CN103198437A - Power grid measurement data and power grid model correction method and device - Google Patents
Power grid measurement data and power grid model correction method and device Download PDFInfo
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Abstract
The embodiment of the invention discloses a power grid measurement data and power grid model correction method. The method comprises the following steps: acquiring power grid measurement data and power grid model data from a data acquisition and monitoring control system; performing state estimation of the actual operating state of the power grid according to the power grid measurement data and the power grid model data; matching the result of the state estimation with the preset conditions, and positioning the error in the power grid measurement data and the power grid model data according to the matching conditions; and correcting the positioned error according to the preset rule. The invention also discloses a power grid measurement data and power grid model correction device. According to the method, the accuracy of the power grid measurement data and the power grid model data can be improved, and a guarantee is provided for performing advanced application based on the data.
Description
Technical field
The application relates to the electric power network technique field, particularly a kind of electrical network metric data and CIM model error correction method and corresponding device thereof.
Background technology
For the development that adapts to power technology with satisfy under the new situation more and more higher need for electricity, the intelligent grid construction is carried out in on a large scale.The key of intelligent grid is intelligent scheduling.So-called intelligent scheduling refers to realize the active of electrical network, intelligent supervision, analysis, early warning, aid decision making and self-healing control by means such as state estimation, optimal load flow calculating, idle work optimization, stability analysis and calculation of fault.The basis of carrying out intelligent scheduling is to provide electrical network metric data and electric network model by network topology structure, realizes by the network topology analysis on this basis.
At present, obtain electrical network metric data and electric network model by the OPEN3000 system usually, but there are some problems in the metric data that OPEN3000 provides and model information, these problems will influence the accuracy of dispatching of power netwoks software analysis.For this reason, the accuracy of electrical network metric data and model information is most important.For improving accuracy, need carry out association by CIM model file, SVG figure and detect, workload is huge, loaded down with trivial details, and need have the managerial personnel of working experience to be competent at.This mode has made obstacle for the error correction of electrical network metric data and electric network model data.
Summary of the invention
For solving the problems of the technologies described above, the embodiment of the present application provides a kind of electrical network metric data and electric network model error correction method and corresponding device thereof, to navigate to mistake fast and to realize error correction, improves the accuracy of electrical network metric data and electric network model.
Electrical network metric data and electric network model error correction method that the embodiment of the present application provides comprise:
Obtain electrical network metric data and electric network model data from data acquisition and supervisor control;
Carry out the state estimation of electrical network actual motion state according to electrical network metric data and electric network model data;
With result and pre-conditioned coupling of state estimation, according to the mistake of match condition location electrical network metric data and the existence of electric network model data;
According to preset rules the mistake of location is corrected.
Preferably, described state estimation of carrying out electrical network actual motion state according to electrical network metric data and electric network model data specifically comprises:
According to electrical network metric data and electric network model The data weighted least-squares method, orthogonal transformation method or carry out the state estimation of electrical network actual motion state based on the direct transform method of piecemeal Givens rotation;
Described direct transform method based on piecemeal Givens rotation is: carry out the Givens orthogonal transformation and handle, utilize the blocking characteristic of Power system state estimation problem to carry out piecemeal to measuring Jacobi matrix, carry out the optimization of column number according to the piecemeal sparsity structure of information matrix, adopt and become rotating shaft by being listed as the unit's strategy that disappears, based on minimum degree principle Dynamic Selection rotating shaft element, inject the minimum principle of element according to non-zero and select the rotation element.
Preferably, the circulation of pre-set threshold value appears surpassing in the described pre-conditioned state estimation result that comprises, if the circulation of pre-set threshold value appears surpassing in the result after the state estimation, be the equipment that has paired running in the electrical network according to the electrical network metric data of match condition location and the mistake of electric network model data existence then.
Preferably, described pre-conditioned state estimation result and the equipment sampled value of comprising there are differences, if the result after the state estimation and equipment sampled value there are differences, be the model parameter mistake according to the electrical network metric data of match condition location and the mistake of electric network model data existence then.
