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Open-Circuit Fault Diagnosis for the Grid-Tied T-Type Inverter Based Only on Three-Phase Currents
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Zhixi WU, Jin ZHAO
CPSS Transactions on Power Electronics and Applications | 2024, 9(3) : 304 - 312
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CPSS Transactions on Power Electronics and Applications | 2024, 9(3): 304-312
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Open-Circuit Fault Diagnosis for the Grid-Tied T-Type Inverter Based Only on Three-Phase Currents
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Zhixi WU, Jin ZHAO
Affiliations
  • Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan 430074 China
  • Zhixi Wu was born in Henan Province, China, in 1996. He received the B.S. degree in automation from Hunan University, Changsha, China, in 2018, and the M.S. degree in control science and engineering from Huazhong University of Science and Technology, Wuhan, China, in 2021. He is currently working toward the Ph.D. degree in artificial intelligence with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interests include multilevel converters and fault diagnosis for power converters.

    Jin Zhao was born in Hubei Province, China, in 1967. He received the B.E. and Ph.D. degrees in Control Science and Engineering from the Department of Control Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China, in 1989 and 1994, respectively. Since 2004, he has been a Full Professor with the School of Artificial Intelligent and Automation, HUST. During 2001-2002, he was a Visiting Scholar in the Power Electronics Research Laboratory, University of Tennessee, Knoxville, USA. He is currently involved in research and applications of power electronics, electrical drives, fault diagnosis and tolerant, and intelligent control. He is the author or co-author of more than 300 technical papers.

Published: 2024-09-10 doi: 10.24295/CPSSTPEA.2024.00012
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Gridtied Ttype inverters are widely used in photovoltaic, electric vehicle charging piles, and other gridtied applications. However, the transistor fault seriously harms the reliability of the inverter. This paper proposed a simple and novel opencircuit fault diagnosis method for the gridtied Ttype inverter based only on the threephase currents. At first, the Hausdorff distances among the threephase normalized current accumulations are obtained to perform the fault detection. The process is tolerant of inverter transient conditions. Then, the fundamental frequency values of the threephase currents are calculated to locate the faulty phase. At last, the faulty transistor is located by some intermediate conditions. In particular, the method is suitable for modulation index regulation. Experiments verify the effectiveness of the proposed method.

Fundamental frequency value  /  grid-tied T-type inverter  /  Hausdorff distance  /  open-circuit fault diagnosis  /  three-phase currents
Zhixi WU, Jin ZHAO. Open-Circuit Fault Diagnosis for the Grid-Tied T-Type Inverter Based Only on Three-Phase Currents[J]. CPSS Transactions on Power Electronics and Applications, 2024 , 9 (3) : 304 -312 . DOI: 10.24295/CPSSTPEA.2024.00012
MULTI-LEVEL inverters are widely used in grid-tied energy conversion systems (such as photovoltaic, electric vehicle charging piles, and wind turbines), because of their higher efficiency, less current harmonics, lower overall costs, and inherent fault-tolerance, compared to two-level converters [1],[2]. Among the many types of multi-level inverters (such as T-type, NPC, NPP, and so on), the T-type inverter performs well in the application of medium switching frequency and tens kilowatt so that it is a kind of crucial grid-tied inverter [3],[4].
Transistor fault seriously harms the reliability of the system, causing energy loss and even system shutdown [5]. Unfortunately, transistors are none other than fragile components in converters. Around 38% of the faults in power systems are due to semiconductor devices [6]. From an industrial perspective [7], 40% of the attention should be on semiconductor devices to improve the reliability of the power system. The T-type inverter has twice as many transistors as the two-level converter, so it faces a greater risk of transistor fault.
The transistor fault types include short-circuit faults and open-circuit faults. Hardware protection circuits often deal with instantaneous overvoltage and overcurrent caused by the short-circuit fault. Open-circuit fault results in output distortion and harmonics, which becomes the major concern of transistor faults [8]. The open-circuit fault diagnosis method can quickly locate the fault transistor, provide the basis for fault tolerant control or maintenance decisions, and greatly improve the system’s reliability.
Recently, much research on open-circuit fault diagnosis has been carried out, which can be simply classified as model-based methods, data-based methods, and signal-based methods. Model-based methods collect system parameters and build accurate circuit models to estimate the residuals, to realize fault diagnosis. Data-based methods mine fault information from a large number of system data by techniques of machine learning or deep learning and obtain fault results by classifiers. Signal-based methods use the voltage or current signals of the system to obtain fault information through signal analysis and processing.
Compared with signal-based methods and data-based methods, model-based methods typically require more types of system parameters. In the case of two-level power converters, models such as the pole-to-pole model [9] and phase voltage model [10] are used to realize fault diagnosis. When applying these models, it is necessary to obtain system parameters such as voltages, currents, inductances and capacitances, or currents and switching states. In the case of multi-level power converters, the models built are more complex and there are more fault states. The average voltage model is built to design fault diagnosis methods for T-type inverters [11], which solve the disturbance caused by working conditions in multi-level converters, such as modulation index regulation. A kind of node-path model and the voltage model are used to diagnose the single open-circuit fault for the T-type inverter [12]. In NPC rectifier fault diagnosis, the line voltage model is built to obtain the error value, and then the diagnosis method is designed based on the error [13]. The two diagnosis methods realize the diagnostic speed of switching period levels because of the combination of switching states. Model-based methods are quick to diagnose and adapt to complex working conditions. However, many system parameters are usually required, and these parameters may not be available or accurate, which may limit the application of model-based methods.
