CN106600138A - Secondary equipment risk assessment method - Google Patents
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Abstract
The invention discloses a secondary equipment risk assessment method. In the method, based on a first-order hidden Markov model, a second-order hidden Markov model is provided and the second-order hidden Markov model is applied to a reliability assessment of secondary equipment, historical running state research to the secondary equipment is increased and considered factors are comprehensive. A method of adopting the second-order hidden Markov model is characterized by firstly, carrying out fault probability calculating on single secondary equipment; and then simulating a failure rate of an investment system of the equipment through Monte Carlo so that an acquired failure rate is reliable. After the secondary equipment is classified, a risk index corresponding to each type of secondary equipment is determined. Therefore, the established risk index accords with characteristics of different types of secondary equipment, and a risk assessment result possesses objectivity and accords with an actual power grid operation condition.
Description
Technical field
The present invention relates to the technical field that secondary equipment in power system is safeguarded, more particularly to a kind of secondary device risk assessment
Method.
Background technology
With the high speed development of power industry, second power equipment quantity gradually increases, and scale also constantly expands, its operation
Maintenance management is also increasingly paid attention to by enterprise.Secondary device risk assessment be consider device security, economy and
The risk of the aspects such as social influence, determines secondary device degree of risk, is that equipment operation, maintenance, maintenance, test, technological transformation etc. are raw
The decision-making for producing management work provides foundation.
Ieee standard 100-1992 is defined as risk " to the probability (i.e. fault rate) and seriousness that do not expect result
Tolerance, generally using probability and the expression-form of result product ", for secondary device, risk is exactly the equipment fault
Probability and failure cause the expectation of consequence.The risky coordinate diagram method of common method of power system device risk assessment, Meng Teka
Luo Fangfa, failure model and effect analysis, ETA etc..According to evaluated system situation and risk control target, can adopt
With different technical methods.
Currently, defect, obstacle and the Risk Management that secondary device is present only rests on the surface analyses to event,
And these analyses are based on qualitative subjective.Secondary device there are which risk, the order of severity of risk much, how to comment
Estimate, how to compare, mostly depending on the subjective judgment of foundation attendant, the effective decision-making of secondary device Risk Pre-control is had greatly
Impact.This can not only make secondary equipment in power system not obtain timely risk control maintenance or early warning, or even be also possible to
Can occur to cause major accident because risk control measure is improper.Therefore, it is necessary to seek one kind can be to electrical secondary system risk
The method of assessment.
The content of the invention
In order to solve the above problems, present invention aim at a kind of secondary equipment in power system methods of risk assessment is provided,
So as to be the O＆M of the secondary device of power grid enterprises assessment and risk control foundation is provided.
Technical scheme：A kind of secondary equipment risk assessment method, comprises the following steps：
Step S1：According to the history data of every kind of secondary device in power system, t-1 moment and t system are obtained
State q and observation output sequence O of system；
Step S2：Number N of system mode q and length M of observation output sequence O can be obtained respectively according to step S1,
The dimension of the consideration due to increased the t to the time, the state-transition matrix A for obtaining and observation output probability matrix B is by two
Dimension is changed into three-dimensional；
Step S3：Three-dimensional state transfer matrix matrix A and observation output probability matrix B according to obtained by step S2, with
And known initial state probabilities π, set up Second-Order Hidden Markov Model λ={ S, O, A1, A2, B1, B2, ∏ }；
Step S4：The Second-Order Hidden Markov Model derived according to step S3 and observation output sequence O, by Viterbi
(Viterbi) algorithm, tries to achieve the status switch S for producing this observation sequence；
Step S5：According to step S3 derive Second-Order Hidden Markov Model observation output sequence O, by it is front to-after
To algorithm (Forward-Backward algorithm), the probability P for producing this observation sequence O is tried to achieve；
Step S6：According to the probability P that step S5 is tried to achieve, using the failure of the method simulation secondary device operation of Monte Carlo
Rate F；
Step S7：Secondary device is classified, corresponding risk indicator is established, is calculated after secondary device failure to system
The loss L for bringing；
Step S8：According to the breakdown loss that the fault rate and step S7 of the simulation of step S6 are tried to achieve, power system is calculated secondary
The integrated risk value of equipment：
In formula：R (t) for secondary device value-at-risk, FiT () is the probability that the i-th class secondary device breaks down, LiT () is
The breakdown loss value of the i-th class secondary device.
