CN108596361A - Selection method for practical measurement protection scheme of power system - Google Patents
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Abstract
The invention discloses a selection method of a practical measurement protection scheme of an electric power system, which comprises the following steps: step S1, establishing a protection return on investment rate evaluation model of the power system measuring device; step S2, determining a mathematical model optimization target of the optimal measurement protection scheme selection problem; step S3, determining the mathematical model constraint condition of the optimal measurement protection scheme selection problem; step S4, establishing a mathematical model of the optimal measurement protection scheme selection problem; step S5, converting the optimization problem into a minimum Steiner tree problem; and step S6, approximately solving the minimum Steiner tree problem by using the improved minimum spanning tree algorithm. The invention fully considers the individual difference of the measuring device of the power system and the cost performance of protecting the specific measuring device, improves the rationality and the practicability of the existing method, has good calculation performance, and can meet the requirement of selecting a measuring protection scheme when the power system defends false data injection attack by time complexity and solving effect of an algorithm.
Description
Technical field
The present invention relates to technical field of power systems, more particularly to a kind of power system practical measures protection scheme selecting party
Method.
Background technology
The same of Automation of Electric Systems level is being greatly improved in the depth integration of advanced Information and Communication Technology and intelligent grid
When, also hidden danger has been buried to electric system infiltration for the Information Security Risk in cyberspace.Data acquisition and monitoring (SCADA)
System is by a large amount of measuring equipments and complex communications networks, in real time by the value of each electrical quantity in intelligent grid dynamic running process
It is aggregated into power regulation department, by state estimation in Energy Management System (Energy Management System, EMS)
The processing of (State Estimation, SE) application, you can realize the perception to intelligent grid real time operation mode.Due to current
There are still the measuring equipment of implicit loophole and the lower transport protocol of safety in SCADA system, once certain measuring equipments or
Communication link is invaded by attacker, injects false data into part measured data, may cause state estimation that can not send out
In the case of now attacking, erroneous judgement is made to the current method of operation of power grid, to seriously affect subsequent electrical network analysis and control, this
It is exactly the action principle of false data injection attacks.
A kind of effective means of defence false data injection attacks is to survey device to part critical quantity in intelligent grid at present
Reinforce protection, makes it that can not be invaded by attacker.An optimization problem can be usually modeled as by measuring the selection of protection scheme, this is excellent
The constraints of change problem is related to the considerable sex chromosome mosaicism of state variable, the steiner tree problem that can be equivalent in graph theory；And optimize
The difference of goal-setting will select optimal measurement protection scheme from different perspectives.Due to protecting needed for different measuring equipments
Cost and it is expected return it is different, reasonably select measure protection scheme be not only related to defence false data injection attacks
Success rate, be also related to the economic benefit of electric power enterprise operation.Rate of return on investment (ROI) is a kind of Economic Model, at present
It is commonly used for assessing the adaptive expectations of enterprise-wide computer system security capital, for the measuring equipment weight in intelligent grid
It after new modeling, can be introduced into optimization aim, a kind of thinking of protection scheme economy is measured as assessment.
In addition, steiner tree problem is a N-P difficulty problem.Practical power systems scale is usually very big, in larger topology
In directly to search the time complexity of minimum Steiner tree be unacceptable.Minimal spanning tree algorithm is that one kind in graph theory is normal
With algorithm, under same topological scale, the time overhead for finding minimum spanning tree will be substantially less that searching minimum Steiner tree.Cause
This, can find approximate minimum Steiner tree, be used at a reasonable time by being improved to minimal spanning tree algorithm in expense
Solve the measurement protection scheme of one group of near-optimization.
Invention content
Technical problem to be solved by the present invention lies in provide a kind of power system practical measurement protection scheme selecting party
Method can effectively improve the selected economy for measuring protection scheme when defending Power system state estimation false data injection attacks
Property, and ensure that measure protection scheme selection has preferable scalability after electric system popularization.
