CN117825866A - Power grid short-circuit capacity on-site detection method and system based on power grid model - Google Patents
Power grid short-circuit capacity on-site detection method and system based on power grid model Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting the short-circuit capacity of a power grid on site based on a power grid model, which relate to the technical field of short-circuit capacity detection of the power grid, and comprise the steps of collecting monitoring data of the power grid, and establishing the power grid model based on the topology structure and parameter information of the power grid; based on the power grid model, calculating short-circuit capacity under the condition of power grid faults through analysis; and establishing a prediction model through the power grid model and the monitoring data, and carrying out early warning on the short-circuit capacity of the power grid. According to the method, the power grid model is built based on the power grid topological structure and the parameter information, so that the accuracy of power grid fault detection and prediction is improved, and the safety and reliability of power grid operation are ensured; the short-circuit capacity under the condition of the power grid fault is analyzed and calculated, the performance of the power grid under the specific fault condition is identified and evaluated, and the risk resistance of the power grid is enhanced; by pre-warning the short-circuit capacity of the power grid, the operation efficiency of the power grid is improved, and economic and safety risks caused by power grid faults are reduced.
Description
Technical Field
The invention relates to the technical field of power grid short-circuit capacity detection, in particular to a power grid short-circuit capacity on-site detection method and system based on a power grid model.
Background
The traditional method for detecting the short-circuit capacity of the power grid generally needs to carry out experiments or use analog calculation in an actual scene, relies on empirical calculation and a simplified model too much, is difficult to accurately describe the actual characteristics of a complex power system, so that the accuracy and reliability of a detection result are challenged, the method generally only considers a static topological structure and fixed parameters, ignores factors of dynamic changes of the power system, causes lack of real-time performance of the result, models complex interaction relationships inside the power grid relatively simply, fails to fully consider a plurality of elements and interaction thereof, and possibly neglects potential fault conditions, the traditional method lacks intelligence and self-adaption, is insufficient in processing high dynamic and nonlinear characteristics of the power system, a new in-situ detection system and method are developed in recent years, and aims to realize accurate detection of the short-circuit capacity of the power grid by using power grid real-time monitoring data and intelligent computing technology.
On an intelligent computing platform, the short-circuit capacity of the power grid can be predicted and calculated by establishing an accurate power grid model and a corresponding algorithm, the short-circuit capacity of the power grid can be monitored and early-warned in real time by the on-site detection system and the on-site detection method, possible fault risks in the power grid can be found in time, and corresponding measures are taken to adjust and repair, so that the stable operation and safety of the power system are ensured.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing method for detecting the short-circuit capacity of the power grid has the problems of low precision, low reliability and low safety, and how to realize real-time monitoring and early warning of the short-circuit capacity of the power grid.
In order to solve the technical problems, the invention provides the following technical scheme: the on-site detection method of the short circuit capacity of the electric network based on the electric network model comprises the steps of collecting monitoring data of the electric network, and establishing the electric network model based on the topological structure and parameter information of the electric network; based on the power grid model, calculating short-circuit capacity under the condition of power grid faults through analysis; and establishing a prediction model through the power grid model and the monitoring data, and carrying out early warning on the short-circuit capacity of the power grid.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: the method comprises the steps that monitoring data of a power grid are collected, wherein parameters in the power grid are monitored through sensors, the parameters comprise current, voltage and power, and the monitoring data of the power grid are collected in real time; collecting power grid topological structure information, acquiring connection relations of a generator, a transformer, a circuit and a load in a power grid through a design drawing and an equipment parameter manual of the power grid, and extracting the power grid topological structure information; collecting element parameter information, acquiring the capacity of a generator and the rated capacity of a transformer by checking technical specifications of the generator and the transformer, and extracting the element parameter information; the voltage sensor and the current sensor are used for real-time measurement, voltage data and current data of the power grid node are collected in real time, and active power and reactive power are calculated and expressed as:
P＝U×I×cosθ
Q＝U×I×sinθ
