CN109859588B - Man-machine confrontation scheduling training simulation system and method for extra-high voltage power grid - Google Patents
Man-machine confrontation scheduling training simulation system and method for extra-high voltage power grid Download PDFInfo
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Abstract
The invention discloses a man-machine confrontation scheduling training simulation system and method for an extra-high voltage power grid, wherein the method comprises the steps of receiving initial system parameters and regulation and control events sent by a student client, and starting a simulation calculation process based on a set initial operation mode; carrying out simulation calculation on the regulation and control event; and performing vulnerability analysis, selecting matched equipment fault events for the trainees according to training topics and topic difficulty levels selected by the trainees, completing automatic question setting, performing simulation calculation on the equipment fault events, and feeding the simulated power grid operation state back to the trainee client. According to the method, intelligent questions can be made according to weak links of power grid simulation and training requirements of students, so that the scientificity of the training simulation questions is improved, and the automatic intelligence level of a dispatcher simulation training system is improved; the invention can conveniently and individually drill, accurately and efficiently improve the dispatching skill level of the trainees and has strong practicability.
Description
Technical Field
The invention relates to a man-machine confrontation scheduling training simulation method and system for an extra-high voltage power grid, and belongs to the field of training simulation of power systems.
Background
With the rapid construction of smart power grids and extra-high voltage power grids, the physical form and the operating characteristics of the power grids are changed remarkably, the power grid characteristics are changed from a region mode to a whole mode, the influence range of local faults is expanded, and even faults in a larger range can be developed, so that power grid dispatchers are urgently required to master the operating characteristics of the extra-high voltage power grids, the capability and quality of driving the extra-high voltage power grids to safely and stably operate are improved, and major accidents of the power grids are effectively avoided. The international multiple blackout accidents show that various safety and stability problems are accompanied in the accident development process, and meanwhile, the blackout accidents are related to the handling capacity of power grid regulation and control operators and whether related power grid accident exercise training is carried out before the accidents. At present, a dispatching training simulation system is the most effective tool for improving the cognitive level of a dispatcher power grid fault and improving the capability of the dispatcher for handling the emergency accident of power grid operation, and plays an important role in improving the dispatching skill of the dispatcher.
The training simulation process of the current dispatching training simulation system is only in a teacher-student double-role manual question setting training mode, a teacher gives questions through a teacher client of the dispatcher training simulation system, namely, a power grid operation fault is set, and a student simulates to perform power grid operation regulation and control operation (answer) through the student client of the dispatcher training simulation system. The training effect of the existing mode depends on the judgment of a teacher individual on the power grid simulation state and the selection of related training questions to a great extent, the subjectivity of the training questions is strong, the training mode is not flexible enough, the automation and intelligence level of the training process is low, and the training quality is not stable.
The existing scheduling training simulation technology mainly has the following defects:
(1) the teacher gives questions manually, the individual subjects of the questions are strong in subjectivity, the relevance with the operation characteristics of the power grid is not strong, and the training practicability is insufficient.
(2) The intelligent computer automatic question setting function is lacked, and customized and personalized training facing to individuals cannot be realized.
(3) The training and practicing mode is not flexible enough, the man-machine confrontation exercise can not be realized, and the experience is poor.
Therefore, a man-machine confrontation simulation training method with high automation and intelligent functions is needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a man-machine confrontation scheduling training simulation system and method for an extra-high voltage power grid.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a man-machine confrontation scheduling training simulation system for an extra-high voltage power grid comprises a system server module and a student client module, wherein the student client module is used for setting initial system parameters and converting regulation and control operations of students into regulation and control events to be sent to the system server module, and the initial system parameters comprise an operation mode, a training theme and a question difficulty level;
the system server module includes: the system comprises a power grid simulation calculation module, a power grid weak link identification module and an automatic question setting module;
the power grid simulation calculation module is used for starting a simulation calculation process based on an initial operation mode set by the student client module; the system server module is also used for carrying out scheduling execution result simulation calculation on the regulation and control events sent by the student client module and received by the system server module; feeding the simulated power grid operation state back to the student client; sending the power grid section information to the power grid weak link identification module;
the power grid weak link identification module is used for analyzing weak points according to the power grid section output by the power grid simulation calculation module, wherein the weak points comprise equipment out-of-limit, loss load, frequency out-of-limit, power grid structure change, power grid risk condition and cut-off equipment, and outputting results to the automatic question setting module;
the automatic question setting module is used for selecting matched equipment faults for the trainees according to the analysis result of the power grid operation weak link identification module and aiming at the training subjects and question difficulty levels set by the trainees, setting equipment fault events to the power grid simulation calculation module,
and the power grid simulation calculation module is also used for carrying out fault simulation on the received equipment fault event sent by the automatic question setting module and feeding back the power grid running state to the student client.
