配电网规划及其自动化（一）第一讲不确定性负荷预测方法

配电网规划及其自动化（一）第一讲不确定性负荷预测方法陈章潮，顾洁上海交通大学，２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫＣｈｅｎＺｈａｎｇｃｈａｏＧｕＪｉｅＳｈａｎｇｈａｉＪｉａｏｔ...     

配电网规划及自动化（二） 第二讲 配电网规划（Ⅰ）

配电网规划及自动化（二）第二讲配电网规划（Ⅰ）陈章潮，顾洁上海交通大学，２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅ（ＳｈａｎｇｈａｉＪｉａｏｔｏ...     

配电网规划及自动化（三）第三讲配电网规划（Ⅱ）

配电网规划及自动化（三）第三讲配电网规划（Ⅱ）陈章潮，顾洁上海交通大学，２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅＳｈａｎｇｈａｉＪｉａｏｔｏｎ...     

基于 BP 神经网络的模糊语言及其规则表示

结合ＢＰ网络和模糊技术,提出一种实现模糊语言表示的神经网络ＦＬＲＮＮ（ＦｕｚｚｙＬｉｎ－ｇｕａＲｅｐｒｅｓｅｎｔａｔｉｏｎＮｅｕｒａｌＮｅｔｗｏｒｋ）和实现模糊推理的神经网络ＦＲＲＮＮ（ＦｕｚｙＲｕｌｅＲｅｐｒｅｓｅｎ－ｔａｔｉｏｎＮｅｕｒａｌＮｅｔｗｏｒｋ）;根据加权判决法,提出反模糊化神经网络ＤＮＮ（ＤｅｆｕｚｚｉｃａｔｉｏｎＮｅｒｕａｌＮｅｔｗｏｒｋ）;并进行了例证．     

基于Oja学习规则的ANN谐波分析算法

传统的ＡＮＮ（人工神经网络）训练过程多建立在Ｈｅｂｂ学习机制的基础上的,但基本的Ｈｅｂｂ学习机制的收敛性往往难以令人满意;在ＡＤＡＬＩＮＥ模型的基础上,提出了一种新的、适用于电力系统谐波分析的ＡＮＮ算法,该算法的训练过程采用了Ｏｊａ学习规则,较好地保证了算法的收敛性。文中给出了利用该算法进行谐波分析的仿真结果,并与傅氏自救和最小二算算法的计算结果进行了比较。算例表明,新算法具有精度高、收敛速度快的     

配电网及自动化讲座（七）──第七讲 国外电气设备发展动态

配电网及自动化讲座（七）第七讲国外电气设备发展动态陈章潮，顾洁上海交通大学，２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫ￥ＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅ（ＳｈａｎｇｈａｉＪｉａ...     

配电网及自动化讲座（六）──第六讲配电网自动化

配电网及自动化讲座（六）──第六讲配电网自动化陈章潮，顾洁上海交通大学２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫ￥ＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅ（ＳｈａｎｇｈａｉＪｉａｏｔｏ...     

配电网规划及自动化（四）第四讲 无功电源优化配置

配电网规划及自动化（四）第四讲无功电源优化配置陈章潮，顾洁上海交通大学，２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅＳｈａｎｇｈａｉＪｉａｏｔｏｎ...     

配电网规划及自动化（五）第五讲配电网中性点接地方式

配电网规划及自动化（五）第五讲配电网中性点接地方式陈章潮，顾洁上海交通大学２０００３０上海ＰＬＡＮＮＩＮＧＡＮＤＡＵＴＯＭＡＴＩＯＮＯＦＰＯＷＥＲＤＩＳＴＲＩＢＵＴＩＯＮＮＥＴＷＯＲＫ￥ＣｈｅｎＺｈａｎｇｃｈａｏ；ＧｕＪｉｅ（ＳｈａｎｇｈａｉＪｉａｏ...     

美国HDTV简易转化技术等34则国际新闻

美国ＨＤＴＶ简易转换技术 　　美国能源部（ＤＯＥ）Ｌｏｓ Ａｌａｍｏｓ国家研究所（ＬＡＮＬ）的科学家们开发了一种新技术，它可以非常容易地将模拟电视转换为高清晰电视（ＨＤＴＶ）。这是通过发射运算法则将一个ＨＤＴＶ数据流压缩至ＨＤＴＶ和模拟电视信号在同一频道发射点上，该技术对电视用户意义重大，因为美国规定２００６年广播电视信号改为ＨＤＴＶ。     

电力系统动态安全分析的ANN动态安全域

采用人工神经网络(ANN)多维空间的强拟合特性，能够较精确地拟合动态安全域，减少了临界点以及稳定点和非稳定点的误判，克服了超平面理论近似拟合电力系统动态安全域误差较大的不足。同时，对于某个具体的故障，采用临界切除时间ter建立ANN稳定裕度拟合器，确定电力系统中运行点的稳定裕度。通过PSASP中的3机9节点模型验证了此理论的正确性，而且准确性优于采用超平面理论拟合的动态安全域。     

A smart distribution system reconfiguration algorithm with optimal active power scheduling considering various types of distributed generators

This paper presents an efficient way of solving the distribution system reconfiguration (DSR) problem in electrical power systems with consideration of different types of distributed generators (DGs). The objective of a DSR is to minimize the system power loss while satisfying the system constraints and keeping the topology of the system radial. In this paper, a new DSR algorithm based on a modified particle swarm optimization (PSO) is proposed to incorporate DGs with the constant voltage control mode. The proposed method is very efficient because it avoids an extra iteration loop for computing the reactive power at PV buses in order to keep the voltage at a specified magnitude. Furthermore, if the reactive power requirement is not met in between the extreme limits, the proposed algorithm strictly searches for the best possible tie switch combination to simultaneously reduce the power loss and ensure that the DGs operate in PV mode within acceptable reactive power limit. The proposed algorithm also integrates hourly DSR with optimal DG active power scheduling considering the DG type, generation limit constraints, and the allowable DG penetration level. The validity and the effectiveness of the proposed method has been tested using standard IEEE 33‐bus and 69‐bus distribution networks with various case studies. Test results show that the proposed method is robust and delivers a minimal average power loss compared with different methods, and it efficiently models DGs in DSR, demonstrating that the presence of DGs can further reduce the system loss. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

