Abstract
Abstract-Wind speed variations make the power of wind turbine system to fluctuate, which could increase the thermal stress of the power converter and reduce its lifetime. In order to relieve this problem, short-term energy storage technologies are applied to improve the thermal performance of a 3 MW grid side wind power converter. The cost, weight and cycle life of the energy storage technologies are evaluated based on a typical low speed high turbulence wind profile. In detail, a wind turbine system model is established and its control strategy is illustrated, which is followed by the power control method of the energy storage system. Then the conventional thermal evaluation approach is simplified for evaluation with long term wind profile. The case studies are done to address the optimal power size and capacity of the energy storage system by comparing the improvement of the thermal performance. Also, the two promising candidates, ultracapacitors and batteries, are compared.
INTRODUCTION
Wind Turbine System (WTS) technology is still booming among the renewable energy technologies [1] [2] [3] [4] . The location is from onshore to offshore for smoother and more available wind source. The power rating of WTS is pushed to multi-MW to reduce the cost per kWh of the energy. As a consequence, the reliability of WTS is becoming more and more critical, because failure has large impact on the power generation and the maintenance of the high power offshore WTS is difficult and costs much. As reported in [5] , one of the main areas of failure is the electrical system. Therefore, the reliability of wind power converters should be taken into account in order to ensure a high availability of the power source and also to obtain low maintenance cost.
INTRODUCTION
According to the test data from the power semiconductor manufacturer and the Coffin-Manson model [6] [7] [8] , the cycle life of power modules is dominated by their mean junction temperature together with the temperature cycling. Thus, the reliability of power converters can be improved by thermaloriented control strategies as well as a good understanding of the mission profile combined with robust design. Several approaches have been proposed in order to achieve better thermal performance, in terms of optimizing of the modulation strategies, improving the topologies of the power converter, and reactive power control methods [9] [10] [11] .
INTRODUCTION
Energy storage technologies could solve the problems caused by the power fluctuations, a common barrier for most sustainable energy. Due to the scale of the capacity, energy storage technologies are usually classified into long-term and short-term [12] . Long-term ones, such as pumped hydro and compressed air, are usually used for power shifting, which means to move the additional electricity generation during the bottom of the consumption to the peak of the consumption. Short-term ones, such as batteries, ultracapacitors and flywheels, are mainly applied for power smoothing or frequency supporting of the power grid. In large scale WTS, the rotor with high moment inertia can be considered as a flywheel for energy storage, and it can be used for power smoothing [13] , [14] . However, due to the limited energy storage capability of the rotor inertia, the power fluctuation in the wind power converter, which is smoothed by the rotor inertia, would still be able to induce significant thermal excursions of the power devices. An ultracapacitor is sized based upon the LVRT requirement for a DFIG wind turbine system, and it can also effectively damp short-term power oscillations and achieve superior transient performance. However, not much detail about the economical characteristics of the energy storage system is given; neither the reliability of wind power converters is taken into account [15] .
INTRODUCTION
In this paper, a short-term Energy Storage System (ESS) is applied in a multi-MW WTS to improve the thermal performance by power smoothing. Since the cost is the main barrier for application of ESS in high power and high capacity, the scope of this paper is to evaluate the power size, capacity at a fixed cost of the ESS, which is large enough for improvement of the thermal performance of wind power converters. In section II, a WTS model is established and its control strategy is illustrated. Then, in section III and IV the power control method of the ESS is proposed and the thermal evaluation approach is simplified for evaluation with long term wind profile. Finally, in section V case studies are done to address the optimal power size and capacity of the ESS by comparing the improvement on thermal performance. Moreover, the two promising candidates, ultracapacitors and batteries, are compared, where not only cost, but also weight and cycle life are taken into account. 
II. WIND TURBINE SYSTEM
The structure of the wind turbine system is shown in Fig. 1 . A single stage gearbox is applied to decrease the pole pairs of the Permanent Magnetic Synchronous Generator (PMSG) in order to avoid a large diameter. A conventional two-level backto-back converter is employed for the power conversion from the generator to the grid. The aerodynamic and generator parameters are listed in Table I . The dc bus voltage is 1100 V which is a common choice for WTS considering that ac distribution line-to-line voltage is 690 V. The switching frequency is fixed at 2 kHz for both generator side and grid side converters.
