CN105069695B - A kind of real-time risk analysis system of intelligent substation and analysis method - Google Patents
A kind of real-time risk analysis system of intelligent substation and analysis method Download PDFInfo
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Abstract
The purpose of the present invention is to provide a kind of real-time risk analysis system of intelligent substation and analysis methods, it is based on Spark big data processing platform and analyzes smart substation equipment monitoring data, call data weighting normalized algorithm, risk moment real-time judgment method determines intelligent substation risk point, once determining that intelligent substation is risky, risk analysis is then carried out by risk association model, orient risk risk factor, and by the hand-held inspection instrument of synchronous transfer when risk analysis fructufy to inspector, to improve the real-time processing speed of intelligent Substation System high speed flow data, so that the real-time of risk analysis is protected.
Description
Technical field
The present invention relates to field of power systems, are specifically related to a kind of real-time risk analysis field of intelligent substation.
Background technique
Intelligent substation is using advanced, reliable, integrated and environmentally friendly smart machine, with whole station information digitalization, communication
Platform network, information sharing are standardized as basic demand, are automatically performed information collection, measurement, control, protection, metering and inspection
Survey etc. basic functions, meanwhile, have support power grid automatically control, intelligence adjust, on-line analysis decision and collaboration interaction etc. it is advanced
The substation of function.
With the development of intelligent substation, the research of monitoring and inspection technology, methods of risk assessment to intelligent substation
Also gradually increase, a kind of relatively conventional intelligent substation monitoring means is that equipment letter is acquired by monitoring unit such as sensors
Breath, then information is reached into backstage and is handled, then Risk-warning, while by posting scanning label on substation equipment, only
Need to scan can quick obtaining facility information, and reach background process.
In the prior art, it describes in patent 201110382500.2 and is adopted by one end of intelligent substation comprehensive monitoring unit
Collect Condition Monitoring Data, the upload of other end completion status monitoring data.The system is subsequent intelligent substation on-line monitoring system
Construction in a systematic way, which is set, provides the solution and embodiment of standardization, is the repair based on condition of component of primary equipment status visualization and power equipment
Powerful support is provided.
Patent 201410199981.7 describes a kind of intelligent controlling device, which includes intelligent substation
Controlling terminal, environment monitoring device, built-in regulating device, external adjusting device, warning device, monitor supervision platform, communication device and
Human-computer interaction interface, these devices are connected directly with intelligent substation controlling terminal, and monitor supervision platform passes through communication device and power transformation
Control of intelligent terminal of standing is connected.It is auxiliary that device is integrated with cooling ventilation, fire alarm, toxic gas safety dumping, floods control etc.
Help the function of device.
Patent 201410402320.X describes a kind of intelligent patrol detection device, which includes being arranged in equipment
On electronic tag, hand-held inspection terminal and MIS database.Hand-held inspection terminal obtains equipment letter by scanning label
Breath, then information is transmitted to MIS database by wireless signal, substation's emphasis equipment routing inspection quality and inspection can be effectively improved
Efficiency.
But the prior art still has following problems to the inspection of intelligent substation: existing monitoring routine inspection mode has
Only relate to a background processing system, there is no consider online lower to dispose inspection terminal；Although what is had has comprehensively considered backstage
Processing system and mobile terminal, but it is only applicable to the monitoring of substation's indoor environment, need manual inspection or terminal not to have
Data processing and real-time display function, only play the role of scanner.Especially to needed in intelligent Substation System high speed pass
Defeated detection data stream, is handled and analytical plan in real time without practicable risk.
Summary of the invention
The purpose of the present invention is to provide real-time risk analysis systems in a kind of intelligent Substation System.
