CN103001328B - Fault diagnosis and assessment method of intelligent substation - Google Patents
Fault diagnosis and assessment method of intelligent substation Download PDFInfo
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Abstract
The invention discloses a fault diagnosis and assessment method of an intelligent substation. The fault diagnosis and assessment method is characterized by including the specific steps that a fault diagnosis and assessment information model of the intelligent substation is constructed; fault and assessment indexes of a primary device, a secondary device and a network device in the intelligent substation are calculated on the basis of the fault diagnosis and assessment information model; a Petri net is used to perform primary fault diagnosis and assessment to the intelligent substation; and final diagnosis and assessment are performed by means of a distributed type expert system based on a knowledge base. The fault diagnosis and assessment method has the advantages of being clear in the diagnosis process, fast and practical in diagnosis methods and accurate and reliable in diagnosis results.
Description
Technical field
The present invention relates to a kind of failure diagnosis and appraisal procedure, relate in particular to a kind of failure diagnosis and appraisal procedure of intelligent substation.
Background technology
Along with deepening continuously of intelligent grid construction, as the intelligent substation construction of intelligent grid core also in develop rapidly.National grid is issued " intelligent substation design specification " by intelligent substation (Smart Substation) definition " smart machine of, low-carbon (LC) advanced, reliable, integrated for adopting, environmental protection; be standardized as basic demand with the information digitalization of entirely standing, communications platform networking, information sharing; automatically complete the basic functions such as information gathering, measurement, control, protection, metering and monitoring, and can support as required that electrical network is controlled in real time automatically, intelligence adjustings, on-line analysis decision-making, work in coordination with the transformer station of the Premium Features such as interaction " in February, 2010.Therefrom can find out, compared with conventional substation, intelligent substation has proposed requirements at the higher level to administering and maintaining work.This specification has defined that intelligent substation should have intelligent alarm and fault message is comprehensively analyzed decision making function first simultaneously: " should set up logic and the inference pattern of transformer station's fault message; realize classification and filtration to fault warning information; under failure condition to comprising that the data such as sequence of events recording signal and protective device, phasor measurement, failure wave-recording carry out data mining; the running status of transformer station is carried out on line real time and reasoning, automatically reports that transformer station is abnormal and propose troubleshooting instruction." the preliminary clear and definite intelligent substation of this definition should have failure diagnosis and evaluation function; but its function definition and not obviously difference of conventional substation, do not embody completely intelligent substation communication network, equipment digitalized, intelligent after new demand to failure diagnosis and evaluation function.How to provide effective monitoring and maintenance service for intelligent substation moves, and the functional requirement that makes monitoring and maintenance service automation, intellectuality reach intelligent substation is in intelligent substation construction from now on, to need one of subject matter solving.
After intelligent substation, its electrical secondary system Structure and form generation revolutionary variation compared with conventional substation, the communication network bearing function logical signal that shows as physics, conventional secondary circuit becomes communication network, and the connection between signal becomes virtual terminal and virtual circuit.Between network physical topology and the input and output of function information and signal, no longer there is one-to-one relationship, traditional failure diagnosis and appraisal procedure are fault detect and the analysis based on secondary electric loop mostly, cannot be applied to intelligent substation, cause the business such as intelligent substation fault detect and analysis to be difficult to carry out.Meanwhile, existing Substation fault diagnosis mostly only utilizes running status and the warning information of primary equipment in transformer station itself or utilizes circuit breaker action situation and Trouble Report to carry out failure diagnosis, provides the probability of equipment fault.Therefore, there is loss of learning in existing Substation fault diagnosis: the one, do not relate to network topology structure in station, do not relate to the impact of topological structure for failure diagnostic process and result, more do not relate to complex topology structure is simplified, its practicality can limit; The 2nd, in failure diagnostic process, do not relate to the impact of secondary communication network in failure diagnostic process, comprise network equipment state, communication network quality, the impact of the factors such as time delay, error code and the packet loss of message transmitting procedure, this is indispensable in intelligent substation failure diagnosis.Petri net theory and expert system are applied comparatively extensive in intelligent substation failure diagnostic process, it is theoretical that but the diagnostic method based on topological structure not yet forms so far, and utilize single Petri net or expert system to carry out failure diagnosis assessment, although process is comparatively quick, but do not merge the panorama information of intelligent substation, cause the accuracy of result not high, the fault freedom under information uncertain condition is also difficult to ensure.
