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141.
Frequency regularization of a linked wind–diesel system using dual structure fuzzy with ultra-capacitor 下载免费PDF全文
Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases. 相似文献
142.
Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square Error (RMSE) and Mean Average Error (MAE) which related to the setting of ANFIS parameters. The experimental results indicated that the training data sample, number, and type of membership functions are the most important factor affecting the performance of the ANFIS network. However, the number of epochs does not necessarily significantly improve the system performance, too many the number of epochs setting may not provide the best results and lead to excessive responding time. The results also demonstrate that three factors, consisted of the engine size, driving speed, and the number of passengers, are important factors that influence the change of vehicle fuel consumption. The selected ANFIS models with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector. 相似文献
143.
根据CD级柴油机的模拟实验与台架试验的基础数据,用人工神经网络的反向传播(BP)方法,对两者的关系进行了研究,给出了人工神经网络的学习速率为0.2,动量因子为0.9,对人工神经网络的拓扑结构也进行了研究,得到了合适的5-7-2拓扑结构及各节点间的权重系数。探讨了用模拟实验数据预测台架试验结果的可能性,通过检验,证明了用人工神经网络方法建立的模型能准确预报柴油机油的台架试验结果。 相似文献
144.
针对常规的智能车传感器固定,视觉范围小,影响智能车的运行速度和准确性问题,提出了基于双舵机的大视觉智能巡线车控制系统。采用激光传感器、将路径识别传感器安装在车架上随动舵机的轮盘上,MCU通过控制随动摆头舵机来带动传感器的运动,使智能车的路径识别角度范围增大。经试验证明,此控制系统紧密结合双舵机的特点,充分发挥装在车架上的随动舵机带动路径识别传感器的优势,增大了智能车巡线过程中能识别到的路径范围,使智能车模实现高速稳定行驶。在不"丢线"的情况下,速度得到较大的提升,能适应各种赛道。 相似文献
145.
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147.
J.P. Torreglosa F. Jurado P. García L.M. Fernández 《Control Engineering Practice》2011,19(10):1182-1194
This paper focuses on describing a control strategy for a real surface tramway powered by a hybrid system based on fuel cell and battery. This tramway, called Metro Centro, serves the centre of Seville, a city in Spain. Currently, it operates as catenary-powered tramway.The configuration and modeling of all principal components of the hybrid system are briefly described. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. The implemented control is based on an equivalent consumption minimization strategy. It allows a suitable energy management of the hybrid system, minimizing the hydrogen consumption. 相似文献
148.
Chi-man Vong Pak-kin Wong Weng-fai Ip 《Engineering Applications of Artificial Intelligence》2011,24(7):1281-1294
Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system. 相似文献
149.
Haijun Zhang Author Vitae Author Vitae M.K.M. Rahman Author Vitae 《Pattern recognition》2009,42(11):2950-2719
A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval. 相似文献
150.
Performance Analysis of The Auxiliary‐Model‐Based Multi‐Innovation Stochastic Newton Recursive Algorithm for Dual‐Rate Systems 下载免费PDF全文
The stochastic Newton recursive algorithm is studied for dual‐rate system identification. Owing to a lack of intersample measurements, the single‐rate model cannot be identified directly. The auxiliary model technique is adopted to provide the intersample estimations to guarantee the recursion process continues. Intersample estimations have a great influence on the convergence of parameter estimations, and one‐step innovation may lead to a large fluctuation or even divergence during the recursion. In the meantime, the sample covariance matrix may appear singular. The recursive process would cease for these reasons. In order to guarantee the recursion process and to also improve estimation accuracy, multi‐innovation is utilized for correcting the parameter estimations. Combining the auxiliary model and multi‐innovation theory, the auxiliary‐model‐based multi‐innovation stochastic Newton recursive algorithm is proposed for time‐invariant dual‐rate systems. The consistency of this algorithm is analyzed in detail. The final simulations confirm the effectiveness of the proposed algorithm. 相似文献