CN114399177A - Scheduling disposal rule mining and generating method and system based on Apriori - Google Patents
Scheduling disposal rule mining and generating method and system based on Apriori Download PDFInfo
- Publication number
- CN114399177A CN114399177A CN202111646787.5A CN202111646787A CN114399177A CN 114399177 A CN114399177 A CN 114399177A CN 202111646787 A CN202111646787 A CN 202111646787A CN 114399177 A CN114399177 A CN 114399177A
- Authority
- CN
- China
- Prior art keywords
- mode
- power grid
- fault
- mining
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005065 mining Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000010219 correlation analysis Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 125000004432 carbon atom Chemical group C* 0.000 claims description 5
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 5
- 230000001052 transient effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 abstract description 9
- 238000012545 processing Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 238000007430 reference method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a scheduling treatment rule mining and generating method and system based on Apriori. The method analyzes and processes the historical data, clusters the mass historical operation modes into a plurality of operation mode clusters according to the key characteristic quantity, sample data in the same operation mode cluster has a relatively close operation mode, and the same safety risk link and disposal measure are provided under the same assessment fault. Through clustering the power grid operation modes, a typical power grid operation mode is extracted, and the completeness of safety and stability analysis and decision making of the power grid is improved. For scenes which do not appear in history, mining a power grid scheduling disposal association rule based on an Apriori algorithm, mining potential association relations between various information changes of a power grid and power grid risk links, between risk links and control measures and between control measures and margin change conditions after the measures are implemented from operation data, and solving the problem of disposal measure generation under the condition that scene matching is unsuccessful.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a scheduling disposal rule mining and generating method and system based on Apriori.
Background
With the development of an extra-high voltage alternating current-direct current hybrid power grid and the large-scale new energy grid connection, the operation characteristics of the power grid are changed profoundly. In addition, natural disasters such as typhoons, ice, rainstorms and the like are frequent, the influence of external environment on the power grid is more prominent, the power grid is in a linkage, multiple and other complex fault forms, and the power grid scheduling operation control is increasingly complex. In the face of a large amount of operation data collected by a scheduling control system in a short time when a fault occurs, scheduling operators need to accurately judge the cause of the fault, master weak links and operation boundaries of a power grid, and take reasonable and effective fault handling measures. The dispatching control means based on operation experience and manual analysis is increasingly insufficient in the aspects of analysis and treatment of complex faults of the alternating current and direct current power grid.
The safe and reliable operation of the power system depends on an information-intensive and knowledge-intensive decision control center 'scheduling support system'. The system collects mass high-value data inside and outside the power grid, and scheduling and controlling are carried out by adopting a two-step scheduling decision mechanism of 'offline rule making' and 'online optimization calculation'. However, in the current scheduling support system, equipment monitoring and manual analysis are mainly used, scheduling personnel are still required to participate in decision and execution links, electronic modeling is realized through knowledge in a large number of text forms such as various scheduling operation rules, accident plans and safety control strategies, various heterogeneous data belong to different systems in the aspects of linkage, multiple fault study and judgment and emergency disposal, the scheduling personnel are difficult to capture key information in a short time, and the accuracy, timeliness and normalization of fault processing are difficult to guarantee.
In the patent document, "a power grid accident handling plan online generation and execution method and device" (CN112183834A), a power-losing electronic system is determined according to fault information, a complex circuit path is searched, all searched complex circuit paths are safely retrieved, and if at least one complex circuit path passing verification exists, an optimal path is selected according to a verification result; if the complex circuit path passing the verification does not exist, the complex circuit path is searched again according to the steps after partial load is cut off according to the general strategy, the safe retrieval and the verification are carried out, the optimal path is selected, and the plan is automatically executed. The method aims at recovering power supply for the power grid accident handling plan, and load shedding measures can be taken under necessary conditions, but the aim of power grid accident handling includes not only recovering power supply, but also isolating accidents to prevent accident expansion and propagation, and the measures which can be taken include unit output adjustment, grid structure adjustment and the like besides load shedding.
