CN113325266A - Power grid fault diagnosis method and system based on fuzzy integral multi-source information fusion - Google Patents
Power grid fault diagnosis method and system based on fuzzy integral multi-source information fusion Download PDFInfo
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Abstract
A power grid fault diagnosis method and system based on fuzzy integral multi-source information fusion comprises the following steps: the system comprises a power grid data acquisition module, a data preprocessing module, a fault feature extraction module, a central control module, a diagnosis model construction module and a fault diagnosis module; the method comprises the steps of preprocessing data information of different monitoring points on the current power grid distribution line by adopting an association analysis algorithm to obtain a suspicious element set, extracting a fault characteristic parameter set from the suspicious element set to construct a power grid multi-target fault diagnosis model, and diagnosing power grid faults based on a fuzzy integral algorithm. The fault processing method based on wavelet transformation can effectively reduce information redundancy, has stronger expansibility and application range and high fault diagnosis accuracy; by adopting the fuzzy integral theory, multi-source information can be effectively integrated to perform fault diagnosis in an information fusion way, the robustness is stronger, the fault coverage rate is extremely high, and the accuracy of power distribution fault detection can be effectively improved.
Description
Technical Field
The invention relates to the technical field of power grid fault diagnosis, in particular to a power grid fault diagnosis method and system based on fuzzy integral multi-source information fusion.
Background
At present, a power grid is a support column of national economy, the development of economy cannot be separated from the development of the power grid, and once a large-area power failure accident occurs, immeasurable loss can be generated. In recent years, in the rapid development stage of power grid construction, the research of the fault diagnosis method of the power grid becomes the key point of the research of scholars at home and abroad, and is also a problem concerned by power equipment manufacturers.
In the prior art, due to the unpredictability of the occurrence of the power grid fault, the principle of a protection device is extremely complex, and more importantly, with the rapid development of a power system, the integrated characteristic of a large power grid becomes obvious day by day, different elements in the system and the coupling relation between the system and the external environment are continuously enhanced, so that the power grid information is diversified and has high coupling degree. Therefore, a great deal of work is needed for further improvement in power grid fault diagnosis, faults are accurately diagnosed in real time, fault equipment is timely repaired, and power supply is recovered, so that the method has very important significance in reducing economic loss and improving power supply reliability.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a power grid fault diagnosis method and system based on fuzzy integral multi-source information fusion, which are used for fusing and integrating power grid multi-source information, realizing automatic diagnosis of power grid faults, avoiding false alarm and missed alarm and effectively improving the fault detection accuracy.
The invention adopts the following technical scheme.
Power grid fault diagnosis system based on fuzzy integral multi-source information fusion includes: the system comprises a power grid data acquisition module, a data preprocessing module, a fault feature extraction module, a central control module, a diagnosis model construction module, a fault diagnosis module, a database construction module, a result feedback module, a data storage module and an update display module;
the power grid data acquisition module is used for acquiring data information of different monitoring points on the current power grid distribution line;
the data preprocessing module is used for preprocessing the acquired data information of different monitoring points on the current power grid distribution line by adopting an association analysis algorithm to determine a suspicious element set of a fault object;
the fault feature extraction module is used for extracting a fault feature parameter set from the suspicious element set and calculating the fault probability of each element in the suspicious element set;
the diagnosis model building module is used for building a power grid multi-target fault diagnosis model according to the fault characteristic parameter set;
the fault diagnosis module is used for diagnosing the power grid fault by utilizing a power grid multi-target fault diagnosis model based on a fuzzy integral algorithm;
the database construction module is used for storing historical operation data of different monitoring points on the power distribution line of the power grid through power grid fault diagnosis;
the result feedback module is used for feeding back the fault detection result;
the data storage module is used for storing the acquired data information, data preprocessing results, fault characteristic parameter sets, fault probabilities, databases, power grid fault areas, power grid multi-target fault diagnosis models, fault diagnosis results and result feedback information of different monitoring points on the current power distribution line of the power grid;
the updating and displaying module is used for updating and displaying the acquired data information, data preprocessing results, fault characteristic parameter sets, fault probabilities, databases, power grid fault areas, power grid multi-target fault diagnosis models, fault diagnosis results and real-time data of result feedback information of different monitoring points on the current power distribution line of the power grid;
and the central control module is used for coordinating and controlling the normal operation of the power grid data acquisition module, the data preprocessing module, the fault characteristic extraction module, the central control module, the diagnosis model construction module, the fault diagnosis module, the database construction module, the result feedback module, the data storage module and the updating display module through the central processing unit.