The intellectuality of intelligent substation primary equipment; make primary equipment body associated with various intelligent apparatus such as merge cells, intelligent assembly and intelligent terminals in electrical secondary system; following these intelligent apparatus will be integrated with primary equipment body; primary equipment directly accesses electrical secondary system network; primary system is associated tightr with electrical secondary system, only relies on primary system and limited protection and record ripple information cannot portray complete, exactly the knowledge feature of intelligent substation fault.As can be seen here, the variation of intelligent substation electrical secondary system structure and improving administering and maintaining the automation of means and intelligent requirements, makes traditional failure diagnosis and appraisal procedure on the diagnosis degree of depth and diagnostic method, can not meet the demand of intelligent substationization operation.
Summary of the invention
Object of the present invention is exactly in order to address the above problem, and a kind of failure diagnosis and appraisal procedure of intelligent substation is provided, and it has, and diagnostic procedure is clear, accurately advantage reliably of diagnostic method Fast Practical, diagnostic result.
To achieve these goals, the present invention adopts following technical scheme:
The failure diagnosis of intelligent substation and an appraisal procedure, concrete steps are:
Step 1: build intelligent substation failure diagnosis and appreciation information model;
Step 2: based on failure diagnosis and appreciation information model, to the primary equipment in intelligent substation, secondary device and network device computes fault and evaluation index;
Step 3: adopt Petri net to carry out primary fault diagnosis and assessment to intelligent substation;
Step 4: adopt the distributed expert system based on knowledge base to carry out last diagnostic and assessment.
The concrete steps of described step 1 are:
(1-1) the functional configuration model by intelligent substation obtains the static information of intelligent substation, utilizes intelligent terminal, intelligent assembly merge cells, intelligent secondary device, transformer station's network message record analysis system to obtain the multidate information of intelligent substation;
(1-2) utilize IEC61850 standard traffic model as transformer station's essential information model, set up the description to communication network base class model;
(1-3) according to networking equipment, network configuration, network configuration information and real time execution information to the model refinement of communication network base class and expansion, set up the UML function information model of communication network;
(1-4) improve the semantic system to logical device, logic ground instance name in IEC61850 model;
(1-5) the UML function information model of communication network and transformer station's essential information model are coordinated, formed the failure diagnosis of intelligent substation and the information model of assessment.
Described static information comprises intelligent substation main electrical scheme topology, the physics of primary equipment and secondary device and logic association information, the networking information of communication network; Described multidate information comprises the position of operation information, circuit breaker and the isolating switch of primary equipment, the action message of protection and control device, the sampled value, the flow information that embody with network message.
Described functional configuration model is the functional configuration file that adopts the configuration language SCL of transformer station to describe.
The concrete steps of described step 2 are:
(2-1) quantity of state, defect, familial defect, mean free error time and the mean availability of primary equipment, secondary device and the network equipment in collection intelligent substation;
(2-2) failure definition rate λ, repair rate μ, equipment health degree σ, device confidence level δ are fault and the evaluation index of primary equipment, secondary device and the network equipment in intelligent substation;
(2-3) utilize the index in step (2-2) to carry out assessment of fault to the discrete component in intelligent substation.
Described quantity of state is technical indicator, performance and the ruuning situation parameter of direct or indirect characterization device state; Described defect refers to that equipment is in operation affects it and completes the various states of predetermined function, point general defect, major defect, urgent defect; Described familial defect is the equipment general character defect being caused by same design, principle of uniformity, same producer, same batch, same device, same technique etc.; The described mean free error time is divided into single device mean free error time and same category of device mean free error time; Described mean availability is divided into single device mean availability and same category of device mean availability.
Described failure rate λ characterizes the probability that element breaks down; Described repair rate μ characterizes the probability of repairing after element fault, obtains according to quantity of state, defect, familial defect, mean free error time and mean availability calculation of parameter; Described device confidence level δ is used for weighing the uncertainty of secondary intelligent apparatus and network equipment working; Equipment health degree σ characterizes and adopts the index that obtains the each parameter factors of certain equipment after different criterion evaluation, each parameter factors index is comprehensively obtained reflecting to the forms data index of the outfit of equipment general level of the health.
The concrete steps of described step 3 are:
(3-1), after transformer station breaks down, Petri net I obtains static information and the multidate information of primary equipment and secondary device from intelligent substation failure diagnosis and appreciation information model;
(3-2) Petri net I, according to the static information of primary equipment and secondary device and multidate information, locates the fault element of system successively, evaluates protection action correctness, and protection action evaluation result is outputed to Petri net II and expert system I;
(3-3) the intelligent apparatus signal configuration information in Petri net II combined with intelligent Fault Diagnosis for Substation and appreciation information model, signal logic correctness between secondary intelligent apparatus is diagnosed, and diagnostic result is outputed to Petri net III and expert system II;
(3-4) Petri net III obtains static information and the multidate information of network from intelligent substation failure diagnosis and appreciation information model, nets the signal diagnostic message of II and communication network is diagnosed, and diagnostic message is sent into expert system III according to Petri;
(3-5) process information of Petri net III output Petri net I, Petri net II, Petri net III, forms primary diagnosis result.