The power grid dispatching field knowledge of the patent document 'a power grid dispatching field knowledge attribute graph model construction method based on CYPHER' (CN112685608A) comes from publications such as 'electric power subject word list', 'power grid dispatching management regulations and implementation methods', 'national power grid dispatching control management regulations', and the like, but the power grid operation mode is complex and changeable, so that the situation that accident handling plan regulations cannot be covered is inevitable, and the power grid dispatching field knowledge attribute graph is not suitable any more.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a scheduling and handling rule mining and generating method and system based on Apriori, which utilize a big data mining technology to extract rules for supporting scheduling operation to form a scheduling and operating rule knowledge base suitable for different operation modes and fault scenes, and are used for simulating and replacing the brains of scheduling personnel to complete complex work such as fault location and reason analysis in a new scene, power grid operation risk discovery after fault, control measure identification, risk change prediction after measure implementation and the like.
Therefore, the invention adopts the following technical scheme: an Apriori-based scheduling handling rule mining and generating method comprises the following steps:
1) acquiring a dispatching and handling rule and mining a sample set, wherein information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault and a handling measure after the fault;
2) determining a scheduling disposal rule by combining a correlation analysis method to mine key characteristic quantities of a sample set, wherein the key characteristic quantities comprise operation state quantities before a power grid fault, a quasi-steady state operation mode, a power grid safety risk link and fault disposal measures, and the power grid operation state quantities comprise section tide, node power, node voltage and steady state frequency;
3) extracting key characteristic quantities of a scheduling treatment rule mining sample set: all historical operation mode data of the power grid, fault information and corresponding quasi-steady state power grid operation mode data under each fault;
4) selecting any mode as a reference value for each fault, judging whether the key connecting line operation state, the section flow, the node power, the node voltage and the steady-state frequency of the fault are consistent with the existing classified reference modes or not for the subsequent to-be-clustered modes, if so, turning to the step 5), and if not, independently using the mode as a new reference mode;
5) judging whether the power grid risk links of the mode to be clustered are consistent with the control station, and if so, bringing the power grid risk links into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered;
6) carrying out integer numbering on clustering mode clusters under the same fault, and using the integer numbers as cluster numbers of the clustering mode clusters under the fault;
7) recalculating the reference modes for all the mode clusters, calculating the Euclidean distance between each mode cluster and the center of the mode cluster, and taking the point with the minimum distance as a new reference mode of the mode cluster; if the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, turning to the step 3);
8) acquiring an online operation mode, performing scene matching according to the key characteristic quantity of the power grid, if matching is successful, indicating that similar scenes exist in a historical database, solving the safety risk by using disposal measures, and issuing and executing the disposal measures after manual confirmation;
9) and for the scene with failed matching, mining the association rule of scheduling treatment based on an Apriori algorithm.
The invention relates to a dispatching disposal rule mining and generating method based on Apriori, which is used for mining dispatching accident disposal rules based on-line safety and stability analysis (DSA) and massive historical operation mode data accumulated by long-term periodic operation and analysis decision results. The method comprises the steps of analyzing and processing historical data based on regularity and repeatability of an actual power grid operation mode, clustering massive historical operation modes into a plurality of operation mode clusters according to key characteristic quantities, enabling sample data in the same operation mode cluster to have a relatively close operation mode, and enabling the sample data to have the same safety risk link and treatment measures under the same assessment fault. Through clustering the power grid operation modes, a typical power grid operation mode is extracted, and the completeness of safety and stability analysis and decision making of the power grid is improved. For scenes which do not appear in history, mining a power grid scheduling disposal association rule based on an Apriori algorithm, mining potential association relations between various information changes of a power grid and power grid risk links, between risk links and control measures and between control measures and margin change conditions after the measures are implemented from operation data, and solving the problem of disposal measure generation under the condition that scene matching is unsuccessful.
Further, the power grid quasi-steady-state mode of the power grid risk link after the fault in the step 1) is generated, the operation states of the safety control and system protection devices are taken into consideration, a current value strategy is identified, the quasi-steady-state operation mode after the fault is generated, transient time domain simulation is carried out based on the detailed model of the alternating current and direct current equipment, whether the system reaches the quasi-steady state or not is judged according to the node voltage fluctuation and frequency fluctuation conditions, and the steady-state operation mode is automatically generated according to the node injection quantity.