wherein, P and Q respectively represent active power and reactive power, U represents voltage, I represents current, sin theta and cos theta are power factors.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: the building of the power grid model comprises a data acquisition moduleThe topological structure information and element parameter information collected by the blocks are used for constructing node branches and branch parameter matrixes, and a topological structure and parameter information model of the power grid is built; the method comprises the steps that nodes and branches are constructed, a power grid is divided into nodes and branches based on collected topological structure information, the nodes represent connection points in the power grid, the branches represent connection lines among the nodes, and a node and branch list of the power grid is built by distributing unique identifiers for each node and branch; establishing a branch parameter matrix comprises the steps of establishing a branch parameter matrix based on collected element parameter information, wherein rows and columns of the branch parameter matrix respectively correspond to node lists of a power grid, and matrix elements represent branch parameters among corresponding nodes; according to the established branch parameter matrix, a node power equation is established, the node power equation indicates that the node input power is equal to the node consumption power, the values of the voltage and the power of each node in the power grid are calculated by solving the node power equation, N nodes are arranged, and the injection power of the ith node is P i +Q i Representing the active power and reactive power injected by the generator, the consumption power is S i +T i Representing active power and reactive power consumed by load, the node voltage is V i cos(θ i )+V i sin(θ i ) X j, represents the voltage amplitude and phase angle of the node, and the node power equation is expressed as:
P i +Q i -(S i +T i )＝V i ×(∑[G ij cos(θ i -θ j )+B ij sin(θ i -θ j )])
wherein P is i Representing the active power injected by the generator of the ith node, Q i Representing the reactive power injected by the ith node generator, S i Representing the active power consumed by the load of the ith node, T i Representing reactive power consumed by the load of the ith node, G ij Indicating the branch admittances of the ith and jth nodes, B ij Representing the branch susceptances of the ith and jth nodes, θ i And theta j Representing the phase angle difference of the i-th and j-th nodes, respectively; each node i corresponds to a node power equation, the power equations of all nodes are combined into a simultaneous equation set,written in matrix form, expressed as:
[P]+[Q]-[S]-[T]＝[V]([G]cos[θ]+[B]sin[θ])
wherein [ P ] is a matrix of active power of injection power, [ Q ] is a matrix of reactive power of injection power, [ S ] is a matrix of active power of consumed power, [ T ] is a matrix of reactive power of consumed power, [ V ] is a matrix of node voltage, [ G ] is a matrix of branch admittance, [ B ] is a matrix of branch susceptance, [ theta ] represents a phase angle difference matrix.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: calculating the short-circuit capacity under the condition of the power grid fault through analysis comprises the steps of calculating the short-circuit capacity based on an established power grid model, selecting a line section as a fault position based on the topology structure and the fault mode of the power grid through simulating the condition of the line short-circuit fault in the power grid, setting the fault type as a three-phase short circuit, indicating that three phases on the line are short-circuited at the same time, calculating fault current based on rated voltage and fault impedance of the line, and indicating that:
wherein I is sc3 Representing three-phase short-circuit current, U representing rated voltage of power grid, Z sc Representing the impedance of the line in the fault condition.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: the method comprises the steps of analyzing and calculating the short-circuit capacity of the power grid under the condition of fault, measuring short-circuit current which occurs in real time in the analysis, and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault by combining a power grid model and real-time monitoring data; applying fault current to a selected fault position by using a short-circuit generator, measuring real-time short-circuit current when a fault occurs by using a current sensor device in an analysis process, and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault, wherein a specific calculation formula is as follows:
wherein S is sc Represents short-circuit capacity, U represents rated voltage of line, Z sc Representing the impedance of the line in the fault condition.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: the method comprises the steps of establishing a prediction model, namely establishing the prediction model to predict the short-circuit capacity of a power grid according to the power grid model and monitoring data through a training model and an optimization algorithm, dividing a data set into a training set and a testing set, wherein the training set is used for training the model and optimizing parameters, and the testing set is used for evaluating the performance and generalization capability of the model; training the model by using a training set through a ridge regression algorithm, and inputting real-time power grid state and monitoring data in the training process to predict the short-circuit capacity of the power grid, wherein the short-circuit capacity is expressed as follows:
y pre ＝w 1 ×F 1 +w 2 ×F 2 +...+w n ×F n
wherein y is pre Representing predicted short-circuit capacity of the network, F 1 ,F 2 ,...,F n Representing characteristics of the electric network, w 1 ,w 2 ,...,w n Is the corresponding weight; predicting the power grid characteristics in the test set based on the trained regression model, and calculating a predicted value y pre Actual observed value S in test set sc And predicted value y pre The comparison is performed to calculate a prediction error for each sample (S sc -y pre ) 2 The prediction errors for all samples are summed and divided by the number of samples n, the result is square root, RMSE is calculated, and the prediction accuracy of the model is evaluated, expressed as:
where RMSE represents root mean square error and n represents the number of samples of the test setQuantity, y represents the actual observed value, y pre Representing model predictions, sqrt represents square root.
