CN109636029A - Power distribution network middle or short term voltage out-of-limit method for early warning based on big data - Google Patents
Power distribution network middle or short term voltage out-of-limit method for early warning based on big data Download PDFInfo
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Abstract
Power distribution network middle or short term voltage out-of-limit method for early warning based on big data.It is related to electric system early warning technology field.Propose a kind of clear logic, step orderly and can centering whether voltage out-of-limit in a short time carries out the power distribution network middle or short term voltage out-of-limit method for early warning based on big data that is good, effectively judging.The present invention passes through fusion distribution network voltage risk analysis various dimensions related data, realize that the separate unit distribution transforming middle or short term voltage out-of-limit under the influence of multiple factors is predicted using random forest technology, identify separate unit distribution transforming middle or short term voltage limit risk type, and out-of-limit risk probability is provided, effectively promote the optimization Governance Ability to the out-of-limit problem of middle or short term distribution network voltage.Have the advantages that on the whole clear logic, step orderly and can centering whether voltage out-of-limit in a short time carries out good, effective judgement.
Description
Technical field
The present invention relates to electric system early warning technology fields.
Background technique
Currently, research of the people to distribution network voltage control technology, develops correlation and is broadly divided into three phases: not considering
Traditional reactive power/voltage control stage of distributed generation resource, the distributed autonomous control stage for considering distributed generation resource and consideration point
The centralized Collaborative Control stage of cloth power supply.The grid company of various countries and research institution fully recognize idle configuration not
Shadow of the factors such as voltage fluctuation and distributed generation resource access caused by foot, burden with power fluctuation to distribution network voltage risk
It rings, and certain research has been made to the basic theory of voltage risk judgment.But matching by distribution automation system data driving
Network voltage risk analysis and anticipation technology, need further to study.Wherein, voltage risk refers to voltage fluctuation to normal
198V-242V except, there is voltage out-of-limit, to impact to operation of power networks.
Summary of the invention
The present invention in view of the above problems, propose a kind of clear logic, step orderly and can centering in a short time voltage whether
It is out-of-limit to carry out the power distribution network middle or short term voltage out-of-limit method for early warning based on big data that is good, effectively judging.
The technical solution of the present invention is as follows: being operated according to the following steps:
1), the influence factor of analysis and research voltage out-of-limit: by the scientific documents guidance of early period and finding, electricity is drafted
Out-of-limit correlative factor is pressed, capacity of distribution transform is specifically included that, holds with Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line
Amount, power factor (PF), power supply distance, bussed supply radius and climate temperature；
2) it, analyzes out-of-limit main reason: transferring the related data that the out-of-limit platform area of overvoltage once occurs, utilize correlation
Data establish the Logic Regression Models of voltage out-of-limit phenomenon Yu each relation factor:
Wherein p is of that month out-of-limit probability, and x is the correlative factor of a upper month voltage out-of-limit, i.e., is with the distribution transforming section time
It is no to cross the border as output variable, it is successively defeated as input variable using each influence factor fluctuation characteristic of transformer for the previous period
Enter in Logic Regression Models, be trained using Logic Regression Models and learn, it is out-of-limit to analyze each area for statistical correlation degree
Main reason；
It obtains, mean value, the mean value of power distribution voltage, the maximum of the maximum difference with Variable power, feeder line power of busbar voltage
Difference and out-of-limit relevance are maximum, are out-of-limit main reason；
3), by the data of main reason relevant to place distribution network voltage risk by extracting, pre-processing and being fused to
In distribution network voltage risk anticipation analysis mark sheet；
4), the following first month prediction:
4.1), training Random Forest model: the data of the main reason of a upper month voltage out-of-limit and this month are out-of-limit
In the data input Random Forest model of situation, several decision trees one are established；
4.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees one and are predicted, are obtained
Occurs most results in several results as the prediction result whether out-of-limit to next month voltage as a result, taking to several；
5), the following second month prediction:
5.1), training Random Forest model: by the data of the main reason of second month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees two are established；
5.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees two and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following second month voltage as a result, taking to several
Fruit；
6), the following third month prediction:
6.1), training Random Forest model: by the data of the main reason of month voltage out-of-limit of third before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees three are established；
6.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees three and are predicted, are obtained
Occurs most results in several results as to the whether out-of-limit prediction knot of the following third month voltage as a result, taking to several
Fruit；
7) it, predicts within following four month:
7.1), training Random Forest model: by the data of the main reason of four month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees four are established；
7.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees four and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following four month voltage as a result, taking to several
Fruit；
8) it, predicts within following five month:
8.1), training Random Forest model: by the data of the main reason of five month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees five are established；
8.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees five and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following five month voltage as a result, taking to several
Fruit；
9) it, predicts within following six month:
9.1), training Random Forest model: by the data of the main reason of six month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees six are established；
9.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees six and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following six month voltage as a result, taking to several
Fruit；
10), result is integrated: being counted to one to six month whether out-of-limit prediction result of voltage of future；It finishes.
