New energy electric driving system current sensor fault diagnosis method, system, device and medium

Abstract

The invention relates to the technical field of fault detection, in particular to a new energy electric driving system current sensor fault diagnosis method, system and device and a medium. The method comprises the steps that a voltage and current basic relational expression is constructed, a current state equation and a fault equation are established and expressed in an augmentation form, a novel interval sliding mode observer is constructed, a fault state threshold value and detection characteristic quantity are given, and fault diagnosis is carried out. A current state equation and a tiny fault equation of an original system are expressed in an augmented form, fault reconstruction is carried out, and the influence of faults on the system is more visually expressed; a novel interval sliding mode observer is used for achieving the effect of reaching a sliding mode surface more quickly and reducing buffeting of sliding mode motion through reasonable parameter design, and therefore the effect of better tracking the actual situation is achieved; and a traditional constant threshold is replaced by an adaptive threshold, so that missing report or false report is prevented, and the robustness of fault diagnosis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a method, system, device and medium for diagnosing faults of a current sensor in a new energy electric drive system. Background Art

[0002] The drive system of permanent magnet synchronous motor is an important part of electric new energy, which is prone to failure, causing serious casualties and property losses. The current sensor is one of the important components of the drive system. There are two main methods for diagnosing the current sensor failure of the new energy electric drive system:

[0003] Diagnostic methods based on circuit models. This type of method usually includes two stages: generating residuals and making diagnostic decisions. First, a hybrid logic dynamic model of the inverter is established, and the estimated value of the fault system output is obtained through the observer. The estimated value is subtracted from the true value to obtain the residual, and then the fault category is determined based on the corresponding decision rules. The paper title is "Research on Fault Diagnosis and Fault-Tolerant Control Based on Sliding Mode Technology and Its Application to High-Speed ​​Trains, Zhang Kangkang, Nanjing University of Aeronautics and Astronautics, December 2018" and the Chinese invention patent with the invention publication number "CN113534035A" and the name "New Energy Electric Vehicle Static Converter Current Sensor Micro-Fault Diagnosis Method". The two methods have clear model mechanisms, are easy to implement, and can perform real-time diagnosis, but the difficulty lies in establishing an accurate analytical mathematical model of the control system and maximizing the reliability of the model.

[0004] Diagnostic method based on signal characteristics. Due to the historical fault data or statistical data sets caused by different signals, it is difficult to confirm which model diagnosis is applicable; when studying many practical fault prediction problems, it is difficult or even impossible to establish a mathematical model of complex components or systems. Therefore, the test and sensor historical data in various stages such as system design, simulation, operation and maintenance become the main means to judge the system performance, that is, data-driven diagnosis method. The whole stage is divided into training stage and testing stage. Related papers and patents such as "Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis" (Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis, Wu Ping, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, August 2021), China Invention Patent Application Publication "A Design Method for a Micro-Fault Diagnosis System for High-Speed ​​Railway Inverters" (CN 106959397A), etc., such methods do not need to know the precise mathematical model of the system, but require a large amount of accurate system data, as well as appropriate data diagnosis methods.

[0005] In summary, the existing technologies have problems such as long diagnosis cycle, large amount of calculation, slow approach rate and obvious jitter when sliding mode motion occurs, poor anti-interference ability of adaptive threshold, high requirements for mathematical model and poor robustness. Summary of the invention

[0006] In view of the problems of existing fault diagnosis methods such as long diagnosis cycle, large amount of calculation, slow approach rate and obvious jitter when sliding mode motion occurs, poor anti-interference ability of adaptive threshold, high requirements for mathematical model and poor robustness, the present invention proposes a fault diagnosis method, system, equipment and medium for current sensor of new energy electric drive system; the method firstly expresses the state equation and fault of the original system in augmented form, reconstructs the fault, and makes the impact of the fault on the system more intuitive; secondly, a new interval sliding mode observer is used to reach the sliding mode surface faster and reduce the jitter of sliding mode motion through reasonable parameter design, so as to achieve better tracking effect of actual situation; at the same time, when fault diagnosis is performed based on the characteristic quantity of current, the inherent or external uncertain disturbance of the system is suppressed by designing adaptive threshold, so as to increase the accuracy of the diagnosis method and improve the diagnosis precision.

