CN109063922B - Power distribution transformer overload prediction method based on cell survival rate - Google Patents
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Abstract
The invention discloses a distribution transformer overload prediction method based on a community check-in rate, which comprises the steps of acquiring and carrying out user number association on customer electric meters and water meters of a multi-meter-in-one community from an electric power user electricity utilization information acquisition system to acquire detailed data; judging and calculating according to the electricity consumption and the water consumption of the customer to obtain the residential area occupancy rate: finally, judging whether the distribution transformer has overload risks or not in a residential quarter saturation state according to a heavy overload judgment rule; effectively solve distribution transformer and lead to being difficult to the accurate problem of developing the prediction of long-term heavy overload in the medium-term because of the mutability and the volatility influence of platform district load, realize carrying out accurate prediction to distribution transformer running state, early warning distribution transformer heavily transships the risk in advance, help promoting distribution transformer's operation and maintenance level, guarantee equipment safety and residential community's power consumption quality.
Description
Technical Field
The invention relates to the technical field of intelligent detection, in particular to a distribution transformer overload prediction method based on a cell entrance rate.
Background
The running state of the distribution transformer directly influences the power supply quality in the transformer area and the safe and reliable power utilization of users; heavy overload operation of a distribution transformer is one of main reasons for causing fault and power failure, and a heavy overload phenomenon is usually accompanied by other problems such as three-phase imbalance and voltage deviation, so that safe and reliable power utilization of users is seriously influenced; in addition, the distribution transformer is in a heavy overload state for a long time, so that abnormal loss of equipment is accelerated, the service life of the equipment is shortened, and fault hidden dangers and operation risks are brought to a power grid. Therefore, the important practical significance is achieved for carrying out overload prediction on the distribution transformer.
The method comprises the following steps of (document [1] (congratulation chapter, King Hai Bo, Quadrature, etc..) power distribution transformer overload prediction [ J ] based on a random forest theory, a power grid technology, 2017, 41 (8): 2594 + 2597) analyzing the change relation of weather indexes, power utilization categories, industry categories and overload occurrence probability of a power distribution transformer, preliminarily discussing possible overload causes, and finally predicting the overload state of the power distribution transformer by using a decision tree model improved based on the random forest theory. In the literature [2] (LI M, ZHOU Q. distribution transformation mid-term heavy and over load pre-warping based on regional regression [ C ]//2015IEEE Eindhoven Powertech, June 29-July 2, 2015, Eindhoven, Netherlands, 2015: 1-5) facing to the high-speed development region with faster load increase, a logistic regression-based heavy overload medium-long term prediction method is proposed from the aspects of users, weather and historical loads, but the selection process of each parameter in the logistic regression model is not given. In the document [3] (Stevenskay, Yan chess, Yan Xiao Hui, etc.. spring festival distribution weight-variable overload prediction based on BP network and gray model [ J ]. electric power science and technology report, 2016, 31 (1): 140-. And (4) predicting the load rate by taking the load change of the distribution transformer before and after spring festival as model input, and further judging the heavy overload condition of the equipment. The heavy overload prediction model obtained by the method does not have generalization capability and is not suitable for rapid analysis of a large-scale power distribution network. Document [4] (PADMANABH K, SINGH M J. load estimating at distribution transformer using IoT based smart meter data [ C ]//2016 IEEE International Conference on control Computing and information, Decumber 14-17, 2016, Noida, India, 2016: 758-. But the method ignores the seasonal annual rule of the load, is sensitive to abnormal values and influences the prediction effect. Document [5] (CAMPEZIDOU S I, GRIJALVA S. distribution transform short-term load for evaluating models [ J ]. IEEE Transactions on Power System, 2016, (19): 267-273) discusses the difference of prediction effects under the processing of aggregation and decomposition models based on a linear regression model, and the load prediction precision can be effectively improved by decomposing training nodes through calculation, but the calculation only adopts weather and time data for model training, and the analysis of the load influence factors of the distribution transformer is insufficient. A trend analysis and exponential weighting model ultra-short term load prediction method is provided in the literature [6] (NGO V C, WU W C, ZHANG B M. ultra-short-term load for estimating using robust expression in distribution networks [ J ]. Journal of Information, Control and Management Systems, 2015, (9): 301-308), and the method can improve the prediction precision of load peaks and valleys and has good adaptability to load missing values and abnormal values.
