## 1 Introduction  
With the increased utilization of low wind speed, blade lengths are becoming increasingly longer, so as to capture more energy at low wind speed. By contrast, in order to control costs, the other parts of the wind turbine have largely remained the same. In fact, this treatment implies that the extreme loads, especially the extreme loads at the blade root are constant or increase within the accepted margin of safety during blade design. Therefore, the extreme load at the blade root is one of the most important constraints when designing of low wind speed blades and blade bolts, even though the blade can reliably endure the loads acting on itself. Of course, the extreme load distribution is also critical factor in the evaluation of the safety of blade. However, in this paper we focus on the extreme load prediction at the blade root because it can be easily extended to get the extreme load distribution. As far as we know, calculating the extreme loads according to the IEC [1] or GL [2] standard is extremely time consuming. Over the last decade, most researchers extrapolated the extreme loads by means of statistical methods which suppose that the distribution of extreme loads at different wind speed meet some statistical models [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Generally, it is reasonable to estimate the extreme loadsthat occur or a long duration based on the measured or simulated data gathered over a short period. However, these methods are not without their drawbacks. These issues include, which distribution method should be used for the estimation of the short-term extreme load distribution? What are the criteria for the selection of the maximum loads used in the extreme load distribution? And how many time series, simulated or measured, are needed for the proper estimated of the extreme load distribution? [4] Naturally, more data gives better results, however by increasing the number of simulated load cases, the time taken to conduct these simulations is drastically increased. During the design of the blade, the discrete engineering method, has been widely adopted to obtain accurate results. This method gives the extreme loads by simulating every load case according to the IEC or GL standard. Most software programs, such as PHATAS in FOCUS [16] and Bladed [17], all employ this method. This method is more accurate than statistical methods. But the process of this method is also time-consuming and unsuitable for the preliminary design due to excessive load cases required to be simulated during the design life of the wind turbine. For simplicity, many research articles about the design methods regarding blade optimization design select parts of the design load cases (DLCs) to get the extreme load and finally run all the DLCs to verify the results to reduce the overall computational time [18; 19; 20; 21; 22]. Even though this process has been used to save time, it takes about 65 h running on a workstation equipped with 40 logical processors [20]. Therefore, this process still represents low efficiency, and it is therefore necessary to propose a new method that can quickly predict the extreme loads for the preliminary design of large-scale wind turbine blades. In this paper, a simplified method is proposed to quickly predict the extreme loads in power production to facilitate the preliminary design of the blades. The optimal algorithm is used to acquire the extreme loads. Because the blade loads are affected by a number of factors, the solution of predicting extreme loads is a combination of multi-variable, multi-constraint, and multi-object optimal problem. An advanced intelligent optimal algorithm should be found to solve this kind of constraint optimal problem. Compared with the genetic algorithm (GA), the PSO algorithm has its own advantages, such as a simpler and less adjusting parameters. Hence, a simplified extreme load prediction model (PSO-ELPM) by use of an improved PSO algorithm [5] is built and certified by comparing with the results from FOCUS using the discrete method.