[0007] The specific implementation contents of the present invention are as follows:

[0008] A method for diagnosing faults of a current sensor in a new energy electric drive system comprises the following steps:

[0009] Step S1: establishing an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter;

[0010] Step S2: performing a non-singular coordinate transformation on the augmented system to obtain a transformed augmented system;

[0011] Step S3: Establish an interval sliding mode observer according to the transformed augmented system, and calculate the residual value between the actual value and the observed value;

[0012] Step S4: construct a fault detection feature quantity according to the set fault diagnosis adaptive threshold and residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

[0013] In order to better implement the present invention, further, the step S1 specifically includes the following steps:

[0014] Step S11: Calculate the estimated values ​​of the three-phase voltages according to the three-phase bridge arm switching functions and the DC side voltage of the three-phase two-level inverter, and establish a hybrid logic dynamic model of the three-phase two-level inverter;

[0015] Step S12: establishing a current state model of the inverter according to the three-phase output current of the three-phase two-level inverter, the small disturbance signal of the inverter, the back potential of the three-phase winding, the constructed coefficient matrix and the hybrid logic dynamic model of the three-phase two-level inverter;

[0016] Step S13: establishing a minor fault model of the current sensor according to the excitation signal of the minor fault of the current sensor and the Hurwitz matrix;

[0017] Step S14: According to the micro-fault model of the current sensor and the current state model of the inverter, an augmented system is obtained by using a state augmentation method.

[0018] In order to better implement the present invention, further, the step S2 specifically includes the following steps:

[0019] Step S21: converting the augmented system into a first-order equation including state variables and output of the augmented system;

[0020] Step S22: According to the constructed non-singular transformation matrix, the first-order equation is subjected to non-singular coordinate transformation to obtain an augmented system after non-singular coordinate transformation.

[0021] In order to better implement the present invention, further, the step S3 specifically includes the following steps:

[0022] Step S31: calculating an upper bound of an estimated value of a state variable and a lower bound of an estimated value of a state variable according to the augmented system after the non-singular coordinate transformation;

[0023] Step S32: establishing an upper bound observer of the interval sliding mode observer according to the upper bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor; establishing a lower bound observer of the interval sliding mode observer according to the lower bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor;

[0024] Step S33: calculating the upper bound of the error according to the upper bound observer of the interval sliding mode observer; calculating the lower bound of the error according to the lower bound observer of the interval sliding mode observer;

[0025] Step S34: construct an error matrix based on the error upper bound and the error lower bound to obtain the residual value between the actual value and the observed value.

[0026] In order to better implement the present invention, further, the step S4 specifically includes the following steps:

[0027] Step S41: Obtain the fault detection feature value F according to the set current sensor minor fault state threshold, current sensor fault state threshold, current sensor failure state threshold and residual value. S ;

[0028] Step S42: diagnose the current sensor fault according to the fault characteristic quantity. If F S =0, the diagnostic current sensor is in normal state; if F S =1, the current sensor is diagnosed to be in a minor fault state; if F S =2, the diagnostic current sensor is in fault state; if F S =3, the diagnostic current sensor is in failure state.

[0029] Based on the above-mentioned new energy electric drive system current sensor fault diagnosis method, in order to better realize the present invention, further, a new energy electric drive system current sensor fault diagnosis system is proposed, which is used to execute the above-mentioned new energy electric drive system current sensor fault diagnosis method; including a construction unit, a coordinate transformation unit, a sliding mode control unit, and a fault diagnosis unit;

[0030] The construction unit is used to establish an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter;

[0031] The coordinate transformation unit is used to perform non-singular coordinate transformation on the augmented system to obtain a transformed augmented system;

[0032] The sliding mode control unit establishes an interval sliding mode observer according to the transformed augmented system and calculates the residual value between the actual value and the observed value;

[0033] The fault diagnosis unit is used to construct a fault detection feature quantity according to a set fault diagnosis adaptive threshold and a residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

[0034] Based on the above-mentioned new energy electric drive system current sensor fault diagnosis method, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned new energy electric drive system current sensor fault diagnosis method is implemented.