Preferably, when state estimation does not restrain, described method also comprises to be revised in the following manner to state estimation procedure: with the state estimation error maximum deviation equipment output of iteration each time, if the error maximum deflection difference value is constantly diminishing, then record this error maximum deviation equipment, if the error maximum deflection difference value constantly enlarges, then record this error maximum deviation equipment so that outputting alarm information is carried out error correcting.
Preferably, when state estimation does not restrain, described method also comprises to be revised in the following manner to state estimation procedure: reduce to carry out the network range of the electrical network of state estimation, described state estimation of carrying out electrical network actual motion state according to electrical network metric data and electric network model data is specially the state estimation of carrying out the actual motion state according to electrical network metric data and the electric network model data electrical network after to the network range that reduces electrical network.
Further preferably: the described network range that reduces to carry out the electrical network of state trajectory comprises:
By selecting different electric pressures to reduce network range; Or, reduce network range by not detecting non-charged island; Or, by switch deciliter and the input of equipment, withdraw from and reduce network range.
The embodiment of the present application also provides a kind of electrical network metric data and electric network model error correction device.This device comprises: data capture unit, state estimation unit, location of mistake unit and error correcting unit, wherein:
Described data capture unit is used for obtaining electrical network metric data and electric network model data from data acquisition and supervisor control;
Described state estimation unit is used for carrying out according to electrical network metric data and electric network model data the state estimation of electrical network actual motion state;
Described location of mistake unit is used for result and pre-conditioned coupling the with state estimation, according to the mistake of match condition location electrical network metric data and the existence of electric network model data;
Described error correcting unit is used for according to preset rules the mistake of location being corrected.
Preferably, when state estimation does not restrain, described device also comprises the first state amending unit, be used for the state estimation error maximum deviation equipment output of iteration each time, if the error maximum deflection difference value is constantly diminishing, then do not record this error maximum deviation equipment, if the error maximum deflection difference value constantly enlarges, then record this error maximum deviation equipment so that outputting alarm information is carried out error correcting.
Preferably, when state estimation does not restrain, described device also comprises the second state amending unit, be used for reducing carrying out the network range of the electrical network of state estimation, described state estimation unit specifically is used for carrying out according to electrical network metric data and the electric network model data electrical network after to the network range that reduces electrical network the state estimation of actual motion state.
The embodiment of the present application is obtained electrical network metric data and electric network model from data acquisition and surveillance, carry out the estimation of electrical network actual motion state according to these two classes data then, come the mistake that exists in positioning measurement data and the model data according to state estimation and pre-conditioned matching result again, and then it is corrected.Compared with prior art, the embodiment of the present application does not need to carry out association again and detects, can navigate to wrong and correction and only need to carry out state estimation, reduced workload, improve the accuracy of electrical network metric data and electric network model, thereby improved guarantee for the operation of the high-level software of topological analysis platform Network Based.
Description of drawings
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, apparently, the accompanying drawing that describes below only is some embodiment that put down in writing among the application, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the process flow diagram of an embodiment of the application's electrical network metric data and electric network model error correction method;
Fig. 2 is an embodiment composition frame chart of the application's electrical network metric data and electric network model error correction device;
Hardware system structure figure when Fig. 3 (a) is electrical network metric data and the design of electric network model error correction device;
Software systems stratal diagram when Fig. 3 (b) is electrical network metric data and the design of electric network model error correction device.
Embodiment
In order to make those skilled in the art person understand technical scheme among the application better, below in conjunction with the accompanying drawing in the embodiment of the present application, technical scheme in the embodiment of the present application is clearly and completely described, obviously, described embodiment only is the application's part embodiment, rather than whole embodiment.Based on the embodiment among the application, those of ordinary skills are not making the every other embodiment that obtains under the creative work prerequisite, all should belong to the scope of the application's protection.