Further, in the step 9), mining is performed on the scheduling disposal association rule based on an Apriori algorithm, potential association relations between various information changes of the power grid and power grid risk links, between risk links and control measures, and between control measures and margin change conditions after the measures are implemented are mined from the operation data, the problem of disposal measure generation under the condition that scene matching is unsuccessful is solved, a strong association rule is selected by setting a minimum support degree and a minimum confidence degree, and the strength of the association rule is determined by the support degree and the confidence degree.
Further, the specific algorithms for support and confidence are as follows:
9-1) the probability of A and B occurring simultaneously can be defined as the support of A and B, and the expression is as follows:
in the above formula, sp (A) is the support; d is the total number of samples;
counting the support degree of the association rule between A and B;
9-2) the percentage of the sample that contains A and also B can be defined as the confidence, the expression is as follows:
in the above formula, the first and second carbon atoms are,
for confidence, count (A) is the number of A contained in the sample.
Further, in the step 9), mining is performed on the scheduling disposal association rule based on an Apriori algorithm, and by setting a rule mining antecedent and consequent constraint, the categories of frequent items are reduced, so as to generate an effective scheduling operation and disposal rule, with a service scenario as a guide.
The other technical scheme adopted by the invention is as follows: an Apriori based scheduling handling rule mining and generating system, comprising:
a mining sample set acquisition unit: acquiring a dispatching and handling rule and mining a sample set, wherein information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault and a handling measure after the fault;
a key feature quantity mining unit: determining a scheduling disposal rule by combining a correlation analysis method to mine key characteristic quantities of a sample set, wherein the key characteristic quantities comprise operation state quantities before a power grid fault, a quasi-steady state operation mode, a power grid safety risk link and fault disposal measures, and the power grid operation state quantities comprise section tide, node power, node voltage and steady state frequency;
a key characteristic amount extraction unit: extracting key characteristic quantities of a dispatching and disposing rule mining sample set, wherein the key characteristic quantities comprise historical operation mode data and fault information of all power grids and corresponding quasi-steady-state power grid operation mode data under each fault;
a mode state judging and classifying unit: selecting any mode as a reference value for each fault, judging whether the key connecting line operation state, the section flow, the node power, the node voltage and the steady-state frequency of the fault are consistent with the existing classified reference mode or not for the subsequent mode to be clustered, if so, switching to a mode risk judging and classifying unit, otherwise, independently using the mode as a new type of reference mode;
a mode risk judging and classifying unit: judging whether the power grid risk links of the mode to be clustered are consistent with the control station, and if so, bringing the power grid risk links into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered;
operation mode cluster numbering unit: carrying out integer numbering on clustering mode clusters under the same fault, and using the integer numbers as cluster numbers of the clustering mode clusters under the fault;
reference mode selection unit: recalculating the reference modes for all the mode clusters, calculating the Euclidean distance between each mode cluster and the center of the mode cluster, and taking the point with the minimum distance as a new reference mode of the mode cluster; if the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, switching to a key characteristic quantity extraction unit;
a scene matching unit: acquiring an online operation mode, performing scene matching according to the key characteristic quantity of the power grid, if matching is successful, indicating that similar scenes exist in a historical database, solving the safety risk by using disposal measures, and issuing and executing the disposal measures after manual confirmation;
a scheduling handling association rule mining unit: and for the scene with failed matching, mining the association rule of scheduling treatment based on an Apriori algorithm.
The invention has the following beneficial effects:
compared with the prior art, the method carries out online completion and dynamic update on the knowledge graph based on the sensitivity information and the online decision information given by the online safety and stability analysis module, and solves the problems that the interpretability is not strong and all operation scenes are difficult to cover by only depending on a data driving mode and offline rules.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a block diagram of the architecture of the system of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following description and drawings.