[0035] Based on the above-mentioned new energy electric drive system current sensor fault diagnosis method, in order to better realize the present invention, further, a computer-readable storage medium is proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned new energy electric drive system current sensor fault diagnosis method is implemented.

[0036] The present invention has the following beneficial effects:

[0037] (1) The present invention expresses the state equation and fault of the original system in an augmented form and reconstructs the fault, so that the impact of the fault on the system can be more intuitively displayed; and uses a new interval sliding mode observer through reasonable parameter design to achieve faster arrival at the sliding surface and reduce the jitter of the sliding mode motion, thereby achieving a better effect of tracking the actual situation. By using a new interval sliding mode observer and sliding mode control, the shortcoming of the existing model-based method that it is unable to establish an accurate system model can be solved by simply designing the sliding surface reasonably.

[0038] (2) When the present invention performs fault diagnosis based on the characteristic quantity of the current, an adaptive threshold is designed to suppress the inherent or external uncertain disturbance of the system, thereby increasing the accuracy of the diagnosis method and improving the diagnosis precision.

[0039] (3) The present invention designs an adaptive control approach rate and adaptively switches the approach rate to reduce the jitter characteristics in the control process and speed up the approach rate, thereby further improving the accuracy of system fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a topological diagram of the circuit involved in the embodiment of the present invention.

[0041] Figure 2 A control diagram of the fault diagnosis method provided by the present invention.

[0042] Figure 3 The present invention provides a flow chart of the fault diagnosis method.

[0043] Figure 4 The state variable Z before and after the fault occurs provided by the embodiment of the present invention 22 Simulation waveform diagram.

[0044] Figure 5 The diagnostic residual e provided in the embodiment of the present invention 22 And simulation waveforms of three state thresholds.

[0045] Figure 6 The fault detection feature value F provided in the embodiment of the present invention is S Simulation waveform diagram. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Embodiment 1:

[0049] This embodiment provides a method for diagnosing a fault of a current sensor of a new energy electric drive system, which specifically includes the following steps:

[0050] Step S1: establishing an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter.

[0051] Furthermore, the step S1 specifically includes the following steps:

[0052] Step S11: Calculate the estimated values ​​of the three-phase voltages according to the three-phase bridge arm switching functions and the DC side voltage of the three-phase two-level inverter, and establish a hybrid logic dynamic model of the three-phase two-level inverter;

[0053] Step S12: establishing a current state model of the inverter according to the three-phase output current of the three-phase two-level inverter, the small disturbance signal of the inverter, the back potential of the three-phase winding, the constructed coefficient matrix and the hybrid logic dynamic model of the three-phase two-level inverter;

[0054] Step S13: establishing a minor fault model of the current sensor according to the excitation signal of the minor fault of the current sensor and the Hurwitz matrix;

[0055] Step S14: According to the micro-fault model of the current sensor and the current state model of the inverter, an augmented system is obtained by using a state augmentation method.

[0056] Step S2: performing a non-singular coordinate transformation on the augmented system to obtain a transformed augmented system.

[0057] Furthermore, the step S2 specifically includes the following steps:

[0058] Step S21: converting the augmented system into a first-order equation including state variables and output of the augmented system;

[0059] Step S22: According to the constructed non-singular transformation matrix, the first-order equation is subjected to non-singular coordinate transformation to obtain an augmented system after non-singular coordinate transformation.

[0060] Step S3: Establish an interval sliding mode observer based on the transformed augmented system and calculate the residual value between the actual value and the observed value.

[0061] Furthermore, the step S3 specifically includes the following steps:

[0062] Step S31: calculating an upper bound of an estimated value of a state variable and a lower bound of an estimated value of a state variable according to the augmented system after the non-singular coordinate transformation;

[0063] Step S32: establishing an upper bound observer of the interval sliding mode observer according to the upper bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor; establishing a lower bound observer of the interval sliding mode observer according to the lower bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor;

[0064] Step S33: calculating the upper bound of the error according to the upper bound observer of the interval sliding mode observer; calculating the lower bound of the error according to the lower bound observer of the interval sliding mode observer;

[0065] Step S34: construct an error matrix based on the error upper bound and the error lower bound to obtain the residual value between the actual value and the observed value.