The power grid fault diagnosis method based on fuzzy integral multi-source information fusion comprises the following steps:
step 1, collecting data information of different monitoring points on a current power grid distribution line;
step 2, preprocessing the acquired data information by adopting a correlation analysis algorithm to obtain a suspicious element set of the fault object;
step 3, extracting a fault characteristic parameter set from the suspicious element set, and calculating the fault probability of each element in the suspicious element set;
step 4, constructing an optimization model for power grid multi-target fault diagnosis according to the fault characteristic parameter set, and carrying out linearization processing on the optimization model for power grid multi-target fault diagnosis to obtain a final power grid multi-target fault diagnosis model;
and 5, diagnosing the power grid fault by utilizing the power grid multi-target fault diagnosis model based on the fuzzy integral algorithm.
Preferably, in step 1, the data information includes fault alarm information of the current power grid distribution line, topology structure information of the power distribution network, and historical operation data of different monitoring points on the current power grid distribution line;
and the fault alarm information of the current power grid distribution line comprises protection action information and breaker information.
Preferably, step 2 comprises:
step 2.1, establishing an association rule set of the data information; the association rule set comprises fault occurrence time, a fault occurrence place, fault equipment and fault alarm information;
and 2.2, according to the association rule set, screening frequent item sets and mining association rules based on an association analysis algorithm, calculating the association confidence of each device with faults, and selecting the device with the threshold value larger than the set association confidence as an element in the suspicious element set.
Preferably, step 3 comprises:
step 3.1, performing wavelet analysis on q elements in the suspicious element set, and forming a p × q order matrix A by using wavelet analysis results of p scales;
step 3.2, calculating the wavelet singularity y for the ith element in the suspicious element set according to the following relational expressioni：
In the formula,
λi＝diag(λ1,λ2,…,λt) The singular value feature matrix of the matrix a is assigned to the ith element in the suspect set, where t is min (p, q), i is 1,2, …,n，
the set of wavelet singularities of each element is the extracted fault feature parameter set;
step 3.3, at fault time t0For the ith element in the suspicious element set, the wavelet fault degree m is calculated according to the following relationWFD：
In the formula,
αFin order to be the degree of confidence,
Fimaxthe maximum amplitude of the change in the electrical signal of the i-th element,
F′ithe degree of amplitude change of the electric signal of the i-th element before and after the failure.
Preferably, in step 4, the objective function in the optimization model for multi-objective fault diagnosis of the power grid satisfies the following relational expression;
minE(X)＝w1E1(X)+w2E2(X)
in the formula,
MinE (X) is an objective function with the minimum error index as the optimum,
E1(X) is an action logic error index which reflects the deviation between the actual state and the expected state of the protection device,
E2(X) is an information communication error index which reflects the deviation between the actual state and the alarm state of the protection device,
w1in order to be the action logic weight coefficient,
w2in order to be the information communication weight coefficient,
x is a fault hypothesis and is formed by the states of all power failure elements;
wherein,
in the formula,
Cjis the actual state of the jth circuit breaker,
for the desired state of the jth circuit breaker,
for the alarm state of the jth circuit breaker,
rkmfor the actual state of the kth station master protection,
for the desired state of the kth station master protection,
rkpfor the actual state of the kth station near backup protection,
for the desired state of the kth station near backup protection,
rksfor the actual state of the kth far backup protection,
for the desired state of the kth far backup protection,
rkfor the actual state of the protection of the kth station,
for the alarm state of the kth station protection,
In step 4, the optimization model for multi-target fault diagnosis of the power grid is subjected to linearization treatment, and the method is an approximate expression for determining the power flow value of each line relative to the output value of each unit and comprises the following steps:
step 4.1, determining that the tidal current values of all lines are in a linear relation with the output value of each unit through tidal current simulation;
and 4.2, performing linear fitting, and obtaining an approximate expression by using a least square fitting method.