II. WIND TURBINE SYSTEM
The control structure for such a wind turbine system is shown in Fig. 2 . For the generator side converter, an advanced torque mode control method [16] is applied to realize both maximum power point tracking (MPPT) and smoother power variations compared with speed mode control. Meanwhile, the current reference on d-axis I * s,d is set to be zero to avoid damaging magnetic of the generator. For the grid side converter, the control strategy is to stabilize the DC bus voltage V dc by regulating the active current I g,d . The current on the q-axis can be controlled to regulate the reactive power injected to the grid. Since the power fluctuations are also related to the bandwidth of the controller, it should be noted that in MPPT, a first-order low pass filter with 5 s time constant is employed to deal with the measured wind speed in order to suppress the power fluctuations, and compared with it the dynamic performance of the other controllers is much faster. Therefore the impact of the control system on the power fluctuations is dominated by the low pass filter.
III. CONTROL STRATEGY OF ENERGY STORAGE SYSTEM
In order to reduce the thermal excursions of the grid side converter caused by the power variations, the ESS is connected to the DC bus, as seen in Fig. 1 , and a low pass filter based power control strategy for the ESS is employed as illustrated in Fig. 3 [17] . The wind power P w is filtered by a low pass filter with the time constant T in order to get smoother power flow P ' o , then the difference between P w and P o , marked as P ' ESS , is supposed to be the power injected into the ESS. However, considering the State Of Charge (SOC) and power rating of the ESS there should be a limitation for P ' ESS , as shown in Fig. 3 . Then, the output of the limitation link, marked as P ESS , is the power to be injected into ESS, and P o , which is the difference between P w and P ESS , will be injected into grid through the grid side converter. It should be noted that the power fluctuation of the grid side converter would be influenced by not only the low pass filter but also the status of the ESS, in terms of SOC and power rating.
a. Design of the low pass filter
If the cutoff frequency of the low pass filter is defined as f o , the following can be obtained,
a. Design of the low pass filter
where T is the time constant of the low pass filter. The function of the low pass filter is to mitigate or reduce the short-term variation of the wind power, whose frequency f s is typically 0.02~0. 
a. Design of the low pass filter
Practically, the power rating of the ESS is not ideal, so in order to get more detail about its impact on the filtering performance of the ESS and also fix the value of time constant T, three study cases are done based on a typical low speed high turbulence (the ratio between the standard deviation and the mean value of the wind speed) wind profile, as seen in Fig. 4 , and the time constant are set to be 5 s, 20 s, and 100 s, respectively.
a. Design of the low pass filter
The impact of the low pass filter on the power flow with various time constants in time domain is shown in Fig. 5 . It can be seen that the output power of the filter (P ESS is relevant to the design of the power rating of the ESS, more comprehensive comparison is indicated by the distribution of P ' ESS in Fig. 6 . As shown the distribution curve with larger time constant lies to the right side of the one with smaller time constant, which means the larger the time constant the higher the power of the ESS. Moreover, when T is 5 s, 0.14 MW power rating of ESS is needed in order to cover 50% of the requirement of the filter, the values of which are 0.21 MW and 0.29 MW respectively, for T=20 s and T=100 s. Due to the cost limitation of the ESS, the maximum power rating of the ESS is supposed to be 0.23 MW, which will be discussed in section V, thus the time constant will be set to 20 s.
a. Design of the low pass filter
The state of charge is another factor that may have impact on the performance of the ESS. Assuming there is no selfdischarging in ESS and the charging/discharging efficiency equals to 100 %, the SOC can be obtained as follows,
a. Design of the low pass filter
where t 0 is the initial time. Since the wind power varies irregularly, it is quite probable that there is a dc bias in P ESS with a longer period. In that case the ESS will be kept at high into three parts according to different SOC, low SOC (0.3-0.4), high SOC (0.9-1.0) and normal SOC (0.4-0.9). During normal SOC, the charging or discharging power P ESS is limited by the power rating of ESS, which is ±P max . During low SOC, the discharging power is suppressed while the charging power limitation is still P max . During high SOC, the charging power is suppressed, while the maximum discharging power is kept at P max .