As a preferred scheme of the power grid short-circuit capacity in-situ detection method based on the power grid model, the invention comprises the following steps: the early warning of the short circuit capacity of the power grid comprises the steps of predicting and early warning the short circuit capacity of the power grid based on RMSE, and when the short circuit capacity of the power grid is detected to exceed a safety range, giving an alarm and taking safety measures, wherein the safety measures comprise automatically disconnecting a fault line and adjusting the running state of the power grid; calculating the short-circuit capacity of the node through historical data, setting a short-circuit capacity early-warning threshold to be 80% of the short-circuit capacity of the node, predicting the short-circuit capacity of the power grid based on a prediction model and real-time monitoring data, wherein the predicted short-circuit capacity exceeds the set early-warning threshold, triggering an alarm by a system, and respectively marking the n nodes as C 1 ,C 2 ,...,C n The corresponding historical short-circuit capacity data is S 1 ,S 2 ,...,S n Calculating a short-circuit capacity early warning threshold value of each node, and for node C i The short circuit capacity early warning threshold value is W i The calculation process is expressed as:
W i ＝S i ×80％
wherein S is i Is node C i Historical short-circuit capacity data of W i The short circuit capacity early warning threshold value is represented, and 80% is the set early warning threshold value proportion; comparing the predicted value y pre Short circuit capacity early warning threshold W of node i When y is pre Greater than W i Indicating that the short-circuit capacity exceeds a set early warning threshold value, and triggering an alarm.
Another object of the present invention is to provide a system for in-situ detecting short-circuit capacity of a power grid based on a power grid model, which can solve the problem that the current detection of short-circuit capacity of a power grid has low accuracy by analyzing and calculating the short-circuit capacity under the condition of power grid faults based on the power grid model.
As a preferred scheme of the grid short-circuit capacity in-situ detection system based on the grid model, the invention comprises the following steps: the system comprises a data acquisition module, a short-circuit capacity analysis module and a monitoring and early-warning module; the data acquisition module is used for acquiring monitoring data of the power grid and establishing a power grid model based on the power grid topological structure and parameter information; the short-circuit capacity analysis module is used for calculating the short-circuit capacity under the condition of power grid faults through analysis based on a power grid model; and the monitoring and early warning module is used for establishing a prediction model through the power grid model and the monitoring data to early warn the short circuit capacity of the power grid.
A computer device comprising a memory storing a computer program and a processor executing the computer program is the step of implementing a grid model based grid short circuit capacity in situ detection method.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a grid model based method of in-situ detection of grid short circuit capacity.
The invention has the beneficial effects that: according to the power grid model-based power grid short-circuit capacity on-site detection method, the power grid model is built based on the power grid topological structure and the parameter information, so that the accuracy of power grid fault detection and prediction is improved, and the safety and reliability of power grid operation are ensured; the short-circuit capacity under the condition of power grid faults is analyzed and calculated, the performance of the power grid under the specific fault condition is identified and evaluated, the risk resistance of the power grid is enhanced, and the large-scale power interruption is prevented; by establishing a prediction model through the power grid model and monitoring data, the short-circuit capacity of the power grid is pre-warned, the running efficiency of the power grid is improved, and the economic and safety risks caused by power grid faults are reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a method for in-situ detection of a short-circuit capacity of a power grid based on a power grid model according to a first embodiment of the present invention.