In order to solve the above technical problem, the present invention provides a kind of power system practicals to measure protection scheme selection method,
Include the following steps：
Step S1 establishes the protection rate of return on investment assessment models of electric system measuring equipment；
Step S2 determines the optimal mathematical model optimizing target for measuring protection scheme select permeability；
Step S3 determines the optimal mathematical model constraints for measuring protection scheme select permeability；
Step S4 establishes the optimal mathematical model for measuring protection scheme select permeability；
Step S5 converts optimization problem to minimum Steiner tree problem；
Step S6 utilizes improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem.
Wherein, the step S1 is specifically risk exposure RE caused by injecting certain measuring equipment according to false data, takes
The annual total cost CP for the risk elimination factor RM%, used safeguard measure that suitable protecting measure is brought and by by protection measure
Effect discount rate PD% caused by device individual performance flaw establishes the protection rate of return on investment assessment of electric system measuring equipment
Model.
Wherein, the protection rate of return on investment assessment models are expressed as：
ROI=(RE × RM%-CP) × PD%/CP.。
Wherein, risk exposure RE is expressed as in 1 year economic loss expectation caused by all kinds of potential false data injection attacks
The sum of value ALE, i.e.,
RE=∑ ALE=∑ SLE × ARO,
Wherein, SLE is economic loss caused by false data injection attacks of generation are possible, and ARO is that the type is attacked
Annual incidence；
Risk elimination factor RM% carries out simulation analysis acquisition by information security risk evaluation frame；
Protect the annual cost C of some measuring equipment that can be calculate by the following formula acquisition：
Wherein, CI is the total initial input for taking hardware, software and service needed for the safeguard measure, and CM is to safeguard the protection
Annual maintenance expense needed for measure, CE take safeguard measure to data acquisition analysis system measuring equipment for assessing
Negative effect, Y indicate the Rated life of the measuring equipment, measure the annual total cost of protection scheme caused by normal work
CP is the sum of measuring equipment annual cost contained in scheme；
Effect discount rate PD% passes through to indexs such as the historical failure record of measuring equipment and measured deviation, Years Of Services
Comprehensive assessment obtains, for characterizing by influence of the factors such as protection measuring equipment operational reliability, measurement accuracy to protecting effect.
Wherein, occur false data injection attacks may caused by economic loss SLE include direct economic loss and
Indirect economic loss two parts, are expressed as：
Include the indirect economic loss of M class direct economic loss DL and N classes in formula, indirect economic loss is to be multiplied by power
The form of weight W is amplified direct economic loss, WACharacterize influence of the intensity of false data injection attacks to SLE.
Wherein, in the step S2, optimization aim is the sum of the rate of return on investment for protecting selected measuring equipment maximum.
Wherein, in the step S3, constraints, which should meet in system measurements Jacobin matrix H, to be measured with all by protection
The submatrix H { P } that the corresponding row of device is formed, the order of * are equal in H and by the corresponding rows of protection measuring equipment P and remove D with all
The submatrix H { P } that the corresponding row of outer other state variables { X D } are constituted, state variable number in the order and D of { X D } | D | it
With, that is, ensure D can be observed by P.
Wherein, the step S5 need to meet following conversion principle simultaneously：
If there are Line Flow measuring equipments on a transmission lines, which, which is corresponded to one of measure, fills
It sets；
If Line Flow measuring equipment is not present on a transmission lines, which is corresponded to one and is mounted on its head
Node injecting power measuring equipment on the busbar of end；
Transmission line must be corresponded with measuring equipment；
There is weight w=ROI per transmission linesmax- ROI, wherein ROI indicate to protect measuring equipment corresponding to the transmission line
Rate of return on investment, ROImaxIndicate maximum rate of return on investment in all measuring equipments for being corresponded to transmission line.
Wherein, the step S6 is specifically included：
Step S61 obtains present node set VcWith current total weight Wc, selection and V from measurement set PcIt is relevant complete
Portion measures subset Pc；
Step S62 about works as cancellation, from P to measuring Jacobin matrix H progress GausscMiddle selection k meet VcObservability
Minimum measure set Pk；
Step S63 measures set P to kkK minimum spanning tree T is obtained using maximum-flow algorithmk, each of spanning tree
Branch corresponds to P by the conversion principlekIn one measurement；
Step S64 introduces tree-pruning mechanism, judges whether the node in minimum spanning tree can be wiped out one by one, if node
It can be wiped out, then trim the subtree that the node and its all descendant nodes are constituted, and updated and measure set PkWith most your pupil
At tree Tk；
Step S65, the minimum spanning tree T trimmed from all kkMiddle selection has one of minimum total weight weight, with it
Total weight update present weight Wc, the node updates present node set V that is included with itc；
It iterates and executes step S61- step S65, until present weight WcNo longer reduce, at this time by the minimum of trimming
Spanning tree TkIn with one of minimum total weight weight, as approximate minimum Steiner tree.
