Aalborg Universitet
Advanced design tools for the reliability of power electronics -- case studies on a
photovoltaic (PV) system
Yang, Yongheng; Sularea, Vasile-Simion; Ma, Ke; Blaabjerg, Frede
Published in:
Proceedings of the 41th Annual Conference of IEEE Industrial Electronics Society, IECON 2015
DOI (link to publication from Publisher):
10.1109/IECON.2015.7392531
Publication date:
2015
Document Version
Accepted manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Yang, Y., Sularea, V-S., Ma, K., & Blaabjerg, F. (2015). Advanced design tools for the reliability of power
electronics -- case studies on a photovoltaic (PV) system. In Proceedings of the 41th Annual Conference of
IEEE Industrial Electronics Society, IECON 2015. (pp. 002828 - 002833). IEEE Press. DOI:
10.1109/IECON.2015.7392531
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Advanced Design Tools for the Reliability of
Power Electronics
– Case Studies on a Photovoltaic (PV) System
Yongheng Yang, Member, IEEE, Vasile-Simion Sularea, Ke Ma, Member, IEEE,
and Frede Blaabjerg, Fellow, IEEE
Center of Reliable Power Electronics (CORPE)
Department of Energy Technology, Aalborg University
Pontoppidanstraede 101, Aalborg 9220, Denmark
yoy@et.aau.dk, vsular14@student.aau.dk, kema@et.aau.dk, fbl@et.aau.dk
Abstract – In many important energy conversion systems, the
power electronic converters are proven to have high failure rates.
At the same time, the failures of the power electronics systems
are becoming more and more unacceptable because of the high
cost of failures and booming power capacity. As a consequence,
an appropriate assessment of reliability performance for the
power electronics is a crucial and emerging need, because it is the
essential information for the design improvements as well as for
the extension of converter lifetime, and reduction of the cost-ofenergy. Unfortunately, there is still lack of suitable and costeffective tools for the reliability assessment in power electronics.
In this paper, an advanced design tool structure, which can
acquire various reliability metrics of the power converters, is
proposed. The proposed reliability design tool is based on the
failure mechanisms in the critical components of the power
electronics system, and the mission profiles in the converter
applications are also taken into account. Finally, the potential
methodologies, challenges and technology trends involved in this
tool structure are also discussed.
Keywords – Power electronics, reliability, design for reliability,
thermal analysis, mission profile, photovoltaic applications
I.
INTRODUCTION
The fast growth in the total installation and individual
capacity makes the failures of the power electronics converters
more critical, and they are also costly to repair because more
and more power electronics are located in remote areas, which
are hard to access [1]-[5]. In many important applications such
as renewable energies, motor drives, power transmission,
electric vehicles, etc., power electronics have particularly
tough operating conditions: they have to withstand a large
amount of power (even up to a few megawatts) with frequent
fluctuations, perform a series of complicated functions, and be
exposed to harsh environment like temperature swings, dust,
vibration, humidity, etc. [1]-[5]. It has been found that in
many of these applications the power electronics tend to be
fragile and have become the “bottleneck” of the whole system,
in respect to the reliability [6]-[18]. This problem will
significantly increase the cost of energy conversion not only
due to the increased maintenances or repairs, but also due to
the reduced energy delivered to the customers.
Specified
lifetime
Mission
Specifications
Design
& Development
Production
Market
×
Unexpected
Failures
High cost !
$$$
(a)
Specified
lifetime
Mission
Specifications
Design
& Development
Production
×
Expected
Failures
+
Reliability
Specifications
Market
+
-
$
Much lower cost !
Reliability
Evaluation Tools
(b)
Fig. 1. Reliability improvement approaches for power electronics products:
(a) typical flow in the past and (b) new flow in the future.
Unfortunately, the reliability improving approach for the
power electronics is still expensive and time consuming. As
shown in Fig. 1(a), a typical flow for improving the reliability
of power converters is indicated from the point view of the
whole life cycle of products. Due to the lack of reliability
assessment in the design as well as the development phases,
the design flaws/weakness has to be identified based on the
failure information or statistics of the field products that have
been massively produced and pushed into the market. It can be
seen that in this approach the design feedbacks/iterations in
order to improve the reliability are quite slow and expensive.
As demonstrated in Fig. 1(b), by introducing the reliability
assessment tool in the design and development phases, a more
promising approach for improving reliability is enabled: the
design flaws/weakness can be quickly identified and corrected
before the projects are put into production and/or market.
Moreover, some reliability targets are able to be integrated
into the specifications of the products at the beginning of the
design, contributing to significant cost reduction and shorter
development cycle for the reliability improvement.
