基于遗传神经网络和群体复合形法的配风方式对锅炉飞灰含碳量影响研究

利用优化递归的BP神经网络进行锅炉飞灰含碳量建模,并对锅炉二次风配风方式的影响进行敏感性分析,同时采用群体复合形法对运行工况寻优,获得各种工况下二次风开度的优化调整方式.优化递归神经网络是以遗传算法来确定神经网络的权值,克服了BP算法易陷入局部极小等缺陷,提高了网络学习速度和精度.通过对某台300MW机组现场试验与计算表明,该方法可以指导运行人员进行二次风开度的优化调整,降低飞灰含碳量,同时也解决了锅炉变工况下运行参数基准值的问题.     

基于遗传算法的BP网络在城区埋地燃气管道风险评价中的应用

将遗传算法和Bp神经网络进行融合,优化神经网络的权值和阈值,充分发挥遗传算法的全局寻优能力和BP算法的局部搜索优势,并与纯BP神经网络进行了比较.城区埋地燃气管道的实例表明,基于遗传算法的BP神经网络不仅收敛速度快,而且易达到最优解.     

基于粒子群优化BP神经网络的风电机组齿轮箱故障诊断方法

提出了一种基于粒子群优化BP神经网络风电机组齿轮箱故障诊断方法。粒子群算法不需要计算梯度,可以兼顾全局寻优和局部寻优。利用粒子群算法对BP网络权值和偏置进行优化,减少了BP神经网络算法陷入局部最优解的风险,提高了神经网络的训练效率,加快了网络的收敛速度。考虑风电齿轮箱振动信号的不确定性、非平稳性和复杂性,提取功率谱熵、小波熵、峭度、偏度、关联维数和盒维数作为故障特征。经测试,算法诊断结果正确,表明了PSO优化BP神经网络用于风电机组齿轮箱故障诊断的有效性和实用性。     

基于果蝇算法优化BP神经网络的齿轮箱故障诊断

为有效提高风电机组齿轮箱故障诊断的快速性和准确性,采用近几年出现的果蝇算法对BP神经网络进行优化,减少了BP神经网络算法陷入局部最优解的风险,显著增强了BP神经网络的泛化能力和全局寻优能力。对比发现,果蝇算法优化后的BP神经网络模型具有比较好的快速性和准确的诊断能力。测试结果表明,果蝇算法优化BP神经网络对风机齿轮箱故障诊断具有可行性和有效性。     

基于BP神经网络和SA_BBO算法的汽轮机组最优运行初压的确定

为确定超超临界机组主汽压力设定值及机组优化运行方式,在对1 000 MW机组进行主汽压力寻优试验研究的基础上,利用BP神经网络建立了汽轮机组滑压特性模型。提出了一种基于模拟退火的生物地理学优化法,将BBO(生物地理学优化算法)算法能较快找到全局最优解的能力和SA(模拟退火)算法较强的局部搜索能力相结合,有效地提高了算法的搜索精度和收敛速度。应用SA-BBO算法对所建机组滑压特性模型进行主蒸汽压力寻优,结果表明机组的滑压曲线与设计值存在较大差别,而且受到环境温度等因素的影响。在不同负荷和相关约束条件下,优化后机组热耗率可降低25～60 kJ/(kW.h),供电煤耗率可降低0.8～2 g/(kW.h)。     

基于遗传算法:BP网络的空调水系统优化控制研究

针对BP神经网络收敛速度慢和易于陷入极小值的问题,采用将遗传算法全局寻优和BP神经网络局部寻优相结合的方法,提高了BP神经网络的计算精度和收敛速度.应用该模型对空调水系统进行了辨识,并以冷水机组和水泵能耗最小为目标进行优化,取得了满意的效果.     

基于GA和模糊关联规则的锅炉脱硝经济性优化

在某660 MW火电机组的厂级监控信息系统(SIS)中选取历史运行数据,利用最小二乘支持向量机(LSSVM)方法建立脱硝经济性预测模型,并基于该模型采用遗传算法进行常运行负荷点的离线寻优以建立离线最优专家数据库(OOED).采用模糊关联规则挖掘(FARM)算法从OOED中提取各调整变量的最优设定值与机组负荷的关联关系,实现电网负荷调度指令下各参数的在线优化调整.结果表明:所提出的脱硝经济性优化方法的优化效果与遗传算法寻优结果接近,且优化时间短,适合火电机组的在线优化控制.     

基于自适应粒子群优化BP神经网络的氨法烟气脱硫效率预测

针对氨法烟气脱硫效率的预测问题,建立了以脱硫系统运行中8个主要参数作为输入变量的BP神经网络模型,采用粒子群优化算法(PSO)对建立的BP神经网络模型的权值进行优化,提出基于粒子群优化算法的BP神经网络(PSO-BP)预测新模型,并利用某电厂脱硫系统20组运行数据对该模型进行了验证.结果表明:采用PSO算法对BP神经网络的权值和阈值进行寻优,避免了网络局部极小值的出现,提高了网络的泛化能力,采用PSO-BP预测模型可以对氨法烟气脱硫效率进行较高精度的预测.     