Example 1
One embodiment of the present invention includes the steps shown in FIG. 1:
step 1 in fig. 1 describes that a scheduling and handling rule is obtained to mine a sample set, and information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault, and a handling measure.
And generating a power grid quasi-steady state mode of the power grid risk link after the fault, considering the current value strategy identification of the running state of the safety control and system protection device, identifying and generating the quasi-steady state running mode after the fault, performing transient time domain simulation based on the detailed model of the AC/DC equipment, judging whether the system reaches the quasi-steady state according to the node voltage fluctuation and frequency fluctuation conditions, and automatically generating the steady state running mode according to the node injection quantity.
Step 2 in fig. 1 describes that the key feature quantities mined by determining the scheduling and handling rules by combining the correlation analysis method include operation state quantities before the power grid fault, quasi-steady-state operation modes, power grid safety risk links, fault handling measures and the like, and the power grid operation state quantities mainly include section flow, node power, node voltage, steady-state frequency and the like.
Step 3 in fig. 1 describes key feature quantities such as all power grid historical operation mode data and fault information of the scheduling and handling rule mining sample set, and quasi-steady-state power grid operation mode data corresponding to each fault.
Step 4 in fig. 1 describes that, for each fault, any one of the modes is selected as a reference value, and for the subsequent mode to be clustered, whether the operation state of the critical connecting line after the fault, the section flow, the node power, the node voltage, and the steady-state frequency of the fault are consistent with the reference modes of the existing classification is determined, if so, the step 5 is performed, otherwise, the method is independently used as a new reference mode.
Step 5 in fig. 1 describes that whether the power grid risk link of the to-be-clustered mode is consistent with the control station is judged, and if so, the power grid risk link is brought into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered.
Step 6 in fig. 1 describes that the clustering method clusters under the same fault are numbered in integer as the cluster numbers of the historical clustering clusters under the fault.
Step 7 in fig. 1 describes that the reference method is recalculated for all the method clusters, each method cluster calculates the euclidean distance between the center of the method cluster and the all the methods, and the point with the minimum distance is used as the new reference method for the method cluster. If the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, turning to the step 3.
Step 8 in fig. 1 describes that an online operation mode is obtained, scene matching is performed according to the key characteristic quantity of the power grid, if matching is successful, it is indicated that similar scenes exist in the historical database, the disposal measures can solve the security risk, and the disposal measures can be issued and executed after manual confirmation.
Step 9 in fig. 1 describes that for the matching failure scenario, the mining of the scheduling handling association rule is performed based on Apriori algorithm.
The scheduling disposal association rule mining based on the Apriori algorithm is used for mining potential association relations between various information changes of a power grid and power grid risk links, between risk links and control measures and between control measures and margin change conditions after the measures are implemented, solving the problem of disposal measure generation under the condition of scene matching failure, generally selecting a strong association rule by setting minimum support degree and minimum confidence degree, and determining the strength of the association rule according to the support degree and the confidence degree, wherein the specific algorithm of the support degree and the confidence degree is as follows:
9-1) the probability of A and B occurring simultaneously can be defined as the support of A and B, and the expression is as follows:
in the above formula, sp (A) is the support; d is the total number of samples;
the support of the association rule between a and B is counted.
9-2) the percentage of the sample that contains A and also B can be defined as the confidence, the expression is as follows:
in the above formula, the first and second carbon atoms are,
for confidence, count (A) is the number of A contained in the sample.
The dispatching disposal association rule mining based on the Apriori algorithm is conducted, a business scene is used as a guide, and the frequent item categories are reduced by setting the constraint of a front item and a back item of the rule mining, so that effective dispatching operation and disposal rules are generated.
Example 2
The present embodiment is a scheduling processing rule mining and generating system based on Apriori, and as shown in fig. 2, the system is composed of a mining sample set obtaining unit, a key feature quantity mining unit, a key feature quantity extracting unit, a mode state judging and classifying unit, a mode risk judging and classifying unit, an operation mode cluster numbering unit, a reference mode selecting unit, a scene matching unit, and a scheduling processing association rule mining unit.