[0066] Step S4: construct a fault detection feature quantity according to the set fault diagnosis adaptive threshold and residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

[0067] Furthermore, the step S4 specifically includes the following steps:

[0068] Step S41: Obtain the fault detection feature value F according to the set current sensor minor fault state threshold, current sensor fault state threshold, current sensor failure state threshold and residual value. S ;

[0069] Step S42: diagnose the current sensor fault according to the fault characteristic quantity. If F S =0, the diagnostic current sensor is in normal state; if F S =1, the current sensor is diagnosed to be in a minor fault state; if F S =2, the diagnostic current sensor is in fault state; if F S =3, the diagnostic current sensor is in failure state.

[0070] Working principle: This embodiment first expresses the state equation and fault of the original system in an augmented form, reconstructs the fault, and makes the impact of the fault on the system more intuitive; secondly, a new interval sliding mode observer is used to reach the sliding mode surface faster and reduce the jitter of the sliding mode motion through reasonable parameter design, so as to achieve a better effect of tracking the actual situation; at the same time, when performing fault diagnosis based on the characteristic quantity of current, an adaptive threshold is designed to suppress the inherent or external uncertain disturbance of the system, thereby increasing the accuracy of the diagnosis method and improving the diagnosis precision.

[0071] Embodiment 2:

[0072] This embodiment is based on the above embodiment 1. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, a specific embodiment is used for explanation. Figure 1 It can be seen that the circuit topology structure involved in the method includes a DC power supply, a three-phase two-level inverter, a permanent magnet synchronous motor, a current sensor and a control module, and the permanent magnet synchronous motor includes three identical equivalent resistances, three identical equivalent inductances and a three-phase winding back electromotive force;

[0073] The three-phase two-level inverter is divided into three-phase bridge arms, and the three-phase bridge arms are all connected in parallel with the DC power supply. The three-phase bridge arm is denoted as the k-phase bridge arm, where k represents the phase sequence, k=a, b, c; in the three-phase bridge arm, each phase bridge arm is composed of two switch tubes connected in series, that is, the inverter circuit contains a total of 6 switch tubes, and the 6 switch tubes are denoted as V kη , η represents the serial number of the switch tube, η = 1, 2; in each phase of the three-phase bridge arm, the switch tube Vk1 , switch tube V k2 Connect in series, switch tube V k1 , switch tube V k2 The connection points are denoted as points

[0074] The three identical equivalent resistors are denoted as resistor R k , three identical equivalent inductors are recorded as inductance L k , the back electromotive force of the three-phase winding is recorded as back electromotive force e k , k = a, b, c, the resistance R k One end and the output point connected to the other end of the inductor L k Connected, inductor L k The other end is connected to the back electromotive force e k The positive electrode, back electromotive force e k The negative pole is grounded;

[0075] The detection end of the current sensor is divided into three phases, denoted as detection end Γ k , k = a, b, c, detection end Γ k Connect at the output point and resistor R k The output end of the current sensor is connected to the input end of the control module, and the output end of the control module is connected to 6 switch tubes V kη ;

[0076] exist Figure 1 In the description, PMSM is a permanent magnet synchronous motor.

[0077] Figure 2 is a control diagram of the fault diagnosis method of this embodiment, Figure 3 is a flow chart of the method for diagnosing faults of a current sensor in a new energy electric drive system according to this embodiment. Figure 2-Figure 3 It can be seen that the steps of the diagnostic method of the present invention are as follows:

[0078] Step S1: The three-phase two-level inverter is recorded as an inverter, and a hybrid logic dynamic model of the inverter is established, and its expression is: in, is the phase voltage U of phase k ko The estimated value of S k is the switching function of the k-phase bridge arm;

[0079] In this embodiment, the switching function S of the k-phase bridge arm is k Determined as follows:

[0080] The specified current flows from the rectifier to the equivalent inductor L k is positive, the current flows from the equivalent inductance L kThe flow to the inverter is negative, and the logical variable σ is defined k , σ k =1 means the k-phase current is positive, σ k =0 means the k-phase current is negative;

