CN110751338A - Construction and early warning method for heavy overload characteristic model of distribution transformer area - Google Patents
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Abstract
The invention discloses a method for constructing and early warning a heavy overload characteristic model of a distribution transformer area, which comprises five steps of collecting heavy overload historical data, preprocessing the data, extracting heavy overload characteristic factors, constructing the heavy overload characteristic model and evaluating the heavy overload characteristic model. Classifying, evolving and differentiating the collected data by using a gene expression to obtain a complete heavy overload feature set; and then, distributing and combining the characteristic variables in the plurality of characteristic sets by using an association rule algorithm to construct a heavy overload characteristic model. And finally, verifying the model by using a Kolmogorov-Similov verification method, predicting the reason of heavy overload, monitoring the distribution transformer area in real time through the heavy overload characteristic model, and sending out early warning by the heavy overload characteristic model when the monitoring data is the same as the prediction result. The invention reduces the probability of heavy overload accidents in the distribution area, reduces economic loss, prolongs the service life of equipment and improves the satisfaction degree of users on power grid services.
Description
Technical Field
The invention belongs to the power grid fault diagnosis technology, and particularly relates to a construction and early warning method of a heavy overload characteristic model of a distribution transformer area.
Background
The distribution transformer area is used as the tail end power supply unit facing low-voltage users, and the running state of power supply equipment in the distribution transformer area directly influences the power supply quality of the distribution transformer area. The heavy overload operation of the equipment is one of the main reasons for causing failure and power failure, the power utilization safety is seriously influenced, the loss of the equipment is accelerated, the service life of the equipment is shortened, and the greater risk is brought to the safety of a power distribution network.
At present, heavy overload treatment and management of a power distribution station area still remain in a traditional after-treatment stage. This kind of passive management and processing mode have also caused the not good condition of effect is managed to heavy overload in present distribution transformer district, can't stop the emergence of the condition such as transformer explosion, and its reason lies in: 1. at present, the overload condition of distribution transformer mainly depends on a metering automatic system, the data acquisition and feedback of the metering automatic system are not in time, and the data of the previous day can be seen after the next day, so that the burning-out event of the distribution transformer due to overload can not be monitored in time; 2. the heavy overload of the transformer is closely related to the electricity utilization behavior of users, but the reasons for the heavy overload are difficult to be clear, so that effective and targeted treatment methods are few; 3. the transformer area transformer can face the risk of accidents due to the fact that the heavy overload condition of the distribution transformer area cannot be predicted in advance due to the lack of necessary analysis and early warning measures.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for constructing and early warning the heavy overload characteristic model of the distribution transformer area is provided, and the problems that in the prior art, the heavy overload reason of the distribution transformer area cannot be effectively analyzed, and early warning cannot be timely achieved are solved.
The technical scheme of the invention is as follows:
a method for constructing and early warning a heavy overload characteristic model of a distribution transformer area comprises the following steps:
step 1, collecting overload historical data of a distribution transformer area;
step 2, preprocessing the collected heavy overload historical data;
step 3, extracting a heavy overload characteristic factor from the preprocessed data;
the method for extracting the heavy overload characteristic factor comprises the following steps:
step 3.1, classifying and sorting the preprocessed data, wherein each type of data forms a characteristic variable;
and 3.2, comparing the single characteristic variable or the combination of the characteristic variables with the characteristic variable of the transformer area heavy overload event to obtain a heavy overload characteristic factor.
Step 4, acquiring all heavy overload characteristic factors and constructing a heavy overload characteristic model;
the construction of the heavy overload characteristic model comprises the following steps:
4.1, for the extracted heavy overload characteristic factors, carrying out evolutionary classification by using a gene expression programming algorithm according to user information, electrical equipment information, environment information, power load characteristic information and electrical equipment running condition information to form a heavy overload association characteristic set;
and 4.2, mutually associating more than two heavy overload association feature sets through an association rule algorithm to form a heavy overload feature model.
And 5, evaluating the heavy overload characteristic model, and sending out heavy overload early warning for the heavy overload characteristic factor which is the same as the evaluation result and is about to occur.
And 6, according to the basic situation of the current distribution transformer area, carrying out real-time monitoring on the heavy overload characteristic model and sending out early warning.
In the step 1, the distribution transformer area heavy overload historical data comprises user information, electrical equipment information, environment information, electrical load characteristic information and electrical equipment running condition information related to the distribution transformer area heavy overload.
The method for preprocessing the overload historical data in the step 2 comprises the following steps: and supplementing missing items in the data and deleting repeated items in the data to obtain a complete data table.
The method for verifying the heavy overload characteristic model comprises the following steps: and verifying the model by adopting a Kolmogorov-Similov verification method, comparing the accumulated frequency distribution and the theoretical frequency distribution of the heavy overload association feature set in the heavy overload feature model, and if an overlapping region exists between the accumulated frequency distribution and the theoretical frequency distribution, determining that the heavy overload association feature set belongs to the reason of heavy overload.