Preferably, step 5 comprises:
step 5.1, acquiring fault characteristic parameters and fault probability of each element in the suspicious element set, namely the wavelet singularity y degree y of the ith elementiSum wavelet fault degree mWFD；
Step 5.2, based on fuzzy integral algorithm, the wavelet singularity y of the ith elementiSum wavelet fault degree mWFDPerforming fusion and obtaining a fusion data set of all elements;
and 5.3, analyzing the fusion data set by using the power grid multi-target fault diagnosis model based on the fuzzy C-means algorithm to obtain a power grid fault diagnosis result.
Step 5.2 comprises the following steps:
step 5.2.1, using fault recording information, relay protection information and transformer substation monitoring information as multi-source information;
step 5.2.2, according to a lambda fuzzy measure algorithm, based on the wavelet singularity y degree of the ith elementiSum wavelet fault degree mWFDObtaining power grid fault fuzzy measure, and calculating influence coefficients of multi-source information on the power grid fault fuzzy measure;
step 5.2.3, constructing a fuzzy measure function by utilizing the value of the multi-source information to influence the coefficient based on a fuzzy integral algorithm, and solving the fuzzy measure function to obtain a decision fuzzy value after the multi-source information is fused, namely the fault information density;
and 5.2.4, storing the multi-source information of which each branch decision fuzzy value of the fault information is greater than or equal to the decision threshold value into a fusion data set.
Preferably, the power grid fault diagnosis method based on fuzzy integral multi-source information fusion further includes:
step 6.1, storing the obtained data information, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the result feedback information;
step 6.2, updating and displaying real-time data of the data information, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the result feedback information;
and 6.3, constructing power grid fault diagnosis by using a database construction program, and storing historical operation data of different monitoring points on the power distribution line of the power grid.
Compared with the prior art, the method for processing the fault information based on the wavelet transform can effectively reduce the redundancy of the information, has stronger expansibility and application range and high fault diagnosis accuracy; by adopting the fuzzy integral theory, multi-source information can be effectively integrated to perform fault diagnosis in an information fusion way, the robustness is stronger, the fault coverage rate is extremely high, and the accuracy of power distribution fault detection can be effectively improved.
Drawings
Fig. 1 is a structural block diagram of a power grid fault diagnosis system based on fuzzy integral multi-source information fusion according to an embodiment of the present invention;
fig. 2 is a flowchart of a power grid fault diagnosis method based on fuzzy integral multi-source information fusion according to an embodiment of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the power grid fault diagnosis system based on fuzzy integral multi-source information fusion includes: the system comprises a power grid data acquisition module, a data preprocessing module, a fault feature extraction module, a database construction module, a central control module, a diagnosis model construction module, a fault diagnosis module, a result feedback module, a data storage module and an update display module.
And the power grid data acquisition module is connected with the data preprocessing module and used for acquiring data information of different monitoring points on the current power grid distribution line through the data acquisition equipment.
And the data preprocessing module is connected with the fault feature extraction module and used for preprocessing the acquired data information of different monitoring points on the current power grid distribution line by adopting an Apriori algorithm through a data preprocessing program and determining a suspicious element set of the fault equipment.
And the fault feature extraction module is connected with the database construction module and used for extracting the fault feature parameter set of the suspicious element set through a feature extraction program and calculating the fault probability of each element in the suspicious element set.
And the database construction module is connected with the central control module and used for diagnosing the power grid faults constructed by the database construction program and storing historical operation data of different monitoring points on the power distribution line of the power grid.
