CN112084709A - Insulation Condition Evaluation Method of Large Generator Based on Genetic Algorithm and Radial Basis Neural Network - Google Patents

Insulation Condition Evaluation Method of Large Generator Based on Genetic Algorithm and Radial Basis Neural Network Download PDF

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CN112084709A
CN112084709A CN202010922999.0A CN202010922999A CN112084709A CN 112084709 A CN112084709 A CN 112084709A CN 202010922999 A CN202010922999 A CN 202010922999A CN 112084709 A CN112084709 A CN 112084709A
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李志成
刘凌
张跃
胡波
梁智明
苏振
唐丽
黄子嘉
杨帅
黄泽
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Dongfang Electric Machinery Co Ltd DEC
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Abstract

本发明公开了一种基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,包括以下步骤:1)测量与大型汽轮发电机定子线棒绝缘老化状态相关的参量;2)对步骤1)测量得到的参量进行分类筛选,然后利用分类筛选后的参量构建数据集;3)建立RBF神经网络;4)利用遗传算法对RBF神经网络中隐藏层的个数、径向基函数的中心及宽度进行优化;5)利用数据集对优化后的RBF神经网络进行训练,然后利用负梯度下降法更新迭代权值;6)利用经步骤5)得到的RBF神经网络评估大型发电机的绝缘状态,该方法能够较为准确评估大型发电机的绝缘状态。

Figure 202010922999

The invention discloses a large-scale generator insulation state evaluation method based on genetic algorithm and radial basis neural network, comprising the following steps: 1) measuring parameters related to the insulation aging state of large-scale steam turbine generator stator bars; Step 1) The measured parameters are classified and screened, and then the parameters after classification and screening are used to construct a data set; 3) RBF neural network is established; Optimize the center and width; 5) Use the data set to train the optimized RBF neural network, and then use the negative gradient descent method to update the iterative weights; 6) Use the RBF neural network obtained in step 5) to evaluate the insulation of large generators state, this method can more accurately evaluate the insulation state of large generators.

Figure 202010922999

Description

基于遗传算法和径向基神经网络的大型发电机绝缘状态评估 方法Insulation Condition Evaluation of Large Generator Based on Genetic Algorithm and Radial Basis Neural Network method

技术领域technical field

本发明涉及一种大型发电机绝缘状态评估方法,具体涉及一种基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法。The invention relates to a large-scale generator insulation state evaluation method, in particular to a large-scale generator insulation state evaluation method based on a genetic algorithm and a radial basis neural network.

背景技术Background technique

大型汽轮发电机是电力系统的关键设备之一,其运行的可靠性关系到电网的运行稳定性,历来受到人们的高度重视,其安全运行的威胁之一主要来自于绝缘体系。在电机运行过程中,定子绕组要受到电、热、机械、化学等多种因素的联合作用,绝缘性能逐步劣化。在绝缘老化严重的情况下,会引起发电机的绝缘故障。由于剩余击穿电压受到很多因素的影响,所以传统上用少量的参数来预测剩余击穿电压的准确性较低。不能很好的满足状态预测的需求。Large-scale steam turbine generator is one of the key equipments in the power system. Its operation reliability is related to the operation stability of the power grid. It has always been highly valued by people. One of the threats to its safe operation mainly comes from the insulation system. During the operation of the motor, the stator winding is subject to the combined action of electrical, thermal, mechanical, chemical and other factors, and the insulation performance is gradually deteriorated. In the case of serious insulation aging, it will cause insulation failure of the generator. Because the residual breakdown voltage is affected by many factors, traditionally, the accuracy of predicting the residual breakdown voltage with a small number of parameters is low. It can not meet the needs of state prediction very well.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于克服上述现有技术的缺点,提供了一种基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,该方法能够较为准确评估大型发电机的绝缘状态。The purpose of the present invention is to overcome the above shortcomings of the prior art, and to provide a large-scale generator insulation state evaluation method based on genetic algorithm and radial basis neural network, which can more accurately evaluate the insulation state of large-scale generators.

为达到上述目的,本发明所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法包括以下步骤:In order to achieve the above object, the method for evaluating the insulation state of a large generator based on a genetic algorithm and a radial basis neural network according to the present invention includes the following steps:

1)测量与大型汽轮发电机定子线棒绝缘老化状态相关的参量;1) Measure the parameters related to the insulation aging state of the large-scale turbine generator stator bar;

2)对步骤1)测量得到的参量进行分类筛选,然后利用分类筛选后的参量构建数据集;2) classifying and screening the parameters obtained in step 1), and then using the parameters after classification and screening to construct a data set;

3)建立RBF神经网络;3) Establish RBF neural network;

4)利用遗传算法对RBF神经网络中隐藏层的个数、径向基函数的中心及宽度进行优化;4) Using genetic algorithm to optimize the number of hidden layers, the center and width of radial basis function in RBF neural network;

5)利用数据集对优化后的RBF神经网络进行训练,然后利用负梯度下降法更新迭代权值;5) Use the data set to train the optimized RBF neural network, and then use the negative gradient descent method to update the iterative weights;

6)利用经步骤5)得到的RBF神经网络评估大型发电机的绝缘状态。6) Use the RBF neural network obtained in step 5) to evaluate the insulation state of the large generator.