A kind of real-time risk analysis system of intelligent substation, the equipment including transformer substation system monitor layer, data Layer lead to
Believe control device, substation's background processor, it is characterised in that: the equipment of transformer substation system monitor layer pass through sensor or
Monitoring device is connected to the communication control unit of data Layer, and communication control unit is by the data information transfer monitored to substation
Background processor, the processing result of substation's background processor are tied processing by terminal box/interface adapter wired mode
Synchronous transfer or is transmitted processing result by the wireless real-time synchronization of radio station to the hand-held inspection instrument of inspector when fruit
To the hand-held inspection instrument of inspector；
Preferably, it includes external communication module, core processor ARM9, Operation display module and number that it is logical, which to hold inspection instrument,
According to preliminary treatment module；
A kind of real-time risk analysis method of intelligent substation, it is characterised in that: obtain the number of devices of transformer substation system it is believed that
Breath, is uploaded to background data server for the data information, then background data server is defeated by the data information received
Enter to the Spark data processing platform (DPP) of the real-time risk analysis system of intelligent substation, Spark data processing platform (DPP) will first receive
Flow data pre-processed, whether then stream data is calculated in real time, risky according to the result judgement calculated in real time,
Risk analysis is obtained as a result, and by the risk by the risk association model reasoning for the intelligent substation established if risky
Result synchronous transfer is analyzed into the hand-held inspection instrument of patrolman；
Preferably, there are three types of the modes for obtaining the device data information of transformer substation system, is respectively: by monitoring device or
Sensor obtains secondary equipment operation data information or intelligent substation operator personal monitoring/statistical information,
Either patrolman obtains inspection data information by hand-held inspection instrument；
Preferably, flow data carries out pretreated method are as follows: allows the monitoring data m of different attributeitIt is mapped to same comment
Sentence in the reference axis of standard, normal value both maps to the section 0-1, assigns further according to it to the influence degree of intelligent substation different
Weight wc, abnormal data using super large value express, obtain monitoring data attribute weight normalization set dit；
Preferably, the construction method of risk association model are as follows: be primarily based on expert-group decision and determine setting for intelligent substation
Standby risk and risk risk factor, establish risk analysis decision table, seek optimal risk reduction using rough set and combine；Then,
Risk association Bayesian network, the i.e. graphic structure of model, the conditional probability point of model are established automatically according to reduction decision table
Cloth then uses Gamma distribution function joint specialist knowledge and incorporates monitoring data to update；
Preferably, it calculates and determines in real time by flow data, when result is more than or equal to risk analysis threshold, then based on building
Risk association model, carry out backward reasoning, algorithm general using wider joint tree reasoning algorithm using in Bayesian inference
Bayesian network is converted to joint tree, inputs evidence at this time are as follows: substation is risky, output are as follows: the side of risk risk factor
Edge probability；When not considering Real-time Monitoring Data, the conditional probability of risk risk factor is initial expert's prior probability, real when considering
When monitoring data, then need update prior probability table, in both cases, before reasoning probability of happening ranking 5 risk induce because
Element, in this, as risk analysis result.
Real-time risk analysis system passes through to intelligent substation real-time monitoring in intelligent Substation System provided by the invention
The processing of flow data is analyzed, and obtains real-time risk risk factor ranking, and by it is wired wireless mode, analysis result is shown
Show on portable equipment.The present invention makes full use of its flow data of Spark and memory operational capability, passes through flow data weight normalizing
Change processing method, risk moment real-time judgment method carrys out real-time computational intelligence substation high speed detection flow data and determines that risk is touched
Send out the moment；Only when determining risky, the reasoning of risk risk factor is carried out based on risk association model, so that the real-time of scheme
It is protected, the real-time processing speed of intelligent substation high speed flow data can be improved, and can pacify in effective guarantee intelligent substation
In the case where complete, it is not necessary to frequently call risk association model, save system resource；It is transported using portable equipment real-time synchronization backstage
Row data, even if it is dynamic also to understand newest risk by logging so that work of transformer substation personnel overhaul at the scene or inspection
State, but also the non-automatic monitoring information of inspection can be reached into backstage by portable equipment typing and be handled in real time, side
Just patrol officer's real-time live checks analysis as a result, and can be by personnel's observed result by logging typing, so that observation knot
It is analyzed when fruit, in order to high speed, reliable, steadily realization intelligent Substation System risk analysis in real time processing.
Detailed description of the invention
The real-time risk analysis system hardware connection figure of Fig. 1 intelligent substation
The hand-held synchronous logging internal structure block diagram of Fig. 2
The real-time risk analysis flow chart of Fig. 3
Fig. 4 monitoring data weight normalized mapping figure
Date stamp sliding window model under Fig. 5 Spark environment calculates abstract graph in real time
The conditional probability distribution setting up procedure of Fig. 6 risk association model
Fig. 7 intelligent substation risk association model construction flow chart
Fig. 8 intelligent substation risk association model
Specific embodiment
The content of present invention is specifically described in conjunction with specific embodiments as follows:
The real-time risk analysis system hardware connection figure of Fig. 1 intelligent substation of the present invention, wherein being monitored for transformer substation system
The equipment such as transformer, bus, breaker, current/voltage mutual inductor, disconnecting switch, arrester of layer pass through sensor or
Monitoring device is connected to the communication control unit of data Layer, and communication control unit is by the data information transfer monitored to substation
Background processor, the processing result of the substation's background processor mode wired by terminal box/interface adapter etc., will be handled
Synchronous transfer or is passed processing result by the wireless real-time synchronization of radio station to the hand-held inspection instrument of inspector when fructufy
The defeated hand-held inspection instrument to inspector.