Further, the power grid weak link identification module comprises a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module and an eighth module;
the first module is used for forming all the cut-off branch and node information according to the input power grid section information;
the second module is used for carrying out topology analysis on the change of the power grid structure according to all the cut-off branch circuits and node information formed by the first module and outputting the change condition of the power grid structure, the risk condition of the power grid and cut-off equipment;
the third module is used for carrying out N-1 scanning calculation to obtain an N-1 scanning calculation result based on all the cut-off branch circuits and node information formed by the first module;
the fourth module is used for analyzing the equipment out-of-limit condition based on the N-1 scanning calculation result of the third module;
the fifth module is used for analyzing the power grid splitting situation based on the N-1 scanning calculation result of the third module and outputting the power grid splitting situation and the on-off equipment;
the sixth module is used for carrying out detailed power flow calculation on the members of the cut-off equipment set causing the situation one by one based on the equipment out-of-limit situation and the power grid disconnection situation of the fourth module and the fifth module, and outputting the power grid equipment out-of-limit situation, the power grid risk situation and the cut-off equipment;
the seventh module is used for calculating the frequency of each split power grid based on the power grid splitting condition analyzed by the fifth module, and outputting a power grid frequency out-of-limit condition, a power grid risk condition and a cut-off device;
and the eighth module is used for carrying out loss load analysis and external full power failure topological analysis of the power plant, and outputting the conditions of load loss, external full stop of the power plant, power grid risk and on-off equipment.
Further, the automatic question setting module comprises a power grid risk evaluation module, a fault equipment sequencing module and a fault equipment selection module,
the power grid risk evaluation module is used for carrying out power grid risk evaluation according to the power grid risk condition and the cut-off equipment sent from the power grid weak link identification module and calculating a power grid risk index caused by each cut-off equipment;
the fault equipment sequencing module is used for dividing the topics of the fault equipment according to the grid risk indexes, dividing the difficulty level of the starting equipment in each topic, and sequencing the cut-off equipment in each level according to the grid risk indexes;
and the fault equipment selection module is used for selecting the disconnection equipment which is most in the front ranking and matched with the training subjects and the subject difficulty grades of the users as fault equipment, and outputting corresponding equipment fault events to carry out fault simulation so as to finish the automatic subject setting function.
Further, the grid risk indicator includes: a single risk indicator;
the individual risk indicators include: the frequency out-of-limit risk index S (f), the voltage out-of-limit risk index S (u), the overheating stability risk index S (i), the loss load risk index S (l), the grid structure change risk index S (c) and S (n) are grid disconnection risk indexes.
Still further, a composite risk indicator, the composite risk indicator being as follows:
S＝wfS(f)+wuS(u)+wiS(i)+wlS(l)+wcS(c)+wnS(n)，
in the formula, wf、wu、wi、wl、wcAnd wnFor the normalized weight of each type of grid risk, the weight satisfies the following constraint: w is af+wu+wi+wl+wc+wn＝1。
In another aspect, the invention provides a simulation training method of a man-machine confrontation scheduling training simulation system for an extra-high voltage power grid, which is provided based on the technical scheme and comprises the following steps:
receiving initial system parameters and a regulation and control event after regulation and control operation conversion, wherein the initial system parameters comprise an operation mode, a training subject and a subject difficulty level, and the initial system parameters are sent by a student client;
starting a simulation calculation process based on an initial operation mode set by a student client module;
carrying out simulation calculation on a scheduling execution result of the regulation and control event and feeding back the simulated power grid operation state to the student client;
performing weak point analysis according to the obtained power grid section information, wherein the weak points comprise equipment out-of-limit, loss load, frequency out-of-limit, power grid structure change, power grid risk condition and cut-off equipment;
according to the analysis result of the weak links of the power grid operation, aiming at the training subjects and the subject difficulty level selected by the student, the student selects matched equipment fault events to finish automatic subject setting, carries out simulation calculation on the equipment fault events and feeds the simulated power grid operation state back to the student client.