Data-based methods are more used in fault diagnosis of two-level converters [14],[15]. There is also precedent for the data-based method in NPC converter [16]. However, it is rarely reported in the diagnosis of T-type converters. Although many of the data-based methods only need to collect a single type of system signal as the input of the learning models, the training of the models needs a lot of training samples. This requires a lot of data acquisition work, and the high computational complexity also increases the difficulty of application.
Signal-based methods have also been widely studied in recent years. In the case of two-level converters, there are many diagnostic methods based only on the three-phase currents [17],[18]. In [19], line voltages are analyzed and used to design the diagnosis method. These methods require only one kind of system signal and are more applicable. Unfortunately, existing signal-based methods in multilevel converter diagnosis still use more system signals or control operations. In the fault diagnosis for grid-tied NPC inverters, the three-phase currents are used as the diagnostic signals, and additional operations such as the underexcited power injection and the switching scheme are performed separately to locate the fault [20],[21]. [22] designs the diagnosis method based on the three-phase currents and grid voltage angle for the T-type rectifier. However, the method is only effective for some transistors. In T-type inverters,[23] uses single-phase line voltage for fault diagnosis, but the performance under modulation index regulation and load regulation is not reported.[24] proposes a fast diagnosis method, but it is based on an additional neutral-point current sensor, three-phase currents, and switching states. In [25], the three-phase currents and two DC-bus capacitor voltages are used to diagnose the open-circuit fault.
In general, fewer diagnostic signals are used in diagnosis methods in favor of their application. There have been many researches and applications on the design of two-level converters using only three-phase currents. However, diagnosis methods for multilevel inverters such as T-type still usually use relatively more diagnostic signals or control operations. This paper proposes an online open-circuit fault diagnosis for the grid-tied T-type inverter based on only three-phase currents. Firstly, the fault mechanism of the inverter is analyzed in detail. Then, the design of the proposed method is given, which consists of three steps. The first step is to obtain the Hausdorff distances among the three-phase normalized current accumulations to perform the fault detection. The second step process is to calculate the fundamental frequency values of the three-phase currents to locate the faulty phase. The third step is to locate the faulty transistor by some intermediate conditions. Finally, simulations and experiments verify the robustness and effectiveness of the proposed method. The contributions are:
1) A novel online diagnosis method for the grid-tied T-type inverter only based on the three-phase currents is proposed. The Hausdorff distances and the fundamental frequency values are used as diagnosis features.
2) The proposed method is suitable for all single transistor open-circuit faults, considering modulation index regulation. The fault diagnosis is tolerant of inverter transient conditions.
Section II introduces the system of the grid-tied T-type inverter with the diagnosis unit. Section III is the analysis of the open-circuit fault mechanism in detail. Section IV introduces the design of the proposed method. Section V shows the simulations and experimental verifications. Section VI is the conclusion of this paper.
As a multi-level converter, the grid-tied T-type inverter has a more complex topology than the two-level converter, as shown in Fig. 1. There are twelve transistors $\left({{\mathrm{S}}_{x1},{\mathrm{\;S}}_{x2},{\mathrm{\;S}}_{x3},{\mathrm{\;S}}_{x4}}\right)$ and their respective fly-wheel diodes $\left({{\mathrm{D}}_{x1},{\mathrm{D}}_{x2},{\mathrm{D}}_{x3},{\mathrm{D}}_{x4}}\right).x$ represents the three phases a, b, c. X represents the three phases A, B, C. N is the neutral point. ${u}_{x\mathrm{\;s}}$ are the three-phase grid voltages. ${L}_{x\mathrm{\;s}}$ represent filter inductances. ${i}_{x}$ are the three-phase currents. The positive directions of ${i}_{x}$ and ${u}_{x\mathrm{\;s}}$ are defined in Fig. 1. ${C}_{1}$ and ${C}_{2}$ are DC bus capacitances. E is the DC power supply. $r$ and ${i}_{0}$ are neutral resistance and current. The PWM inverter control system collects ${i}_{x}$ and ${u}_{xs}$, and then outputs the three-phase modulated voltage signals ${u}_{x}$ according to the given power ${p}^{* }$. Drive signals can be calculated by ${u}_{x}$. When ${u}_{x}> 0$, there are switching states P and O in the X phase. When ${u}_{x}< 0$, there are switching states N and O in the X phase. The relationship between the switching state and the switch combination is shown in Table I. The proposed fault diagnosis unit only collects ${i}_{x}$ to perform the fault diagnosis, without any other system signals (system parameters, control parameters, or other electrical signals).
