CN109670696B - Line heavy overload prediction method based on big operation data - Google Patents
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Abstract
The invention provides a line heavy overload prediction method based on operation big data, which adopts a big data processing thought to process and summarize distribution transformation historical data, improves the usage amount of original data, avoids the influence of malformed data on a prediction result, and improves the credibility of a calculation result.
Description
Technical Field
The invention relates to the technical field of distribution line overload prediction, in particular to a line heavy overload prediction method based on operation big data.
Background
The line heavy overload is an important index for operation of the distribution network, and describes the difference between the operation current condition and the allowable load condition of the line, the line heavy load generally means that the line current exceeds 80% of the allowable current, and the continuous operation can cause line loss increase, lead temperature increase and operation risk increase of the line; overload generally means that the operating current exceeds the maximum allowable circuit, and continuous operation causes serious heating of a lead, and a fuse can be protectively disconnected, power supply is stopped, and even safety accidents are caused when the fuse is serious. By accurately predicting the heavy overload of the line, the effectiveness of power grid planning can be effectively improved.
At present, the load prediction mostly adopts a total load prediction mode to predict the whole power grid managed by one unit. The method needs small calculation data amount, and only needs global parameters such as the historical load condition of the whole network and the social and economic development prediction index of the whole network. The prediction result is more general and macroscopic, and the method is more suitable for providing reference for the construction of a transformer substation, cannot give a result to a single line, and cannot give a basis for the construction and transformation of distribution network line equipment.
Disclosure of Invention
The invention aims to provide a line heavy overload prediction method based on operation big data, so as to solve the problems in the background technology.
The invention is realized by the following technical scheme: a line heavy overload prediction method based on operation big data comprises the following steps:
s1, acquiring historical operation data of all distribution transformers on a distribution line, and filling up the historical operation data of gaps by adopting a distribution network state estimation method;
s2, acquiring the annual maximum load time T of each distribution transformer according to the completed historical distribution transformer operation data 0 、T 1 、T 2 、…、T n And annual maximum load value P corresponding to the maximum load time 0 、P 1 、P 2 、…、P n Calculating the sum P of the maximum load values of all distribution transformer years of the distribution line nz ；
S3, calculating the annual load simultaneous rate S of each distribution transformer 0 、S 1 、S 2 、…、S n The calculation method comprises the following steps: s. the n ＝P n /P nz ；
S4, to instituteThe annual load concurrency rate of each distribution transformer is subjected to one-time curve fitting and prediction to obtain the annual load concurrency rate S in the next n years 1 ′、S 2 ′、S 3 ′、…、S n ′；
S5, fitting and predicting the annual maximum load value of each distribution transformer in the next n years by adopting a quadratic curve, obtaining the annual maximum load value of each distribution transformer in the next n years, and calculating the sum P of the annual maximum load values of all distribution transformers of the distribution line in the next n years nz ′；
S6, obtaining the annual load concurrency rate S according to the step S4 1 ′、S 2 ′、S 3 ′、…、S n ' and the sum P of the annual maximum load value obtained in step S5 nz ', calculating the maximum moment load P of the line n ', line maximum time load P n The calculation method of' is as follows: p n ′＝P nz ′*S n ′；
S7, according to the maximum allowable load P of the distribution line max Calculating the predicted annual load rate R of the line n Line predicted annual load rate R n The calculation method comprises the following steps:
in the formula, P max Maximum allowable load for the line;
s8, predicting the annual load rate R according to the line n And judging the line load condition.
Optionally, the power distribution network state estimation method is one of a least square method state estimation method adopting a three-phase model and a newton method, a state estimation method based on measurement transformation, a robust estimation method, and a power distribution network state estimation method based on a power distribution matching power flow technology.
Optionally, the method further includes performing state estimation on the distribution lines according to all historical time section data by using the supplemented distribution transformation historical operating data obtained in step S1, and obtaining all historical data of all outlets of the distribution lines or any node, so as to form a historical curve. Optionally, in step S8, when the line predicts the annual load rate R n If the predicted annual load rate is more than 100%, the line is overloaded, and when the predicted annual load rate of the line is more than 80% < R n If the current is less than 100%, the line is heavily overloaded.
Compared with the prior art, the invention has the following beneficial effects:
according to the line heavy overload prediction method based on the large data operation, the large data processing thought is adopted to process and summarize distribution transformation historical data, the usage amount of original data is increased, the influence of malformed data on prediction results is avoided, the credibility of calculation results is improved, the method is oriented to the original data with huge data amount, and a parallel calculation method commonly used for large data processing can be used, so that the processing efficiency is improved.
