CN109697563B - Electric power information physical system risk guarantee early warning method considering hidden faults - Google Patents
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Abstract
The invention discloses a risk guarantee early warning method for a power information physical system considering hidden faults. The first step is as follows: establishing a risk propagation architecture of the power information physical system; the second step is that: aiming at risk propagation, constructing a risk propagation model of the electric power information physical system; the third step: the method comprises the steps that risk propagation of hidden faults in the electric power information physical system is processed in a classified mode and fusion processing is conducted through analyzing the influence of the hidden faults on the electric power information physical system; the fourth step: the spatial influence of the risk on the electric power information physical system is analyzed to find out the nodes with high risk sensitivity of the system, and guarantee early warning and adjustment are carried out on the nodes with high risk sensitivity, so that the electric power information physical system can normally operate. According to the method, the space influence of the risk on the electric power information physical system is analyzed, the influence of the hidden fault on risk propagation is analyzed, the high-sensitivity nodes of the system risk are found out, the influence of the risk propagation on each device of the system is analyzed, the risk change process is revealed, and the development of risk early warning measures is guided.
Description
Technical Field
The invention belongs to the field of space risk propagation calculation analysis of an electric power information physical system, and particularly relates to an electric power information physical system risk guarantee early warning method considering hidden faults.
Background
In recent years, under the promotion of the rapid development of energy conservation and emission reduction and information technology worldwide, the power system is undergoing a deep revolution. Along with the smart grid stepping into the mature development period, the power system will evolve to a new generation power system in technical characteristics and to the energy internet in functional form. The power grid is merged with other energy sources, energy systems and information systems in an accelerating way in an unprecedented and unified trend.
With the accelerated promotion of the electrification process, the high-proportion access of new energy and the wide application of novel energy utilization equipment, the 'cloudiness moving intelligence' technology is deeply integrated, the physical characteristics, the operation mode and the market form of the traditional power grid are fundamentally changed, and the gradual change of 'wide interconnection, intelligent interaction, flexibility, safety and controllability' is generated. To achieve the above object, the introduction and convergence of advanced information communication technology is particularly critical to power systems. The electric power information physical system is a novel system formed by deeply fusing information resources and an electric power system, and has the adaptability, flexibility, safety and reliability obviously superior to those of the existing intelligent electric power system. However, due to the high integration of the information system and the power system, the complexity of operation and control of the novel power system, namely the power information physical system, is greatly enhanced, and higher requirements are provided for the reliable and safe operation of the system. At the present stage, the risk research of the power information physical system needs to be developed urgently, and a foundation is laid for the large-scale engineering practice of the power information physical system. Since the risk of the information system is propagated to the power system, thereby jeopardizing the normal operation of the power system, research aiming at the risk propagation process is particularly necessary in the risk research of the power information physical system.
At present, the discussion of risk propagation of the power information physical system is limited, and the risk propagation of the power information physical system is still in a starting stage. Moreover, the existing research does not deeply analyze the influence of the hidden fault in the risk propagation of the power information physical system, and does not deeply analyze the risk propagation coupling relation among all layers of the power information physical system.
The disadvantages of the prior art are summarized as follows:
the prior art has the defects that: the existing research does not deeply analyze risk propagation coupling relation among layers of the power information physical system.
The prior art has the defects that: the existing research does not deeply analyze the influence of hidden faults existing in the system on risk propagation.
The prior art has the defects of 3: the existing research does not deeply analyze the influence of risk propagation on each device of the system
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a risk guarantee early warning method for an electric power information physical system considering hidden faults, which comprehensively considers the characteristics of risk propagation in different spaces and spaces, and the influence of hidden faults existing in the system on risk propagation and the influence of risk propagation on each device of the system.
Aiming at the characteristics of a power information physical system, the invention establishes an optimization model of system risk state propagation and solves risk state propagation matrixes inside an information layer, an electric power communication layer, an electric power layer, an information layer and an electric power communication layer and an electric power communication layer. The elements in the matrix correspond to risk propagation processes of the information layer, the power layer and the power communication layer, respectively. Meanwhile, the influence of the dominant fault and the recessive fault on the element stable state of the power information physical system after the risk is applied is quantified based on 5 types of risk state propagation matrixes inside an information layer, an electric power communication layer, an electric power layer, an information layer and an electric power communication layer and an electric power communication layer; and finally, through analyzing the space influence of the risk on the electric power information physical system, finding out a system risk high-sensitivity node, and performing risk guarantee early warning processing by using the risk high-sensitivity node.
