CN113516280A - Optimization method for power grid equipment fault probability prediction based on big data - Google Patents
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Abstract
The invention discloses an optimization method for power grid equipment fault probability prediction based on big data, which comprises the steps of collecting historical defect data, online monitoring data and meteorological data of power grid equipment; training a plurality of fault probability prediction models based on machine learning aiming at each type of materials; and predicting the fault probability of the material by using the fault probability prediction model and combining the collected data. According to the invention, through multi-model fusion and multi-data source fusion, the precision of defect material prediction is improved.
Description
Technical Field
The invention relates to the technical field of power grid equipment fault probability prediction optimization, in particular to a power grid equipment fault probability prediction optimization method based on big data.
Background
The stable and healthy operation of the power grid system is very important for people's lives. However, the grid system is too large and the plant may not always operate perfectly. Extreme weather, emergency, equipment aging, etc. can cause grid faults.
For equipment materials of a power grid, three types are mainly used: daily equipment materials, emergency equipment materials and major disaster defect materials. The invention mainly aims at emergency equipment materials. When equipment fails, warehouses at various places need to be prepared for replacement to ensure the normal operation of the power grid. However, the warehouse in each place needs to purchase the amount of each type of material, so that the material is not lacked, and the material is not excessively stored, which becomes a problem worthy of research.
However, in the power grid system, different areas and different material data are distributed very differently, as shown in fig. 1. Taking the real data distribution of the Xiuwen county as an example, the distribution of the body, the composite insulator and the hardware fitting body is different. Meanwhile, it can be seen that the distribution of the data is not very regular. Therefore, to realize accurate prediction of the probability of the defective materials of the power grid hierarchy, it is impossible to realize that one model is suitable for prediction of all the material fault probabilities.
Meanwhile, the sensor data of the power grid are massive, and the current big data technology is mature, so that the big data technology is utilized to predict the fault probability of the multi-model equipment defect by combining historical defect data, on-line monitoring data and meteorological data.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides an optimization method for power grid equipment fault probability prediction based on big data, which can solve the problem of low prediction accuracy in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of collecting historical defect data, online monitoring data and meteorological data of the power grid equipment; training a plurality of fault probability prediction models based on machine learning aiming at each type of materials; and predicting the fault probability of the material by using the fault probability prediction model and combining the collected data.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: the failure probability prediction model comprises a negative feedback neural network model, a gradient lifting tree, an extreme gradient lifting tree model, a random forest model, a naive Bayes model and a logistic regression model.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: training the fault probability prediction model to obtain a predicted value y1The method comprises the following steps of (1),
wherein the content of the first and second substances,
a predicted value of defect material, f, for the gradient lifting tree and the extreme gradient lifting modelkFor the kth classification regression tree, K is scoreAnd the number of the class regression trees, gamma is the space for classifying the regression trees, the target optimization index is loglos, and the regression problem is converted into fitting of probability values.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein the content of the first and second substances,
defect material prediction for negative feedback neural network model, w1Is a parameter of the first layer, σ is an activation function, w2Is a weight parameter of the second layer.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
where θ is the model parameter obtained by training, T is the transposition operation, and x is the parameter.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: the defect material comprises a hardware fitting body, a stay wire body, a concrete pole and a porcelain insulator.
The invention discloses a preferable scheme of a big data-based power grid equipment fault probability prediction optimization method, wherein the method comprises the following steps: the device also comprises a CPU plug-in, an overhead conductor, a switching contactor, a charging module, a composite insulator and a distribution transformer.
The invention has the beneficial effects that: according to the invention, through multi-model fusion and multi-data source fusion, the precision of defect material prediction is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flowchart of an optimization method for big data-based power grid device failure probability prediction according to a first embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a comparison between prediction accuracy and a curve of an optimization method for grid equipment fault probability prediction based on big data according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, for a first embodiment of the present invention, there is provided a method for optimizing a prediction of a failure probability of a grid device based on big data, including:
s1: and acquiring historical defect data, online monitoring data and meteorological data of the power grid equipment.
S2: for each type of material, a plurality of fault probability prediction models are trained based on machine learning.
S3: and predicting the fault probability of the material by using a fault probability prediction model and combining the collected data.
Specifically, the failure probability prediction model comprises a negative feedback neural network model, a gradient lifting tree, an extreme gradient lifting tree model, a random forest model, a naive Bayes model and a logistic regression model.
Training a fault probability prediction model to obtain a predicted value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein the content of the first and second substances,
a predicted value of defect material, f, for the gradient lifting tree and the extreme gradient lifting modelkAnd converting the regression problem into fitting to probability values for the kth classification regression tree, wherein K is the number of classification regression trees, gamma is the space of the classification regression trees, and the target optimization index is loglos.
Prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein the content of the first and second substances,
defect material prediction for negative feedback neural network model, w1Is a parameter of the first layer, σ is an activation function, w2Is a weight parameter of the second layer.
Prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
where θ is the model parameter obtained by training, T is the transposition operation, and x is the parameter.
The defect materials comprise a hardware fitting body, a stay wire body, a concrete pole, a porcelain insulator, a CPU plug-in unit, an overhead conductor, a switching contactor, a charging module, a composite insulator and a distribution transformer.