与大型汽轮发电机定子线棒绝缘老化状态相关的参量包括绝缘电阻R、极化指数PI、吸收比DAR、介质损耗tanδ、介损增量Δtanδ、电容量C及电容增加量ΔC。The parameters related to the insulation aging state of large-scale steam turbine generator stator bars include insulation resistance R, polarization index PI, absorption ratio DAR, dielectric loss tanδ, dielectric loss increment Δtanδ, capacitance C and capacitance increase ΔC.

利用负梯度下降法更新迭代权值包括以下步骤:Updating iterative weights using negative gradient descent includes the following steps:

利用负梯度下降法更新迭代RBF神经网络中输入层的权值、隐含层的权值及输出层的权值。Negative gradient descent method is used to update the weights of input layer, hidden layer and output layer in iterative RBF neural network.

步骤3)中利用经步骤2)分类筛选后的参量构建RBF神经网络。In step 3), an RBF neural network is constructed using the parameters classified and screened in step 2).

步骤4)的具体操作过程为:The specific operation process of step 4) is:

4a)对参量进行归一化处理;4a) Normalize the parameters;

4b)将归一化处理后的参数进行相关性分析,以剔除相关性低于预设值的参量;4b) carrying out a correlation analysis on the normalized parameters to eliminate the parameters whose correlation is lower than the preset value;

4c)利用遗传算法生成初始种群;4c) Generating an initial population by genetic algorithm;

4d)利用种群参数,令Kmeans算法中k=N,将参量进行分类,根据分类结果得分类中心,即隐藏层径向基函数的中心c,隐藏层径向基函数的宽度σ根据

Figure BDA0002667368150000021
计算,其中,ci和cj分别为第i个和第j个节点的中心向量;4d) Using the population parameters, set k=N in the Kmeans algorithm, classify the parameters, and obtain the classification center according to the classification result, that is, the center c of the hidden layer radial basis function, and the width σ of the hidden layer radial basis function according to
Figure BDA0002667368150000021
Calculate, where c i and c j are the center vectors of the i-th and j-th nodes, respectively;

4e)利用隐藏层的神经元个数N、径向基函数的中心c及宽度σ构建RBF训练模型,对数据集进行训练,并利用负梯度下降法更新权重,直到精度满足要求或迭代次数达到最大值为止,得训练后的RBF神经网络;4e) Use the number of neurons in the hidden layer N, the center c and the width σ of the radial basis function to build an RBF training model, train the data set, and update the weights using the negative gradient descent method until the accuracy meets the requirements or the number of iterations reaches Up to the maximum value, the trained RBF neural network is obtained;

4f)利用训练后的RBF神经网络对剩余击穿电压进行预测,然后计算每个种群的适应度;4f) Use the trained RBF neural network to predict the remaining breakdown voltage, and then calculate the fitness of each population;

4g)判断是否满足预设终止条件,当满足预设终止条件时,则转至步骤4h);否则,则对种群根据各自适应度进行选择交叉变异,然后转向步骤4e);4g) Judging whether the preset termination conditions are met, when the preset termination conditions are met, then go to step 4h); otherwise, the population is selected and cross-mutated according to each degree of adaptation, and then go to step 4e);

4h)按照训练结果选择种群中自适应度最优的个体作为RBF神经网络的隐藏层层数N,然后对数据集进行训练,获取经遗传算法改进的RBF神经网络的各参数。4h) According to the training result, select the individual with the best degree of self-adaptation in the population as the number of hidden layers N of the RBF neural network, and then train the data set to obtain the parameters of the RBF neural network improved by the genetic algorithm.

本发明具有以下有益效果:The present invention has the following beneficial effects:

本发明所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法在具体操作时,利用遗传算法对RBF神经网络中隐藏层的个数、径向基函数的中心及宽度进行优化,解决RBF神经网络预测中难以确定最佳隐藏层数的问题,以提高预测的准确性,然后对优化后的RBF神经网络进行训练,然后利用训练后的RBF神经网络评估大型发电机的绝缘状态,评估的准确性较高,有效解决传统依靠单变量老化因子进行电机绝缘状态预测的不准确、不全面的问题。The method for evaluating the insulation state of a large generator based on the genetic algorithm and the radial basis neural network of the present invention uses the genetic algorithm to evaluate the number of hidden layers, the center and the width of the radial basis function in the RBF neural network. Optimization, to solve the problem that it is difficult to determine the optimal number of hidden layers in the prediction of RBF neural network to improve the accuracy of prediction, then train the optimized RBF neural network, and then use the trained RBF neural network to evaluate the insulation of large generators The accuracy of the evaluation is relatively high, which effectively solves the inaccurate and incomplete problem of traditionally relying on the single-variable aging factor to predict the motor insulation state.

附图说明Description of drawings

图1为本发明的流程图;Fig. 1 is the flow chart of the present invention;

图2为本发明的系统图。FIG. 2 is a system diagram of the present invention.