As shown in fig. 1, the technical scheme adopted by the invention comprises the following steps:
the first step is as follows: establishing a risk propagation architecture of the power information physical system;
the risk propagation architecture of the power information physical system in the invention is as follows: in the electric power information physical system, risks are repeatedly propagated among an electric power layer, an electric power communication layer and an information layer, so that faults are continuously propagated to adjacent elements, next adjacent elements and farther elements, and finally, the process of large-area faults of the whole network is called risk propagation.
The power information physical system is divided into three spaces, namely a power layer, a power communication layer and an information layer. The information layer is connected with the power communication layer, and the power communication layer is connected with the power layer; the power layer refers to a multi-element power network; the power communication layer is a space for information transmission between the power layer and the information layer; the information layer refers to a space where sensing measurement information, external input information and control decision information are located.
The risk of the information layer may propagate to the power communication layer and through the power communication layer to the power layer. Likewise, the risk of the power layer may propagate to the power communication layer and through the power communication layer to the information layer.
According to risk propagation of the electric power information physical system, the risk propagation of the electric power information physical system can be divided into risk propagation of five subsystems: risk propagation of the first subsystem is risk propagation inside the information layer, for example, the information layer is threatened by safety due to the failure of the information equipment; risk transmission of the second subsystem is risk transmission inside the electric power layer, such as a large-area power failure accident caused by a fault of the electric power layer; risk propagation of the third subsystem is risk propagation inside the power communication layer, for example, propagation of risks in the power communication network, which finally causes continuous increase of information transmission delay rate and packet loss rate inside the power communication layer and congestion of a communication channel; the risk propagation of the fourth subsystem is risk propagation between the power layer and the power communication layer, such as power supply interruption causing breakdown of the communication device; the risk propagation of the fifth subsystem is risk propagation between the information layer and the power communication layer, and if the information layer is damaged, the information transmission delay rate of the power communication layer is increased continuously.
The power information physical system has elements, and the elements refer to devices in an information layer, a power layer and a power communication layer, for example, a generator in the power layer, a data acquisition device in the information layer, a router in the power communication layer, and the like, and are all elements of the power information physical system. Regarding one element as a node in the model, the node ordinal number is denoted as i (i ═ 1, 2.. multidot.n), n represents the total number of nodes of the electric information physical system, and the operation condition of the node in a time period when the node is subjected to a risk is regarded as a risk state of the node in the time period.
The risk transmission analysis used in the present invention means: the risk refers to various uncertain factors possibly borne by the equipment and the system, and the system and the equipment are changed from normal operation to fault operation under the influence of the uncertain factors. The behavior of a node at a time may be represented by the risk state at that time. In actual operation, the risk status of the system can be represented by 1 or 0, where 1 represents faulty operation of the system and 0 represents normal operation of the system. Under the risk of a certain unknown influence, the initial operating conditions of each node are known and can be represented by 0 or 1.
However, under the action of a certain risk, only the initial operating conditions of each device of the system can be obtained, and it can be determined whether each node of the system is in normal operation or in failure when the risk is just born, but with the action of the risk, what changes of the operating conditions of each node of the system will occur is unknown, and it is necessary to predict the probability of failure of each node of the system in the future, where the predicted node risk state represents the possibility of failure operation of the node at a certain time and is represented by a probability x (x is less than or equal to 1), that is, the node has a probability failure of x at the time. Therefore, the risk propagation judgment of the power information physical system in the invention refers to the judgment of the probability change of the fault operation of each node of the system under the action of a certain risk.
The second step is that: aiming at risk propagation, constructing a risk propagation model of the electric power information physical system;
the random dynamic process described by the risk propagation model of the invention is as follows: the risk state of the element at the previous moment can influence the element with physical and logical connection at the next moment with fault propagation probability to change the element from normal operation to fault operation, so that the risk state is continuously propagated to the adjacent element with physical and logical connection. According to the known risk state of the element at the previous moment and the fault propagation probability, the risk state of the element at any moment can be simulated.
