A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control(AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper.Also, a new hybrid scheme of the improved teaching learning based optimization-differential evolution(hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative(PID), integral double derivative(IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade－Correlation Neural Network

In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

A Two-Stage Approach for Large-Scale Cascaded Hydropower System Operations

The paper presents a two-stage approach to cope with the long-term optimal operation of cascaded hydropower systems. This approach combines progressive optimality algorithm (POA) with quadratic programming (QP) to improve the optimization results. POA is used at the first stage to generate a local optimal result, which will be selected as the initial feasible solution of QP method employed at the second stage. Around the initial solution, a rational local search range for QP method is then determined, where the nonlinear water level function and tailrace level function can be linearized nearly with high accuracy. The simplified optimization problem is formulated as a QP model with a quadratic generation function and a linear set of constraints, and solved using the available mathematic optimization software package. Simulation is performed on the long term operation of Hongshui River hydropower system which is located in southwest China and consists of 9 built hydropower plants. Results obtained from the proposed approach show a significant increase in the total energy production compared to the results from POA.

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

Research on Double Cascade Control Algorithm for Self-Balancing Two-Wheeled Vehicle

To solve the problem of self-balancing two-wheeled vehicle, this article presents double cascade PID control algorithm. This method reduces the coupling of balance control, speed control and direction control, because of the special system structure. This article successfully solved the sensor fusion of gyroscope and accelerometer by using Kalman filtering algorithm, and adding in fuzzy PID algorithm to improve the flexibility of the steering system, thus greatly improving the accuracy and response rate of the system.

NEW MULTIOBJECTIVE SIMULATED ANNEALING ALGORITHM AND ITS APPLICATION TO TURBINE CASCADES’ DESIGN

A new kind of multiobjective simulated annealing algorithm is proposed,in which the concept of non dominated character is introduced and a new multiobjective acceptance criterion is set up.The optimization example of a typical mathematical problem with two minimum objective functions indicates that all of the solutions contract to the set of the non dominated points,and the variation trend of the optimal solutions is verified to be identical with that obtained using Genetic Algor thms.The new developed algorithm is then applied to the multiobjective optimization design of turbine cascades,in which it is coupled with the aerodynamics computation of the cascade flow fields and performance and the calculated loss coefficient and work potential of the cascade are considered as the objective functions,thus setting up a technique to the engineering optimization design for the cascades.The optimization results,by the view of a group of optimal solutions,show that the algorithm is superior to the traditional technique of multiobjective optimization design and can be applied to more than two objective optimization cascade design problem or other engineering multiobjective optimization designs.

基于Adaboost cascade的车牌检测技术

研究基于Adaboost cascade算法的车牌检测技术,针对收费站车辆照片,选用局部矩形特征,利用cascade算法得到了98%的识别率,误识对象一般都集中在真实目标附近,从而验证了方法的可行性.

快速特征金字塔和Soft-Cascade在折角塞门图像故障检测中的应用

为了提升列车折角塞门的故障检测效率,提出了一种基于快速特征金字塔和Soft-Cascade的故障图像检测算法。首先,构建快速特征金字塔模型来提取图像多尺度聚合通道特征;其次,利用向量化后的多尺度聚合通道特征来训练Soft-Cascade故障分类器;最后,利用训练好的分类器来判断待检折角塞门是否含有故障。实验结果表明:该算法的故障检测正确率为97.33%,离线检测速度高达43fps(每张图像仅需23ms),检测效率高于其他算法。该算法训练时间短,检测速度快,硬件要求低,能满足列车折角塞门的故障检测要求。

基于连续型Adaboost算法和Cascade结构的红外人脸检测

自由立体显示技术中,人脸位置的探测与跟踪是关键之一。由于光照变化等因素的影响,对多人的脸部位置的探测很难达到快速、准确的目的。提出一种基于连续型Adaboost算法和Cascade结构的新方法。该方法采用红外主动照明模式,通过隔离可见光照,基本消除了光照变化对人脸检测造成的影响。新检测算法中Adaboost检测速度很快,Cascade结构可以检测那些难以识别的人脸,大大地提高了人脸检测的速度和鲁棒性。对视频流图像进行的检测实验中,没有出现人脸"漏检",极少出现非人脸的"误检"。检测速度在Windows XP,Penti-um IV,图片分辨率为640×480的条件下,可达25 f/s,完全达到了实时性的要求。另外,实验证明该方法对于人脸表情变化和人脸小角度倾斜也具有鲁棒性。

Heisenberg群上的cascade算法和多分辨分析

研究了Heisenberg群上的cascade算法的收敛性与正交的多分辨分析之间的关系。证明了cascade算法的(强)收敛性和完全重构条件可诱导一个正交的多分辨分析,并且反向结果也成立。

基于Open CASCADE的服装排料系统的研究与开发

文章针对服装人工排料劳动强度大、周期长及排料图不便于归档的问题,利用Visual C++6.0开发环境,并结合CAD开发平台Open CASCADE,研发了一套操作便捷的计算机辅助服装排料系统。该系统采用改进的遗传算法,即混合多种交叉、变异算子并采用快速解码方法,将不规则的衣片排料转化成矩形排料来实现服装的自动排料。通过平移、旋转和镜像等操作实现人机交互排料及优化自动排料。实验结果表明,该系统的使用能够缩短排料时间,并提高布料利用率。

Identification of critical nodes for cascade faults of grids based on electrical PageRank

In this paper,the electrical PageRank method is proposed to identify the critical nodes in a power grid considering cascading faults as well as directional weighting.This method can rapidly and accurately focus on the critical nodes in the power system.First,the proposed method simulates the scenario in a grid after a node is attacked by cascading faults.The load loss of the grid is calculated.Second,the electrical PageRank algorithm is proposed.The nodal importance of a grid is determined by considering cascading faults as well as directional weights.The electrical PageRank values of the system nodes are obtained based on the proposed electrical PageRank algorithm and ranked to identify the critical nodes in a grid.Finally,the effectiveness of the proposed method is verified using the IEEE39 node system.The proposed method is highly effective in preventing the occurrence of cascading faults in power systems.

