CN109063922A - A kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate - Google Patents
A kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate Download PDFInfo
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The distribution transformer heavy-overload prediction technique based on cell occupancy rate that the invention discloses a kind of, by obtaining and carrying out from power user power consumption information acquisition system, Customs Assigned Number is associated with client's ammeter of multiple-in-one cell, water meter acquires detailed data；Cell occupancy rate is calculated according to power user consumption and water consumption judgement: finally determining that cell moves in whether distribution transformer under saturation state occurs to overload risk according to heavy-overload decision rule；Effectively solve the problems, such as that the mutability of the area distribution transformer Yin Tai load and fluctuation influence lead to be difficult to long-term heavy-overload prediction in accurately development, it realizes and Accurate Prediction is carried out to distribution transformer operating status, give warning in advance distribution transformer heavy-overload risk, the O&M for helping to be promoted distribution transformer is horizontal, ensures the power quality of equipment safety and community resident.
Description
Technical field
The present invention relates to intelligent testing technology fields, it particularly relates to a kind of distribution transformer based on cell occupancy rate
Think highly of overload prediction method.
Background technique
The operating status of distribution transformer directly affects power supply quality and the reliable electricity consumption of user security in platform area；Distribution becomes
The heavy-overload operation of depressor is one of the main reason for causing fault outage, and heavy-overload phenomenon is generally also along with three-phase injustice
The other problems such as weighing apparatus, variation seriously affect the reliable electricity consumption of user security；In addition, distribution transformer is in mistake again for a long time
Load state can accelerate the abnormal waste of equipment, reduce service life of equipment, bring potential faults and operation risk to power grid.Cause
This, carries out the prediction of distribution transformer heavy-overload and is of great practical significance.
(He Jianzhang, Wang Haibo, Ji Zhixiang wait distribution transformer heavy-overload of the based on random forest theory pre- to document [1]
Survey [J] electric power network technique, 2017,41 (8): 2594-2597) analyze meteorological index, electricity consumption classification, category of employment and distribution change
The variation relation of depressor heavy-overload probability of happening, and may the heavy-overload origin cause of formation carried out Primary Study, finally using being based on
The theoretical improved decision-tree model of random forest predicts distribution transformer heavy-overload state.Document [2] (LI M, ZHOU
Q.Distribution transformer mid-term heavy and over load pre-warning based on
Logistic regression [C] // 2015IEEE Eindhoven Powertech, June 29-July 2,2015,
Eindhoven, Netherlands, 2015:1-5) towards the faster high speed development region of load growth, from user, meteorology and go through
History load sets out, and proposes the heavy-overload medium- and long-term forecasting method that logic-based returns, but does not provide every in Logic Regression Models
The selection course of parameter.(Shi Changkai, Yan Wenqi, Zhang Xiaohui wait Spring Festival distribution transforming of the based on BP network and gray model to document [3]
Heavy-overload predicts [J] power science and Technology, 2016,31 (1): 140-145) for the heavy-overload phenomenon during the Spring Festival,
It is proposed the heavy-overload prediction technique based on BP neural network and gray model.The load variations of distribution transformer around the Spring Festival are made
Load factor is predicted for mode input, and then judges equipment heavy-overload situation.The heavy-overload prediction obtained by this method
Model does not have generalization ability, can not adapt to the quick analysis of large-scale distribution network.Document [4] (PADMANABH K, SINGH
M J.Load forecasting at distribution transformer usingIoT based smart meter
data[C]//2016 IEEE International Conference on Contemporary Computing and
Informatics, December 14-17,2016, Noida, India, 2016:758-763) with one week for the period, to 6000
Totally 7 type load curves are studied on the same day weekly in a resident's platform area, and meteorological and economic data is added, using difference
Machine learning method load curve is predicted.But this method has ignored year in the season rule of load, and to exception
Value is more sensitive, and prediction effect is affected.Document [5] (CAMPEZIDOU S I, GRIJALVA S.Distribution
transformer short-term load forecasting models[J].IEEE Transactions on Power
System, 2016, (19): 267-273) it is discussed based on linear regression model (LRM) and is predicted in the case where polymerizeing and decomposing two kinds of model treatments
The difference of effect is illustrated that load prediction precision can be effectively improved by decomposing training node by example, but calculated
Example is used for model training only with weather and time data, insufficient to the factor analysis of distribution transformer loading effects.Document [6]
(NGO V C, WU W C, ZHANG B M.Ultra-short-term load forecasting using robust
exponentially weighted method in distribution networks[J].Journal of
Information, Control and Management Systems, 2015, (9): 301-308) propose a kind of trend point
The very Short-Term Load Forecasting Method of analysis and exponential weighting model, this method can be improved the precision of prediction to load peak valley, and right
Load missing values and well adapting to property of exceptional value.