From the above, most of the current researches take distribution transformer load prediction as an entry point, including extrapolating a typical change rule of the load by using historical load data, adding external indirect factor analysis such as meteorological conditions, economic indexes and the like to the association degree of the load, trying to construct a load prediction model by using various machine learning methods, and finally judging the overload on the basis of a load prediction result. However, the current various load prediction methods are limited in prediction accuracy, so that heavy overload prediction based on a load prediction result cannot adapt to the actual service situation. For short-term load prediction, the current means for eliminating heavy overload is mainly implemented by line switching and equipment transformation with long period, and the short-term prediction result is not enough to provide enough time margin to eliminate the hidden danger of heavy overload; for medium and long-term load prediction, the sudden change and the fluctuation of the station load are considered, and the load prediction result cannot meet the basic accuracy for judging the heavy overload.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a distribution transformer overload prediction method based on a cell entrance rate.
The technical scheme of the invention is realized as follows:
a distribution transformer overload prediction method based on a cell occupancy rate comprises the following steps:
s1, acquiring detailed data of client electric meters and water meters of the multi-meter-in-one cell from the power consumer electricity consumption information acquisition system;
s2, carrying out user number association on the ammeter and the water meter file according to detail data collected by the client ammeter and the water meter in the multi-meter-in-one community;
s3, dividing the customers into a living user k and a non-living user n-k according to the electricity consumption D and the water consumption S of the customers, and calculating to obtain the residence rate lambda of the cell; wherein:
λ＝k/n×100％
s4, obtaining rated capacity S of distribution transformer from power consumer electricity consumption information acquisition systemNAnd real-time power S at different time pointstObtaining the maximum real-time power S of the distribution transformer in the time interval T when the residence rate lambda of the cell is obtainedtmax：
S5, according to the relation between the maximum power of the distribution transformer and the residence rate of the cell in the time period T:
a＝Stmax/λ＝Stmax×n/k
calculating the cell occupancy rate changed to lambda in the time period T1Maximum power of the distribution transformer:
Stmaxλ1＝λ1×a＝λ1×Stmax×n/k；
s6, calculating the load rate delta of the distribution transformer:
δ＝Stmax/SN×100％
respectively calculating the entrance rate warning value lambda for triggering the heavy load condition of the distribution transformer0.8And an occupancy warning value lambda triggering an overload condition of the distribution transformer1.0：
λ0.8＝0.8×SN×k/Stmax/n×100％
λ1.0＝SN×k/Stmax/n×100％
Judging whether the distribution transformer has overload risk or not in a residential quarter saturation state according to a heavy overload judgment rule; wherein: the heavy overload judgment rule is as follows:
when the load factor delta is in the interval of [ 80%, 100% ], judging that the distribution transformer is in a heavy load state;
and when the load factor delta is larger than 100%, judging that the distribution transformer is in an overload state.
Further, the determination rule for classifying the client into the checked-in user and the unchecked user in step S3 is:
when client AiPower consumption DA< P kilowatt-hours and customer AiWater consumption SAIf W ton, then the customer A is judgediDoes not camp on the cell;
when client AiPower consumption DANot less than P kilowatt hours and client AiWater consumption SAWhen the load is more than or equal to W tons, judging that the client A is a client AiDoes not camp on the cell;
wherein: n ═ 1, 2,. n; n is the total number of clients in the cell, and P is more than or equal to 0; w is more than or equal to 0.
Further, in step S3, P is 0 and/or W is 0.
