The Multi-objective Fuzzy Optimal Power Flow Model and its Neural Network Algorithm

This paper has presented a multi-objective fuzzy optimal power flow medel.Inthe model , the multiple objectives, such as the minimum generation cost and the minimum powerloss, have been considered simultaneously, A new algorithm based on neural network models is aisopresented,in which the neural networks are employed to express, the membership function of fuzzysets and solve the optimization problems. The validity of model and algorithm is verified with numerical examples.     

THE REAL RESISTIVE MODEL FOR FREQUENCY -DOMAIN ANALYSIS

A new model for frequency - domain analysis - the real resistive model which is based on Complex Network Theory is proposed in this paper. The network equations formed with the new model without contain complex numbers because there are only sources and resirtive elements in the model. A CAA algorithm for frequency analysis by using the new model is also given. It is shown that the trouble with the complex operation in the traditional algorithm has been avoided. The algorithm presented in this paper is simple and convenient,hence it is easy to popularize.     

Distribution Network Structure Planning Based on Multi-population Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem, which is difficult to solve properly by using traditional optimization methods. The authors put forward Multiple Population Immune Genetic Algorithm (MPIGA)for optimal planning of distribution network structure, and do optimal search to different aspects of optimization goals. During the genetic evolution process, biologic immune mechanism is introduced to do some immune operator operation on chromosomes of each population, which can interact mutually by the shift of excellent units. By this way, it can effectively prevent population retrogression, promote diversity and the whole optimal searching ability of genetic algorithm. In order to minimize network annual expenditure, a mathematic model is established. The optimal solution is obtained by this algorithm, which has been illustrated effectively by specific examples at the same time.     

THE OPTIMAL DESIGN AND ENGINEERING APPLICATION OF DISTRIBUTION

This paper introduces the metod of optimal design of municipal water distribution,The main points are the theory,mathematical model,program frame figure and example in practice of the optimal method of hydraulics.The program is applicable to the design and calculation of the pipe network in a city. It gives a simple and convenient calculation method for multiple-scheme design and scheme cmparison of water supply system.     

THE ECONOMIC DISPATCH OF REAL POWER WITH SECURITY

This paper presents an approach to studing the economic dispatch of real power with N-1 security with the network flow programming (NFP), the N-1 line - security constraints are first introduced into the network f olw model and the problem of N-1 security economic dispatch becomes an incremental metwork flow model. Therefore, the shortage of NFP is mitigated to some extent, The solution adopted is the "out-of-kilter"algorithm(OKA), which is characterized by fast calculation and reliable convergency. The model and solution are tested on IEEE 30-bus system, and the results show that the proposed approach is feasible.     

元动作装配单元误差传递模型及有效路径求解方法

在对机械产品进行"功能-运动-动作"结构化分解的基础上,将影响元动作装配单元装配精度的误差源分为零部件的形状误差、位置误差、装配位置误差和运动误差等四类误差源。引入误差链接模型作为元动作装配单元误差关联关系的基本封装单元,构建结构化误差关联模型——链接网络和链接矩阵,形象描述误差间的耦合嵌套关系。提出基于误差链接模型的装配误差传递路径求解方法,用老鼠迷宫算法搜索所有可能的误差传递路径,以装配精度最高作为判别依据,得到各误差分量的有效传递路径。以蜗杆转动元动作装配单元蜗杆轴线平行度误差有效传递路径为目标,对上述方法进行验证,结果表明该方法能够高效地搜索到所有误差传递路径,并快速获得有效传递路径。该方法的提出为整机装配过程质量预测与控制提供了理论依据。     

Distributed Cutting Stock System Based on Heuristic Search and Knapsack Algorithm

A approximate algorithm is presented by which satisfactory results of two dimensional optimal layout belonged to NP complete problems can be obtained efficiently and rapidly. The algorithm integrated with widespread of computer network has been applied to the distributed cutting stock system Adopting Heuristic search and Knapsack algorithm, the algorithm makes each layout optimal locally and obtained the approximate optimal solution. It is compared with other two approximate algorithms and pointed out their shortcomings. In the end of the article a layout example was presented.     

Fast Algorithm About Unit Commitment Based on Revised BP Artificial Neural Network and Dynamic Search

In order to reduce the operation cost and optimize the unit commitment,the fast algorithm about unit commitment based on revised BP ANN(Artificial Neural Network) and dynamic search is discussed.The BP ANN is trained with Levenberg-Marquardt algorithm,which aiming at its drawback of the storage of some matrices that can be quite large for certain problems,and a revised algorithm is presented.The BP ANN is used to generate a pre-schedule according to the input load profile.Then the dynamic search is performed some stages where the commitment states of some of the units are not certain.The experimental results indicate that the proposed algorithm can reduce the execution time and memory space without degrading the quality of the generation schedule.     

A new pattern recognition method of gas sensor array detection

BP neural network based gas sensor array detection pattern recognition has some disadvantages, such as slow convergence and local minimum problem. A modified immune neural network model which combines BP algorithm and immune algorithm is proposed to enhance global search capability and improve the performance of the neural network model. Orthogonal test is adopted to design the study samples of neural network. This ensures the accuracy of neural network while reducing the number of samples. The simulation results show that the proposed pattern recognition method solves the cross sensitivity of gas sensor effectively, overcomes the disadvantages of traditional BP neural network and improves the learning speed and detection accuracy.     