具体实施方式Detailed ways

下面结合附图对本发明做进一步详细描述:Below in conjunction with accompanying drawing, the present invention is described in further detail:

本发明针对大型发电机定子绝缘老化状态的评估与预测问题,解决了传统依靠单变量老化因子进行电机绝缘状态预测的不准确、不全面的问题。同时,使用遗传算法解决RBF神经网络预测中难以确定最佳隐藏层数的问题,以提高预测的准确性。同时通过建立实验平台,选取测量装置对所需的参量进行测量。然后用测得的数据对RBF神经网络进行训练及预测,具体过程为:The invention aims at the evaluation and prediction of the insulation aging state of the stator of a large generator, and solves the inaccurate and incomplete problems of traditionally relying on the single-variable aging factor to predict the insulation state of the motor. At the same time, genetic algorithm is used to solve the problem that it is difficult to determine the optimal number of hidden layers in the prediction of RBF neural network, so as to improve the accuracy of prediction. At the same time, by establishing an experimental platform, a measuring device is selected to measure the required parameters. Then use the measured data to train and predict the RBF neural network. The specific process is as follows:

径向基神经网络Radial Basis Neural Network

径向基神经网络RBF是具有单隐层的三层前向网络,第一层为输入层,由信号源节点组成;第二层为隐藏层,隐藏层中神经元的变换函数即径向基函数为对中心点径向对称且衰减的非负线性函数,径向基函数为局部响应函数;第三层为输出层,为对输入模式做出的响应;输入层仅起到传输信号作用,输入层和隐含层之间可以看做连接权值为1的连接层,输出层与隐含层所完成的任务是不同的,因此他们的学习策略也不同;输出层是对线性权进行调整,采用的是线性优化策略,因而学习速度较快;而隐含层是对激活函数(格林函数,高斯函数,一般取后者)的参数进行调整,采用的是非线性优化策略,因而学习速度较慢。The radial basis neural network RBF is a three-layer forward network with a single hidden layer. The first layer is the input layer, which is composed of signal source nodes; the second layer is the hidden layer, and the transformation function of the neurons in the hidden layer is the radial basis. The function is a non-negative linear function that is radially symmetric and attenuated to the center point, and the radial basis function is a local response function; the third layer is the output layer, which is the response to the input mode; the input layer only plays the role of transmitting signals, The input layer and the hidden layer can be regarded as a connection layer with a connection weight of 1. The tasks performed by the output layer and the hidden layer are different, so their learning strategies are also different; the output layer adjusts the linear weights , adopts a linear optimization strategy, so the learning speed is faster; while the hidden layer adjusts the parameters of the activation function (Green function, Gaussian function, generally the latter), and adopts a nonlinear optimization strategy, so the learning speed is faster. slow.

训练的目的是求两层的最终权值Cj、Dj及Wj。The purpose of training is to find the final weights Cj, Dj and Wj of the two layers.

训练的过程分为两步:第一步是无监督学习,训练确定输入层与隐含层之间的权值Cj、Dj;第二步是有监督学习,训练确定隐含层与输出层间的权值Wj。The training process is divided into two steps: the first step is unsupervised learning, the training determines the weights Cj and Dj between the input layer and the hidden layer; the second step is supervised learning, and the training determines the difference between the hidden layer and the output layer. The weight Wj of .

训练前提供输入向量X、对应的目标输出向量Y和径向基函数的宽度向量Dj。The input vector X, the corresponding target output vector Y and the width vector Dj of the radial basis function are provided before training.

在输入训练集数据进行训练时,各参数的表达及计算方法为:When inputting the training set data for training, the expression and calculation method of each parameter are as follows:

输入向量X:X=[x1,x2,...,xn]T,其中,n为输入层的神经元个数,即输入训练集参数种类数。Input vector X: X=[x 1 , x 2 ,..., x n ] T , where n is the number of neurons in the input layer, that is, the number of types of parameters in the input training set.

输出向量Y:Y=[y1,y2,,...,yq]T,其中,q为输出层的神经元个数,即期望值。Output vector Y: Y=[y 1 , y 2 , . . . , y q ] T , where q is the number of neurons in the output layer, that is, the expected value.

初始化隐含层至输出层的权值:WK=[wk1,wk2,...,wkp]T,其中k=(1,2,…q)Initialize the weights from the hidden layer to the output layer: W K = [w k1 , w k2 ,..., w kp ] T , where k=(1,2,...q)

其中,p为隐藏层的神经元数,q为输出层的神经元数。Among them, p is the number of neurons in the hidden layer, and q is the number of neurons in the output layer.

参考中心初始化方法利用的隐藏层到输出层的权值初始化方法为:The weight initialization method from the hidden layer to the output layer used by the reference center initialization method is:

Figure BDA0002667368150000051
其中,mink为训练集中第k个神经元中所有期望输出的最小值;maxk为训练集中第k个输出神经元中所有期望输出的最大值,其中,j=(1,2,…,p)。
Figure BDA0002667368150000051
where mink is the minimum value of all expected outputs in the kth neuron in the training set; maxk is the maximum value of all expected outputs in the kth output neuron in the training set, where j=(1,2,...,p) .

初始化各隐含层神经元的中心参数Cj=[cj1,,cj2,...cjn]T,其初始化方法为:Initialize the central parameter C j =[c j1, , c j2 ,...c jn ] T of each hidden layer neuron, and the initialization method is:

Figure BDA0002667368150000052
Figure BDA0002667368150000052

其中,p为隐含层的神经元总个数,j=(1,2,…,p)。Among them, p is the total number of neurons in the hidden layer, j=(1,2,...,p).

mini为训练集中第i个特征所有输入信息的最小值,maxi为训练集中第i个特征所有输入信息的最大值。mini is the minimum value of all input information of the ith feature in the training set, and maxi is the maximum value of all input information of the ith feature in the training set.