Therefore current most of research is using distribution transformer load prediction as point of penetration, including the use of history
The external indirect factor analysis such as meteorological condition, economic indicator and load is added in the typical change rule of load data extrapolation load
Correlation degree, attempt using all kinds of machine learning methods construct load forecasting model, finally be based on load prediction results counterweight
Overload is judged.But current limitation of all kinds of load forecasting methods on precision of prediction causes based on load prediction results
Heavy-overload prediction does not adapt to business actual conditions.For short-term load forecasting, it is contemplated that at present eliminate heavy-overload means with
Implementation cycle longer route cut change with scrap build based on, short-term prediction result is not enough to provide time enough nargin and disappears
Except heavy-overload hidden danger；For Mid-long term load forecasting, it is contemplated that the mutability and fluctuation of platform area load, load prediction results sheet
Body is not able to satisfy counterweight and overloads the basic accuracy judged.
For the problems in the relevant technologies, currently no effective solution has been proposed.
Summary of the invention
For above-mentioned technical problem in the related technology, the present invention proposes a kind of distribution transformer based on cell occupancy rate
Heavy-overload prediction technique.
The technical scheme of the present invention is realized as follows:
A kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate, comprising the following steps:
S1, client's ammeter that multiple-in-one cell is obtained from power user power consumption information acquisition system, water meter acquisition are bright
Count evidence accurately；
S2, ammeter and water meter archives progress user are carried out to multiple-in-one cell client ammeter, water meter acquisition detailed data
Number association；
S3, according to power user consumption D and water consumption S, client is divided into and moves in user k and does not move in user n-k, and is calculated
Obtain cell occupancy rate λ；Wherein:
λ=k/n × 100%
S4, distribution transformer rated capacity S is obtained from power user power consumption information acquisition systemNAnd different time points
Realtime power St, maximum realtime power S of the distribution transformer in period T when obtaining cell occupancy rate λtmax:
S5, according to the relationship of distribution transformer maximum power and cell occupancy rate in period T:
A=Stmax/ λ=Stmax×n/k
It calculates when cell occupancy rate becomes λ in period T1When distribution transformer maximum power:
Stmaxλ1=λ1× a=λ1×Stmax×n/k；
S6, distribution transformer load factor δ is calculated:
δ=Stmax/SN× 100%
Calculate separately the occupancy rate warning value λ of triggering distribution transformer fully loaded transportation condition0.8Carrier strip is crossed with triggering distribution transformer
The occupancy rate warning value λ of part1.0:
λ0.8=0.8 × SN×k/Stmax/ n × 100%
λ1.0=SN×k/Stmax/ n × 100%
Determine that cell moves in whether distribution transformer under saturation state occurs to overload risk according to heavy-overload decision rule；Its
In: heavy-overload decision rule are as follows:
When load factor δ is in [80%, 100%] section, judgement distribution transformer is heavy condition；
As load factor δ > 100%, determine that distribution transformer is overload.
Further, client is divided into the decision rule moved in user and do not move in user in step S3 are as follows:
As client AiElectricity consumption DA< P kilowatt hour and client AiWater consumption SA< W ton hour, then determine client AiIt does not move in
The cell；
As client AiElectricity consumption DA>=P kilowatt hour and client AiWater consumption SA>=W ton hour, then determine client AiIt does not move in
The cell；
Wherein: i=1,2 ... n；N is client's sum of cell, P >=0；W≥0.
Further, P=0 and/or W=0 in step S3.
Beneficial effects of the present invention: the mutability and fluctuation influence for effectively solving the area distribution transformer Yin Tai load cause
The problem of being difficult to long-term heavy-overload prediction in accurately development, realizes and carries out Accurate Prediction to distribution transformer operating status, in advance
Early warning distribution transformer heavy-overload risk, the O&M for helping to be promoted distribution transformer is horizontal, ensures that equipment safety and cell occupy
The power quality of the people.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is a kind of distribution transformer heavy-overload prediction technique flow chart based on cell occupancy rate according to the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art's every other embodiment obtained belong to what the present invention protected
Range.