Wherein, whether the principle that can be trimmed to about is judgement minimum spanning tree interior joint：
Do not include in node v and its descendant nodes D (v) in state variable set D corresponding to any one state variable
Node；
After wiping out { v ∪ D (v) }, all injecting power measuring equipments on { v ∪ D (v) } interior joint should all be gone
It removes.
The advantageous effect of the embodiment of the present invention is：The present invention has fully considered the individual difference of electric system measuring equipment
And the cost performance of the specific measuring equipment of protection, the reasonability and practicability of existing method are improved, and with good computational
Can, selection, which measures, when the time complexity and solution effect of algorithm can meet electric system defence false data injection attacks protects
The needs of shield scheme.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is the flow diagram that a kind of power system practical of the embodiment of the present invention measures protection scheme selection method.
Fig. 2 is the detailed process signal that a kind of power system practical of the embodiment of the present invention measures protection scheme selection method
Figure.
Fig. 3 is the topology diagram of the 14 node modular systems of IEEE comprising measurement information in the embodiment of the present invention.
Fig. 4 is the stream that improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem is utilized in the embodiment of the present invention
Cheng Tu.
Fig. 5 is the maximum flow model for establishing minimum spanning tree in the embodiment of the present invention.
Specific implementation mode
The explanation of following embodiment is refer to the attached drawing, can be to the specific embodiment implemented to the example present invention.
It please refers to shown in Fig. 1, the embodiment of the present invention provides a kind of power system practical measurement protection scheme selection method, packet
Include following steps：
Step S1 establishes the protection rate of return on investment assessment models of electric system measuring equipment；
Step S2 determines the optimal mathematical model optimizing target for measuring protection scheme select permeability；
Step S3 determines the optimal mathematical model constraints for measuring protection scheme select permeability；
Step S4 establishes the optimal mathematical model for measuring protection scheme select permeability；
Step S5 converts optimization problem to minimum Steiner tree problem；
Step S6 utilizes improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem.
It is further illustrated below in conjunction with Fig. 3 to Fig. 5.
Step S1 establishes the protection rate of return on investment assessment models of electric system measuring equipment
The development of information attack technology makes to include inevitably that part is hidden in the presence of safety in intelligent grid SCADA system
The measuring equipment of trouble；It is largely communicated using non-proprietary agreements such as TCP/IP, also makes it easier to the prestige by information attack
The side of body.Information attack person usually utilizes the loophole in measuring equipment or communication protocol, invasion field device to capture transmission channel or straight
The computer for meeting infiltration electric system regulation and control department interferes the normal work of SCADA system to distort measurement information.Table 1 is listed
It is directed to the common information attack type of intelligent grid SCADA system and corresponding defensive measure.
The common information attack type of 1 intelligent grid SCADA system of table and corresponding defensive measure
Take the cost needed for different defensive measures different, the protection effect of generation is also different, and measures dress in addition
Reliability, the measurement accuracy set are also not exactly the same, it is necessary to consider false data and inject wind caused by certain measuring equipment
Danger exposure RE, the risk elimination factor RM% for taking suitable protecting measure to bring, used safeguard measure annual total cost CP with
And it by the factors such as effect discount rate PD% caused by protection measuring equipment individual performance flaw, establishes protection electric system and measures
The rate of return on investment assessment models of device.