初始化宽度向量Dj=[dj1,dj2,...djn],宽度向量影响着神经元对输入信息的作用范围:宽度越小,相应隐含层神经元作用函数的形状越窄,那么处于其他神经元中心附近的信息在该神经元处的响应就越小,计算方法为:Initialize the width vector D j =[d j1 , d j2 ,...d jn ], the width vector affects the scope of the neuron's action on the input information: the smaller the width, the narrower the shape of the corresponding hidden layer neuron action function, Then the information located near the center of other neurons has a smaller response at the neuron, and the calculation method is:

Figure BDA0002667368150000061
Figure BDA0002667368150000061

df为宽度调节系数,取值小于1,其作用是使每个隐含层神经元更容易实现对局部信息的感受能力,有利于提高RBF神经网络的局部响应能力。df is the width adjustment coefficient, and its value is less than 1. Its function is to make each hidden layer neuron easier to realize the ability to perceive local information, which is beneficial to improve the local response ability of the RBF neural network.

计算隐含层神经元的输出zj

Figure BDA0002667368150000062
Calculate the output z j of the hidden layer neurons,
Figure BDA0002667368150000062

其中,Cj为隐含层第j个神经元的中心向量,Dj为隐含层第j个神经元的宽度向量,||·||为欧式范数;Among them, Cj is the center vector of the jth neuron in the hidden layer, Dj is the width vector of the jth neuron in the hidden layer, and ||·|| is the Euclidean norm;

计算输出层神经元的输出Y=[y1,y2,...,yq]T

Figure BDA0002667368150000063
Calculate the output Y=[y 1 , y 2 , ..., y q ] T of the output layer neurons,
Figure BDA0002667368150000063

其中,wkj为输出层第k个神经元与隐含层第j个神经元间的调节权重;Among them, w kj is the adjustment weight between the kth neuron in the output layer and the jth neuron in the hidden layer;

Figure BDA0002667368150000064
Figure BDA0002667368150000064

Figure BDA0002667368150000065
Figure BDA0002667368150000065

Figure BDA0002667368150000066
Figure BDA0002667368150000066

其中,wkj(t)为第k个输出神经元与第j个隐含层神经元之间在第t次迭代计算时的调节权重;cji(t)为第j个隐含层神经元对于第i个输入神经元在第t次迭代计算时的中心分量;dji(t)为与中心cji(t)对应的宽度;η为学习因子;E为RBF神经网络的评价函数,其中,Among them, w kj (t) is the adjustment weight between the k-th output neuron and the j-th hidden layer neuron during the t-th iteration calculation; c ji (t) is the j-th hidden layer neuron For the center component of the i-th input neuron in the t-th iteration calculation; d ji (t) is the width corresponding to the center c ji (t); η is the learning factor; E is the evaluation function of the RBF neural network, where ,

Figure BDA0002667368150000071
Figure BDA0002667368150000071

其中,Olk为第k个输出神经元在第l个输入样本时的期望输出值;ylk为第k个输出神经元在第l个输入样本时的网络实际输出值。Among them, Olk is the expected output value of the kth output neuron at the lth input sample; ylk is the actual output value of the network at the lth input sample of the kth output neuron.

当E小于预期的误差ε时,训练结束。When E is less than the expected error ε, the training ends.

遗传算法Genetic Algorithm

遗传算法(Genetic Algorithm)是一类借鉴生物界的进化规律(适者生存,优胜劣汰遗传机制)演化而来的随机化搜索方法,它是由美国的J.Holland教授1975年首先提出,其主要特点是直接对结构对象进行操作,不存在求导和函数连续性的限定;具有内在的隐并行性和更好的全局寻优能力;采用概率化的寻优方法,能够自动获取和指导优化的搜索空间,自适应地调整搜索方向,不需要确定的规则。遗传算法的这些性质,可以很好地解决组合优化、机器学习方面的问题,它是现代有关智能计算中的关键技术。Genetic algorithm (Genetic Algorithm) is a random search method evolved from the evolutionary laws of biology (survival of the fittest, survival of the fittest genetic mechanism). It was first proposed by Professor J. Holland in the United States in 1975. It operates directly on structural objects, and there is no restriction on derivation and function continuity; it has inherent implicit parallelism and better global optimization capabilities; using probabilistic optimization methods, it can automatically obtain and guide the optimization search space, adaptively adjusts the search direction, and does not require definite rules. These properties of genetic algorithm can well solve the problems of combinatorial optimization and machine learning, and it is a key technology in modern intelligent computing.

遗传算法的基本运算过程为:The basic operation process of the genetic algorithm is:

a)初始化:设置进化代数计数器t=0,设置最大进化代数T,随机生成M个个体作为初始群体P(0);a) Initialization: set the evolutionary algebra counter t=0, set the maximum evolutionary algebra T, and randomly generate M individuals as the initial population P(0);

b)个体评价:计算群体P(t)中各个个体的适应度;b) Individual evaluation: calculate the fitness of each individual in the group P(t);

c)选择运算:将选择算子作用于群体,选择的目的是将优化的个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代,选择操作是建立在群体中个体的适应度评估基础上的;c) Selection operation: The selection operator is applied to the group. The purpose of selection is to directly inherit the optimized individual to the next generation or generate new individuals through pairing and crossover and then inherit it to the next generation. The selection operation is based on the adaptation of individuals in the group. on the basis of degree evaluation;

d)交叉运算:将交叉算子作用于群体,遗传算法中起核心作用的就是交叉算子;d) Crossover operation: The crossover operator is applied to the population, and the core role in the genetic algorithm is the crossover operator;

e)变异运算:将变异算子作用于群体,即是对群体中的个体串的某些基因座上的基因值作变动,群体P(t)经过选择、交叉、变异运算之后得到下一代群体P(t+1);e) Mutation operation: The mutation operator is applied to the population, that is, the gene values on some loci of the individual strings in the population are changed. After the population P(t) is selected, crossed, and mutated, the next generation population is obtained. P(t+1);

f)终止条件判断:若t=T,则以进化过程中所得到的具有最大适应度的个体作为最优解输出,终止计算。f) Judgment of termination condition: if t=T, the individual with the maximum fitness obtained in the evolution process is taken as the output of the optimal solution, and the calculation is terminated.