In this paper, an advanced design tool (Design For
Reliability - DFR), which can acquire the reliability metrics of
power converter, is thus proposed. It is based on the failure
mechanisms in the critical components of the entire power
electronics, and the mission profiles for the whole converter
system are also taken into account. The tools are demonstrated
on a single-phase transformerless PV power converter. Some
important metrics for the converter design can be quickly
identified and evaluated according to the proposed design tool.
It is concluded that with the proposed design tool structure,
more detailed information related to reliability performances
in power converters can be obtained before the converters
actually fail in the real-field, and many potential research
topics can be also enabled.
II.
Identification
x Critical components
x Failure mehanisms
x Major stress & strength
Stress Analysis
Strength Modeling
x Mission profile translation
x Multi-physics stress
x Multi-time scales stress
x Component-based
x Accelerated/Limit test
x Degradation model
Reliability Mapping
x Stress organization
x Variation & statistics
x Multi-components system
GENERAL S TRUCTURE AND F LOW TO ACCESS
RELIABILITY P ERFORMANCES
The reliability research in power electronics has been
carried out for decades. As the state-of-the-art trend, the
reliability engineering in power electronics is now moving
from a solely statistical approach that has been proven to be
unsatisfactory in the automotive industry, to a more physicsbased approach which involves not only the statistics but also
the analysis of root causes behind the failures [10]-[15]. In this
physics-of-failure based approach, the correct mapping of
loading profile which can trigger the failures of components
(the stress analysis), as well as the strength modeling which
reflects how much loading the components can withstand (the
strength tests), are two of the most important activities for
reliability assessment.
A promising tool structure is thereby depicted in Fig. 2,
which can be generally categorized as four groups of activities
or approaches. In this structure, the critical components as
well as the major failure mechanisms in the power converter
system are first identified. Then, based on the interested
failure mechanisms in the critical devices/components, the
corresponding stresses and the ability of components to
withstand the stresses (also referred as strength) are tested and
modeled separately. Finally, a series of algorithms and
statistical distributions are introduced to map the stress and
strength information of the components to the reliability
metrics of the entire power converter. The reliability metrics
may include either direct reliability performances like lifetime,
robustness, probability of failures changing with time, etc., or
indirect reliability-related performances like the maximum
stress level, stress margin, optimal component rating, etc.
Reliability Metrics
Indirect
Fig. 2. Proposed Design For Reliability (DFR) tool structure and research
activities for evaluating and designing the reliability metrics of power
electronics.
A. Basic Efficiency and Reliability Analysis
As it is shown in Fig. 2, the DFR tool enables a basic
analysis of the power converter candidates in terms of
efficiency and reliability. It can directly translate the mission
profile specified by the users into power losses and thermal
loading on the power electronics devices/components (e.g.,
IGBTs and MOSFETs). Taking a single-phase transformerless
PV inverter shown in Fig. 3 as an example, real-field mission
Full-Bridge
PV Strings/Modules
iPV
Mission
Profile
S1
D 1 S3
D3
A
CPV
C
D 2 S4
Grid
vg
Cf
B
S2
LCL- Filter
L2
L1
D4
O
CP
Ground
Fig. 3. A single-phase grid-connected transformerless PV system with an
LCL filter.
TABLE I.
PARAMETERS OF THE SINGLE-PHASE PV SYSTEM.
Parameter
Grid voltage amplitude
Grid frequency
DC-link capacitor
LCL filter
III. D EMONSTRATIONS OF THE DFR T OOL ON A S INGLE P HASE T RANSFORMERLESS PV CONVERTER
Direct
x Bx lifetime
x Robustness
x Reliability/unreliability
x Thermal loading
x Voltage/current stress
x Stress margin
Switching frequency
Symbol
vgn
Ȧ0
Cpv
L1
Cf
L2
fsw
Value
325 V
2ʌî50 rad/s
2200 ȝF
3.6 mH
4.7 ȝF
4 mH
10 kHz
profiles can be easily translated into the corresponding power
losses and thermal loading on the power switching devices.
The system parameters are listed in Table I. Fig. 4 shows two
considered real-field mission profiles of different PV sites
(Aalborg-Denmark and Arizona-USA). In accordance to the
DFR tool shown in Fig. 2, the mission profiles are translated,
and the resultant loading profiles as well as the efficiency
curves are presented in Fig. 5. When applying the mission
profile to different transformerless PV inverters, the thermal
10
0
-10
-20
Solar irradiance (kW/m2)
Ambient temp. (°C)
20
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
1
0.5
0
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
40
20
0
5 î105
Solar irradiance (kW/m2)
Ambient temp. (°C)
30
5 î105
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
5 î105
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
5 î105
3
2
1
0
Junction temp. (°C)
100
Conversion efficiency (%)
(a)
(b)
Fig. 4. Two considered mission profiles from different locations (top: ambient temperature; bottom: solar irradiance): (a) Aalborg and (b) Arizona.