Further, the automatic question setting steps are as follows:
according to the power grid risk condition and the cut-off equipment output by the power grid operation weak link, performing power grid risk evaluation, and calculating a power grid risk index caused by each cut-off equipment;
dividing the subjects of the fault equipment according to the power grid risk indexes, dividing the difficulty levels of the starting equipment in each subject, and finally sequencing the cut-off equipment in each level from large to small according to the power grid risk indexes;
and selecting the switching-on and switching-off equipment which is matched with the training subject and the difficulty level of the user and is ranked most at the front as fault equipment, and outputting corresponding equipment fault events to perform fault simulation so as to finish the automatic question setting function.
The invention achieves the following beneficial effects:
(1) the man-machine confrontation simulation training method provided by the invention is a brand-new dispatching simulation training mode for extra-high voltage power grid dispatching personnel training, is different from a conventional teacher and student dual-angle tone simulation training mode, can realize single-person targeted intelligent training, and greatly improves the training flexibility and the confrontation in the training process.
(2) The power grid weak link identification content, method and process provided by the invention fully consider the actual requirements of the ultra-high voltage large power grid dispatching training and have high practical value.
(3) The automatic question setting method provided by the invention has high intelligence and scientificity, and the software is used for performing targeted question setting according to the current weak link of power grid simulation, so that the subjectivity of manual question setting is avoided, the scientificity and intelligence of training question setting are realized, and the quality and efficiency of student simulation training are greatly improved.
(4) The power grid risk index calculation method provided by the invention can effectively distinguish the influence degree of different on-off equipment on the power grid risk, provides support for the pertinence of training questions, and improves the practicability of a man-machine confrontation training system.
Drawings
FIG. 1 is a schematic diagram of a simulation system for human-computer training for countermeasure scheduling according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a power grid weak link identification module framework according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a human-machine confrontation dispatch training simulation system according to another embodiment of the present invention;
FIG. 4 is a flow chart of an automatic question setting method according to an embodiment of the present invention;
fig. 5 is a flowchart of a method for selecting a faulty device according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1: FIG. 1 is a schematic diagram of a simulation system for human-computer training for countermeasure scheduling according to an embodiment of the present invention; as shown in FIG. 1, a man-machine confrontation scheduling training simulation system facing an extra-high voltage power grid is provided, and comprises a system server module and a student client module.
The student client module is used for setting initial system parameters and converting the regulation and control operation of the student into a regulation and control event and sending the regulation and control event to the system server module, wherein the initial system parameters comprise an operation mode, a training subject and a subject difficulty grade;
the system server module includes: the system comprises a power grid simulation calculation module, a power grid weak link identification module and an automatic question setting module;
the power grid simulation calculation module is used for carrying out simulation calculation on a scheduling execution result on the regulation and control event sent by the student client module and received by the system server module, feeding the simulated power grid operation state back to the student client, and sending power grid section information to the power grid weak link identification module;
the power grid weak link identification module is used for analyzing weak points according to the power grid section output by the power grid simulation calculation module, wherein the weak points comprise equipment out-of-limit, loss load, frequency out-of-limit, power grid structure change, power grid risk condition and cut-off equipment, and outputting results to the automatic question setting module;
the automatic question setting module is used for selecting matched equipment faults for the trainees according to the analysis result of the power grid operation weak link identification module and aiming at the training subjects and question difficulty levels set by the trainees, setting equipment fault events to the power grid simulation calculation module,
and the power grid simulation calculation module is also used for carrying out fault simulation on the received equipment fault event sent by the automatic question setting module and feeding back the power grid running state to the student client.
