CN117578444B - Power distribution network safety state estimation method and system based on event triggering mechanism - Google Patents
Power distribution network safety state estimation method and system based on event triggering mechanism Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Abstract
The invention belongs to the technical field of state estimation of an active power distribution network, and discloses a power distribution network safety state estimation method and system based on an event triggering mechanism, so as to cope with the condition that the active power distribution network is deceptively attacked. According to the invention, the data meeting the triggering condition in the measurement data measured by the measuring device in the active power distribution network is selectively transmitted through the event triggering mechanism, and is transmitted to the estimation center through the communication network for state estimation; establishing a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link; constructing a multi-cell collector estimator according to a state equation and a measurement equation of the power distribution network considering an event triggering mechanism and a spoofing attack; obtaining the gain of the assembler estimator; and carrying the gain of the estimator back to the multi-cell assembler estimator to perform state estimation on the power distribution network. The method and the device are beneficial to saving more network communication resources and improving the capability of the power distribution network for resisting deception attack and the accuracy of the power distribution network security state estimation.
Description
Technical Field
The invention belongs to the technical field of state estimation of an active power distribution network, and particularly relates to a power distribution network safety state estimation method and system based on an event triggering mechanism, which are suitable for the situation that the active power distribution network is deception attacked.
Background
With the rapid increase of energy demand and the rapid development of new energy technologies, a large number of distributed power supplies are connected into a power distribution network, so that the traditional power distribution network is promoted to be converted into an active power distribution network. In order to ensure stable operation and control of the active power distribution network, the real-time operation state of the power distribution network needs to be accurately perceived by using state estimation, basic data is provided for safe operation and economic dispatching of a system, and high-precision state estimation of the active power distribution network depends on accurate measurement information.
With the continuous expansion of the power distribution network and the wide configuration of the power distribution remote terminal units (Distribution Remote Terminal Unit, DRTUs), the phase measurement units (Phasor Measurement Unit, PMUs) and the photovoltaic measurement devices, a large amount of data is transmitted to the remote estimation center through the limited resource channel, so that a great pressure is brought to the communication network, communication blockage is caused by the transmission of redundant information, and the problem of incomplete information is also brought. In order to improve the resource utilization rate and ensure the estimation performance of the system, event triggering is adopted for data transmission, and the method is generally used for determining the data transmission time by judging whether the triggering condition is met or not, unlike the traditional time triggering, but the static event triggering mechanism has a fixed triggering threshold value and lacks certain flexibility.
For example, patent document 1 discloses a power distribution network state estimation method based on an event-triggered transmission mechanism and hybrid measurement, which adopts a static event-triggered strategy to transmit measurement data meeting trigger conditions, so that the data transmission frequency is reduced to a certain extent, and the pressure of a communication bandwidth is relieved. However, the static event trigger mechanism has a fixed trigger threshold, and cannot be dynamically adjusted according to the change of the measured data at different moments, so that flexibility of the mechanism in controlling data transmission is limited, and thus, a state estimation result is inaccurate.
Patent document 2 discloses a method for estimating the self-adaptive event triggering state of a T-S fuzzy system under random network attack, which establishes a network attack model of replay attack, spoofing attack and denial of service attack, considers the influence of the network attack on transmission data, and adopts a self-adaptive event triggering mechanism to improve the utilization rate of resources. Patent document 2 considers the influence of network attack, but cannot be applied to the problem of state estimation of an active power distribution network, and the flexibility of the adaptive event triggering mechanism needs to be improved.
Furthermore, network security issues of active distribution networks become particularly prominent. Network attacks have attracted considerable attention, with spoofing attacks being attractive because of their strong concealment and the need to acquire global information. Considering that the actual occurrence probability of the attack is difficult to accurately obtain in the actual engineering, the invention provides an active power distribution network member safety state estimation method based on an event triggering mechanism under the spoofing attack from the perspective of improving the state estimation performance under the network attack occurrence condition.
Reference to the literature
Patent document 1 chinese invention patent application publication No.: CN111245099a, publication date: 2020.06.05;
patent document 2 chinese invention patent application publication No.: CN113741198A, publication date: 2021.12.03.
disclosure of Invention
The invention aims to provide a power distribution network safety state estimation method based on an event triggering mechanism, and the method aims at the problem of limited network resources, designs a novel event triggering mechanism to save more network resources, and designs a multi-cell collector estimator with the capability of resisting deception attack aiming at the situation that an active power distribution network is subjected to deception attack so as to improve the accuracy of power distribution network safety state estimation.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the power distribution network safety state estimation method based on the event triggering mechanism comprises the following steps:
step 1, selectively transmitting data meeting a trigger condition in measurement data measured by a measurement device in an active power distribution network by adopting an event trigger mechanism, and transmitting the data to an estimation center through a communication network for state estimation;
step 2, establishing a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link;
step 3, a system state model and a mixed measurement model are established according to the characteristics of the power distribution network, and a multi-cell collector estimator is established according to the power distribution network state model and the measurement model which consider an event triggering mechanism and a spoofing attack;
step 4, obtaining the gain of the multi-cell member estimator;
and 5, carrying out state estimation on the power distribution network by taking the gain of the obtained estimator back to the multi-cell member estimator.