遗传算法是计算数学中用于解决最佳化的搜索算法,是进化算法的一种,它是依据生物学的基因的遗传变异的思想形成的。遗传算法是一种全局寻优的算法,可以避免陷入局部最优解和过拟合现象,并且通过交叉变异的方法可以很大程度地提高寻优的收敛速度。Genetic algorithm is a search algorithm used to solve optimization in computational mathematics. It is a kind of evolutionary algorithm. It is formed based on the idea of genetic variation of biological genes. Genetic algorithm is a global optimization algorithm, which can avoid falling into the local optimal solution and over-fitting, and can greatly improve the convergence speed of optimization through the method of crossover mutation.

遗传算法首先要进行编码然后进行再进一步完成基因的选择,交叉和变异。Genetic Algorithm firstly performs coding and then further completes gene selection, crossover and mutation.

遗传算法优化RBF神经网络的步骤为:The steps of genetic algorithm optimization of RBF neural network are:

1)为了方便基因的交叉变异,本发明采用二进制编码,生成初始种群,生成任意N组中心向量ci及宽度参数δi的网络参数;1) In order to facilitate the crossover variation of genes, the present invention adopts binary coding to generate an initial population, and generate any network parameters of N groups of center vectors c i and width parameters δ i ;

2)运用梯度下降法对网络参数ci和δi进行训练,求取网络的权值wi及种群个体的适应度f,适应度f为:2) Use the gradient descent method to train the network parameters c i and δ i , and obtain the weight wi of the network and the fitness f of the individual population, and the fitness f is:

Figure BDA0002667368150000081
Figure BDA0002667368150000081

其中,E为误差函数,Cmax=max{E};Among them, E is the error function, C max =max{E};

3)对步骤2)中适应度高的个体进行交叉变异操作;3) Perform crossover mutation operation on individuals with high fitness in step 2);

例如,随机选取个体a1,其对应的适应度为f1,随机选取个体为a2,其适应度为f2,选取个体an,其适应度为fn;在交叉变异中选取适应度较高的部分个体进行二进制码的部分位次进行交换或者取反;进而生成新的二进制码即新的ci和δiFor example, randomly select individual a1, its corresponding fitness is f1, randomly select individual a2, its fitness is f2, select individual an, its fitness is fn; select some individuals with higher fitness in the crossover mutation. Part of the binary code is exchanged or reversed; and a new binary code is generated, namely new c i and δ i ;

4)将步骤3)中得到的新的种群ci和δi继续运用步骤2)进行训练。4) Continue to use step 2) to train the new populations c i and δ i obtained in step 3).

5)当误差函数满足误差精度要求后,训练停止,取此时适应度最高的ci和δi作为RBF神经网络的最优参数值。5) When the error function meets the error accuracy requirements, the training stops, and the c i and δ i with the highest fitness at this time are taken as the optimal parameter values of the RBF neural network.

融合遗传算法后设计的新型RBF神经网络进行定子线棒的状态评估步骤为:The new RBF neural network designed after the fusion of the genetic algorithm is used to evaluate the state of the stator bars as follows:

1)选取与大型汽轮发电机定子线棒绝缘老化状态相关的电参量及非电参量作为待测量的参量类型。绝缘电阻R、极化指数PI和吸收比DAR能很好地反应绝缘受潮和积污情况,是定子绝缘吸湿,脏污特性调查的灵敏参数,介质损耗tanδ、介损增量Δtanδ、电容量C和电容增加量ΔC等可以反应绝缘中气隙、分层的大小和绝缘整体缺陷,一般常根据tanδ、C随电压的变化趋势评定绝缘的老化程度,若Δtanδ或ΔC接近于0,则表明绝缘无缺陷。当绝缘中含有大量的气隙时,随着绝缘上外施交流电压的升高,流过绝缘的泄漏电流会随着气隙放电的增强而突变,并出现第一和第二电流激增点,因此交流电流增加率ΔI与内部气隙分布状况和数量有关,也可以反映绝缘的整体老化状态。局部放电对绝缘的损坏最为严重,放电主要来源于绝缘内部的气隙击穿,最大局部放电量Qmax反映了绝缘内部最大的局部缺陷,可有效判断绝缘的局部老化情况。研究表明,上述特征量都与剩余击穿电压具有相关性。根据IEC标准,定子线棒绝缘剩余击穿电压UBD可以用来反应绝缘老化的程度,当UBD下降到初始击穿电压的一半时,则认为绝缘达到了寿命终点。1) Select the electrical parameters and non-electrical parameters related to the aging state of the stator bar insulation of large-scale steam turbine generators as the parameter types to be measured. Insulation resistance R, polarization index PI and absorption ratio DAR can well reflect the moisture and contamination of the insulation, and are sensitive parameters for the investigation of the moisture absorption and contamination characteristics of stator insulation, dielectric loss tanδ, dielectric loss increment Δtanδ, capacitance C and capacitance increase ΔC can reflect the size of air gaps, delaminations and overall insulation defects in the insulation. Generally, the aging degree of insulation is often evaluated according to the change trend of tanδ and C with voltage. If Δtanδ or ΔC is close to 0, it indicates that the insulation flawless. When the insulation contains a large number of air gaps, with the increase of the AC voltage applied to the insulation, the leakage current flowing through the insulation will change abruptly with the enhancement of the air gap discharge, and the first and second current surge points will appear. Therefore, the AC current increase rate ΔI is related to the distribution and quantity of the internal air gaps, and can also reflect the overall aging state of the insulation. Partial discharge damages the insulation the most seriously. The discharge mainly comes from the breakdown of the air gap inside the insulation. The maximum partial discharge Qmax reflects the largest local defect inside the insulation, which can effectively judge the local aging of the insulation. Studies have shown that the above characteristic quantities are all related to the residual breakdown voltage. According to the IEC standard, the residual breakdown voltage U BD of the stator bar insulation can be used to reflect the degree of insulation aging. When U BD drops to half of the initial breakdown voltage, the insulation is considered to have reached the end of its life.

