CN110929226B - Power distribution network power failure prediction method, device and system - Google Patents
Power distribution network power failure prediction method, device and system Download PDFInfo
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Abstract
The application relates to a power failure prediction method, device and system for a power distribution network, wherein the method comprises the following steps: acquiring current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment; processing current memory node fact data according to the training data set to obtain a power failure feature vector; and searching a memory tree model of the corresponding distribution equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding distribution equipment. When a new fact appears, the application firstly utilizes the memory tree to recall, calculates an internal evaluation outage prior probability, completes outage prediction, improves the accuracy of outage prediction, ensures the optimal running state of the power distribution network on the premise of safety and stability, provides reliable outage early warning for operation and maintenance personnel by the outage prediction result, makes related work in advance, prevents the occurrence of the outage, and reduces economic loss caused by outage.
Description
Technical Field
The application relates to the technical field of power outage prediction, in particular to a power outage prediction method, device and system for a power distribution network.
Background
The power supply reliability is one of the most important indexes for measuring the management level of a power supply enterprise, the running condition of a power system and the management level of the power enterprise can be displayed, and meanwhile, the method has important guiding effects on the aspects of planning, design, overhaul and maintenance of a power grid, equipment operation and the like. The power distribution network is an intermediate link for connecting users and a power system, and the power supply reliability of the power distribution network is a quite important factor for measuring the quality of electric energy. Through analysis and research of the power supply reliability, a relatively weak link of the power distribution network is discovered, the development of local economy is linked, and measures are formulated to improve the power supply reliability, so that higher electric energy quality can be provided, and higher social benefits are created.
During the period of peak-welcome summer, the power consumption of residential users in a power distribution area is increased rapidly, and the power load reaches the highest peak in one year. With the increase of the social electricity consumption, the distribution network is easy to cause frequent power failure. The distribution network is frequently powered off, so that not only is the electric power service not full for users, but also great inconvenience is brought to daily life of residents, and even serious commercial economic loss is caused.
In the implementation process, the inventor finds that at least the following problems exist in the conventional technology: the traditional power failure prediction of the power distribution network is judged through manual experience, so that the power failure prediction error is large, the workload is large and the labor cost is high.
Disclosure of Invention
Based on the above, it is necessary to provide a power distribution network power outage prediction method, device and system, aiming at the problems of large power outage prediction error, large workload and high labor cost in the traditional power outage prediction of a power distribution network through manual experience judgment.
In order to achieve the above object, an embodiment of the present invention provides a power outage prediction method for a power distribution network, including the following steps:
acquiring current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment;
Processing current memory node fact data according to the training data set to obtain a power failure feature vector;
Searching a memory tree model of the corresponding distribution equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding distribution equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
In one embodiment, the memory node fact data includes any one or any combination of the following: heavy overload duration, heavy three-phase imbalance duration, daily maximum active load rate, daily maximum three-phase imbalance, daily average active load rate, average three-phase imbalance, and current day power outage event data.
In one embodiment, the step of searching the memory tree model of the corresponding distribution equipment based on the power outage feature vector to obtain the power outage probability prediction result of the corresponding distribution equipment comprises:
obtaining memory node fact data of a prediction period based on a power failure probability prediction result;
And updating the current memory node fact data in the memory tree model into the memory node fact data of the prediction period.
In one embodiment, the step of updating the current memory node fact data in the memory tree model to the memory node fact data of the prediction period includes:
deleting current memory node fact data in the memory tree model; and adds the memory node fact data of the prediction period to a corresponding position in the memory node fact data corresponding to the current time.
In one embodiment, the step of deleting current memory node fact data in the memory tree model comprises:
in the memory tree model, pointing a parent node corresponding to the memory node to be deleted to be a child node of the memory node to be deleted; the memory node to be deleted is the memory node corresponding to the current memory node fact data.
In one embodiment, the step of adding the memory node fact data of the prediction period to the corresponding location in the corresponding current memory node fact data includes:
Establishing newly added memory nodes of the memory node fact data corresponding to the prediction period;
setting the newly added memory node as the memory root of the memory tree model, and pointing the newly added memory node to the previous node of the memory tree model.