Described network static information comprises network configuration information and secondary intelligent apparatus configuration information.Network configuration information comprises network topology structure (Star network, ring network), method of service (MMS, GOOSE), subnet (VLan) division methods, addressing method (clean culture, broadcast, multicast), network bandwidth information; Substation secondary intelligent apparatus configuration information comprises protection configuration, merge cells configuration, intelligent terminal configuration, intelligent assembly configuration information.
Described network multidate information comprises transformer station's fault message and network dynamic indicator.Transformer station's fault message comprises secondary intelligent apparatus action message, the malfunction of secondary intelligent apparatus, tripping information, network equipment failure information; Network dynamic indicator comprises network equipment confidence level, network dynamic flow, offered load, network delay, packet loss, the error rate.
Described primary diagnosis result comprises that primary diagnosis result comprises the signal logic of the primary equipment breaking down, the secondary device breaking down, secondary device mistake, the network equipment breaking down.
The concrete steps of described step 4 are:
(4-1) three expert systems are carried out respectively the assessment of a dark step to primary equipment, secondary device and the network equipment;
(4-2) three expert systems are uploaded to assessment result by the result of each self-evaluating again and are transported in integrated expert system, and each assessment result is carried out complex reasoning by integrated expert system, obtains complete assessment result.
Described assessment content contains the following aspects: after primary equipment fault, if all protections or control element correct operation, fault is excised smoothly, relevant secondary device signal logic and the transmission quality on network thereof are evaluated, determined whether electrical secondary system exists risk or hidden danger in function signal configuration and network configuration; If there is protection or the control element of tripping, malfunction; analyze from the following aspect reason: itself breaks down secondary device; secondary device signal logic configuration error; networking equipment fault; because the bad dropout causing of unreasonable or network quality or the signal of network configuration send mistake, troubleshooting suggestion is finally proposed.
The expert system knowledge base basic structure that this method adopts, factor of influence is by network information secondary intelligent apparatus information structure, the parameter that is divided into static configuration information, kinetic measurement information and calculates by above-mentioned information.
Knowledge base is made up of fault case, inference rule, and the retrieval of fault case is to find and similar cases to be diagnosed according to certain search strategy.In the reasoning based on case, how at a high speed, the retrieval that effectively completes case is very important.In native system, the search strategy of case is utilized similarity.Similarity is for characterizing the similarity degree for the treatment of diagnostic message and known fault case, and each information occurs different to the sensitiveness of fault.So consider the problem of weighting.Basic ideas are as follows:
Analysis integrated by fault case, summarize electrical secondary system failure cause and the large sequence of communication network failure phenomenon two.Basic fault reason sequence is made as X={X
1, X
2, X
3..., X
n, fundamemtal phenomena sequence is made as Y={Y
1, Y
2, Y
3... Y
n.X in formula
iand Y
jrepresent fault i and phenomenon j, X is to the available fuzzy matrix R=of the association (r of Y like this
ij) represent i.e. X=RY, the element r in matrix
ij(degree of association of failure cause and phenomenon) calculates the susceptibility of different faults according to each parameter, and the calculated value of sequence X is similarity.
The basic model structure of rule adopts following form:
RULE< rule name >
WHEN< condition >
IF< factor of influence l>THEN< weight >;
IF< factor of influence n>THEN< weight >; (n>1)
ENDRULE< rule name >
For example associated for breaker failure and network service, condition can be made as breaker failure, and factor of influence can be made as network equipment confidence level, offered load, network delay, the error rate etc., obtains its comprehensive evaluation value by factor of influence and weight calculation.
The result finally different inference methods being obtained is comprehensively analyzed.If different inference methods obtain similar result, this result is more credible.If the result difference being drawn by different reasonings is very large, according to similarity judgement, if similarity, close to 1, is as the criterion with the reasoning of case, otherwise is as the criterion with rule-based reasoning.