When analyzing the open-circuit fault mechanism, the working mode of the T-type inverter should be analyzed first. The inverter has three switching states(P, O, N). Considering the current direction, there are six current flow paths, as shown in Fig. 2. They are ${\mathrm{p}}_{+ }\left({{C}_{1}\rightarrow {\mathrm{S}}_{x1}\rightarrow {L}_{x\mathrm{\;s}}\rightarrow {u}_{x\mathrm{\;s}}}\right),{\mathrm{p}}_{- }\left({{u}_{x\mathrm{\;s}}\rightarrow {L}_{x\mathrm{\;s}}\rightarrow {\mathrm{D}}_{1}\rightarrow {C}_{1}}\right)$, ${\mathrm{O}}_{+ }\left({{\mathrm{S}}_{x3}\rightarrow {\mathrm{D}}_{x2}\rightarrow {L}_{x\mathrm{\;s}}\rightarrow {u}_{x\mathrm{\;s}}}\right)$, ${\mathrm{O}}_{- }\left({{u}_{x\mathrm{\;s}}\rightarrow {L}_{x\mathrm{\;s}}\rightarrow {\mathrm{S}}_{x2}\rightarrow {\mathrm{D}}_{x3}}\right)$, ${\mathrm{n}}_{+ }\left({{C}_{2}\rightarrow {\mathrm{D}}_{x4}\rightarrow }\right.{L}_{x\mathrm{\;s}}\rightarrow {u}_{xs})$, and ${\mathrm{n}}_{- }\left({{u}_{x\mathrm{\;s}}\rightarrow {L}_{x\mathrm{\;s}}\rightarrow {\mathrm{S}}_{x4}\rightarrow {C}_{2}}\right)$. In path ${\mathrm{p}}_{- },{\mathrm{o}}_{- }$, and ${\mathrm{n}}_{- }$, the current direction is negative. In paths ${\mathrm{p}}_{+ },{\mathrm{o}}_{+ }$, and ${\mathrm{n}}_{+ }$, the current direction is positive. In paths ${\mathrm{p}}_{- }$ and ${\mathrm{p}}_{+ }$, the switching state is $\mathrm{P}$. In paths ${\mathrm{o}}_{- }$ and ${\mathrm{o}}_{+ }$, the switching state is O. In paths ${\mathrm{n}}_{- }$, and ${\mathrm{n}}_{+ }$, the switching state is N.
During the healthy operation of the inverter, the energy is transferred from the DC side to the grid side, which means that both ${i}_{x}$, and ${u}_{x\mathrm{\;s}}$ are in the positive direction. The pole-to-pole voltage ${U}_{\mathrm{{XN}}}$ is determined by the switching state and the capacitor voltages. The relationship between ${i}_{x}$ and ${U}_{\mathrm{{XN}}}$ is shown in Fig. 3. The fault mechanisms of the current path under the health condition, the ${\mathrm{S}}_{x4}$ fault condition, and the ${\mathrm{S}}_{x2}$ fault condition are analyzed below. The ${S}_{x1}$ fault condition and the ${\mathrm{S}}_{x3}$ fault condition are similar to the previous two fault conditions, except that the direction of each parameter is opposite.
As shown in Fig. 3(a), path ${\mathrm{o}}_{- }$ and ${\mathrm{n}}_{- }$ exist in the negative current period. The power sources in ${\mathrm{n}}_{- }$ are ${C}_{2}$ and ${u}_{x\mathrm{\;s}}$. The power sources in ${\mathrm{o}}_{- }$ is ${u}_{x\mathrm{\;s}}$. n_increases the amplitude of ${i}_{x}$, and ${\mathrm{o}}_{- }$ decreases it. The controller makes the current approximately sinusoidal by controlling the action time of the two paths.
When ${\mathrm{S}}_{x4}$ is faulty, path ${\mathrm{n}}_{- }$ cannot be conducted, which distorts the current in the negative period as shown in Fig. 3 (b). Assuming the negative current exists, path ${p}_{- }$ will replace the original path ${\mathrm{n}}_{- }$ to act, resulting in a decrease in the negative current. Path o_ does the same thing. So there is no negative current. In particular, the switch state O turns on both ${\mathrm{S}}_{x3}$ and ${\mathrm{S}}_{x2}$. Under the action of ${u}_{x\mathrm{\;s}}$, the amplitude of the positive current will increase in the O state. The presence of the positive current initiates paths ${\mathrm{p}}_{+ }$ and ${\mathrm{o}}_{+ }$. ${\mathrm{o}}_{+ }$ increases the amplitude of the positive current, but ${\mathrm{p}}_{+ }$ decreases it.
When ${\mathrm{S}}_{x2}$ is faulty, path ${\mathrm{o}}_{- }$ cannot be conducted, which distorts the current in the negative period as shown in Fig. 3 (c). The current distortion in ${\mathrm{S}}_{x2}$ fault is similar to that in ${\mathrm{S}}_{x4}$ fault, but it has a more complex distortion mode. Assuming the negative current exists, path ${p}_{- }$ will replace the original path ${o}_{- }$ to act, resulting in a decrease in the negative current. The action can be severe, resulting in zero amplitude of the negative current. In particular, the switch state O also turns on ${\mathrm{S}}_{x3}$, making the presence of the positive current with path ${\mathrm{o}}_{+ }$. Switching state N conducts path ${\mathrm{n}}_{+ }$ and path ${\mathrm{n}}_{- }$ to decrease the positive current amplitude and increase the negative current amplitude, respectively. With the increase of the amplitude of ${u}_{x}$, the amplitude of the negative current increases, and the amplitude of the positive current decreases sharply.