Correlative factor in the step 2) according to voltage out-of-limit repeatedly establishes Logic Regression Models, and x is used as and matches each time
Varying capacity is supplied with Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line capacity, power factor (PF), power supply distance, bus
One in electric radius and climate temperature.
The present invention is realized more by fusion distribution network voltage risk analysis various dimensions related data using random forest technology
Separate unit distribution transforming middle or short term voltage out-of-limit prediction under the influence of weight factor, identifies separate unit distribution transforming middle or short term voltage limit risk type,
And out-of-limit risk probability is provided, effectively promote the optimization Governance Ability to the out-of-limit problem of middle or short term distribution network voltage.On the whole
Have the advantages that clear logic, step orderly and can centering whether voltage out-of-limit in a short time carries out good, effective judgement.
Detailed description of the invention
Flow diagram is predicted in this case of Fig. 1.
Specific embodiment
The present invention operates according to the following steps:
1), the influence factor of analysis and research voltage out-of-limit: by the scientific documents guidance of early period and finding, electricity is drafted
Out-of-limit correlative factor is pressed, capacity of distribution transform is specifically included that, holds with Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line
Amount, power factor (PF), power supply distance, bussed supply radius and climate temperature；
2) it, analyzes out-of-limit main reason: transferring the related data that the out-of-limit platform area of overvoltage once occurs, utilize correlation
Data establish the Logic Regression Models of voltage out-of-limit phenomenon Yu each relation factor:
Wherein p is of that month out-of-limit probability, and x is the correlative factor of a upper month voltage out-of-limit, i.e., is with the distribution transforming section time
It is no to cross the border as output variable, it is successively defeated as input variable using each influence factor fluctuation characteristic of transformer for the previous period
Enter in Logic Regression Models, be trained using Logic Regression Models and learn, it is out-of-limit to analyze each area for statistical correlation degree
Main reason；
It obtains, mean value, the mean value of power distribution voltage, the maximum of the maximum difference with Variable power, feeder line power of busbar voltage
Difference and out-of-limit relevance are maximum, are out-of-limit main reason；
3), by the data of main reason relevant to place distribution network voltage risk by extracting, pre-processing and being fused to
In distribution network voltage risk anticipation analysis mark sheet；The out-of-limit factor correlation acquisition Data Data quality of distribution transforming is to future anticipation model
It has a significant impact, needs to pre-process Raw data quality and cleaned；Power distribution voltage is greater than 0 less than 110 or greater than 330
Data point is set to invalid data, deletes the invalid power distribution voltage data of whole data；According to low-voltage overvoltage standard, to acquisition
Transformer situation of crossing the border is identified and is marked that mesolow is identified as -1 in data, is normally identified as 0, high pressure is identified as 1.
Based on the data after data prediction and cleaning, the anticipation of distribution network voltage risk and assistant analysis characteristic are calculated；Each moon matches
The fluctuation characteristics such as time variant voltage and power, busbar voltage, feeder line power, and related fixed attribute correlative factor, by structuring number
According to importing database purchase；
Pre- flow gauge is as shown in Figure 1；
4), the following first month prediction:
4.1), training Random Forest model: the data of the main reason of a upper month voltage out-of-limit and this month are out-of-limit
In the data input Random Forest model of situation, several decision trees one are established；
4.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees one and are predicted, are obtained
Occurs most results in several results as the prediction result whether out-of-limit to next month voltage as a result, taking to several；With
Mode is extracted using to sample and variable dual random when the training of machine forest model, constructs short-run decision tree several different,
It is prejudged from different dimensions to whether power distribution voltage crosses the border, last according to the minority is subordinate to the majority, principle realization is pre- to voltage out-of-limit
Sentence, and obtains its out-of-limit probability；
5), the following second month prediction:
5.1), training Random Forest model: by the data of the main reason of second month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees two are established；
5.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees two and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following second month voltage as a result, taking to several
Fruit；
6), the following third month prediction:
6.1), training Random Forest model: by the data of the main reason of month voltage out-of-limit of third before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees three are established；
6.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees three and are predicted, are obtained
Occurs most results in several results as to the whether out-of-limit prediction knot of the following third month voltage as a result, taking to several
Fruit；
7) it, predicts within following four month:
7.1), training Random Forest model: by the data of the main reason of four month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees four are established；
7.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees four and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following four month voltage as a result, taking to several
Fruit；
8) it, predicts within following five month:
8.1), training Random Forest model: by the data of the main reason of five month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees five are established；
8.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees five and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following five month voltage as a result, taking to several
Fruit；
9) it, predicts within following six month:
9.1), training Random Forest model: by the data of the main reason of six month voltage out-of-limit before this month and
In the data input Random Forest model of of that month out-of-limit situation, several decision trees six are established；
9.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees six and are predicted, are obtained
Occurs most results in several results as the prediction knot whether out-of-limit to the following six month voltage as a result, taking to several
Fruit；
10), result is integrated: being counted to one to six month whether out-of-limit prediction result of voltage of future；It finishes.