Before introducing the application's various embodiment in detail, the indivedual concepts that earlier the application related to give brief explanation:
1, CIM model: CIM model (Common Information Model, common information model) is that have nothing to do with specific implementation, as to be used for describing a management information conceptual model.The CIM model is divided into two parts: CIM standard (CIM Specification) and CIM pattern (CIM Schema).The CIM standard provides the formal definition of model, has described language, name, meta schema and to the mapping techniques of other administrative models (as SNMP MIB); The CIM pattern has then provided the description of realistic model.The CIM model is made of for three layers kernel model, common model and extended model.Kernel model is the set of a series of classes, connection and attribute, and this group of objects provides all management domains general essential information model; Common model provides the Common Information Model in cura specialis territory, and these specific management domains are as system, application program, network and equipment etc.; Extended model represents the particular technology expansion of universal model.
By the CIM modeling, can access the abstract and expression of entity in the management domain, comprise their attribute, operation and relation.Such model is independent of any concrete database, application, agreement and platform.Therefore, the application based on different platform that the CIM model requires different developers to provide all adopts a kind of form of standard to describe management data, so that data can be shared between multiple application.CIM adopts OO mode to make up a kind of new structure that is applicable to management system, network and conceptual model.The CIM modeling is a kind of universal method.The CIM modeling in cura specialis territory is to expand on the basis of kernel model and common model.
Generally speaking common information model (CIM) is a standard, defined the model of a self-consistentency, according to this model, the network equipment, system and application program can show relevant their information, and make these information be utilized CIM can describe such as desktop software and hardware configuration, the sequence number of CPU package blocks and the information such as traffic level on certain special routers port by management tool.
2, SVG:SVG scalable vector graphics (Scalable Vector Graphics) is based on extend markup language (XML), is used for describing a kind of graphical format of two-dimension vector graphics.SVG is a kind of new two-dimension vector graphics form that W3C (World Wide Web ConSor-tium Internet normal structure) formulated in August, 2000, also is the network vector graphics standard in the standard.The XML grammer is deferred in the SVG strictness, and describes picture material with the descriptive language of text formatting, is a kind of therefore and the irrelevant vector graphics format of image resolution ratio.
3, SCADA:SCADA (Supervisory Control And Data Acquisition) system, i.e. data acquisition and supervisor control.The application of SCADA system is very wide, and it can be applied to the data acquisition and numerous areas such as supervision control and process control in fields such as electric system, water supply system, oil, chemical industry.On electric system and electric railway, claim telecontrol system again.The SCADA system is production run control and the dispatch automated system based on computing machine.It can monitor the operational outfit at scene and control, to realize every functions such as data acquisition, equipment control, measurement, parameter adjusting and various types of signal warning.Because each application is to the requirement difference of SCADA, the development of the SCADA system in different application field is also incomplete same.
Referring to Fig. 1, the figure shows the flow process of an embodiment of the application's electrical network metric data and electric network model error correction method.This embodiment comprises:
Step S101: obtain electrical network metric data and electric network model from data acquisition and supervisor control;
Step S102: the state estimation of carrying out electrical network actual motion state according to electrical network metric data and electric network model data;
State estimation is the base application of Power System Analysis, its objective is the actual motion state of estimating electrical network according to the measurement information of electrical network.More accurate for the result who makes state estimation, can carry out the identification operation of bad data to the data of from the SCADA storehouse, extracting.The concrete grammar of identification can adopt the residual error search procedure, also can adopt based on type one by one and estimate identification method, the latter is the once recurrence to the former, but it no longer carries out overall iteration correction when getting rid of a suspicious data, but utilize residual error sensitivity and Jacobi matrix directly to revise the residual sum state, make computing technique be brought up to the level of only the minority coherent element being operated by overall matrix operant level, make the identification time of residual error search procedure shorten several magnitude, make its tight search logic be able to practicality.Adopt one by one type to estimate that identification method carries out the bad data identification, the identification process of bad data not only reliably but also quick.