NEURAL NETWORK APPROACH TO DYNAMIC VOLTAGE STABILITY PREDICTION

ABSTRACT

The application of artificial neural network (ANN) on dynamic voltage stability analysis is presented. Two ANN models have been utilized, in which the first ANN model is used to classify the power system as to whether it is dynamically stable or unstable. Then the second ANN model is used for the dynamically stable system to predict the voltage magnitudes at load busbars. Both ANN models are based on the multiperceptron model, and the training is done using the error back propagation scheme. The training set patterns are generated by carrying out dynamic simulations, using induction motor and constant P-Q load models. This paper highlights the method for selection of the optimum number of training sets so as to minimise the time taken in the ANN learning process. The performance of the ANN models have been tested and shown to give good results.     

基于分类回归树算法的在线静态电压稳定裕度评估

随着电力负荷的不断增加,电压稳定问题日益凸显;为防止电压失稳,电网运行人员迫切需要了解电网当前的运行状态。提出运用分类回归树算法构建决策树模型,对静态电压稳定裕度进行在线评估。首先根据PV曲线分析和电压稳定裕度计算获得决策树训练样本集;然后通过模态分析和相关系数分析降低属性集的维度,确定训练决策树的输入属性;最后利用离线数据,运用决策树分类回归树算法训练出在线电压稳定裕度的评估规则。以某省实际电网为例对所提方法进行验证,其决策规则具有较高的准确性,能够实时地评估电网运行状态。     

人工神经网络的混合电力补偿控制器设计

针对电力系统谐波补偿控制问题,基于人工神经网络技术,提出了电力补偿控制器设计方法,并给出了人工神经网络的结构、网络训练和测试的具体方法。实现了电力系统中谐波电流的自动检测,并根据谐波电流的具体情况实现补偿元件的自动切换以达到对谐波电流的补偿。仿真结果证明,利用神经网络技术实现对混合电力补偿器的控制是提高其动态性能的有效途径。     

电力系统实用动态安全域及其搜索方法的改进

电力系统动态安全域（DSR）可为电力系统提供更为丰富的安全信息．实现电力系统的在线安全性评估与优化。经过大量的仿真实验发现，应用现有的搜索动态安全域临界超平面方法所得到的结果与理论分析得到的结果存在着很多差异。为此，分析了某些情况下应用现有实用动态安全域搜索程序昕得结果存在偏差的原因，给出其理论上的解释。并通过增加非独立节点（一般选取平衡节点作为非独立节点）约束条件和改进相应的搜索方法解决了出现的偏差问题。完善了实用动态安全域（PDSR）的方法。并在CEPRI-36节点交直流混合系统和太平洋联络线交直流并联输电系统算例上进行了验证，得到了满意的结果。改进后的实用动态安全域及其搜索方法使动态安全域超平面的搜索更加精确，对实际系统更具有指导意义。     

An Integrated Approach to Economic Emission Load Dispatching Using Neural Network and Goal-Attainment Methods

This paper presents an integrated approach comprising artificial neural network (ANN) and goal-attainment (GA) methods to economic emission load dispatching (EELD) in power system operation and scheduling phases. The GA method, which is one of the most powerful tools for multiobjective optimization problems, is quantitatively performed to grasp trade-off relations between the two conflicting objectives (economy and emission impact) in the training set creation phase. Finally, a radial basis function ANN is trained by the orthogonal least squares learning algorithm to reach the optimal generations. The ANN models so developed have been tested to solve EELD problem on 6-bus and 71-bus power systems. The test results demonstrate that the proposed approach is capable of obtaining well-coordinated optimal solutions suitable both in accuracy and speed while allowing flexibility in the operation of the generators.     

基于有功功率小扰动分析的动态安全域求解

动态安全域(DSR)的暂态稳定边界可以近似表示为超平面,由此提出了一种DSR的快速求解方法.该方法分别对事故前系统的稳定运行点、事故中系统的故障轨迹和事故后系统的暂态稳定性进行有功功率的小扰动分析,然后依据整个暂态响应过程中状态变量的连续性,将不同阶段的分析结果联系起来,最终推导出了超平面形式的DSR解析表达式.应用暂态能量函数分析给出了注入空间上的实用动态安全判据,以此来量化暂态稳定性指标,从而实现对事故后系统的有功功率小扰动分析.该方法的有效性在New England 10机39节点系统上得到了验证.     

Application of artificial neural networks in voltage stabilityassessment

Voltage stability problems have been one of the major concerns for electric utilities as a result of system heavy loading. This paper reports on an investigation on the application of ANNs in voltage stability assessment. A multilayer feedforward artificial neural network (ANN) with error backpropagation learning is proposed for calculation of voltage stability margins (VSM). Based on the energy method, a direct mapping relation between power system loading conditions and the VSMs is set up via the ANN. A systematic method for selecting the ANN's input variables was developed using sensitivity analysis. The effects of ANN's training pattern sensitivity problems were also studied by dividing system operating conditions into several loading levels based on sensitivity analysis. Extensive testing of the proposed ANN-based approach indicate its viability for power system voltage stability assessment. Simulation results on five test systems are reported in the paper