CN112464995A - Power grid distribution transformer fault diagnosis method and system based on decision tree algorithm - Google Patents
Power grid distribution transformer fault diagnosis method and system based on decision tree algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of computers, and particularly discloses a power grid distribution transformer fault diagnosis method based on a decision tree algorithm. The method comprises the following steps: acquiring historical fault alarm data, and determining a standard fault sample according to the historical fault alarm data; performing attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target testing set; carrying out data mining on the target training set by using a decision tree algorithm to form an initial decision tree for diagnosing the distribution transformer fault of the power grid; pruning the initial decision tree through a target test set to obtain a target decision tree; generating a fault diagnosis model according to the classification rule determined by the target decision tree; and diagnosing the power grid distribution transformer fault based on the fault diagnosis model. By the method, the efficiency and the accuracy of fault diagnosis can be improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a power grid distribution transformer fault diagnosis method and system based on a decision tree algorithm.
Background
Electric power is an important energy source for national economic construction and is a material foundation for social development. With the development of social economy and the progress of the living standard of people, modern power systems are gradually enlarged and complicated, and the demand of people for continuous and stable electric energy is larger and larger. With an increase in the demand for electricity, a failure of the power system is inevitable. In general, if a power system fails due to the fact that voltage or current data of a distribution transformer is out of limit, a distribution network dispatcher needs to check alarm position processing data from a database, call account data and original messages, and check distribution transformer matching problems one by one. The manual troubleshooting process is very complicated, needs a large amount of manpower and is easy to make mistakes, so that the fault diagnosis efficiency and the accuracy are low.
Therefore, in order to quickly and accurately diagnose the power distribution network fault, ensure the safe and stable operation of the power system and enhance the reliability and continuity of power supply, a high-quality power distribution network fault diagnosis and identification system is obviously very important.
Disclosure of Invention
The invention provides a power distribution transformer fault diagnosis method and system based on a decision tree algorithm, and aims to solve the technical problems of low power distribution network fault diagnosis efficiency and low accuracy in the related technology.
The first aspect of the embodiment of the invention discloses a power grid distribution transformer fault diagnosis method based on a decision tree algorithm, which comprises the following steps:
acquiring historical fault alarm data, and determining a standard fault sample according to the historical fault alarm data;
performing attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target testing set;
carrying out data mining on the target training set by using a decision tree algorithm to form an initial decision tree for diagnosing the power grid distribution transformer fault;
pruning the initial decision tree through the target test set to obtain a target decision tree;
generating a fault diagnosis model according to the classification rule determined by the target decision tree;
and diagnosing the power grid distribution transformer fault based on the fault diagnosis model.
As an alternative implementation, in the first aspect of the embodiment of the present invention, the determining a standard fault sample according to the historical fault alarm data includes:
and performing quality screening on the historical fault alarm data according to a preset quality evaluation rule, screening out missing, repeated and format error data, and generating a standard fault sample.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the diagnosing a power distribution transformation fault based on the fault diagnosis model includes:
when a fault alarm is received, acquiring real-time alarm data from alarm information, inputting the real-time alarm data into the fault diagnosis model, and judging whether a fault occurs;
and if the fault occurs, determining that the alarm information is correct, and selecting a matched fault processing scheme according to the type of the alarm information.
The second aspect of the embodiment of the invention discloses a power grid distribution transformer fault diagnosis system based on a decision tree algorithm, which comprises:
the data acquisition module is used for acquiring historical fault alarm data;
the sample determining module is used for determining a standard fault sample according to the historical fault alarm data;
the attribute selection module is used for carrying out attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target test set;
the data mining module is used for carrying out data mining on the target training set by utilizing a decision tree algorithm to form an initial decision tree for power grid distribution transformer fault diagnosis;
a pruning processing module, configured to perform pruning processing on the initial decision tree through the target test set to obtain a target decision tree;
the model generation module is used for generating a fault diagnosis model according to the classification rule determined by the target decision tree;
and the fault diagnosis module is used for diagnosing the power distribution transformer fault based on the fault diagnosis model.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the sample determination module is specifically configured to perform quality screening on the historical failure alarm data according to a preset quality evaluation rule, screen out missing, duplicate, and format-wrong data, and generate a standard failure sample.
As an optional implementation manner, in a second aspect of the embodiment of the present invention, the fault diagnosis module includes:
the acquisition submodule is used for acquiring real-time alarm data from the alarm information when the fault alarm is received;
the fault judgment submodule is used for inputting the real-time alarm data into the fault diagnosis model and judging whether a fault occurs;
and the scheme selection submodule is used for determining that the alarm information is correct when the fault judgment submodule judges that a fault occurs, and selecting a matched fault processing scheme according to the type of the alarm information.