In a specific embodiment, the trainee client module may be disposed on a terminal such as a mobile phone, a tablet, or a PC, and may be connected to the system server through a wire or wirelessly. The trainees interact with the training system provided with the system server module through the trainee client,
example 2: on the basis of the above embodiments, the present embodiment further provides a component module of the power grid weak link identification module. FIG. 2 is a schematic diagram of a power grid weak link identification module framework according to an embodiment of the present invention; as shown in fig. 2: the power grid weak link identification module comprises a first module (namely a module 1), a second module (namely a module 2), a third module (namely a module 3), a fourth module (namely a module 4), a fifth module (namely a module 5), a sixth module (namely a module 6), a seventh module (namely a module 7) and an eighth module (namely a module 8);
the first module is used for forming all the cut-off branch and node information according to the input power grid section information;
the second module is used for carrying out topology analysis on the change of the power grid structure according to all the cut-off branch circuits and node information formed by the first module and outputting the change condition of the power grid structure, the risk condition of the power grid and cut-off equipment;
the third module is used for carrying out N-1 scanning calculation to obtain an N-1 scanning calculation result based on all the cut-off branch circuits and node information formed by the first module;
the fourth module is used for analyzing the equipment out-of-limit condition based on the N-1 scanning calculation result of the third module;
the fifth module is used for analyzing the power grid splitting situation based on the N-1 scanning calculation result of the third module and outputting the power grid splitting situation and the cut-off equipment;
the sixth module is used for carrying out detailed power flow calculation on the cut-off equipment set members causing the situation one by one on the basis of the equipment out-of-limit situation and the power grid disconnection situation of the fourth module and the fifth module, and outputting the power grid equipment out-of-limit situation, the power grid risk situation and the cut-off equipment;
the seventh module is used for calculating the frequency of each split power grid based on the power grid splitting condition analyzed by the fifth module and outputting a power grid frequency out-of-limit condition, a power grid risk condition and a cut-off device;
and the eighth module is used for analyzing loss load and the topology of the external full power failure of the power plant, and outputting the conditions of load loss, external full stop of the power plant, power grid risk and equipment on and off.
The power grid weak link identification module comprises a power grid weak link identification module, a power grid fault detection module and a power grid fault diagnosis module, wherein the power grid weak link identification module comprises a plurality of modules, and the power grid fault detection module comprises a plurality of modules.
Example 3: on the basis of the above embodiments, the present embodiment further provides a composition of an automatic question generation module. As shown in fig. 3, the automatic question setting module includes a power grid risk evaluation module, a fault device sorting module and a fault device selection module,
the power grid risk evaluation module is used for carrying out power grid risk evaluation according to the power grid risk condition and the cut-off equipment sent from the power grid weak link identification module and calculating a power grid risk index caused by each cut-off equipment;
the fault equipment sequencing module is used for performing theme division on the fault equipment according to the power grid risk indexes, performing difficulty and easiness grade division on the starting equipment in each theme, and finally sequencing the cut-off equipment in each grade according to the power grid risk indexes;
and the failure equipment selection module is used for selecting the disconnection equipment which is most in the front of the sequence and matched with the training subjects and the subject difficulty grades of the users as failure equipment, and outputting corresponding equipment failure events to perform failure simulation so as to finish the automatic subject setting function.
The input of the automatic question setting module is the power grid risk condition output by the power grid weak link identification module, the training subjects and the question difficulty level output by the on-off equipment (power grid weak link) and the student client end module, and the output is the equipment fault event, so that the automatic question setting function is completed.
In a particular embodiment, the grid risk indicators include: a single risk indicator; individual risk indicators include: the frequency out-of-limit risk index S (f), the voltage out-of-limit risk index S (u), the overheating stability risk index S (i), the loss load risk index S (l), the grid structure change risk index S (c) and S (n) are grid disconnection risk indexes. The grid risk indicator further comprises: the comprehensive risk indexes are as follows:
S＝wfS(f)+wuS(u)+wiS(i)+wlS(l)+wcS(c)+wnS(n)，
in the formula, wf、wu、wi、wl、wcAnd wnFor the normalized weight of each type of grid risk, the weight satisfies the following constraint: w is af+wu+wi+wl+wc+wn＝1。
The automatic question setting module provided by the embodiment sorts according to the difficulty degree of faulty equipment, and combines the selection of students and the automatic selection of the most appropriate questions according to the weak links of the power grid, so that the personalized customization of the students is completed, the question coverage is appropriate, a good training effect can be achieved, the defect of subjective question setting of individuals is avoided, and the training efficiency is greatly improved.
Implementation 5: the embodiment provides a simulation training method of a man-machine confrontation scheduling training simulation system for an extra-high voltage power grid, which is provided based on the technical scheme and comprises the following steps:
the trainees interact with a system server provided with a training system through trainee clients, and initial system parameters including operation modes, training subjects and subject difficulty levels are set; the system server receives initial system parameters sent by the student client and a regulation and control event after regulation and control operation conversion;
starting a simulation calculation process based on an initial operation mode set by a student client module;
carrying out simulation calculation on a scheduling execution result of the regulation and control event and feeding back the simulated power grid operation state to the student client;
performing weak point analysis according to the obtained power grid section information, wherein the weak points comprise equipment out-of-limit, loss load, frequency out-of-limit, power grid structure change, power grid risk condition and cut-off equipment;
according to the analysis result of the weak link of the power grid operation, aiming at the training subjects and the subject difficulty level selected by the student, selecting matched equipment fault events for the student to finish automatic subject setting, carrying out simulation calculation on the equipment fault events and feeding the simulated power grid operation state back to the student client; the trainees perform regulation and control operation through the trainee client based on the power grid running state, namely, the training answering process is executed.