2)对大型汽轮发电机加速老化的线棒进行测量,得到所需要的参量;2) Measure the accelerated aging wire rod of the large turbo-generator to obtain the required parameters;

在烘箱内或者采用加热板的方式对定子线棒进行热老化试验,应用激振器对定子线棒施加振动激励来模拟定子线棒受到的振动的情况,对线棒两端加电压来模拟其所受的电老化的影响。在测量参数方面使用IR测试仪进行绝缘电阻测量。IR测试仪包括有欧姆表、内置发电机及发电机,发电机用于产生高直流电压,将电压施加到定子绝缘的表面,并使电流绕绝缘表面流动,给出以欧姆为单位的IR读数,然后用1min和10min时的绝缘电阻值计算PI,用30s和60s的绝缘电阻值计算DAR。用介质损耗测试仪测量介质损耗tanδ及介损增量Δtanδ。通过测量电容电压和电容电流求取电容量C及电容增加量ΔC。通过电流互感器测量泄漏电流的值。采用局部放电测量仪器测量最大局部放电量Qmax。采用电压表测量剩余击穿电压。为更好的控制老化试验时的温度情况,本发明采用红外点阵测温法对温度进行检测。对形变量的测量可以通过位移传感器来完成。The thermal aging test is carried out on the stator bar in an oven or by means of a heating plate, and a vibration exciter is used to apply vibration excitation to the stator bar to simulate the vibration of the stator bar, and a voltage is applied to both ends of the bar to simulate its vibration. The effects of electrical aging. Insulation resistance measurement was performed using an IR tester in terms of measurement parameters. The IR tester includes an ohmmeter, a built-in generator, and a generator. The generator is used to generate a high DC voltage, apply the voltage to the surface of the stator insulation, and cause current to flow around the surface of the insulation, giving an IR reading in ohms , and then calculate PI with the insulation resistance values at 1min and 10min, and calculate DAR with the insulation resistance values at 30s and 60s. Use a dielectric loss tester to measure the dielectric loss tanδ and the dielectric loss increment Δtanδ. Calculate the capacitance C and the capacitance increase ΔC by measuring the capacitance voltage and capacitance current. The value of the leakage current is measured by the current transformer. A partial discharge measuring instrument is used to measure the maximum partial discharge quantity Q max . Use a voltmeter to measure the residual breakdown voltage. In order to better control the temperature during the aging test, the present invention adopts the infrared lattice temperature measurement method to detect the temperature. The measurement of the deformation amount can be done by a displacement sensor.

3)运用皮尔逊相关性分析将所测得的参量进行分类筛选,去掉与剩余击穿电压相关度不是很大的因素,皮尔逊算法是用于度量两个变量X和Y之间的相关(线性相关),其值介于-1与1之间,其计算公式为:3) Use Pearson correlation analysis to classify and filter the measured parameters, and remove the factors that are not very correlated with the residual breakdown voltage. The Pearson algorithm is used to measure the correlation between two variables X and Y ( Linear correlation), its value is between -1 and 1, and its calculation formula is:

Figure BDA0002667368150000101
Figure BDA0002667368150000101

其中,ρx,y为相关系数,cov(X,Y)为两个变量的协方差,δx、δy分别为两个参量的标准差,当相关系数大于0.7时,则认为两个量相关性很高,否则,则被认为是冗余参量并将其剔除掉,为提升模型的收敛速度和精确度,将上面选择好的参量进行归一化处理,即Among them, ρ x, y is the correlation coefficient, cov(X, Y) is the covariance of the two variables, δ x and δ y are the standard deviations of the two parameters, respectively. When the correlation coefficient is greater than 0.7, the two variables are considered to be The correlation is very high, otherwise, it is considered as redundant parameters and eliminated. In order to improve the convergence speed and accuracy of the model, the parameters selected above are normalized, namely

Figure BDA0002667368150000111
Figure BDA0002667368150000111

其中,x,y∈Rn;xmin=min(x);xmax=max(x);Among them, x, y∈R n ; x min =min(x); x max =max(x);