From the above analysis, it can be obtained that the current will be distorted in the negative period under ${\mathrm{S}}_{x2}$ fault and ${\mathrm{S}}_{x4}$ fault. Only the positive current exists in the distortion period under ${\mathrm{S}}_{x4}$ fault. The proportion of negative current is higher than that of positive current in the distortion period under ${\mathrm{S}}_{x2}$ fault.
Only the three-phase currents are used to design the opencircuit diagnosis method in this paper. The method is a step-by-step process. The first step is to obtain the Hausdorff distances among the three-phase normalized current accumulations to perform the fault detection. The second step process is to calculate the fundamental frequency values of the three-phase currents to locate the faulty phase. The third step is to locate the faulty transistor. The ${\mathrm{S}}_{x1}/{\mathrm{S}}_{x3}$ or ${\mathrm{S}}_{x2}/{\mathrm{S}}_{x4}$ fault is located by the three-phase normalized current accumulations and the ${\mathrm{S}}_{x2}/{\mathrm{S}}_{x3}$ or ${\mathrm{S}}_{x1}/{\mathrm{S}}_{x4}$ fault is located by the fundamental frequency values between the phases.
To adapt to the effect of load and other changes on the three-phase currents, the current normalization operation is applied. In the healthy condition, the phase difference between the three currents is ${120}^{\circ }$. Let ${I}_{\mathrm{m}}$ be the peak value of currents. ${i}_{\mathrm{d}},{i}_{\mathrm{q}}$ and ${i}_{\mathrm{m}}$ are the intermediate variables.(1) can be obtained as:
$\left\{\begin{array}{l}{i}_{\mathrm{d}}= \sqrt{\frac{2}{3}}{i}_{\mathrm{a}}- \sqrt{\frac{1}{6}}{i}_{\mathrm{b}}= \sqrt{\frac{1}{6}}{i}_{\mathrm{c}}\\{i}_{\mathrm{q}}= \sqrt{\frac{1}{2}}{i}_{\mathrm{b}}- \sqrt{\frac{1}{2}}{i}_{\mathrm{c}}\\{i}_{\mathrm{m}}= \sqrt{\frac{2}{3}\left({{i}_{\mathrm{a}}^{2}+ {i}_{\mathrm{q}}^{2}}\right)} ={I}_{\mathrm{m}}\end{array}\right.$
The normalized currents ${i}_{x}^{- }$ can be calculated as:
${i}_{x}^{- }= \frac{{i}_{x}}{{I}_{\mathrm{m}}}$
Let the grid frequency be fs and the current sampling frequency of the fault diagnosis unit be f. Define the size of the sliding window per unit current period to be L, and L can be calculated as (3). ${i}_{xj}^{- }$ is the $j$ -th sampling point of ${i}_{x}^{- }$ in the window.
$ L =\frac{f}{{f}_{\mathrm{s}}}$
As analyzed in Section III, transistor fault will cause current distortion in positive or negative periods. The steady-state current is sinusoidal under healthy conditions. Therefore, the three-phase normalized current accumulations ${i}_{xs}$ can be used to indicate the current distortion:
${i}_{x\mathrm{\;s}}= \mathop{\sum }\limits_{{j = 1}}^{L}{i}_{xj}^{- }$
The Hausdorff distances among ${i}_{x\mathrm{\;s}}$ are defined in a sliding window of length ${2L}.{i}_{xsj}$ is the $j$ -th sampling point of ${i}_{xs}$ in the window. The maximum value ${i}_{xs}^{\max }$ and the minimum value ${i}_{xs}^{\min }$ of the ${i}_{x\mathrm{\;s}}$ points in the sliding window should be obtained:
$\left\{\begin{array}{l}{i}_{xs}^{\max }= \max \left({{i}_{xs1},{i}_{xs2},\ldots,{i}_{xs2L}}\right)\\{i}_{xs}^{\min }= \min \left({{i}_{xs1},{i}_{xs2},\ldots,{i}_{xs2L}}\right)\end{array}\right.$
The Hausdorff distances can be defined as:
$\left\{\begin{matrix}{H}_{\mathrm{{ab}}}= \max \left\lbrack {\max \left({{i}_{\mathrm{{as}}}^{\min }- {i}_{\mathrm{{bs}}}^{\max },0}\right),\max \left({{i}_{\mathrm{{bs}}}^{\min }- {i}_{\mathrm{{as}}}^{\max },0}\right)}\right\rbrack \\{H}_{\mathrm{{bc}}}= \max \left\lbrack {\max \left({{i}_{\mathrm{{bs}}}^{\min }- {i}_{\mathrm{{cs}}}^{\max },0}\right),\max \left({{i}_{\mathrm{{cs}}}^{\min }- {i}_{\mathrm{{bs}}}^{\max },0}\right)}\right\rbrack \\{H}_{\mathrm{{ca}}}= \max \left\lbrack {\max \left({{i}_{\mathrm{{cs}}}^{\min }- {i}_{\mathrm{{as}}}^{\max }}\right),0}\right\rbrack,\max \left({{i}_{\mathrm{{as}}}^{\min }- {i}_{\mathrm{{cs}}}^{\max },0}\right)\\ H =\operatorname{mid}\left({{H}_{\mathrm{{ab}}},{H}_{\mathrm{{ba}}},{H}_{\mathrm{{ca}}}}\right)\end{matrix}\right.$
