Many coherent cooperative diversity techniques for wireless relay networks have recently been suggested to improve the overall system performance in terms of the achievable data rate or bit error rate (BER) with low decoding complexity and delay. However, these techniques require channel state information (CSI) at the transmitter side, at the receiver side, or at both sides. Therefore, due to the overhead associated with estimating CSI, distributed differential space-time coding techniques have been suggested to overcome this overhead by detecting the information symbols without requiring any (CSI) at any transmitting or receiving antenna. However, the latter techniques suffer from low performance in terms of BER as well as high latency and decoding complexity. In this paper, a distributed differential beamforming technique with power allocation is proposed to overcome all drawbacks associated with the later techniques without needing CSI at any antenna and to be used for cooperative communication networks. We prove through our analytical and simulation results that the proposed technique outperforms the state-of-the-art techniques in terms of BER with comparably low decoding complexity and latency.
#2Mohd Helmi Suid(UTeM: Universiti Teknikal Malaysia Melaka)H-Index: 4
Last. Mohd Ashraf Ahmad(UTeM: Universiti Teknikal Malaysia Melaka)H-Index: 1
view all 3 authors...
The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification empl...
The effects of global climate change are beginning to exhibit notable impact across the world and within the next 20 years are predicted to worsen and have the potential to become irreversible. It is crucial that sustainable solutions are created and implemented before it is too late. A large part of the solution is the increased use of vehicles powered with renewably sourced electricity rather than fossil fuels. While this technology change is environmentally justified, its feasibility from a l...
This paper presents a boost type Vienna Rectifier with an Elman back propagation neural network algorithm for maximum power point tracking (MPPT) from the wind energy system. The preferred control algorithm deals with non-linear problems with improved conversance precision and reduced learning time. In this system, boost type Vienna Rectifier is employed as a machine side converter for single stage energy conversion of AC to DC with the enhanced output voltage and a grid side converter worked fo...
Last. Zhenyu Chen(NCEPU: North China Electric Power University)H-Index: 2
view all 4 authors...
Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount of data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operating condition of the turbine. In this paper, the idea of deep learning is introduced into wind turbine condition monitoring. After se...
The wind energy sources as an alternative to conventional sources to generate power has been increasingly popular due to abundant in nature and pollution free. The wind energy sources are undepletable, however, they are not able to generate as much electricity due to their intermittent nature. Therefore, a wind energy conversion system (WECS) with accurate maximum power point tracking (MPPT) inverter controller is essential in order to improve the wind energy capturing capability. This paper aim...
The dynamic behavior of modern multi-Megawatt wind turbines has become an important design consideration. The power generated by wind turbine depends mainly on the interaction between the blades and the flowing wind. Therefore, the major aspects related to the operational reliability of a wind turbine are the aerodynamics characteristics and integrity of the blades. This paper demonstrates the results of the application of wavelet transform combined with experimental modal analysis as a tool for...
Last. Jiakang Song(HEU: Harbin Engineering University)H-Index: 1
view all 4 authors...
Abstract Wind energy is the intermittent energy and its output has great volatility. How to accurately predict wind power output is a problem that many researchers have been paying attention to and urgently need to solve. In this paper, a deep belief network (DBN) model is developed for wind power forecasting. The numerical weather prediction (NWP) data was selected as the input of the proposed model and the data directly affects the prediction precision. The NWP data and wind data in the wind f...
#1Xiong Luo(USTB: University of Science and Technology Beijing)H-Index: 23
#2Jiankun Sun(USTB: University of Science and Technology Beijing)H-Index: 3
Last. Zijun Zhang(CityU: City University of Hong Kong)H-Index: 25
view all 8 authors...
Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combina...
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coe...
Accurate wind speed forecasting is a fundamental requirement for large-scale integration of wind power generation. However, the intermittent and stochastic nature of wind speed makes this task challenging. Artificial neural networks (ANNs) are widely used in this area; however, they may fail to provide the accuracy that may be required. This is due to applying shallow architectures with error-prone hand-engineered features. This paper proposes a deep neural network (DNN) architecture with stacke...