Fig. 2 is an overall flowchart of a power grid short-circuit capacity in-situ detection system based on a power grid model according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a method for in-situ detecting a short circuit capacity of a power grid based on a power grid model, including:
s1: and collecting monitoring data of the power grid, and establishing a power grid model based on the power grid topological structure and parameter information.
Further, collecting monitoring data of the power grid comprises monitoring parameters in the power grid through a sensor, wherein the parameters comprise current, voltage and power, and collecting the monitoring data of the power grid in real time; collecting power grid topological structure information, acquiring connection relations of a generator, a transformer, a circuit and a load in a power grid through a design drawing and an equipment parameter manual of the power grid, and extracting the power grid topological structure information; collecting element parameter information, acquiring the capacity of a generator and the rated capacity of a transformer by checking technical specifications of the generator and the transformer, and extracting the element parameter information; the voltage sensor and the current sensor are used for real-time measurement, voltage data and current data of the power grid node are collected in real time, and active power and reactive power are calculated and expressed as:
P＝U×I×cosθ
Q＝U×I×sinθ
wherein, P and Q respectively represent active power and reactive power, U represents voltage, I represents current, sin theta and cos theta are power factors.
It should be noted that, establishing the power grid model includes constructing node branches and branch parameter matrixes based on the topological structure information and element parameter information collected by the data acquisition module, and establishing a topological structure and parameter information model of the power grid; the method comprises the steps that nodes and branches are constructed, a power grid is divided into nodes and branches based on collected topological structure information, the nodes represent connection points in the power grid, the branches represent connection lines among the nodes, and a node and branch list of the power grid is built by distributing unique identifiers for each node and branch; establishing a branch parameter matrix comprises the steps of establishing a branch parameter matrix based on collected element parameter information, wherein rows and columns of the branch parameter matrix respectively correspond to node lists of a power grid, and matrix elements represent branch parameters among corresponding nodes; according to the established branch parameter matrix, a node power equation is established, the node power equation indicates that the node input power is equal to the node consumption power, the values of the voltage and the power of each node in the power grid are calculated by solving the node power equation, N nodes are arranged, and the injection power of the ith node is P i +Q i Representing the active power and reactive power injected by the generator, the consumption power is S i +T i Representing active power and reactive power consumed by load, the node voltage is V i cos(θ i )+V i sin(θ i ) X j, represents the voltage amplitude and phase angle of the node, and the node power equation is expressed as:
P i +Q i -(S i +T i )＝V i ×(∑[G ij cos(θ i -θ j )+B ij sin(θ i -θ j )])
wherein P is i Representing the active power injected by the generator of the ith node, Q i Representing the reactive power injected by the ith node generator, S i Representing the active power consumed by the load of the ith node, T i Representing reactive power consumed by the load of the ith node, G ij Indicating the branch admittances of the ith and jth nodes, B ij Representation ofThe ith and jth node branch susceptances, θ i And theta j Representing the phase angle difference of the i-th and j-th nodes, respectively; each node i corresponds to a node power equation, the power equations of all the nodes are combined into a simultaneous equation set, and the simultaneous equation set is written into a matrix form and expressed as:
[P]+[Q]-[S]-[T]＝[V]([G]cos[θ]+[B]sin[θ])
wherein [ P ] is a matrix of active power of injection power, [ Q ] is a matrix of reactive power of injection power, [ S ] is a matrix of active power of consumed power, [ T ] is a matrix of reactive power of consumed power, [ V ] is a matrix of node voltage, [ G ] is a matrix of branch admittance, [ B ] is a matrix of branch susceptance, [ theta ] represents a phase angle difference matrix.
It should also be noted that the physical connection relationship of the power system, including the layout between nodes and branches. The collection of topological structure information is helpful for understanding the overall structure of the power grid, a comprehensive model of the power grid is established by collecting topological structure and element parameter information, accurate input is provided for subsequent system analysis, unique identifiers are distributed for each node and each branch, the identification and management of the system are facilitated, the modeling process of a complex system is simplified, the electrical characteristics among nodes in the power system are more effectively described by establishing a branch parameter matrix, the accuracy of the model is improved, accurate calculation of voltage and power of each node in the power grid is realized by analyzing a node power equation, and a foundation is provided for real-time monitoring and optimization of the system. The power equations of all the nodes are combined into simultaneous equations, and the simultaneous equations are expressed in a matrix form, so that the comprehensive analysis and the solution of the whole power grid system are facilitated.