In addition, the distribution and transformation load and the distribution and transformation load simultaneous rate are predicted, the problem that some user characteristic details are neglected is avoided through a more detailed prediction method, and a basis is provided for later possible more detailed prediction according to line branches and line segments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a line overload prediction method based on big operational data according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, the following detailed description is provided in conjunction with the accompanying drawings for further explanation of the present invention.
Referring to fig. 1, a line heavy overload prediction method based on big operational data includes the following steps:
s1, acquiring historical operation data of all distribution transformers on a distribution line, and filling up the vacant historical operation data by adopting a distribution transformer network state estimation method;
the power distribution network state estimation method comprises a least square method state estimation method adopting a three-phase model and a Newton method, a state estimation method based on measurement transformation, a robust estimation method and a power distribution network state estimation method based on a power distribution matching power flow technology, and can obtain all historical data of outlets of all power distribution lines or any nodes by performing state estimation on the power distribution lines according to all historical time section data, so that a historical curve is formed.
S2, acquiring the annual maximum load time T of each distribution transformer according to the completed distribution transformer historical operation data 0 、T 1 、T 2 、…、T n And annual maximum load value P corresponding to the maximum load time 0 、P 1 、P 2 、…、P n And calculating the sum P of the maximum load values of all distribution transformer years of the distribution line nz According to P nz 、T n And P n The relationship of (A) is shown in Table 1. As can be seen from Table 1, when T is n Sum P of annual maximum load values of all distribution and transformation at a time nz When the load is maximum, the moment is the moment with the heaviest load of the power distribution network;
TABLE 1
| Year 0 | 1 year | 2 years old | n years old | |
| Distribution transformer 1 | P 0 1 | P 1 1 | P 2 1 | P n 1 |
| Distribution transformer 2 | P 0 2 | P 1 2 | P 2 2 | P n 2 |
| Distribution transformer m | P 0 m | P 1 m | P 2 m | P n m |
| Sum of maximum load values of distribution transformer | P 0 z | P 1 z | P 2 z | P nz |
S3, acquiring annual load concurrency rate S of each distribution transformer 0 、S 1 、S 2 、…、S n The specific calculation method is as follows; s n ＝P n /P nz Wherein n represents year, and n may be 0, 1, 2, 3, …, n.
S4, fitting and predicting the load simultaneous rate of each distribution transformer in each year by adopting a primary curve n Is a primary curve of time independent variable t, which is based on the minimum valueAnd (3) establishing a curve fitting expression by using a curve fitting algorithm of the second multiplication: y = ax + b, wherein y represents the annual load simultaneity S n X represents a time independent variable t, the annual historical data of each distribution transformer are substituted into a primary curve expression, the numerical values of a and b in the expression are calculated, and therefore the load synchronization rate S is obtained n A linear equation for the time argument t.
According to annual load simultaneity rate S n With respect to the equation of one time of the time argument t, by substituting the year n into the time argument t, the load coincidence rate of the nth year and the annual load coincidence rate S in the next n years can be obtained 1 ′、S 2 ′、S 3 ′、…、S n ′。
S5, fitting and predicting the annual maximum load value of each distribution transformer in the next n years by adopting a quadratic curve, wherein the annual maximum load P of the distribution transformer can be assumed n The method is characterized in that a quadratic curve of a time independent variable t is established by adopting a currently common curve fitting algorithm based on a least square method, and a quadratic curve fitting expression is established: y = ax 2 + bx + c, where y represents the annual maximum load P of the distribution transformer n X represents a time independent variable t, the annual historical data of each distribution transformer is substituted into a quadratic curve expression, the numerical values of a, b and c in the expression are calculated, and therefore the maximum load P of the distribution transformer is obtained n Quadratic equation with respect to the time argument t.