S2, sequentially constructing risk state propagation matrixes of 5 types of subsystems inside an information layer, inside a power communication layer, inside the power layer, between the information layer and the power communication layer and between the power layer and the power communication layer:
aiming at each type of risk state propagation matrix, the following method is adopted to establish:
s21, assuming that a certain subsystem has n nodes in total, the risk propagation time T is divided into m time periods at equal intervals, each time period corresponds to one risk state propagation, that is, there are m times of risk state propagation, and the operation condition of each node in a certain time period when the node bears a risk is taken as the risk state of the node in the time period: the risk state is 0 to indicate that the node normally operates, and the risk state is 1 to indicate that the node fails to operate; the risk state x indicates that the node has the probability of x to operate and fail; when the risk state of a certain node is 1, that is, a fault occurs, the node that normally operates around the node is affected, and the change of the risk state of the system node in different time periods can be regarded as the change of the risk state of the system node in different risk propagation times.
Under the condition that connectivity exists between two nodes, one node i with the risk state of 1 influences the other node j with the risk state of 0, and a risk state propagation process exists between the nodes; the existence of connectivity between two nodes means that there is a physical and logical connection between the two nodes.
S22, the node state of the subsystem is represented as Si (t)，si (t)Representing the risk state of the node i in the time period of the t-th risk propagation, wherein t represents the ordinal number of the time period, and m represents the total number of the time period, namely the total number of the risk propagation;
s23, calculating the fault propagation probability p of the node i in the t-th risk propagation time period and the node j in the next t + 1-th risk propagation time period changing from normal to faultijThe following formula:
wherein s isi (t)Representing the risk status, s, of node i in the time period of the t-th risk spreadj (t+1)Representing the risk state of a node j in a t +1 th risk propagation time period, P { } represents an event of which the risk state is 1 in the t +1 th risk propagation time period and the event occurrence probability is P on the premise that the risk state of the node i in the t +1 th risk propagation time period is 1 and the risk state of the node j in the t +1 th risk propagation time period is 0ijI.e. the fault propagation probability;
and a matrix formed by fault propagation probabilities between each node and the nodes with connectivity serves as a risk state propagation matrix P, and is marked as P ═ (P ═ P)ij)n×n；
S3, solving a risk state propagation matrix:
in m risk propagation time periods, namely m times of risk propagation, an optimization model is established on the basis of minimizing the sum of squares of errors of the actual risk state and the risk state vector obtained by theoretical calculation, and a relatively accurate risk state propagation matrix P is obtained.
In practice, the risk state propagation matrices for adjacent time segments are not exactly the same, S(t)And S(t) Total memory between PAnd in error, establishing a model according to a criterion that the sum of squared errors f (P) is minimum, so that the risk propagation influence of m risk propagation time periods is equivalently quantized by one risk state propagation matrix.
S31, establishing an optimization model with the following formula:
s.t.{pij≥0,i,j＝1,2,…,n
wherein S is(t)Is the system risk state vector at the time period of the tth risk spread,
S(t)＝(s1 (t)，s2 (t)，s3 (t)，…，sn (t))，si (t)a node state representing that the ith node of the system is in a time period of the tth risk propagation, t being 1,2, …, m; f (P) represents the vector S(t)And S(t)The sum of the squares of the errors of the elements between P, representing the transpose of the vector, i.e. the vector S(t+1)-S(t)The transposed vector of P;
the above is the construction of the risk state propagation matrix of the subsystem, and the risk state propagation matrices of the 5 types of subsystems are constructed by adopting the above steps.
In the present invention, 5 types of risk propagation processes all have corresponding risk state propagation matrixes, as shown in fig. 2. If the information layer has a (i ═ 1, 2.., a) nodes, then in the risk state propagation matrix of risk propagation within the information layer, p is presentijAnd (i 1, 2., a; j 1, 2., a) represents information interaction of the information layer and reflects risk influence of the information layer. If the power communication layer has b (i ═ 1, 2., b) nodes, p is in a risk state propagation matrix of risk propagation in the power communication layerijAnd ( i 1, 2.. multidot.b; j 1, 2.. multidot.b) represents the information interaction of the power communication layer and represents the risk influence of the power communication layer. The power layer has c (i ═ 1, 2.., c) nodes, and in the risk state propagation matrix of risk propagation inside the power layer, p isij( i 1, 2.. times.c; j 1, 2.. times.c) represents a power layer signalThe risk influence of the power layer is reflected through information interaction; the information layer has d (i ═ 1, 2., d) nodes and d (i ═ 1, 2., d) nodes of the power communication layer to transmit information. Then in the risk state propagation matrix of risk propagation between the information layer and the power communication layer, pij(i 1, 2.. multidot.d; j 1, 2.. multidot.d) represents information interaction between the information layer and the power communication layer; the power communication layer has e (i ═ 1, 2.., e) nodes and e (i ═ 1, 2.., e) nodes of the power layer to transmit information. Then in the risk state propagation matrix of risk propagation between the power layer and the power communication layer, pijAnd ( i 1, 2.. times, e; j 1, 2.. times, e) represents information interaction of the power communication layer and the power layer.