A mining sample set acquisition unit: acquiring a dispatching and handling rule and mining a sample set, wherein information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault and a handling measure after the fault;
a key feature quantity mining unit: determining a scheduling disposal rule by combining a correlation analysis method to mine key characteristic quantities of a sample set, wherein the key characteristic quantities comprise operation state quantities before a power grid fault, a quasi-steady state operation mode, a power grid safety risk link and fault disposal measures, and the power grid operation state quantities comprise section tide, node power, node voltage and steady state frequency;
a key characteristic amount extraction unit: extracting key characteristic quantities of a dispatching and disposing rule mining sample set, wherein the key characteristic quantities comprise historical operation mode data and fault information of all power grids and corresponding quasi-steady-state power grid operation mode data under each fault;
a mode state judging and classifying unit: selecting any mode as a reference value for each fault, judging whether the key connecting line operation state, the section flow, the node power, the node voltage and the steady-state frequency of the fault are consistent with the existing classified reference mode or not for the subsequent mode to be clustered, if so, switching to a mode risk judging and classifying unit, otherwise, independently using the mode as a new type of reference mode;
a mode risk judging and classifying unit: judging whether the power grid risk links of the mode to be clustered are consistent with the control station, and if so, bringing the power grid risk links into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered;
operation mode cluster numbering unit: carrying out integer numbering on clustering mode clusters under the same fault, and using the integer numbers as cluster numbers of the clustering mode clusters under the fault;
reference mode selection unit: recalculating the reference modes for all the mode clusters, calculating the Euclidean distance between each mode cluster and the center of the mode cluster, and taking the point with the minimum distance as a new reference mode of the mode cluster; if the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, switching to a key characteristic quantity extraction unit;
a scene matching unit: acquiring an online operation mode, performing scene matching according to the key characteristic quantity of the power grid, if matching is successful, indicating that similar scenes exist in a historical database, solving the safety risk by using disposal measures, and issuing and executing the disposal measures after manual confirmation;
a scheduling handling association rule mining unit: and for the scene with failed matching, mining the association rule of scheduling treatment based on an Apriori algorithm.
The method comprises the steps of mining a power grid quasi-steady state mode of a power grid risk link after a fault in a sample set acquisition unit, taking operation states of a safety control and system protection device into consideration, identifying a current value strategy, generating a quasi-steady state operation mode after the fault, carrying out transient time domain simulation based on a detailed model of alternating current and direct current equipment, judging whether a system reaches a quasi-steady state or not according to node voltage fluctuation and frequency fluctuation conditions, and automatically generating a steady state operation mode according to node injection quantity.
The scheduling disposal association rule mining unit performs scheduling disposal association rule mining based on an Apriori algorithm, potential association relations between various information changes of the power grid and power grid risk links, between the risk links and control measures and between the control measures and margin change conditions after the measures are implemented are mined from operation data, the problem of disposal measure generation under the condition that scene matching is unsuccessful is solved, a strong association rule is selected by setting minimum support and minimum confidence, and the strength of the association rule is determined by the support and the confidence.
The specific algorithm of the support degree and the confidence degree is as follows:
9-1) the probability of A and B occurring simultaneously can be defined as the support of A and B, and the expression is as follows:
in the above formula, sp (A) is the support; d is the total number of samples;
support count for association rule between A and B；
9-2) the percentage of the sample that contains A and also B can be defined as the confidence, the expression is as follows:
in the above formula, the first and second carbon atoms are,
for confidence, count (A) is the number of A contained in the sample.
The scheduling disposal association rule mining unit performs scheduling disposal association rule mining based on an Apriori algorithm, and reduces the categories of frequent items by setting rule mining antecedent and consequent constraints with the direction of a service scene, thereby generating effective scheduling operation and disposal rules.
Although the present invention has been described in terms of the preferred embodiment, it is not intended that the invention be limited to the embodiment. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. The scope of the invention should therefore be determined with reference to the appended claims.