Risk exposure RE is represented by 1 year economic loss desired value caused by all kinds of potential false data injection attacks
The sum of ALE, i.e.,
RE=∑ ALE=∑ SLE × ARO,
Wherein, SLE is economic loss caused by false data injection attacks of generation are possible, and ARO is that the type is attacked
Annual incidence.SLE should include direct economic loss and indirect economic loss two parts, be represented by
The indirect economic loss of M class direct economic loss DL and N classes is considered in formula altogether, indirect economic loss is to be multiplied by
The form of weight W is amplified direct economic loss.WAInfluence of the intensity of false data injection attacks to SLE is characterized, is led to
Often less than 1.
A variety of safeguard measures, which are usually combined, is applied to protected measuring equipment, defends false data injection to attack to improve it
The ability hit.By taking all kinds of safeguard measures, partial risks exposure can be eliminated to a certain extent, be expressed as risk elimination
Rate RM%.RM% can carry out simulation analysis acquisition by information security risk evaluation frame.
Protect the annual cost C of some measuring equipment that can be calculate by the following formula acquisition：
Wherein, CI is the total initial input for taking hardware, software and service needed for the safeguard measure, and CM is to safeguard the protection
Annual maintenance expense needed for measure, CE take safeguard measure to data acquisition and monitoring measuring equipment for assessing
(SCADA) negative effect, Y indicate the Rated life of the measuring equipment caused by the normal work of system.Measure protection side
The annual total cost CP of case is the sum of measuring equipment annual cost contained in scheme.
Effect discount rate PD% can be by fingers such as the historical failure record of measuring equipment and measured deviation, Years Of Services
It marks comprehensive assessment to obtain, for characterizing the shadow by factors such as protection measuring equipment operational reliability, measurement accuracy to protecting effect
It rings.
Amid all these factors, the assessment models of rate of return on investment are represented by：
ROI=(RE × RM%-CP) × PD%/CP.
Step S2 determines the optimal mathematical model optimizing target for measuring protection scheme select permeability
In order under the premise of ensuring that false data injection attacks can be detected always, given in protection scheme to measuring
With optimal economy, the optimal mathematical model optimizing target for measuring protection scheme select permeability when safeguard measure is taken in measurement
It can be designed to：
It is maximum to measure protection scheme gross investment return rate.P is to measure in protection scheme comprising all measuring equipments in formula
Set, ROIp are the rate of return on investment for protecting a measuring equipment p in P.
Step S3 determines the optimal mathematical model constraints for measuring protection scheme select permeability
In order to ensure by protecting one group of measuring equipment P that can make the false data injection attacks for one group of state variable D
Can be detected always, constraints should meet in system measurements Jacobin matrix H with it is all corresponding by protection measuring equipment
The submatrix H { P } that is formed of row, the order of * is equal in H with all by the corresponding rows of protection measuring equipment P and other shapes in addition to D
The sum of the submatrix H { P } that the corresponding row of state variable { X D } are constituted, state variable number in the order and D of { X D } | D |, i.e.,：
rank(H{P},*)=rank (H{P},{X\D})+|D|.
Step S4 establishes the optimal mathematical model for measuring protection scheme select permeability
According to above-mentioned steps, by defending based on Power system state estimation false data injection attacks and rate of return on investment
Optimal measurement protection scheme select permeability data model be：
s.t.rank(H{P},*)=rank (H{P},{X\D})+|D|.
Step S5 converts optimization problem to minimum Steiner tree problem
According to graph theory relative theory, finds one group of measurement P and make one group of state variable D Observable, be equivalent to open up in network
The tree for finding the corresponding network node of all state variables in a connection D is flutterred, meanwhile, every branch of this tree must be by
A measurement is corresponded to according to following principle：
(1) if there are Line Flow measurements on this branch, this branch is corresponded into one of measure.Such as Fig. 3
Shown, branch 6-11 can correspond to Line Flow and measure (7)；
(2) if there is no Line Flows to measure on this branch, this branch is corresponded to one and is mounted on its head end
Or the node injecting power on endpoint node measures.As shown in figure 3, branch 6-12 can correspond to the measurement of node injecting power
(18)；
(3) branch must be corresponded with measuring equipment, not reproducible correspondence；
Measurement corresponding to whole branches of all trees for meeting mentioned above principle, you can constitute one group of feasible measurement set
P, such tree are known as steiner tree.Weight w=ROI is assigned for every branch of steiner treemax- ROI, wherein ROI indicate protection
The corresponding rate of return on investment measured of the branch, ROImaxIt indicates all in P and measures maximum protection rate of return on investment, then total power
Measurement set P* corresponding to one steiner tree of weight minimum constitutes optimal measurement protection scheme.