[0081] The switch tube V kn The switching signal u kn The symbol "-" indicates logical negation, u kn =1 means switch tube V kn In the on state, u kn =0 means switch tube V kn In the disconnected state, the switching function S of the k-phase bridge arm k The expression is:

[0082]

[0083] Sampling the three-phase output current i of the inverter a ,i b ,i c , establish the current state equation of the inverter, its expression is:

[0084]

[0085] in, The equivalent current i is measured for the network a ,i b ,i c The derivative of R is the equivalent resistance R k The resistance value, L is the equivalent inductance L k The inductance value, e a is the back electromotive force of phase a winding, e b is the back electromotive force of phase b winding, e c is the back electromotive force of the c-phase winding, G is the coefficient matrix 1, d is the unknown disturbance signal of the inverter;

[0086] Define the minor fault of the current sensor as minor fault f, and establish the minor fault equation, which is expressed as: in, is the derivative of the small fault f, A f is the Hurwitz matrix, is the excitation signal of a minor fault. In this embodiment, The values ​​are as follows:

[0087]

[0088] The state augmentation method is used to establish an augmented system for the micro-fault equation and the state space equation of the inverter. The expression of the augmented system is as follows:

[0089]

[0090] Step S2: Abstract the hybrid logic dynamic model of the inverter of the PMSM drive system with a current sensor minor fault into the following general form:

[0091]

[0092] Where x=(i a i b i c ) T is the state variable, is the derivative of x, G is the coefficient matrix of the perturbation, Full of order,

[0093] The column is full of rank.

[0094] There exists a non-singular transformation moment T such that Among them, B 21 ∈R (n+q-p)×m , E2=(0E 22 ) T , B 12 ∈R p×m , T∈R (n+q)×(n+q) , A4∈R p×p , B 22 ∈R p×m , E 22 ∈R q×q , A2∈R (n+q-p)×p , G2∈R p×r , A1∈R (n+q-p)×(n+q-p) , A3∈R p×(n+q-p) , E2∈R p×q , z1∈R n+q-p , z2∈R p , B 11 ∈R (n+q-p)×m , G1∈R (n+q-p)×r .

[0095] Non-singular coordinate transformations do not change the characteristics of the system, so Then the augmented system can be further expressed as follows through non-singular coordinate transformation:

[0096]

[0097] Where z1∈R n+q-p , z2∈R p , z 21 ∈Rp-q , z 22 ∈R q ,

[0098] Step S3: For the augmented system after non-singular coordinate transformation, record are z1, z 21 , z 22 The upper bound of the estimated value of are z1, z 21 , z 22 The lower bound of the estimated value of , the upper bound of the designed new interval sliding mode observer is:

[0099]

[0100] The lower bound observer is:

[0101]

[0102] Among them, the matrix K1, K 21 , K 22 So that A1-K1, are all Hurwitz matrices. A new adaptive reaching law for upper bound observers The expression is:

[0103]

[0104]

[0105]

[0106] Among them, λ is an adjustable parameter 1, and 0<λ<1; α is an adjustable parameter 2, β is an adjustable parameter 3, α and β are both positive odd numbers and satisfy 1<α / β<2. Similarly, the new adaptive reaching law of the lower bound observer is similar to it.

[0107] Set the state variables z1, z 21 , z 22 Estimated value of Written as:

[0108]

[0109]

[0110]

[0111] Among them, the weight factor And at any time, it is satisfied

[0112] Defining Error Then the expressions for the upper and lower bounds of the error are:

[0113]

[0114]

[0115] The available error is:

[0116]

[0117] Step S4: Given a current sensor minor fault state threshold T th1 , Current sensor fault state threshold T th2 and the current sensor failure state threshold T th3 .

[0118] In this embodiment, the current sensor minor fault state threshold T th1 , Current sensor fault state threshold T th2 and the current sensor failure state threshold T th3 The given process is as follows:

[0119] First, define the residual e 22 as follows:

[0120]

[0121] Where, e is the base of the natural logarithm function, e 22 (0) represents the residual e 22 The initial value at t = 0, τ is the time constant, ∫ represents the integral sign; |||| is the norm symbol, This is the small fault excitation signal corresponding to the failure of the current sensor. The critical value of

[0122] Current sensor minor fault state threshold T th1 , Current sensor fault state threshold T th2 and the current sensor failure state threshold T th3 They are defined as follows:

[0123]

[0124]

[0125]

[0126] in, This is the fault excitation signal corresponding to a minor fault in the current sensor. The critical value of The fault excitation signal corresponding to the current sensor failure The critical value of This is the fault excitation signal corresponding to a major fault of the current sensor. The critical value of .