It needs to pre-process training data before model training, the out-of-limit situation distribution of distribution transforming is obvious uneven, and it is first right to need
Data distribution is modified, and considers that examinee produces environmental samples distribution asymmetry, normal voltage and the voltage ratio that crosses the border in training sample
Example is set as 10:1, owes example-based approach equilibrium data using normal transformer.There are dimension gaps for each dimension data of characteristic table, will
Hough transformation is between 0-1.Training set and test set: it uses and randomly selects and predict two ways month by month.
It realizes the early warning of middle or short term voltage out-of-limit, needs to consider machine learning time span problem, model prejudges time span
It should be consistent with model training time span.In conjunction with actual production environment, time span select 1 month it is relatively reasonable, i.e., with
Cross the border situation and front 1 month (or 2-6 months) of this month is trained, and is realized using of that month data to (or 2-6 1 month following
A month) out-of-limit early warning, each moon prediction result is integrated, to realize middle or short term voltage out-of-limit early warning.In actual use, by
The month where month and prediction result where main reason in input is closer, and the accuracy of prediction is higher, therefore, each
The moon is both needed to predict the following half a year.
Mode is extracted using to sample and variable dual random when Random Forest model training, constructs several different determine
Plan tree, different decision trees prejudge from different dimensions to whether power distribution voltage crosses the border, last root as the expert of different field
According to the minority is subordinate to the majority, principle is realized to voltage out-of-limit anticipation, and obtains its out-of-limit probability.Random Forest model needs training 6
It is secondary, respectively obtain the anticipation result and anticipation probability of each moon step-length of 1-6, and accordingly it is comprehensive obtain it is following cross the border within 6 months situation and
Probability.
Correlative factor in the step 2) according to voltage out-of-limit repeatedly establishes Logic Regression Models, and x is used as and matches each time
Varying capacity is supplied with Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line capacity, power factor (PF), power supply distance, bus
One in electric radius and climate temperature.
Claims (2)
1. the power distribution network middle or short term voltage out-of-limit method for early warning based on big data, which is characterized in that operated according to the following steps:
1) it, the influence factor of analysis and research voltage out-of-limit: by the scientific documents guidance of early period and finding, drafts voltage and gets over
The correlative factor of limit, specifically include that capacity of distribution transform, with Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line capacity,
Power factor (PF), power supply distance, bussed supply radius and climate temperature；
2) it, analyzes out-of-limit main reason: transferring the related data that the out-of-limit platform area of overvoltage once occurs, utilize related data
Establish the Logic Regression Models of voltage out-of-limit phenomenon Yu each relation factor:
Whether wherein p is of that month out-of-limit probability, and x is the correlative factor of a upper month voltage out-of-limit, i.e., sent out with the distribution transforming section time
Life is crossed the border as output variable, is sequentially input and is patrolled as input variable using each influence factor fluctuation characteristic of transformer for the previous period
In volume regression model, it is trained and is learnt using Logic Regression Models, statistical correlation degree, out-of-limit main in each area of analysis
The origin cause of formation；
Obtain, the mean value of busbar voltage, the mean value of power distribution voltage, the maximum difference with Variable power, feeder line power maximum difference
It is maximum with out-of-limit relevance, it is out-of-limit main reason；
3), by the data of main reason relevant to place distribution network voltage risk by extracting, pre-processing and be fused to distribution
In net voltage risk anticipation analysis mark sheet；
4), the following first month prediction:
4.1), training Random Forest model: by the data of the main reason of a upper month voltage out-of-limit and of that month out-of-limit situation
Data input Random Forest model in, establish several decision trees one；
4.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees one and are predicted, if obtaining
It is dry to occur most results in several results as the prediction result whether out-of-limit to next month voltage as a result, taking；
5), the following second month prediction:
5.1), training Random Forest model: by the data of the main reason of second month voltage out-of-limit and this month before this month