4)运用遗传算法改进的RBF神经网络建模4) RBF neural network modeling improved by genetic algorithm

4a)初始化数据集4a) Initialize the dataset

对实验测得的介电参量数据进行归一化处理,以减少不同数量级的数据对预测的影响并减小计算量;Normalize the experimentally measured dielectric parameter data to reduce the influence of data of different magnitudes on prediction and reduce the amount of calculation;

4b)将步骤4a)得到的介电参量数据进行相关性分析剔除相关性低的参量;4b) performing a correlation analysis on the dielectric parameter data obtained in step 4a) to eliminate parameters with low correlation;

4c)利用遗传算法生成初始种群;4c) Generating an initial population by genetic algorithm;

4d)利用种群参数,令Kmeans算法中k=N,将介电参量数据进行分类,根据分类结果得到分类中心,即隐藏层径向基函数的中心c,宽度σ可以根据

Figure BDA0002667368150000112
计算,其中,ci和cj分别是第i个和第j个节点的中心向量;4d) Using the population parameters, let k=N in the Kmeans algorithm, classify the dielectric parameter data, and obtain the classification center according to the classification result, that is, the center c of the hidden layer radial basis function, and the width σ can be determined according to
Figure BDA0002667368150000112
Calculate, where c i and c j are the center vectors of the i-th and j-th nodes, respectively;

4e)利用隐藏层的神经元个数N、径向基函数的中心c及宽度σ构建RBF训练模型,对数据集进行训练,并利用负梯度下降法更新权重,直到精度满足要求或迭代次数达到最大值,得训练后的RBF神经网络;4e) Use the number of neurons in the hidden layer N, the center c and the width σ of the radial basis function to build an RBF training model, train the data set, and update the weights using the negative gradient descent method until the accuracy meets the requirements or the number of iterations reaches The maximum value is the trained RBF neural network;

4f)利用训练后的RBF神经网络对剩余击穿电压进行预测,然后计算每个种群的适应度,即平均绝对百分误差MAPE;4f) Use the trained RBF neural network to predict the remaining breakdown voltage, and then calculate the fitness of each population, that is, the mean absolute percentage error MAPE;

4g)判断条件是否停止,否,则对种群根据各自适应度进行选择交叉变异,然后转向步骤4e),是,则转至步骤4h);4g) Judging whether the condition is stopped, if not, then select crossover mutation for the population according to each adaptive degree, and then turn to step 4e), if yes, then turn to step 4h);

4h)按照训练结果选择种群中适应度最优的个体作为RBF神经网络中隐藏层的层数N,然后对整训练集数据进行训练,获取经遗传算法改进的RBF神经网络的各参数;4h) according to the training result, select the individual with the best fitness in the population as the number N of hidden layers in the RBF neural network, and then train the entire training set data to obtain the parameters of the RBF neural network improved by the genetic algorithm;

4i)利用遗传算法改进的RBF神经网络对待预测剩余击穿电压进行预测,得到剩余击穿电压预测值。4i) The RBF neural network improved by genetic algorithm is used to predict the remaining breakdown voltage to be predicted, and the predicted value of the remaining breakdown voltage is obtained.

通过扫描电镜法和热重分析法对云母老化后的分层现象和气隙分布进行观察,将分层和气隙的老化情况进行等级划分,将其划分为4个等级,如表1所示:The delamination phenomenon and air gap distribution after aging of mica were observed by scanning electron microscopy and thermogravimetric analysis, and the aging conditions of delamination and air gap were classified into 4 grades, as shown in Table 1:

表1Table 1

Figure BDA0002667368150000121
Figure BDA0002667368150000121

4g)层次分析法在问题情境及策略目标的基础上,通过主观性的判断和问题因素之间相互作用和相互包含关系的分析,将各个因素按照不同的层次进行归纳,得到问题的层次结构图。通常来说,多层次分析结构中由高到低依次是目标层、标准层和决策方案层。由此,可以把较为复杂的问题转化为条理清晰,逻辑性强的多层次问题。在建立问题的层次结构图后,层次分析法应当将同层次各个因素之间进行相对重要性的排序,并根据序列结果确定因素的权重,从而为最终的决策提供定量化标准,因为电机环境运行温度对定子线棒老化的速度有一定的影响,所以利用层次分析法将神经网络预测的剩余击穿电压,发电机运行时周围的环境温度以及老化等级按照层次分析法的原则将三者综合考虑,给出最终的运行状态评估结果。4g) Analytic Hierarchy Process: On the basis of the problem situation and strategic objectives, through subjective judgment and analysis of the interaction and mutual inclusion between the problem factors, the various factors are summarized according to different levels, and the hierarchical structure diagram of the problem is obtained. . Generally speaking, the multi-level analysis structure is the target layer, the standard layer and the decision plan layer in order from high to low. In this way, more complex problems can be transformed into multi-level problems with clear order and strong logic. After establishing the hierarchical structure diagram of the problem, the AHP should rank the relative importance of each factor at the same level, and determine the weight of the factors according to the sequence results, so as to provide a quantitative standard for the final decision, because the motor environment runs The temperature has a certain influence on the aging speed of the stator bar, so the residual breakdown voltage predicted by the neural network, the ambient temperature and the aging level of the generator during operation are comprehensively considered according to the principle of AHP. , giving the final running status evaluation result.