In addition, on the basis of the method for estimating the safety state of the power distribution network based on the event triggering mechanism, the invention also provides a power distribution network safety state estimation system which is adaptive to the method and based on the event triggering mechanism, and the method adopts the following technical scheme:
an event trigger mechanism based power distribution network security state estimation system, comprising:
the event triggering mechanism transmission module is used for selectively transmitting the data meeting the triggering condition in the measurement data measured by the measurement device in the active power distribution network and transmitting the data to the estimation center through the communication network for state estimation;
the model building module is used for building a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link;
the estimator construction module is used for building a system state model and a hybrid measurement model according to the characteristics of the power distribution network, and building a multi-cell gatekeeper estimator according to the power distribution network state model and the measurement model which consider an event triggering mechanism and a spoofing attack;
the gain calculation module is used for obtaining the gain of the member estimator;
and the state estimation module is used for bringing the gain back to the multi-cell assembler estimator to perform state estimation on the power distribution network.
In addition, on the basis of the method for estimating the safety state of the power distribution network based on the event triggering mechanism, the invention also provides computer equipment which comprises a memory and one or more processors.
The memory stores executable codes, and the processor is used for realizing the steps of the power distribution network safety state estimation method based on the event triggering mechanism when executing the executable codes.
In addition, on the basis of the method for estimating the safety state of the power distribution network based on the event triggering mechanism, the invention further provides a computer readable storage medium on which a program is stored. The program, when executed by a processor, is configured to implement the steps of the method for estimating a safe state of a power distribution network based on an event triggering mechanism described above.
The invention has the following advantages:
as described above, the invention relates to a method and a system for estimating the safety state of a power distribution network based on an event triggering mechanism. The method of the invention applies a multicellular body set member estimation algorithm to unknown but bounded system noise, and the unknown but bounded assumption of the system noise is more in line with engineering practice. The invention adopts a novel event triggering mechanism, introduces five adjustment parameters and two auxiliary dynamic systems, and greatly improves the flexibility of the event triggering mechanism. The method further reduces the data transmission quantity while ensuring the state estimation precision, balances the network resource consumption and the system estimation performance, improves the event triggering flexibility and saves more network bandwidth resources. In addition, the invention considers the integrity of communication data between the spoofing attack influence sensor and the remote estimation center, considers the link constraint of an attack channel, and provides a multi-cell body member estimation method considering the spoofing attack and based on a novel event triggering mechanism. The method and the device are beneficial to improving the accuracy of the safety state estimation of the power distribution network.
Drawings
Fig. 1 is a flowchart of a method for estimating a security state of a power distribution network based on an event trigger mechanism and hybrid measurement under a spoofing attack in an embodiment of the present invention.
Fig. 2 is a flow chart of a spoofing attack and event trigger mechanism in a power distribution network in accordance with an embodiment of the present invention.
Fig. 3 is a schematic diagram of real and estimated values of the real part of voltage.
Fig. 4 is a schematic diagram of the real and estimated values of the imaginary voltage.
FIG. 5 is a graph of event trigger rates under a novel and conventional dynamic event trigger mechanism.
Fig. 6 is a graph of root mean square error for multiple cell assembler estimation based on novel and conventional dynamic event triggering mechanisms.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
example 1
The embodiment 1 describes a method for estimating the safe state of a power distribution network based on an event triggering mechanism, as shown in fig. 1, which includes the following steps:
and step 1, selectively transmitting the data meeting the triggering conditions in the measurement data measured by the measuring device in the active power distribution network by adopting an event triggering mechanism, and transmitting the data to an estimation center through a communication network to perform state estimation.
The invention provides a novel event triggering mechanism, which is based on the following principle:
adding an auxiliary offset variable S based on the traditional dynamic event triggering mechanism s,k+1 And three adjustable parameters And->The constitution has two auxiliary variables theta s,k And S is s,k Five adjustable parameters +.>ρ s And eta s The event triggering mechanism of the system improves the flexibility of the event triggering mechanism and reduces the triggering times.