In said method, described secondary device fault rate is calculated based on the method for second order HMM
, wherein A1、B1The respectively state transition probability matrix observation output probability matrix of t, A2And B2Respectively record t-
The state transition probability matrix observation output probability matrix at 1 and t two moment, A2And B2For three-dimensional matrice；
A1=(aij)N×N,A2=(aijk)N×N×N
B1=(bi(Ot))N×M, B2=(bij(Ot))N×N×M
In said method, the establishment of the risk indicator is the classification according to secondary device determining, secondary will be set
Body of getting everything ready is categorized as monitoring measurement class and relay protection and automaton class.
Further, the risk indicator of described monitoring measurement class secondary device is should in the middle-and-high-ranking of power system according to it
With difference establishing, including：In electric power system fault positioning, it is considered to the decline of fault location precision after the equipment fault；
In Power system state estimation, it is considered to the decline of precision of state estimation after the equipment fault.
Further, the risk indicator of described relay protection and automaton class secondary device is to electricity after its failure
The loss size of Force system, including：System loading cuts down probability, expects to lose loading.
In said method, described secondary device is repairable elements in its life-span stable phase, wherein using second order
The probability P that hidden Markov model is tried to achieve is the reliable probability of equipment, the secondary device failure probability F's in follow-up calculating
Computing formula is：F=1-P.
Due to adopting above-mentioned technical proposal, it is an advantage of the current invention that：The present invention is commented for secondary equipment in power system risk
The method estimated provides reference, establishes the system of a set of complete secondary device risk assessment.In single order hidden Markov mould
On the basis of type, propose the method for Second-Order Hidden Markov Model and apply it on the reliability assessment of secondary device,
Increased the history run state research to secondary device, it is considered to which factor is more fully.Using Second-Order Hidden Markov Model
Method carries out first probability of malfunction calculating to single secondary device, then by the failure of the Monte Carlo simulation equipment investment system
Rate, so as to get fault rate it is more reliable.And the present invention establishes each class secondary device phase after classifying to secondary device
The risk indicator answered.Therefore, the risk indicator of foundation more conforms to the characteristic of different classes of secondary device, makes the knot of risk assessment
Fruit has more objectivity and more meets electrical network practical operation situation.
Description of the drawings
Fig. 1 is the secondary equipment risk assessment method calculation flow chart of the present invention；
Fig. 2 is the fault rate tub curve of the secondary device of the present invention；
Fig. 3 is the schematic diagram of the Second-Order Hidden Markov Model of 4 hidden states of the present invention.
Specific embodiment
In order that the object of the invention, technical scheme and advantage are clearer, the present invention is made into one with reference to embodiment
The detailed description of step.