The invention has the beneficial effects that: effectively solve distribution transformer and lead to being difficult to the accurate problem of developing the prediction of long-term heavy overload in the medium-term because of the mutability and the volatility influence of platform district load, realize carrying out accurate prediction to distribution transformer running state, early warning distribution transformer heavily transships the risk in advance, help promoting distribution transformer's operation and maintenance level, guarantee equipment safety and residential community's power consumption quality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flow chart of a distribution transformer overload prediction method based on a cell occupancy rate according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1, a method for predicting overload of a distribution transformer based on a cell occupancy rate according to an embodiment of the present invention includes the following steps:
s1, acquiring detailed data of client electric meters and water meters of the multi-meter-in-one cell from the power consumer electricity consumption information acquisition system;
s2, carrying out user number association on the ammeter and the water meter file according to detail data collected by the client ammeter and the water meter in the multi-meter-in-one community;
s3, dividing the customers into a living user k and a non-living user n-k according to the electricity consumption D and the water consumption S of the customers, and calculating to obtain the residence rate lambda of the cell; wherein:
λ＝k/n×100％
s4, obtaining rated capacity S of distribution transformer from power consumer electricity consumption information acquisition systemNAnd real-time power S at different time pointstObtaining the maximum real-time power S of the distribution transformer in the time interval T when the residence rate lambda of the cell is obtainedtmax：
S5, according to the relation between the maximum power of the distribution transformer and the residence rate of the cell in the time period T:
a＝Stmax/λ＝Stmax×n/k
calculating the cell occupancy rate changed to lambda in the time period T1Maximum power of the distribution transformer:
Stmaxλ1＝λ1×a＝λ1×Stmax×n/k；
s6, calculating the load rate delta of the distribution transformer:
δ＝Stmax/SN×100％
respectively calculating the entrance rate warning value lambda for triggering the heavy load condition of the distribution transformer0.8And an occupancy warning value lambda triggering an overload condition of the distribution transformer1.0：
λ0.8＝0.8×SN×k/Stmax/n×100％
λ1.0＝SN×k/Stmax/n×100％
Judging whether the distribution transformer has overload risk or not in a residential quarter saturation state according to a heavy overload judgment rule; wherein: the heavy overload judgment rule is as follows:
when the load factor delta is in the interval of [ 80%, 100% ], judging that the distribution transformer is in a heavy load state;
and when the load factor delta is larger than 100%, judging that the distribution transformer is in an overload state.
In this embodiment, the determination rule for dividing the client into the checked-in user and the unchecked user in step S3 is as follows:
when client AiPower consumption DA< P kilowatt-hours and customer AiWater consumption SAIf W ton, then the customer A is judgediDoes not camp on the cell;
when client AiPower consumption DANot less than P kilowatt hours and client AiWater consumption SAWhen the load is more than or equal to W tons, judging that the client A is a client AiDoes not camp on the cell;
wherein: n ═ 1, 2,. n; n is the total number of clients in the cell, and P is more than or equal to 0; w is more than or equal to 0.
In this embodiment, in step S3, P is 0 and/or W is 0.
Specifically, the embodiment of the invention selects 2 cells with multiple expressions in one and 2 distribution transformers in a certain area for description, but the applicability and the protection range of the invention are not limited to the cells with multiple expressions in one and also to the data acquisition from the power utilization information acquisition system of the power consumer;
s1, acquiring detailed data of a customer ammeter and a water meter of the multi-meter-in-one cell A, B from the power consumer electricity consumption information acquisition system;
s2, carrying out user number association on the electric meter and the water meter files according to the detail data collected by the customer electric meter and the water meter of the multi-meter-in-one cell, wherein the number of the archives of the cell A is 308 households and the number of the archives of the cell B is 352 households after the association is carried out;
s3, dividing the customers into a living user k and a non-living user n-k according to the electricity consumption D and the water consumption S of the customers, and calculating to obtain the residence rate lambda of the cell;
the number of the residents in the cell A is 244 households, and the number of the residents in the cell B is 294 households;
cell a occupancy λA＝244/308×100％＝79.22％；
Cell B occupancy λB＝294/352×100％＝83.52％；
S4, obtaining rated capacity S of distribution transformer of community A from power consumer electricity consumption information acquisition systemNARated capacity S of distribution transformer of 630kVA, cell BNBIs 630kVA, and real-time power S at different time pointst. When the residence rate of the cell A is 79.22%, the maximum real-time power of a distribution transformer of the cell A in a time period T is 203 kVA; when the occupancy rate of the cell B is 83.52%, the maximum real-time power of a distribution transformer of the cell A in the time period T is 547 kVA:
s5, the community occupancy rate can reflect the community load level, so the relation between the maximum power of the distribution transformer and the community occupancy rate in the period T, the a of the community A can be establishedA256.25KVA, a of cell BB654.93 KVA. When the cell occupancy rate becomes lambda within the time period T1Then the maximum power of the distribution transformer of the cell A can be obtained as Stmaxλ1＝λ1X 256.25KVA, maximum power of distribution transformer in district B is Stmaxλ1＝λ1×654.93KVA；
S6, the occupancy rate of the cell in the time period T is 100%, namely, the load rate of the distribution transformer of the cell A is 40.67% in the occupancy saturation state, namely, the distribution transformer of the cell A cannot generate heavy overload risk in the occupancy saturation state; the load rate of the distribution transformer of the cell B is 103.96%, namely, the distribution transformer of the cell B can be overloaded under the saturation state;
calculating an entering rate warning value for triggering the heavy load overload condition of the distribution transformer, and calculating an entering rate warning value for triggering the heavy load condition of the distribution transformer in the cell B:
λ0.8＝0.8×630×294/547/352×100％＝76.95％
the occupancy alert value that triggers the overload condition:
λ1.0＝630×294/547/352×100％＝96.20％
namely, the occupancy rate of the cell B is 76.95%, and when the number of residents is 270, the distribution transformer of the cell B has the overload risk; the occupancy rate of the cell B is 96.20%, and when the number of residents is 338, the distribution transformer of the cell B has overload risk.