The relationship between time period and risk propagation in the invention: the risk propagation process is divided into equal time intervals, each time interval is marked as one risk propagation, namely the change of the risk state of the system node in different time intervals can be regarded as the change of the risk state of the system node in different risk propagation times.
The risk state transmission in the invention refers to: node slave risk state si (t)After j risk transmissions, the risk state changes to si (t+j)This process is referred to as risk state propagation.
S32, according to the historical risk data, the system risk state vector after each risk propagation of each subsystem in the historical risk data and the known initial risk state vector S of each subsystem bearing risk are utilized in the information layer, the electric power communication layer, the electric power layer and the electric power communication layer, and the information layer and the electric power communication layer(0)Solving the optimization model to obtain respective risk state propagation matrix P of the five subsystems in the risk propagation process, wherein S(0)＝(s1 (0)，s2 (0)，s3 (0)，…，sn (0))si (0)( i 1, 2.. n.) represents the initial risk state of the ith node in the subsystem, i.e., node i has si (0)Is out of order;
through the steps, the 5-type risk state propagation matrix under each specific risk can be obtained. Each specific risk generates a specific initial risk state for each element, the initial risk states of each element constituting an initial risk state vector for the subsystem. Thus, the initial risk state vector for each subsystem corresponds to the risk state propagation matrix for one subsystem.
The 5 types of risk state propagation matrixes collectively reflect the risk propagation process of the power information physical system. And the risk of any layer is propagated to any direction, and the risk propagation analysis can be carried out based on the corresponding risk state propagation matrix. For example: if the risk comes from the information layer and propagates to the power communication layer and the power layer, the matrices required for the calculation are sequentially inside the information layer, between the information layer and the power communication layer, inside the power communication layer, between the power layer and the power communication layer, and inside the power layer. If the risk comes from the power communication layer and propagates to the power layer, the matrices required for the calculation are sequentially inside the power communication layer, between the power layer and the power communication layer, and inside the power layer.
The third step: by analyzing the influence of the hidden faults on the electric power information physical system, risk propagation of the hidden faults in the electric power information physical system is processed in a classified mode, and fusion processing is carried out
For normal operation, the function whose element failure is not obvious is called a recessive function, and the recessive function fails to be a recessive failure. As long as no accident occurs, the hidden fault cannot be reflected. The hidden fault has great harm and is easy to cause secondary loss, thereby causing great damage to human bodies and equipment.
The method considers the hidden fault damage by adding the hidden fault occurrence probability parameter, and analyzes and judges the stable state of each node after risk propagation in more detail by considering the occurrence and non-occurrence of the hidden fault.
The third step is specifically as follows:
it is assumed that at most one component has a recessive fault at the same time as each dominant fault occurs. Under the action of a certain risk, the risk state of the system is continuously changed through a risk state propagation matrix. The risk propagation for each time and after is divided into two categories, namely how to propagate the risk after the hidden fault occurs to the element j and how to propagate the risk without the hidden fault occurring to the element j.
The present invention knows the particular component that is likely to fail in the shadow, i.e., knows which component is the component that is likely to fail in the shadow, but does not know when the component failed in the shadow. Before the hidden failure of the element actually occurs, the risk probability is obtained by means of prediction.
In the case of hidden failure of the element j during the k-th risk propagation, the risk state of the element j is represented by sj (k)The change is 1, which indicates a 100% probability failure of the component in the event of a latent failure of component j.