In one embodiment, the method further comprises the steps of:
sequentially detecting memory node impression values and memory decay values of the memory node fact data;
And deleting corresponding memory node fact data in the memory tree model when the memory node impression value is smaller than the memory impression threshold value and the memory decline value is smaller than the forgetting threshold value.
On the other hand, the embodiment of the invention also provides a power distribution network power failure prediction device, which comprises:
The data acquisition unit is used for acquiring current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment;
The data processing unit is used for processing the current memory node fact data according to the training data set to obtain a power failure feature vector;
The power outage prediction unit is used for searching a memory tree model of the corresponding distribution equipment based on the power outage characteristic vector to obtain a power outage probability prediction result of the corresponding distribution equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
On the other hand, the embodiment of the invention also provides a power distribution network power failure prediction system, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of any power distribution network power failure prediction method when executing the computer program.
On the other hand, the embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the power distribution network power failure prediction method of any one of the above steps are realized.
One of the above technical solutions has the following advantages and beneficial effects:
In each embodiment of the power distribution network power outage prediction method, current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment are obtained; processing current memory node fact data according to the training data set to obtain a power failure feature vector; based on the power outage feature vector, searching a memory tree model of the corresponding distribution equipment to obtain a power outage probability prediction result of the corresponding distribution equipment, and realizing power outage prediction of the power distribution network. The application can build a memory tree model for each distribution transformer device, and uses the fact data of the corresponding memory nodes of the distribution transformer device as facts to build a distribution transformer memory tree. When a new fact appears, firstly, the self memory tree is used for recall, an internal evaluation outage prior probability is calculated, outage prediction is completed, outage prediction accuracy is improved, the optimal running state of the power distribution network is guaranteed on the premise of safety and stability, a outage prediction result provides reliable outage early warning for operation and maintenance personnel, related work is made in advance, the situation is prevented, and economic losses caused by outage are reduced.
Drawings
FIG. 1 is a schematic diagram of an application environment of a power distribution network outage prediction method according to an embodiment;
FIG. 2 is a first flow chart of a power distribution network outage prediction method according to one embodiment;
FIG. 3 is a second flow chart of a power distribution network outage prediction method according to one embodiment;
FIG. 4 is a third flow chart of a power distribution network outage prediction method according to one embodiment;
FIG. 5 is a schematic diagram of building a memory tree model in one embodiment;
FIG. 6 is a schematic diagram of a memory tree model with nodes added in one embodiment;
FIG. 7 is a schematic structural diagram of a power distribution network power outage prediction apparatus according to an embodiment;
fig. 8 is a schematic structural diagram of a power distribution network outage prediction system according to an embodiment.
Detailed Description
In order that the application may be readily understood, a more complete description of the application will be rendered by reference to the appended drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The power failure prediction method of the power distribution network can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the distribution transformer device 104 via a network. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the distribution transformer device 104 may be implemented by a stand-alone distribution transformer device or a distribution transformer device cluster formed by a plurality of distribution transformer devices.
In one embodiment, as shown in fig. 2, a power outage prediction method for a power distribution network is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and includes the following steps:
Step S210, obtaining current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment.
The distribution transformer equipment can be, but is not limited to, overhead lines, towers, cables, distribution transformers, switching equipment, reactive compensation capacitors and the like. The memory node fact data refers to the fact data of the corresponding memory node in the memory tree. For example, the memory node fact data may be, but is not limited to, three-phase imbalance data for the corresponding device. The training data is data for training a data mining model in the data mining process; a training data set refers to a collection of multiple training data.
Specifically, a training data set can be obtained in advance by training historical memory node fact data, and then the training data set corresponding to the equipment can be obtained; the memory tree model is obtained by establishing the historical memory node fact data of the corresponding distribution equipment, and then the current memory node fact data of the corresponding distribution equipment can be obtained by inquiring the memory tree model.
Step S220, according to the training data set, the current memory node fact data is processed to obtain the power failure feature vector.