Step S6 utilizes improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem
It is a N-P difficulty problem, time complexity that minimum Steiner tree is directly found in power network topology shown in Fig. 3
Growth rate with topological scale is unacceptable, it is therefore necessary to carry out reasonable approximation to it, utilize introducing tree-pruning
Minimal spanning tree algorithm selects the measurement protection scheme of one group of near-optimization.
Flow chart using improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem is as shown in Figure 4.
The first step obtains present node set Vc(being initialized as whole node V in topology) and current total weight WcIt is (initial
It turns to 0), selection and V from measurement set PcIt is relevant all to measure subset Pc.It is related to node v to measure p, refers to measuring p
The node injecting power on v is directly measured, or is measured and the trend in v phase connecting lines.
Second step about works as cancellation, from P to measuring Jacobin matrix H progress GausscMiddle selection k meet VcObservability
Minimum measures set Pk.Wherein k is an adjustable approximation factor, adjusts the value of k, you can the trade-off problem solution time opens
Pin and the approximate optimal solution precision solved.
Third walks, and set P is measured to kkK minimum spanning tree T is obtained using max-flow (Max Flow) algorithmk, generate
Each branch of tree corresponds to P according to principle in step S5kIn one measurement.
By taking topology in Fig. 3 as an example, if present node set Vc={ v1,v2,v4,v5,v6, specify v1For reference mode, then
Ensure VcObservable one group of measurement set can be Pk={ 1,6,12,14 } measures set P with this groupkRelevant topology side Ek=
{e1,e2,e5,e7,e10}.Establish maximum flow model as shown in Figure 4, reference mode v1It is appointed as finally obtained spanning tree Tk's
Root node, temporarily first from EkIn specify one and v1Connected side e1It is added to TkIn, this can be by by maximum flow model in Fig. 5
It is set as 1 realization corresponding to maximum, the minimum discharge on the side of e1, the maximum stream flow on remaining each side is set as 1, and minimum capacity is set as 0.
Maximum flow problem is solved using Ford-Fulkerson algorithms, is such as ensureing side e1Flow has solution in the case of being 1, then according to most
Big flow model can be obtained the correspondence on measurement and side；If ensureing side e1Flow be 1 in the case of without solution, then need e1
Remove Tk, and select another and v1T is added in connected sidek, repeat aforesaid operations.Because of V0It can be by PkObservation, therefore centainly can be with
The correspondence of one group of measurement and side is acquired, such as These sides constitute one
Connect VcMinimum spanning tree.
4th step introduces tree-pruning mechanism, judges whether the node in minimum spanning tree can be wiped out one by one, decision principle
For：(1) section in state variable set D corresponding to any one state variable is not included in node v and its descendant nodes D (v)
Point；(2) after wiping out { v ∪ D (v) }, all injecting power measuring equipments on { v ∪ D (v) } interior joint should all remove.
If node can be wiped out, trim the subtree that the node and its all descendant nodes are constituted, and update measure set Pk and
Minimum spanning tree Tk.
5th step, the minimum spanning tree T trimmed from all kkMiddle selection with one of minimum total weight weight, with it
Total weight update present weight Wc, the node updates present node set V that is included with itc.Iterate execution first to the 5th
Step, until present weight WcNo longer reduce.At this time by the minimum spanning tree T of trimmingkIn with one of minimum total weight weight, i.e.,
For approximate minimum Steiner tree, corresponding measurement set PkAs practical measure with near-optimization rate of return on investment is protected
Shield scheme.