IV. THERMAL PERFORMANCE EVALUATION METHOD
The power size and capacity of the ESS are evaluated by measuring the improvement on thermal performance of the grid side power converter. The conventional thermal performance evaluation method is applied [19] , as shown in Fig. 8 . Firstly, the wind speed V w is transferred to electrical power (V G , I G ) by the wind turbine and generator model. Then with the loss model and thermal model the temperature of the power devices are gained.
IV. THERMAL PERFORMANCE EVALUATION METHOD
The power loss is calculated based on a look-up table, where the switching loss is defined as a function of the blocking voltage and conduction current in order to reduce the computational effort [20] . The thermal model is composed of the power loss and thermal impedance between the different parts in the power devices. All of the data for the power loss model and thermal model of the power switch can be found in the product datasheet from the manufacturers. Actually, in this application, the HiPak IGBT Modules 5SNA 2400E170100 from ABB are employed. More details of the thermal model can be found in [11] , [19] .
IV. THERMAL PERFORMANCE EVALUATION METHOD
It should be noted that the thermal behavior of the heatsink is more complicated for modeling, because it depends on the cooling technique, like air cooling and liquid cooling, and also the physical characteristic of the heatsink. In fact, the thermal capacitance of the heatsink is much larger than that of the power device, which means the temperature of the heatsink is much more stable than the power device. Therefore, the temperature of the heatsink is assumed to be constant at 50 ℃ for simplification.
IV. THERMAL PERFORMANCE EVALUATION METHOD
Based on the above method, the thermal performance of the wind power converter with a specific wind speed can be investigated. However, in order to evaluate the thermal performance with a long wind profile, which is necessary for the evaluation of the power size and capacity of the ESS, the evaluation method needs to be simplified due to the simulation time. The power and junction temperature in the grid side converter based on a 10-min wind profile are evaluated with the thermal evaluation method mentioned above, and good correlation between the power and temperature profile is found, as seen in Fig. 9 . It should be noted that, the good linear correlation between P o and T j_IGBT is more or less due to the assumption that the reactive power is zero and the heatsink's temperature is constant. But the assumption can be accepted in the evaluation of the ESS. According to (4), the loss model and thermal model in Fig. 8 used to obtain temperature from power can be simplified to shorten the simulation time. Meanwhile, the correlation between the wind speed and the power of the grid side converter is also investigated, but since the correlation is poor, the wind turbine and the generator model are still needed in the simplified thermal performance evaluation method.
V. EVALUATION OF ENERGY STORGAE SYSTEM
Since the proposed method is to utilize a small ESS with high power and low capacity to smooth the short-term wind power fluctuation, only two promising energy storage technologies Lithium-ion (Li-ion) batteries and ultracapacitors (Ultracap.) are considered. Due to the high complexity of the maintenance, the flywheel energy storage technology is not considered [21] . The physical and economical specifications of the two candidates are listed in Table II. The total cost of ESS is calculated as the following [24] ,
V. EVALUATION OF ENERGY STORGAE SYSTEM
where the UnitCost E is the unit capacity cost of the ESS and the UnitCost power is the unit power cost of the ESS, including the unit power cost of the energy storage device and the bidirectional power converter, which usually costs 700 €/kW. Assuming the cost of the ESS is fixed to be 1/10 of the total cost of the 3 MW WTS, which is typically 3 M€ [25] , the function curve between the power and capacity of the ESS can be obtained based on (5) as shown in Fig. 10 . The Li-ion battery has larger maximum capacity, which is 375 kWh, while the ultracapacitor has larger maximum power rating, which is 0.23 MW. In order to investigate the optimal power and capacity of the ESS and also the better choice between the two energy storage technologies, three cases with different power and capacity of the ESS are studied, as seen in Fig. 10 . Case #2 is chosen to be the cross point of the two curves, where the two candidates have the same power and capacity. In case #1, the power of the two candidates is lower than case #2 and the Liion battery, which has higher capacity, is chosen. In case #3, the power is higher than case #2 and beyond the maximum power of the Li-ion battery, thus only ultracapacitor is chosen. The parameters of the three cases are listed in Table III. Power behavior of the system and the thermal performance of the grid side power converter are shown in Fig. 11 . In case #1, as the power compensation capability of the ESS is low, the power of the grid side converter is almost the same with the case without ESS, thus little improvement on thermal excursions can be found. In case #2 and case #3, the power compensation capability of the ESS is relatively higher, therefore both the power and junction temperature of the grid side converter are smoother than in case #1.