基于遗传算法的BP神经网络在小麦蛋白质含量预测中的应用

为了快速、简便、准确地测定小麦蛋白质的含量,本文提出了应用近红外光谱分析技术结合遗传算法(GA)的BP神经网络的建模方法。采用SPXY算法对光谱数据进行了合理划分,并运用连续投影算法(SPA)将预处理过的数据压缩,对光谱数据提取最佳敏感波点作为GA-BP神经网络的输入,建立小麦蛋白质含量的校正模型。模型的预测均方根误差和预测相关系数为1.3379和0.979,并与BP神经网络所建立的校正模型进行了比较。结果表明：GA-BP神经网络所建模型收敛速度快、训练时间短、准确度也较高,能够实现对小麦蛋白质含量快速高效的检测。     

Design on Contingency Classifier of Functioned Link Neural Network

Contingency analysis is thekey computational issue in power system steady state security analysis and reliability calculations. This task requires a large amount of CPU time. In order to reduce effectively requirements of computationin outagesimulation ofbulk power system,a functioned link neural network (FLNN) classified model and algorithm employed to identify contingencies is presented. For the sake of gaining post-accident information of system states, a group of performance index (PI) is designed according to the performance characters relative to the changes of base case.Moreover, a neural network classifieris constructed. A varietyof the effects of PI and combinations of PI on the proposed classifieris discussed. That branch flow performance indices are better than the others is explanted. The resultsof classification by applied the FLNNclassifierto the IEEE-RTS24 show that it not only make network and algorithmsimpler, but also improvethe speed and accurate of contingency analysis.     

PTS Function Usage of 96 SCM in Microprocessor-based Relay System

This paper presents structure, characteristic and condition of microprocessor based relay communication system. A available start network configuration for the system is put forward. Message manager is used in the net. Some problems appearing in serial communication of field unit are also discussed. A way is brought forward for the communication system, which is an application of PTS function of 80C196KC SCM. The order is proposed for the PTS's one transmission model, which is the data longness must be destined in the net message agreement. A data redundance technic is adopted for improved message dependability.     

Distribution of trust model in P2P network based on equilibrium theory

There are a large number of malicious attack nodes and collusion groups in P2P(peer to peer) network, and the existing trust models improve the P2P network environment to some extent, but the emphasis of the models are different, which are unable to fully solve large-scale malicious attacks and deception. Therefore, a P2P trust model based on equilibrium theory is presented. The model consists of the construction of the trust structure, the malicious node detection and trust speculation. It constructs trust network according to the equilibrium theory, uses the equilibrium theory to define nodes balance factor, detects balance malicious nodes by calculating the impact of malicious behavior on the network, and adopts trust inference algorithm to estimate trust nodes to prevent distrust network nodes being added. Experimental results show that the model is reliable, and the algorithm is efficient and robust.     

APPLICATION OF A CONVEX NETWORK FLOW PROGRAMMING TO THE ECONOMIC POWER DISPATCH

Analyses the characteristics of convex network flow programming of the economic power dispatch with security and presents a rapid algorithm. The numerical examples are given to demonstrate the effectiveness of the algorithm.     

AN OPTIMIZATION METHOD OF THERMAL PLANT UNIT COMMITMENT PROBLEM

In this paper, the Lagrangian method for thermal plant unit commitment and some properties of its Lagrangian multiplier are discussed. Besides, a polynomial algorithm for updating the Lagramgian multiplier is proposed, the critical point of operating unit commitment is derived, and the computational quatity is less than that of the method of subgradient. AS a result, the consumption of coal of the thermal plant is greatly reduced and greater economic benefits will gained.     

BP Neural Networks and Its Application in Lung Cancer Intelligent Diagnosis System

The learning algorithm of networks is discussed. The programming example of 3 layer BP networks is given with Visual C++6.0 program langue. Based on this model, a lung cancer intelligent diagnosis system is successfully implemented. Furthermore, the paper introduces network's structure design, preferencesand the source of stylebookdatum in factual applications. The ameliorative arithmetic is applied to the study of networks and BP dynamic evolving process is designed. The experiments indicatecell images are recognized and classified by the trained neural network. The study illustrates the system has feasibility and clinical value in lung cancer diagnosis.     

基于无迹卡尔曼滤波神经网络的光伏发电预测

针对光伏发电系统在不同天气状况下发电功率预测精度不高的问题,在分析传统方法的基础上,提出一种无迹卡尔曼滤波神经网络光伏发电预测方法。该方法利用无迹卡尔曼滤波实时更新神经网络模型的权重,以直流电压和电流作为系统的输入,以有功功率和无功功率作为系统的输出,分别建立两个独立的双输入单输出功率预测模型。实验结果表明：所提出的方法对有功功率和无功功率的预测精度分别为97.3%和94.2%,并且对天气具有良好的鲁棒性。     

THE DECOUPLJNG CALCULATION OF MULTI-AREA POWER SYSTEM LOAD FREQUENCY CONTROL

The calculating burden of the decoupling judgement of multi-area power system load frequeney centrel is very large by tranditional methods. This paper changcs the decoupling judgement problem into a minimum cost network flow problem. The solution algorithm is out-of-kilter . algorithm which is characterized by simple manipaultion, fast and reliable convergence. In the paper, we discuss and calculate the decoupling problem of two arca power system load frequency control. The results show that the proposed approach is feasible and effective.