Embodiments of the invention：The step of Fig. 1 is secondary equipment risk assessment method of the present invention flow chart.It is described secondary to set
The foundation of Second-Order Hidden Markov Model in standby methods of risk assessment is comprised the following steps：
Step S1：According to the history data of every kind of secondary device in power system, t-1 moment and t system are obtained
State q and observation output sequence O of system；
Step S2：Number N of system mode q and length M of observation output sequence O can be obtained respectively according to step S1,
The dimension of the consideration due to increased the t to the time, the state-transition matrix A for obtaining and observation output probability matrix B is by two
Dimension is changed into three-dimensional；
Step S3：Three-dimensional state transfer matrix matrix A and observation output probability matrix B according to obtained by step S2, with
And known initial state probabilities π, set up Second-Order Hidden Markov Model λ={ S, O, A1, A2, B1, B2, П }；
Further, the output probability of the observation, not only with state S of t systemjCorrelation, and during with t-1
State S of etching systemiCorrelation, so, second order HMM is defined as seven tuples λ={ S, O, an A1, A2, B1, B2, ∏ }, each element
Implication it is as follows：
S：The hidden state set of second order HMM is expressed as S, S={ S1, S2...Si, Sj...SN, the hidden state of t system
For qt, it is known that qt∈{S1, S2...Si, Sj...SN}。
O：The set expression of the observation of second order HMM is O, O={ O1, O2...OM, the observation probability of t system is
Ot, it is known that Ot∈{O1, O2...OM}。
A1, A2：T-1 moment and the state transition probability matrix of t.A1=(aij)N×N, A2=(aijk)N×N×N, during t+1
The hidden state carved is qt+1, corresponding system mode is Sk。
A1={ aij=P (qt=Sj|qt-1=Si)}
A2={ aijk=P (qt+1=Sk|qt=Sj, qt-1=Si), 1≤i, j, k≤N (1)
B1, B2：T-1 moment and the observation output probability matrix of t.
B1=(bi(Ot))N×M, B2=(bij(Ot))N×N×M。
B1={ bj(Ot)=P (Ot=Or|qt=Sj)}
B2={ bij(Ot)P(Ot=Or|qt=Sj, qt-1=Si),
1≤i, j≤N, 1≤r≤M (2)
∏：The initial state probabilities set of second order HMM.П={ π1, π2...πN}。
πi=P (q1=Si), 1≤i≤N (3)
Step S4：The Second-Order Hidden Markov Model λ derived according to step S3 and observation output sequence O, by Viterbi
(Viterbi) algorithm, tries to achieve the status switch S for producing this observation sequence.
Further, viterbi algorithm (Viterbi Algorithm) is expressed as follows：
Defined variable δtIt is q that (i, j) is t status switch1, q2..., qt(qt-1=Si, qt=Sj) when produce observation
The output sequence O of value1, O2..., OtMaximum of probability, algorithmic procedure is as follows：
Initialization：
δ2(i, j)=πiaijbi(O1)bij(O2), 1≤i, j≤N； (4)
Ψ2(i, j)=0,1≤i, j≤N； (5)
Iterative calculation：
Where (1≤j, k≤N, 2≤t≤T-1)
Termination：
Recall in path：
First from front to back search one by one reaches the optimal path of current state to viterbi algorithm, then by backward tracing from rear
The hidden state sequence of optimum is found forward.The present invention tries to achieve the path of secondary device failure, the i.e. event of module using this algorithm
Barrier state.
Step S5：According to step S3 derive Second-Order Hidden Markov Model λ observation output sequence O, by it is front to-after
To algorithm (Forward-Backward algorithm), the probability P for producing this observation sequence O is tried to achieve；
Further, Forward-backward algorithm (Forward-Backward algorithm) is expressed as follows respectively：
1) forwards algorithms (Forward Algorithm)：
Define forward variable：
αt(i, j)=P (O1, O2..., Ot, qt-1=Si, qt=Sj|λ)
By forward variable αt(i, j) is according to following steps iteration：
Initialization：
α2(i, j)=πibi(O1)aijbij(O2), 1≤j≤N (11)
Iterative calculation：
Termination：
2) backward algorithm (Backward Algorithm)
Define backward variable：
βt(i, j)=P (Ot+1, Ot+2..., OT|qt-1=Si, Ot, qt=Sj, λ)
By backward variable βt(i, j) is according to following steps iteration：
Initialization：
βT(i, j)=1 (14)
Iterative calculation：
Observation sequence O={ O can be obtained by Forward-backward algorithm1, O2…OMProduce probability：
Forward-backward algorithm (Forward-Backward algorithm) be used for solve setting models λ=A, B,
∏ } and observation output probability O={ O1, O2...OMIn the case of, seek the probability P (O | λ) for producing this observation sequence.The present invention
The probability of secondary device failure is tried to achieve using this algorithm.