S2: based on the grid model, the short-circuit capacity under the condition of grid faults is calculated through analysis.
Further, calculating the short-circuit capacity under the condition of the power grid fault by analyzing comprises calculating the short-circuit capacity based on the established power grid model, selecting a line section as a fault position based on the topological structure and the fault mode of the power grid by simulating the condition of the line short-circuit fault in the power grid, setting the fault type as a three-phase short circuit, indicating that three phases on the line are short-circuited at the same time, calculating the fault current based on the rated voltage and the fault impedance of the line, and indicating that:
wherein I is sc3 Representing three-phase short-circuit current, U representing rated voltage of power grid, Z sc Representing the impedance of the line in the fault condition.
It should be noted that, calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault by analyzing and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault also includes measuring the short-circuit current occurring in real time in the analysis, combining the power grid model and the real-time monitoring data; applying fault current to a selected fault position by using a short-circuit generator, measuring real-time short-circuit current when a fault occurs by using a current sensor device in an analysis process, and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault, wherein a specific calculation formula is as follows:
wherein S is sc Represents short-circuit capacity, U represents rated voltage of line, Z sc Representing the impedance of the line in the fault condition.
It should also be noted that by monitoring the short-circuit current in the fault in real time, and combining with the power grid model, the short-circuit capacity of the power grid can be more accurately analyzed, which is critical to the design and fault prevention of the power grid, the short-circuit condition can be rapidly and accurately simulated and measured by using the short-circuit generator and the current sensor, the real-time fault response capability is provided for the power grid operator, the power grid operator can better understand the limitation and the weak point of the system, so that measures are taken to enhance the reliability of the power grid, and the accurate calculation of the short-circuit capacity is helpful for the optimization of the maintenance and operation of the power grid, especially when planning the upgrade and emergency response strategies.
S3: and establishing a prediction model through the power grid model and the monitoring data, and carrying out early warning on the short-circuit capacity of the power grid.
Further, establishing a prediction model comprises the steps of according to a power grid model and monitoring data, establishing a prediction model to predict the short-circuit capacity of the power grid through a training model and an optimization algorithm, dividing a data set into a training set and a testing set, wherein the training set is used for training and parameter optimization of the model, and the testing set is used for evaluating the performance and generalization capability of the model; training the model by using a training set through a ridge regression algorithm, and inputting real-time power grid state and monitoring data in the training process to predict the short-circuit capacity of the power grid, wherein the short-circuit capacity is expressed as follows:
y pre ＝w 1 ×F 1 +w 2 ×F 2 +...+w n ×F n
wherein y is pre Representing predicted short-circuit capacity of the network, F 1 ,F 2 ,...,F n Representing characteristics of the electric network, w 1 ,w 2 ,...,w n Is the corresponding weight; predicting the power grid characteristics in the test set based on the trained regression model, and calculating a predicted value y pre Actual observed value S in test set sc And predicted value y pre The comparison is performed to calculate a prediction error for each sample (S sc -y pre ) 2 The prediction errors for all samples are summed and divided by the number of samples n, the result is square root, RMSE is calculated, and the prediction accuracy of the model is evaluated, expressed as:
wherein RMSE represents root mean square error, n represents the number of samples of the test set, y represents the actual observed value, y pre Representing model predictions, sqrt represents square root.
