CN111740582A - Wireless charging system PFC fault detection method based on improved HMM - Google Patents

Wireless charging system PFC fault detection method based on improved HMM Download PDF

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CN111740582A
CN111740582A CN202010176325.0A CN202010176325A CN111740582A CN 111740582 A CN111740582 A CN 111740582A CN 202010176325 A CN202010176325 A CN 202010176325A CN 111740582 A CN111740582 A CN 111740582A
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fault
pfc
hidden markov
model
improved
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谭林林
王若隐
黄天一
李乘云
李昊泽
黄学良
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Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M1/00—Details of apparatus for conversion
    • H02M1/42—Circuits or arrangements for compensating for or adjusting power factor in converters or inverters
    • H02M1/4208—Arrangements for improving power factor of AC input
    • H02M1/4225—Arrangements for improving power factor of AC input using a non-isolated boost converter
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/10—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles characterised by the energy transfer between the charging station and the vehicle
    • B60L53/12—Inductive energy transfer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/28—Testing of electronic circuits, e.g. by signal tracer
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a PFC fault detection method of a wireless charging system based on an improved HMM, which introduces an improved Hidden Markov Model (HMM) for a PFC fault detection aiming at a Power Factor Correction (PFC) device in the wireless charging system of an electric vehicle. The invention selects the boost chopper circuit as the basic topology of the PFC device to detect the fault. The method comprises the following steps: training an improved hidden Markov model of the PFC device under each fault condition; the inductive current I of each fault condition of the PFC device1Output current I2Inputting the output voltage U into the improved hidden Markov models of the fault states trained in the step 1); using Viterbi algorithm to determine each changeAnd outputting a probability value by using the hidden Markov model, wherein the model corresponding to the maximum value is the current fault of the PFC. According to the method, the HMM and the genetic algorithm are combined, required training samples are greatly reduced, the training time of the fault model is short, and the fault recognition rate is high.