A third aspect of the embodiments of the present invention discloses a computer-readable storage medium storing a computer program, where the computer program enables a computer to execute the method for diagnosing a distribution transformer fault of a power grid based on a decision tree algorithm disclosed in the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention discloses an electronic device, including:
a processor;
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method as previously described.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
the power grid distribution transformer fault diagnosis method based on the decision tree algorithm comprises the following steps,
acquiring historical fault alarm data, and determining a standard fault sample according to the historical fault alarm data; performing attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target testing set; carrying out data mining on the target training set by using a decision tree algorithm to form an initial decision tree for diagnosing the distribution transformer fault of the power grid; pruning the initial decision tree through a target test set to obtain a target decision tree; generating a fault diagnosis model according to the classification rule determined by the target decision tree; and diagnosing the power grid distribution transformer fault based on the fault diagnosis model.
Under the method, the power distribution network fault diagnosis method suitable for various adverse conditions can be applied to the algorithm of the power distribution transformation mismatch problem based on the classification decision tree, so that the dispatching personnel can be assisted to quickly identify the fault, the fault diagnosis efficiency and accuracy are improved, and the safe and stable operation of the power distribution network is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a method for power grid distribution fault diagnosis based on a decision tree algorithm in accordance with an exemplary embodiment;
FIG. 2 is a flow diagram illustrating another method for power grid distribution fault diagnosis based on a decision tree algorithm in accordance with an exemplary embodiment;
FIG. 3 is a flow diagram illustrating a discrimination rule for a fault alarm in accordance with an exemplary embodiment;
fig. 4 is a block diagram illustrating a power distribution transformation fault diagnosis system based on a decision tree algorithm according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a flowchart illustrating a method for diagnosing a distribution transformer fault based on a decision tree algorithm according to an exemplary embodiment. As shown in fig. 1, the method includes the following steps.
As an alternative embodiment, the determining the standard fault sample according to the historical fault alarm data may include:
and (4) performing quality screening on the historical fault alarm data according to a preset quality evaluation rule, screening out missing, repeated and format error data, and generating a standard fault sample.
In embodiments of the present invention, if the alarm event is a voltage violation, for example, then accordingly, the fault alarm data may include correlation matches, transformation ratios, voltage, current information, and the like; in addition, the quality assessment rules described above may be used to screen out data that is missing, duplicate, and misformatted in the historical fault alarm data.
And 102, the fault diagnosis system selects attributes of the standard fault sample according to the characteristic attributes to obtain a target training set and a target testing set.
In the embodiment of the invention, data discretization can be carried out on the standard fault sample, the attribute type for judging the mismatch fault of the distribution transformer is set, and the discretized standard fault sample is classified according to the set attribute type to obtain the target training set and the target test set.
And 103, the fault diagnosis system performs data mining on the target training set by using a decision tree algorithm to form an initial decision tree for power grid distribution transformer fault diagnosis.
In the embodiment of the invention, the classification tree is a common classification method, belongs to a learning algorithm of a supervised machine, and selects samples from a stack of samples to be tested, each sample has a group of corresponding attributes and categories, and the attributes and the categories are determined in advance, so that a classifier is obtained through supervised learning, and the classifier can give correct classification to new samples. The classification decision tree model represents a tree structure for sample classification of instances, and a decision tree generally consists of two parts, namely directed edges and nodes, wherein each node in a sample consists of an internal node and a leaf node. Internal nodes typically represent features or attributes, and leaf nodes represent individual individuals, belonging to a category that cannot be subdivided. When the nodes of the sample are classified and divided, testing is carried out on a certain feature vector of the sample from a root node, the sample is distributed to the sub-nodes according to the test result of the sample, and each sample sub-node corresponds to the value of the feature. Setting corresponding threshold values, and moving downwards continuously and recursively until the leaf nodes are reached, and then allocating the sample instances to the types of the leaf nodes.
And step 104, the fault diagnosis system prunes the initial decision tree through the target test set to obtain a target decision tree.
In the embodiment of the invention, the classification decision tree pruning is proposed aiming at the problem of premature fitting of the decision tree. The classification decision tree pruning is generally divided into first pruning and second pruning. The construction method of the tree stopping in advance belongs to pruning in advance, if a certain key node is determined not to be split any more, the tree node is pruned, and if pruning stops, the node becomes a leaf node in a sample. And selecting a class containing the most classes in the sample subset as a class of the sample node for the leaf node. Post pruning is to prune the fully grown tree, by removing the branches of the nodes, replace with leaf nodes, which are marked with the category with the highest frequency of occurrence among the categories in the subset. As the CART algorithm is adopted, the algorithm selects a post-pruning method.