According to distribution maximum load P n Substituting year n into time independent variable t to obtain annual maximum load value of distribution transformer in the nth year and annual maximum load value of distribution transformer in the next n years, summarizing the annual maximum load values of the distribution transformers, and calculating the sum P of annual maximum load values of all distribution transformers in the next n years nz ', as shown in Table 2;
TABLE 2
S6, obtaining the annual load concurrency rate S in the next n years according to the step S4 1 ′、S 2 ′、S 3 ′、…、S n ', and the sum P of annual maximum load values of all distribution transformation of the distribution line in the next n years obtained in step S5 nz ', calculating the maximum moment load P of the line n ' the concrete calculation method is as follows:
P n ′＝P nz ′*S n ′
s7, according to the maximum allowable load P of the distribution line max Calculating the predicted annual load rate R of the line n The specific calculation method comprises the following steps:
in the formula, P max For maximum allowable load of line, load P at maximum moment of line obtained n ', and maximum allowable load P of line calculated according to conventional method max In the belt-in type, the predicted annual load rate R of the line is finally obtained n 。
S8, predicting the annual load rate R according to the line n Judging the line load condition;
predicted annual load rate R when line is connected n If the load is more than 100%, the line is overloaded, and the annual load rate R is predicted when the line n If =100%, the line is full, and when the predicted annual load rate of the line is 80% < R n If the predicted annual load rate is less than 30R, the line is overloaded again n If less than 50%, the line is half loaded, and when the line predicts the annual load rate R n If the load is less than 30%, the line is lightly loaded.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A line heavy overload prediction method based on big operation data is characterized by comprising the following steps:
s1, acquiring historical operation data of all distribution transformers on a distribution line, and filling up the vacant historical operation data by adopting a distribution network state estimation method;
s2, acquiring the annual maximum load time T of each distribution transformer according to the completed historical distribution transformer operation data 0 、T 1 、T 2 、…、T n And annual maximum load value P corresponding to the maximum load time 0 、P 1 、P 2 、…、P n Calculating the sum P of the maximum load values of all distribution transformer years of the distribution line nz ；
S3, calculating the annual load simultaneous rate S of each distribution transformer 0 、S 1 、S 2 、…、S n The calculation method comprises the following steps: s. the n ＝P n /P nz ；
S4, adopting primary curve fitting and predicting to the annual load simultaneous rate of each distribution transformer to obtain the annual load simultaneous rate S in the next n years 1 ′、S 2 ′、S 3 ′、…、S n ′；
S5, fitting and predicting the annual maximum load value of each distribution transformer in the next n years by adopting a quadratic curve, obtaining the annual maximum load value of each distribution transformer in the next n years, and calculating the sum P of the annual maximum load values of all distribution transformers of the distribution circuit in the next n years nz ′；
S6, obtaining the annual load concurrency rate S according to the step S4 1 ′、S 2 ′、S 3 ′、…、S n ' and the sum P of the annual maximum load value obtained in step S5 nz ', calculating the maximum moment load P of the line n ', line maximum time load P n The calculation method of' is as follows: p n ′＝P nz ′*S n ′；
S7, according to the maximum allowable load P of the distribution line max Calculating the predicted annual load rate R of the line n Line predicted annual load rate R n The calculation method comprises the following steps:
in the formula, P max Maximum allowable load for the line;
s8, predicting the annual load rate R according to the line n And judging the line load condition.
2. The line heavy overload prediction method based on the big operation data as claimed in claim 1, wherein the power distribution network state estimation method is one of a least square state estimation method using a three-phase model and a newton method, a state estimation method based on measurement transformation, a robust estimation method, and a power distribution network state estimation method based on a power distribution matching load flow technique.
3. The line overload prediction method based on operation big data as claimed in claim 1, further comprising using the supplemented distribution transformation historical operation data obtained in step S1, and performing state estimation on the distribution lines according to all historical time profile data, so as to obtain all historical data of all outlets of the distribution lines or any nodes, thereby forming a historical curve.
4. The method according to claim 1, wherein in step S8, when the line forecasts the annual load rate R, the method for forecasting the line heavy overload based on the big operation data is characterized in that n >When the load is 100%, the line is overloaded, and when the predicted annual load rate of the line is 80%<R n <At 100%, the line is heavily overloaded.