In addition, more accurate for the result who makes state estimation, can also carry out data deburring processing to electric network data and model data.This function is mainly used under real time execution, when withdrawing from real time, can mend error correction by reading historical data simultaneously.Its principle is: preserve 10 continuous cross-sections (5 minutes sections) in (1) internal memory forever; (2) vertical remote measurement, the remote signalling of doing 10 sections judged, if (the remote signalling: saltus step, remote measurement: the great variety of nonzero value) of this state and place section is then recorded in unusual fluctuations; (3) do state estimation and remote signalling misidentification for each section, if state estimation does not restrain, then the burr with this section is described as warning; (4) if state estimation convergence, and judge this remote measurement, remote signalling is bad data, then the burr with this section is described as mistake; (5) if state estimation convergence, and judge this remote measurement, remote signalling is qualified data, then the burr with this section is described as prompting.
For the concrete grammar of state estimation, can adopt weighted least-squares method, the advantage of this method is the statistical property that does not need stochastic variable, and is objective criteria with the residual sum of squares (RSS) minimum of measuring value.This method supposition measurement amount, is estimated to have optimum unanimity and is not had fine tradition characteristics such as inclined to one side the measurement amount of desirable normal distribution according to desirable normal distribution.But when containing bad data in the data of normal distribution, it is far away that the estimated result of WLS can depart from true value.And under actual conditions, metric data and incomplete strict Normal Distribution cause bad data to be difficult to finish and detect and identification.The concrete steps of weighted least-squares method comprise:
Set up following objective function:
Adopt process of iteration to ask its quantity of state, the iteration correction formula is:
Except above-mentioned weighted least-squares method is carried out state estimation, can also adopt the orthogonal transformation method, this method can change the normal equation form into based on the update equation of weighted least-squares method state estimation:
H
TR
-1HΔx＝H
TR
-1r
In the formula: r is for measuring residual error, r=z-h (x).
The computation process of orthogonal transformation method is first calculated gains matrix (H
TR
~ 1H) (being information matrix) carried out the factor again and decomposed, at this moment the conditional number of gain matrix be Jacobi matrix H conditional number square, so find the solution the ill character that has increased former problem greatly by the normal equation form.The approach that improves the state estimation numerical stability is to adopt orthogonal transformation algorithm.Because its norm was constant after a certain matrix carried out orthogonal transformation, it is constant that the matrix of coefficients on equation both sides carries out its conditional number of orthogonal transformation simultaneously, so do not influence the equation stability of solution.
For the Power system state estimation problem, the precision of measurement has determined the size of its weight factor in the state estimation objective function, and precision is more high, and the weight factor of giving is more big.The weight of various measurements may differ 102～104 times in the practicality, and the pathosis of state estimation problem is quite serious.The computing velocity of orthogonal transformation method state estimation is relatively slow, but has fabulous numerical stability, is the algorithm that is most widely used in the Power system state estimation.At quantity of state in the Power system state estimation problem and measure the actual state that most nonzero elements of Jacobi matrix all occur in pairs, present embodiment preferably adopts the Length Factor Method in Power System State based on piecemeal Givens rotation.This algorithm belongs to Givens orthogonal transformation method, but in the processing of Givens orthogonal transformation, utilize the blocking characteristic of Power system state estimation problem to carry out piecemeal to measuring Jacobi matrix, carry out the optimization of column number according to the piecemeal sparsity structure of information matrix, adopt to become rotating shaft by being listed as the unit's strategy that disappears, based on minimum degree principle Dynamic Selection rotating shaft element, inject the minimum principle of element according to non-zero and select to rotate element, both reduce required memory headroom, obviously improved execution efficient again.Show from operating experience, compare with traditional Givens orthogonal transformation method that this algorithm has remarkable advantages (save about 30% CPU time) carrying out efficient.