Claims (10)
1. An Apriori-based scheduling handling rule mining and generating method is characterized by comprising the following steps of:
1) acquiring a dispatching and handling rule and mining a sample set, wherein information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault and a handling measure after the fault;
2) determining a scheduling disposal rule by combining a correlation analysis method to mine key characteristic quantities of a sample set, wherein the key characteristic quantities comprise operation state quantities before a power grid fault, a quasi-steady state operation mode, a power grid safety risk link and fault disposal measures, and the power grid operation state quantities comprise section tide, node power, node voltage and steady state frequency;
3) extracting key characteristic quantities of a scheduling treatment rule mining sample set: all historical operation mode data of the power grid, fault information and corresponding quasi-steady state power grid operation mode data under each fault;
4) selecting any mode as a reference value for each fault, judging whether the key connecting line operation state, the section flow, the node power, the node voltage and the steady-state frequency of the fault are consistent with the existing classified reference modes or not for the subsequent to-be-clustered modes, if so, turning to the step 5), and if not, independently using the mode as a new reference mode;
5) judging whether the power grid risk links of the mode to be clustered are consistent with the control station, and if so, bringing the power grid risk links into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered;
6) carrying out integer numbering on clustering mode clusters under the same fault, and using the integer numbers as cluster numbers of the clustering mode clusters under the fault;
7) recalculating the reference modes for all the mode clusters, calculating the Euclidean distance between each mode cluster and the center of the mode cluster, and taking the point with the minimum distance as a new reference mode of the mode cluster; if the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, turning to the step 3);
8) acquiring an online operation mode, performing scene matching according to the key characteristic quantity of the power grid, if matching is successful, indicating that similar scenes exist in a historical database, solving the safety risk by using disposal measures, and issuing and executing the disposal measures after manual confirmation;
9) and for the scene with failed matching, mining the association rule of scheduling treatment based on an Apriori algorithm.
2. The Apriori-based scheduling and handling rule mining and generating method according to claim 1, wherein the grid quasi-steady-state mode of the grid risk link after the fault in step 1) is generated, the operation states of the safety control and system protection devices are taken into consideration, a current value strategy is identified, the quasi-steady-state operation mode after the fault is generated, transient time domain simulation is performed based on a detailed model of alternating current/direct current equipment, whether the system reaches a quasi-steady state or not is judged according to node voltage fluctuation and frequency fluctuation conditions, and a steady-state operation mode is automatically generated according to the node injection quantity.
3. The Apriori-based mining and generating method for the scheduling handling rules according to claim 1, wherein in step 9), mining is performed for the scheduling handling association rules based on an Apriori algorithm, mining potential association relations between various types of information changes of a power grid and changes of a risk link of the power grid, between the risk link and a control measure, between the control measure and a margin change condition after the measures are implemented from operation data, solving the problem of generating the handling measures when the scene matching is unsuccessful, and selecting strong association rules by setting a minimum support degree and a minimum confidence degree, wherein the strength of the association rules is determined by the support degree and the confidence degree.
4. The Apriori-based mining and generating method for scheduling treatment rules according to claim 3, wherein the specific algorithms for support and confidence are as follows:
9-1) the probability of A and B occurring simultaneously can be defined as the support of A and B, and the expression is as follows:
in the above formula, sp (A) is the support; d is the total number of samples;
counting the support degree of the association rule between A and B;
9-2) the percentage of the sample that contains A and also B can be defined as the confidence, the expression is as follows:
in the above formula, the first and second carbon atoms are,
for confidence, count (A) is the number of A contained in the sample.
5. The Apriori-based mining and generating method for scheduling handling rules according to claim 1, wherein in step 9), mining is performed for scheduling handling association rules based on an Apriori algorithm, and by setting a rule mining antecedent and consequent constraint with the guidance of a service scenario, the categories of frequent items are reduced, so as to generate effective scheduling operation and handling rules.