Example 6: on the basis of the above embodiments, the present embodiment provides a method for performing vulnerability analysis according to obtained power grid section information, including:
forming all the cut-off branch and node information according to the input power grid section information;
according to all the cut-off branch and node information, carrying out power grid structure change topology analysis, and outputting a power grid structure change condition, a power grid risk condition and cut-off equipment;
based on all the cut-off branch and node information, carrying out N-1 scanning calculation to obtain an N-1 scanning calculation result, and analyzing the equipment out-of-limit condition based on the N-1 scanning calculation result;
analyzing the power grid splitting condition based on the N-1 scanning calculation result, and outputting the power grid splitting condition and the cut-off equipment;
based on the equipment out-of-limit condition and the power grid disconnection condition, performing detailed load flow calculation on the members of the disconnected equipment set which cause the condition one by one, and outputting the power grid equipment out-of-limit condition, the power grid risk condition and the disconnected equipment;
calculating the frequency of each split power grid based on the power grid splitting condition, and outputting a power grid frequency out-of-limit condition, a power grid risk condition and a switching-on/switching-off device;
and carrying out loss load analysis and external full power failure topological analysis of the power plant, and outputting the load loss, the external full stop condition of the power plant, the power grid risk condition and the on-off equipment.
The method can realize intelligent question setting according to weak links of power grid simulation and training requirements of students, so that the scientificity of the training simulation question setting is improved, and the automatic intelligence level of a dispatcher simulation training system is improved. The trainees can conveniently and conveniently carry out personalized drilling through the man-machine confrontation simulation training so as to accurately and efficiently improve the self dispatching skill level, so the invention has strong practicability at the same time.
Example 7: on the basis of the above embodiment, the present embodiment includes a method flow for automatically setting questions.
The basic principle of the automatic question setting method is shown in fig. 4, the input of the automatic question setting method is the power grid risk condition, the power grid weak link which is the on-off equipment, and the training subject and the question difficulty level output by the client of the student, and the output is the equipment fault event, so as to complete the automatic question setting function.
The automatic question-making method is described as follows:
and the power grid risk evaluation module carries out power grid risk evaluation according to the input power grid risk condition and the cut-off equipment, and calculates a power grid risk index caused by each cut-off equipment, wherein the higher the index is, the larger the power grid risk is represented.
Dividing the subjects of the fault equipment according to the power grid risk indexes, dividing the difficulty levels of the starting equipment in each subject, and finally sequencing the cut-off equipment in each level from large to small according to the power grid risk indexes; the higher the resulting grid risk index value, the higher the ranking of the faulty device.
The faulty equipment selection module selects the top ranked ones that match the user's training topic and difficulty rating
And the on-off equipment is used as fault equipment, and corresponding equipment fault events are output to carry out fault simulation, so that the automatic question setting function is completed.
The invention provides an automatic training question making technology based on intellectualization, namely, aiming at a weak link of power grid operation, faults which are most easy to occur are screened out for automatic question making, so that the subjectivity of manual question making is avoided, and the scientificity and practicability of training are improved;
on the basis of the embodiment, preferably, different power grid risks are caused by different cut-off devices, and in order to quantitatively score the severity of the power grid risks, the power grid risk index calculation method is designed and divided into single risk index calculation and comprehensive risk index calculation, and specifically, the method comprises the following steps:
(1) single grid risk indicator calculation
1) Frequency out-of-limit risk indicator
Wherein f is the system frequency; f. ofh、flFor warning upper and lower limits of frequency, fh_lim、fl_limFour limits are set for the upper and lower frequency risk limits according to relevant requirements.
2) Voltage out-of-limit risk indicator
Node x voltage out-of-limit risk indicator:
in the formula uxIs the voltage of node x; u. ofh、ulFor upper and lower voltage warning limits uh_lim、ul_limFour limits are set for the upper and lower voltage risk limits according to relevant requirementsAnd (4) determining.