As shown in Figure 1, a kind of distribution transformer heavy-overload prediction based on cell occupancy rate according to an embodiment of the present invention
Method, comprising the following steps:
S1, client's ammeter that multiple-in-one cell is obtained from power user power consumption information acquisition system, water meter acquisition are bright
Count evidence accurately；
S2, ammeter and water meter archives progress user are carried out to multiple-in-one cell client ammeter, water meter acquisition detailed data
Number association；
S3, according to power user consumption D and water consumption S, client is divided into and moves in user k and does not move in user n-k, and is calculated
Obtain cell occupancy rate λ；Wherein:
λ=k/n × 100%
S4, distribution transformer rated capacity S is obtained from power user power consumption information acquisition systemNAnd different time points
Realtime power St, maximum realtime power S of the distribution transformer in period T when obtaining cell occupancy rate λtmax:
S5, according to the relationship of distribution transformer maximum power and cell occupancy rate in period T:
A=Stmax/ λ=Stmax×n/k
It calculates when cell occupancy rate becomes λ in period T1When distribution transformer maximum power:
Stmaxλ1=λ1× a=λ1×Stmax×n/k；
S6, distribution transformer load factor δ is calculated:
δ=Stmax/SN× 100%
Calculate separately the occupancy rate warning value λ of triggering distribution transformer fully loaded transportation condition0.8Carrier strip is crossed with triggering distribution transformer
The occupancy rate warning value λ of part1.0:
λ0.8=0.8 × SN×k/Stmax/ n × 100%
λ1.0=SN×k/Stmax/ n × 100%
Determine that cell moves in whether distribution transformer under saturation state occurs to overload risk according to heavy-overload decision rule；Its
In: heavy-overload decision rule are as follows:
When load factor δ is in [80%, 100%] section, judgement distribution transformer is heavy condition；
As load factor δ > 100%, determine that distribution transformer is overload.
In the present embodiment, client is divided into the decision rule moved in user and do not move in user in step S3 are as follows:
As client AiElectricity consumption DA< P kilowatt hour and client AiWater consumption SA< W ton hour, then determine client AiIt does not move in
The cell；
As client AiElectricity consumption DA>=P kilowatt hour and client AiWater consumption SA>=W ton hour, then determine client AiIt does not move in
The cell；
Wherein: i=1,2 ... n；N is client's sum of cell, P >=0；W≥0.
In the present embodiment, P=0 and/or W=0 in step S3.
Specifically, case study on implementation of the present invention selects 2, somewhere multiple-in-one cell and 2 station power distribution transformers to be said
It is bright, but the applicability of the content of present invention and protection scope are not limited to multiple-in-one cell, are also not limited to from power consumer and use
Power utilization information collection system obtains data；
S1, client's ammeter that multiple-in-one cell A, B is obtained from power user power consumption information acquisition system, water meter acquisition
Detailed data；
S2, ammeter and water meter archives progress user are carried out to multiple-in-one cell client ammeter, water meter acquisition detailed data
Number association, the archives amount that cell A is obtained after association is 308 families, and the archives amount of cell B is 352 families；
S3, according to power user consumption D and water consumption S, client is divided into and moves in user k and does not move in user n-k, and is calculated
Obtain cell occupancy rate λ；
The amount of moving in of cell A is 244 families, and the amount of moving in of cell B is 294 families；
Cell A occupancy rate λA=244/308 × 100%=79.22%；
Cell B occupancy rate λB=294/352 × 100%=83.52%；
S4, the distribution transformer rated capacity S that cell A is obtained from power user power consumption information acquisition systemNAFor
The distribution transformer rated capacity S of 630kVA, cell BNBFor the 630kVA and realtime power S of different time pointst.From system
When can obtain cell A occupancy rate 79.22%, maximum realtime power 203kVA of the cell A distribution transformer in period T；Cell B
When occupancy rate 83.52%, maximum realtime power 547kVA of the cell A distribution transformer in period T:
S5, cell occupancy rate can reflect cell load level, therefore can establish distribution transformer maximum power in period T
With the relationship of cell occupancy rate, a of cell AA=256.25KVA, a of cell BB=654.93KVA.When cell is moved in period T
Rate becomes λ1, then the distribution transformer maximum power that cell A can be obtained is Stmaxλ1=λ1The distribution of × 256.25KVA, cell B become
Depressor maximum power is Stmaxλ1=λ1×654.93KVA；
Cell occupancy rate is 100% in S6, period T, that is, is moved under saturation state, the distribution transformer load factor of cell A
It is 40.67%, i.e. cell A, which moves in distribution transformer under saturation state, will not occur heavy-overload risk；The distribution transformer of cell B
Load factor 103.96%, i.e. cell B, which move in distribution transformer under saturation state, can occur to overload risk；
The occupancy rate warning value for obtaining triggering distribution transformer heavy duty overload condition can be calculated, cell B triggers distribution transformer
The occupancy rate warning value of device fully loaded transportation condition:
λ0.8=0.8 × 630 × 294/547/352 × 100%=76.95%
Trigger the occupancy rate warning value of overload condition:
λ1.0=630 × 294/547/352 × 100%=96.20%
I.e. the occupancy rate of cell B is 76.95%, and when resident is 270 family, cell B distribution transformer has heavily loaded risk；It is small
The occupancy rate of area B is 96.20%, and when resident is 338 family, cell B distribution transformer has overload risk.
