricity utilization loads of the low-voltage users meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance management are met.
2. The distribution transformer three-phase load unbalance problem diagnosis and treatment method based on big data analysis according to claim 1, characterized by further comprising the steps of: judging whether the line loss value of the distribution transformer area in the selected time window is changed remarkably, if so, further carrying out relevance judgment on the time point distribution and the duration of the line loss value change remarkably and the time point distribution and the duration dimension of the three-phase unbalance change remarkably; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
3. The method for diagnosing and treating the distribution transformer three-phase load unbalance problem based on big data analysis according to claim 1, wherein the obtaining of the distribution transformer area and branch line low-voltage three-phase unbalance significant change time point distribution, duration and three-phase unbalance degree conditions in the time window specifically comprises:
and the distribution transformer area and the branch line low-voltage three-phase unbalance significant change time point distribution, duration and three-phase unbalance conditions are obtained by processing, counting and analyzing the time window and adopting the system distribution transformer operating current data and the primary leakage protection current data.
4. The big data analysis-based distribution transformer three-phase load imbalance problem diagnosis and treatment method according to claim 1, wherein the data sources for performing cluster analysis on the electricity consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average electricity consumption, daily electricity consumption fluctuation, electricity consumption peak period and electricity consumption valley period are specifically as follows: and actively and periodically calling and measuring the electric quantity data according to the daily electricity consumption data of the low-voltage users of the intelligent electric meter in a period of time and the daily time distribution and time division points of the low-voltage users.
5. The distribution transformer three-phase load unbalance problem diagnosis and treatment method based on big data analysis according to claim 1, characterized by further comprising the steps of: and (3) carrying out on-site targeted survey and measurement on the line loss of the distribution transformer area and the electricity utilization habit of the user so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the user have a strong correlation with the three-phase imbalance obvious change.
6. The utility model provides a distribution transformer three-phase unbalanced load problem diagnosis treatment system based on big data analysis which characterized in that includes:
the data acquisition module is used for acquiring power consumption data;
a memory to store a computer program executable by the processor;
processor capable of carrying out the method steps of any one of claims 1 to 4 when the processor runs the computer program.
7. The utility model provides a distribution transformer three-phase unbalanced load problem diagnosis treatment system based on big data analysis which characterized in that includes:
the data acquisition module is used for acquiring power consumption data;
the three-phase imbalance analysis module is used for selecting a time window before and after the distribution low-voltage three-phase imbalance is remarkably changed to obtain the distribution and transformation area in the time window and the distribution and transformation area and the time point distribution, duration and three-phase imbalance conditions of the low-voltage three-phase imbalance remarkable change of the branch line;
the power consumption habit analysis module is used for carrying out cluster analysis on the power consumption habits of all low-voltage users under the distribution transformer from dimensions including daily average power consumption, daily power fluctuation, power consumption peak periods and power consumption valley periods to obtain the power consumption habits of the low-voltage users;
the strong association analysis module is used for judging whether the electricity utilization habits of the users in the selected time window are obviously changed or not, if so, further carrying out association judgment on the time point distribution of the obvious change of the electricity utilization habits of the users, the time point distribution of the obvious change of the duration and the three-phase imbalance and the dimension of the duration, and analyzing the strong association relationship between the three-phase imbalance and the electricity utilization habits of the users;
the three-phase unbalance management module is used for calculating the low-voltage load quantity and time distribution requirements of phases to be adjusted according to the time point distribution and duration of the significant change of the low-voltage three-phase unbalance and the three-phase load unbalance degree condition; and analyzing by combining the phase data received by the low-voltage user and the electricity utilization habits of the users to obtain daily average electricity consumption, daily electricity fluctuation, peak electricity consumption and valley electricity consumption, and selecting the optimized combination of the electricity utilization loads of the low-voltage users meeting the conditions to adjust the phase sequence until the requirements of adjusting the phase low-voltage load and time distribution of the three-phase imbalance management are met.
8. The distribution transformer three-phase load unbalance problem diagnosis and treatment system based on big data analysis according to claim 7, characterized by further comprising:
the line loss analysis module is used for judging whether the line loss value of the distribution transformer area in the selected time window is changed remarkably or not, and if yes, further carrying out relevance judgment on the time point distribution and the duration of the line loss value which are changed remarkably and the time point distribution and the duration dimension of the three-phase unbalance remarkable change; and if the strong correlation exists, performing line loss abnormity analysis to eliminate the three-phase imbalance problem caused by line loss reasons.
9. The distribution transformer three-phase load unbalance problem diagnosis and treatment system based on big data analysis according to claim 7, characterized by further comprising:
and the field survey verification module is used for carrying out field targeted survey on the line loss of the distribution transformer area and the electricity utilization habit conditions of the users so as to verify whether the obvious change of the line loss and the obvious change of the electricity utilization habit of the users have a strong correlation with the three-phase imbalance obvious change.
10. The distribution transformer three-phase load imbalance problem diagnosis and treatment system based on big data analysis according to claim 7, wherein the data acquisition module acquires data including acquisition system distribution transformer operating current data, primary leakage protection current data, low-voltage user daily electricity consumption data of the intelligent electric meter in a period of time and electricity quantity data actively and periodically called by dividing time points according to daily time distribution.
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| CN113036786A (en) * | 2021-03-05 | 2021-06-25 | 云南电网有限责任公司电力科学研究院 | Low-voltage distribution transformer user phase sequence identification and three-phase imbalance adjustment method |
| CN113484626A (en) * | 2021-08-05 | 2021-10-08 | 南方电网科学研究院有限责任公司 | Detection method and related device for potential three-phase imbalance of distribution network area |
| CN114280385A (en) * | 2021-12-30 | 2022-04-05 | 北京京能高安屯燃气热电有限责任公司 | Service power three-phase voltage balance judgment method based on real-time monitoring |
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| CN114280385B (en) * | 2021-12-30 | 2023-12-26 | 北京京能高安屯燃气热电有限责任公司 | Real-time monitoring-based station service three-phase voltage balance judging method |
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