Preferably, this embodiment should also be described in that the present invention integrates meteorological data, historical defect data, and online monitoring data to form rich data so as to improve the prediction accuracy, and trains a plurality of failure probability prediction models for various materials in each county, and then integrates the plurality of models to obtain higher prediction accuracy.
Furthermore, the random forest is used for model fusion, the stability of fault probability is improved, and the prediction precision is also improved, the multiple fault probability prediction models are trained simultaneously through fusion of multiple data sources (historical data, online monitoring data and), then the prediction results of the models are fused, the prediction precision is improved, and the recall ratio reaches 95.5% (recall) in terms of fault probability prediction of the distribution transformer, namely 95.5% of equipment faults can be covered.
Example 2
Referring to fig. 2, a second embodiment of the present invention is different from the first embodiment in that an experimental test of an optimization method for predicting a failure probability of a power grid device based on big data is provided, which specifically includes:
the traditional technical scheme has the problems of incomplete data and single model, namely, the fault probability of the material is predicted only by adopting historical defect data and a single fault probability prediction model, high-precision data is difficult to obtain if the data is too little, and the single model is difficult to accurately predict the fault probability of the material in multiple counties and counties; in order to verify that the method of the present invention has higher precision compared with the conventional method, the present embodiment uses the conventional method to perform real-time measurement comparison.
And (3) testing environment: according to the method, historical state data, meteorological data and online monitoring data are adopted, the fault probability of various devices in each district and county is predicted, and the fault probability is compared with the fault probability prediction of a single data source and a single model.
Referring to fig. 2, it can be seen that the method improves the precision of predicting the defective materials through multi-model fusion and fusion of multiple data sources.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A big data-based optimization method for power grid equipment fault probability prediction is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting historical defect data, online monitoring data and meteorological data of the power grid equipment;
training a plurality of fault probability prediction models based on machine learning aiming at each type of materials;
and predicting the fault probability of the material by using the fault probability prediction model and combining the collected data.
2. The optimization method for big data based power grid equipment fault probability prediction according to claim 1, wherein the method comprises the following steps: the failure probability prediction model comprises a negative feedback neural network model, a gradient lifting tree, an extreme gradient lifting tree model, a random forest model, a naive Bayes model and a logistic regression model.
3. The optimization method for big data based grid equipment fault probability prediction according to claim 1 or 2, wherein the method comprises the following steps: training the fault probability prediction model to obtain a predicted value y1The method comprises the following steps of (1),
wherein the content of the first and second substances,
a predicted value of defect material, f, for the gradient lifting tree and the extreme gradient lifting modelkAnd converting the regression problem into fitting to probability values for the kth classification regression tree, wherein K is the number of classification regression trees, gamma is the space of the classification regression trees, and the target optimization index is loglos.
4. The optimization method for big data based power grid equipment fault probability prediction according to claim 3, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein the content of the first and second substances,
defect material prediction for negative feedback neural network model, w1Is a parameter of the first layer, σ is an activation function, w2Is a weight parameter of the second layer.
5. The optimization method for big data based power grid equipment fault probability prediction according to claim 4, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
6. the optimization method for big data based power grid equipment fault probability prediction according to claim 5, wherein the method comprises the following steps: prediction value
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
where θ is the model parameter obtained by training, T is the transposition operation, and x is the parameter.
7. The optimization method for big data based power grid equipment fault probability prediction according to claim 6, wherein the method comprises the following steps: the defect material comprises a hardware fitting body, a stay wire body, a concrete pole and a porcelain insulator.
8. The optimization method for big data based power grid equipment fault probability prediction according to claim 7, wherein the method comprises the following steps: the device also comprises a CPU plug-in, an overhead conductor, a switching contactor, a charging module, a composite insulator and a distribution transformer.
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| CN116780583A (en) * | 2023-06-06 | 2023-09-19 | 广东中特建设集团有限公司 | Intelligent energy storage method, system, equipment and medium for photovoltaic power generation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111639815A (en) * | 2020-06-02 | 2020-09-08 | 贵州电网有限责任公司 | Method and system for predicting power grid defect materials through multi-model fusion |
| CN112308278A (en) * | 2019-08-02 | 2021-02-02 | 中移信息技术有限公司 | Method, device, equipment and medium for optimizing prediction model |
| CN112365077A (en) * | 2020-11-20 | 2021-02-12 | 贵州电网有限责任公司 | Construction method of intelligent storage scheduling system for power grid defective materials |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112308278A (en) * | 2019-08-02 | 2021-02-02 | 中移信息技术有限公司 | Method, device, equipment and medium for optimizing prediction model |
| CN111639815A (en) * | 2020-06-02 | 2020-09-08 | 贵州电网有限责任公司 | Method and system for predicting power grid defect materials through multi-model fusion |
| CN112365077A (en) * | 2020-11-20 | 2021-02-12 | 贵州电网有限责任公司 | Construction method of intelligent storage scheduling system for power grid defective materials |
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| CN116780583A (en) * | 2023-06-06 | 2023-09-19 | 广东中特建设集团有限公司 | Intelligent energy storage method, system, equipment and medium for photovoltaic power generation |
| CN116780583B (en) * | 2023-06-06 | 2024-04-16 | 广东中特建设集团有限公司 | Intelligent energy storage method, system, equipment and medium for photovoltaic power generation |
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