It should be noted that, the early warning of the short-circuit capacity of the power grid includes predicting and early warning the short-circuit capacity of the power grid based on RMSE, and when the short-circuit capacity of the power grid is detected to exceed the safety range, an alarm is sent out and safety measures are taken, wherein the safety measures include automatically disconnecting a fault line and adjusting the running state of the power gridA state; calculating the short-circuit capacity of the node through historical data, setting a short-circuit capacity early-warning threshold to be 80% of the short-circuit capacity of the node, predicting the short-circuit capacity of the power grid based on a prediction model and real-time monitoring data, wherein the predicted short-circuit capacity exceeds the set early-warning threshold, triggering an alarm by a system, and respectively marking the n nodes as C 1 ,C 2 ,...,C n The corresponding historical short-circuit capacity data is S 1 ,S 2 ,...,S n Calculating a short-circuit capacity early warning threshold value of each node, and for node C i The short circuit capacity early warning threshold value is W i The calculation process is expressed as:
W i ＝S i ×80％
wherein S is i Is node C i Historical short-circuit capacity data of W i The short circuit capacity early warning threshold value is represented, and 80% is the set early warning threshold value proportion; comparing the predicted value y pre Short circuit capacity early warning threshold W of node i When y is pre Greater than W i Indicating that the short-circuit capacity exceeds a set early warning threshold value, and triggering an alarm.
It should also be noted that, through real-time monitoring and prediction of the short-circuit capacity, the system can discover potential power grid problems in advance, so that measures can be taken in time to prevent power grid faults, personalized early warning thresholds are set according to the historical data of each node, the early warning system is more accurate and sensitive, automatic safety measures can effectively reduce risks and losses caused by power grid faults, the overall stability and reliability of the power grid are increased, and the power grid operators can make more data-driven decisions by using the historical data and accurate prediction results provided by the RMSE.
Example 2
In order to verify the beneficial effects of the invention, the invention carries out scientific demonstration through economic benefit calculation and simulation experiments.
Firstly, selecting a medium-scale urban power grid as a test object, wherein the power grid comprises 4 main power distribution nodes, installing current and voltage sensors at each node, continuously monitoring the current and the voltage for 24 hours, collecting current and voltage data from node 1 to node 4, assuming that each node is subjected to three-phase short circuit, calculating short circuit current, and calculating the short circuit capacity of each node by using a short circuit model.
Referring to table 1, the test data were subjected to a recording analysis.
Table 1 table of experimental data records
It can be seen from the table that the short circuit capacity of node 4 is highest, probably because it is connected with more loads or has higher transmission capacity, the short circuit capacity of node 1 is lowest, indicating that it may be a weak link in the power grid, the active power and reactive power calculations of each node show that node 4 is highest in load, while node 1 is lowest, these data help to evaluate the load condition and stability of each node, the short circuit capacity data indicate that node 4 may be under greater stress in case of extreme faults, these information are crucial to optimizing the power grid design and emergency preparation, this embodiment not only provides a specific method for short circuit capacity evaluation of the power grid, but also supports decision making of the power grid operation through objective data analysis, helps to improve the reliability and safety of the power grid, so my invention is creative.
Example 3
Referring to fig. 2, for one embodiment of the present invention, a power grid short-circuit capacity in-situ detection system based on a power grid model is provided, which includes a data acquisition module, a short-circuit capacity analysis module, and a monitoring and early warning module.
The data acquisition module is used for acquiring monitoring data of the power grid and establishing a power grid model based on the power grid topological structure and parameter information; the short-circuit capacity analysis module is used for calculating the short-circuit capacity under the condition of power grid faults through analysis based on a power grid model; and the monitoring and early warning module is used for building a prediction model through the power grid model and the monitoring data to early warn the short circuit capacity of the power grid.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, 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 the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, 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 the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The power grid short circuit capacity on-site detection method based on the power grid model is characterized by comprising the following steps of:
collecting monitoring data of a power grid, and establishing a power grid model based on a power grid topological structure and parameter information;
based on the power grid model, calculating short-circuit capacity under the condition of power grid faults through analysis;
and establishing a prediction model through the power grid model and the monitoring data, and carrying out early warning on the short-circuit capacity of the power grid.