The measurement data comprise alternating voltage, alternating current, branch power and injection power, and measurement information provided by a sensor in the photovoltaic system; the measurement output is:
wherein y is s,k The input for the s (s.epsilon.1, 2, …, τ) th event trigger; τ represents the total number of event triggers; for the s-th event trigger, the event trigger sequence is expressed as Representing the r-th trigger time.
The triggering conditions of the flexible event triggering mechanism are as follows:
wherein,the (1) th trigger time of the(s) th event trigger is represented; />Representing the whole positive integer; />Is the measurement of the transmission quantity at the latest moment, y s,k Is the input of an event trigger, +.>And eta s Is a given parameter.
And->Respectively indicate->And->Is a binary norm of (c).
θ s,k And S is s,k For the auxiliary offset variable, the following formula is calculated:
wherein θ s,k+1 And S is s,k+1 The values of two auxiliary offset variables representing the s-th event trigger at the time k+1; ρ s Andfor a given parameter->And->Is a given scalar.
μ s,k Indicating whether or not the current time measurement is transmitted, only when mu s,k =1, measure y s,k To a communication network.
In order to solve the problem of insufficient flexibility of an event triggering mechanism, the invention introduces five adjustment parametersη s And ρ s And two auxiliary variables θ s,k+1 And S is s,k+1 The flexibility of the event triggering mechanism is greatly improved.
The invention further reduces the data transmission quantity and balances the network resource consumption and the system estimation performance while ensuring the state estimation precision through the novel event triggering mechanism.
And 2, establishing a measurement model under spoofing attack by utilizing the norm bounded false injection signal constrained by the link.
Considering that the integrity of communication data between a sensor and a remote estimation center is damaged by an attacker injecting an attack vector, and considering the link constraint of an attack channel, the invention utilizes an unknown bounded noise signal to describe false data spoofing attack.
The metrology model under spoofing attacks is as follows:
in the step 2, the measurement model under the spoofing attack is as follows:
wherein,
representing measurements transmitted to the zero-order keeper under a spoofing attack; />Representing a measure of the transmission of the s-th event trigger to the zero-order keeper under a spoofing attack; />Representing the amount of data transmitted to the communication network.
β k Represents the link constraint of attack signals and has upper and lower bounds, meets the following conditionsWhen beta is k =0, no spoofing attack occurs; beta k E (0, 1), spoofed attack signal is attenuated; beta k > 1, spoofing attack signal is enhanced, +.>Representing beta k Upper bound of (2); zeta type k Is a false signal injected by an attacker, which satisfies +.>Wherein (1)>Representation->Is a lower bound of (c).
Subsequently, the amount of the influence of the spoofing attack is measured with a zero-order holderProcessing is performed so that the amount received by the k moment estimation center is +.>Can be expressed as: />
Wherein,representing the quantity measurement received by the estimation center corresponding to the s-th event trigger; if the k time is the non-trigger time, namely: />From the zero-order hold mechanism, it is possible to obtain: />
And 3, establishing a system state model and a hybrid measurement model according to the characteristics of the power distribution network, and constructing a multi-cell collector estimator according to the power distribution network state model and the measurement model considering an event triggering mechanism and a spoofing attack.
The system state model is shown in a formula (6);
x k+1 ＝A k x k +u k +ω k (6)
wherein x is k+1 、x k State variables, x, representing time k+1 and time k, respectively k Is defined asx pv,k And x d,k Representing the photovoltaic system status and the three-phase node voltage of the network part, respectively. A is that k Is a state transition matrix vector, u k Representing the change trend of the state track, and updating the state track and the state track on line by adopting a Holt-windows double-index smoothing method; omega k Representing unknown but bounded process noise, satisfying +.>|ω l,k I is omega l,k Absolute value of omega l,k Is omega k Is>Is omega l,k Is a lower bound of (c).
The ac voltage, ac current, branch power and injection power from PMU and DRTU acquisitions in the network part and the measurement information provided by the sensors in the photovoltaic system, i.e. the photovoltaic system measurements, PMU measurements and DRTU measurements, are measured.
Total measurement vectorIs defined as:
wherein,representing m-dimensional Euclidean space, y pv,k 、y pm,k And y dr,k Respectively representing photovoltaic system measurement, PMU measurement and DRTU measurement; the measurement output of the active distribution network is expressed as:
y k ＝g(x k )+v k (8)
wherein,measurement functions of photovoltaic system, PMU and DRTU, respectively,>unknown but bounded measurement noise for photovoltaic systems, PMUs and DRTUs, respectively, satisfy|v ξ,k I is v ξ,k Absolute value of v ξ,k V is k Is the xi-th component of->Representing v ξ,k Is a lower bound of (c).
ζ is an index symbol representing the index of a scalar in the associated vector.