The power outage feature vector can be used for indicating the feature vector of the power outage factor of the corresponding equipment.
Specifically, according to the acquired training data set, current memory node fact data of the distribution transformer equipment are processed, and then a power failure feature vector can be obtained.
Step S230, searching a memory tree model of the corresponding equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
The power outage probability prediction result can be used for indicating the probability of the power outage of the distribution transformer equipment in the next time period (for example, the next day).
Specifically, according to the processed power outage characteristic vector, searching a memory tree model corresponding to the distribution transformer equipment to obtain a power outage probability prediction result of the distribution transformer equipment in the next time period.
Specifically, in the embodiment of the power outage prediction method for the power distribution network, current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment are obtained; processing current memory node fact data according to the training data set to obtain a power failure feature vector; based on the power outage feature vector, searching a memory tree model of the corresponding distribution equipment to obtain a power outage probability prediction result of the corresponding distribution equipment, and realizing power outage prediction of the power distribution network. A memory tree model is built for each distribution transformer device, and the fact data of the corresponding memory nodes of the distribution transformer device is selected as facts to build a distribution transformer memory tree. When a new fact appears, firstly, the self memory tree is used for recall, an internal evaluation outage prior probability is calculated, outage prediction is completed, outage prediction accuracy is improved, the optimal running state of the power distribution network is guaranteed on the premise of safety and stability, a outage prediction result provides reliable outage early warning for operation and maintenance personnel, related work is made in advance, the situation is prevented, and economic losses caused by outage are reduced.
In a specific embodiment, the memory node fact data includes any one or any combination of the following data: heavy overload duration, heavy three-phase imbalance duration, daily maximum active load rate, daily maximum three-phase imbalance, daily average active load rate, average three-phase imbalance, and current day power outage event data.
The current day power outage event data can be used for indicating whether power outage occurs to the distribution transformer equipment on the current day. For example, the daily power outage event data may be 0 and 1, wherein the daily power outage event data is 0 indicates that the distribution transformer device has not failed, and the daily power outage event data is 1 indicates that the distribution transformer device has failed.
In one example, taking the regional office's distribution equipment as an example, the following variables and features are employed: the memory decay coefficient is alpha, the forgetting threshold value is delta, the memory period is T, and the memory node fact data are the heavy overload time length f zgzsc (hereinafter denoted as f 1), the heavy three-phase unbalance time length f zscbphsc (hereinafter denoted as f 2), the daily maximum active load rate f max_ygfzl (hereinafter denoted as f 3), the daily maximum three-phase unbalance degree f max_sxbphd (hereinafter denoted as f 4), the daily average active load rate f ave_ygfzl (hereinafter denoted as f 5), the average three-phase unbalance degree f ave_sxbphd (hereinafter denoted as f 6) and the current power outage event data f is_poweroff (hereinafter denoted as f 7) of the distribution transformer.
In one embodiment, as shown in fig. 3, a power outage prediction method for a power distribution network is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and includes the following steps:
step S310, obtaining current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment.
Step S320, according to the training data set, the current memory node fact data is processed to obtain the power failure feature vector.
Step S330, searching a memory tree model of the corresponding equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
Step S340, based on the power failure probability prediction result, the memory node fact data of the prediction period is obtained.
Wherein, the memory node fact data of the prediction period refers to memory node fact data occurring in a period corresponding to the power outage probability prediction result.
For example, if the outage probability prediction result is for the distribution transformer equipment prediction of the following day, the memory node fact data of the prediction period corresponds to the memory node fact data occurring in the following day.
Step S350, updating the current memory node fact data in the memory tree model into the memory node fact data of the prediction period.
The specific content process of step S310, step S320 and step S330 may refer to the above, and will not be described herein.
Specifically, a memory tree model is built for the distribution transformer equipment, memory node fact data of the distribution transformer equipment is selected, and a distribution transformer memory tree is built. When a new fact appears, the self memory tree is used for recall, an internal evaluation outage prior probability is calculated, outage prediction is completed, after the later outage fact is confirmed, the memory node fact data of a prediction period is used for dynamically updating the memory tree model structure and the memory impression of the memory nodes, so that the power distribution network can guarantee the optimal running state on the premise of safety and stability, a outage prediction result provides reliable outage early warning for operation and maintenance personnel, related work is made in advance, and economic losses caused by outage are reduced. The power failure prediction implementation process is simplified, and the robustness is good.