Step S103: with result and pre-conditioned coupling of state estimation, according to the mistake of match condition location electrical network metric data and model data existence;
State estimation result can reflect the mistake that electrical network metric data and electric network model exist, and different error condition results estimated there are differences, and then can judge the mistake that exists in electrical network metric data and the model data according to these results.Such as, when having paired running equipment in the electrical network, metric data may be inaccurate, bigger circulation can appear in this result that can be reflected as state estimation in state estimation, therefore, by to the distinguishing of circulation, can play corresponding mistake in identification, positioning measurement data and the model data.Also such as, (looped network comprises the electricity ring of same electric pressure and strides the electromagnetism ring that electric pressure forms by transformer under the looped network situation.The estimated result of electricity ring is relevant with resistance, reactance and the capacitance of respective lines; The estimated result of electromagnetism ring is also relevant with resistance, reactance and the tap joint position of transformer), if there were bigger difference in the result of state estimation and sampled value during looped network was estimated, then model parameter may go wrong, therefore, can by detect looped network estimate in difference degree between state estimation result and the sampled value, and then the corresponding mistake that exists according to this difference degree identification, location electrical network metric data and model data.
Step S104: the mistake of location is corrected according to preset rules.
Present embodiment obtains electrical network metric data and electric network model from data acquisition and surveillance, carry out the estimation of electrical network actual motion state according to these two classes data then, come the mistake that exists in positioning measurement data and the model data according to state estimation and pre-conditioned matching result again, and then it is corrected.Compared with prior art, present embodiment does not need to carry out association again and detects, can navigate to wrong and correction and only need to carry out state estimation, reduced workload, improve the accuracy of electrical network metric data and electric network model, thereby improved guarantee for the operation of the high-level software of topological analysis platform Network Based.
In the above-described embodiments, the situation that may occur is that state estimation result existence does not restrain, the result of state estimation in this case, need revise state estimation procedure, so that can reflect the mistake that may exist in electrical network metric data and the model data more realistically.Such as, if it is not under the situation that causes by model data that state estimation does not restrain, because model parameter be to making error be deposited near the model corresponding equipment, therefore, can change state estimation and do not restrain by seeking the error maximum device, its implementation is: with the state estimation error maximum deviation equipment output of iteration each time, if this end value is constantly being dwindled, then warehousing is not restrained if constantly expand to, then warehousing, and outputting alarm is in order in time correct.Also such as, if not restraining appears in state estimation, then can reduce network range, the mode that reduces network range comprises: reduce network range by selecting different electric pressures; Or, reduce network range by not detecting non-charged island; Or, by switch deciliter and the input of equipment, withdraw to reduce network range.
Except passing through state estimation location, correction electric network data and the model data that foregoing is mentioned, can also check correction to the mistake that aspects such as unsettled equipment, different electric pressure equipment hybrid junction, model parameter, remote signalling occur by the network topology analysis, below narration respectively:
For the unsettled equipment of error correction: in the process of network modelling, sometimes can produce device definition, but not have the problem of connection, these problems are difficult on the figure to be found, can only find in unsettled equipment, three classes are arranged by network topology, one class is that an end points has connected, but the equipment that the another one end points is unsettled, this often occurs on the line, and also having a kind of is all unsettled equipment of two ends, also having a class is that two ends have all connected, but tie point is unsettled.Here related CIM class: Terminal, ConnectivityNode, the Terminal that its principle is equipment with the corresponding type of its type should have conform to equipment some Terminal not and any ConnectivityNode not related.From the ConnectivityNode class, find that it links to each other with some Terminal.Here face we need filter out some SCADA equipment, as preparing and carrying usefulness, PT, pressure change, institute's change, ground connection, neutral point, Qi Beibian on the name, being equipped with the equipment of change, passage, switching, used change, lightning arrester, unit transformer.Because these equipment are not conductive equipment, any electric network of getting along well links to each other.