3.1) in the case of an element j known to possibly have a hidden fault, performing calculation processing on the element containing the hidden fault by adopting the following modes:
S＝(1-a)·S2+a·S1
wherein, S1 represents the steady risk state vector of the system when the element j has a hidden fault during the k-th risk propagation, S2 represents the steady risk state vector of the system when the element j has no hidden fault, a represents the probability of the element j having a hidden fault, and S represents the steady risk state vector of the system considering the hidden fault occurrence probability;
the stable risk state vectors S1 and S2 of the two systems are obtained by the following processing, but the processing of the stable risk state vector S1 of the system sets the risk state of the element j in the risk state vector obtained after the k-1 th risk propagation to 1 as the initial risk state vector at the k-th risk propagation:
3.1.1) obtaining the risk state propagation matrix P of the 5-class subsystem by utilizing the data calculation of the risk state composition of all elements under any known historical riski；
3.1.2) under the action of a certain future risk, after each risk propagation, according to the layer where the risk is started and the risk propagation sequence, combining the node serial numbers in each layer and the initial risk states of each node, sequentially selecting the risk state propagation matrix of the related subsystem in the risk propagation, substituting the system risk state vector which is sequentially and continuously multiplied to the last risk propagation, and obtaining the system risk state vector after the current risk propagation, wherein the specific expression is as follows:
S(k)＝S(k-1)·Pk
where k denotes the number of risk propagation orders, S(k)Representing the system risk state vector after the k-th risk propagation, S(k-1)Represents the system risk state vector after the k-1 th risk propagation, PkA risk state propagation matrix representing the subsystems involved in the k-th risk propagation;
for example, the information layer is propagated to the power layer via the power communication layer, and the risk state propagation matrices of the subsystems involved in the risk propagation include risk state propagation matrices within the information layer, between the information layer and the power communication layer, within the power communication layer, between the power communication layer and the power layer, and within the power layer, and a total of five risk state propagation matrices are sequentially multiplied.
3.1.3) continuously repeating the processes of the steps 3.1.1) -3.1.2) until the risk state vector of the electric power information physical system reaches a stable state, namely, no new failure node is generated, wherein the failure node is a node with a risk state different from 0, the state of each node is not changed, the risk propagation is finished, and at the moment, the risk state vector of the electric power information physical system is taken as a stable risk state vector.
According to the method, a 5-class risk state propagation matrix P of the power information physical system is obtained by utilizing the system risk state and the optimization model of different risk state propagation times of the power information physical system under the action of historical risks, and the matrix P represents the state influence of each node on other nodes in an information layer, an electric power communication layer, an electric power layer, an information layer and an electric power communication layer and an electric power communication layer in the system.
The fourth step: the spatial influence of the risk on the electric power information physical system is analyzed to find out the nodes with high risk sensitivity of the system, and guarantee early warning and adjustment are carried out on the nodes with high risk sensitivity, so that the electric power information physical system can normally operate.
In the second step and the third step, the risk propagation process of the electric power information physical system is influenced by the hidden fault of the system, the self space structure and the external bearing risk, and has certain uncertainty. The node states of the system are jointly represented by a risk state propagation matrix, risk propagation times and an initial risk state, the initial risk state is known in a certain risk propagation process, the risk state propagation matrix is also known by the system decision, and the state change process of each node after the risk is used for establishing a two-dimensional view representation by taking the risk propagation times as an X axis and the risk state as a Y axis so as to reveal the influence of the risk propagation on the system nodes.
And in the fourth step, the risk state of each node does not change any more, namely a new failure node is not generated any more, the complete risk propagation process is considered to be finished, wherein if after the risk propagation process is finished, a certain node is changed into a risk state x from an initial risk state of 0 through at least one risk propagation, and the risk state x of the node is the maximum value in the risk states of all the nodes after the risk propagation process is finished, the node is a risk high-sensitivity node of the system, and the risk high-sensitivity node is subjected to risk early warning and measures such as timely adding redundant equipment and timing maintenance are taken so as to ensure the normal operation of the system and the node.
According to the method, a fault propagation process is applied to the electric power information physical system, a risk propagation mechanism of the electric power information physical system is researched based on an optimized model, propagation processes inside an information layer, an electric power communication layer, between the information layer and the electric power communication layer and between the electric power layer and the electric power communication layer of risks are considered comprehensively, the influence of a hidden fault is quantized, a risk evolution process of the electric power information physical system is simulated in detail, the evolution problem of risk propagation of the electric power information physical system considering the hidden fault is solved, and based on the problem, a risk high-sensitivity node is found, and the risk guarantee early warning problem of the electric power information physical system is solved.
The risk state propagation matrix provided by the invention is determined by the structure of the power information physical system and the internal relation of each network space, is not influenced by external conditions and human beings, fully reflects the information interaction inside an information layer, inside a power communication layer, inside the power layer, between the information layer and the power communication layer and between the power layer and the power communication layer, quantifies the influence of the risk state of one node on the state of another node into the corresponding fault influence probability in the matrix, can quantify the network structure of the power information physical system and the propagation path of the risk, and reveals the risk change process.