Fig. 2 is the internal structure chart of the hand-held synchronous inspection instrument of inspector in the present invention, and hand-held synchronous inspection instrument passes through outer
The limited serial ports or wireless module of portion's communication module receive the processing result of the background processor in standing, and by core processing
Device ARM9 is shown after being handled in the LCD display of Operation display module, and inspector can pass through Operation display module
The corresponding control instruction of key-press input, meanwhile, hand-held synchronous inspection instrument also has data preliminary treatment module, to inspection data
Preliminary processing is carried out, and not necessarily uploads the interior background processor that arrives at a station and is handled, and by wired or wireless
Mode, so that the risk analysis result of intelligent substation background processing system is synchronous with the holding of hand-held inspection terminal.
Fig. 3 is flow chart of data processing in the real-time risk analysis system of intelligent substation of the present invention, for intelligent substation system
Data monitoring in system, there are three types of approach: (1) by monitoring device or sensor monitor transformer, bus, breaker, electric current/
Voltage transformer, disconnecting switch, the operation data information of arrester, other secondary devices, and data information is uploaded to backstage
Data server；(2) intelligent substation operator personal monitoring/statistical information, and by data information typing to from the background
Data server；(3) data information of patrolling is uploaded to the data service of background processor by hand-held inspection instrument by patrolman
Device, then the data information received is input to the real-time risk analysis system of intelligent substation by background data server
Spark data processing platform (DPP), Spark data processing platform (DPP) first pre-process the flow data received, then carry out risk
Risk analysis is calculated according to the risk association model of the result combination intelligent substation of the judgement in moment real-time judgment
As a result, and by the hand-held inspection instrument of the risk analysis result synchronous transfer to patrolman, in order to which patrolman obtains in time
Risk analysis is known as a result, carrying out risk investigation.
The processing method of the Spark data processing platform (DPP) of risk analysis system real-time for intelligent substation, particular content
It is as follows:
(1) flow data carries out pretreated method are as follows:
It is not comprehensive or have the monitoring record of burst etc. that the unconcerned record field of application system, completion are filtered out first, most
Stand-by data are subjected to weight normalized afterwards.The thought of use is to allow the monitoring data m of different attributeitIt is mapped to same
In the reference axis of a judgment criteria, the section 0-1 is both mapped to, influence degree of intelligent substation is assigned further according to it different
Weight wc obtains monitoring data mitWeight normalized value dit。
Specific mapping method is as shown in Figure 4:
As monitoring data mitWhen not in normal floating range, d at this timeitFor a super large value, such as 99999；
As monitoring data mitWhen without floating space, i.e., substation is to certain mitValue require stringent, be only some fixed
Value, works as mitWhen for desired value, ditValue is 0, and otherwise value is super large value, such as 99999；
As monitoring data mitWhen in normal floating range, then dit=[0-1] * wc；
(2) method that flow data calculates in real time are as follows:
Flow data calculates in real time, makes full use of the memory rapid computations and Spark of Spark data processing platform (DPP)
Advantage of the Streaming subframe in terms of flow data processing calculates the flow data after the normalization of weight, one in real time
Denier calculated result triggers risk threshold value, then Data application system is called to carry out corresponding operation.
(a) flow data calculates in real time
The present invention is based on date stamp sliding window models, thought are handled using window, to the weight stream in unit window
Data are added up in real time, if it is less than activation threshold value, are then slided into next window automatically, are recalculated accumulated value.Once
Accumulated value is more than or equal to activation threshold value, then single thread calls risk association model, carries out risk analysis, and forces to be switched to next
A window, other threads continue real-time accumulation operations.