For the different electric pressure equipment of error correction hybrid junction: when network modelling, sometimes can connect three sides of transformer wrong electric pressure, though this also can be found by dynamic coloring on figure, be not easy to find that different electric pressure hybrid junctions can make the confusion as a result of state estimation but also have some.Wherein related CIM class is VoltageLevel.BaseVoltage, ConductingEquipment.Base Voltage.Its principle is: whether (1) uses the EquipmentContainer.BaseVoltage of the associated ConnectivityNode of all Terminal of ConductingEquipment.BaseVoltage and ConductingEquipment consistent, its prerequisite is in this CIM file, and its container of all ConnectivityNode is VoltageLevel.(2) if its container of ConnectivityNode is Substation, can only use the rack analysis in the station so earlier, inquire all electric pressures in each transformer station, then all equipment that connects together (comprising coil and circuit) is traveled through, see whether their all electric pressures are consistent.
For the error correction apparatus parameter: in network modelling, when beginning appears in regular meeting, do not take parameter, so parameter do not have the problem of typing, therefore need find out these parameters and alarm.Related CIM class: all ConductingEquipment.In native system, the logic of realization is: R, X have one to be 0 circuit in (1) all circuit; (2) each coil X is 0 transformer in all two-winding transformer; (3) each coil X has one less than 0 transformer in all two-winding transformer; (4) each coil X has one to be 0 transformer in all three-winding transformer; (5) high-voltage coil and low-voltage coil X is not for just in all three-winding transformer, and intermediate voltage winding X is not negative transformer; (6) in all circuit R greater than the circuit of 3 times of X; (7) X is not equal to 0 in all circuit and the main transformer, but less than 0.01 or greater than 100 equipment (famous value), perhaps greater than 1 equipment (perunit value).
For the error correction remote signalling: in the process that transform at old station, the measurement that might use measures mistake, the metrology applications at old station is arrived new website, will cause what see at the interface so is good measurement, but the problem that actual topology is not but crossed, therefore need this because the YX mistake causes the problem of whole station dead electricity to be found out.The CIM class that it is related: Measurement, MeasurementValue.Its principle is according to the result after the topology, lists the tabulation of dead island, and can list equipment all in the dead island, and the dead island that contains non-vanishing analog quantity in all dead islands is marked with different colours.List the node number less than 10 island alive, and can list equipment all in the island alive, and provide the quality of balance on this work island automatically, mark with different colours for unbalanced island alive.
For checking quality of balance and direction: this function is mainly used when state estimation does not restrain, and this function need be carried out under topology.It mainly comprises: the bus quality of balance checks: be node with the bus, check inflow and the outflow of all elements that bus connected.Transformer station's quality of balance checks: check the circuit inflow of this transformer station and the outflow of main transformer (attention can not be used low-pressure side load bus, because the measurement on the load bus might not be accurate, using the low-pressure side of main transformer is to measure more accurately); Line balance checks: check inflow and the outflow at circuit two ends; The main transformer quality of balance checks: check each inflow and outflow of surveying of main transformer; Check in the other direction: if the value of quality of balance, then has a measurement close to the addition of two values in the other direction.For plural measurement, get one of them and measure, if the value of quality of balance close to the twice of this value, then this measurement should be in the other direction.
Check remote signalling, remote measurement for association: this function is mainly used to check doomed dead certificate and the unmatched situation of remote signalling remote measurement, mainly looks into the problem of the value of YX, can cooperate usefulness with historical measurement of inspection.Its principle is: (1) is suspended to all measurements on the switch; (2) list all position of the switch for dividing, but remote measurement arranged with all position of the switch for closing, be zero switch but measure.
Foregoing has been described in detail the application's electrical network metric data and the embodiment of electric network model error correction method, and correspondingly, the application also provides a kind of electrical network metric data and electric network model error correction device embodiment.Referring to Fig. 2, the figure shows the application's electrical network metric data and the structured flowchart of electric network model error correction device embodiment.This device comprises: data capture unit 201, state estimation unit 202, location of mistake unit 203 and error correcting unit 204, wherein:
Location of mistake unit 203 is used for result and pre-conditioned coupling the with state estimation, according to the mistake of match condition location electrical network metric data and the existence of electric network model data;
The course of work of this device embodiment is: data capture unit 201 obtains electrical network metric data and electric network model data from data acquisition and supervisor control, state estimation unit 202 carries out the state estimation of electrical network actual motion state according to electrical network metric data and electric network model data then, again by result and pre-conditioned mate of location of mistake unit 203 with state estimation, according to the mistake that match condition location electrical network metric data and model data exist, the mistake of location is corrected according to preset rules by error correcting unit 204 at last.