Described basic fault reason sequence X={ X
1, X
2, X
3..., X
nand fundamemtal phenomena sequence be made as Y={Y
1, Y
2, Y
3... Y
ncan consult Fig. 9:
In Fig. 9, Xi and Yj corresponding relation have uncertainty, may be one-to-many, many-one also or many-to-many relationship, RULE is the rule of correspondence of failure diagnosis based on fundamemtal phenomena analysis of failure fundamental cause.In table, data are only that reason that part is possible and the phenomenon of appearance are enumerated, and do not represent all, in actual moving process, can increase at any time related data.
Beneficial effect of the present invention:
The present invention is based on intelligent substation network topology structure, utilize distributed Petri nets to carry out preliminary failure diagnosis, obtain primary diagnosis result, according to the accuracy requirement to fault diagnosis result, can utilize the distributed expert system in the present invention to carry out deeper failure diagnosis, finally draw more accurate diagnostic result.The present invention is applicable to the simple intelligent substation failure diagnosis of topological structure, and therefore the present invention is also applicable to the Fault Diagnosis for Substation process that network topology structure is comparatively complicated.The present invention has adopted distributed model, clear layer, structure understands, fully utilize intelligent substation internal information, bag expands primary equipment information, secondary device information and network topology structure information etc., therefore, the present invention has applied widely, and careful reliable, the accurate believable advantage of diagnostic result of diagnostic procedure, for intelligent substation failure diagnosis and assessment are had laid a good foundation.
Brief description of the drawings
Fig. 1 is intelligent substation failure diagnosis and appreciation information modeling;
Fig. 2 adopts the electrical secondary system network model expansion of UML description and the flow chart with transformer station's essential information model coordination thereof;
Fig. 3 is unit state transition diagram in transformer station;
Fig. 4 is system configuration and the diagnostic process of intelligent substation failure diagnosis and assessment;
Fig. 5 is fault generating process;
Fig. 6 is failure diagnostic process;
Fig. 7 is fault diagnosis expert system simplified structure diagram;
Fig. 8 is electrical secondary system function and knowledge representation and inference pattern with network communicating function;
Fig. 9 is the corresponding relation figure of basic fault reason sequence and fundamemtal phenomena sequence.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
Step 1: build intelligent substation failure diagnosis and appreciation information model
Intelligent substation failure diagnosis and appreciation information can be divided into static information and multidate information, static information comprises intelligent substation main electrical scheme topology, the physics of primary equipment and secondary device and logic association information, the networking information of communication network, these can obtain by the functional configuration model of intelligent substation (the functional configuration file that adopts the configuration language SCL of transformer station to describe).Multidate information comprises the position of operation information, circuit breaker and the isolating switch of primary equipment, the action message of protection and control device, and the sampled value, the flow information etc. that embody with network message, its acquisition of information approach is as shown in Figure 1.
In intelligent substation, protection and measurement and control device, electronic mutual inductor, merge cells, intelligent terminal have had the standard traffic model that meets IEC61850, can be used as essential information model.In standard to communication network also some model description, as subnet is divided, accessing points description etc., but from failure diagnosis and evaluation function angle, its model description is also imperfect.According to the demand of failure diagnosis and evaluation function, adopt the describing method of IEC61850 UML (UML), set up communication network base class model description (in Fig. 2, empty frame is with interior part), on this basis for networking equipment, network configuration, network configuration information and real time execution information are carried out refinement and expansion to base class model, set up the UML function information model of communication network.The new established model of communication network and transformer station's essential information model (empty frame part in addition in Fig. 2) are coordinated, formed the failure diagnosis of intelligent substation and the information model of assessment.
Transformer station's essential information model is with certain semanteme, but because IEC61850 is to logical device, logical node instance name is not made semantic convention, therefore if " action of phase spacing I section " is although this semanteme can be carried by IEC61850 model, but but can only give expression to " distance action " such semanteme, be to have the incomplete phenomenon of agreement in the semantic system of IEC61850, for intelligent substation failure diagnosis and assessment, these semantemes can't meet the semantic meaning representation of fault diagnosis functions, if be applied to failure diagnosis, must encapsulate and in modeling process, expand intelligent substation failure diagnosis semanteme simultaneously at UML.And the model after expansion semanteme must compatible existing model.The semanteme that logical node level can be expressed in IEC61850 is by this structural bearing of Instance Name of the standard name+logical node of logical node prefix+logical node class, wherein logical node prefix and logical node Instance Name are not arranged semanteme in IEC61850, if by the definition that standardizes of the semanteme of logical node prefix and Instance Name, just can express the semanteme that failure diagnosis is generally used.
Step 2: based on failure diagnosis and appreciation information model definition assessment of failure index
To the primary equipment in intelligent substation, secondary device and the network equipment, collect following calculation of parameter assessment of failure index:
1. quantity of state S: the parameters such as technical indicator, performance and the ruuning situation of direct or indirect characterization device state.
