helicopter-MFCS

1节点固定为平衡节点，其他节点为pq节点，程序可以计算网络损耗，支路功率，输电效率，平衡节点功率，潮流分布。(One node is fixed at a balanced node, other node to node pq, the program can calculate the network loss, the slip power, the transmission efficiency of node power balance, the trend of the distribution.)

Abstract—Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and d by the appliance control interfaces developed in this

模块化多电平仿真分析,可用于电力系统分析计算，高压直流仿真分析，毕业论文必备模型。(Modular multi-level simulation analysis, can be used for power system analysis and calculation, high voltage DC simulation analysis, the essential model of graduation thesis.)

system will automatically

本程序利用MATLAB软件对小干扰情况下电力系统的稳定情况进行了编程(The program using MATLAB software for small interference case the stability of power system has been programmed)

Field oriented control of induction motor

bp神经网络的非线性系统建模————非线性函数拟合(nonlinear system modeling bp neural network nonlinear function fitting )

ecg de noising for Matlab data sd

多维变量的寻优算法，遗传算法解决多变量的最优化问题。(multivariable GA)

说明：  the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]: Y=φβ+ξ (7) β=〖(φ^T φ)〗^(-1) φ^T Y (8) where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.