V. EVALUATION OF ENERGY STORGAE SYSTEM
In order to investigate the utilization of the energy storage of the ESS, the state of charge is compared both in time domain and by statistical approach, as shown in Fig. 12 and Fig. 13 . Since the energy of the ESS decreases, its SOC becomes more unstable: the SOC in case #1 is kept in a quite narrow range, the SOC in case #3 is evenly distributed in the whole range, while the SOC in case #2 has a tradeoff performance between case #1 and case #3. It can be concluded that compared with case #2 and case #3, the energy utilization
V. EVALUATION OF ENERGY STORGAE SYSTEM
Number of cycles Number of cycles Number of cycles in case #1 is too low, which means the 270 kWh for the energy of ESS is far too large.
V. EVALUATION OF ENERGY STORGAE SYSTEM
Based on the simplified thermal evaluation method mentioned above, the temperature of the power devices in different cases with the 10-hour wind profile is obtained. Then the thermal cycles are counted by Rainflow algorithm [26] and the distribution of the thermal cycles related to the amplitude (∆T) and the period (t cycle ) can be seen in Fig. 14 . Generally, compared with the thermal performance of the grid side converter in Fig. 15(a) , where no ESS is employed, the thermal cycles are reduced to some degree when the ESS is applied. More details are,  In case #1 (0.05 MW, 270 kWh), where the capacity of the ESS is much larger, the improvement on the distribution of the thermal cycles is not significant.
V. EVALUATION OF ENERGY STORGAE SYSTEM
 In case #2 (0.177 MW, 6.25 kWh), where the power rating of the ESS is much larger than that of case #1, obvious improvement on distribution of thermal cycles can be found. Furthermore, the thermal cycles with amplitude ∆T above 20 ℃ are almost eliminated and the maximum number of thermal cycles is reduced from 30 to 18.
V. EVALUATION OF ENERGY STORGAE SYSTEM
 In case #3 (0.2 MW, 2.6 kWh), where the power rating of the ESS is almost the same with case #2 and the capacity of the ESS is half, the improvement on distribution of the thermal cycles is almost the same with case #2.
V. EVALUATION OF ENERGY STORGAE SYSTEM
It can be concluded that, high power of the ESS is more important than large capacity for improving the thermal performance of the grid side power converter. Moreover, in order to have the same improvement on thermal performance of the power converter, there is no large difference between Liion batteries and ultracapacitors from a cost perspective. But if the weight and cycle life are also taken into account, as shown in Table II and III, the ultracapacitors could be a better choice, because the weight of the ultracapacitor is not large and its cycle life is much longer than Li-ion battery. Another significant feature of the energy storage technologies is the charging/discharging time. For ultracapacitors, the value is 1 s, while for Li-ion batteries the value is 100 s [27] . Since the periods of most thermal cycles are between 1 s and 120 s, the ultracapacitors are better than Li-ion batteries from a dynamic point of view.
I. CONCLUSION
Energy storage technologies are sizing to reduce the power fluctuation in wind power converters and thereby to relieve the thermal stress. It can be concluded that a 200 kW and 2.6 kWh ultracapacitor module can be employed into a 3 MW wind power system for significant improvement on the thermal performance of the grid side converter, where the cost of the ESS is 1/10 of the total cost of the wind power system and the weight is 0.52 t, which is light enough to be put into the nacelle of the 3 MW wind power system. Additionally, the power of the ESS plays more significant role than capacity of the ESS for improving thermal performance of the grid side wind power converter. Therefore ultracapacitors, which also have much longer cycle life than Lithium-ion batteries, are better choice for this purpose. Although the cost of energy storage technologies in high power application is still high so far, there is an opportunity to utilize a small and relatively cheap ultracapacitor to obtain significant improvement of thermal performance of the grid side wind power converter, since the price of energy storage technologies keeps going down.

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