Transformer internal insulation life prediction method and system based on BP neural network

Abstract

The invention discloses a BP neural network-based transformer internal insulation life prediction method and system, and the method comprises the steps: firstly obtaining transformer historical operation data, carrying out the preprocessing of the transformer historical operation data through employing a main analytic hierarchy process, and then training a BP neural network model through employing the preprocessed transformer historical operation data, when training begins, optimizing initial network parameters of the BP neural network model by using an ant colony algorithm combined with a genetic algorithm; and predicting hot-spot temperature information of the transformer based on the trained BP neural network model, inputting a hot-spot temperature information prediction value into the insulation life evaluation model to obtain an insulation life evaluation value, and converting the insulation life evaluation value into a final insulation life prediction value based on a Monte Carlo method. The ant colony algorithm is improved by utilizing the genetic algorithm, so that the search efficiency is improved, and falling into a local optimal solution is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of transformer operation reliability, and in particular relates to a method and system for predicting the internal insulation life of a transformer based on a BP neural network. Background Art

[0002] The safe and reliable operation of transformers directly affects the normal operation of the power system. Once a transformer fails, it will not only interrupt the power supply of the system, but also greatly affect industrial and agricultural production and the normal life of the people, and even endanger personal and equipment safety. Improving the reliability of transformers by accurately predicting their life has become an urgent problem to be solved, which is very important for ensuring the reliable operation of the power system.

[0003] Existing literature points out that temperature is the main cause of insulation fatigue failure. Under high temperature, the dielectric will deteriorate significantly in a short time. Even if the temperature is not too high, the insulation performance will deteriorate irreversibly if the temperature is too long. The higher the temperature, the faster the insulation ages and the shorter the life. In addition, the main reason for the aging of transformer oil due to local overheating is that the oil will decompose into a variety of trace gases that can be dissolved in the oil. In summary, the hot spot temperature information of the transformer can be used to reflect the approximate life of the transformer, thereby predicting the life of the transformer.

[0004] The invention patent with application number CN202111458661.5 provides a transformer life prediction method, which integrates the dynamic parameter characteristics with the static parameter information of the transformer to be predicted and inputs them into the BP neural network to train the life prediction model. The dynamic parameter information includes at least one of the hot spot temperature of the transformer insulation winding, the transformer load voltage, the transformer load current and the transformer magnetic flux, and the static parameter information includes at least one of the moisture content of the transformer insulation paper, the water-soluble acid component in the transformer oil and the transformer oil acidity value. Although this method realizes the prediction of the remaining life of the transformer, there is a problem of low prediction accuracy of the BP neural network.

[0005] The invention patent with application number CN202311701302.7 provides a prediction method for the remaining life of dry-type transformers based on an improved ant colony optimization BP neural network. This method uses an ant colony algorithm to optimize the BP neural network to train data and obtain a remaining life prediction model. Although this method optimizes the BP neural network through the ant colony algorithm and reasonably adjusts the initial parameters of the BP neural network, it makes up for the problem of insufficient prediction accuracy of the neural network and improves the accuracy of the prediction of the remaining service life of the transformer, but this method has the problem of too long training time and is prone to falling into the local optimal solution.

[0006] In summary, the existing technology of optimizing BP neural network using ant colony algorithm has the problem of too long training time and easy to fall into the local optimal solution. Summary of the invention

[0007] The purpose of the present invention is to provide a method, system, device and medium for predicting the internal insulation life of a transformer based on a BP neural network, which can speed up the algorithm search efficiency and avoid falling into a local optimal solution in view of the above-mentioned problems existing in the prior art.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] In a first aspect, the present invention provides a method for predicting the internal insulation life of a transformer based on a BP neural network, the prediction method comprising the following steps:

[0010] S1. Obtain the historical operation data of the transformer and pre-process it using the principal hierarchy analysis method;

[0011] S2. Using the pre-processed historical operation data of the transformer to train the BP neural network model, during the training, using the ant colony algorithm combined with the genetic algorithm to optimize the initial network parameters of the BP neural network model, and then training the BP neural network model based on the optimal initial network parameters;

[0012] S3, predicting the hot spot temperature information of the transformer based on the trained BP neural network model;

[0013] S4, inputting the predicted value of the hot spot temperature information into the insulation life assessment model to obtain an insulation life assessment value;

[0014] S5. Convert the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method.