The multi-cell set member estimator is designed as follows:
wherein,and->Respectively representing a predicted value and an estimated value of the state; />Is a predicted quantity at time k+1 obtained by a measurement equation; />Representation measure +.>And forecast quantity->Difference between them.
K k+1 Representing the estimator gain, willMultiplying by estimator gain K k+1 As correction amount, pre-measure the state +.>Correcting to obtain corrected estimated amount +.>As a final state estimator.
The above equation (10) shows that only the estimator gain K is in the design process of the estimator k+1 Unknown, the next step is to build a hybrid measurement model with a second order nonlinear function by simplifying the hybrid measurement model; simplified second order nonlinear function using taylor expansion techniqueLinearizing; respectively solving multiple cell shapes containing non-triggering errors and linearization errors; solving a multi-cell shape containing estimation errors; the estimator gain is obtained by minimizing the F radius of the set of multi-cell estimates.
And 4, obtaining the gain of the assembler estimator.
And 4.1. Simplifying the mixed measurement model.
Let k moment mixing amount measurement y k The j-th component in the voltage is the voltage amplitude of the p-phase of the iota nodeExpressed as:
wherein,and->Representing the real and imaginary parts, v, respectively, of the iota node p-phase voltage j,k Representing node voltage amplitudeCorresponding measurement noise; measure the voltage amplitude->Square processing is performed to obtain square +.>The method comprises the following steps:
wherein,square representing node voltage amplitude +.>The corresponding measurement noise is expressed as:
further can obtain the measurement noiseHas an upper bound of the form:
wherein phi is j,k Representation ofUpper bound of (2); />Representing v j,k Is a lower bound of (c).
And->Respectively indicate->And->Is the absolute value of (c).
And->Respectively represent the real part of the node voltage at the moment k>And imaginary part->Is a predicted value of (a).
And->Respectively represent the real part of the node voltage at the moment k>And imaginary part->The upper prediction error bound of (2), namely:
the system metrology model is then simplified to:
wherein,and->Representing the mixed amount measurement, measurement function and measurement noise of the reconstruction after squaring, respectively, +.>And->The xi-th component of (2) is expressed as:
wherein g ξ (x k ) Representing the measurement function g (x k ) Is the xi-th component of (c).
Satisfy->Wherein phi is ξ,k Representation->Upper bound of phi ξ,k Expressed as:
measuring noiseIs included in a multicellular shape<0,R k >In (i.e.)>Wherein R is k Representing multiple cells<0,R k >The element of the xi row and the xi column of the outline matrix is phi ξ,k The remaining elements are 0.
Step 4.2. Nonlinear measurement functionLinearization.
Will nonlinear measurement functionState prediction value +.>Taylor expansion is performed as follows:
wherein,representing a measurement function->Predicted quantity at time k+1, e k+1|k True value x representing state at time k+1 k+1 And state prediction value->Is referred to as the prediction error.
Jacobian matrix at time k+1, < >>As linearization error, expressed as:
wherein,a hessian matrix at time k+1; />
I represents an identity matrix with suitable dimensions;is Cronecker product.
Δ k+1|k Is an uncertain vector and satisfies delta k+1|k The value is less than or equal to 1, and delta is obtained k+1|k ||≤1，||Δ k+1|k I is delta k+1|k Is a binary norm of (c).
P k+1|k For multiple sets of cellular predictions<0,P k+1|k >Form matrix of (c) satisfying e k+1|k ∈<0,P k+1|k >。
P k+1|k The specific expression of (c) will be given in the estimator design process.
Step 4.3. Establishing a prediction error e k+1|k And estimation error e k+1 And solving for a multicell shape containing non-triggering errors and linearization errors<0,Σ k+1 >And<0,Θ k+1 >。
obtaining a k+1 moment prediction error e according to the system state model and the measurement model in the formula (6) and the formula (15) k+1|k And estimation error e k+1 The specific expression of (2) is as follows:
e k+1|k ＝A k e k +ω k (20)
wherein,representing the quantity measurement, σ, received by the estimation center at time k+1 k+1 Output vector y for event trigger kr And input vector y k The difference at time k+1, the so-called non-trigger error, is defined as +.>
Wherein the method comprises the steps ofσ s,k+1 Representing the non-triggering error generated by the s (s epsilon {1,2, …, τ }) th event trigger; there is a positive scalar +.>And->So that the error sigma is not triggered k+1 Constrained to the following polytypes:
σ k+1 ∈<0,Σ k+1 > (22)
wherein:
wherein Σ is k+1 Representing multiple cells<0,Σ k+1 >Is a matrix of profiles;and χ (x) s,k+1 Respectively->Sum sigma s,k+1 Is a lower bound of (c).