And the central control module is connected with the diagnosis model building module and is used for coordinating and controlling the normal operation of the power grid data acquisition module, the data preprocessing module, the fault feature extraction module, the database building module, the central control module, the diagnosis model building module, the fault diagnosis module, the result feedback module, the data storage module and the update display module through the central processing unit.
And the diagnosis model building module is connected with the fault diagnosis module and used for building a power grid multi-target fault diagnosis model according to the fault characteristic parameter set through a model building program.
And the fault diagnosis module is connected with the result feedback module and used for diagnosing the power grid fault by utilizing the power grid multi-target fault diagnosis model according to the fuzzy integral algorithm-based theory through a fault diagnosis program.
And the result feedback module is connected with the data storage module and used for feeding back the fault detection result through a result feedback program.
And the data storage module is connected with the updating display module and used for storing the acquired data information of different monitoring points on the current power distribution line of the power grid, a data preprocessing result, a fault characteristic parameter set, the fault probability, a database, a power grid fault area, a power grid multi-target fault diagnosis model, a fault diagnosis result and result feedback information through a memory.
And the updating display module is used for updating and displaying the acquired data information of different monitoring points on the current power distribution line of the power grid, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the real-time data of the result feedback information through the display.
As shown in fig. 2, the power grid fault diagnosis method based on fuzzy integral multi-source information fusion includes:
step 1, acquiring data information of different monitoring points on the current power distribution line of the power grid by using data acquisition equipment through a power grid data acquisition module.
Specifically, in step 1, the data information includes fault alarm information of the current power grid distribution line, topology structure information of the power distribution network, and historical operation data of different monitoring points on the current power grid distribution line; the fault warning information of the current power grid distribution line comprises protection action information and breaker information.
And 2, preprocessing the acquired data information of different monitoring points on the current power grid distribution line by using a data preprocessing program and an Apriori algorithm through a data preprocessing module, and determining a suspicious element set of the fault equipment.
Specifically, step 2 comprises:
step 2.1, establishing an association rule set of the data information; the association rule set comprises fault occurrence time, fault occurrence place, fault equipment and fault alarm information;
and 2.2, according to the association rule set, screening frequent item sets and mining association rules based on an association analysis algorithm, calculating the association confidence of each device with faults, and selecting the device with the threshold value larger than the set association confidence as an element in the suspicious element set.
And 3, extracting a fault feature parameter set of the suspicious element set by using a feature extraction program through a fault feature extraction module, and calculating the fault probability of each element in the suspicious element set.
Specifically, step 3 includes:
step 3.1, performing wavelet analysis on q elements in the suspicious element set, and forming a p × q order matrix A by using wavelet analysis results of p scales;
step 3.2, calculating the wavelet singularity y for the ith element in the suspicious element set according to the following relational expressioni：
In the formula,
λi＝diag(λ1,λ2，…，λt) The singular value feature matrix of the matrix a is assigned to the ith element in the suspect set, where t is min (p, q), i is 1,2, …, n,
the set of wavelet singularities of each element is the extracted fault feature parameter set;
step 3.3, at fault time t0For the ith element in the suspicious element set, the wavelet fault degree m is calculated according to the following relationWFD：
In the formula,
αFin order to be the degree of confidence,
Fimaxthe maximum amplitude of the signal change for the ith element,
f' i is the change degree of the amplitude value of the signal of the ith element before and after the fault, and the following relation is satisfied:
in the formula, when t < t0Then F isifMax (d (t)); when t is more than or equal to t0Then F isibMax (d (t)); wherein D (t) is a detail coefficient,
the wavelet failure degree of each element is the failure probability of the element.
And 4, constructing a power grid multi-target fault diagnosis model according to the fault characteristic parameter set by using the model construction program through the diagnosis model construction module.