2. defect F: referring to that equipment is in operation affects it and complete the various states of predetermined function, point general defect, major defect, urgent defect.
3. familial defect F
f: the equipment general character defect being caused by same design, principle of uniformity, same producer, same batch, same device, same technique etc.
4. mean free error time T
a: the mean free error time is divided into single device mean free error time and same category of device mean free error time.
5. mean availability U
a: mean availability is divided into single device mean availability and same category of device mean availability.
On this basis, definition 4 index: failure rate λ, repair rate μ, equipment health degree σ, device confidence level δ.
Failure rate λ characterizes the probability that element breaks down, and repair rate μ characterizes the probability of repairing after element fault, according to S, F, F
f, T
a, U
aobtain etc. calculation of parameter.
Equipment health degree σ characterizes primary equipment body in the normal a certain status level to fault, for the working state evaluation of primary equipment, calculates according to λ and μ.
Device confidence level δ is used for weighing the uncertainty of secondary intelligent apparatus and network equipment working.Secondary device and the network equipment are made up of software and hardware conventionally, the defect of soft and hardware can cause that device can not complete its predetermined function under defined terms, thereby the operating state of device can be divided into by normal condition (S0), hardware normal software mistake (S1), hardware fault software normal (S2), four kinds of states of hardware fault software error (S3) and form, the conversion between four kinds of states as shown in Figure 3.State S0 lower device is normally worked, and state S1, S2, S3 have formed the abnormal working position of device.The operation probability of every kind of state is the confidence level of device under this state.Because the State space transition of device is a random process, thus Markov chain calculation element confidence level can be adopted,
Transition matrix based on Markov chain is:
These parameters can be used for discrete component to carry out assessment of fault; also can carry out the evaluation of transformer station's functional subsystem; as the reliability at certain protection interval of transformer station can be passed through primary equipment body confidence level; input circuit confidence level; protective device confidence level, the mathematics weighted calculation of trip(ping) circuit confidence level obtains.
Step 3: adopt Petri net to carry out primary fault diagnosis and assessment
The flow process that adopts Petri net to carry out the diagnosis of intelligent substation primary fault and to assess is as shown in Fig. 4 (the following part of dotted line).
Petri net is four-tuple PN=(P, a T; F,M
0), be a kind of patterned modeling tool, and there is perfect matrix operation theory.It can clearly describe and expression system in concurrent, asynchronous or circulation occur phenomenon or event.
Wherein, P=(p
1, p
2... p
m) be the finite aggregate of storehouse institute (primary equipment, secondary intelligent apparatus and logical message thereof number mapping), the p of storehouse institute
1, p
2... p
mrepresent the initial intermediateness that maybe may exist of respective element; T=(t
1, t
2... t
n) be the finite aggregate of transition, transition t
1, t
2t
nrepresent to make storehouse in the cloth willing satisfied condition of starting going to a nursery; F is arc collection or flow relation collection, reflected storehouse and transition between ordinal relation, represent by directed edge; Mark M is a m × 1 dimension group (m is the number of storehouse institute), its corresponding storehouse institute of one of them element, and the state of mark representative system, generally value is integer 0 or 1.While being illustrated on Petri figure, if having Tuo Ken (being pore) in storehouse institute, the corresponding relevant position identifying of storehouse institute is 1; Otherwise as no holder is not agree, and is 0.M
0be initial marking, described the initial condition of the system that simulated.
If have connection arc from the P of storehouse institute to transition t, claim the input magazine that P is t institute, count I (t)=P.Contrary, if from transition t to storehouse, the P of institute has connection arc, claims the output storehouse that P is t institute, counts O (t)=P.On figure, the P of storehouse institute represents with circle, and transition t represents with vertical line, and flow relation is with representing with the arc of arrow, holder agree with storehouse in pore represent.
Failure diagnostic process based on Petri net generally divides two to walk greatly:
1, set up the Petri graphical model of failure diagnosis
According to the logical relation structure Petri net diagnostic model of the topological structure of electrical network and fault element and protection, circuit breaker action; by the reasoning from logic process of Petri net graphic simulation electric network failure diagnosis process, wherein the foundation of graphical model is the key of failure diagnosis.
2, mathematical reasoning
First according to Petri web frame, construct its incidence matrices C;
Wherein, iff (P, t) ∈ F represents to have directed arc F from the P of storehouse institute to transition t, and 1 represents that directed arc F points to the P of storehouse institute.