In the data input Random Forest model of out-of-limit situation, several decision trees two are established；
5.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees two and are predicted, if obtaining
It is dry to occur most results in several results as the prediction result whether out-of-limit to the following second month voltage as a result, taking；
6), the following third month prediction:
6.1), training Random Forest model: by the data of the main reason of third before this month month voltage out-of-limit and this month
In the data input Random Forest model of out-of-limit situation, several decision trees three are established；
6.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees three and are predicted, if obtaining
It is dry most results occur as to the whether out-of-limit prediction result of the following third month voltage as a result, taking in several results；
7) it, predicts within following four month:
7.1), training Random Forest model: by the data of the main reason of four month voltage out-of-limit and this month before this month
In the data input Random Forest model of out-of-limit situation, several decision trees four are established；
7.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees four and are predicted, if obtaining
It is dry to occur most results in several results as the prediction result whether out-of-limit to the following four month voltage as a result, taking；
8) it, predicts within following five month:
8.1), training Random Forest model: by the data of the main reason of five month voltage out-of-limit and this month before this month
In the data input Random Forest model of out-of-limit situation, several decision trees five are established；
8.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees five and are predicted, if obtaining
It is dry to occur most results in several results as the prediction result whether out-of-limit to the following five month voltage as a result, taking；
9) it, predicts within following six month:
9.1), training Random Forest model: by the data of the main reason of six month voltage out-of-limit and this month before this month
In the data input Random Forest model of out-of-limit situation, several decision trees six are established；
9.2) it, predicts: the data of the main reason of of that month voltage out-of-limit being inputted in all decision trees six and are predicted, if obtaining
It is dry to occur most results in several results as the prediction result whether out-of-limit to the following six month voltage as a result, taking；
10), result is integrated: being counted to one to six month whether out-of-limit prediction result of voltage of future；It finishes.
2. the power distribution network middle or short term voltage out-of-limit method for early warning according to claim 1 based on big data, which is characterized in that
Correlative factor in the step 2) according to voltage out-of-limit repeatedly establishes Logic Regression Models, each time x as capacity of distribution transform, match
Variable power, power distribution voltage, busbar voltage, feeder line power, feeder line capacity, power factor (PF), power supply distance, bussed supply radius and
One in climate temperature.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110266000A (en) * | 2019-06-17 | 2019-09-20 | 国网江苏省电力有限公司 | A kind of out-of-limit analysis of causes method of distribution network voltage, system and storage medium |
| CN110739698A (en) * | 2019-11-20 | 2020-01-31 | 国网山东省电力公司潍坊市寒亭区供电公司 | 10kV bus voltage and public distribution transformer outlet voltage allocation method and system |
| CN117614132A (en) * | 2023-11-27 | 2024-02-27 | 广州航海学院 | Distribution transformer voltage out-of-limit portrait method and device for power distribution network |
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| CN108022001A (en) * | 2017-09-20 | 2018-05-11 | 河海大学 | Short term probability density Forecasting Methodology based on PCA and quantile estimate forest |
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| CN110266000A (en) * | 2019-06-17 | 2019-09-20 | 国网江苏省电力有限公司 | A kind of out-of-limit analysis of causes method of distribution network voltage, system and storage medium |
| CN110266000B (en) * | 2019-06-17 | 2022-07-19 | 国网江苏省电力有限公司 | Power distribution network voltage out-of-limit reason analysis method, system and storage medium |
| CN110739698A (en) * | 2019-11-20 | 2020-01-31 | 国网山东省电力公司潍坊市寒亭区供电公司 | 10kV bus voltage and public distribution transformer outlet voltage allocation method and system |
| CN110739698B (en) * | 2019-11-20 | 2023-07-28 | 国网山东省电力公司潍坊市寒亭区供电公司 | Method and system for allocating 10kV bus voltage and public distribution transformer outlet voltage |
| CN117614132A (en) * | 2023-11-27 | 2024-02-27 | 广州航海学院 | Distribution transformer voltage out-of-limit portrait method and device for power distribution network |
| CN117614132B (en) * | 2023-11-27 | 2024-06-11 | 广州航海学院 | Distribution transformer voltage out-of-limit portrait method and device for power distribution network |
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