The system voltage out-of-limit risk index:
in the formula exIs a voltage node x importance factor, and exAnd (3) setting manually according to the importance degree of the nodes, wherein X is the set of all out-of-limit nodes.
3) Risk index of superheat stability
Equipment x overheating stability risk index:
in the formula ixIs the current of device x; i.e. idFor the overload warning threshold of the installation, set in dependence on the mode of operation, according to the relevant requirements, ilimThe threshold value of the overload risk of the equipment is generally taken as the value of the limit capacity of the equipment.
The system overheating stability risk index is as follows:
in the formula exIs an electrical node x importance factor, and exAnd (3) setting manually according to the importance degree of the nodes, wherein X is the set of all out-of-limit nodes.
4) Loss load risk indicator
In the formula IlimA risk threshold (percentage) is set for loss of load,
Lloseis a set of off-loaded nodes, eiIs the importance factor of the load node i, and eiIf the load is less than 1, manually setting according to the load importance degree;
is the load loss of node i, PLThe total load of the power grid before the accident.
5) Grid structure change risk indicator
The power grid structure change comprises the following steps: and the electromagnetic ring network in the power supply area is subjected to ring opening (topology analysis result), the interconnection condition of the power supply area is changed, and the like.
6) Grid splitting risk indicator
(2) Integrated risk indicator calculation
S＝wfS(f)+wuS(u)+wiS(i)+wlS(l)+wcS(c)+wnS(n)
In the formula, w is the normalized weight of each type of power grid risk and is set manually.
The following constraint is satisfied: w is af+wu+wi+wl+wc+wn＝1。
According to the automatic question setting scheme provided by the invention, different power grid risks can be caused according to different on-off equipment, in order to quantitatively grade the severity of the power grid risks, a power grid risk index calculation method is added, and the method is divided into single risk index calculation and comprehensive risk index calculation, so that the influence degrees of different on-off equipment on the power grid risks can be effectively distinguished, the risk evaluation range is more reasonable, the automatic question setting is more reasonable and targeted, and the training effect is greatly improved.
In a specific embodiment, the disconnection devices are sorted from large to small according to a single risk indicator or a comprehensive risk indicator caused by the disconnection devices to serve as alternative fault device sequences (or alternative question sequences) of different training subjects, and the specific sorting method is as follows:
(1) frequency off-limit risk disconnect device sequencing
And according to the magnitude of the frequency out-of-limit risk index caused by the equipment disconnection, performing descending sorting on all the disconnected equipment to serve as a frequency risk theme-oriented alternative fault equipment sequence, wherein the sorting represents the difficulty coefficient of the equipment fault at the same time, and the equipment fault difficulty coefficient is higher before the ranking.
(2) Voltage out-of-limit risk disconnect equipment sequencing
And according to the magnitude of the out-of-limit risk index of the system voltage caused by the equipment after being switched on and switched off, sequencing all the switched-on and switched-off equipment in a descending order to serve as a voltage risk theme-oriented alternative fault equipment sequence, wherein the sequencing simultaneously represents the difficulty coefficient of the equipment fault, and the higher the difficulty coefficient of the equipment fault is, the higher the ranking is.
(3) Order of equipment for switching on and off subject of risk of overheating stability
And sequencing all the cut-off equipment in a descending order according to the size of the system overheating stability risk index caused by the cut-off of the equipment, wherein the sequencing is used as an alternative fault equipment sequence facing the overheating stability risk theme, the sequencing simultaneously represents the difficulty coefficient of the equipment fault, and the equipment fault difficulty coefficient is higher before the ranking.
(3) Loss load (plant stop) risk shutdown equipment sequencing
And according to the size of a system loss load risk index caused by the equipment after being disconnected, sequencing all the disconnected equipment in a descending order to serve as a candidate fault equipment sequence facing the loss load risk theme, wherein the sequencing simultaneously represents the difficulty coefficient of the equipment fault, and the equipment fault difficulty coefficient is higher before the ranking is higher.
(4) Power grid structure change risk on-off equipment sequencing
And sequencing all the cut-off equipment in a descending order according to the size of the risk index of the change of the power grid structure caused by the cut-off of the equipment, wherein the sequencing is used as a candidate fault equipment sequence facing the risk theme of the power grid structure, the sequencing simultaneously represents the difficulty coefficient of the equipment fault, and the more the ranking, the higher the difficulty coefficient of the equipment fault is.