The method for monitoring the heavy overload characteristic model in real time and sending out early warning in the step 6 comprises the following steps: the heavy overload characteristic model is connected with a monitoring terminal of the distribution transformer area, the distribution transformer area is monitored in real time, monitoring data automatically form a heavy overload associated characteristic set in the heavy overload characteristic model, and when the heavy overload associated characteristic set belongs to the reason of heavy overload, the heavy overload characteristic model gives out early warning.
The invention has the beneficial effects that:
the invention discloses a method for collecting and processing heavy overload accident data of a distribution transformer area and intelligently early warning, which extracts heavy overload characteristic factors from the collected and preprocessed historical data of the distribution transformer area of a power grid, establishes a heavy overload characteristic set, and continuously differentiates and evolves characteristic variables in the characteristic set by using a gene expression algorithm to construct a perfect heavy overload characteristic model. And verifying the model by adopting a Kolmogorov-Similov verification method to obtain the reason of the occurrence of the heavy overload accident. And monitoring the distribution transformer area in real time through a heavy overload characteristic model, sending heavy overload early warning to the impending heavy overload characteristic factors close to the prediction reason in real time, and making a heavy overload prevention and treatment method according to the prediction result. And a comprehensive and effective basis is provided for equipment maintenance of the distribution and transformation area. The probability of heavy overload accidents in the power distribution area is reduced, economic loss is reduced, the service life of equipment is prolonged, and the satisfaction degree of users on power grid services is improved.
Drawings
FIG. 1 is a schematic flow chart of heavy overload data mining and intelligent early warning according to the present invention;
FIG. 2 is a schematic diagram of the model structure of the present invention.
Detailed Description
A method for constructing and early warning a heavy overload characteristic model of a distribution transformer area comprises the following steps:
step 1, collecting overload historical data of a distribution transformer area;
the distribution transformer area heavy overload historical data comprises user information, electrical equipment information, environment information, electrical load characteristic information and electrical equipment operation condition information related to heavy overload of the distribution transformer area.
Further, sources of the distribution transformer area heavy overload historical data comprise an EMS system, a distribution network automation system, a PMS production management system, a marketing system, a 95598 customer service system, a power utilization information acquisition system and the like. As long as the data related to heavy overload of the distribution transformer area is acquired and summarized, the data is stored in a memory.
Step 2, preprocessing the collected heavy overload historical data; the method comprises the steps of supplementing missing items in the data and deleting repeated items in the data so as to obtain complete overload historical data information and ensure the authenticity of the data.
Step 3, extracting a heavy overload characteristic factor from the preprocessed data;
the method for extracting the heavy overload characteristic factor comprises the following steps:
step 3.1, classifying and sorting the preprocessed data, wherein each type of data forms a characteristic variable;
and 3.2, comparing the single characteristic variable or the combination of the characteristic variables with the characteristic variable of the transformer area heavy overload event to obtain a heavy overload characteristic factor.
Step 4, acquiring all heavy overload characteristic factors and constructing a heavy overload characteristic model;
the construction of the heavy overload characteristic model comprises the following steps:
4.1, for the extracted heavy overload characteristic factors, carrying out evolutionary classification by using a gene expression programming algorithm according to user information, electrical equipment information, environment information, power load characteristic information and electrical equipment running condition information to form a heavy overload association characteristic set;
and 4.2, mutually associating more than two heavy overload association feature sets through an association rule algorithm to form a heavy overload feature model.
As shown in fig. 2, the causes of heavy overload are divided into five feature sets, that is, user information, electrical equipment information, environmental information, electrical load feature information, and electrical equipment operating condition information, each feature set is continuously evolved and differentiated into a plurality of feature variables according to a gene expression method, for example, the environmental information includes feature variables such as weather information, holiday information, group activities, and the like, and each feature variable is refined again. For example, the weather information is divided into sunny days, rainy days, cloudy days, etc. Through continuous evolution and differentiation, a complete set of characteristics was obtained.
And then, carrying out combined distribution on the characteristic variables in each characteristic set through a correlation rule algorithm, and mutually correlating the characteristic variables to form a heavy overload characteristic model.
And 5, evaluating the heavy overload characteristic model, and sending out heavy overload early warning for the heavy overload characteristic factor which is the same as the evaluation result and is about to occur.
In the whole data mining and model building process, computer programs needed to be used comprise Tableau, Echarts, R languages and the like; and importing the collected distribution transformer area heavy overload historical data into a processor, driving a computer program by the processor to quickly and accurately output heavy overload characteristic factors related to heavy overload accidents, forming a plurality of complete heavy overload characteristic sets through classification, and finally obtaining cumulative frequency distribution and theoretical frequency distribution of the heavy overload associated characteristic sets.
And finally, evaluating the model by adopting a Kolmogorov-Schmilov check method, comparing the accumulated frequency distribution and the theoretical frequency distribution of the heavy overload correlation characteristic set in the heavy overload characteristic model, and if an overlapping region exists between the accumulated frequency distribution and the theoretical frequency distribution, determining that the heavy overload correlation characteristic set belongs to the reason of heavy overload.