[0127] Define the fault detection feature F s , whose expression is:

[0128] F s =(sign(||e 22 ||-T th1 )+1) / 2+(sign(||e 22 ||-T th2 )+1) / 2+(sign(||e 22 ||-T th3 )+1) / 2

[0129] According to the fault detection feature F s To diagnose a current sensor fault:

[0130] If F s =0, the current sensor is considered to be in normal state;

[0131] If F s =1, the current sensor is considered to be in a minor fault state;

[0132] If F s =2, the current sensor is considered to be in a fault state;

[0133] If F s =3, the current sensor is considered to be in a malfunctioning state.

[0134] The diagnosis is complete.

[0135] In order to better illustrate the technical effect of this embodiment, this embodiment is simulated. Figure 4 As shown in the figure, the state variable z before and after the minor fault occurs in the embodiment 22 And the observed value As can be seen from the figure, when a small fault occurs, the state variable z 22 And the observed value changes have occurred.

[0136] like Figure 5 The diagnostic residual e in this embodiment is shown as 22 And the simulation waveforms of the three state thresholds. It can be seen from the figure that when the micro fault occurs for 0.217 seconds, e 22 The sudden increase exceeds the current sensor's minor fault state threshold T th1, 0.231 seconds e 22 The current sensor suddenly increases and exceeds the fault state threshold T th2 , 0.307 seconds e 22 The current sensor suddenly increases and exceeds the current sensor failure threshold T th3 .

[0137] like Figure 6 The fault detection feature quantity F in this embodiment is shown as s As can be seen from the figure, the fault detection feature F s At 0.217 seconds, it changes from 0 to 1, which indicates that a minor fault has occurred in the current sensor. At 0.231 seconds, it changes from 1 to 2, which indicates that a fault has occurred in the current sensor. At 0.307 seconds, it changes from 2 to 3, which indicates that the current sensor has failed.

[0138] This embodiment uses a novel interval sliding mode observer and sliding mode control, and can solve the shortcoming of existing model-based methods that cannot establish an accurate system model by simply designing the sliding surface; by designing an adaptive control approach rate, the jitter characteristics in the control process are reduced and the approach rate is accelerated by adaptively switching the approach rate, thereby further improving the accuracy of system fault diagnosis; by designing an adaptive diagnostic threshold to suppress the inherent or external uncertain disturbances of the system, the diagnostic accuracy is improved; and the current sensor fault diagnosis is realized and the accuracy of the fault diagnosis result is verified.

[0139] The other parts of this embodiment are the same as those of the above-mentioned embodiment 1, and thus will not be described in detail.

[0140] Embodiment 3:

[0141] This embodiment, based on any one of the above-mentioned embodiments 1-2, proposes a new energy electric drive system current sensor fault diagnosis system, which is used to execute the above-mentioned new energy electric drive system current sensor fault diagnosis method; including a construction unit, a coordinate transformation unit, a sliding mode control unit, and a fault diagnosis unit;

[0142] The construction unit is used to establish an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter;

[0143] The coordinate transformation unit is used to perform non-singular coordinate transformation on the augmented system to obtain a transformed augmented system;

[0144] The sliding mode control unit establishes an interval sliding mode observer according to the transformed augmented system and calculates the residual value between the actual value and the observed value;

[0145] The fault diagnosis unit is used to construct a fault detection feature quantity according to a set fault diagnosis adaptive threshold and a residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

[0146] This embodiment also proposes an electronic device, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned new energy electric drive system current sensor fault diagnosis method is implemented.

[0147] This embodiment also proposes a computer-readable storage medium, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned new energy electric drive system current sensor fault diagnosis method is implemented.

[0148] The other parts of this embodiment are the same as any one of the above-mentioned embodiments 1-2, so they will not be repeated here.