In a specific embodiment, the step of updating the current memory node fact data in the memory tree model to the memory node fact data of the prediction period includes:
deleting current memory node fact data in the memory tree model; and adds the memory node fact data of the prediction period to a corresponding position in the memory node fact data corresponding to the current time.
Specifically, the current memory node fact data of the distribution transformer equipment is deleted from the memory tree, the memory node fact data of the prediction period is added to the root of the memory tree, and the memory tree structure is updated.
Further, the memory node fact data of the prediction period is saved with a copy Temp, the current memory node fact data is deleted from the memory tree, the Temp is added to the root of the memory tree, and thus the memory tree structure update is completed.
In a specific embodiment, the step of deleting the current memory node fact data in the memory tree model comprises:
in the memory tree model, pointing a parent node corresponding to the memory node to be deleted to be a child node of the memory node to be deleted; the memory node to be deleted is the memory node corresponding to the current memory node fact data.
Specifically, the subsequent node of the father node of the memory node to be deleted is pointed to be the child node of the memory node to be deleted, and then the deleting operation of the node is completed.
In a specific embodiment, the step of adding the memory node fact data of the prediction period to the corresponding location in the corresponding current memory node fact data comprises:
Establishing newly added memory nodes of the memory node fact data corresponding to the prediction period;
setting the newly added memory node as the memory root of the memory tree model, and pointing the newly added memory node to the previous node of the memory tree model.
Specifically, newly creating a new memory node corresponding to the memory node fact data of the prediction period, setting an initial node memory impression, setting the new memory node as a memory root and pointing to a previous node, and further obtaining a memory tree after the new memory node.
In one embodiment, as shown in fig. 4, a power outage prediction method for a power distribution network is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and includes the following steps:
Step S410, obtaining current memory node fact data of the distribution transformer device and a training data set of the distribution transformer device.
Step S420, according to the training data set, the current memory node fact data is processed to obtain the power failure feature vector.
Step S430, searching a memory tree model of the corresponding equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
Step S440, detecting the memory node impression value and the memory decay value of each memory node fact data in turn.
Wherein the impression value of the memory node can be used for indicating the impression degree of the corresponding memory node in the memory tree; the memory decay value may be used to indicate the degree of decay of a corresponding memory node in the memory tree.
In step S450, when the memory node impression value is smaller than the memory impression threshold value and the memory decay value is smaller than the forgetting threshold value, deleting the corresponding memory node fact data in the memory tree model.
The specific content process of step S410, step S420 and step S430 may refer to the above, and will not be described herein.
Specifically, a memory tree model is built for the distribution transformer equipment, memory node fact data of the distribution transformer equipment is selected, and a distribution transformer memory tree is built. Whenever a new fact appears, the self memory tree is used for recall, an internal evaluation outage prior probability is calculated, and outage prediction is completed. In addition, the memory nodes in the memory tree can be subjected to memory forgetting judgment processing, when the memory node impression value is smaller than the memory impression threshold value and the memory decay value is smaller than the forgetting threshold value, namely the memory nodes meet the memory forgetting condition, the corresponding memory node fact data in the memory tree model can be deleted, the memory forgetting process is further completed, the memory tree model is optimized, and the robustness is better.
In order to solve the problem that the power failure prediction of the power distribution network is large in power failure prediction error, large in workload and high in labor cost through manual experience judgment. In one example, the operation of power distribution network outage prediction is specifically described.
Step 1: let n=1 distribution transformer equipment in total, the memory fade coefficient is α=0.05, the forgetting threshold value is δ=0.01, the memory period is t=24 h, the memory node fact data is the heavy overload time length (hereinafter denoted as f 1), the heavy three-phase imbalance time length (hereinafter denoted as f 2), the daily maximum active load factor (hereinafter denoted as f 3), the daily maximum three-phase imbalance (hereinafter denoted as f 4), the daily average active load factor (hereinafter denoted as f 5), the average three-phase imbalance (hereinafter denoted as f 6) and the current power outage event data (hereinafter denoted as f 7) of the distribution transformer equipment.