The risk state propagation matrix obtained based on the optimization model is determined by the structure of the risk state propagation matrix, and the risk high-sensitivity nodes can be effectively reflected through the system risk propagation evolution process obtained through the matrix, so that the node risk guarantee early warning measures can be guided to be developed.
The risk of the power information physical system refers to that one or more devices of an information layer, a power communication layer and a power layer are in fault and cannot normally operate due to the fault caused by the action of external factors on corresponding devices or the fault of the devices in the power information physical system.
When a certain risk acts on the electric power information physical system, the influence of the risk is unknown, the electric power information physical system risk guarantee early warning method considering the hidden fault provided by the invention can be firstly utilized to judge the risk high-sensitivity nodes in the electric power information physical system, so that the information of the risk high-sensitivity nodes is transmitted to the master control console through the communication device, the master control console issues corresponding control instructions to guide corresponding control equipment to take protective measures on the risk high-sensitivity nodes, and the normal operation of the system is guaranteed
The invention has the beneficial effects that:
according to the method, aiming at the characteristics of the electric power information physical system, an electric power information physical system risk propagation framework considering the hidden fault is established, a risk propagation model considering the hidden fault is established, and a risk state propagation matrix is solved by utilizing an optimized model. The risk propagation model starts from the system structure of the electric power information physical system, considers the propagation mechanism of risks and simulates the fault evolution process of the electric power information physical system. The method solves the problems of risk propagation characteristics, analysis of influence of hidden faults on risk propagation, risk early warning and the like.
The risk state propagation matrix provided by the invention is determined by the structures inside the information layer, the electric power communication layer, the electric power layer, the information layer and the electric power communication layer, and the electric power layer and the electric power communication layer and the internal connection of each network, and is not influenced by external conditions and human beings. The influence of the risk state of one node on the state of the other node can specifically obtain the corresponding fault influence probability in the matrix, the influence of the dominant fault and the recessive fault on the stable state of the element of the power information physical system after the risk is received is obtained, the network structure and the risk propagation path of the power information physical system are obtained, and the risk change process is revealed.
According to the method, the space influence of the risk on the electric power information physical system is analyzed, the influence of the hidden fault on risk propagation is analyzed, the high-sensitivity nodes of the system risk are found, the influence of the risk propagation on each device of the system is analyzed, the risk guarantee early warning measures can be accurately carried out, and the safe operation of the electric power information physical system is guaranteed.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is an illustration of a category 5 risk state propagation matrix of the present invention.
FIG. 3 is a diagram of a physical system of power information as employed by the present invention.
Fig. 4 is a risk propagation diagram for nodes 1,2, 3 in an embodiment of the invention.
Fig. 5 is a risk propagation diagram for nodes 4, 5, 6 in an embodiment of the invention.
Fig. 6 is a risk propagation diagram for nodes 7, 8, 9, 10 in an embodiment of the invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
The embodiment and the implementation process of the complete method according to the invention are as follows:
first step of: constructing a power information physical system model shown in FIG. 3, wherein the model comprises 10 nodes, 4 information nodes, 2 communication nodes and 4 power nodes, and the node r1And node r6Forming an information-power communication layer, node r5And node r10Constituting a power-power communication layer.
The second step is that: at a certain external Risk Risk1Under the action, the initial risk state of the node of the power information physical system is R0After six risk propagations, the state of the electric power information physical system is kept stable, and the risk state change of the node is shown in table 1. Based on the risk propagation process and the risk state propagation model, 5 types of risk state propagation matrixes P of the system can be obtained1. In the same way, Risk comes from the outside2Under the action of R0The initial risk state and propagation states of each node of (1,0,1,0,0,0,0,0, 0) are known, and a 5-class risk state propagation matrix P at the class-class risk can be obtained2. Risk according to any historyiIts class 5 risk state propagation matrix PiCan all be calculated to obtain
TABLE 1 System Risk State under Risk propagation (Risk)1)
The third step: suppose that when the 3 rd risk is propagated, the power node 7 has a hidden fault, and the state of the node 7 is suddenly changed to 1. And establishing a risk propagation schematic diagram by taking the risk propagation times as an X axis and the risk state as a Y axis, wherein after a hidden fault occurs, the state of each node of the system after each risk propagation, namely a risk propagation evolutionary diagram, is shown in fig. 4, fig. 5 and fig. 6. As can be seen from fig. 4, 5 and 6, when the system is at risk from the information layer and the power layer and there is a hidden fault, the probability of the fault at all nodes is significantly improved compared to the case without considering the hidden fault.