Description

Wireless charging system PFC fault detection method based on improved HMM
Technical Field
The invention belongs to the technical field of wireless power transmission, and particularly relates to a PFC fault detection method of a wireless charging system based on an improved HMM.
Background
In order to save energy and reduce environmental pollution, electric vehicles are concerned and popularized in various countries all over the world, the development of the electric vehicles is influenced by battery capacity and charging facilities, and the wireless power transmission technology solves the problems of interface limitation, safety and the like of the traditional wired charging mode, so that the wireless charging mode is gradually developed into the main charging mode of the electric vehicles. The wireless power transmission technology is a novel power transmission technology at present, can effectively avoid physical connection to realize power transmission, and common transmission modes include induction type and magnetic coupling resonance. The power can reach several watts to dozens of kilowatts, and the power requirement of charging the electric automobile is met.
The wireless transmitting end that charges of electric automobile usually links to each other with the electric wire netting, and the commercial power is generally through sending the ray circle after rectification and high frequency contravariant, if direct filtering mode after the rectification directly provides direct current power supply to the back stage circuit, the input current of circuit just is a sharp-pointed pulse, and this kind of sharp-pointed pulse contains many harmonic components. Harmonics can have two serious consequences: on one hand, the large EMI (electromagnetic interference) can be generated on a line, and the secondary effect caused by harmonic resistance loss even causes the voltage waveform of a power grid to generate sinking distortion near the peak value, thereby causing large pollution to the power grid; on the other hand, although the fundamental wave and the voltage of the current are in phase, the power factor of the circuit is greatly reduced by harmonic components. Therefore, a Power Factor Correction (PFC) device is generally added between the rectification and the high frequency inverter circuit.
The PFC device plays a role in starting and stopping in a wireless charging system, can improve the power factor of electric equipment, and can reduce the interference of harmonic components on a power grid, so that once the PFC device breaks down, the PFC device not only has serious influence on a superior rectifying circuit and the power grid, but also can cause irreversible damage to a back and high-frequency inverter circuit. The fault of the PFC device directly influences the normal work of the wireless charging system of the electric automobile and even damages the whole wireless charging system. Therefore, a fast and accurate PFC fault detection method is needed to minimize the damage to the wireless charging system of the electric vehicle when the PFC fault detection method fails. The fault characteristic parameters in the existing fault detection technology are mostly set according to expert experience, the practicability is greatly reduced, and the accuracy is insufficient.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a PFC (power factor correction) fault detection method of a wireless charging system based on an improved HMM (hidden Markov model), which improves the self defects of the hidden Markov model and solves the problems of low detection speed and low accuracy in PFC fault detection.
A hidden markov model is a type of markov chain whose states are not directly observable but observable through a sequence of observation vectors, each observation vector being represented as a variety of states by some probability density distribution, each observation vector being generated by a sequence of states having a corresponding probability density distribution. Therefore, the hidden markov model is a double random process- -a hidden markov chain with a certain number of states and a set of display random functions, denoted as λ (pi, a, B), which has been successfully used in the field of speech recognition.
The purpose of the invention is realized by the following technical scheme:
a PFC fault detection method of a wireless charging system based on an improved HMM comprises the following steps:
1) training the PFC device for an improved hidden markov model in each fault case:
1-1) adopting a PFC device with a boost chopper circuit topology to convert the current I flowing through an inductor in a converter1Output current I2And the output voltage U is taken as a characteristic parameter of the fault;
1-2) measuring characteristics in each case of a fault conditionParameters, the sequence of observation states forming the hidden Markov model O ═ I1,I2,U]；
1-3) completing the establishment of the hidden Markov model, wherein lambda is (pi, A, B), wherein pi is the probability of the initial state, A is a state transition probability matrix, and B is a probability matrix of the observed value. According to the characteristics of the boost chopper circuit, the initial state probability is pi ═ 1000, the number of hidden states is 4, and the state transition probability matrix is set as:
Figure BDA0002410949430000021
1-4) training an initial value of an optimal observation value probability matrix B by using a genetic algorithm, wherein the initialization of lambda ═ pi, A and B is completed.
1-5) obtaining initial parameters, then reestimating the parameters Pi, A and B of the model by using a Bowmville algorithm to obtain a reestimated model
Figure BDA0002410949430000022
This process is repeated until a set convergence range is reached, at which time
Figure BDA0002410949430000023
Is the model to be obtained;
2) detecting the fault of the PFC device, comprising the following steps:
2-1) converting the inductor current I in each fault situation of the PFC device1Output current I2Inputting the output voltage U into the improved hidden Markov model of each fault state trained in the step 1);
2-2) solving the output probability value of each improved hidden Markov model by using a Viterbi algorithm, wherein the model corresponding to the maximum value is the current fault of the PFC.
The technical scheme is further characterized in that the fault detection method for the PFC of the wireless charging system of the electric vehicle based on the improved hidden Markov model is mainly related to aging and failure of a capacitor and a switching tube in a PFC device.
The technical scheme is further characterized in that the PFC fault detection method of the electric vehicle wireless charging system based on the improved hidden Markov model is characterized in that fault detection can be carried out under both dynamic and static conditions.
The invention has the beneficial effects that: the improved hidden Markov model is used as a powerful statistical analysis model, is a method based on a statistical pattern recognition theory, can well process a dynamic process, introduces a genetic algorithm, enables a training model to achieve global optimization, greatly improves a fault recognition rate, and has a recognition speed higher than that of a traditional inverter fault.