Step 2: a memory tree model is built as shown in fig. 5. Wherein, the part in the dotted line is a memory fact node, f 7 is a fact result, f 1～f6 is a fact factor, alpha is a memory decline coefficient, beta is a memory node impression, and t is a fact occurrence event.
Step 3: a training data set is generated. The original data set of the regional local distribution transformer is set as t= { T 1,t2,…,tn }, wherein T i (i=1, 2, …, n) is the operation data of the ith distribution transformer, T i＝{s1,s2,…,sm }, and s j (j=1, 2, …, m) is the operation data of the distribution transformer at the moment jWherein x j is the timestamp of time j,/>For the active load factor at time j,/>For phase A current at time j,/>Is B-phase current at j-The C-phase current at time j. The training dataset is calculated as follows:
A calculation date d k：dk＝date(xj);
Date d k maximum active load f 3 (k) is calculated:
Calculate heavy overload duration f 1 (k) for date d k: wherein I (x) is an indication function;
The average active load factor f 5 (k) for date d k is calculated:
Calculating three-phase imbalance x sxbphd (j) at time j:
Calculate the maximum three-phase imbalance f 4 (k) for date d k:
calculating a heavy three-phase imbalance f 2 (k)：f2 (k)＝Δt∑I(xsxbphd (j)＞60,jindk of date d k);
Calculate the average three-phase imbalance f 6 (k) for date d k:
Through the calculation, a training data set T '= { T' 1,t'2,…,t'n }, where T 'i (i=1, 2, …, n) generates training data for the ith distribution equipment, let T' i＝{f1,f2,…,fm }, where f k (j=1, 2, …, m) sets a data tag of l= { L 1,l2,…,ln } on a training sample fk＝[dk,f1 (k),f2 (k),f3 (k),f4 (k),f5 (k),f6 (k)]. of a date j, where L i (i=1, 2, …, n) is a tag of the ith distribution equipment, let L i＝{y1,y2,…,ym }, and where y k (j=1, 2, …, m) is a tag of the date k, indicating whether a power failure accident occurs on a subsequent day. The training data set is used for raw memory generation. The training data set generated for this formulation over 10 consecutive days is shown in the following table.
| Sequence number | dk | f1 (k) | f2 (k) | f3 (k) | f4 (k) | f5 (k) | f6 (k) | f7 (k) |
| 1 | 2018/5/1 | 0.00 | 21.32 | 12.50 | 0.50 | 63.16 | 33.73 | 0 |
| 2 | 2018/5/2 | 0.00 | 14.52 | 7.73 | 0.50 | 62.00 | 34.69 | 0 |
| 3 | 2018/5/3 | 0.00 | 11.38 | 8.01 | 1.50 | 67.95 | 32.55 | 0 |
| 4 | 2018/5/4 | 0.00 | 9.78 | 6.37 | 0.25 | 66.04 | 29.50 | 0 |
| 5 | 2018/5/5 | 0.00 | 10.37 | 6.85 | 1.00 | 72.86 | 40.08 | 0 |
| 6 | 2018/5/6 | 0.00 | 12.65 | 7.85 | 0.00 | 63.54 | 31.52 | 0 |
| 7 | 2018/5/7 | 0.00 | 13.15 | 9.12 | 0.00 | 59.26 | 32.59 | 0 |
| 8 | 2018/5/8 | 0.00 | 12.17 | 7.53 | 0.00 | 57.75 | 28.07 | 0 |
| 9 | 2018/5/9 | 0.00 | 11.18 | 6.95 | 0.25 | 60.00 | 32.93 | 0 |
| 10 | 2018/5/10 | 0.00 | 10.75 | 6.28 | 0.25 | 67.57 | 37.02 | 0 |
| … | … | … | … | … | … | … | … | … |
| 59 | 2.18/6/28 | 0.00 | 20.47 | 14.86 | 0.00 | 42.11 | 20.39 | 0 |
Step 4: 1-58 groups of data are selected as training samples, sample data are input, and a memory tree is searched. Let sample of ith distribution transformer on date k be fi,k＝[di,k,fi,1 (k),fi,2 (k),fi,3 (k),fi,4 (k),fi,5 (k),fi,6 (k)],li,k＝yi,k. to find out the memory node whose training sample is closest to the memory tree and the power failure label corresponding to the memory node. Wherein, nodes= { (f j,dis)|dis＝||fi,k-fj||2,fj in tree },
