CN103018592B - The traction transformer faults diagnostic method of internal model control based PID controller - Google Patents
The traction transformer faults diagnostic method of internal model control based PID controller Download PDFInfo
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Abstract
The present invention is the traction transformer faults diagnostic method of internal model control based PID controller.The method application traction transformer device structure model knowledge sets up tractive transformer double_layer construction model, utilizes voltage transformer (VT) current transformer to record actual tractive transformer operational factor, is diagnosed by parameters input diagnostic system.The present invention simultaneously diagnosis point in order to off-line and inline diagnosis two parts, improve diagnosis speed, diagnosed by consistance and trace back because diagnosis last diagnostic is out of order element and fault type.Shown by simulation result, the method can diagnose out the faults such as tractive transformer short circuit in winding, open circuit, turn-to-turn ground connection rapidly and accurately, is a kind of new traction transformer faults diagnostic method.
Description
Technical field
The invention belongs to tractive power supply system fault diagnosis technology field, particularly relate to a kind of traction transformer faults diagnostic method of internal model control based PID controller.
Background technology
Tractive power supply system is the electric power system of railway, once its certain section failure causes power failure, not only affect the normal operation of the train of supply line of institute, in the rail transportation system of current large density large discharge, even chain reaction may be caused, cause the operation of many rail tracks not smooth, cause great economic loss.As the important component part of tractive power supply system, the safe operation of tractive transformer is directly connected to the reliable and stable work of tractive power supply system, and then is related to the normal orderly function of railway.Therefore, the normal operation of tractive transformer is to tractive power supply system, the important guarantee that the railway system normally runs.In order to ensure the unimpeded of railway operation, require that tractive transformer has high operation availability reliability and security.But because the problem of device fabrication problem and running environment and working time, inevitably there are some faults in tractive transformer.So, for the normal operation of guarantee tractive transformer, just need the running status grasping tractive transformer in real time, when its generation small fault runs on up-set condition, want to go forward side by side row relax by Timeliness coverage, avoid fault to further develop damage equipment and cause power failure; Meanwhile, when its generation catastrophic failure causes power failure, need to find failure cause as soon as possible and adopt an effective measure as much as possible to restore electricity, reduce because of the economic loss brought that has a power failure to shorten power off time.
Current tractive transformer diagnostic method mainly contains two aspects: the first first observes single parameter as temperature, ultrasound wave, insulating paper water cut etc., by analytical parameters change, transformer is diagnosed, but this method can only diagnose fault in a certain respect, comprehensive diagnos cannot be carried out; The second is the method for artificial intelligence, as the application of expert system, and the application of artificial neural network, but expert system experimental knowledge obtains difficulty, and artificial neural network needs a large amount of sample datas, and the development time of diagnostic system is all longer.
Internal model control based PID controller (Model-basedDiagnosis, MBD) method puts forward nineteen seventies, MBD uses diagnosis object theory structure and knowledge, the accumulative process be ignorant, the modeling of simultaneity factor and the diagnostic reasoning of system are distinct two parts, have good independence and transplantability.Document " Liu Zhigang; Zhong Wei; Deng Yunchuan; Qu Changjun. the internal model control based PID controller method of traction substation fault. Proceedings of the CSEE; 2010,30 (34): 36-41 " MBD is used for the diagnosis of traction substation fault, but in the diagnosis of tractive transformer in-house facility, adopted equivalence element mode simple, to the concrete diagnosing malfunction of tractive transformer, only can not can diagnose the fault of tractive transformer two lateral circuit; In addition, diagnostic mode is only the mode of individual layer modeling and immediate reasoning, can not carries out effectively and comprehensive diagnostic the fault of tractive transformer.In order to change difficulty that traction transformer faults diagnosis aspect runs into and make up the deficiency that expert system exists, MBD method is used for tractive transformer modeling line transformer fault diagnosis of going forward side by side and there is very important practical significance.
Summary of the invention
The object of the invention is: the traction transformer faults diagnostic method that a kind of internal model control based PID controller is provided, in order to solve the deficiency of tractive transformer routine diagnostic method.To achieve these goals, the technical solution used in the present invention is a kind of traction transformer faults diagnostic method of internal model control based PID controller, is that in the electric power system of railway, key equipment tractive transformer provides real-time fault diagnosis to tractive power supply system.Described method comprises following means:
Step 1: according to tractive transformer structure, sets up tractive transformer Structural abstraction model, and build diagnostic system, concrete grammar is: using the phase voltage of transformer and phase current as system model variable, describe the annexation of each winding; Adopt hierarchy abstract model: ground floor sets up small components model, describe the normal behaviour of small components; The second layer sets up large component models, describes the fault behavior between the fault behavior of small components and small components; Topological fault between small components is converted into the internal fault of large element by hierarchical model, is conducive to the analysis of fault.