Date stamp sliding window model, the variation for mainly considering date stamp are big to time window size, window sliding
The influence of the parameters such as small, the appearance of energy real-time judgment anomalous event, wherein date stamp refers to and returns in unit window to weight
The one stream monitor value changed carries out the result after calculating in real time.In unit window, timestamp and date stamp are incremented by simultaneously, as long as he
Either one reach switch window condition, then be switched to next time window.When date stamp trigger data application system is called
Condition then calls application system to carry out data application.
The process object of date stamp sliding window model is that the formal definitions of Monitoring data flow are as follows: time series monitoring
Data are come according to the sequence of its time, and assume the monitoring frequency freq (unit time/second) of each attribute value of every record
Unanimously.A monitoring record of t moment is indicated with m (t), then { m (t), t=1,2,3 ..., T } is a time series.Monitoring note
Record m (t)={ m1t,m2t,...,mnt}(mitFor record attribute value, and i ∈ (1,2 ... n)).
The major parameter of date stamp sliding window model and defined below: the data stream sliced sheet time is batchDuration；
Time window size is TW (unit second), TW=N*batchDuration (N is positive integer)；Date stamp window size is DW, DW
Change because of the variation of TW, DW=TW*freq*n (n is positive integer)；Date stamp window ratio dr；Date stamp security window size
SW, SW=DW*dr；Last time resets moment lct, is initialized as 0；Data computer capacity is { mmax(0,lct),...,mT, wherein T is
Current point in time.
Date stamp is calculated by formula 1:
In formula, ditIt is monitoring data mitBy weightization treated value；mitIt is i-th of data field in single record
In the value of t moment；Lct is last time to reset the moment；T is current time；N is the field number of single record.
When meeting IV >=SW or T-LCT=TW, then lct=T, IV=0 are enabled, and be switched to next window, i.e., new
Window in recalculate date stamp.
When N is sufficiently small, such as less than 10, then it need not force to switch next window, window can be waited to slide automatically.As IV >
Then risk has triggered when SW, single thread log-on data application system, other threads continue date stamp and calculate work.(b) Spark ring
Date stamp sliding window model calculates abstract graph in real time under border
Real-time calculating process makes full use of the RDD of Spark Streaming to divide thought, the monitoring fluxion that will be arrived in real time
According to being divided into Batch small one by one with the mode of isochronous surface, and Spark processing engine is transferred to go to handle them.Spark
Streaming constantly calculates the slice of data in a period in an i.e. window, and a sliding is arranged and is spaced, and makes window
Constantly sliding, to handle newest nearest monitoring data.
It is as shown in Figure 5 that date stamp sliding window model under Spark environment calculates abstract graph in real time.
(3) construction method of the risk association model of intelligent substation
It is primarily based on equipment Risk and risk risk factor that expert-group decision determines intelligent substation, establishes risk analysis
Decision table is sought optimal risk reduction using rough set and is combined, i.e. risk analysis decision table after acquisition reduction；Then, according to
Risk analysis decision table after reduction establishes the risk association Bayesian network-i.e. graphic structure of model automatically, and model
Conditional probability distribution then uses Gamma distribution function joint specialist knowledge and incorporates monitoring data to update.
Specifically, the step of establishing the decision table of risk analysis is as follows: risk association system S five-tuple { U, A1,A2,
V, f } it indicates, wherein U is the set of risk factors；A1 is conditional attribute set, and A2 is conclusion attribute set；V is attribute value Va
Set, Va indicate the risky situation of equipment under, risk risk factor occur a possibility that, andThere is f
(a1,a2)=Va, a risk analysis decision table is just constituted by this " attribute-value " relationship.Wherein data Va must be discrete
Change, size 0,1,2 can be understood as the probability that when equipment is risky risk risk factor occurs almost without, it is smaller, compared with
Greatly.
The step of reduction risk analysis decision table, is as follows:
1) the risk risk factor that relative redundancy is big in table is deleted.
2) it is determined to eliminate the redundant attributes in each decision rule according to the complexity of equipment and importance.
The conditional probability distribution of risk association model then uses Gamma distribution function joint specialist knowledge and incorporates monitoring number
According to as shown in Figure 6 come the specific steps that update.
Wherein intelligent substation risk association model construction flow chart is as shown in fig. 7, the risk through above method building is closed
Gang mould type (omitting conditional probability) as shown in figure 8, the symbol meaning in Fig. 8 referring to table 1.The graphic structure of risk association model is adopted
It is expressed with Bayesian network, is designed as three-decker, top layer's node is risk risk factor；Middle layer node is equipment
Risk；Lowest level node is substation's risk.