The minimum distance dis i,k：disi,k＝||fi,k-fnode||2 can be obtained. Set in nodes set, training samples are Bnodes from the node set with a distance of less than thr=0.8 from the memory tree and elements have been ordered from small to large: bnodes = { (f j,labelj)|(fj, dis) ∈nodes and dis < thr }. If dis i,k > thr, go to step 5. If dis i,k is less than or equal to thr and label i,k＞labelnode, go to step 6. If dis i,k is less than or equal to thr and label i,k≠labelnode, the memory impression of the memory node is updated, where the value w thr =3: otherwise, the memory impression of the Node is updated according to the following formula, and the value ω dis =2.5: /(I) After the jump is executed, returning to the step 4 until the creation of the memory tree is completed.
Step 5: and newly adding a memory node. New memory node t n+1 is newly built, and the initial node memory impression is set as follows: The new memory node is set at the memory root and points to the previous node, and the memory tree after the memory node is newly added is shown in fig. 6.
Step 6: updating the memory tree. And (3) saving the copy Temp of the node to be updated, deleting the node from the memory tree by using the step 7, and adding the Temp to the root of the memory tree by using the step 5, so as to finish the updating of the memory tree structure. Updating the node memory impression by using the following method:
Step 7: and deleting the memory node. And pointing the successor node of the father node of the memory node to be deleted to be the child node of the node, and completing the deleting operation of the node.
Step 8: and predicting power failure on the following day. After the ith distribution transformer generates operation data of a new day, calculating a power failure feature vector, wherein f i,new = [2018/6/28,0.00,20.47,14.86,0.00,42.11,20.39], searching a memory tree, and finding a memory node set Result with a short distance between the feature vector and the memory tree and good impression and a power failure label of a corresponding node.
The power outage probability prediction result is:
p(label＝1)＝1-p(label＝0)＝1-0.9358＝0.0642
Here, label=1 indicates a power outage, label=0 indicates no power outage, so the power outage probability per day on which the prediction date is 2018/6/28 is 0.0642, and no power outage probability is 0.9358, and the power outage prediction is completed.
Step 9: the actual occurrence result in the step 9 is label' =1, that is, without power outage, a new data sample, that is, a data sample with a serial number of 59 in the data set can be obtained, and the memory tree of the distribution transformer is updated according to the step 4 by using the sample.
Step 10: memory forgetfulness. If a certain node beta j＜βthr =0.001 and alpha ωj < delta=0.0001 of the memory tree, taking omega=0.1, deleting the node by utilizing the step 7, and completing the memory forgetting process.
The power failure prediction of the distribution transformer equipment is carried out through the process, and the result with the prediction date of 2018/6/28 can be obtained as follows: the outage probability is 0.0642 and the uninterrupted probability is 0.9358. The prediction result can be provided for the workers for operation and maintenance of the distribution transformer, and is used as a reliable reference for operation and maintenance, measures are taken in time for the distribution transformer with high probability of power failure, so that the distribution transformer is prevented from happening, or power supply work is ensured, the power supply reliability of a platform area is ensured, and the economic benefit of the platform area is ensured.
It should be understood that, although the steps in the flowcharts of fig. 2-4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or steps.