Drawings
Fig. 1 is a schematic diagram of a transmitting terminal of a wireless charging system for an electric vehicle for fault detection according to the present invention.
FIG. 2 is a flow chart of the improved hidden Markov model training of the present invention.
FIG. 3 is a flow chart of a genetic algorithm used in the present invention.
Fig. 4 is a flow chart of PFC fault detection of an electric vehicle wireless charging system based on an improved hidden markov model according to the present invention.
FIG. 5 is a diagram of multi-point crossing and multi-point variation in the present invention, wherein FIG. 5(a) is a diagram of multi-point crossing and FIG. 5(b) is a diagram of multi-point variation.
Detailed Description
The technical solutions of the present invention are described in detail below with reference to the accompanying drawings and specific embodiments, but the scope of the present invention is not limited thereto.
The invention discloses a PFC fault detection method of an electric vehicle wireless charging system based on an improved hidden Markov model, which mainly comprises the training of a fault model and the detection of faults.
The invention discloses a PFC fault detection method of a wireless charging system based on an improved HMM, which comprises the following steps of:
1) training the PFC device for an improved hidden markov model in each fault case:
1-1) the PFC device of the invention adopts a boost chopper circuit topology, and the PFC device is arranged in a converterCurrent I flowing through the inductor1Output current I2And the output voltage U is taken as a characteristic parameter of the fault;
1-2) measuring characteristic parameters for each fault state, forming an observation state sequence O ═ I for the hidden Markov model1,I2,U]；
1-3) completing the establishment of the hidden Markov model, wherein lambda is (pi, A, B), wherein pi is the probability of the initial state, A is a state transition probability matrix, and B is a probability matrix of the observed value. According to the characteristics of the boost chopper circuit, the initial state probability is pi ═ 1000, the number of hidden states is 4, and the state transition probability matrix is set as:
Figure BDA0002410949430000031
1-4) training an initial value of an optimal observation value probability matrix B by using a genetic algorithm, wherein the steps are shown in a figure 3:
a. and (3) encoding: encoding the B initial value
The invention adopts a binary coding method, and the value range of the initial value B is [0, 1 ]]If it is represented by a string of binary coded symbols of length 64, a total of 2 can be generated64Different codes are adopted, and the corresponding relation during parameter coding is as follows:
Figure BDA0002410949430000041
the encoding precision is as follows:
Figure BDA0002410949430000042
assume that the code for an individual is:
X:a64a63a62…a2a1(3)
the corresponding decoding formula is:
Figure BDA0002410949430000043
the initial value B meets the following constraint conditions:
Figure BDA0002410949430000044
b. fitness function
Reflecting the merits of each chromosome, P (O | λ) is the probability of knowing the observed sequence O and the model λ, resulting in the sequence of each state, which is the optimization target, and the chromosome with the maximum P (O | λ) is the best chromosome, and P (O | λ) is usually found by the Viterbi algorithm. Since the recognition algorithm of the present invention is also a Viterbi algorithm, the specific steps of the algorithm will be introduced in the recognition stage, where P (O | λ) is used as the optimization target, and the fitness of an individual is represented by the log-likelihood probability of each training sample
f(λ)＝ln(P(O(k)|λ)) (6)
O(k)Is the k-th observation sequence of the training model, P (O)(k)| λ) is obtained by using the Viterbi algorithm.
c. Designing genetic operators
The genetic operator comprises a crossover operator and a mutation operator, the crossover operator is equivalent to a local search operation, two filial generations near a parent generation are grown, the mutation operator enables the individual to jump out of the current local search area, and the two can be combined to better embody the optimization of the genetic algorithm, so that the multipoint crossover and the multipoint mutation are adopted in the method as shown in figure 5:
d. termination criteria
I.e., maximum evolutionary algebra, the present invention is set to 150.
At this point, the genetic algorithm is completed, and an optimal initial value of B is obtained, so that the initialization of the model λ ═ (pi, a, B) is completed.
1-5) obtaining initial parameters, then reestimating the parameters Pi, A and B of the model by using a Bowmville algorithm to obtain a reestimated model
Figure BDA0002410949430000052
The method comprisesThe following:
first, we define a variable ξt(i, j) indicating that the Markov model is at θ at time t in the presence of the observation sequence O and the model λiState and at time t +1jIs a probability of
ξt(i,j)＝P(O,qt＝θi,qt+1＝θj|λ) (7)
Further obtain the
ξt(i,j)＝[αt(i)aijbj(ot+1)βt+1(j)]/P(O|λ) (8)
So that the Markov model is in state θ at time tiHas a probability of
Figure BDA0002410949430000053
In the above formula, the first and second carbon atoms are,
Figure BDA0002410949430000054
the expression state is represented by thetaiTransfer to thetajThe desired number of values of (c). The Baum-Welch algorithm reestimation formula is as follows:
Figure BDA0002410949430000055
Figure BDA0002410949430000056
Figure BDA0002410949430000057
obtaining a reestimated model
Figure BDA0002410949430000058
This process is repeated until a set convergence range is reached, at which time
Figure BDA0002410949430000059
The model is obtained.
2) The specific process of detecting the fault of the PFC device is shown in fig. 4:
2-1) converting the inductor current I in each fault situation of the PFC device1Output current I2Inputting the output voltage U into the improved hidden Markov model of each fault state trained in the step 1);
2-2) calculating the output probability value of each improved hidden Markov model by using a Viterbi algorithm, wherein the model corresponding to the maximum value is the current fault of the PFC, and the method comprises the following steps:
first, a variable is definedt(i)：
Figure BDA0002410949430000061
Indicating that state S is reached along a path at time tiAnd generates observation sequence { O1,O2,…OtGet the maximum probability.t(i) An iterative algorithm may be used to calculate:
(1) initialization:
t(i)＝πibi(O1),1≤i≤N,ψ1(i)＝0 (14)
(2) iterative computation
Figure BDA0002410949430000062
Figure BDA0002410949430000063
(3) Final calculation
Figure BDA0002410949430000064
Here P is the maximum probability output value P (O | λ) we require.
The above embodiments are only for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all the equivalent changes or modifications made according to the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims (3)