According to the calculation result, capacity increasing transformation or transformer addition can be carried out on the distribution transformer of the community B in advance, the situation that the distribution transformer is overloaded when the residents of the community increase is ensured, and the safety of the distribution transformer and the electricity utilization safety of the community users are ensured
Therefore, by means of the technical scheme, the problem that medium and long-term heavy overload prediction is difficult to accurately carry out due to the influence of the mutability and the fluctuation of the transformer area load of the distribution transformer is effectively solved, the running state of the distribution transformer is accurately predicted, the heavy overload risk of the distribution transformer is early warned, the operation and maintenance level of the distribution transformer is improved, and the equipment safety and the electricity utilization quality of community residents are guaranteed.
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 that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (3)
1. A distribution transformer overload prediction method based on a cell occupancy rate is characterized by comprising the following steps:
s1, acquiring detailed data of client electric meters and water meters of the multi-meter-in-one cell from the power consumer electricity consumption information acquisition system;
s2, carrying out user number association on the ammeter and the water meter file according to detail data collected by the client ammeter and the water meter in the multi-meter-in-one community;
s3, dividing the customers into a living user k and a non-living user n-k according to the electricity consumption D and the water consumption S of the customers, and calculating to obtain the residence rate lambda of the cell; wherein:
λ＝k/n×100％
s4, obtaining rated capacity S of distribution transformer from power consumer electricity consumption information acquisition systemNAnd real-time power S at different time pointstObtaining the maximum real-time power S of the distribution transformer in the time interval T when the residence rate lambda of the cell is obtainedtmax：
S5, according to the relation between the maximum power of the distribution transformer and the residence rate of the cell in the time period T:
a＝Stmax/λ＝Stmax×n/k
calculating the cell occupancy rate changed to lambda in the time period T1Maximum power of the distribution transformer:
Stmaxλ1＝λ1×a＝λ1×Stmax×n/k；
s6, calculating the load rate delta of the distribution transformer:
δ＝Stmax/SN×100％
respectively calculating the entrance rate warning value lambda for triggering the heavy load condition of the distribution transformer0.8And an occupancy warning value lambda triggering an overload condition of the distribution transformer1.0：
λ0.8＝0.8×SN×k/Stmax/n×100％
λ1.0＝SN×k/Stmax/n×100％
Judging whether the distribution transformer has overload risk or not in a residential quarter saturation state according to a heavy overload judgment rule; wherein: the heavy overload judgment rule is as follows:
when the load factor delta is in the interval of [ 80%, 100% ], judging that the distribution transformer is in a heavy load state;
and when the load factor delta is larger than 100%, judging that the distribution transformer is in an overload state.
2. The method of claim 1, wherein the decision rule of dividing the clients into the checked-in users and the unchecked users in step S3 is:
when client AiPower consumption DA< P kilowatt-hours and customer AiWater consumption SAIf W ton, then the customer A is judgediDoes not camp on the cell;
when client AiPower consumption DANot less than P kilowatt hours and client AiWater consumption SAWhen the load is more than or equal to W tons, judging that the client A is a client AiDoes not camp on the cell;
wherein: n ═ 1, 2,. n; n is the total number of clients in the cell, and P is more than or equal to 0; w is more than or equal to 0.
3. The method of claim 2, wherein P-0 and/or W-0 in step S3 is used for predicting the overload of the distribution transformer based on the cell occupancy.
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