And 105, generating a fault diagnosis model by the fault diagnosis system according to the classification rule determined by the target decision tree.
In the embodiment of the invention, the classification rule represents the knowledge in the if-else form, and the rule forms an assumption by a leader (antecedent) and a successor (sequence). Referring to fig. 2, fig. 2 is a flowchart illustrating a rule for determining a fault alarm according to an embodiment of the present invention. Wherein, the cross-line proportion can be divided by three dimensions of large, medium and small, the duration is divided by two dimensions of long and short, wherein the words such as "large", "medium", "small", "long" and "short" in the rule are replaced by definite threshold values.
And 106, diagnosing the power distribution transformer fault by the fault diagnosis system based on the fault diagnosis model.
In the embodiment of the invention, for example, when the same user has two measuring points of a high voltage supply meter and a high voltage supply meter, voltage data is often switched between a high voltage side and a low voltage side, so that the voltage is out of limit. Due to the fact that equipment on the user side is updated, transformation ratio data in a metering system or a marketing system are not timely synchronized or not regularly filled, or due to the fact that the transformation ratio data cannot be synchronously updated, calculation of equivalent measured data of PQIU (power, electric quantity, current and voltage) is directly influenced. The method analyzes specific reasons by using the trained fault diagnosis model, establishes data rules, judges based on the decision tree principle, trains decision tree parameters, loads subsequent parameters, analyzes the voltage and current alarm reasons, and establishes data set rules, thereby judging whether the alarm is a false alarm or not and improving the accuracy of judging the false alarm.
Therefore, by implementing the power distribution transformation fault diagnosis method based on the decision tree algorithm described in fig. 1, the power distribution network fault diagnosis method which is suitable for various adverse conditions can be applied to the algorithm of the power distribution transformation mismatch problem based on the classification decision tree, so that the dispatch personnel can be assisted to quickly identify the fault, the fault diagnosis efficiency and accuracy are improved, and the safe and stable operation of the power distribution network is ensured.
Referring to fig. 3, fig. 3 is a schematic flow chart of another power distribution transformation fault diagnosis method based on a decision tree algorithm according to an embodiment of the present invention. As shown in fig. 3, the power distribution transformation fault diagnosis method based on the decision tree algorithm may include the following steps:
in the embodiment of the invention, the power distribution transformer fault diagnosis method based on the decision tree algorithm comprises steps 201 to 205, and for the description of the steps 201 to 205, please refer to the detailed description of the first embodiment for the steps 101 to 105, which is not described again in the embodiment of the invention.
In the embodiment of the invention, after the fault alarm is received and before the real-time alarm data is acquired from the alarm information, the integrity of the alarm information, including the time, the place, the equipment number, the alarm type and the severity of the alarm data, can be checked, and whether the alarm time is lost or not needs to be checked. The alarm place needs to be matched with the alarm equipment line, the alarm equipment number needs to be matched with the ledger data information, and the like. And waiting for the extraction and confirmation of the alarm type, and checking whether obvious false alarm information exists. And if the verification result of the alarm information shows that the alarm information is complete, acquiring real-time alarm data from the alarm information.
In the embodiment of the invention, the alarm information can be divided into three types: voltage out-of-limit, protection device action and circuit breaker trip; when the alarm information is determined to be correct, namely when the power distribution network is determined to have a fault, the fault processing scheme which is stored in advance and matched with the alarm information is selected from the database according to the type of the alarm information, and the fault processing efficiency can be further improved.
Therefore, by implementing the power distribution transformation fault diagnosis method based on the decision tree algorithm described in fig. 3, the power distribution network fault diagnosis method which is suitable for various adverse conditions can be applied to the algorithm of the power distribution transformation mismatch problem based on the classification decision tree, so that the dispatch personnel can be assisted to quickly identify the fault, the fault diagnosis efficiency and accuracy are improved, and the safe and stable operation of the power distribution network is ensured.
Fig. 4 is a block diagram illustrating a power distribution transformation fault diagnosis system based on a decision tree algorithm according to an exemplary embodiment. As shown in fig. 4, the system includes:
a data acquisition module 301 for acquiring historical fault alarm data and providing it to a sample determination module 302.
A sample determination module 302 for determining a standard fault sample from the historical fault alarm data and providing the sample to an attribute selection module 303.
The attribute selection module 303 is configured to perform attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target test set, and provide the target training set and the target test set to the data mining module 304 and the pruning processing module 305, respectively.
And the data mining module 304 is configured to perform data mining on the target training set by using a decision tree algorithm to form an initial decision tree for power distribution transformation fault diagnosis, and trigger the pruning processing module 305 to start.
