In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

In this paper, an approach for robust sensor fault reconstruction of wind turbine systems in the presence of simultaneous uncertainty and disturbance is proposed. For this purpose, an adaptive sliding mode observer is designed such that the fault is reconstructed through an online adaptive law. The significance of the proposed approach in addition to its robustness against the bounded disturbances and uncertainties is that it does not require the fault and uncertainty bounds to be known a priori. An efficient algorithm is presented to adjust the design parameters based on the Linear Matrix Inequality (LMI) concept. The proposed approach is applied to a 5MWs wind turbine system and simulation results demonstrate the accuracy and desirable performance of the approach.

Introduction Wind is known as an intermittent event because of its rapid change in direction and value. Various effects of storm on civil aviation, besides of its danger to the urban, industrial and agricultural areas, make it very important to forecast wind in appreciate lead time. Direct effect of wind on many industries, specially its role in energy generation and increasing share of wind energy in the market, made it very important. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Material and methods Different kind of observation systems including in-situ devices and remote sensing devices are useful to measure wind, and different methods are useful to detect and estimate probability of extreme events as well as forecast the wind speed. Different methods for detection and forecasting of wind have been invented and several works were done for comparing and improving them. In-situ measuring devices include, cup anemometer, ultrasonic anemometer and hotwire anemometer, while remote sensing measuring devices include, SODAR, LiDAR and radar. SODAR, LiDAR and radar operate in a similar manner except that they use different kind of pulses for transition. Generally, both the intensity and the Doppler frequency shift of the return signal are analyzed to determine wind speed, wind direction and turbulence. In spite of in-situ measuring instruments which measure the wind at a single point, remote sensing devices measure the wind in several points or a limited area. Each measuring device has its advantage and limitations witch has been listed in the paper. Wind farm deployment is moving from flat to complex terrains because of the availability of stronger winds there. The cost of site assessment through local sensing techniques is also growing due to the increasing height of meteorological masts. The maintenance required after installing the setup makes this approach even more expensive. On the other hand, remote sensing technologies are cheaper solutions, but their accuracy in complex terrains is still questionable. Turbulence also needs to be considered when measuring the wind. Turbulence is caused by (i) friction with the earth’ s surface, that is flow disturbances caused by the topographical features and (ii) thermal effects that can cause air masses to move vertically as a result of variations in temperature. Turbulent flow is chaotic with a variable pattern over a short time frame but it has a relatively constant average over longer time periods. Wind turbulence is the rapid disturbances or irregularities in the wind speed, direction, and vertical component. The most common indicator of turbulence is the standard deviation (σ ) of wind speed. σ normalized with the average wind speed gives the Turbulence Intensity (TI) of a site. Results and discussion Various methods classified according to time-scales or methodology, are available for wind forecasting. According to the time-scales, wind forecasting methods can be divided into 4 categories. (i) ultra-short-term forecasting: from few minutes to 1 hour ahead, (ii) Short-term forecasting: from 1 hour to several hours ahead, (iii) medium-term forecasting: from several hours to 1 week ahead and (iv)long-term forecasting: from 1 week to 1 year or more ahead. Each category has its own application in industry. The rapid increase in numbers of connectable devices, the expansion of networks, the implementation of new networks, and the requirement for field workers to be completely mobile but always connected (with laptops, smart tablets, smart phones), makes even more imperative the implementation of some form of Unified Communications. Otherwise it takes too long to adapt to changes. Under this paradigm the communications medium from the central server to a remote station, and around the remote station may still be varied (fibre, cable, cellular, satellite, ADSL, Radio, Microwave, WiFi, Ethernet etc. ). However the interconnection method between the different medium link modules is all the same-Ethernet, with Power over Ethernet (PoE) where practicable. New frameworks in observation systems like IOT (Internet Of Things), make a revolution in measuring methods along with data transfer. In IOT, all of the data sources (sensors), end user devices (displays, databases), and even a data source and sink (an actuator, smart phone) are connected to the Internet and have two ways communication. Conclusion This paper review the wind measuring devices along with the new frameworks of measuring methods like IOT and then presents a comparison between different wind forecasting methods. Spatial correlation method has been depicted by use of measured data of two ultrasonic wind sensors of IKIA (Imam Khomeini International Airport) in March 31st 2015. Results show strong dependencies of the observed data of two sites, and wind speed and direction in second site, follow the first site with a delay. Comparison between wind measurement by radiosonde and VVP and CAPPI products of S-band weather radar in Ahwaz shows good consistency at higher at elevation.

The upcoming study investigates the design and use of vertical-axis wind turbines for power extraction in Chahnimeha, Zabol. In Sistan and Baluchestan provinces, due to the vastness and climatic barriers, the use of renewable energy can greatly contribute to the well-being of people. Using the meteorological data of this province, the average wind speed in the Chahnime region is estimated at 6.4 m/s. At first, 4 airfoils with the highest lift-to-drag coefficient have been selected and studied for wind turbine design. By choosing the best airfoil among the four examined ones, a wind turbine with 3 different blade sizes and rotor radius was designed. The wind turbine, which is designed with a blade length of 3 meters and a rotor radius of 1.5 m, has the best performance. The vertical axis wind turbine has been investigated in 4 models with 3, 5, 7, and 9 blades. The power factor of the 3-bladed turbine is equal to 0.30, and of the 7-bladed wind turbine is equal to 0.45. Among the examined wind turbines, the best wind turbine with 7 blades was chosen. The reduction of wind speed before the blades is influenced by the solidity of the wind turbine. The study of wind turbine exergy was used to investigate the environmental effects such as humidity and temperature on the performance of wind turbines in the climatic region of Zabol. The exergy efficiency of the designed 3-blade and 7-blade wind turbine is equal to 45 and 75%, which shows the effect of temperature and relative humidity on the wind turbine efficiency in a climate region. The results of this study clearly show that it is possible to use a 7-blade vertical axis wind turbine to provide electricity to areas far from the grid and to produce scattered.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

In this study, weather conditions such as air humidity, temperature air, and wind speed were investigated in relation to wind turbine efficiency with the approach of an exergy study. In this study, the wind speed has been investigated in two different climatic regions of Iran with an approximate distance of 1200 km, in the names of Ardabil and Marvast. The amount of wind density of Ardabil is equal to 66 (kW/m2) and Marvast is equal to 123 (kW/m2). Power production using a 10 (Kw) wind turbine in the Ardabil region is 2.3 (MWh) and in the Marvast region is 3.2 (MWh) per year. The highest wind turbine exergy efficiency is 0.48 in the Ardabil region, and the highest exergy efficiency in the Marvast region is 0.18. The amount of reduction of CO2 gas production, using wind turbines in comparison to gas and diesel power plants in Ardabil, are 1.1 and 2.1 tons and in Marvast are 1.5 and 2.9 tons per year. This reduction in CO2 greenhouse gas per year is equal to using a forest region of 1000 (m2) to 3000 (m2). The use of wind turbines reduces the fuel consumption of diesel power plants in the Ardabil region for the amount of 797.4 liters and in the Marvast region for the amount of 1244 liters of diesel per year. According to this review, it can be concluded that in addition to wind speed, air humidity plays a significant role in the selection, installation, and commissioning of wind turbines in the region. According to this survey, it can be seen that in the Ardabil region, the wind speed of the wind turbine has a higher exergy efficiency than in the Marvast region, and it can be concluded that the wind turbine has performed better in the Ardabil region.