Linearization errorConstrained to the following polytypes:
wherein Θ is k+1 Representing multiple cells<0,Θ k+1 A figure matrix. Definition of the definitionΘ k+1 The ith column element of the ith row of (2) is +.>
Wherein,is an interval matrix Γ g,k+1 The ith row and jth column element,/>Representing interval element +.>Is set, and the radius of (a) is set. For convenience of presentation, let->Indicating measurement noise->Non-trigger error sigma k+1 And linearization error->And, namely:
wherein,is multi-cellular->Is a matrix of profiles; multiple cell shape->The calculation can be made by the following formula:
step 4.4. Deriving a set of multiple-cell estimates that can contain the true value of the system state
Given the estimator gain K k+1 . Assume an estimation error e k Satisfy e k ∈<0,P k >Then, a k+1 time prediction error e is obtained k+1|k And estimation error e k+1 Respectively satisfy e k+1|k ∈<0,P k+1|k >And e k+1 ∈<0,P k+1 >。
Multi-cell prediction set<0,P k+1|k >And a set of multi-cell estimatesIs of the shape matrix P of (2) k+1|k And P k+1 The method comprises the following steps:
wherein Q is k Representation comprising process noise omega k Is a multicellular form of (2)<0,Q k >The ith row and the ith column elements of the outline matrix areThe rest elements are 0; />The representation comprises beta k+1 ζ k+1 Is->Is defined as:
wherein matrix xi k+1 The ith row and jth column elements of (a) Is interval matrix->The ith row and jth column element,/>Is interval element->Is a central value of (a).
Matrix phi k+1 The ith column elements of the ith row of (a) are respectively
Representing interval element +.>Is set, and the radius of (a) is set.
According toFurther deriving a set of multi-cell estimates comprising system state realism values
Step 4.5. By minimizing the set of multiple-cell estimatesTo obtain the estimator gain K k+1 。
<0,P k+1 >Appearance matrix P k+1 The F-norm of (c) satisfies the following relationship:
wherein:
to make multicellular bodiesThe minimum F radius of (B) to obtain the estimator gain K k+1 The method comprises the following steps:
and 5, carrying out state estimation on the power distribution network by taking the gain of the obtained estimator back to the multi-cell member estimator.
As shown in fig. 2, the PMU, the DRTU and the photovoltaic measurement device acquire measurement from the active power distribution network, and the acquired measurement needs to be determined by a new event trigger to determine whether the transmission condition is met.
If the current measurement meets the transmission condition, the measurement is transmitted through a communication network, the measurement is influenced by external spoofing attack in the transmission process, and the measurement influenced by the spoofing attack is transmitted to an estimation center; and if the current measurement does not meet the transmission condition, transmitting the transmission quantity at the latest moment to an estimation center by using the zero-order retainer instead of the current measurement.
And the estimation center carries out active power distribution network state estimation by utilizing the proposed multi-cell assembler estimator according to the system model of the active power distribution network and the obtained quantity measurement, so as to obtain a system state estimation value.
Fig. 3 and 4 show the real, estimated and estimated boundaries of the real and imaginary parts of the node voltage, respectively.
It is apparent from fig. 3 and fig. 4 that, although the system is affected by the cooperative effect of the spoofing attack and the event trigger, the multi-cell estimator provided by the invention still has a better estimation effect, and the true value of the system is between the estimation boundaries, so that the accuracy of the estimation of the safety state of the power distribution network can be ensured by the multi-cell estimator.
Fig. 5 and 6 illustrate the event trigger rates under the new and legacy dynamic event trigger mechanisms and the root mean square error of the multi-cell assembler estimates based on the new and legacy dynamic event trigger mechanisms, respectively.
As can be seen from a comparison between fig. 5 and fig. 6, the event trigger rate under the event trigger mechanism is far smaller than that of the conventional dynamic event trigger mechanism at each sampling time, and the corresponding root mean square errors are not greatly different, which further verifies that the proposed multi-cell assembler estimator based on the event trigger mechanism can ensure higher state estimation accuracy while reducing the data transmission quantity.
Example 2
Embodiment 2 describes a system for estimating a safe state of a power distribution network based on an event triggering mechanism, which is based on the same inventive concept as the method for estimating a safe state of a power distribution network based on an event triggering mechanism in embodiment 1.
Specifically, the system for estimating the safety state of the power distribution network based on the event triggering mechanism comprises the following components:
the event triggering mechanism transmission module is used for selectively transmitting the data meeting the triggering condition in the measurement data measured by the measurement device in the active power distribution network and transmitting the data to the estimation center through the communication network for state estimation;
the model building module is used for building a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link;
the estimator construction module is used for building a system state model and a hybrid measurement model according to the characteristics of the power distribution network, and building a multi-cell gatekeeper estimator according to the power distribution network state model and the measurement model which consider an event triggering mechanism and a spoofing attack;
the gain calculation module is used for obtaining the gain of the member estimator;
and the state estimation module is used for bringing the gain back to the multi-cell assembler estimator to perform state estimation on the power distribution network.