In one embodiment, as shown in fig. 7, there is provided a power distribution network power outage prediction apparatus, including:
a data obtaining unit 710, configured to obtain current memory node fact data of the distribution transformer device and a training data set of the corresponding distribution transformer device;
The data processing unit 720 is configured to process current memory node fact data according to the training data set to obtain a power failure feature vector;
The power outage prediction unit 730 is configured to search a memory tree model of the corresponding distribution equipment based on the power outage feature vector, and obtain a power outage probability prediction result of the corresponding distribution equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
For specific limitations of the power outage prediction apparatus for a power distribution network, reference may be made to the above limitation of the power outage prediction method for a power distribution network, and no further description is given here. All or part of each module in the power distribution network power failure prediction device can be realized by software, hardware and a combination thereof. The modules can be embedded in or independent of a processor in the power distribution network power failure prediction system in a hardware mode, and can also be stored in a memory in the power distribution network power failure prediction system in a software mode, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, as shown in fig. 8, a power distribution network power outage prediction system is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of any one of the power distribution network power outage prediction methods described above when executing the computer program.
Wherein the processor is operable to perform the steps of:
acquiring current memory node fact data of the distribution transformer equipment and a training data set of the distribution transformer equipment;
Processing current memory node fact data according to the training data set to obtain a power failure feature vector;
Searching a memory tree model of the corresponding distribution equipment based on the power failure feature vector to obtain a power failure probability prediction result of the corresponding distribution equipment; the memory tree model is obtained by establishing historical memory node fact data of the corresponding equipment; the training data set is obtained by training the historical memory node fact data.
In one embodiment, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power distribution network power outage prediction method of any one of the above.
Those skilled in the art will appreciate that implementing all or part of the above-described embodiments of the method may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of embodiments of the division methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The power failure prediction method for the power distribution network is characterized by comprising the following steps of:
Acquiring current memory node fact data of distribution transformer equipment and a training data set corresponding to the distribution transformer equipment;
processing the current memory node fact data according to the training data set to obtain a power failure feature vector;
Searching a memory tree model corresponding to the distribution transformer equipment based on the power outage characteristic vector to obtain a power outage probability prediction result corresponding to the distribution transformer equipment; the memory tree model is established based on historical memory node fact data corresponding to the distribution transformer equipment; the training data set is obtained by training the historical memory node fact data;
The power outage probability prediction result corresponding to the distribution transformer equipment is obtained by searching a memory tree model corresponding to the distribution transformer equipment based on the power outage feature vector, and the method comprises the following steps:
After the ith distribution transformer equipment generates new operation data of one day, calculating a power failure feature vector, searching a memory tree model, finding a memory node set Result with a short distance between the power failure feature vector and the memory tree model and a power failure label of a corresponding memory node, and determining a power failure probability prediction Result as follows:
p(label＝1)＝1-p(label＝0)
Wherein label=1 represents a power outage, label=0 represents a non-power outage, I is an I-th distribution transformer, f j is fact data of a j-th memory node, dis represents a distance between the memory node and a memory tree model, β i is a memory node impression of the I-th distribution transformer, k is a date, (f j,dis,βi, k) represents a power outage feature vector, I ((f j,dis,βi, k) in Result and label =1) represents a memory node set label whose distance between the power outage feature vector and the memory tree model is shorter than a preset distance threshold when a power outage event occurs at the I-th distribution transformer on the date k, β j is a memory impression of a j-th memory node of the memory tree model, β j I is a memory impression of a memory node j corresponding to the memory node set label I, p (label=1) represents a power outage probability, p (label=0) represents a probability of the non-power outage, e represents an exponential function, I ((f j,dis,βi, k) in Result and label =0) represents a memory node set label whose distance between the feature vector and the memory tree model is shorter than the preset distance threshold when the power outage event occurs at the I-th distribution transformer on the date k, And the sum of the memory impressions of the power failure events of all the distribution equipment is represented.
2. The power distribution network outage prediction method of claim 1, wherein the memory node fact data comprises any one or any combination of: heavy overload duration, heavy three-phase imbalance duration, daily maximum active load rate, daily maximum three-phase imbalance, daily average active load rate, average three-phase imbalance, and current day power outage event data.