Since the hidden fault occurrence probability parameter is 0.2, the system stable state under both the two conditions is obtained, and the final stable state vector of each node can be obtained, as shown in table 2. The hazard of the hidden fault is considered in risk propagation, so that the predicted state of the node obtained by the risk propagation model of the electric power information physical system is more accurate.
Table 2 system risk status considering hidden faults
The fourth step: as can be seen from fig. 4, fig. 5, and fig. 6, after the nodes 4, 7, 8, and 9 propagate risks for the first few times, the states of the nodes are significantly higher than those of other nodes, that is, the nodes 4, 7, 8, and 9 are risk-sensitive nodes, and it is necessary to ensure that risk early warning measures of the nodes are carried out in time.
According to the risk propagation method, a risk propagation mechanism of the electric power information physical system is researched, the influence of the propagation process and the hidden fault in and across the space of the risk is comprehensively considered, the risk evolution process of the electric power information physical system is simulated, the system risk propagation evolution process obtained through the risk state propagation matrix can effectively reflect the nodes with high risk sensitivity, the development of node risk early warning measures is guided, and certain feasibility and practicability are achieved.
Claims (2)
1. A risk guarantee early warning method for an electric power information physical system considering hidden faults is characterized by comprising the following steps:
the first step is as follows: establishing a risk propagation architecture of the power information physical system;
the method comprises the following steps that a power information physical system is divided into three spaces, wherein the three spaces are a power layer, a power communication layer and an information layer respectively; according to risk propagation of the electric power information physical system, the risk propagation of the electric power information physical system can be divided into risk propagation of five subsystems: the risk propagation of the first subsystem is risk propagation inside the information layer; the risk propagation of the second subsystem is risk propagation inside the power layer; risk propagation of the third subsystem is risk propagation inside the electric power communication layer; the risk propagation of the fourth subsystem is risk propagation between the power layer and the power communication layer; the risk propagation of the fifth subsystem is risk propagation between the information layer and the electric power communication layer; the method comprises the following steps that elements exist in the power information physical system, wherein the elements refer to devices in an information layer, a power layer and a power communication layer, one element is used as one node in a model, the node number is represented as i (i is 1, 2.. multidot.n), n represents the total number of nodes of the power information physical system, and the operation condition of a time period when the node bears a risk is used as a risk state of the node in the time period;
the second step is that: aiming at risk propagation, constructing a risk propagation model of the electric power information physical system;
s2, sequentially constructing risk state propagation matrixes of 5 types of subsystems inside an information layer, inside a power communication layer, inside the power layer, between the information layer and the power communication layer and between the power layer and the power communication layer:
aiming at each type of risk state propagation matrix, the following method is adopted to establish:
s21, assuming that a certain subsystem has n nodes in total, the risk propagation time T is divided into m time periods at equal intervals, each time period corresponds to one risk state propagation, that is, there are m times of risk state propagation, and the operation condition of each node in a certain time period when the node bears a risk is taken as the risk state of the node in the time period: the risk state is 0 to indicate that the node normally operates, and the risk state is 1 to indicate that the node fails to operate;
s22, the node state of the subsystem is represented as Si (t)，si (t)Representing the risk state of the node i in the time period of the t-th risk propagation, wherein t represents the ordinal number of the time period, and m represents the total number of the time period, namely the total number of the risk propagation;
s23, calculating the fault propagation probability p of the node i in the t-th risk propagation time period and the node j in the next t + 1-th risk propagation time period changing from normal to faultijThe following formula:
pij＝P{sj (t+1)＝1|si (t)＝1,sj (t)＝0}
wherein s isi (t)Representing the risk status, s, of node i in the time period of the t-th risk spreadj (t+1)Representing the risk state of a node j in a t +1 th risk propagation time period, P { } represents an event of which the risk state is 1 in the t +1 th risk propagation time period and the event occurrence probability is P on the premise that the risk state of the node i in the t +1 th risk propagation time period is 1 and the risk state of the node j in the t +1 th risk propagation time period is 0ijI.e. the fault propagation probability;
and a matrix formed by fault propagation probabilities between each node and the nodes with connectivity serves as a risk state propagation matrix P, and is marked as P ═ (P ═ P)ij)n×n；