Then according to fault message, obtain M
0(being the initial condition of Petri net), and the transition sequence U of solving system
n, transition sequence U represents whether transition node meets trigger condition, meet the transition of trigger condition input magazine in possess the Tuo Ken that can trigger, the corresponding vector of transition sequence U puts 1, otherwise is 0.；
Finally, application state equation M
n+1=M
n+ CU
n+1, derive stable state Petri net, determine fault element according to decision principle.
The generating process of fault can be described as: the action of element fault-protection action-circuit breaker.This trigger process can be described as the Petri pessimistic concurrency control as Fig. 5.
In this model, comprise three class libraries institutes.The first P of class libraries institute
1for fault element storehouse institute, comprise circuit, bus, transformer etc.; The second P of class libraries institute
2for protection relay class libraries institute, comprise the protection R that fault element is installed; The 3rd P of class libraries institute
3for circuit breaker storehouse institute, comprise P
2the circuit breaker CB that middle protection is corresponding.Transition t
1trigger table is shown with fault and occurs, and protection occurs to fault simultaneously; Transition t
2trigger and represent protection action, send trip signal, tripping circuit breaker.
Theoretical according to Petri net, the initial state of initial condition representative system of network.In Fig. 5, if element fault, the P of storehouse institute under initial condition
1in be distributed into one holder agree, next transition node t
1meet trigger condition and light a fire, be i.e. the P of storehouse institute
1in Tuo Ken transfer to P
2in, this just represents that protection relay detects fault, t after protection action
2can trigger, final holder agree be transferred to the P of circuit breaker storehouse institute
3in, fault has been excised in the action of expression circuit breaker.The simulation process of Here it is fault forward, in the time carrying out failure diagnosis, process will be just in time contrary, utilize Petri pessimistic concurrency control to diagnose out element fault by breaker protection action, as shown in Figure 6.It is diagnosed as: when initial condition, by the fault information flags circuit breaker class libraries institute of receiving, by the P of circuit breaker storehouse institute
3mid-ly start going to a nursery willingly, pass through t
2igniting and then t
1igniting, holder agree finally be moved to P
1in, thereby diagnosis P
1storehouse in element fault.
The present invention has defined a distributed Petri nets system; be divided into three sub-Petri nets; function is defined as follows: after transformer station breaks down; Petri net I is as diagnosis entrance; obtain the Static and dynamic information of primary equipment and secondary device; location primary system fault element, evaluates protection action correctness.Export protection action evaluation result to Petri net II, Petri net II combined with intelligent device signal configuration information, diagnoses the signal logic correctness between secondary intelligent apparatus.Petri net III obtains the Static and dynamic information of network, nets the signal diagnostic message of II and communication network is diagnosed according to Petri.
In diagnostic procedure, determine real fault element according to evaluation indexes such as element fault probability, protection action probability and fault confidence levels, obtain primary diagnosis result.Primary diagnosis result comprises the signal logic of the primary equipment breaking down, the secondary device breaking down, secondary device mistake, the network equipment breaking down.After Petri net is diagnosed, middle diagnostic result can be offered to expert system and do further diagnosis and assessment.
The intelligent substation multidimensional information that wherein each sub-Petri net obtains is described below:
Transformer station's fault message: secondary intelligent apparatus action message, the malfunction of secondary intelligent apparatus, tripping information, network equipment failure information
Substation secondary intelligent apparatus configuration information: protection configuration, merge cells configuration, intelligent terminal configuration, intelligent assembly configuration
Network configuration information: network topology (Star network, ring network), method of service (MMS, GOOSE), subnet (VLan) division methods, addressing method (clean culture, broadcast, multicast), the network bandwidth.
Network dynamic indicator: network equipment confidence level, network dynamic flow, offered load, network delay, packet loss, the error rate.
Step 4: adopt the distributed expert system based on knowledge base to carry out last diagnostic and assessment, as shown in Fig. 4 (the above part of dotted line).
Artificial intelligence is a frontier science technology emerging in computer science, utilize computer mould personification intelligent behavior, complete the task that can show human intelligence.Fault diagnosis expert system is the knowledge that the mankind are had the multidigit expert aspect failure diagnosis, experience, reasoning, the mainframe computer program being compiled into after technical ability is comprehensive, it can utilize computer system to help people's analyze and solve to describe in words, the challenge of thinking reasoning, the original working range of expansion computer system makes computer system have thinking ability, can carry out " dialogue " with policymaker, and apply inference mode decision recommendation is provided, expert system is very extensive in the application of fault diagnosis field, fault detection and diagnosis technology combines with expert system, the safety and reliability of engineering is guaranteed.