Specifically, in the step 4, the objective function in the optimization model for multi-objective fault diagnosis of the power grid meets the following relational expression;
minE(X)＝w1E1(X)+w2E2(X)
in the formula,
MinE (X) is an objective function with the minimum error index as the optimum,
E1(X) is an action logic error index which reflects the deviation between the actual state and the expected state of the protection device,
E2(X) is an information communication error index which reflects the deviation between the actual state and the alarm state of the protection device,
w1in order to be the action logic weight coefficient,
w2in order to be the information communication weight coefficient,
x is a fault hypothesis and is formed by the states of all power failure elements;
wherein,
in the formula,
Cjis the actual state of the jth circuit breaker,
for the desired state of the jth circuit breaker,
for the alarm state of the jth circuit breaker,
rkmfor the actual state of the kth station master protection,
for the desired state of the kth station master protection,
rkpfor the actual state of the kth station near backup protection,
for the desired state of the kth station near backup protection,
rksfor the actual state of the kth far backup protection,
for the desired state of the kth far backup protection,
rkfor the actual state of the protection of the kth station,
for the alarm state of the kth station protection,
In step 4, the optimization model of the power grid multi-target fault diagnosis is subjected to linearization treatment, and the method is an approximate expression for determining the power flow value of each line relative to the output value of each unit and comprises the following steps:
step 4.1, determining that the tidal current values of all lines are in a linear relation with the output value of each unit through tidal current simulation;
and 4.2, performing linear fitting, and obtaining an approximate expression by using a least square fitting method.
Through linear fitting, not only is the expression analyzed for the goodness of fit, but also the coefficients and constants in the expression are reasonably explained.
Step 5, diagnosing the power grid fault by using a fault diagnosis program and a power grid multi-target fault diagnosis model through a fault diagnosis module according to a fuzzy integral algorithm-based theory; and feeding back the fault detection result by using a result feedback program through a result feedback module.
Specifically, step 5 comprises:
step 5.1, acquiring fault characteristic parameters and fault probability of each element in the suspicious element set, namely the wavelet singularity y degree y of the ith elementiSum wavelet fault degree mWFD。
Step 5.2, based on fuzzy integral algorithm, the wavelet singularity y of the ith elementiSum wavelet fault degree mWFDFusion is performed and a fused data set of all elements is obtained.
Specifically, step 5.2 comprises:
step 5.2.1, using fault recording information, relay protection information and transformer substation monitoring information as multi-source information;
step 5.2.2, according to a lambda fuzzy measure algorithm, based on the wavelet singularity y degree of the ith elementiSum wavelet fault degree mWFDObtaining power grid fault fuzzy measure, and calculating influence coefficients of multi-source information on the power grid fault fuzzy measure;
step 5.2.3, constructing a fuzzy measure function by utilizing the value of the multi-source information to influence the coefficient based on a fuzzy integral algorithm, and solving the fuzzy measure function to obtain a decision fuzzy value after the multi-source information is fused, namely the fault information density;
and 5.2.4, storing the multi-source information of which each branch decision fuzzy value of the fault information is greater than or equal to the decision threshold value into a fusion data set.
And 5.3, analyzing the fusion data set by using the power grid multi-target fault diagnosis model based on the fuzzy C-means algorithm to obtain a power grid fault diagnosis result.
The power grid fault diagnosis method based on fuzzy integral multi-source information fusion further comprises the following steps:
and 6.1, storing the acquired data information of different monitoring points on the current power distribution line of the power grid, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the result feedback information by using the memory through the data storage module.
And 6.2, updating and displaying the acquired data information of different monitoring points on the current power distribution line of the power grid, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the real-time data of the result feedback information by using the display through the updating and displaying module.