According to calculated result, capacity-increasing transformation or newly-increased transformer can be carried out to cell B distribution transformer in advance, it is ensured that cell
Resident does not occur distribution transformer heavy-overload situation when increasing, it is ensured that the Electrical Safety of distribution transformer safety and community user
It can be seen that effectively solving the prominent of the area distribution transformer Yin Tai load by means of above-mentioned technical proposal of the invention
It the problem of denaturation and fluctuation influence lead to be difficult to long-term heavy-overload prediction in accurately development, realizes and shape is run to distribution transformer
State carries out Accurate Prediction, and give warning in advance distribution transformer heavy-overload risk, and the O&M for helping to be promoted distribution transformer is horizontal, protects
Hinder the power quality of equipment safety and community resident.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (3)
1. a kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate, which comprises the following steps:
S1, client's ammeter that multiple-in-one cell is obtained from power user power consumption information acquisition system, water meter acquire detail number
According to；
S2, ammeter and water meter archives progress Customs Assigned Number are carried out to multiple-in-one cell client ammeter, water meter acquisition detailed data
Association；
S3, according to power user consumption D and water consumption S, client is divided into and moves in user k and does not move in user n-k, and is calculated
Cell occupancy rate λ；Wherein:
λ=k/n × 100%
S4, distribution transformer rated capacity S is obtained from power user power consumption information acquisition systemNAnd different time points is real-time
Power St, maximum realtime power S of the distribution transformer in period T when obtaining cell occupancy rate λtmax:
S5, according to the relationship of distribution transformer maximum power and cell occupancy rate in period T:
A=Stmax/ λ=Stmax×n/k
It calculates when cell occupancy rate becomes λ in period T1When distribution transformer maximum power:
Stmaxλ1=λ1× a=λ1×Stmax×n/k；
S6, distribution transformer load factor δ is calculated:
δ=Stmax/SN× 100%
Calculate separately the occupancy rate warning value λ of triggering distribution transformer fully loaded transportation condition0.8With triggering distribution transformer overload condition
Occupancy rate warning value λ1.0:
λ0.8=0.8 × SN×k/Stmax/ n × 100%
λ1.0=SN×k/Stmax/ n × 100%
Determine that cell moves in whether distribution transformer under saturation state occurs to overload risk according to heavy-overload decision rule；Wherein:
Heavy-overload decision rule are as follows:
When load factor δ is in [80%, 100%] section, judgement distribution transformer is heavy condition；
As load factor δ > 100%, determine that distribution transformer is overload.
2. a kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate according to claim 1, feature
It is, client is divided into the decision rule moved in user and do not move in user in step S3 are as follows:
As client AiElectricity consumption DA< P kilowatt hour and client AiWater consumption SA< W ton hour, then determine client AiIt is small that this is not moved in
Area；
As client AiElectricity consumption DA>=P kilowatt hour and client AiWater consumption SA>=W ton hour, then determine client AiIt is small that this is not moved in
Area；
Wherein: i=1,2 ... n；N is client's sum of cell, P >=0；W≥0.
3. a kind of distribution transformer heavy-overload prediction technique based on cell occupancy rate according to claim 2, feature
It is, P=0 and/or W=0 in step S3.
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| CN113902148A (en) * | 2021-12-03 | 2022-01-07 | 广东电网有限责任公司东莞供电局 | Load detection method and device of transformer, computer equipment and storage medium |
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| CN113902148A (en) * | 2021-12-03 | 2022-01-07 | 广东电网有限责任公司东莞供电局 | Load detection method and device of transformer, computer equipment and storage medium |
| CN113902148B (en) * | 2021-12-03 | 2022-04-05 | 广东电网有限责任公司东莞供电局 | Load detection method and device of transformer, computer equipment and storage medium |
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| Publication number | Publication date |
|---|---|
| CN109063922B (en) | 2021-04-09 |
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