It should be noted that, in the power distribution network safety state estimation system based on the event triggering mechanism, the implementation process of the functions and roles of each functional module is specifically shown in the implementation process of the corresponding steps in the method in the above embodiment 1, and will not be described herein again.
Example 3
Embodiment 3 describes a computer device for implementing the steps of the method for estimating the safety state of the power distribution network based on the event triggering mechanism described in embodiment 1.
The computer device includes a memory and one or more processors. Executable code is stored in the memory for implementing the steps of the method for estimating the security state of the distribution network based on the event triggering mechanism when the executable code is executed by the processor.
In this embodiment, the computer device is any device or apparatus having data processing capability, which is not described herein.
Example 4
Embodiment 4 describes a computer readable storage medium for implementing the steps of the method for estimating a safety state of a power distribution network based on an event triggering mechanism described in embodiment 1.
The computer readable storage medium of this embodiment 4 has a program stored thereon, which when executed by a processor, is configured to implement the steps of the method for estimating a safe state of a power distribution network based on an event triggering mechanism.
The computer readable storage medium may be an internal storage unit of any device or apparatus having data processing capability, such as a hard disk or a memory, or may be an external storage device of any device having data processing capability, such as a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), or the like, which are provided on the device.
The foregoing description is, of course, merely illustrative of preferred embodiments of the present invention, and it should be understood that the present invention is not limited to the above-described embodiments, but is intended to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.
Claims (4)
1. The power distribution network safety state estimation method based on the event triggering mechanism is characterized by comprising the following steps of:
step 1, selectively transmitting data meeting a trigger condition in measurement data measured by a measurement device in an active power distribution network by adopting an event trigger mechanism, and transmitting the data to an estimation center through a communication network to perform state estimation;
step 2, establishing a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link;
step 3, a system state model and a mixed measurement model are established according to the characteristics of the power distribution network, and a multi-cell collector estimator is established according to the power distribution network state model and the measurement model which consider an event triggering mechanism and a spoofing attack;
step 4, obtaining the gain of the multi-cell member estimator;
step 5, carrying out state estimation on the power distribution network by taking the gain of the obtained estimator back to the multi-cell member estimator;
in the step 1, the measurement data includes ac voltage, ac current, branch power and injection power, and measurement information provided by a sensor in the photovoltaic system; the measurement output is:
wherein y is s,k For the input of the s-th event trigger, s epsilon {1,2, …, τ }, τ representing the total number of event triggers; for the s-th event trigger, the event trigger sequence is expressed as
Wherein,represents the r-th trigger time; the trigger conditions of the event trigger mechanism are as follows:
wherein,the (1) th trigger time of the(s) th event trigger>Representing the whole positive integer; y is s,k Is the input of an event trigger, +.>Is the latest transmission quantity measurement,/-at the moment>And eta s Is a given parameter;
and->Respectively indicate->And->Is a binary norm of (2);
θ s,k and S is s,k For the auxiliary offset variable, the following formula is calculated:
wherein θ s,k+1 And S is s,k+1 The values of two auxiliary offset variables representing the s-th event trigger at the time k+1; ρ s Andfor a given parameter->And->Is a given scalar;
μ s,k indicating whether or not the current time measurement is transmitted, only when mu s,k =1, measure y s,k Transmitting to a communication network;
in the step 2, the measurement model under the spoofing attack is as follows:
wherein,
representing measurements transmitted to the zero-order keeper under a spoofing attack; />Representing an amount of data transmitted to the communication network; />Representing a measure of the transmission of the s-th event trigger to the zero-order keeper under a spoofing attack;
β k represents the link constraint of attack signals and has upper and lower bounds, meets the following conditionsWhen beta is k =0, no spoofing attack occurs; beta k E (0, 1), spoofed attack signal is attenuated; beta k > 1, spoofing attack signal is enhanced, +.>Representing beta k Upper bound of (2); zeta type k Is a false signal injected by an attacker, which satisfies +.>Wherein (1)>Representation->Upper bound of (2);