Among the said apparatus embodiment when state estimation does not restrain, said apparatus also comprises the first state amending unit 205, be used for the state estimation error maximum deviation equipment output of iteration each time, if this error maximum deflection difference value is constantly diminishing, then give up, do not restrain if constantly expand to, then record this error maximum deviation equipment so that outputting alarm information is carried out error correcting.In addition, when state estimation did not restrain, said apparatus just can adopt increased the correction that following functional unit is realized state estimation.Be that said apparatus embodiment can also comprise the second state amending unit, be used for reducing carrying out the network range of the electrical network of state estimation, described state estimation unit specifically is used for reducing according to electrical network metric data and electric network model the state estimation of the electrical network actual motion state behind the network range of electrical network.
In order more to know the technical scheme of the application's device embodiment, the application gives electrical network metric data and system architecture and the framework of electric network model error correction device when specific design as shown in Figure 3, and wherein Fig. 3 (a) is the hardware system structure figure of said apparatus embodiment realization; Fig. 3 (b) is the stratal diagram of the software systems of said apparatus embodiment realization.The hardware system of said apparatus embodiment is positioned at safety three districts, links to each other with the PI bus and obtains model, figure and real time data, and the PI bus is positioned at safety three districts.The level of whole software system comprises five levels: ground floor is the system hardware layer.Each server and workstation adopt various hardware architecture workstations, server and the microcomputer based on the RISC/CISC chip, as COMPAQ, ALPHA, SUN, IBM, HP and all kinds of PC; The second layer is operating system layer.Operating system adopt ripe meet international industrial standard in real time, multi-user, the pure WINDOWSNT4.0/2000/XP of multitask, POSIX/UNIX or LINUX operating system; The 3rd layer is the general-purpose platform layer.General-purpose platform can be regarded a middleware software bag between upper layer application system and bottom different hardware system, the different operating system as, this software package is kept apart upper layer application and first floor system effectively, for the design and running of upper layer application provides a kind of development environment and operation platform; The 4th layer is parameter management layer.The parameter of each department is merged, and metric data and figure in the association are for the operation of system is laid a solid foundation; Layer 5 is the functional module layer.This layer is the main body of native system, and the standardized data access interface that utilizes Data support to provide adopts C/S model to obtain required information, carries out corresponding analytic function.
Need to prove: easy for what narrate, what the various distortion implementations of above-described embodiment of this instructions and embodiment stressed all is and the difference of other embodiment or mode of texturing that identical similar part is mutually referring to getting final product between each situation.Especially, for several improved procedures of device embodiment, because it is substantially similar in appearance to method embodiment, so describe fairly simplely, relevant part gets final product referring to the part explanation of method embodiment.Each unit of device embodiment described above can or can not be physically to separate also, both can be positioned at a place, perhaps also can be distributed under a plurality of network environments.In actual application, can select wherein some or all of unit to realize the purpose of present embodiment scheme according to the actual needs, those of ordinary skills namely can understand and implement under the situation of not paying creative work.
The above only is the application's embodiment; should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the application's principle; can also make some improvements and modifications, these improvements and modifications also should be considered as the application's protection domain.
Claims (10)
1. an electrical network metric data and electric network model error correction method is characterized in that described method comprises:
Obtain electrical network metric data and electric network model data from data acquisition and supervisor control;
Carry out the state estimation of electrical network actual motion state according to electrical network metric data and electric network model data;
With result and pre-conditioned coupling of state estimation, according to the mistake of match condition location electrical network metric data and the existence of electric network model data;
According to preset rules the mistake of location is corrected.