Fault diagnosis expert system, refer to that computer is gathering after the information of diagnosed object, the various rules of integrated use (expertise), carry out a series of reasoning, can also call at any time if desired various application programs, in running, ask for after necessary information to user, can find rapidly final fault or most possible fault, then be confirmed by user.Expert system method for diagnosing faults can illustrate with the simplified structure shown in Fig. 7, and it is made up of database, knowledge base, man-machine interface, inference machine etc.
The function of its each several part is:
(1) database is made up of dynamic data base and static database two parts conventionally.Static database is metastable parameter, as the rated voltage of equipment, rated current, rated frequency etc.; Dynamic data base is equipment detected state parameter in service, as voltage, electric current, frequency etc.;
(2) knowledge base is the set of expert's domain knowledge, the knowledge of depositing comprises system works environment, systematic knowledge (running status and the system topological knowledge of reflection system), fault eigenvalue, fault diagnosis algorithm, inference rule etc., the causality of reflection system, is used for carrying out fault reasoning;
(3) man-machine interface is bridge and the window of man-machine information interaction;
(4) inference machine is the organizational controls mechanism of expert system, uses various rules according to the informix obtaining, and carries out failure diagnosis, output diagnostic result.
Utilize the expert system structure of Petri primary fault diagnostic data as shown in Fig. 4 (the above part of dotted line).This method has defined a distributed expert system simultaneously, (expert system 1. to comprise three sub-expert systems, expert system 2., expert system is 3.) and an integrated expert system, each expert's function is defined as follows: in Petri net diagnostic procedure, middle diagnostic result separately can be offered to three expert systems, three expert systems by inference rule respectively according to corresponding Petri net diagnostic result to primary system, electrical secondary system and network carry out meticulousr assessment, each assessment result is finally carried out complex reasoning by integrated expert system, obtain complete assessment result.Assessment content contains the following aspects: after primary equipment fault; if all protections or control element correct operation; fault is excised smoothly; relevant secondary device signal logic and the transmission quality on network thereof are evaluated, determined whether electrical secondary system exists risk or hidden danger in function signal configuration and network configuration.If there is protection or the control element of tripping, malfunction; analyze from the following aspect reason: itself breaks down secondary device; secondary device signal logic configuration error; networking equipment fault; because the bad dropout causing of unreasonable or network quality or the signal of network configuration send mistake, troubleshooting suggestion is finally proposed.
The expert system knowledge base basic structure that the present invention adopts as shown in Figure 8.In figure, factor of influence is by network information secondary intelligent apparatus information structure, the parameter that is divided into static configuration information, kinetic measurement information and calculates by above-mentioned information.
Knowledge base is made up of fault case, inference rule, and the retrieval of fault case is to find and similar cases to be diagnosed according to certain search strategy.In the reasoning based on case, how at a high speed, the retrieval that effectively completes case is very important.In native system, the search strategy of case is utilized similarity.Similarity is for characterizing the similarity degree for the treatment of diagnostic message and known fault case, and each information occurs different to the sensitiveness of fault.So consider the problem of weighting.Basic ideas are as follows:
Analysis integrated by fault case, summarize electrical secondary system failure cause and the large sequence of communication network failure phenomenon two.Basic fault reason sequence is made as X={X
1, X
2, X
3..., X
n, fundamemtal phenomena sequence is made as Y={Y
1, Y
2, Y
3... Y
n.X in formula
iand Y
jrepresent fault i and phenomenon j, X is to the available fuzzy matrix R=of the association (r of Y like this
ij) represent i.e. X=RY, the element r in matrix
ijaccording to each parameter, the susceptibility of different faults is calculated, the calculated value of sequence X is similarity.
The basic model structure of rule adopts following form:
RULE< rule name >
WHEN< condition >
IF< factor of influence I>THEN< weight >;
IF< factor of influence n>THEN< weight >; (n>1)
ENDRULE< rule name >
For example associated for breaker failure and network service, condition can be made as breaker failure, and factor of influence can be made as network equipment confidence level, offered load, network delay, the error rate etc., obtains its comprehensive evaluation value by factor of influence and weight calculation.
The result finally different inference methods being obtained is comprehensively analyzed.If different inference methods obtain similar result, this result is more credible.If the result difference being drawn by different reasonings is very large, according to similarity judgement, if similarity, close to 1, is as the criterion with the reasoning of case, otherwise is as the criterion with rule-based reasoning.