(5) Power grid splitting risk on-off equipment sequencing
And according to the size of a power grid splitting risk index caused by the fact that the equipment is disconnected, performing descending sorting on all the disconnected equipment to serve as an alternative fault equipment sequence facing to the power grid splitting risk theme, wherein the sorting represents the difficulty coefficient of the equipment fault at the same time, and the equipment fault difficulty coefficient is higher before the ranking is higher.
(6) Power grid comprehensive risk on-off equipment sequencing
And sequencing all the cut-off equipment in a descending order according to the size of the comprehensive risk index of the power grid caused by the cut-off of the equipment, wherein the sequencing is used as an alternative fault equipment sequence facing the comprehensive risk theme of the power grid, the sequencing simultaneously represents the difficulty coefficient of the equipment fault, and the more the ranking is, the higher the difficulty coefficient of the equipment fault is.
In a specific embodiment, based on the technical solution of the above embodiment, the method for selecting the faulty device is as follows:
the theme obtained by theme division of the fault equipment according to the grid risk index comprises the following steps: the method comprises a frequency out-of-limit risk theme, a voltage out-of-limit theme, an overheating stability risk theme, a loss load risk theme, a power grid structure change risk theme, a power grid splitting risk theme and a comprehensive risk theme.
Dividing fault equipment sequences corresponding to training themes into three grade sequences of easy, medium and difficult according to training themes selected by users, such as a comprehensive risk theme, a frequency out-of-limit risk theme, a voltage out-of-limit risk theme and the like; selecting the most front equipment from the corresponding grade sequence according to the question difficulty grade selected by the user; and setting equipment faults and outputting fault events. The principle of the faulty device selection method is shown in fig. 5.
In a specific embodiment, based on the technical solution of the above embodiment, a preferred method for classifying difficulty levels of faulty devices is as follows:
according to a training theme selected by a user, the theme sequenced fault equipment sequence is further divided into three difficulty grades of 'easy', 'medium' and 'difficult', and the division method comprises the following steps: the sorted fault equipment sequence is divided into three fault difficulty grades according to the difficulty parameter, and the specific calculation method is shown in table 1.
TABLE 1 alternate Fault device difficulty ratings
E in the above tablef、Mf、Eu、Mu、Ei、Mi、El、Ml、Ec、Mc、En、MnE, M is a 'difficulty parameter', which can be flexibly set by the user according to specific conditions.
According to the invention, through interaction between the server and the client, a man-machine confrontation training drill mode is realized, and the flexibility, individuation and customization of training are improved, so that the invention has extremely high application value;
as will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. A man-machine confrontation scheduling training simulation system for an extra-high voltage power grid is characterized by comprising a system server module and a student client module, wherein the student client module is used for setting initial system parameters and converting regulation and control operations of students into regulation and control events to be sent to the system server module, and the initial system parameters comprise an operation mode, a training theme and a question difficulty level;
the system server module includes: the system comprises a power grid simulation calculation module, a power grid weak link identification module and an automatic question setting module; the power grid simulation calculation module is used for starting a simulation calculation process based on an initial operation mode set by the student client module; the system server module is also used for carrying out scheduling execution result simulation calculation on the regulation and control events sent by the student client module and received by the system server module; feeding the simulated power grid operation state back to the student client; sending the power grid section information to the power grid weak link identification module; the power grid weak link identification module is used for analyzing weak points according to the power grid section output by the power grid simulation calculation module, wherein the weak points comprise equipment out-of-limit, loss load, frequency out-of-limit, power grid structure change, power grid risk condition and cut-off equipment, and outputting results to the automatic question setting module; the automatic question setting module is used for selecting matched equipment faults for the trainees according to the analysis result of the power grid operation weak link identification module and aiming at the training subjects and question difficulty levels set by the trainees, setting equipment fault events to the power grid simulation calculation module,
the power grid simulation calculation module is also used for carrying out fault simulation on the received equipment fault event sent by the automatic question setting module and feeding back the running state of the power grid to the student client;