Claims (5)

1.一种基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,其特征在于,包括以下步骤:1. a large-scale generator insulation state assessment method based on genetic algorithm and radial basis neural network, is characterized in that, comprises the following steps: 1)测量与大型汽轮发电机定子线棒绝缘老化状态相关的参量;1) Measure the parameters related to the insulation aging state of the large-scale turbine generator stator bar; 2)对步骤1)测量得到的参量进行分类筛选,然后利用分类筛选后的参量构建数据集;2) classifying and screening the parameters obtained in step 1), and then using the parameters after classification and screening to construct a data set; 3)建立RBF神经网络;3) Establish RBF neural network; 4)利用遗传算法对RBF神经网络中隐藏层的个数、径向基函数的中心及宽度进行优化;4) Using genetic algorithm to optimize the number of hidden layers, the center and width of radial basis function in RBF neural network; 5)利用数据集对优化后的RBF神经网络进行训练,然后利用负梯度下降法更新迭代权值;5) Use the data set to train the optimized RBF neural network, and then use the negative gradient descent method to update the iterative weights; 6)利用经步骤5)得到的RBF神经网络评估大型发电机的绝缘状态。6) Use the RBF neural network obtained in step 5) to evaluate the insulation state of the large generator. 2.根据权利要求1所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,其特征在于,与大型汽轮发电机定子线棒绝缘老化状态相关的参量包括绝缘电阻R、极化指数PI、吸收比DAR、介质损耗tanδ、介损增量Δtanδ、电容量C及电容增加量ΔC。2. the large-scale generator insulation state assessment method based on genetic algorithm and radial basis neural network according to claim 1, is characterized in that, the parameter relevant with large-scale steam turbine generator stator bar insulation aging state comprises insulation resistance R , polarization index PI, absorption ratio DAR, dielectric loss tanδ, dielectric loss increment Δtanδ, capacitance C and capacitance increase ΔC. 3.根据权利要求1所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,其特征在于,利用负梯度下降法更新迭代权值包括以下步骤:3. the large-scale generator insulation state assessment method based on genetic algorithm and radial basis neural network according to claim 1, is characterized in that, utilizes negative gradient descent method to update iteration weight and comprises the following steps: 利用负梯度下降法更新迭代RBF神经网络中输入层的权值、隐含层的权值及输出层的权值。Negative gradient descent method is used to update the weights of input layer, hidden layer and output layer in iterative RBF neural network. 4.根据权利要求1所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,其特征在于,步骤3)中利用经步骤2)分类筛选后的参量构建RBF神经网络。4. the large-scale generator insulation state assessment method based on genetic algorithm and radial basis neural network according to claim 1, is characterized in that, utilizes the parameter after step 2) classification screening to construct RBF neural network in step 3). 5.根据权利要求1所述的基于遗传算法和径向基神经网络的大型发电机绝缘状态评估方法,其特征在于,步骤4)的具体操作过程为:5. the large-scale generator insulation state assessment method based on genetic algorithm and radial basis neural network according to claim 1, is characterized in that, the concrete operation process of step 4) is: 4a)对参量进行归一化处理;4a) Normalize the parameters; 4b)将归一化处理后的参数进行相关性分析,以剔除相关性低于预设值的参量;4b) carrying out a correlation analysis on the normalized parameters to eliminate the parameters whose correlation is lower than the preset value; 4c)利用遗传算法生成初始种群;4c) Generating an initial population by genetic algorithm; 4d)利用种群参数,令Kmeans算法中k=N,将参量进行分类,根据分类结果得分类中心,即隐藏层径向基函数的中心c,隐藏层径向基函数的宽度σ根据
Figure FDA0002667368140000021
计算,其中,ci和cj分别为第i个和第j个节点的中心向量;
4d) Using the population parameters, set k=N in the Kmeans algorithm, classify the parameters, and obtain the classification center according to the classification result, that is, the center c of the hidden layer radial basis function, and the width σ of the hidden layer radial basis function according to
Figure FDA0002667368140000021
Calculate, where c i and c j are the center vectors of the i-th and j-th nodes, respectively;
4e)利用隐藏层的神经元个数N、径向基函数的中心c及宽度σ构建RBF训练模型,对数据集进行训练,并利用负梯度下降法更新权重,直到精度满足要求或迭代次数达到最大值为止,得训练后的RBF神经网络;4e) Use the number of neurons in the hidden layer N, the center c and the width σ of the radial basis function to build an RBF training model, train the data set, and update the weights using the negative gradient descent method until the accuracy meets the requirements or the number of iterations reaches Up to the maximum value, the trained RBF neural network is obtained; 4f)利用训练后的RBF神经网络对剩余击穿电压进行预测,然后计算每个种群的适应度;4f) Use the trained RBF neural network to predict the remaining breakdown voltage, and then calculate the fitness of each population; 4g)判断是否满足预设终止条件,当满足预设终止条件时,则转至步骤4h);否则,则对种群根据各自适应度进行选择交叉变异,然后转向步骤4e);4g) Judging whether the preset termination conditions are met, when the preset termination conditions are met, then go to step 4h); otherwise, the population is selected and cross-mutated according to each degree of adaptation, and then go to step 4e); 4h)按照训练结果选择种群中自适应度最优的个体作为RBF神经网络的隐藏层层数N,然后对数据集进行训练,获取经遗传算法改进的RBF神经网络的各参数。4h) According to the training result, select the individual with the best degree of self-adaptation in the population as the number of hidden layers N of the RBF neural network, and then train the data set to obtain the parameters of the RBF neural network improved by the genetic algorithm.
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