6. An Apriori-based scheduling handling rule mining and generating system, comprising:
a mining sample set acquisition unit: acquiring a dispatching and handling rule and mining a sample set, wherein information contained in each sample includes a power grid operation mode before and after a fault, a safety control and system protection device operation state, a power grid risk link after the fault and a handling measure after the fault;
a key feature quantity mining unit: determining a scheduling disposal rule by combining a correlation analysis method to mine key characteristic quantities of a sample set, wherein the key characteristic quantities comprise operation state quantities before a power grid fault, a quasi-steady state operation mode, a power grid safety risk link and fault disposal measures, and the power grid operation state quantities comprise section tide, node power, node voltage and steady state frequency;
a key characteristic amount extraction unit: extracting key characteristic quantities of a dispatching and disposing rule mining sample set, wherein the key characteristic quantities comprise historical operation mode data and fault information of all power grids and corresponding quasi-steady-state power grid operation mode data under each fault;
a mode state judging and classifying unit: selecting any mode as a reference value for each fault, judging whether the key connecting line operation state, the section flow, the node power, the node voltage and the steady-state frequency of the fault are consistent with the existing classified reference mode or not for the subsequent mode to be clustered, if so, switching to a mode risk judging and classifying unit, otherwise, independently using the mode as a new type of reference mode;
a mode risk judging and classifying unit: judging whether the power grid risk links of the mode to be clustered are consistent with the control station, and if so, bringing the power grid risk links into the existing classified reference mode; otherwise, the method is independently used as a new reference mode until all the historical modes are clustered;
operation mode cluster numbering unit: carrying out integer numbering on clustering mode clusters under the same fault, and using the integer numbers as cluster numbers of the clustering mode clusters under the fault;
reference mode selection unit: recalculating the reference modes for all the mode clusters, calculating the Euclidean distance between each mode cluster and the center of the mode cluster, and taking the point with the minimum distance as a new reference mode of the mode cluster; if the reference mode of the mode cluster changes or the iteration times are reached, finishing clustering, otherwise, switching to a key characteristic quantity extraction unit;
a scene matching unit: acquiring an online operation mode, performing scene matching according to the key characteristic quantity of the power grid, if matching is successful, indicating that similar scenes exist in a historical database, solving the safety risk by using disposal measures, and issuing and executing the disposal measures after manual confirmation;
a scheduling handling association rule mining unit: and for the scene with failed matching, mining the association rule of scheduling treatment based on an Apriori algorithm.
7. The Apriori-based scheduling and handling rule mining and generating system according to claim 6, wherein a grid quasi-steady-state mode of a grid risk link after a fault in the mining sample set acquisition unit is generated, a safety control and system protection device operation state is taken into consideration, a current value strategy is identified, a quasi-steady-state operation mode after the fault is generated, transient time domain simulation is performed based on an alternating current-direct current equipment detailed model, whether the system reaches a quasi-steady state or not is judged according to node voltage fluctuation and frequency fluctuation conditions, and a steady-state operation mode is automatically generated according to node injection quantity.
8. The Apriori-based scheduling treatment rule mining and generating system according to claim 6, wherein Apriori algorithm-based scheduling treatment association rule mining is performed in the scheduling treatment association rule mining unit, potential association relations between various types of information changes of a power grid and power grid risk links, between risk links and control measures, between control measures and margin change conditions after the measures are implemented are mined from operating data, the problem of generation of treatment measures when scene matching is unsuccessful is solved, a strong association rule is selected by setting minimum support and minimum confidence, and the strength of the association rule is determined by the support and the confidence.
9. The Apriori-based scheduling treatment rule mining and generating system according to claim 8, wherein a specific algorithm of support degree and confidence degree is as follows:
9-1) the probability of A and B occurring simultaneously can be defined as the support of A and B, and the expression is as follows:
in the above formula, sp (A) is the support; d is the total number of samples;
counting the support degree of the association rule between A and B;
9-2) the percentage of the sample that contains A and also B can be defined as the confidence, the expression is as follows:
in the above formula, the first and second carbon atoms are,
for confidence, count (A) is the number of A contained in the sample.