基于模态阻尼比的风电叶片气弹特性研究

以考虑叶片挥舞和摆振两个方向自由度的气弹分析模型为基础,通过Matlab软件编写仿真分析程序,对某5 MW叶片的叶素气弹阻尼、叶片模态和整体叶片模态气动阻尼比等进行仿真分析。结果表明,该叶片模型在正常设计运行范围内,各阶模态气动阻尼比均为正值,气弹特性稳定;并提出将整体叶片模态气动阻尼比随桨矩角变化曲线同风电机组正常运行风速随桨矩角变化曲线相结合,进行风电机组叶片气弹稳定性的判定方法。     

基于大数据的燃煤电站空冷岛运行实时优化策略

为提高某电厂2×600 MW超临界直接空冷燃煤机组的节能效果,采用大数据分析方法,对机组历史运行数据进行筛选得到最佳数据库,同时建立BP神经网络预测模型,预测实时机组净发电功率.最终从机组实时运行背压偏置范围中找出实时机组运行数据对应负荷及相应边界条件下的最佳背压,以指导其运行优化水平,计算节电量.结果 表明:利用最佳背压思路进行寻优,最后通过仿真得到的优化效果较稳定,且当环境温度较低或机组处于中段负荷运行时,节能效果更明显.     

A neural network-based power system stabilizer using power flowcharacteristics

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS     

A neural network based power system stabilizer suitable for on-linetraining-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS     

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system     

Optimization of turbine cold-end system based on BP neural network and genetic algorithm

The operation condition of the cold-end system of a steam turbine has a direct impact on the economy and security of the unit as it is an indispensible auxiliary system of the thermal power unit. Many factors influence the cold-end operation of a steam turbine; therefore, the operation mode needs to be optimized. The optimization analysis of a 1000 MW ultra-supercritical (USC) unit, the turbine cold-end system, was performed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimization analysis. The optimized condenser pressure under different conditions was obtained, and it turned out that the optimized parameters were of significance to the performance and economic operation of the system.     

Artificial neural network power system stabilizers in multi-machinepower system environment

The effectiveness of an artificial neural network (ANN), functioning as a power system stabilizer (PSS), in damping multi-mode oscillations in a five-machine power system environment is investigated in this paper. Accelerating power of the generating unit is used as the input to the ANN PSS. The proposed ANN PSS using a multilayer neural network with error-backpropagation training method was trained over the full working range of the generating unit with a large variety of disturbances. The ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Results show that the proposed ANN PSS can provide good damping for both local and inter-area modes of oscillations     

Neural network identification of power system transfer functions

This paper describes an investigation into the use of a multilayered neural network for measuring the transfer function of a power system for use in power system stabilizer (PSS) tuning and assessing PSS damping. The objectives are to quickly and accurately measure the transfer function relating the electric power output to the AVR PSS reference voltage input of a system with the plant operating under normal conditions. In addition, the excitation signal used in the identification procedure is such that it will not adversely affect the terminal voltage or the system frequency. This research emphasized the development of a neural network that is easily trained and robust to changing system conditions. Performance studies of the trained neural network are described. Simulation studies suggest the practical feasibility of the algorithm as a stand-alone identification package and as a portion of a self-tuning algorithm requiring identification in the strategy. The same technique applied to a forward modelling scheme can be used to test the damping contribution from different control strategies     

基于遗传算法的BP神经网络在水利定额编制中的应用

针对BP神经网络易陷入局部最优和遗传算法全局搜索速度过慢的缺点及水利定额编制中存在非线性和复杂性的实际状况,提出采用遗传算法(GA)优化BP神经网络在水利定额编制中的问题。实例分析表明,优化后模型(GA-BP神经网络)结合了BP神经网络的非线性逼近、局部寻优能力和遗传算法的全局搜索特性,在稳定性、预测精度、收敛速度上均优于BP神经网络,可运用于水利定额编制。     

遗传算法优化BP神经网络在大坝扬压力预测中的应用

针对BP神经网络的局部极小和收敛慢等问题,提出了利用遗传算法的选择、交叉和变异操作优化BP神经网络的权值和阈值,将优化后的BP神经网络用于预测大坝扬压力。通过实例应用,将遗传算法优化的BP神经网络与逐步回归、BP神经网络预测相对比,结果表明遗传算法优化的BP神经网络收敛快且预测结果精度高。     

Analysis of torsional oscillations using an artificial neuralnetwork

A novel approach using an artificial neural network (ANN) is proposed for the analysis of torsional oscillations in a power system. In the ANN those system variables, such as generator loadings and the capacitor compensation ratio, which have major impact on the damping characteristics of torsional oscillation modes were used as the inputs. The outputs of the neural net provided the desired eigenvalues for torsional modes. Once the connection weights of the neural network have been learned using a set of training data derived offline, the neural network can be applied to torsional analysis in real-time situations. To demonstrate the effectiveness of the proposed neural net, torsional analysis was performed on the IEEE First Benchmark Model. It is concluded from the test results that accurate assessment of the torsional mode eigenvalues can be achieved by the neural network in a very efficient manner     

基于果蝇算法与自适应性遗传算法组合优化神经网络的光伏电站短期出力预测

光伏电站的输出功率会随着很多因素发生波动,若能够提高光伏系统出力预测的准确性,则能有效地降低光伏电站并网后对电网造成的冲击,提高电力系统的稳定性。建立了果蝇算法与自适应遗传算法组合优化的BP神经网络的预测模型。从预测结果可以发现,采用组合优化算法的BP神经网络模型能够有效避免地BP神经网络易陷入局部极小值点的缺陷,相比于仅优化权值和阈值的BP神经网络模型提高了预测精度,具有一定的应用价值。