WIND

说明：  快速滑膜控制的开关磁阻电机调速系统，双闭环设计(Speed control system of switched reluctance motor with fast slide film control)

用拟牛顿法求非线性方程组 fi( x1, x2, ?, xn ) = 0, i = 1, 2, ?, n 的一组实数解。(A quasi-Newton method is used to find a set of real solutions of the nonlinear equations.)

基于pscad的电气化铁路对周围发电厂负序影响的割集仿真模型，倾情奉献。(Pscad electrified railway based on negative sequence power plants around the cut set of simulation models Decent.)

电力系统稳态分析-课程作业-用牛顿拉夫逊法求解潮流-C++下编译通过，注释清晰，思路清楚，值得参考。(Steady power system analysis- coursework- Newton Raphson method to solve the current-C++ compiler, comments clear, clear thinking, good reference.)

该文件是优秀论文并带有相应的源码。在Matlab软件平台上，对所提出的基于遗传算法的电力系统电力市场分段竞价交易算法进行了阶梯型报价曲线、线性报价曲线这两种常用报价曲线情况下的算例验证，计算结果显示算法的寻优效果因报价曲线性质的不同而略有区别，但两种情况均可得到优化的购电费用。实验结果证明了该算法应用于分段竞价模型的有效性和可行性，且不受报价曲线形式的限制。(The document is an excellent paper with a corresponding source code. In the Matlab software platform for the proposed genetic algorithm-based power systems market segment auction algorithms offer ladder-type curve, the two commonly used linear bidding curve bidding curve example in case of validation, the results show algorithm optimization results for the different nature of bidding curve slightly different, but both cases can be optimized for power purchase costs. Experimental results show that the algorithm is applied to sub-effectiveness and feasibility of bidding model, and is not limited form of bidding curve.)

用前馈神经网络对数字图像进行去噪，最终效果去噪良好，能够清晰显现原图像。(With feedforward neural networks for digital image denoising, denoising good final results, we can clearly show the original image.)

压缩空气储能仿真simulink模型，适合matlab 2017及以上版本(model of CAES of simulink)

pscad ieee标准系统搭建详细说明，包含各模型具体标准数据及说明(PSCAD IEEE standard system set up detailed description, including the specific standard data and instructions of each model)

说明：  热力系统中将水力模型与热力模型同时考虑，使用牛顿拉夫逊算法进行联合求解。(Hydraulic model and thermal model are considered simultaneously in the thermal system, and Newton Raphson algorithm is used to solve them jointly.)

将txt格式的文档导入到LMS测试软件中(load the txt file to LMS testlab)