A nonlinear wave coupling algorithm and its programing and application in plasma turbulences

The fully developed turbulence can be regarded as a nonlinear system,with wave coupling inside,which causes the nonlinear energy to transfer,and drives the turbulence to develop further or be suppressed.Spectral analysis is one of the most effective methods to study turbulence system.In order to apply it to the study of the nonlinear wave coupling process of edge plasma turbulence,an efficient algorithm based on spectral analysis technology is proposed to solve the nonlinear wave coupling equation.The algorithm is based on a mandatory temporal static condition with the nonideal spectra separated from the ideal spectra.The realization idea and programing flow are given.According to the characteristics of plasma turbulence,the simulation data are constructed and used to verify the algorithm and its implementation program.The simulation results and experimental results show the accuracy of the algorithm and the corresponding program,which can play a great role in the studying the energy transfer in edge plasma turbulences.As an application,the energy cascade analysis of typical edge plasma turbulence is carried out by using the results of a case calculation.Consequently,a physical picture of the energy transfer in a kind of fully developed turbulence is constructed,which confirms that the energy transfer in this turbulent system develops from lower-frequency region to higher-frequency region and from linear growing wave to damping wave.

Improved hyper-spherical search algorithm for voltage total harmonic distortion minimization in 27-level inverter

Multi-level inverters(MLIs)have become popular in different applications such as industrial power control systems and distributed generations.There are different forms of MLIs.The cascaded MLIs(CMLIs)have some special advantages among them such as more different output voltage levels using the same number of components and higher power quality.In this paper,a 27-level inverter switching algorithm considering total harmonic distortion(THD)minimization is investigated.Switching angles of the inverter switches are achieved by minimizing a THD-based objective function.In order to minimize the THD-based objective function,the hyper-spherical search(HSS)algorithm,as a novel optimization algorithm,is improved and the results of improved HSS(IHSS)are compared with HSS algorithm and other five evolutionary algorithms to show the advantages of IHSS algorithm.

Influence Maximization for Cascade Model with Diffusion Decay in Social Networks

Maximizing the spread of influence is to select a set of seeds with specified size to maximize the spread of influence under a certain diffusion model in a social network. In the actual spread process, the activated probability of node increases with its newly increasing activated neighbors, which also decreases with time. In this paper, we focus on the problem that selects k seeds based on the cascade model with diffusion decay to maximize the spread of influence in social networks. First, we extend the independent cascade model to incorporate the diffusion decay factor, called as the cascade model with diffusion decay and abbreviated as CMDD. Then, we discuss the objective function of maximizing the spread of influence under the CMDD, which is NP-hard. We further prove the monotonicity and submodularity of this objective function. Finally, we use the greedy algorithm to approximate the optimal result with the ration of 1 ? 1/e.

面向共驱混沌同步密钥分发的Cascade-Biconf信息协商算法

针对传统Cascade算法协商效率低、吞吐量不高的问题,提出了一种面向共驱混沌同步密钥分发的Cascade-Biconf信息协商算法。该算法基于奇偶校验和二分法纠错算法的原理,将所得密钥进行置乱、分块并传输校验值用以二分法纠错。与传统Cascade算法不同,Cascade-Biconf信息协商算法通过优化分块的大小降低了协商过程中的交互次数和计算量。仿真结果表明:在通信双方初始密钥误码率为0.1的情况下,Cascade-Biconf信息协商算法可将传统Cascade算法的协商效率由0.76提升到0.84,总吞吐量提升27.9%。

基于级联碰撞缺陷数据库的源项对辐照微结构演化影响团簇动力学模拟研究

团簇动力学(CD)方法是模拟核材料在高能粒子辐照下微观结构演化的重要方法之一,源项是团簇动力学方法的关键输入。经典CD方法中源项通常采用经验拟合得到,未能充分利用原子尺度获得的初始缺陷信息。随着分子动力学等方法的发展,级联碰撞缺陷数据库大为丰富,结合初级离位原子(PKA)能谱足以得到更为合理的源项。由于级联碰撞缺陷数据库的能量值数量相对于准连续PKA能谱仍然偏少,本文提出了5种从准连续PKA能谱得到级联能量分立值的抽样算法,并基于团簇动力学方法模拟低剂量中子辐照纯钨实验对算法进行了验证和比较。

基于改进的Cascade-RCNN网络的人员检测算法

为解决人员密集情景下行人检测存在大量的目标误检、漏检的情况,本文提出了一种改进的基于Cascade-RCNN的目标检测网络,提高了人员检测的准确率。对目前检测效果较好的Cascade-RCNN做了一些改进:选用ResNeXt101代替ResNet作为骨干网络,以便提取更加充分的特征;为了获得更好的标记框,用kmeans聚类算法得到更符合目标形态的anchor长宽比例,通过WBF算法融合多个模型的结果得到更精确的边界框,同时引入多尺度训练以提高对小尺度目标的检测能力。实验结果表明,在CrowdHuman公开数据集上,用ResNeXt101提取特征其得分提高了3.7%,用kmeans聚类算法生成anchor比例和WBF算法融合多预测框其准确率提升了0.7%和1.2%,最终整体性能较基础Cascade-RCNN提升近6%。