2. The grid model-based grid short circuit capacity in-situ detection method as claimed in claim 1, wherein: the method comprises the steps that monitoring data of a power grid are collected, wherein parameters in the power grid are monitored through sensors, the parameters comprise current, voltage and power, and the monitoring data of the power grid are collected in real time;
collecting power grid topological structure information, acquiring connection relations of a generator, a transformer, a circuit and a load in a power grid through a design drawing and an equipment parameter manual of the power grid, and extracting the power grid topological structure information;
collecting element parameter information, acquiring the capacity of a generator and the rated capacity of a transformer by checking technical specifications of the generator and the transformer, and extracting the element parameter information;
the voltage sensor and the current sensor are used for real-time measurement, voltage data and current data of the power grid node are collected in real time, and active power and reactive power are calculated and expressed as:
P＝U×I×cosθ
Q＝U×I×sinθ
wherein, P and Q respectively represent active power and reactive power, U represents voltage, I represents current, sin theta and cos theta are power factors.
3. The grid model-based grid short circuit capacity in-situ detection method as claimed in claim 2, wherein: the power grid model establishment comprises the steps of constructing node branches and branch parameter matrixes based on topological structure information and element parameter information collected by the data acquisition module, and establishing a topological structure and parameter information model of a power grid;
the method comprises the steps that nodes and branches are constructed, a power grid is divided into nodes and branches based on collected topological structure information, the nodes represent connection points in the power grid, the branches represent connection lines among the nodes, and a node and branch list of the power grid is built by distributing unique identifiers for each node and branch;
establishing a branch parameter matrix comprises the steps of establishing a branch parameter matrix based on collected element parameter information, wherein rows and columns of the branch parameter matrix respectively correspond to node lists of a power grid, and matrix elements represent branch parameters among corresponding nodes;
according to the established branch parameter matrix, a node power equation is established, the node power equation indicates that the node input power is equal to the node consumption power, the values of the voltage and the power of each node in the power grid are calculated by solving the node power equation, N nodes are arranged, and the injection power of the ith node is P i +Q i Representing the active power and reactive power injected by the generator, the consumption power is S i +T i Representing active power and reactive power consumed by load, the node voltage is V i cos(θ i )+V i sin(θ i ) X j, represents the voltage amplitude and phase angle of the node, and the node power equation is expressed as:
P i +Q i -(S i +T i )＝V i ×(∑[G ij cos(θ i -θ j )+B ij sin(θ i -θ j )])
wherein P is i Representing the active power injected by the generator of the ith node, Q i Representing the reactive power injected by the ith node generator, S i Representing the active power consumed by the load of the ith node, T i Representing reactive power consumed by the load of the ith node, G ij Indicating the branch admittances of the ith and jth nodes, B ij Representing the branch susceptances of the ith and jth nodes, θ i And theta j Representing the phase angle difference of the i-th and j-th nodes, respectively;
each node i corresponds to a node power equation, the power equations of all the nodes are combined into a simultaneous equation set, and the simultaneous equation set is written into a matrix form and expressed as:
[P]+[Q]-[S]-[T]＝[V]([G]cos[θ]+[B]sin[θ])
wherein [ P ] is a matrix of active power of injection power, [ Q ] is a matrix of reactive power of injection power, [ S ] is a matrix of active power of consumed power, [ T ] is a matrix of reactive power of consumed power, [ V ] is a matrix of node voltage, [ G ] is a matrix of branch admittance, [ B ] is a matrix of branch susceptance, [ theta ] represents a phase angle difference matrix.
4. A grid model based on-site detection method of grid short circuit capacity as claimed in claim 3, wherein: calculating the short-circuit capacity under the condition of the power grid fault through analysis comprises the steps of calculating the short-circuit capacity based on an established power grid model, selecting a line section as a fault position based on the topology structure and the fault mode of the power grid through simulating the condition of the line short-circuit fault in the power grid, setting the fault type as a three-phase short circuit, indicating that three phases on the line are short-circuited at the same time, calculating fault current based on rated voltage and fault impedance of the line, and indicating that:
wherein I is sc3 Representing three-phase short-circuit current, U representing rated voltage of power grid, Z sc Representing the impedance of the line in the fault condition.