A pruning processing module 305, configured to perform pruning processing on the initial decision tree through the target test set to obtain a target decision tree, and trigger the model generating module 306.
The model generating module 306 is configured to generate a fault diagnosis model according to the classification rule determined by the target decision tree, and provide the fault diagnosis model to the fault diagnosis module 307.
And a fault diagnosis module 307, configured to diagnose the power distribution transformer fault based on the fault diagnosis model.
As an optional implementation manner, the sample determining module 302 may be specifically configured to perform quality screening on historical failure alarm data according to a preset quality evaluation rule, screen out missing, duplicate, and format-wrong data, and generate a standard failure sample.
As another alternative implementation, the fault diagnosis module 307 may include:
the acquisition submodule is used for acquiring real-time alarm data from the alarm information when the fault alarm is received;
the fault judgment submodule is used for inputting the real-time alarm data into the fault diagnosis model and judging whether a fault occurs;
and the scheme selection submodule is used for determining that the alarm information is correct when the fault judgment submodule judges that the fault occurs, and selecting a matched fault processing scheme according to the type of the alarm information.
Therefore, by implementing the system described in fig. 4, the power distribution network fault diagnosis method suitable for various adverse conditions can be applied to the algorithm of the power distribution transformation mismatch problem based on the classification decision tree, so that the dispatch personnel can be assisted to quickly identify the fault, the fault diagnosis efficiency and accuracy are improved, and the safe and stable operation of the power distribution network is ensured.
The present invention also provides an electronic device, including:
a processor;
the system comprises a memory, wherein computer readable instructions are stored on the memory, and when the computer readable instructions are executed by a processor, the power grid distribution transformer fault diagnosis method based on the decision tree algorithm is realized.
In an exemplary embodiment, the present invention further provides a computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for diagnosing the grid distribution transformer fault based on the decision tree algorithm as described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (6)
1. A power grid distribution transformation fault diagnosis method based on a decision tree algorithm is characterized by comprising the following steps:
acquiring historical fault alarm data, and determining a standard fault sample according to the historical fault alarm data;
performing attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target testing set;
carrying out data mining on the target training set by using a decision tree algorithm to form an initial decision tree for diagnosing the power grid distribution transformer fault;
pruning the initial decision tree through the target test set to obtain a target decision tree;
generating a fault diagnosis model according to the classification rule determined by the target decision tree;
and diagnosing the power grid distribution transformer fault based on the fault diagnosis model.
2. The method of claim 1, wherein said determining standard fault samples from said historical fault alarm data comprises:
and performing quality screening on the historical fault alarm data according to a preset quality evaluation rule, screening out missing, repeated and format error data, and generating a standard fault sample.
3. The method according to claim 1 or 2, wherein the diagnosing the grid distribution transformer fault based on the fault diagnosis model comprises:
when a fault alarm is received, acquiring real-time alarm data from alarm information, inputting the real-time alarm data into the fault diagnosis model, and judging whether a fault occurs;
and if the fault occurs, determining that the alarm information is correct, and selecting a matched fault processing scheme according to the type of the alarm information.
4. A power grid distribution transformer fault diagnosis system based on a decision tree algorithm is characterized by comprising:
the data acquisition module is used for acquiring historical fault alarm data;
the sample determining module is used for determining a standard fault sample according to the historical fault alarm data;
the attribute selection module is used for carrying out attribute selection on the standard fault sample according to the characteristic attributes to obtain a target training set and a target test set;
the data mining module is used for carrying out data mining on the target training set by utilizing a decision tree algorithm to form an initial decision tree for power grid distribution transformer fault diagnosis;
a pruning processing module, configured to perform pruning processing on the initial decision tree through the target test set to obtain a target decision tree;
the model generation module is used for generating a fault diagnosis model according to the classification rule determined by the target decision tree;
and the fault diagnosis module is used for diagnosing the power distribution transformer fault based on the fault diagnosis model.
5. The system of claim 4, wherein the sample determination module is specifically configured to perform quality screening on the historical failure alarm data according to a preset quality evaluation rule, screen out missing, duplicate, and mis-formatted data, and generate a standard failure sample.
6. The system of claim 4 or 5, wherein the fault diagnosis module comprises:
the acquisition submodule is used for acquiring real-time alarm data from the alarm information when the fault alarm is received;
the fault judgment submodule is used for inputting the real-time alarm data into the fault diagnosis model and judging whether a fault occurs;
and the scheme selection submodule is used for determining that the alarm information is correct when the fault judgment submodule judges that a fault occurs, and selecting a matched fault processing scheme according to the type of the alarm information.
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