Comparative analysis of different methods in estimating wind speed distribution, and evaluation of large‐scale wind turbine performance in Rahva‐Bitlis, Turkey

Abstract

In this study, wind characteristics and electricity generation potential from wind energy were investigated in the Bitlis‐Rahva region in eastern Turkey. Ten‐minute wind data from the Bitlis meteorological station were used in the study. The determination of the wind speed (WS) distribution was carried out using the WindPRO program together with the Weibull and Rayleigh distributions. R2, RMSE, and χ2 goodness‐of‐fit tests were used to measure the accuracy of the velocity distribution process. A digital elevation model of the power generation site was created to evaluate wind data and predict turbine performance. Wind power generation performance was evaluated using five different wind turbines (WTs). A power curve model was developed for the WTs to determine the capacity for energy generation. According to the error tests, it has been determined that the results obtained with the WindPRO program can represent the observed wind data most accurately. The results from the WindPRO program analysis showed an average annual WS of 3.26 m/s and a power density of 49.77 W/m2. Increasing height was found to increase WS and power density. The prevailing wind direction was found to be South‐Southwest with a frequency value of 27.5%. The largest capacity factor was obtained from the WT with the largest rotor diameter and rated power. Out of all the WTs examined, the turbine with the largest rated power and rotor diameter produced the greatest amount of energy. According to the capacity factor values calculated for the selected WTs, the region is not considered efficient for wind energy investments. However, as WS and power density increase with increasing altitude, it is thought that different higher parts of the region may be more efficient in terms of wind energy utilization.

Optimal siting and sizing of custom power system and smart parking lot in the active distribution network

“…In this study, backward/forward sweep power flow is used to obtain the mentioned constraints. Branch-current to bus voltage matrix and bus-injection to branch-current matrix are defined as (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) to buses voltage deviation in this section. All of the equations are written on radial distribution system, which is studied in [32]: Acquire the initial optimal plans for each objective Perform TOPSIS approach for all plans through (29)(30)(31)(32)(33)(34)(35) Sort all plans based on mean distance from anti-ideal solution Final ranking of the plans End FIGURE 3 Flowchart of the proposed method.…”

“…In [23], the authors introduce a new strategy for pre-allocation and dispatch of mobile power sources within a power-transportation coupled network, targeting efficient distribution network fault recovery under extreme weather conditions. Optimal siting and sizing of wind turbine (WT) is done by using the WindPRO program to estimate wind speed [24]. In [25], entropy weights TOPSIS was used to analyze the economic, social and ecological impact of the traffic networks.…”