10. The Apriori-based scheduling handling rule mining and generating system according to claim 6, wherein Apriori algorithm-based scheduling handling association rule mining in the scheduling handling association rule mining unit performs scheduling handling association rule mining, and by setting rule mining antecedent and consequent constraints with the guidance of a service scenario, frequent item categories are reduced, thereby generating effective scheduling operation and handling rules.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111646787.5A CN114399177A (en) | 2021-12-30 | 2021-12-30 | Scheduling disposal rule mining and generating method and system based on Apriori |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111646787.5A CN114399177A (en) | 2021-12-30 | 2021-12-30 | Scheduling disposal rule mining and generating method and system based on Apriori |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114399177A true CN114399177A (en) | 2022-04-26 |
Family
ID=81229622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111646787.5A Pending CN114399177A (en) | 2021-12-30 | 2021-12-30 | Scheduling disposal rule mining and generating method and system based on Apriori |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114399177A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115098242A (en) * | 2022-08-24 | 2022-09-23 | 广州市城市排水有限公司 | Real-time acquisition and processing method and system for deep tunnel surveying and mapping data |
-
2021
- 2021-12-30 CN CN202111646787.5A patent/CN114399177A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115098242A (en) * | 2022-08-24 | 2022-09-23 | 广州市城市排水有限公司 | Real-time acquisition and processing method and system for deep tunnel surveying and mapping data |
| CN115098242B (en) * | 2022-08-24 | 2022-11-08 | 广州市城市排水有限公司 | Real-time acquisition and processing method and system for deep tunnel surveying and mapping data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107358366B (en) | Distribution transformer fault risk monitoring method and system | |
| CN106504116B (en) | Stability evaluation method based on power grid operation and transient stability margin index correlation | |
| CN109146093B (en) | Power equipment field investigation method based on learning | |
| CN106779505B (en) | Power transmission line fault early warning method and system based on big data driving | |
| CN106054104B (en) | A kind of intelligent electric meter failure real-time predicting method based on decision tree | |
| CN111639921B (en) | Intelligent equipment fault judgment and auxiliary disposal method based on expert system | |
| CN102638100B (en) | District power network equipment abnormal alarm signal association analysis and diagnosis method | |
| CN105044497B (en) | A kind of traction convertor intelligent fault analysis method | |
| MX2013000577A (en) | Machine learning for power grids. | |
| CN113189451A (en) | Power distribution network fault positioning studying and judging method, system, computer equipment and storage medium | |
| CN113435759B (en) | Primary equipment risk intelligent assessment method based on deep learning | |
| CN112818008A (en) | Intelligent diagnosis method, system, medium and electronic equipment for nuclear power debugging faults | |
| CN113283602A (en) | Power grid system fault diagnosis method based on offline knowledge learning and online dynamic correction | |
| CN110968703B (en) | Method and system for constructing abnormal metering point knowledge base based on LSTM end-to-end extraction algorithm | |
| CN117394529A (en) | SCADA-based auxiliary decision method and system for main distribution network loop-closing reverse power supply control conditions | |
| CN109308587A (en) | The reliability analysis system and its intelligent diagnosing method of electric automobile charging pile | |
| CN114399177A (en) | Scheduling disposal rule mining and generating method and system based on Apriori | |
| CN114487705A (en) | Power grid equipment fault positioning detection method | |
| CN104657814A (en) | Extraction definition method based on EMS system for relay protection device signal template | |
| Xu et al. | Fault diagnosis and identification of malfunctioning protection devices in a power system via time series similarity matching | |
| CN111736636A (en) | Flooded waterwheel room early warning method and system based on knowledge graph | |
| CN114265837A (en) | Station-side interactive data feature extraction method | |
| CN114626440A (en) | Monitoring signal objectification analysis method and device | |
| CN107958505A (en) | A kind of stable intelligent inspection system for operating condition and its control method | |
| Ding et al. | Reliability evaluation of direct current distribution system for intelligent buildings based on big data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221108 Address after: 310014 No. 1, Huadian lane, Zhaohui eighth District, Gongshu District, Hangzhou City, Zhejiang Province Applicant after: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Address before: The eight district of Hangzhou city in Zhejiang province 310014 Huadian Zhaohui under No. 1 Lane Applicant before: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Applicant before: NARI NANJING CONTROL SYSTEM Co.,Ltd. Applicant before: NARI Group Corp. |