Step 2: off-line search tractive transformer abstract model, obtains all analytical redundancy relation of diagnostic system and the minimum candidate's conflict set associated by them;
Step 3: the false voltage current data being recorded tractive transformer reality by voltage transformer (VT) summation current transformer;
Step 4: obtain Min-conflicts collection by minimum candidate's conflict set: the traction transformer faults status information recorded by current-voltage transformer brings analytical redundancy relation into, whether inspection analytical redundancy relation meets; If do not meet, then produce deviation between system prediction and actual observed value, then the back-up environment of this analytical redundancy relation is a Min-conflicts collection;
Step 5: by the Min-conflicts collection of gained, what utilize artificial intelligence field touches the minimal hitting set that collection computing method ask Min-conflicts collection, obtains all minimum candidate diagnosis;
Step 6: carry out Trouble Match to the element in minimum candidate diagnosis, finally obtains concrete fault element and fault type thereof.
Due to the precision problem of modeling, part analysis redundancy relationship existing under normal operation and observation being false, getting rid of by setting a permissible error value erroneous judgement that precision problem causes.
Be in the process of Trouble Match, utilize element fault probability size, the fault of candidate's element is sorted, preferentially Trouble Match is carried out to the fault mode that probability of malfunction is maximum, if mate the unsuccessful fault that probability of malfunction is little of considering again.
Beneficial effect of the present invention is:
1, the method for the present invention based on model, directly carries out inline diagnosis to traction transformer faults, requirement of real time, and the diagnostic result obtained is objective and accurate.
2, the present invention's application is the model structure knowledge of tractive transformer, overcomes traditional expert system diagnostic method experimental knowledge and collects the deficiencies such as difficulty.
Accompanying drawing explanation
The actual diagnostic process of Fig. 1
The traction transformer faults diagnostic flow chart of Fig. 2 internal model control based PID controller
Fig. 3 tractive transformer equivalent circuit diagram
Embodiment
Below in conjunction with accompanying drawing and concrete embodiment, the present invention is further detailed explanation.
Fig. 1 is the flow process diagnosed for actual traction transformer faults, and its idiographic flow as shown in Figure 2.
Fig. 2 is the traction transformer faults diagnostic criteria process flow diagram of internal model control based PID controller.In Fig. 2, the traction transformer faults diagnostic method of internal model control based PID controller comprises:
Step 1: according to tractive transformer structure, sets up tractive transformer Structural abstraction model, obtains diagnostic system;
Using the phase voltage of transformer and phase current as system model variable, the annexation of each winding is described.Adopt hierarchy abstract model: ground floor sets up small components model, describe the normal behaviour of small components; The second layer sets up large component models, describes the fault behavior between the fault behavior of small components and small components.Topological fault between small components is converted into the internal fault of large element by hierarchical model, is conducive to the analysis of fault.This tractive transformer can be expressed as COMPS={T1_AB by abstract element, T1-BC, T1, T21_TRF1, T22TRF2, T21, T22}, and the implication representated by the symbol of each abstract element is as shown in table 1.
Implication in table 1 element composition representated by each symbol
Fig. 3 is VX type tractive transformer one phase equivalent circuit figure, and for conversion is to the isoboles of primary side, wherein A, B represent A phase and the B phase of power circuit respectively, and T, F, N represent contact net respectively, positive feeder and the earth.First following observational variable is had for the single transformer shown in Fig. 3: { VA, VB, VAB, IA, IB, VTR, VRF, VFT, IT, IR, IF}, VA, VB represents the phase voltage of AB end of incoming cables, end of incoming cables, VAB, VTR, VRF, VFT is respectively line voltage alternate separately, and IA, IB are the current value of end of incoming cables inflow transformer, IT, IR, IF are the current value that leading-out terminal flows out transformer.
When then transformer normally runs, the electric current and voltage of whole transformer is constrained to:
For T1_AB, also have voltage relationship:
VAB＝VA-VB(3)
For T21_TRF1, also have voltage relationship:
0＝VTR+VRF+VFT(4)
Above formula (1) ~ (4) are relational model when transformer normally runs, and are used in the modeling of ground floor discrete component.Normal model relational application is determined element fault in based on conforming diagnostic method, but concrete fault type cannot be determined.Thus in the large element set of the second layer, introduce the fault type of various element, carry out Trouble Match by tracing back because of diagnosis and just can diagnose out concrete element fault type.