[0015] The S2 comprises the following steps:

[0016] S21, inputting the pre-processed historical operation data of the transformer;

[0017] S22, parameter initialization: The elements composed of the initial weights and thresholds generated by each node of the BP neural network are recorded as set I wi , let the number of ants in the ant population be N, the path coordinates of the ants be the weights and thresholds of each node in the BP neural network, let the path i→j be from the starting point e through the nodes i and j to the end point s, and obtain the pheromone concentration of the path i→j through the non-uniform distribution processing; the calculation formula for the non-uniform distribution processing is:

[0018]

[0019] In the above formula, v ijis the pheromone concentration from node i to node j; d ij is the Euclidean distance between node i and node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration;

[0020] S23, path selection: place all ants at the starting point and move them according to the following formula:

[0021]

[0022] In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration on the path from node i to node j at time t; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j;

[0023] S24. When all ants have traversed the elements in the set, the pheromone concentration on the path is updated according to the following formula:

[0024] v ij (t+n)=(1-ρ)v ij (t)+Δv ij (t,t+n)+(h×(LL i→j )) / L i→j ;

[0025] In the above formula, v ij (t+n) is the pheromone concentration on the path from node i to node j at time t+n; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; L i→j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient;

[0026] S25, calculating the fitness function value of each ant, forming a sub-population of ants whose fitness values ​​are less than a preset threshold, substituting them into a genetic algorithm for crossover mutation, and forming a new ant population with the sub-population after crossover mutation and the ants whose fitness values ​​are greater than or equal to the preset threshold;

[0027] S26, return to step S23 to iterate until the maximum number of iterations is reached, output the ant with the highest fitness value as the optimal solution, and substitute it into the BP neural network as the optimal initial network parameter;

[0028] S27, calculating the network error of the BP neural network;

[0029] S28, determine whether the network error of the BP neural network is less than the preset error threshold ε0; if so, the training ends; if not, update the network parameters of the BP neural network and return to step S27 to continue training.

[0030] In S24, the pheromone volatility coefficient ρ is adjusted according to the following formula:

[0031]

[0032] In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1).

[0033] In step S25, the specific steps of crossover mutation include:

[0034] A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula:

[0035]

[0036] In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of newly generated individuals after the crossover;

[0037] The calculation formula of the crossover probability is:

[0038]

[0039] In the above formula, b max , b min is the maximum and minimum value of the crossover probability; J max represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals undergoing crossover; J avg Represents the average fitness value of the sub-population;

[0040] A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is:

[0041]

[0042] In the above formula, P i is the probability of individual i being selected in the subpopulation; f i is the fitness function of individual i;

[0043] A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain a mutant individual.

[0044] The insulation life assessment model is:

[0045]

[0046] In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant; θ H is the hot spot temperature value.

[0047] In a second aspect, the present invention provides a transformer internal insulation life prediction system based on an improved BP neural network, the prediction system comprising a data acquisition module, a model training module, a hot spot temperature prediction module, and an insulation life calculation module;

[0048] The data acquisition module is used to acquire the historical operation data of the transformer and pre-process it using the principal hierarchy analysis method;

[0049] The model training module is used to train the BP neural network model using the preprocessed historical operation data of the transformer. During the training, the initial network parameters of the BP neural network model are first optimized using an ant colony algorithm combined with a genetic algorithm, and then the BP neural network model is trained based on the optimal initial network parameters;

[0050] The hot spot temperature prediction module is used to predict the hot spot temperature information of the transformer based on the trained BP neural network model;

[0051] The insulation life calculation module is used to input the hot spot temperature information prediction value into the insulation life evaluation model to obtain the insulation life evaluation value, and convert the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method.