The Hausdorff distance is symmetric, meaning that ${H}_{\mathrm{{ab}}}=$ ${H}_{\mathrm{{ba}}},{H}_{\mathrm{{bc}}}= {H}_{\mathrm{{cb}}}$, and ${H}_{\mathrm{{ac}}}= {H}_{\mathrm{{ca}}}$. In the healthy condition, H is close to zero. In the faulty condition, H is a positive value. The current accumulation calculated for an ideal half-period normalized current is $\mathop{\sum }\limits_{{j = 1}}^{L}\left|{\sin \left(\frac{2\pi j}{L}\right)}\right|/2$. Therefore, the fault detection function can be to determine whether H exceeds threshold th, whose value is $k\mathop{\sum }\limits_{{j = 1}}^{L}\left|{\sin \left(\frac{2\pi j}{L}\right)}\right|/2$.(0.2, 0.5) is considered as the value range of k. The fault detection function is
$\left\{\begin{array}{l} H >{th},\text{ faulty condition }\\ H \leq {th},\text{ healthy condition }\end{array}\right.$
Fault phase location is performed after an open-circuit fault is detected. The fundamental frequency values Dx of ${i}_{x}$ are used as fault features. Let ${i}_{xj}$ is the $j$ -th sampling point of ${i}_{x}$ in the sliding window. Define ${D}_{x}^{\sin }$ and ${D}_{x}^{\cos }$ to be the sine and cosine components of Dx, respectively. ${D}_{x}^{\sin },{D}_{x}^{\cos }$ and Dx can be calculated as (8),(9) and (10), respectively.
${D}_{x}^{\sin }= \mathop{\sum }\limits_{{j = 1}}^{L}\left\lbrack {{i}_{xj}\sin \left(\frac{2\pi j}{L}\right)/L}\right\rbrack $
${D}_{x}^{\cos }= \mathop{\sum }\limits_{{j = 1}}^{L}\left\lbrack {{i}_{xj}\cos \left(\frac{2\pi j}{L}\right)/L}\right\rbrack $
${D}_{x}= \sqrt{{D}_{x}^{\sin }\cdot {D}_{x}^{\sin }+ {D}_{x}^{\cos }\cdot {D}_{x}^{\cos }}$
The calculation of Dx is independent of the start of the sliding window and is easy to perform online. Because of the current distortion, the fundamental frequency of the fault phase is smaller than that of the other two phases. Therefore, the fault phase location function is
${D}_{x}= \min \left({{D}_{\mathrm{a}},{D}_{\mathrm{b}},{D}_{\mathrm{c}}}\right), x -\text{phase fault}$
After the phase location, the X -phase fault is obtained, and then ${\mathrm{S}}_{x1}/{\mathrm{S}}_{x3}$ or ${\mathrm{S}}_{x2}/{\mathrm{S}}_{x4}$ fault location is performed. As analyzed in Section III, the current distortion caused by ${\mathrm{S}}_{x1}/{\mathrm{S}}_{x3}$ occurs in the positive period of the current, and the current distortion caused by ${\mathrm{S}}_{x2}/{\mathrm{S}}_{x4}$ occurs in the negative period of the current. The distortion in the negative period will cause the positive current accumulation to be greater than the negative current accumulation. In the same way, the distortion in the positive period will cause the negative current accumulation to be greater than the positive current accumulation. Therefore, the defined ${i}_{x\mathrm{\;s}}$ can perform this location process as shown in (12).
$\left\{\begin{array}{l}{i}_{x\mathrm{\;s}}> 0,{\mathbf{S}}_{x2}/{\mathbf{S}}_{x4}\text{ fault is located }\\{i}_{x\mathrm{\;s}}< 0,{\mathbf{S}}_{x1}/{\mathbf{S}}_{x3}\text{ fault is located }\end{array}\right.$
According to the analysis in Section III, the current distortions under ${\mathrm{S}}_{x1}$ fault and ${\mathrm{S}}_{x3}$ are similar, and the same is true between ${\mathrm{S}}_{x2}$ fault and ${\mathrm{S}}_{x4}$. However, the proportions of positive current and negative current in the distortion period are important features for similarity discrimination, which are reflected in the fundamental frequency values Dx. With X -phase ${\mathrm{S}}_{x1}/{\mathrm{S}}_{x4}$ fault, Dx will be reduced and less than half of the normal fundamental frequency value. With X -phase ${\mathrm{S}}_{x2}/{\mathrm{S}}_{x3}$ fault, Dx will be reduced but greater than half of the normal fundamental frequency value. Half of the normal fundamental frequency value ${D}_{\mathrm{n}}$ can be calculated in real time using the fundamental frequency value ${D}_{\mathrm{n}}^{\max }$ obtained from the other two healthy phases within a sliding window L, as shown in (13) and (14). ${D}_{\mathrm{n}j}^{\max }$ is the $j$ -th sampling point of ${D}_{\mathrm{n}}^{\max }$ in the window. ${k}_{1}$ is a threshold with a value range of(1.05,1.1)to resist the adverse effects of noise on the location.