1. A PFC fault detection method of a wireless charging system based on an improved HMM is characterized by comprising the following steps:
1) training the PFC device for an improved hidden markov model in each fault case:
1-1) adopting a PFC device with a boost chopper circuit topology to convert the current I flowing through an inductor in a converter1Output current I2And the output voltage U is taken as a characteristic parameter of the fault;
1-2) measuring characteristic parameters for each fault state, forming an observation state sequence O ═ I for the hidden Markov model1,I2,U]；
1-3) establishing a hidden Markov model of lambda (pi, A, B), wherein pi is the probability of an initial state, A is a state transition probability matrix, and B is a probability matrix of an observed value; according to the characteristics of the boost chopper circuit, the initial state probability is pi ═ 1000, the number of hidden states is 4, and the state transition probability matrix is set as:
Figure FDA0002410949420000011
1-4) training an initial value of an optimal observation value probability matrix B by using a genetic algorithm, wherein the initialization of lambda (pi, A and B) is completed;
1-5) obtaining initial parameters, then reestimating the parameters Pi, A and B of the model by using a Bowmville algorithm to obtain a reestimated model
Figure FDA0002410949420000012
This process is repeated until a set convergence range is reached, at which time
Figure FDA0002410949420000013
Is the model to be obtained;
2) detecting the fault of the PFC device, comprising the following steps:
2-1) converting the inductor current I in each fault situation of the PFC device1Output current I2Inputting the output voltage U into the improved hidden Markov models of the fault states trained in the step 1);
2-2) solving the output probability value of each improved hidden Markov model by using a Viterbi algorithm, wherein the model corresponding to the maximum value is the current fault of the PFC.
2. The improved HMM based wireless charging system PFC fault detection method of claim 1, wherein the faults include aging and failure of capacitors and switching tubes in a PFC device.
3. The improved HMM-based wireless charging system PFC fault detection method of claim 1, wherein the fault detection can be performed in both dynamic and static situations.
CN202010176325.0A 2020-03-13 2020-03-13 Wireless charging system PFC fault detection method based on improved HMM Pending CN111740582A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723546A (en) * 2021-09-03 2021-11-30 江苏理工学院 Bearing fault detection method and system based on discrete hidden Markov model

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107102223A (en) * 2017-03-29 2017-08-29 江苏大学 NPC photovoltaic DC-to-AC converter method for diagnosing faults based on improved hidden Markov model GHMM

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107102223A (en) * 2017-03-29 2017-08-29 江苏大学 NPC photovoltaic DC-to-AC converter method for diagnosing faults based on improved hidden Markov model GHMM

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723546A (en) * 2021-09-03 2021-11-30 江苏理工学院 Bearing fault detection method and system based on discrete hidden Markov model
CN113723546B (en) * 2021-09-03 2023-12-22 江苏理工学院 Bearing fault detection method and system based on discrete hidden Markov model

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Application publication date: 20201002