subsequently, the amount of the influence of the spoofing attack is measured with a zero-order holderProcessing is performed so that the amount received by the k moment estimation center is +.>Expressed as: />
Wherein,representing the quantity measurement received by the estimation center corresponding to the s-th event trigger; if the k time is the non-trigger time, namely: />According to the zero-order hold mechanism, the following is obtained: />
In the step 3, the system state model is shown in a formula (6);
x k+1 ＝A k x k +u k +ω k (6)
wherein x is k+1 、x k State variables, x, representing time k+1 and time k, respectively k Is defined asx pv,k And x d,k Respectively representing the state of the photovoltaic system and the three-phase node voltage of the network part;
A k is a state transition matrix vector, u k Representing the change trend of the state track, and updating the state track and the state track on line by adopting a Holt-windows double-index smoothing method; omega k Representing unknown but bounded process noise, satisfying
Wherein, |ω l,k I is omega l,k Absolute value of omega l,k Is omega k Is used as a reference to the first component of (a),is omega l,k Upper bound of (2);
in the step 3, the ac voltage, ac current, branch power and injection power collected by the phasor measurement unit PMU and the distribution remote terminal unit DRTU in the network part and the measurement information provided by the sensors in the photovoltaic system, that is, PMU measurement, DRTU measurement and photovoltaic system measurement are measured; total measurement vectorIs defined as:
wherein,representing m-dimensional Euclidean space, y pv,k 、y pm,k And y dr,k Respectively representing photovoltaic system measurement, PMU measurement and DRTU measurement; the measurement output of the active distribution network is expressed as:
y k ＝g(x k )+v k (8)
wherein,measuring functions of the photovoltaic system, the PMU and the DRTU respectively; />
Unknown but bounded measurement noise for photovoltaic systems, PMUs and DRTUs, respectively, and satisfy|v ξ,k I is v ξ,k Absolute value of v ξ,k V is k Is the xi-th component of->Representing v ξ,k Upper bound of (2);
in the step 3, the multi-cell assembler estimator is designed as follows:
wherein,and->Respectively representing a predicted value and an estimated value of the state; />Is a predicted quantity at time k+1 obtained by a measurement equation; />Representation measure +.>And forecast quantity->A difference between them;
K k+1 representing the estimator gain, willMultiplying by estimator gain K k+1 As a correction amount, a state is predictedCorrecting to obtain corrected estimated amount +.>As a final state estimate;
the step 4 specifically comprises the following steps:
step 4.1, simplifying the mixed measurement model;
let k moment mixing amount measurement y k The j-th component in the voltage is the voltage amplitude of the p-phase of the iota nodeExpressed as:
wherein,and->Representing the real and imaginary parts, v, respectively, of the iota node p-phase voltage j,k Representing node voltage magnitude +.>Corresponding measurement noise; measure the voltage amplitude->Square processing is performed to obtain square +.>The method comprises the following steps:
wherein,square representing node voltage amplitude +.>The corresponding measurement noise is expressed as:
further obtain the measurement noiseHas an upper bound of the form:
wherein phi is j,k Representation ofUpper bound of (2); />Representing v j,k Upper bound of (2);
and->Respectively indicate->And->Absolute value of (2);
and->Respectively represent the real part of the node voltage at the moment k>And imaginary part->Is a predicted value of (2);
and->Respectively represent the real part of the node voltage at the moment k>And imaginary part->The upper prediction error bound of (2), namely:
the system metrology model is then simplified to:
wherein,and->Respectively representing the mixed quantity measurement, the measurement function and the measurement noise of the reconstruction after the square processing;
and->The xi-th component of (2) is expressed as:
wherein g ξ (x k ) Representing the measurement function g (x k ) Is the xi-th component of (2);
satisfy->Wherein phi is ξ,k Representation->Upper bound of phi ξ,k Expressed as:
measuring noiseIs included in a multicellular shape<0,R k >In (i.e.)>Wherein R is k Representing multiple cells<0,R k >The element of the xi row and the xi column of the outline matrix is phi ξ,k The rest elements are 0;
step 4.2. Nonlinear measurement functionLinearizing;
will nonlinear measurement functionState prediction value +.>Taylor expansion is performed as follows:
wherein,representing a measurement function->Predicted quantity at time k+1, e k+1|k True value x representing state at time k+1 k+1 And state prediction value->Is called the prediction error;
jacobian matrix at time k+1, < >>As linearization error, expressed as:
wherein,a hessian matrix at time k+1; />
I represents an identity matrix with suitable dimensions;is Cronecker product;
Δ k+1|k is an uncertain vector and satisfies delta k+1|k The value is less than or equal to 1, and delta is obtained k+1|k ||≤1，||Δ k+1|k I is delta k+1|k Is a binary norm of (2);
P k+1|k for multiple sets of cellular predictions<0,P k+1|k >Form matrix of (c) satisfying e k+1|k ∈<0,P k+1|k >；
Step 4.3. Establishing a prediction error e k+1|k And estimation error e k+1 Is a specific expression of (2);