And 6.3, constructing power grid fault diagnosis by using a database construction program through a database construction module, and storing historical operation data of different monitoring points on the power distribution line of the power grid.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Compared with the prior art, the method for processing the fault information based on the wavelet transform can effectively reduce the redundancy of the information, has stronger expansibility and application range and high fault diagnosis accuracy; by adopting the fuzzy integral theory, multi-source information can be effectively integrated to perform fault diagnosis in an information fusion way, the robustness is stronger, the fault coverage rate is extremely high, and the accuracy of power distribution fault detection can be effectively improved.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. A power grid fault diagnosis system based on fuzzy integral multi-source information fusion is characterized in that,
the system comprises: the system comprises a power grid data acquisition module, a data preprocessing module, a fault feature extraction module, a central control module, a diagnosis model construction module, a fault diagnosis module, a database construction module, a result feedback module, a data storage module and an update display module;
the power grid data acquisition module is used for acquiring data information of different monitoring points on the current power grid distribution line;
the data preprocessing module is used for preprocessing the acquired data information of different monitoring points on the current power grid distribution line by adopting an association analysis algorithm to determine a suspicious element set of a fault object;
the fault feature extraction module is used for extracting a fault feature parameter set from the suspicious element set and calculating the fault probability of each element in the suspicious element set;
the diagnosis model building module is used for building a power grid multi-target fault diagnosis model according to the fault characteristic parameter set;
the fault diagnosis module is used for diagnosing the power grid fault by utilizing a power grid multi-target fault diagnosis model based on a fuzzy integral algorithm;
the database construction module is used for storing historical operation data of different monitoring points on the power distribution line of the power grid through power grid fault diagnosis;
the result feedback module is used for feeding back the fault detection result;
the data storage module is used for storing the acquired data information, data preprocessing results, fault characteristic parameter sets, fault probabilities, databases, power grid fault areas, power grid multi-target fault diagnosis models, fault diagnosis results and result feedback information of different monitoring points on the current power distribution line of the power grid;
the updating and displaying module is used for updating and displaying the acquired data information, data preprocessing results, fault characteristic parameter sets, fault probabilities, databases, power grid fault areas, power grid multi-target fault diagnosis models, fault diagnosis results and real-time data of result feedback information of different monitoring points on the current power distribution line of the power grid;
and the central control module is used for coordinating and controlling the normal operation of the power grid data acquisition module, the data preprocessing module, the fault characteristic extraction module, the central control module, the diagnosis model construction module, the fault diagnosis module, the database construction module, the result feedback module, the data storage module and the updating display module through the central processing unit.
2. A power grid fault diagnosis method based on fuzzy integral multi-source information fusion is suitable for the power grid fault diagnosis system based on fuzzy integral multi-source information fusion in claim 1, and is characterized in that,
the method comprises the following steps:
step 1, collecting data information of different monitoring points on a current power grid distribution line;
step 2, preprocessing the acquired data information by adopting a correlation analysis algorithm to obtain a suspicious element set of the fault object;
step 3, extracting a fault characteristic parameter set from the suspicious element set, and calculating the fault probability of each element in the suspicious element set;
step 4, constructing an optimization model for power grid multi-target fault diagnosis according to the fault characteristic parameter set, and carrying out linearization processing on the optimization model for power grid multi-target fault diagnosis to obtain a final power grid multi-target fault diagnosis model;
and 5, diagnosing the power grid fault by utilizing the power grid multi-target fault diagnosis model based on the fuzzy integral algorithm.
3. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 2,
in the step 1, the data information comprises fault alarm information of the current power grid distribution line, topological structure information of the power distribution network and historical operation data of different monitoring points on the current power grid distribution line;
and the fault alarm information of the current power grid distribution line comprises protection action information and breaker information.
4. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 2,
the step 2 comprises the following steps:
step 2.1, establishing an association rule set of the data information; the association rule set comprises fault occurrence time, a fault occurrence place, fault equipment and fault alarm information;
and 2.2, according to the association rule set, screening frequent item sets and mining association rules based on an association analysis algorithm, calculating the association confidence of each device with faults, and selecting the device with the threshold value larger than the set association confidence as an element in the suspicious element set.
5. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 4,
the step 3 comprises the following steps:
step 3.1, performing wavelet analysis on q elements in the suspicious element set, and forming a p × q order matrix A by using wavelet analysis results of p scales;
step 3.2, calculating the wavelet singularity y for the ith element in the suspicious element set according to the following relational expressioni：
In the formula,
λi＝diag(λ1，λ2，...，λt) The singular value feature matrix of the matrix a is assigned to the ith element in the suspect set, where t min (p, q), i 1,2,.., n,
the set of wavelet singularities of each element is the extracted fault feature parameter set;
step 3.3, at fault time t0For the ith element in the suspicious element set, the wavelet fault degree m is calculated according to the following relationWFD：
In the formula,
αFin order to be the degree of confidence,
Fimaxthe maximum amplitude of the change in the electrical signal of the i-th element,
F′ithe degree of amplitude change of the electric signal of the i-th element before and after the failure.
6. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 2,
in step 4, the objective function in the optimization model for the multi-objective fault diagnosis of the power grid meets the following relational expression;
minE(X)＝w1E1(X)+w2E2(X)
in the formula,
MinE (X) is an objective function with the minimum error index as the optimum,
E1(X) is an action logic error index which reflects the deviation between the actual state and the expected state of the protection device,
E2(X) is an information communication error index which reflects the deviation between the actual state and the alarm state of the protection device,
w1in order to be the action logic weight coefficient,
w2in order to be the information communication weight coefficient,
x is a fault hypothesis and is formed by the states of all power failure elements;
wherein,
in the formula,
Cjis the actual state of the jth circuit breaker,
for the desired state of the jth circuit breaker,
for the alarm state of the jth circuit breaker,
rkmis as followsThe actual state of the k primary protections,
for the desired state of the kth station master protection,
rkpfor the actual state of the kth station near backup protection,
for the desired state of the kth station near backup protection,
rksfor the actual state of the kth far backup protection,
for the desired state of the kth far backup protection,
rkfor the actual state of the protection of the kth station,
for the alarm state of the kth station protection,
7. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 6,
in step 4, the optimization model of the power grid multi-target fault diagnosis is subjected to linearization treatment, and the method is an approximate expression for determining the power flow value of each line relative to the output value of each unit and comprises the following steps:
step 4.1, determining that the tidal current values of all lines are in a linear relation with the output value of each unit through tidal current simulation;
and 4.2, performing linear fitting, and obtaining an approximate expression by using a least square fitting method.
8. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 2,
the step 5 comprises the following steps:
step 5.1, acquiring fault characteristic parameters and fault probability of each element in the suspicious element set, namely the wavelet singularity y degree y of the ith elementiSum wavelet fault degree mWFD；
Step 5.2, based on fuzzy integral algorithm, the wavelet singularity y of the ith elementiSum wavelet fault degree mWFDPerforming fusion and obtaining a fusion data set of all elements;
and 5.3, analyzing the fusion data set by using the power grid multi-target fault diagnosis model based on the fuzzy C-means algorithm to obtain a power grid fault diagnosis result.
9. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 7,
step 5.2 comprises the following steps:
step 5.2.1, using fault recording information, relay protection information and transformer substation monitoring information as multi-source information;
step 5.2.2, according to a lambda fuzzy measure algorithm, based on the wavelet singularity y degree of the ith elementiSum wavelet fault degree mWFDObtaining power grid fault fuzzy measure, and calculating influence coefficients of multi-source information on the power grid fault fuzzy measure;
step 5.2.3, constructing a fuzzy measure function by utilizing the value of the multi-source information to influence the coefficient based on a fuzzy integral algorithm, and solving the fuzzy measure function to obtain a decision fuzzy value after the multi-source information is fused, namely the fault information density;
and 5.2.4, storing the multi-source information of which each branch decision fuzzy value of the fault information is greater than or equal to the decision threshold value into a fusion data set.
10. The grid fault diagnosis method based on fuzzy integral multi-source information fusion of claim 2,
the power grid fault diagnosis method based on fuzzy integral multi-source information fusion further comprises the following steps:
step 6.1, storing the obtained data information, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the result feedback information;
step 6.2, updating and displaying real-time data of the data information, the data preprocessing result, the fault characteristic parameter set, the fault probability, the database, the power grid fault area, the power grid multi-target fault diagnosis model, the fault diagnosis result and the result feedback information;
and 6.3, constructing power grid fault diagnosis by using a database construction program, and storing historical operation data of different monitoring points on the power distribution line of the power grid.
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