5. The method for in-situ detection of short circuit capacity of a power grid based on a power grid model as set forth in claim 4, wherein: the method comprises the steps of analyzing and calculating the short-circuit capacity of the power grid under the condition of fault, measuring short-circuit current which occurs in real time in the analysis, and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault by combining a power grid model and real-time monitoring data;
applying fault current to a selected fault position by using a short-circuit generator, measuring real-time short-circuit current when a fault occurs by using a current sensor device in an analysis process, and calculating the short-circuit capacity of the power grid under the condition of line short-circuit fault, wherein a specific calculation formula is as follows:
wherein S is sc Represents short-circuit capacity, U represents rated voltage of line, Z sc Representing the impedance of the line in the fault condition.
6. The method for in-situ detection of short circuit capacity of a power grid based on a power grid model as set forth in claim 5, wherein: the method comprises the steps of establishing a prediction model, namely establishing the prediction model to predict the short-circuit capacity of a power grid according to the power grid model and monitoring data through a training model and an optimization algorithm, dividing a data set into a training set and a testing set, wherein the training set is used for training the model and optimizing parameters, and the testing set is used for evaluating the performance and generalization capability of the model;
training the model by using a training set through a ridge regression algorithm, and inputting real-time power grid state and monitoring data in the training process to predict the short-circuit capacity of the power grid, wherein the short-circuit capacity is expressed as follows:
y pre ＝w 1 ×F 1 +w 2 ×F 2 +...+w n ×F n
wherein y is pre Representing predicted short-circuit capacity of the network, F 1 ,F 2 ,...,F n Representing characteristics of the electric network, w 1 ,w 2 ,...,w n Is the corresponding weight;
predicting the power grid characteristics in the test set based on the trained regression model, and calculating a predicted value y pre Actual observed value S in test set sc And predicted value y pre The comparison is performed to calculate a prediction error for each sample (S sc -y pre ) 2 The prediction errors for all samples are summed and divided by the number of samples n, the result is square root, RMSE is calculated, and the prediction accuracy of the model is evaluated, expressed as:
wherein RMSE represents root mean square error, n represents the number of samples of the test set, y represents the actual observed value, y pre Representation model pre-processingMeasured, sqrt represents square root.
7. The grid model based on-site detection method for short circuit capacity of a grid as set forth in claim 6, wherein: the early warning of the short circuit capacity of the power grid comprises the steps of predicting and early warning the short circuit capacity of the power grid based on RMSE, and when the short circuit capacity of the power grid is detected to exceed a safety range, giving an alarm and taking safety measures, wherein the safety measures comprise automatically disconnecting a fault line and adjusting the running state of the power grid;
calculating the short-circuit capacity of the node through historical data, setting a short-circuit capacity early-warning threshold to be 80% of the short-circuit capacity of the node, predicting the short-circuit capacity of the power grid based on a prediction model and real-time monitoring data, wherein the predicted short-circuit capacity exceeds the set early-warning threshold, triggering an alarm by a system, and respectively marking the n nodes as C 1 ,C 2 ,...,C n The corresponding historical short-circuit capacity data is S 1 ,S 2 ,...,S n Calculating a short-circuit capacity early warning threshold value of each node, and for node C i The short circuit capacity early warning threshold value is W i The calculation process is expressed as:
W i ＝S i ×80％
wherein S is i Is node C i Historical short-circuit capacity data of W i The short circuit capacity early warning threshold value is represented, and 80% is the set early warning threshold value proportion;
comparing the predicted value y pre Short circuit capacity early warning threshold W of node i When y is pre Greater than W i Indicating that the short-circuit capacity exceeds a set early warning threshold value, and triggering an alarm.
8. A system employing the grid model-based grid short circuit capacity in-situ detection method of any one of claims 1 to 7, characterized by: the system comprises a data acquisition module, a short-circuit capacity analysis module and a monitoring and early-warning module;
the data acquisition module is used for acquiring monitoring data of the power grid and establishing a power grid model based on the power grid topological structure and parameter information;
the short-circuit capacity analysis module is used for calculating the short-circuit capacity under the condition of power grid faults through analysis based on a power grid model;
and the monitoring and early warning module is used for establishing a prediction model through the power grid model and the monitoring data to early warn the short circuit capacity of the power grid.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the grid model-based grid short circuit capacity in-situ detection method of any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the grid model based grid short circuit capacity in situ detection method according to any one of claims 1 to 7.
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