Following embodiment is used to verify the ability that improved minimum-cost spanning tree algorithm solves approximate optimal solution.Such as：When in Fig. 3
The ROI of measuring equipment presses 2 value of table, and when k is set as 15, and it includes arbitrary m=2 to enable in D, 4,7,9,11 state variables, successively
Seek the approximate maximum measurement protection scheme of gross investment return rate.For each value of m, all repeatedly 20 groups of D of random configuration, and
The gross investment return rate acquired is averaged, ROI when m takes different value is obtainedm.Meanwhile it acquiring different m using the method for exhaustion and taking
Global optimum gross investment return rate ROI* when value.By r=ROIm/ ROI* is listed in Table 3.As can be seen from Table 3, in m=2,
When 4,7,9,11, thus the ratio of approximate optimal solution and globally optimal solution demonstrates improved minimum-cost spanning tree algorithm all close to 1
It can be in the hope of the measurement protection scheme of near-optimization.
Each measuring equipment rate of return on investment ROI in the given 14 node modular systems of IEEE of table 2
The ratio of approximate optimal solution and globally optimal solution when 3 difference m values of table
Following embodiment is for verifying improved minimum-cost spanning tree algorithm for network topology scale with preferable expansible
Property.K=15 is set, improved minimum-cost spanning tree algorithm solution is utilized respectively in 14,30,57,118 node modular systems of IEEE
Near-optimization measures protection scheme, the false data injection attacks for defending the state variable for different proportion.Above-mentioned
The statistical data of network topology is as shown in table 4.Each case repeats experiment 50 times, takes the average value of time overhead, is listed in table 5
In.As can be seen from Table 5, improved minimum-cost spanning tree algorithm time overhead is with the secondary growth of topological scale, and direct solution is minimum
The time overhead of steiner tree problem will increase with topological scaled index, thus the present invention has better scalability.
4 IEEE of table, 14,30,57,118 node modular system network structure statistical data
Time overhead (the unit that near-optimization measures protection scheme is solved under 5 heterogeneous networks scale of table：ms)
The actual use scene of this method combination electric system measuring equipment establishes the investment repayment of protection measuring equipment
Rate assessment models；Meanwhile to measure the minimum optimization aim of protection scheme gross investment return rate, with selected measurement protection scheme
It can be always ensured that false data injection attacks are successfully detected as constraints, establish the mathematical model of optimization problem, and pass through
The improved minimum-cost spanning tree algorithm of tree-pruning mechanism is introduced, the measurement for solving near-optimization within the scope of expense at a reasonable time is protected
Shield scheme.The present invention has fully considered the individual difference of electric system measuring equipment and the cost performance of the specific measuring equipment of protection,
The reasonability and practicability of existing method are improved, and there is good calculated performance, the time complexity of algorithm and solution are imitated
Selection measures the needs of protection scheme when fruit can meet electric system defence false data injection attacks.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (10)
1. a kind of power system practical measures protection scheme selection method, include the following steps：
Step S1 establishes the protection rate of return on investment assessment models of electric system measuring equipment；
Step S2 determines the optimal mathematical model optimizing target for measuring protection scheme select permeability；
Step S3 determines the optimal mathematical model constraints for measuring protection scheme select permeability；
Step S4 establishes the optimal mathematical model for measuring protection scheme select permeability；
Step S5 converts optimization problem to minimum Steiner tree problem；
Step S6 utilizes improved minimum-cost spanning tree algorithm approximate solution minimum Steiner tree problem.
2. selection method as described in claim 1, which is characterized in that the step S1 is specifically to inject certain according to false data
Risk exposure RE, the risk elimination factor RM% for taking suitable protecting measure to bring, used safeguard measure caused by measuring equipment
Annual total cost CP and by the effect discount rate PD% caused by protection measuring equipment individual performance flaw, establish power train
The protection rate of return on investment assessment models of system measuring equipment.