S3, solving a risk state propagation matrix:
s31, establishing an optimization model with the following formula:
wherein S is(t)Is the system risk state vector, S, at the time period of the tth risk propagation(t)＝(s1 (t),s2 (t),s3 (t),…,sn (t))，si (t)A node state representing that the ith node of the system is in a time period of the tth risk propagation, t being 1,2, …, m; f (P) represents the vector S(t)And S(t)The sum of the squares of the errors of the elements between P, representing the transpose of the vector, i.e. the vector S(t +1)-S(t)The transposed vector of P;
s32, according to the historical risk data, the risk state vector of each subsystem after each risk propagation in the historical risk data and the known initial risk state vector S of each subsystem bearing risks are utilized(0)Solving optimization modelObtaining respective risk state propagation matrixes P of the five subsystems in the risk propagation process, wherein S(0)＝(s1 (0),s2 (0),s3 (0),…,sn (0))，si (0)(i 1, 2.. n.) represents the initial risk state of the ith node in the subsystem, i.e., node i has si (0)Is out of order;
the third step: the method comprises the steps that risk propagation of hidden faults in the electric power information physical system is processed in a classified mode and fusion processing is conducted through analyzing the influence of the hidden faults on the electric power information physical system;
the third step is specifically as follows:
3.1) in the case of an element j known to possibly have a hidden fault, performing calculation processing on the element containing the hidden fault by adopting the following modes:
S＝(1-a)·S2+a·S1
wherein, S1 represents the steady risk state vector of the system when the element j has a hidden fault during the k-th risk propagation, S2 represents the steady risk state vector of the system when the element j has no hidden fault, a represents the probability of the element j having a hidden fault, and S represents the steady risk state vector of the system considering the hidden fault occurrence probability;
the stable risk state vectors S1 and S2 of the two systems are obtained by the following processing, but the processing of the stable risk state vector S1 of the system sets the risk state of the element j in the risk state vector obtained after the k-1 th risk propagation to 1 as the initial risk state vector at the k-th risk propagation:
3.1.1) obtaining the risk state propagation matrix P of the 5-class subsystem by utilizing the data calculation of the risk state composition of all elements under any known historical riski；
3.1.2) after each risk transmission, according to the layer where the risk starts and the risk transmission sequence, combining the node serial numbers in each layer and the initial risk state of each node, sequentially selecting the risk state transmission matrix of the related subsystems in the risk transmission, substituting the system risk state vector which is sequentially and continuously multiplied to the last risk transmission, and obtaining the system risk state vector after the current risk transmission, wherein the specific expression is as follows:
S(k)＝S(k-1)·Pk
where k denotes the number of risk propagation orders, S(k)Representing the system risk state vector after the k-th risk propagation, S(k-1)Represents the system risk state vector after the k-1 th risk propagation, PkA risk state propagation matrix representing the subsystems involved in the k-th risk propagation;
3.1.3) continuously repeating the processes of the steps 3.1.1) -3.1.2) until the risk state vector of the electric power information physical system reaches a stable state, namely, no new failure node is generated, wherein the failure node is a node with a risk state different from 0, and the state of each node is not changed, so that risk propagation is finished, and the risk state vector of the electric power information physical system is taken as a stable risk state vector;
the fourth step: the method comprises the steps of finding out a system risk high-sensitivity node by analyzing the space influence of risks on the electric power information physical system, and carrying out guarantee early warning and adjustment on the risk high-sensitivity node to enable the electric power information physical system to normally operate;
and in the fourth step, the risk state of each node does not change any more, namely a new failure node is not generated any more, the complete risk propagation process is considered to be finished, wherein if after the risk propagation process is finished, a certain node is changed into a risk state x from an initial risk state of 0 through at least one risk propagation, and the risk state x of the node is the maximum value in the risk states of all the nodes after the risk propagation process is finished, the node is a risk high-sensitivity node of the system, and risk early warning is carried out on the risk high-sensitivity node and redundant equipment and regular maintenance measures are added in time to ensure the normal operation of the system and the node.
2. The electric power information physical system risk guarantee early warning method considering hidden faults is characterized in that:
in the second step and the third step, the states of all nodes of the system are represented by a risk state propagation matrix, risk propagation times and an initial risk state together, and a two-dimensional view representation is established by using the risk propagation times as an X axis and the risk state as a Y axis in the state change process of all nodes subjected to risks.
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