The heavy overload characteristic model is connected with a monitoring terminal of the distribution transformer area, the distribution transformer area is monitored in real time, monitoring data automatically form a heavy overload associated characteristic set in the heavy overload characteristic model, and when the heavy overload associated characteristic set belongs to the reason of heavy overload, the heavy overload characteristic model gives out early warning. The staff finds out a corresponding overload characteristic set according to the early warning information, then determines a overload characteristic factor according to the overload characteristic set, and then makes emergency treatment measures according to the overload characteristic factor.
Claims (7)
1. A method for constructing and early warning a heavy overload characteristic model of a distribution transformer area is characterized by comprising the following steps of: it comprises the following steps:
step 1, collecting overload historical data of a distribution transformer area;
step 2, preprocessing the collected heavy overload historical data;
step 3, extracting a heavy overload characteristic factor from the preprocessed data;
step 4, acquiring all heavy overload characteristic factors and constructing a heavy overload characteristic model;
step 5, verifying the heavy overload characteristic model to obtain the reason of heavy overload;
and 6, according to the basic situation of the current distribution transformer area, carrying out real-time monitoring on the heavy overload characteristic model and sending out early warning.
2. The method for constructing and early warning of the heavy overload characteristic model of the distribution transformer area as claimed in claim 1, wherein: the distribution transformer area heavy overload historical data comprises user information, electrical equipment information, environment information, electrical load characteristic information and electrical equipment operation condition information related to heavy overload of the distribution transformer area.
3. The method for constructing and early warning of the heavy overload characteristic model of the distribution transformer area as claimed in claim 1, wherein: the method for preprocessing the overload historical data in the step 2 comprises the following steps: and filling up missing items in the data and deleting repeated items in the data.
4. The method for constructing and early warning the heavy overload characteristic model of the distribution transformer area according to claim 1 or 2, wherein the method comprises the following steps: the method for extracting the heavy overload characteristic factor in the step 3 comprises the following steps:
step 3.1, classifying and sorting the preprocessed data, wherein each type of data forms a characteristic variable;
and 3.2, comparing the single characteristic variable or the combination of the characteristic variables with the characteristic variable of the transformer area heavy overload event to obtain a heavy overload characteristic factor.
5. The method for constructing and early warning of the heavy overload characteristic model of the distribution transformer area as claimed in claim 1, wherein: the construction of the heavy overload characteristic model in the step 4 comprises the following steps:
4.1, for the extracted heavy overload characteristic factors, carrying out evolutionary classification by using a gene expression programming algorithm according to user information, electrical equipment information, environment information, power load characteristic information and electrical equipment running condition information to form a heavy overload association characteristic set;
and 4.2, mutually associating more than two heavy overload association feature sets through an association rule algorithm to form a heavy overload feature model.
6. The method for constructing and early warning of the heavy overload characteristic model of the distribution transformer area as claimed in claim 1, wherein: the method for verifying the heavy overload characteristic model comprises the following steps: and evaluating the model by adopting a Kolmogorov-Schmilov check method, comparing the accumulated frequency distribution and the theoretical frequency distribution of the heavy overload correlation characteristic set in the heavy overload characteristic model, and if an overlapping region exists between the accumulated frequency distribution and the theoretical frequency distribution, determining that the heavy overload correlation characteristic set belongs to the reason of heavy overload.
7. The method for constructing and early warning the heavy overload characteristic model of the distribution transformer area according to claim 1 or 7, wherein the method comprises the following steps: the method for monitoring the heavy overload characteristic model in real time and sending out early warning in the step 6 comprises the following steps: the heavy overload characteristic model is connected with a monitoring terminal of the distribution transformer area, the distribution transformer area is monitored in real time, monitoring data automatically form a heavy overload associated characteristic set in the heavy overload characteristic model, and when the heavy overload associated characteristic set belongs to the reason of heavy overload, the heavy overload characteristic model gives out early warning.
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Cited By (5)
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| CN112257923A (en) * | 2020-10-21 | 2021-01-22 | 国网冀北电力有限公司承德供电公司 | Heavy overload early warning method and device and electronic equipment |
| CN112258342A (en) * | 2020-10-21 | 2021-01-22 | 国网冀北电力有限公司承德供电公司 | Heavy overload early warning method and device and electronic equipment |
| CN112800577A (en) * | 2020-12-17 | 2021-05-14 | 北京国电通网络技术有限公司 | Method and device for analyzing overload reason of distribution transformer and electronic equipment |
| CN112994250A (en) * | 2021-04-20 | 2021-06-18 | 广东电网有限责任公司佛山供电局 | Heavy overload event monitoring method and device, electronic equipment and storage medium |
| CN113570109A (en) * | 2021-06-25 | 2021-10-29 | 广西电网有限责任公司南宁供电局 | Distribution transformer weight overload prediction method |
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