3. selection method as claimed in claim 2, which is characterized in that the protection rate of return on investment assessment models are expressed as：
ROI=(RE × RM%-CP) × PD%/CP.。
4. selection method as claimed in claim 2, which is characterized in that risk exposure RE is expressed as all kinds of potential falsenesses in 1 year
The sum of economic loss desired value ALE caused by Data Injection Attacks, i.e.,
RE=∑ ALE=∑ SLE × ARO,
Wherein, SLE is that economic loss, ARO are the year of the type attack caused by false data injection attacks of generation are possible
Spend incidence；
Risk elimination factor RM% carries out simulation analysis acquisition by information security risk evaluation frame；
Protect the annual cost C of some measuring equipment that can be calculate by the following formula acquisition：
Wherein, CI is the total initial input for taking hardware, software and service needed for the safeguard measure, and CM is to safeguard the safeguard measure
Required annual maintenance expense, CE take safeguard measure to the normal of data acquisition analysis system measuring equipment for assessing
Negative effect, Y indicate the Rated life of the measuring equipment caused by work, and the annual total cost CP for measuring protection scheme is
The sum of measuring equipment annual cost contained in scheme；
Effect discount rate PD% passes through to index comprehensives such as the historical failure record of measuring equipment and measured deviation, Years Of Services
Assessment obtains, for characterizing by influence of the factors such as protection measuring equipment operational reliability, measurement accuracy to protecting effect.
5. selection method as claimed in claim 4, which is characterized in that caused by false data injection attacks of generation are possible
Economic loss SLE includes direct economic loss and indirect economic loss two parts, is expressed as：
Include the indirect economic loss of M class direct economic loss DL and N classes in formula, indirect economic loss is to be multiplied by weight W
Form direct economic loss is amplified, WACharacterize influence of the intensity of false data injection attacks to SLE.
6. selection method as described in claim 1, which is characterized in that in the step S2, optimization aim is selected by protection
The sum of the rate of return on investment of measuring equipment maximum.
7. selection method as described in claim 1, which is characterized in that in the step S3, constraints should meet system quantities
It surveys in Jacobin matrix H and is equal in H and institute with all submatrix H { P } formed by the corresponding row of protection measuring equipment, the order of *
There are the submatrix H { P }, { X that by the corresponding rows of protection measuring equipment P and the corresponding row of other state variables { X D } are constituted in addition to D
D } order and D in state variable number | D | the sum of, that is, ensure D can be observed by P.
8. selection method as claimed in claim 7, which is characterized in that the step S5 need to meet following conversion principle simultaneously：
(1) if there are Line Flow measuring equipments on a transmission lines, which is corresponded into one of measuring equipment；
(2) if Line Flow measuring equipment is not present on a transmission lines, which is corresponded to one and is mounted on its head
Node injecting power measuring equipment on the busbar of end；
(3) transmission line must be corresponded with measuring equipment；
(4) there is weight w=ROI per transmission linesmax- ROI, wherein ROI indicate to protect measuring equipment corresponding to the transmission line
Rate of return on investment, ROImaxIndicate maximum rate of return on investment in all measuring equipments for being corresponded to transmission line.
9. selection method as claimed in claim 8, which is characterized in that the step S6 is specifically included：
Step S61 obtains present node set VcWith current total weight Wc, selection and V from measurement set PcRelevant whole amount
Survey subset Pc；
Step S62 about works as cancellation, from P to measuring Jacobin matrix H progress GausscMiddle selection k meet VcObservability is most
Small measurement set Pk；
Step S63 measures set P to kkK minimum spanning tree T is obtained using maximum-flow algorithmk, each branch of spanning tree
Correspond to P by the conversion principlekIn one measurement；
Step S64 introduces tree-pruning mechanism, judges whether the node in minimum spanning tree can be wiped out one by one, if node can quilt
It wipes out, then trims the subtree that the node and its all descendant nodes are constituted, and update and measure set PkWith minimum spanning tree
Tk；
Step S65, the minimum spanning tree T trimmed from all kkMiddle selection has one of minimum total weight weight, with its total power
Update present weight W againc, the node updates present node set V that is included with itc；
It iterates and executes step S61- step S65, until present weight WcNo longer reduce, is generated at this time by the minimum of trimming
Set TkIn with one of minimum total weight weight, as approximate minimum Steiner tree.
10. selection method as claimed in claim 9, which is characterized in that whether judgement minimum spanning tree interior joint can be trimmed to about
Principle be：
(1) section in state variable set D corresponding to any one state variable is not included in node v and its descendant nodes D (v)
Point；
(2) after wiping out { v ∪ D (v) }, all injecting power measuring equipments on { v ∪ D (v) } interior joint should all remove.
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