${D}_{\mathrm{n}}^{\max }= \left\lbrack {\left({{D}_{\mathrm{a}}+ {D}_{\mathrm{b}}+ {D}_{\mathrm{c}}}\right)- \min \left({{D}_{\mathrm{a}},{D}_{\mathrm{b}},{D}_{\mathrm{c}}}\right)}\right\rbrack /4 $
${D}_{\mathrm{n}}= {k}_{1}\max \left({{D}_{\mathrm{n}1}^{\max },{D}_{\mathrm{n}2}^{\max },\ldots,{D}_{\mathrm{n}L}^{\max }}\right)$
The last location process can be performed as (15) and (16). ${D}_{xj}$ is the $j$ -th sampling point of Dx in the sliding window. Finally, the faulty transistor can be diagnosed.
${D}_{xn}= \min \left({{D}_{x1},{D}_{x2},\ldots,{D}_{xL}}\right)$
$\left\{\begin{array}{l}{D}_{xn}> {D}_{n},{\mathbf{S}}_{x2}/{\mathbf{S}}_{x3}\text{ fault is located }\\{D}_{xn}< {D}_{n},{\mathbf{S}}_{x1}/{\mathbf{S}}_{x4}\text{ fault is located }\end{array}\right.$
A flowchart of the proposed diagnosis method is given as shown in Fig. 4, which consists of the fault detection, fault phase location, and fault transistor location. The diagnosis results are obtained in the corresponding steps.
The experimental platform used for verification is shown in Fig. 5. The main circuit uses 10-FY12NMA160SH01 IGBT modules and a TMS320F28335 controller. DC source is FTG100-800. The current sampling frequency f is 5kHz. The grid source frequency fs is 50Hz and RMS is 50V. The switching frequency fk is 10kHz, and the dead time td is 2 $\mu \mathrm{s}$. The capacitances ${C}_{1}$ and ${C}_{2}$ are both ${550\mu }\mathrm{F}$, and the filter inductances Lxs are all 2.632mH. The open-circuit fault is realized by the host shielding the driver signal. The thresholds k and ${k}_{1}$ are set to 0.3 and 1.08, respectively. The experiments verify the robustness and effectiveness of fault diagnosis under the modulation index regulation.
The start-up process, DC side voltage changing, and output power adjustment are the most common working transient conditions of the inverter. The fault diagnosis unit should not be triggered by mistake under these conditions. The performance of the proposed method under the transient conditions is evaluated below.
Fig. 6 shows the performance verification in the start-up process. The three-phase currents fluctuate caused of the grid voltages before the action of the driven signals. The current normalization operation limits the three-phase current to the unit range. When the driven signals are applied, the currents have a large response, which causes the abrupt change of ixs. However, the Hausdorff distance H is significantly reduced, which avoids the false trigger.
Fig. 7 shows the performance verification with changing given power. The given current peak value is 3A before 1.4s, and 2A at 1.4s. The current normalization operation limits the three-phase current to the unit range in the whole process. The amplitudes of the currents decrease. However, ${i}_{\mathrm{{xs}}}$ and the Hausdorff distance H are all no significant changes, which also avoids the false trigger.
Fig. 8 shows the performance verification with changing DC source E. E is 300V before 1.065s, and 350s at 1.065s. The inverter takes a long time to reach a steady state. The current normalization operation limits the three-phase current to the unit range in the whole process. The amplitudes of the currents increase first and then decrease. However, ${i}_{x\mathrm{\;s}}$ and the Hausdorff distance H are all no significant changes, which also avoids the false trigger.
Since the modulation index controls the action time of the switching state, it can be seen from Section III that modulation index regulation has an important impact on the current distortion under open-circuit fault. In this section, the size of E is changed to realize the modulation index regulation, and then the diagnostic effectiveness of the proposed method is verified.
Fig. 9 shows the experimental results of ${\mathrm{S}}_{\mathrm{{a1}}}$ fault with a modulation index of 0.8 . Set ${\mathrm{S}}_{\mathrm{{al}}}$ fault at 1.14s, and the positive period of ${i}_{\mathrm{a}}$ is distorted. The current normalization operation limits the three-phase current to the unit range in the whole process. The Hausdorff distance H is greater than th at about 1.185s, and the fault is detected. After the fault is detected, ${D}_{\mathrm{a}}$ $<{D}_{\mathrm{b}}< {D}_{\mathrm{c}}$ and a-phase fault is detected. At the same time, ${i}_{\mathrm{{as}}}<$ 0 and ${D}_{\mathrm{{an}}}< {D}_{\mathrm{n}}$. Therefore, ${\mathrm{S}}_{\mathrm{{al}}}/{\mathrm{S}}_{\mathrm{a}3}$ fault is detected and ${\mathrm{S}}_{\mathrm{{al}}}/{\mathrm{S}}_{\mathrm{a}4}$ fault is detected. Finally, ${\mathrm{S}}_{\mathrm{{al}}}$ fault is correctly diagnosed.