IGBT junction temperature
50
0
Diode junction temperature
-50
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
5 î105
150
Conversion efficiency (%)
Junction temp. (°C)
200
IGBT junction temperature
Diode junction temperature
100
50
0
0
0.5
1
1.5
2
2.5
3
3.5
Time of a year (mins)
4
4.5
5 î105
100
80
60
40
20
0
0
1000
2000
3000
4000
5000
Time of 5 days (mins)
6000
7000
0
1000
2000
3000
4000
5000
Time of 5 days (mins)
6000
7000
100
80
60
40
20
0
(b)
(a)
Fig. 5. Thermal loading profile and the conversion efficiency of 5 days translated from the mission profiles shown in Fig. 4 (top: Aalborg, bottom: Arizona):
(a) device thermal loading and (b) instantaneous conversion efficiency in the case of a five-day operation.
system conversion efficiency is higher in Arizona, leading to
more energy production through the year.
loading and/or power loss profiles can readily be obtained
using the DFR tool. Thus, a basic comparison between those
candidates can be done in terms of efficiency and reliability,
as exemplified in [1], [4].
B. Reliability Assessment using the DFR Tool
It can be observed in Fig. 5(a) that the PV inverter located
in Arizona experienced a relatively “flat” junction temperature
loading on the power devices compared to that of the PV
system in Aalborg, which thus contributes to less temperature
cycles. However, the mean junction temperature of the power
devices in Arizona is higher than that of the PV inverter
installed in Aalborg, which may accelerate the inverter
degradation. In addition, using the DFR tool, the instantaneous
conversion efficiency under different mission profiles can also
be obtained as shown in Fig. 5, where it can be seen that the
The translated power losses and the thermal loading of the
power electronics devices (e.g., Fig. 5(a)) can be used to
estimate the total energy yield through the period of this
mission profile, and also the reliability of the power devices,
respectively. However, the thermal loading profiles have to be
properly interpreted by means of a counting algorithm (e.g.,
rain-flow counting) [10], [17], [18] or Monte-Carlo analysis
[19], [20], which are able to extract necessary information
from the thermal loading profile according to certain lifetime
models, e.g., the Coffin-Mason model [5], [9]. According to
Fig. 5(a), the rain-flow counting of the thermal loading for the
Number of cycles
Number of cycles
40
20
0
13
Tj
m
400
200
0
26
10
16
( oC
)
19
6
Tj
8 o )
C
2(
ǻT j/
m
13
32
( oC
)
5
38
9 o )
C
2(
ǻT j/
(a)
(b)
Fig. 6. Rain-flow counting results of the thermal loading shown in Fig. 5(a): (a) Aalborg and (b) Arizona.
50
45
5
4
3
Damage – chip
Damage – bond
Damage – base
2
1
0
Accumulated damage (%)
Accumulated damage (%)
6
Damage – chip
Damage – bond
Damage – base
40
35
30
25
20
15
10
5
0
1
2
3
Time (mins)
4
5 î105
0
0
1
2
3
4
Time (mins)
5
6î105
(b)
(a)
Fig. 7. Accumulated chip, bond (wire), and base damage of the power devices of the PV inverter: (a) in Aalborg and (b) in Arizona.
power devices in the PV inverter has been done, and the
results are shown in Fig. 6. It can be identified that the thermal
loading of the PV inverter in Arizona has a larger number of
high mean junction temperatures (i.e., Tjm) and a smaller
number of junction temperature variations (i.e., ǻTj/2). This is
in a close agreement with the analysis presented in § III.A.
However, the higher mean junction temperature may pose a
bigger challenge to the lifetime of the power devices. In other
words, the PV inverter in Arizona may have a shorter lifetime,
if operating under the mission profile shown in Fig. 4(b). By
applying the obtained counting results to suitable lifetime
models, a quantitative lifetime prediction can be achieved. In
the proposed design tool, different lifetime (damage) models
can be integrated. To demonstrate, in this paper, the LESIT
model [21], [22] has been adopted and applied to the thermal
loading profile (c.f., Fig. 6), and thus the reliability can be
predicted in terms of damage. The lifetime model is given as,
Ea
(1)
)
N f A ˜ 'T j D ˜ exp(
kb ˜ Tjm
where Nf is the number of cycles to fail, and the parameters
are explained in Table II.