What transformer fault was common comprises phase fault, ground short circuit, turn-to-turn short circuit etc., and what have can set up direct fault model relational expression, as phase fault ground short circuit etc.During as A phase ground short circuit, VA=0, IA+IB ≠ 0; AB phase fault, VAB=0, IA=IB.Some failure ratios are as not obvious in fault signatures such as turn-to-turn short circuits, just cannot being undertaken tracing back because of diagnosis by setting up fault model, can only diagnose decision element fault by consistance and can not judge concrete fault type.
Step 2: off-line search tractive transformer abstract model, obtains all analytical redundancy relation of system and the minimum candidate's conflict set associated by them;
The layout of the supervising device of tractive transformer is all constant, can produce some fixing analytical redundancy relation only comprising observable quantity by the information of measurement mechanism.Adopt searching algorithm, off-line search is carried out to the normal model that system is set up, obtains analytical redundancy relation, and then its minimum support environment (being also Min-conflicts collection candidate) can be obtained.The Min-conflicts collection candidate one being obtained all analytical redundancy relation of tractive transformer and correspondence by off-line search has 8.Such as: namely { T21_TRF1} is minimum candidate's conflict set, and the analytical redundancy relation of its correspondence is " 0=-IT1-IR1-IF1 " for MinCSC3={{T21_TRF1}, 0=-IT1-IR1-IF1}.
Step 3: the false voltage current data being recorded tractive transformer reality by voltage transformer (VT) summation current transformer;
Systematic observation device voltage transformer (VT) summation current transformer is mainly used in recording geometry and provides system status information for diagnostic system diagnosis, here voltage transformer (VT), current mulual inductor malfunction is not considered yet, therefore suppose that measurement mechanism all normally works, to survey data be accurately.
Step 4: brought into by fault data in analytical redundancy relation, obtains Min-conflicts collection by minimum candidate's conflict set;
Suppose that this tractive transformer is at T1 leading-out terminal A phase ground short circuit and T2 secondary side RF2 phase fault, because R is rail also i.e. F winding earth short circuit, obtain the measured value of tractive transformer voltage current transformer under this fault as shown in table 2 and table 3 by emulation.
The measured value of voltage transformer (VT) under table 2 failure condition
The measured value of current transformer under table 3 failure condition
Table 2 table 3 gained measured value is brought in the analytical redundancy relation of minimum candidate's conflict set, its amplitude is got to the vector value of gained and obtains absolute residuals.Owing to relatively not having confidence level between absolute residuals, introduce relative residual error, relative residual error is the ratio of variable maximum amplitude in absolute residuals and analytical redundancy relation.Result of calculation is as shown in table 4.
The residual error of table 4 analytical redundancy relation
Due to the precision problem of modeling, part analysis redundancy relationship existing under normal operation and observation being false, getting rid of by setting a permissible error value erroneous judgement that precision problem causes.Here the relative residual error arranging permission is 0.2, then from table 4, relative residual error is greater than in minimum candidate's conflict set of 0.2 and obtains Min-conflicts collection MinCs={MinCSC1, MinCSC4} and MinCsC={{T1_AB}, { T22_TRF2}}.
Step 5: by the Min-conflicts collection of gained, what utilize artificial intelligence field touches the minimal hitting set that collection computing method ask Min-conflicts collection, obtains all minimum candidate diagnosis;
By Min-conflicts collection MinCs={MinCSC1, MinCSC4} and MinCsC={{T1_AB}, T22_TRF2}}, then try to achieve minimal hitting set MinHs={T1_AB, T22_TRF2}
Arrive this, by normal model and the consistance reasoning diagnosis of system element, fault element { T1_AB, T22_TRF2} can have been drawn.
Step 6: carry out Trouble Match to the element in minimum candidate diagnosis, finally obtains concrete fault element and fault type thereof.
For the ease of finding out the concrete fault type of fault element rapidly, according to Practical Project data, can suppose that the probability of short trouble is 0.6, the probability of earth fault is 0.4, and the probability of disconnection fault is 0.2.
Carrying out, in the process that Abductive reasoning mates candidate diagnosis, according to probability of malfunction size, first carrying out Trouble Match to the fault mode that probability of malfunction is maximum, if mate the unsuccessful fault that probability of malfunction is little of considering again.By the hypothesis of probability of malfunction above, the probability of malfunction of fault element is sorted, probability of malfunction maximum front 5 kinds as shown in table 5.
