Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.

Recently Vehicular Cloud Computing (VCC) has become an attractive solution that support vehicle's computing and storing service requests. This computing paradigm insures a reduced energy consumption and low traffic congestion. Additionally, VCC has emerged as a promising technology that provides a virtual platform for processing data using vehicles as infrastructures or centralized data servers. However, vehicles are deployed in open environments where they are vulnerable to various types of attacks. Furthermore, traditional cryptographic algorithms failed in insuring security once their keys compromised. In order to insure a secure vehicular platform, we introduce in this paper a new decoy technology DT and user behavior profiling (UBP) as an alternative solution to overcome data security, privacy and trust in vehicular cloud servers using a fog computing architecture. In the case of a malicious behavior, our mechanism shows a high efficiency by delivering decoy files in such a way making the intruder unable to differentiate between the original and decoy file.

With the rapid growth of Internet of Things (IoT) devices around the world, thousands of mobile users share many data with each other daily. IoT communication has been developed in the past few years to ensure direct connection among mobile users. However, wireless vulnerabilities exist that cause security concerns for IoT device-to-device (D2D) communication. This has become a serious debate, especially in smart environments where highly sensitive information is exchanged. In this paper, we study the security requirements in IoT D2D communication. In addition, we propose a novel authentication approach called Secure Key Exchange with QR Code (SeKeQ) to verify user identity by ensuring an automatic key comparison and providing a shared secret key using Diffie-Hellman key agreement with an SHA-256 hash. To evaluate the performance of SeKeQ, we ran a testbed using devices with a WiFi-Direct communication interface. The obtained results depict that our proposal can offer the required security functions including key exchange, data confidentiality, and integrity. In addition, our proposal can reach the same security performances as MANA (Manual Authentication) and UMAC (Universal-Hashing Message Authentication Code) but with 10 times fewer key computations and reduced memory occupancy.

The proposed work presented in this paper is mainly focused on the control of the active and reactive stator powers generated by a wind energy conversion system (WECS) based on the dual feed induction generator (DFIG). This control is achieved by acting on the rotor side converter (RSC) to extract the maximum power from the wind turbine (WT) while regulating the rotor currents. Furthermore, another control objective is achieved by acting on the grid side converter (GSC), in which the DC bus voltage is maintained constant and a unity power factor is ensured. To do that, a new robust control known as active disturbance rejection control (ADRC) has been proposed and applied to the WECS. This control is based on the extended state observer (ESO), which is the main core of this algorithm; it makes the estimation and cancellation of the total effect of various uncertainties (internal and external disturbances) possible in real time. To validate the effectiveness of the proposed approach, the system was modeled and simulated by using the Matlab/Simulink software. Two tests, namely, tracking and robustness tests, were performed to compare the proposed ADRC technique and classical PI controllers. The obtained results are promising and have shown that the proposed control strategy based on ADRC, especially when varying the mode parameters, is performant and very useful.

The satellite, as small as the Cubesat concept, requires employment of limited small boards. In the modular architecture, each single subsystem has a dedicated hardware and software. The approach, which has been taken in this paper, based on two design directives: the integration of the maximum subsystems within the same unit taking into account that single subsystems can be setup without modifying the operation of the remaining subsystems, and the elimination of nonessential elements. In this paper, we will describe in details the design of integrated store-and-forward APRS (automatic packet reporting system) payload and OBDH (on board data handling) subsystems using one chip based on the fixed point DSP (digital signal processor) to design a low-cost nanosatellite. By an optimal software design that takes the maximum advantages from DSP architecture facilities, good real time experimental performances have been obtained for the APRS payload and OBDH implemented modules. Due to the environment in space and the constraints of the nanosatellite, different measures had to be taken when the system was designed.

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have been evaluated and compared to observational data, and also with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of 10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.