The present invention is with module M on measuring equipment in secondary device1Reliability assessment as a example by, make further
It is bright.
Two circuit boards are included in the module, each module has four hidden states (being represented with HS), its corresponding hidden Ma Er
Can husband's model as shown in Figure 3.The function of module is different, and the schematic diagram of its corresponding hidden Markov model can be varied from,
The present invention makes following explanation in case of simplest 4 hidden states.
The meaning of each hidden status representative is as follows：
HS1 --- two circuit boards are normal
HS2 --- circuit board 1 is normal, the failure of circuit board 2
The failure of HS3 --- circuit board 1, circuit board 2 is normal
HS4 --- the equal failure of two circuit boards
We can obtain the corresponding observer state of module.By taking module M1 as an example, Yj1=M1Represent M1It is in running order,
Yj2=M1Represent M1In malfunction.
With module M1As a example by, it is known that the state-transition matrix A of t, observation probability matrix B and probability matrix π,
It is as follows.
π=[0.999 6.088e-4 40172e-4 5.384e-7]
In second order HMM, matrix π is constant for probability, state-transition matrix A2With observation probability matrix B2It is contemplated that
The module status at two moment of t-1 moment and t, therefore it is three-dimensional matrice.A1And B1Respectively it is included in A2And B2Interior
Two-dimensional matrix, is expressed as follows respectively：
By the data of t and the state-transition matrix at t-1 moment and observation probability matrix by second order forwards algorithms, after
Module M can be obtained to algorithm and viterbi algorithm0Probability and path, as shown in table 1." index " in form represents observation
The state of the module for arriving, wherein Y1Represent normal, Y2Failure is represented, " probability " represents the maximum likelihood for the observer state occur,
" path " represents the hidden status switch of the maximum possible for generating the observer state.
The M of table 11Probability and path
In the same manner, the reliable probability of other modules, and the module status corresponding to different probability can be obtained.
Because each module in individual equipment is connected each other, so the total probability of malfunction of equipment is：
Step S6：Can be further explained, according to the probability F that step S5 is tried to achieve0Simply discrete component is put into probability of malfunction,
Using the method for Monte Carlo, according to distribution of the equipment in electrical network, simulate it and run failure rate F after 10000 times.
Due to the equipment only faulty and normal two states, therefore its state is simulated using binomial distribution.What is obtained after simulation secondary sets
Standby failure-rate data is more reliable.
Step S7：Secondary device is classified, corresponding risk indicator is established, is calculated after secondary device failure to system
The loss L for bringing.The establishment of wherein risk indicator is the classification according to secondary device determining.Secondary device is specifically classified
For monitoring measurement class, relay protection and automaton class.
The risk indicator of monitoring measurement class secondary device is established according to it in the difference of the middle-and-high-ranking application of power system.
Including：In electric power system fault positioning, it is considered to the decline of fault location precision after the equipment fault；Estimate in POWER SYSTEM STATE
On meter, it is considered to the decline of precision of state estimation after the equipment fault.
The risk indicator of relay protection and automaton class secondary device is to the loss of power system after its failure
Size.Probability is cut down including system loading, expect to lose loading.
Step S8：According to the breakdown loss that the fault rate and step S7 of the simulation of step S6 are tried to achieve, power system is calculated secondary
The integrated risk value of equipment.Risk assessment, the public affairs of its foundation are carried out to secondary equipment in power system using the appraisal procedure of probability
Formula is as follows：
In formula：R (t) for secondary device value-at-risk, FiT () is the probability that the i-th class secondary device breaks down, LiT () is
The breakdown loss value of the i-th class secondary device.