establishing a set of multiple cells of non-triggered errors and linearization errors<0,Σ k+1 >And<0,Θ k+1 >；
obtaining a k+1 moment prediction error e according to the system state model and the measurement model in the formula (6) and the formula (15) k+1|k And estimation error e k+1 The specific expression of (2) is as follows:
e k+1|k ＝A k e k +ω k (20)
wherein,representing the quantity measurement received by the k+1 time estimation center;
e k representing the estimated error at time k; sigma (sigma) k+1 Output vector for event triggerAnd input vector y k The difference at time k+1, the so-called non-trigger error, is defined as +.>
Wherein,σ s,k+1 representing the non-triggering error generated by the s-th event trigger, s ε {1,2, …, τ }; there is a positive scalar +.>And->So that the error sigma is not triggered k+1 Constrained to the following polytypes:
σ k+1 ∈<0,Σ k+1 > (22)
wherein:
wherein Σ is k+1 Representing multiple cells<0,Σ k+1 >Is a matrix of the outline of (a),and χ (x) s,k+1 Respectively->Sum sigma s,k+1 Upper bound of (2);
linearization errorConstrained to the following polytypes:
wherein Θ is k+1 Representing multiple cells<0,Θ k+1 >Is a matrix of profiles; definition of the definitionΘ k+1 The ith column element of the ith row of (2) is +.>
Wherein the method comprises the steps ofIs an interval matrix Γ g,k+1 The ith row and jth column element,/>Representing interval element +.>Radius of (2); for convenience of presentation, let->Indicating measurement noise->Non-trigger error sigma k+1 And linearization error->And, namely:
wherein,is multi-cellular->Is a matrix of profiles; multiple cell shape->Calculated by the following formula:
step 4.4. Deriving a set of multiple-cell estimates that can contain the true value of the system state
Given the estimator gain K k+1 Assume that the error e is estimated k Satisfy e k ∈<0,P k >Then, a k+1 time prediction error e is obtained k+1|k And estimation error e k+1 Respectively satisfy e k+1|k ∈<0,P k+1|k >And e k+1 ∈<0,P k+1 >；
Multi-cell prediction set<0,P k+1|k >And a set of multi-cell estimatesIs of the shape matrix P of (2) k+1|k And P k+1 The method comprises the following steps:
wherein Q is k Representation comprising process noise omega k Is a multicellular form of (2)<0,Q k >The ith row and the ith column elements of the outline matrix areThe rest elements are 0; />The representation comprises beta k+1 ζ k+1 Is->Is defined as:
wherein matrix xi k+1 The ith row and jth column elements of (a)Is an interval matrixThe ith row and jth column element,/>Is interval element->Is a central value of (2);
matrix phi k+1 The ith column elements of the ith row of (a) are respectively
Representing interval element +.>Radius of (2);
according toFurther deriving a set of multi-cell estimates comprising a true value of the system state +.>
Step 4.5. Multiple cells by minimizing estimation error<0,P k+1 >To obtain the estimator gain K k+1 ；
<0,P k+1 >Appearance matrix P k+1 The F-norm of (c) satisfies the following relationship:
wherein:
making multicellular bodiesThe minimum F radius of (B) to obtain the estimator gain K k+1 The method comprises the following steps:
and carrying the gain of the obtained estimator back to the multi-cell member estimator to perform state estimation on the power distribution network.
2. An event trigger mechanism based power distribution network security state estimation system for implementing the event trigger mechanism based power distribution network security state estimation method as set forth in claim 1, wherein,
the system for estimating the safety state of the power distribution network based on the event triggering mechanism comprises the following components:
the event triggering mechanism transmission module is used for selectively transmitting the data meeting the triggering condition in the measurement data measured by the measurement device in the active power distribution network and transmitting the data to the estimation center through the communication network for state estimation;
the model building module is used for building a measurement model under spoofing attack by utilizing a norm bounded false injection signal constrained by a link;
the estimator construction module is used for building a system state model and a hybrid measurement model according to the characteristics of the power distribution network, and building a multi-cell gatekeeper estimator according to the power distribution network state model and the measurement model which consider an event triggering mechanism and a spoofing attack;
the gain calculation module is used for obtaining the gain of the member estimator;
and the state estimation module is used for bringing the gain back to the multi-cell assembler estimator to perform state estimation on the power distribution network.
3. A computer device comprising a memory and one or more processors, the memory having executable code stored therein, wherein the processor, when executing the executable code, performs the steps of the event trigger mechanism based power distribution network security state estimation method of claim 1.
4. A computer readable storage medium having stored thereon a program, which when executed by a processor, implements the steps of the method for estimating a security state of a power distribution network based on an event triggering mechanism as claimed in claim 1.
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