Described basic fault reason sequence X={ X
1, X
2, X
3..., X
nand fundamemtal phenomena sequence be made as Y={Y
1, Y
2, Y
3... Y
ncan consult Fig. 9:
In Fig. 9, Xi and Yj corresponding relation have uncertainty, may be one-to-many, many-one also or many-to-many relationship, RULE is the rule of correspondence of failure diagnosis based on fundamemtal phenomena analysis of failure fundamental cause.In table, data are only that reason that part is possible and the phenomenon of appearance are enumerated, and do not represent all, in actual moving process, can increase at any time related data.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendments that creative work can make or distortion still in protection scope of the present invention.
Claims (6)
1. the failure diagnosis of intelligent substation and an appraisal procedure, is characterized in that, concrete steps are:
Step 1: build intelligent substation failure diagnosis and appreciation information model;
Step 2: based on failure diagnosis and appreciation information model, to the primary equipment in intelligent substation, secondary device and network device computes fault and evaluation index;
Step 3: adopt Petri net to carry out primary fault diagnosis and assessment to intelligent substation;
Step 4: adopt the distributed expert system based on knowledge base to carry out last diagnostic and assessment;
The concrete steps of described step 3 are:
(3-1), after transformer station breaks down, Petri net I obtains static information and the multidate information of primary equipment and secondary device from intelligent substation failure diagnosis and appreciation information model;
(3-2) Petri net I, according to the static information of primary equipment and secondary device and multidate information, locates the fault element of system successively, evaluates protection action correctness, and protection action evaluation result is outputed to Petri net II and expert system I;
(3-3) the intelligent apparatus signal configuration information in Petri net II combined with intelligent Fault Diagnosis for Substation and appreciation information model, signal logic correctness between secondary intelligent apparatus is diagnosed, and diagnostic result is outputed to Petri net III and expert system II;
(3-4) Petri net III obtains static information and the multidate information of network from intelligent substation failure diagnosis and appreciation information model, nets the signal diagnostic message of II and communication network is diagnosed, and diagnostic message is sent into expert system III according to Petri;
(3-5) process information of Petri net III output Petri net I, Petri net II, Petri net III, forms primary diagnosis result.
2. a kind of failure diagnosis of intelligent substation and appraisal procedure as claimed in claim 1, is characterized in that, the concrete steps of described step 1 are:
(1-1) the functional configuration model by intelligent substation obtains the static information of intelligent substation, utilizes intelligent terminal, intelligent assembly merge cells, intelligent secondary device, transformer station's network message record analysis system to obtain the multidate information of intelligent substation;
(1-2) utilize IEC61850 standard traffic model as transformer station's essential information model, set up the description to communication network base class model;
(1-3) according to networking equipment, network configuration, network configuration information and real time execution information to the model refinement of communication network base class and expansion, set up the UML function information model of communication network;
(1-4) improve the semantic system to logical device, logic ground instance name in IEC61850 model;
(1-5) the UML function information model of communication network and transformer station's essential information model are coordinated, formed the failure diagnosis of intelligent substation and the information model of assessment.
3. a kind of failure diagnosis of intelligent substation and appraisal procedure as claimed in claim 2, is characterized in that, described static information comprises intelligent substation main electrical scheme topology, the physics of primary equipment and secondary device and logic association information, the networking information of communication network; Described multidate information comprises the position of operation information, circuit breaker and the isolating switch of primary equipment, the action message of protection and control device, the sampled value, the flow information that embody with network message.
4. a kind of failure diagnosis of intelligent substation and appraisal procedure as claimed in claim 2, is characterized in that, described functional configuration model is the functional configuration file that adopts the configuration language SCL of transformer station to describe.
5. a kind of failure diagnosis of intelligent substation and appraisal procedure as claimed in claim 1, is characterized in that, the concrete steps of described step 2 are:
(2-1) quantity of state, defect, familial defect, mean free error time and the mean availability of primary equipment, secondary device and the network equipment in collection intelligent substation;
(2-2) failure definition rate λ, repair rate μ, equipment health degree σ, device confidence level δ are fault and the evaluation index of primary equipment, secondary device and the network equipment in intelligent substation;
(2-3) utilize the index in step (2-2) to carry out assessment of fault to the discrete component in intelligent substation.
6. a kind of failure diagnosis of intelligent substation and appraisal procedure as claimed in claim 1, is characterized in that, the concrete steps of described step 4 are:
(4-1) three expert systems are carried out respectively the assessment of a dark step to primary equipment, secondary device and the network equipment;
(4-2) three expert systems are uploaded to assessment result by the result of each self-evaluating again and are transported in integrated expert system, and each assessment result is carried out complex reasoning by integrated expert system, obtains complete assessment result.
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