Referring to Fig. 2 and Fig. 3, the present invention is according to probability of equipment failure tub curve, it is considered to which equipment is in the steady of rate of breakdown
In periodically, speculate that the probability that current device breaks down should with when breaking down using the method for Second-Order Hidden Markov Model
The situation of the hidden state of equipment.Fault rate of the equipment in running is simulated with the method for Monte Carlo, is obtained more
Believable failure-rate data.Secondary device is classified, different risk indicators are established according to different classes of equipment, obtained
Consequential Loss L caused after secondary device failure.Set finally by possible loss computation model and value-at-risk computation model
Standby quantization risk evaluation result, for follow-up works such as secondary device risk managements reference frame is provided.Operations staff is set to have pin
Equipment operation, service work are carried out to property, the secondary professional operational management of optimization is lectotype selection, determine equipment answers no carrying out
Change and objective basis are provided.
Claims (6)
1. a kind of secondary equipment risk assessment method, it is characterised in that comprise the following steps：
Step S1：According to the history data of every kind of secondary device in power system, t-1 moment and t system are obtained
State q and observation output sequence O；
Step S2：Number N of system mode q and length M of observation output sequence O can be obtained respectively according to step S1, due to
The consideration of the t to the time is increased, the state-transition matrix A for obtaining and the dimension of observation output probability matrix B are by two dimension change
For three-dimensional；
Step S3：Three-dimensional state transfer matrix matrix A and observation output probability matrix B according to obtained by step S2, Yi Jiyi
The initial state probabilities π for knowing, sets up Second-Order Hidden Markov Model λ={ S, O, A1, A2, B1, B2, Π }；
Step S4：The Second-Order Hidden Markov Model derived according to step S3 and observation output sequence O, by Viterbi
(Viterbi) algorithm, tries to achieve the status switch S for producing this observation sequence；
Step S5：The output sequence O of the Second-Order Hidden Markov Model observation derived according to step S3, by front to-backward calculation
Method (Forward-Backward algorithm), tries to achieve the probability P for producing this observation sequence O；
Step S6：According to the probability P that step S5 is tried to achieve, using fault rate F of the method simulation secondary device operation of Monte Carlo；
Step S7：Secondary device is classified, corresponding risk indicator is established, is calculated and is brought to system after secondary device failure
Loss L；
Step S8：According to the breakdown loss that the fault rate and step S7 of the simulation of step S6 are tried to achieve, secondary equipment in power system is calculated
Integrated risk value：
In formula：R (t) for secondary device value-at-risk, FiT () is the probability that the i-th class secondary device breaks down, LiT () is i-th
The breakdown loss value of class secondary device.
2. secondary equipment risk assessment method according to claim 1, it is characterised in that：Described secondary device fault rate
It is to be calculated based on the method for second order HMM, wherein A1、B1The respectively state transition probability square of t
Battle array observation output probability matrix, A2And B2Respectively record the state transition probability matrix observation at two moment of t-1 and t defeated
Go out probability matrix, A2And B2For three-dimensional matrice；
A1=(aij)N×N, A2=(aijk)N×N×N
B1=(bi(Ot))N×M, B2=(bij(Ot))N×N×M
3. secondary equipment risk assessment method according to claim 1, it is characterised in that：The establishment of the risk indicator is
Determined according to the classification of secondary device, will secondary device be specifically categorized as monitoring measurement class and relay protection and automatically dress
Put class.
4. secondary equipment risk assessment method according to claim 3, it is characterised in that：Described monitoring measurement class is secondary
The risk indicator of equipment be according to it in the difference of the middle-and-high-ranking application of power system establishing, including：In electric power system fault
In positioning, it is considered to the decline of fault location precision after the equipment fault；In Power system state estimation, it is considered to the equipment fault
The decline of precision of state estimation afterwards.
5. secondary equipment risk assessment method according to claim 3, it is characterised in that：Described relay protection and automatically
The risk indicator of control device class secondary device be after its failure to power system loss size, including：System loading is cut down
Loading is lost in probability, expectation.
6. secondary equipment risk assessment method according to claim 1, it is characterised in that：Described secondary device is in its longevity
Repairable elements are in life stable phase, wherein adopting the probability P that Second-Order Hidden Markov Model is tried to achieve for the reliability of equipment
Probability, the computing formula of the secondary device failure probability F in follow-up calculating is：F=1-P.
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