Fig. 10 shows the experimental results of ${\mathrm{S}}_{\mathrm{a}3}$ fault with modulation index0.8. Set ${\mathrm{S}}_{\mathrm{a}3}$ fault at 1.17s, and the positive period of ${i}_{\mathrm{a}}$ is distorted. The current normalization operation limits the three-phase current to the unit range in the whole process. The Hausdorff distance H is greater than th at about 1.23s, and the fault is detected. After the fault is detected, ${D}_{\mathrm{a}}<$ ${D}_{\mathrm{b}}< {D}_{\mathrm{c}}$ and a-phase fault is detected. At the same time, ${i}_{\mathrm{{as}}}< 0$ and ${D}_{\mathrm{{an}}}> {D}_{\mathrm{n}}$. Therefore, ${\mathrm{S}}_{\mathrm{{a1}}}/{\mathrm{S}}_{\mathrm{a}3}$ fault is detected and ${\mathrm{S}}_{\mathrm{a}2}/{\mathrm{S}}_{\mathrm{a}3}$ fault is detected. Finally, ${\mathrm{S}}_{\mathrm{a}3}$ fault is correctly diagnosed.
Fig. 11 shows the experimental results of ${\mathrm{S}}_{\mathrm{b}1}$ fault with a modulation index being 0.2 . With the decrease of the modulation index, the current distortion becomes more and more serious, which is consistent with the results of the analysis in Section III. The decrease in the modulation index increases the growth time of the negative current. The results of the proposed diagnosis in those experiments are the same as the experimental results at the modulation index of 0.8, which are correct. The average diagnosis time is about two current cycles.
Fig. 12 shows the experimental results of ${\mathrm{S}}_{\mathrm{a}3}$ fault with a modulation index being 0.2 . With the decrease of the modulation index, the current distortion becomes also more and more serious, which is consistent with the results of the analysis in Section III. The decrease of the modulation index decreases the growth time of the positive current. The results of the proposed diagnosis in those experiments are the same as the experimental results at the modulation index of 0.8, which is correct. The average diagnosis time is about two current cycles.
The proposed method is compared with previous diagnosis methods as shown in Table II. The comparison indexes include diagnosis types, diagnosis variables, application objects, converter types, model dependence, cost, application range (whether to consider the modulation regulation,’*’ represents *, no validation no validation), and diagnostic time. From the comparison, the advantages of the proposed method are: 1) Only the three-phase currents are needed for the diagnosis design, and the implemental cost of the proposed method is low. 2) The proposed method is suitable for all single transistor opencircuit faults, considering modulation index regulation.
A novel online open-circuit fault diagnosis method for grid-tied T-type inverters based on only three-phase currents is proposed in this paper, which is suitable for all single transistor fault diagnosis. The distortion mechanism of the currents is analyzed in detail. The Hausdorff distances among the three-phase normalized current accumulations and the fundamental frequency values of the three-phase currents are used as diagnosis features. Compared with the existing methods, fewer system signals are used, which reduces the cost of diagnosis and the difficulty of implementation. Experimental results show the robustness under inverter transient conditions and effectiveness of fault diagnosis even with modulation index regulation.
  • China National Science Foundation(62073147)
  • China National Science Foundation(61573159)
[1]
A. Poorfakhraei , M. Narimani , A. Emadi . "A review of multilevel inverter topologies in electric vehicles: current status and future trends". in IEEE Open Journal of Power Electronics, 2021. 2. 155-170.
[2]
F. Blaabjerg , M. Liserre , K. Ma . "Power electronics converters for wind turbine systems". in IEEE Transactions on Industry Applications, 2012. 48 (2): 708-719.
[3]
M. Schweizer J. W. Kolar . "Design and implementation of a highly efficient three-level T-type converter for low-voltage applications". in IEEE Transactions on Power Electronics, 2013. 28 (2): 899-907.
[4]
Z. Huang , D. Zhou , L. Wang , Z. Shen , Y. Li . "A review of sin-gle-stage multiport inverters for multisource applications". in IEEE Transactions on Power Electronics, 2023. 38 (5): 6566-6584.
[5]
L. Alhmoud B. Wang . "A review of the state-of-the-art in wind-ener-gy reliability analysis". in Renewable and Sustainable Energy Reviews, 2018. 81 (2): 1643-1651.
[6]
B. Gou , X. Ge , S. Wang , X. Feng , J. B. Kuo , T. G. Habetler . "An open-switch fault diagnosis method for single-phase PWM rectifier using a model-based approach in high-speed railway electrical traction drive system". in IEEE Transactions on Power Electronics, 2016. 31 (5): 3816-3826.
[7]
J. Falck , C. Felgemacher , A. Rojko , M. Liserre , P. Zacharias . "Reli-ability of power electronic systems: An industry perspective". in IEEE Industrial Electronics Magazine, 2018. 12 (2): 24-35.
[8]
Z. Yang Y. Chai . "A survey of fault diagnosis for onshore grid-con-nected converter in wind energy conversion systems". in Renewable and Sustainable Energy Reviews, 2016. 66. 345-359.