TABLE II.
PARAMETERS OF THE LIFETIME MODEL USED IN THIS PAPER.
Parameter
Curve fitting factor
Curve fitting factor
Activation energy
Boltzmann constant
Symbol
A
Į
Ea
kb
Value
3.025î105 K-Į
-5.039
9.891î10-20 J
1.381î10-23 J/K
The accumulative damage of the power devices of the PV
inverter in the two cases is shown in Fig. 7, and accordingly
the lifetime can be estimated as,
LF
t mp
D
t mp
¦ N fi
(2)
in which LF is the predicted lifetime, tmp is the mission profile
duration, D is the accumulated damage, and Nfi is the number
of cycles to fail at the ith stress of Tjm and ǻTj according to (1).
It can be seen from Fig. 7 that the damage of the power
electronics devices/components of the PV inverter in Arizona
accumulates at a faster rate compared to that of the PV
inverter in Aalborg. Consequently, the PV inverter installed in
Arizona will go into the end of life much faster as well,
according to (2). This is also in a close agreement with the
above discussion. The results shown in Fig. 7 indicate that, the
power devices of higher reliability have to be chosen in the
design and planning phases for the PV inverters in Arizona,
leading to higher cost and more investments, although a higher
total annual energy yield is possible to achieve (Fig. 5(b)). Fig.
8 further summarizes the reliability prediction procedure in
details, which is integrated in the design tool – DFR tool.
C. LCOE Analysis with the DFR Tool
As both the reliability/lifetime (i.e., the downtime during
operation) and the energy yield are the key indicators of the
Levelized Cost Of Energy (LCOE) [7], [8], [23], [24], the
DFR tool also enables an access to the LCOE of the power
converter candidates using the obtained energy yield as well
reached, the maintenance of the PV inverter will be performed,
imposing the corresponding maintenance cost. Thus, the
present total maintenance cost of the PV inverter, Mc(Pr), is
calculated by reducing the (future) expenses occurring at the
end of the power devices lifetime for repairing the PV inverter
to the corresponding present value, as follows:
n
(1+ g) j
(6)
M c Pr = ¦ LFj Pr ˜ Rc ˜ Pr ˜
(1+ d) j
j=1
in which n is the PV inverter system operational lifetime (e.g.,
30 years), Rc (€/kW) is the present value of the PV inverter
repair cost per kW of the power rating, g (%) is the annual
inflation rate, d (%) is the annual discount rate, and LFj(Â) is
defined as:
C
Mission Profile
Mission Profile
Translation System Models
Counting Algorithms:
ƒ Level crossing
ƒ Rain-flow
ƒ Simple range counting
ƒ ...
Thermal Loading Loading Profile
Intepretation
Counting
Lifetime Prediction
Lifetime
Models
Extracted Thermal Info.:
ƒ Tjm – mean junction temp.
ƒ ǻTj – temp. cycle amplitude
ƒ ton – cycle period
ƒ ...
LFj Pn Fig. 8. Flow-chart of the mission profile based reliability analysis approach
for PV inverters, which has been integrated in the proposed design tool.
as the lifetime data. As a consequence, means to reduce the
LCOE can be initiated, e.g., using highly efficient power
converters and/or applying advanced control strategies to
lower the thermal loading. In addition, the resultant LCOE
from the DFR tool is also of significant usefulness for
renewable energy system planning in the consideration of the
mission profiles. Here, the LCOE calculation procedure based
on the DFR results is demonstrated. As known, the PV
inverter LCOE (€/Wh) is a function of the inverter power
rating (denoted as Pr), and it can be expressed as [7], [8],
LCOE Pr Cinv Pr E y Pr (3)
where Cinv(Â) (€) is the present total cost of the PV inverter
during its lifetime and Ey(Â) (Wh) is the total energy injected
into the grid by the PV inverter during its lifetime. Since the
PV inverter is required to operate in the Maximum Power
Point Tracking (MPPT) mode [25], [26], it holds that Pr = Pn,
with Pn being the inverter nominal power.
The present total cost of the PV inverter depends on the
corresponding manufacturing and maintenance costs [8]:
(4)
Cinv Pr Cm Pr M c Pr in which Cm(Pr) (€) is the PV inverter manufacturing cost and
Mc(Pr) (€) is the present total maintenance cost of the PV
inverter during its lifetime. The PV inverter manufacturing
cost is proportional to the inverter power rating:
(5)
Cm Pr cm ˜ Pr C0
where cm is the proportionality factor (€/kW) and C0 is the
initial cost. C0 can be considered as zero since it is much
lower than the total cost of the PV inverter.