Specifically, intelligent substation risk association model can use a binary group B=<G, θ>expression.Wherein, G is one
A directed acyclic graph, node and risk factors correspond, and directed edge indicates the condition dependence between risk factors；And
It has ready conditions independent hypothesis: a given risk factors Ri, its non-risk factors given independently of the father node by risk factors Ri
Any risk factors subset that Ri descendant nodes are constituted.θ is the parameter sets for delineating model local condition probabilityIndicate each value e of equipment Risk Eii, in its corresponding risk risk factor
Set RSiIn some specific distribution rsiUnder conditional probability,Indicate the value of substation's risk IS
Is is in equipment Risk set ESiIn some specific distribution esiUnder conditional probability.eiWith is can value 1 or 2,1 represent
There is no risks, and there are risks for 2 representatives.If certain equipment Risk is influenced by n risk risk factor, then the father of the equipment Risk
Node distribution rs has 2n kind value.
1 substation of table and substation important equipment list E
Tab.1 Important substation equipments
(4) risk analysis result
Risk association model based on building, it is anti-using being carried out in Bayesian inference using wider joint tree reasoning algorithm
To reasoning, which is converted to joint tree for Bayesian network, reduces the difficulty of probability inference calculating, improves reasoning
Speed.It is calculated in real time by flow data, when being as a result more than or equal to risk analysis threshold, then the risk association model based on building,
Backward reasoning is carried out using wider joint tree reasoning algorithm using in Bayesian inference, which converts Bayesian network
For joint tree, input evidence at this time are as follows: substation is risky, i.e. evidence { IS }=2 is exported at this time are as follows: risk induce because
The marginal probability of element.When not considering Real-time Monitoring Data, the conditional probability of risk risk factor is initial expert's prior probability, when
Consider Real-time Monitoring Data, then needs to update prior probability table.It can respectively in both cases, 5 before reasoning probability of happening ranking
Risk risk factor.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (1)
1. a kind of real-time risk analysis method of intelligent substation, it is characterised in that: the device data information of transformer substation system is obtained,
The data information is uploaded to background data server, then the data information received is input to by background data server
The Spark data processing platform (DPP) of the real-time risk analysis system of intelligent substation, the stream that Spark data processing platform (DPP) will first receive
Data are pre-processed, and risk moment real-time judgment are then carried out, according to the wind of the result combination intelligent substation of the judgement
Risk analysis is calculated as a result, and patrolling the risk analysis result synchronous transfer to the hand-held of patrolman in dangerous correlation model
Cha Yizhong；The construction method of risk association model are as follows: firstly, based on expert-group decision determine intelligent substation equipment Risk and
Risk risk factor establishes risk analysis decision table, seeks optimal risk reduction using rough set and combines；Then, according to reduction
Decision table establishes risk association Bayesian network, the i.e. graphic structure of model automatically, and the conditional probability distribution of model then uses
Gamma distribution function joint specialist knowledge simultaneously incorporates monitoring data to update；
Wherein, there are three types of the modes for obtaining the device data information of transformer substation system, is respectively: by monitoring device or sensor
Secondary equipment operation data information or intelligent substation operator personal monitoring/statistical information are obtained, either
Patrolman obtains inspection data information by hand-held inspection instrument；
Wherein, flow data carries out pretreated method are as follows: allows the monitoring data m of different attributeitIt is mapped to the same judgment criteria
Reference axis on, normal value both maps to the section 0-1, and different weights is assigned to the influence degree of intelligent substation further according to it
Wc, abnormal data are expressed using super large value, obtain monitoring data mitBy weightization treated value dit；
The processing method of risk analysis are as follows: calculate and determine in real time by flow data, when result be more than or equal to risk analysis threshold,
The then risk association model based on building carries out reverse push using wider joint tree reasoning algorithm using in Bayesian inference
Bayesian network is converted to joint tree, inputs evidence at this time by reason, the algorithm are as follows: substation is risky, output are as follows: risk lures
The marginal probability of hair factor；When not considering Real-time Monitoring Data, the conditional probability of risk risk factor is that initial expert's priori is general
Rate then needs to update prior probability table, in both cases, 5 before reasoning probability of happening ranking when considering Real-time Monitoring Data
Risk risk factor, in this, as risk analysis result.
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