3. The power outage prediction method according to claim 1, wherein the step of searching the memory tree model corresponding to the distribution transformer equipment based on the power outage feature vector to obtain the power outage probability prediction result corresponding to the distribution transformer equipment comprises:
Obtaining memory node fact data of a prediction period based on the power failure probability prediction result;
And updating the current memory node fact data in the memory tree model into the memory node fact data of the prediction period.
4. A power distribution network outage prediction method according to claim 3, wherein the step of updating current memory node fact data in the memory tree model to memory node fact data of the prediction period comprises:
Deleting the current memory node fact data in the memory tree model; and adding the memory node fact data of the prediction period to a corresponding position in the memory node fact data corresponding to the current time period.
5. The power distribution network outage prediction method according to claim 4, wherein said step of deleting said current memory node fact data in said memory tree model comprises:
In the memory tree model, pointing a parent node corresponding to a memory node to be deleted to be a child node of the memory node to be deleted; the memory node to be deleted is a memory node corresponding to the current memory node fact data.
6. The power distribution network outage prediction method according to claim 4, wherein said step of adding said predicted period of memory node fact data to a corresponding location in said current memory node fact data comprises:
Establishing a new memory node corresponding to the memory node fact data of the prediction period;
setting the newly added memory node as a memory root of the memory tree model, and pointing the newly added memory node to a previous node of the memory tree model.
7. The power distribution network outage prediction method according to claim 1, further comprising the step of:
Sequentially detecting a memory node impression value and a memory decay value of each memory node fact data;
and deleting the corresponding memory node fact data in the memory tree model when the memory node impression value is smaller than a memory impression threshold value and the memory decline value is smaller than a forgetting threshold value.
8. The utility model provides a distribution network power failure prediction device which characterized in that includes:
the data acquisition unit is used for acquiring current memory node fact data of the distribution transformer equipment and a training data set corresponding to the distribution transformer equipment;
The data processing unit is used for processing the current memory node fact data according to the training data set to obtain a power failure feature vector;
The power failure prediction unit is used for searching a memory tree model corresponding to the distribution transformer equipment based on the power failure characteristic vector to obtain a power failure probability prediction result corresponding to the distribution transformer equipment; the memory tree model is established based on historical memory node fact data corresponding to the distribution transformer equipment; the training data set is obtained by training the historical memory node fact data;
The power outage probability prediction result corresponding to the distribution transformer equipment is obtained by searching a memory tree model corresponding to the distribution transformer equipment based on the power outage feature vector, and the method comprises the following steps:
After the ith distribution transformer equipment generates new operation data of one day, calculating a power failure feature vector, searching a memory tree model, finding a memory node set Result with a short distance between the power failure feature vector and the memory tree model and a power failure label of a corresponding memory node, and determining a power failure probability prediction Result as follows:
p(label＝1)＝1-p(label＝0)
Wherein label=1 represents a power outage, label=0 represents a non-power outage, I is an I-th distribution transformer, f j is fact data of a j-th memory node, dis represents a distance between the memory node and a memory tree model, β i is a memory node impression of the I-th distribution transformer, k is a date, (f j,dis,βi, k) represents a power outage feature vector, I ((f j,dis,βi, k) in Result and label =1) represents a memory node set label whose distance between the power outage feature vector and the memory tree model is shorter than a preset distance threshold when a power outage event occurs at the I-th distribution transformer on the date k, β j is a memory impression of a j-th memory node of the memory tree model, β j I is a memory impression of a memory node j corresponding to the memory node set label I, p (label=1) represents a power outage probability, p (label=0) represents a probability of the non-power outage, e represents an exponential function, I ((f j,dis,βi, k) in Result and label =0) represents a memory node set label whose distance between the feature vector and the memory tree model is shorter than the preset distance threshold when the power outage event occurs at the I-th distribution transformer on the date k, And the sum of the memory impressions of the power failure events of all the distribution equipment is represented.
9. A power distribution network outage prediction system comprising a memory and a processor, said memory storing a computer program, wherein said processor, when executing said computer program, performs the steps of the power distribution network outage prediction method according to any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the power distribution network outage prediction method according to any one of claims 1 to 7.
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