[9]
Z. Li , H. Ma , Z. Bai , Y. Wang , B. Wang . "Fast transistor open-circuit faults diagnosis in grid-tied three-phase VSIs based on average bridge arm pole-to-pole voltages and error-adaptive thresholds". in IEEE Trans-actions on Power Electronics, 2018. 33 (9): 8040-8051.
[10]
H. Zhang , C. Sun , Z. Li , J. Liu , H. Cao , X. Zhang . "Voltage vector error fault diagnosis for open-circuit faults of three-phase four-wire ac-tive power filters". in IEEE Transactions on Power Electronics, 2017. 32 (3): 2215-2226.
[11]
Y. Liang , R. Wang , B. Hu . "Single-switch open-circuit diagnosis method based on average voltage vector for three-level T-Type inverter". in IEEE Transactions on Power Electronics, 2021. 36 (1): 911-921.
[12]
B. Wang , Z. Li , Z. Bai , P. T. Krein , H. Ma . "A voltage vector resid-ual estimation method based on current path tracking for T-type inverter open-circuit fault diagnosis". in IEEE Transactions on Power Electron-ics, 2021. 36 (12): 13460-13477.
[13]
L. M. A. Caseiro A. M. S. Mendes . "Real-time IGBT open-circuit fault diagnosis in three-level neutral-point-clamped voltage-source recti-fiers based on instant voltage error". in IEEE Transactions on Industrial Electronics, 2015. 62 (3): 1669-1678.
[14]
Z. Huang , Z. Wang , H. Zhang . "A diagnosis algorithm for multiple open-circuited faults of microgrid inverters based on main fault compo-nent analysis". in IEEE Transactions on Energy Conversion, 2018. 33 (3): 925-937.
[15]
Y. Xia Y. Xu . "A transferrable data-driven method for IGBT open-circuit fault diagnosis in three-phase inverters". in IEEE Trans-actions on Power Electronics, 2021. 36 (12): 13478-13488.
[16]
W. Yuan , Z. Li , Y. He , R. Cheng , L. Lu , Y. Ruan . "Open-circuit fault diagnosis of NPC inverter based on improved 1-D CNN network". in IEEE Transactions on Instrumentation and Measurement, 2022. 71. 1-11.
[17]
F. Wu J. Zhao . "A real-time multiple open-circuit fault diagnosis method in voltage-source-inverter fed vector controlled drives". in IEEE Transactions on Power Electronics, 2016. 31 (2): 1425-1437.
[18]
N. M. A. Freire , J. O. Estima , A. J. Marques Cardoso . "Open-circuit fault diagnosis in PMSG drives for wind turbine applications". in IEEE Transactions on Industrial Electronics, 2013. 60 (9): 3957-3967.
[19]
X. Wu , C. -Y. Chen , T. -F. Chen , S. Cheng , Z. -H. Mao , T. -J. Yu , K. Li . "A fast and robust diagnostic method for multiple open-circuit faults of voltage-source inverters through line voltage magnitudes analysis". in IEEE Transactions on Power Electronics, 2020. 35 (5): 5205-5220.
[20]
U.-M. Choi , J. -S. Lee , F. Blaabjerg , K. -B. Lee . "Open-circuit fault diagnosis and fault-tolerant control for a grid-connected NPC inverter". in IEEE Transactions on Power Electronics, 2016. 31 (10): 7234-7247.
[21]
U.-M. Choi , H. -G. Jeong , K. -B. Lee , F. Blaabjerg . "Method for detecting an open-switch fault in a grid-connected NPC inverter sys-tem". in IEEE Transactions on Power Electronics, 2012. 27 (6): 2726-2739.
[22]
J.-S. Lee K. -B. Lee . "An open-switch fault detection method and tolerance controls based on SVM in a grid-connected T-type rectifier with unity power factor". in IEEE Transactions on Industrial Electron-ics, 2014. 61 (12): 7092-7104.
[23]
K.-H. Chao , L. -Y. Chang , F. -Q. Xu . "Three-level T-type inverter fault diagnosis and tolerant control using single-phase line voltage". in IEEE Access, 2020. 8. 44075-44086.
[24]
J. He , N. A. O. Demerdash , N. Weise , R. Katebi . "A fast on-line diagnostic method for open-circuit switch faults in SiC-MOSFET-based T-type multilevel inverters". in IEEE Transactions on Industry Applica-tions, 2017. 53 (3): 2948-2958.
[25]
U. -M. Choi , K. -B. Lee , F. Blaabjerg . "Diagnosis and tolerant strat-egy of an open-switch fault for T-type three-level inverter systems". in IEEE Transactions on Industry Applications, 2014. 50 (1): 495-508.
Year 2024 volume 9 Issue 3
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doi: 10.24295/CPSSTPEA.2024.00012
  • Receive Date:2024-03-08
  • Online Date:2025-07-05
  • Published:2024-09-10
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  • Received:2024-03-08
  • Revised:2024-06-03
  • Accepted:2024-06-20
Funding
China National Science Foundation(62073147)
China National Science Foundation(61573159)
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    Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan 430074 China

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Jin zhao.
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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