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Three-Winding Distribution Transformer This demonstration illustrates the use of the linear transformer to simulate a three-winding distribution transformer rated 75 kVA - 14400/120/120 V. G. Sybille (Hydro-Quebec)

Beam width is the aperture angle from where most of the power is radiated. The two main considerations of this beam width are Half Power Beam Width (HPBW) and First Null Beam Width (FNBW).

仿真纵联差动保护，有一定的学习意义。模拟输电线路的闭锁式距离纵联保护建立 SIMULINK 仿真模型。加深我们对电气工程和 MATLAB 仿真的理解和认识。(The simulation longitudinal differential protection has certain learning significance. The SIMULINK simulation model is established to simulate the closed range longitudinal protection of transmission lines. Deepen our understanding and understanding of electrical engineering and MATLAB simulation.)

8 queen problem with SA

本实验指导书是配合计算机仿真技术的教学，为学生做实验提供的指导性材料。为便于学习，书中对各种仿真方法均备有范例程序，所有程序均采用MATLAB语言编写，并上机调试通过。(This study guide book is in line with the teaching of computer simulation technology for students to experiment with materials to provide guidance. To facilitate learning, in the book are available in a variety of simulation examples of procedures, all procedures are used MATLAB language, and debugging through the machine.)

说明：  发电微网控制;电力系统;MATLAB程序(Generation Microgrid control;Power system;Matlab program)

远程操作串行端口源码，非常值得收藏~~~~~~~~~(Remote COntrol Serial Port，very precious)

采用动态加权综合评价方法建立评价模型，增大了评价结果的客观性。首先对评价指标数据进行归一化处理，然后选取偏大型正态分布函数作为动态加权函数建立评价模型，从而对评价指标观测值进行排序，最后用决策(Dynamic weighted comprehensive uation method to establish uation model, increasing the objectivity of uation results. First uation data normalization, and then the partial large normal distribution function as a dynamic weighting function uation model, and thus the uation observations are sorted, with the final decisions)

双馈电机simulink仿真，定子电压定向，pq解耦控制(Simulink simulation DFIG stator voltage oriented, pq decoupling control)

实验中使用随机反向传播算法对构造的神经网络进行学习，最终得到构造的神经网络的权值矩阵。(Experiment using the random back-propagation algorithm to construct the neural network learning, the final structure of the neural network obtained the weight matrix.)