Therefore, in the MPPT operation mode, the PV inverter
cost is proportional to Pn. The total maintenance cost, Mc,
depends on the PV inverter reliability features, which in turn
depend on the power rating of the PV inverter. Based on the
DFR tool, the lifetime (in years) of the PV inverter power
devices is initially calculated. It is assumed that each time
when the end-of-life of the PV inverter power devices is
­1 if lifetime expires at the j
®
¯ 0 else
th
operation year
(7)
with 1 ” j ” n. Notably, the repair cost Rc in (4) consists of
both the purchase cost of the failed power devices, as well as
the potential labor and transportation costs for repairing the
PV inverter. Therefore, the LCOE can be calculated using the
DFR tool.
The LCOE calculation is demonstrated on a 3-kW singlephase PV inverter system installed in Aalborg (c.f., Fig. 3)
under the mission profile shown in Fig. 4(a). Accordingly, the
nominal power Pn = 3 kW, the following parameters are
chosen for the case study: n = 30 years, cm = 200 €/kW, Rc =
200 €/kW, g = 2 %, and d = 5 %. Based on the design tool –
DFR tool shown in Fig. 2 and the detailed lifetime prediction
approach illustrated in Fig. 8, the thermal loading, the power
losses, as well as the annual energy yield can be calculated
through the year. The calculated lifetime is around 38 years,
which is higher than the designed lifetime (i.e., 30 years), thus
guaranteeing no failures of the power devices will occur
during that period, leading to no maintenance cost during
operation. According to (4) and (5), the total cost of the PV
inverter is only the inerter construction cost: Cinv(Pr) = Cm(Pr)
= cmÂPr = 200 €/kW î 3 kW = 600 €, and thus the LCOE can
be approximated as 0.17 €/kWh.
It should be pointed out that, in this demonstration, the
following assumptions are made for simplicity:
1) In the reliability (lifetime) calculation, only the thermal
cycles induced by the mission profile are considered,
where the grid fundamental-frequency thermal loading
cycles are ignored;
2) The energy production is calculated without considering
the power losses on the passive components;
3) The LCOE presented above is only for the PV inverters,
while the PV panel cost also accounts for a major share of
the total cost of the entire PV system, also including
circuit boards and capacitors, etc.
Nevertheless, it is still possible to use the proposed DFR tool
for the LCOE analysis as long as more detailed thermal,
electrical, and loss models are incorporated in the tool.
D. Expanded Use of the DFR Tool
Beyond the above analysis enabled the DFR tool and with
the development of advanced monitoring technologies, it is
possible to acquire mission profiles with different sampling
rate, e.g., from micro-seconds to minutes, and thus the DFR
tool can extensively be used. However, for the electrical
systems and the junction temperature of the switching devices,
it may take up to seconds to come into steady-state. Moreover,
the cycle period, ton, is taken into account in some lifetime
models [9], [10]. Therefore, the available mission profile with
a certain resolution may have some impacts on the reliability
analysis. It is also of interest to investigate what the
appropriate resolution is for lifetime estimation in power
converter applications. The introduced DFR tool can also be
employed to analyze how the resolution of the mission
profiles will affect the lifetime prediction.
Furthermore, the power electronics as well as the power
converter technologies have been experiencing a very fast
growth [27], [28]. In the future, more power electronics
devices will come into the market, and this will also advance
the power converters technology and its applications, e.g., in
the renewable energy systems. When such advanced power
electronics systems come into reality, the DFR tool can also
be adopted for reliability-oriented analysis and design, where
minor changes may be required. For instance, the database
including the thermal data of the power devices and the
topology data should be updated accordingly.
IV. CONCLUSIONS
In this paper, an advanced design tool for reliability of the
power electronics converters has been presented. The
proposed Design For Reliability (DFR) tool is based on the
physics-of-failure mechanisms, and it takes the costumerspecified mission profiles as the input. As the outcomes of the
DFR tool, the thermal loading, the power losses, the efficiency
of the critical components and also the power converters can
be obtained through the DFR tool. The application of the DFR
tool is demonstrated on a single-phase transformerless PV
inverter in this paper when considering two real-field mission
profiles from different PV locations, where the efficiency, the
thermal loading, and thus the lifetime (in terms of
accumulated damage) of the power devices are analyzed. A
basic comparison has also been done. In addition, the DFR
tool also enables the LCOE analysis, which is also
exemplified on the PV inverter system in this paper. Further
applications of the proposed DFR tool are highlighted as well,
which can be future research perspectives.
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