the automatic question setting module comprises a power grid risk evaluation module, a fault equipment sequencing module and a fault equipment selection module, wherein the power grid risk evaluation module is used for carrying out power grid risk evaluation according to the power grid risk condition and the cut-off equipment sent from the power grid weak link identification module and calculating a power grid risk index caused by each cut-off equipment; the fault equipment sequencing module is used for dividing the topics of the fault equipment according to the grid risk indexes, dividing the difficulty level of the starting equipment in each topic, and sequencing the cut-off equipment in each level according to the grid risk indexes; the fault equipment selection module is used for selecting the cut-off equipment which is matched with the training subjects and the subject difficulty levels of the users and is ranked most forward as fault equipment, and outputting corresponding equipment fault events to perform fault simulation so as to finish the automatic subject setting function;
the grid risk indicator comprises a single risk indicator, and the single risk indicator comprises: the frequency out-of-limit risk index S (f), the voltage out-of-limit risk index S (u), the overheating stability risk index S (i), the loss load risk index S (l), the grid structure change risk index S (c) and S (n) are grid disconnection risk indexes;
1) frequency out-of-limit risk indicator
Wherein f is the system frequency; f. ofh、flFor warning upper and lower limits of frequency, fh_lim、fl_limUpper and lower frequency risk limits;
2) voltage out-of-limit risk indicator
Node x voltage out-of-limit risk indicator:
in the formula uxIs the voltage of node x; u. ofh、ulFor upper and lower voltage warning limits uh_lim、ul_limVoltage risk upper and lower limits;
the system voltage out-of-limit risk index:
in the formula exIs an importance factor of the voltage node x, and ex＜1，exPresetting, wherein X is a set of all out-of-limit nodes;
3) risk index of superheat stability
Equipment x overheating stability risk index:
in the formula ixIs the current of device x; i.e. idFor device overload warning threshold, ilimIs a device overload risk threshold;
the system overheating stability risk index is as follows:
in the formula exIs an electrical node x importance factor, and ex＜1，exPresetting that X is a set of all out-of-limit nodes;
4) loss load risk indicator
In the formula IlimA risk threshold is set for the loss of load,
Lloseis a set of off-loaded nodes, eiIs the importance factor of the load node i, and ei＜1，eiPresetting;
is the load loss of node i, PLThe total load of the power grid before the accident;
5) grid structure change risk indicator
6) Grid splitting risk indicator
2. The human-machine confrontation scheduling training simulation system as claimed in claim 1, wherein the power grid weak link identification module comprises a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module and an eighth module;
the first module is used for forming all the cut-off branch and node information according to the input power grid section information;
the second module is used for carrying out topology analysis on the change of the power grid structure according to all the cut-off branch circuits and node information formed by the first module and outputting the change condition of the power grid structure, the risk condition of the power grid and cut-off equipment;
the third module is used for carrying out N-1 scanning calculation to obtain an N-1 scanning calculation result based on all the cut-off branch circuits and node information formed by the first module;
the fourth module is used for analyzing the equipment out-of-limit condition based on the N-1 scanning calculation result of the third module;
the fifth module is used for analyzing the power grid splitting situation based on the N-1 scanning calculation result of the third module and outputting the power grid splitting situation and the on-off equipment;
the sixth module is used for carrying out detailed power flow calculation on the members of the cut-off equipment set causing the situation one by one based on the equipment out-of-limit situation and the power grid disconnection situation of the fourth module and the fifth module, and outputting the power grid equipment out-of-limit situation, the power grid risk situation and the cut-off equipment;
the seventh module is used for calculating the frequency of each split power grid based on the power grid splitting condition analyzed by the fifth module, and outputting a power grid frequency out-of-limit condition, a power grid risk condition and a cut-off device;
and the eighth module is used for carrying out loss load analysis and external full power failure topological analysis of the power plant, and outputting the conditions of load loss, external full stop of the power plant, power grid risk and on-off equipment.
3. The human-machine confrontation dispatching training simulation system as claimed in claim 1, wherein the grid risk indicators comprise: a composite risk indicator, the composite risk indicator being as follows:
S＝wfS(f)+wuS(u)+wiS(i)+wlS(l)+wcS(c)+wnS(n)，
in the formula, wf、wu、wi、wl、wcAnd wnFor the normalized weight of each type of grid risk, the weight satisfies the following constraint: w is af+wu+wi+wl+wc+wn＝1。
4. The extra-high voltage power grid-oriented man-machine countermeasure scheduling training simulation system as claimed in claim 1, wherein the theme obtained by theme division of the fault equipment according to the power grid risk indicator comprises: the method comprises a frequency out-of-limit risk theme, a voltage out-of-limit theme, an overheating stability risk theme, a loss load risk theme, a power grid structure change risk theme, a power grid splitting risk theme and a comprehensive risk theme.
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