The various fault mode of table 5 and qualitative probability of malfunction thereof
With reference to each fault mode qualitative probabilistic that table 5 provides, prioritizing selection fault mode: { T1_AB, { groundA}}, T22_TRF2, { groundF}} mates, and arranging maximum permission residual error is 0.2, finally the match is successful, determines that fault type is { T1_AB, { groundA}}, T22_TRF2, { groundF}}, can judge T1_AB thus, A phase earth fault, T22_TRF2, F ground connection, also i.e. FR short circuit.
Claims (3)
1. a traction transformer faults diagnostic method for internal model control based PID controller, is that in the electric power system of railway, key equipment tractive transformer provides real-time fault diagnosis to tractive power supply system, it is characterized in that, said method comprising the steps of:
Step 1: according to tractive transformer structure, sets up tractive transformer Structural abstraction model, and build diagnostic system, concrete grammar is: using the phase voltage of transformer and phase current as system model variable, describe the annexation of each winding; Adopt hierarchy abstract model: ground floor sets up small components model, describe the normal behaviour of small components; The second layer sets up large component models, describes the fault behavior between the fault behavior of small components and small components; Topological fault between small components is converted into the internal fault of large element by hierarchical model, this tractive transformer is expressed as COMPS={T1_AB by abstract element, T1_BC, T1, T21_TRF1, T22TRF2, T21, T22}, the implication representated by the symbol of each abstract element is: T1_AB is first single transformer first side winding of VX tractive transformer, is independently small components model; T1_BC is second single transformer first side winding of VX tractive transformer, is independently small components model; T1 is that VX draws primary side, and be large component models, T1_AB and T1_BC is its inner small components; T21_TRF1 is first single transformer secondary side winding of VX tractive transformer, is independently small components model; T22_TRF2 is second single transformer secondary side winding of VX tractive transformer, is independently small components model; T21 is that VX draws first Circuit Fault on Secondary Transformer, is large component models, and T21_TRF1 is its inner small components; T22 is that VX draws second Circuit Fault on Secondary Transformer, is large component models, and T22_TRF2 is its inner small components; Transformer fault comprises phase fault, ground short circuit and turn-to-turn short circuit, fault model relational expression is set up to phase fault and ground short circuit: during A phase ground short circuit, when VA=0, IA+IB ≠ 0, AB phase fault, VAB=0, IA=IB, wherein, VA represents the phase voltage of A end of incoming cables, VAB is the line voltage that AB is alternate, and IA, IB are the current value of end of incoming cables inflow transformer; Shorted-turn fault feature is not obvious, cannot set up fault model, can only decision element fault and can not judge concrete fault type;
Step 2: off-line search tractive transformer abstract model, obtains all analytical redundancy relation of diagnostic system and the minimum candidate's conflict set associated by them;
Step 3: the false voltage current data being recorded tractive transformer reality by voltage transformer (VT) summation current transformer;
Step 4: obtain Min-conflicts collection by minimum candidate's conflict set: the traction transformer faults status information recorded by current-voltage transformer brings analytical redundancy relation into, whether inspection analytical redundancy relation meets; If do not meet, then produce deviation between system prediction and actual observed value, then the back-up environment of this analytical redundancy relation is a Min-conflicts collection;
Step 5: by the Min-conflicts collection of gained, what utilize artificial intelligence field touches the minimal hitting set that collection computing method ask Min-conflicts collection, obtains all minimum candidate diagnosis;
Step 6: carry out Trouble Match to the element in minimum candidate diagnosis, finally obtains concrete fault element and fault type thereof.
2. method according to claim 1, it is characterized in that, described when setting up tractive transformer Structural abstraction model, due to the precision problem of modeling, part analysis redundancy relationship existing under normal operation and observation being false, getting rid of by setting a permissible error value erroneous judgement that precision problem causes.
3. method according to claim 1, it is characterized in that, in the process of described Trouble Match, utilize element fault probability size, the fault of candidate's element is sorted, preferentially Trouble Match is carried out to the fault mode that probability of malfunction is maximum, if mate the unsuccessful fault that probability of malfunction is little of considering again.
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| US20160140263A1 (en) * | 2014-11-18 | 2016-05-19 | General Electric Company | System and method for determining the current and future state of health of a power transformer |
| CN104407267B (en) * | 2014-11-26 | 2017-10-20 | 中国船舶重工集团公司第七一二研究所 | A kind of transformer fault detection method |
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| CN112486035A (en) * | 2020-11-18 | 2021-03-12 | 上海电力大学 | Software and hardware simulation system for early fault research of transformer |
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