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| CN110895728B (en) * | 2019-11-28 | 2023-06-13 | 海南电网有限责任公司 | Automatic planning method for line path of grid distribution network |
| CN110879335B (en) * | 2019-12-16 | 2022-05-10 | 深圳供电局有限公司 | Method for evaluating heavy overload condition of power distribution network line |
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103814394A (en) * | 2011-08-16 | 2014-05-21 | 佳境有限公司 | Estimation and management of loads in electric vehicle networks |
| CN104574209A (en) * | 2015-01-07 | 2015-04-29 | 国家电网公司 | Modeling method of urban electrical network distribution transform weight overload mid-term forewarning model |
| CN105303263A (en) * | 2015-11-16 | 2016-02-03 | 深圳合纵能源技术有限公司 | Load prediction system of regional power grid and method thereof |
| CN105654196A (en) * | 2015-12-29 | 2016-06-08 | 中国电力科学研究院 | Adaptive load prediction selection method based on electric power big data |
| CN105811398A (en) * | 2016-03-16 | 2016-07-27 | 中国农业大学 | Monitoring data-based determination method and system of maximum load coincidence factor |
| CN106952027A (en) * | 2017-03-11 | 2017-07-14 | 国网浙江省电力公司台州供电公司 | A kind of 10kV distribution network lines plan access capacity computational methods |
| CN107203837A (en) * | 2017-05-03 | 2017-09-26 | 国网浙江省电力公司 | Region entirety economic life of equipment measuring method based on rate of load condensate and simultaneity factor |
| CN107909230A (en) * | 2018-01-12 | 2018-04-13 | 国网福建省电力有限公司 | A kind of modeling method of the short-term Early-warning Model of rural power grids distribution transforming heavy-overload |
| CN108876131A (en) * | 2018-06-06 | 2018-11-23 | 国家电网有限公司 | Deposit project preferred method based on power grid effect contribution degree |
| CN108960475A (en) * | 2017-12-28 | 2018-12-07 | 海南电网有限责任公司 | A kind of Distribution Network Load Data feature extracting method based on big data |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8392031B2 (en) * | 2011-02-28 | 2013-03-05 | General Electric Company | System and method for load forecasting |
| WO2015039668A2 (en) * | 2013-09-18 | 2015-03-26 | Danmarks Tekniske Universitet | Autonomous voltage load controller |
| US20160004798A1 (en) * | 2014-07-07 | 2016-01-07 | Ingersoll-Rand Company | Determining the performance of an environmental system using a predictive model |
| US20170030949A1 (en) * | 2015-07-29 | 2017-02-02 | Alcatel-Lucent Usa Inc. | Electrical load prediction including sparse coding |
-
2018
- 2018-12-13 CN CN201811521306.6A patent/CN109670696B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103814394A (en) * | 2011-08-16 | 2014-05-21 | 佳境有限公司 | Estimation and management of loads in electric vehicle networks |
| CN104574209A (en) * | 2015-01-07 | 2015-04-29 | 国家电网公司 | Modeling method of urban electrical network distribution transform weight overload mid-term forewarning model |
| CN105303263A (en) * | 2015-11-16 | 2016-02-03 | 深圳合纵能源技术有限公司 | Load prediction system of regional power grid and method thereof |
| CN105654196A (en) * | 2015-12-29 | 2016-06-08 | 中国电力科学研究院 | Adaptive load prediction selection method based on electric power big data |
| CN105811398A (en) * | 2016-03-16 | 2016-07-27 | 中国农业大学 | Monitoring data-based determination method and system of maximum load coincidence factor |
| CN106952027A (en) * | 2017-03-11 | 2017-07-14 | 国网浙江省电力公司台州供电公司 | A kind of 10kV distribution network lines plan access capacity computational methods |
| CN107203837A (en) * | 2017-05-03 | 2017-09-26 | 国网浙江省电力公司 | Region entirety economic life of equipment measuring method based on rate of load condensate and simultaneity factor |
| CN108960475A (en) * | 2017-12-28 | 2018-12-07 | 海南电网有限责任公司 | A kind of Distribution Network Load Data feature extracting method based on big data |
| CN107909230A (en) * | 2018-01-12 | 2018-04-13 | 国网福建省电力有限公司 | A kind of modeling method of the short-term Early-warning Model of rural power grids distribution transforming heavy-overload |
| CN108876131A (en) * | 2018-06-06 | 2018-11-23 | 国家电网有限公司 | Deposit project preferred method based on power grid effect contribution degree |
Non-Patent Citations (6)
| Title |
|---|
| A Method for Evaluating Reserve Capacity in Middle and Long Term Electric Power Balance;Yongkai Zhang 等;《2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)》;20150330;第1-4页 * |
| Exergetic comparison of wind energy storage with ice making cycle versus mini-hydrogen economy cycle in off-grid district cooling;Birol Kılkıs;《ScienceDirect》;20170517;第1-12页 * |
| 中压配电网规划实用方法研究;朱凤娟;《中国优秀硕士学位论文全文数据库》;20130331;第1-81页 * |
| 城镇配电网负荷特性及变压器经济容量研究;蒋旻奕;《中国优秀硕士学位论文全文数据库》;20180331;第1-59页 * |
| 基于曲线拟合原理的负荷预测研究;李莎;《中国优秀硕士学位论文全文数据库》;20170430;第1-62页 * |
| 海南电网典型降损方案分析及降损空间研究;陈奕达;《科技资讯》;20151031;第32-33页 * |
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