2. method according to claim 1 is characterized in that, described state estimation of carrying out electrical network actual motion state according to electrical network metric data and electric network model data specifically comprises:
According to electrical network metric data and electric network model The data weighted least-squares method, orthogonal transformation method or carry out the state estimation of electrical network actual motion state based on the direct transform method of piecemeal Givens rotation;
Described direct transform method based on piecemeal Givens rotation is: carry out the Givens orthogonal transformation and handle, utilize the blocking characteristic of Power system state estimation problem to carry out piecemeal to measuring Jacobi matrix, carry out the optimization of column number according to the piecemeal sparsity structure of information matrix, adopt and become rotating shaft by being listed as the unit's strategy that disappears, based on minimum degree principle Dynamic Selection rotating shaft element, inject the minimum principle of element according to non-zero and select the rotation element.
3. method according to claim 1, it is characterized in that, the circulation of pre-set threshold value appears surpassing in the described pre-conditioned state estimation result that comprises, if the circulation of pre-set threshold value appears surpassing in the result after the state estimation, be the equipment that has paired running in the electrical network according to the electrical network metric data of match condition location and the mistake of electric network model data existence then.
4. method according to claim 1, it is characterized in that, described pre-conditioned state estimation result and the equipment sampled value of comprising there are differences, if the result after the state estimation and equipment sampled value there are differences, be the electric network model error in data according to the electrical network electrical network metric data of match condition location and the mistake of electric network model data existence then.
5. method according to claim 1, it is characterized in that, when state estimation does not restrain, described method also comprises to be revised in the following manner to state estimation procedure: with the state estimation error maximum deviation equipment output of iteration each time, if the error maximum deflection difference value constantly diminishes, then do not record this error maximum deviation equipment, if the error maximum deflection difference value constantly enlarges, then record this error maximum deviation equipment so that outputting alarm information is carried out error correcting.
6. method according to claim 1, it is characterized in that, when state estimation does not restrain, described method also comprises to be revised in the following manner to state estimation procedure: reduce to carry out the network range of the electrical network of state estimation, described state estimation of carrying out electrical network actual motion state according to electrical network metric data and electric network model data is specially the state estimation of carrying out the actual motion state according to electrical network metric data and the electric network model data electrical network after to the network range that reduces electrical network.
7. method according to claim 6 is characterized in that, the described network range that reduces to carry out the electrical network of state estimation comprises:
By selecting different electric pressures to reduce network range; Or, reduce network range by not detecting non-charged island; Or, by switch deciliter and the input of equipment, withdraw from and reduce network range.
8. an electrical network metric data and electric network model error correction device is characterized in that this device comprises: data capture unit, state estimation unit, location of mistake unit and error correcting unit, wherein:
Described data capture unit is used for obtaining electrical network metric data and electric network model data from data acquisition and supervisor control;
Described state estimation unit is used for carrying out according to electrical network metric data and electric network model data the state estimation of electrical network actual motion state;
Described location of mistake unit is used for result and pre-conditioned coupling the with state estimation, according to the mistake of match condition location electrical network metric data and the existence of electric network model data;
Described error correcting unit is used for according to preset rules the mistake of location being corrected.
9. device according to claim 8, it is characterized in that, when state estimation does not restrain, described device also comprises the first state amending unit, be used for the state estimation error maximum deviation equipment output of iteration each time if the error maximum deflection difference value is constantly diminishing, is not then recorded this error maximum deviation equipment, if the error maximum deflection difference value constantly enlarges, then record this error maximum deviation equipment so that outputting alarm information is carried out error correcting.
10. device according to claim 9, it is characterized in that, when state estimation does not restrain, described device also comprises the second state amending unit, be used for reducing carrying out the network range of the electrical network of state estimation, described state estimation unit specifically is used for carrying out according to electrical network metric data and the electric network model data electrical network after to the network range that reduces electrical network the state estimation of actual motion state.
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