[0149] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for diagnosing faults of a current sensor in a new energy electric drive system, characterized in that: The specific steps include: Step S1: establishing an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter; Step S2: performing a non-singular coordinate transformation on the augmented system to obtain a transformed augmented system; Step S3: Establish an interval sliding mode observer according to the transformed augmented system, and calculate the residual value between the actual value and the observed value; Step S4: construct a fault detection feature quantity according to the set fault diagnosis adaptive threshold and residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

2. A method for diagnosing faults of a current sensor in a new energy electric drive system according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S11: Calculate the estimated values ​​of the three-phase voltages according to the three-phase bridge arm switching functions and the DC side voltage of the three-phase two-level inverter, and establish a hybrid logic dynamic model of the three-phase two-level inverter; Step S12: establishing a current state model of the inverter according to the three-phase output current of the three-phase two-level inverter, the small disturbance signal of the inverter, the back potential of the three-phase winding, the constructed coefficient matrix and the hybrid logic dynamic model of the three-phase two-level inverter; Step S13: establishing a minor fault model of the current sensor according to the excitation signal of the minor fault of the current sensor and the Hurwitz matrix; Step S14: According to the micro-fault model of the current sensor and the current state model of the inverter, an augmented system is obtained by using a state augmentation method.

3. A method for diagnosing faults of a current sensor in a new energy electric drive system according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S21: converting the augmented system into a first-order equation including state variables and output of the augmented system; Step S22: According to the constructed non-singular transformation matrix, the first-order equation is subjected to non-singular coordinate transformation to obtain an augmented system after non-singular coordinate transformation.

4. A method for diagnosing faults of a current sensor in a new energy electric drive system according to claim 1, characterized in that: The step S3 specifically comprises the following steps: Step S31: calculating an upper bound of an estimated value of a state variable and a lower bound of an estimated value of a state variable according to the augmented system after the non-singular coordinate transformation; Step S32: establishing an upper bound observer of the interval sliding mode observer according to the upper bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor; establishing a lower bound observer of the interval sliding mode observer according to the lower bound of the estimated value of the state variable, the set adjustable parameter and the set weight factor; Step S33: calculating the upper bound of the error according to the upper bound observer of the interval sliding mode observer; calculating the lower bound of the error according to the lower bound observer of the interval sliding mode observer; Step S34: construct an error matrix based on the error upper bound and the error lower bound to obtain the residual value between the actual value and the observed value.

5. The method for diagnosing faults of a current sensor in a new energy electric drive system according to claim 1, characterized in that: The step S4 specifically comprises the following steps: Step S41: Obtain the fault detection feature value F according to the set current sensor minor fault state threshold, current sensor fault state threshold, current sensor failure state threshold and residual value. S ; Step S42: diagnose the current sensor fault according to the fault characteristic quantity. If F S =0, the diagnostic current sensor is in normal state; if F S =1, the current sensor is diagnosed to be in a minor fault state; if F S =2, the diagnostic current sensor is in fault state; if F S =3, the diagnostic current sensor is in failure state.

6. A new energy electric drive system current sensor fault diagnosis system, used to execute the new energy electric drive system current sensor fault diagnosis method as claimed in claim 1; characterized in that: It includes a construction unit, a coordinate transformation unit, a sliding mode control unit, and a fault diagnosis unit; The construction unit is used to establish an augmented system according to the constructed micro-fault model of the current sensor of the new energy electric drive system and the current state model of the inverter; The coordinate transformation unit is used to perform non-singular coordinate transformation on the augmented system to obtain a transformed augmented system; The sliding mode control unit establishes an interval sliding mode observer according to the transformed augmented system and calculates the residual value between the actual value and the observed value; The fault diagnosis unit is used to construct a fault detection feature quantity according to a set fault diagnosis adaptive threshold and a residual value, and diagnose the current sensor fault according to the fault detection feature quantity.

7. An electronic device, characterized in that: It comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the fault diagnosis method of the current sensor of the new energy electric drive system as described in any one of claims 1-5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as described in claim 7, the new energy electric drive system current sensor fault diagnosis method as described in any one of claims 1-5 is implemented.

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