[0052] The model training module is used to perform training according to the following steps:

[0053] S21, inputting the pre-processed historical operation data of the transformer;

[0054] S22, parameter initialization: The elements composed of the initial weights and thresholds generated by each node of the BP neural network are recorded as set I wi , let the number of ants in the ant population be N, the path coordinates of the ants be the weights and thresholds of each node in the BP neural network, let the path i→j be from the starting point e through the nodes i and j to the end point s, and obtain the pheromone concentration of the path i→j through the non-uniform distribution processing; the calculation formula for the non-uniform distribution processing is:

[0055]

[0056] In the above formula, v ij is the pheromone concentration from node i to node j; d ij is the Euclidean distance between node i and node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration;

[0057] S23, path selection: place all ants at the starting point and move them according to the following formula:

[0058]

[0059] In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration on the path from node i to node j at time t; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j;

[0060] S24. When all ants have traversed the elements in the set, the pheromone concentration on the path is updated according to the following formula:

[0061] v ij (t+n)=(1-ρ)v ij (t)+Δv ij (t,t+n)+(h×(LL i→j )) / L i→j ;

[0062] In the above formula, v ij (t+n) is the pheromone concentration on the path from node i to node j at time t+n; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; L i→j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient;

[0063] S25, calculating the fitness function value of each ant, forming a sub-population of ants whose fitness values ​​are less than a preset threshold, substituting them into a genetic algorithm for crossover mutation, and forming a new ant population with the sub-population after crossover mutation and the ants whose fitness values ​​are greater than or equal to the preset threshold;

[0064] S26, return to step S23 to iterate until the maximum number of iterations is reached, output the ant with the highest fitness value as the optimal solution, and substitute it into the BP neural network as the optimal initial network parameter;

[0065] S27, calculating the network error of the BP neural network;

[0066] S28, determine whether the network error of the BP neural network is less than the preset error threshold ε0; if so, the training ends; if not, update the network parameters of the BP neural network and return to step S27 to continue training.

[0067] The model training module adjusts the pheromone volatility coefficient ρ according to the following formula:

[0068]

[0069] In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1).

[0070] The model training module performs crossover mutation according to the following steps:

[0071] A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula:

[0072]

[0073] In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of newly generated individuals after the crossover;

[0074] The calculation formula of the crossover probability is:

[0075]

[0076] In the above formula, b max , b min is the maximum and minimum value of the crossover probability; J max represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals undergoing crossover; J avg Represents the average fitness value of the sub-population;

[0077] A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is:

[0078]

[0079] In the above formula, P i is the probability of individual i being selected in the subpopulation; f i is the fitness function of individual i;

[0080] A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain a mutant individual.

[0081] The insulation life assessment model is:

[0082]

[0083] In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant; θ H is the hot spot temperature value.

[0084] In a third aspect, the present invention provides a transformer internal insulation life prediction device based on a BP neural network, the prediction device comprising a memory and a processor;

[0085] The memory is used to store computer program code and transmit the computer program code to the processor;

[0086] The processor is used to execute the aforementioned method according to the instructions in the computer program code.

[0087] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the aforementioned method when executed by a processor.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] 1. The transformer internal insulation life prediction method based on BP neural network described in the present invention first obtains the transformer historical operation data, and pre-processes it using the principal hierarchy analysis method, then uses the pre-processed transformer historical operation data to train the BP neural network model, and at the beginning of the training, uses the ant colony algorithm combined with the genetic algorithm to optimize the initial network parameters of the BP neural network model; predicts the hot spot temperature information of the transformer based on the trained BP neural network model, inputs the hot spot temperature information prediction value into the insulation life evaluation model, obtains the insulation life evaluation value, and converts the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method; the above design uses the genetic algorithm to improve the ant colony algorithm, speeds up the search efficiency and avoids falling into the local optimal solution. Therefore, the present invention can speed up the algorithm search efficiency and avoid falling into the local optimal solution.

[0090] 2. The transformer internal insulation life prediction method based on BP neural network of the present invention aims at the problem that the initial information concentration of the ant colony algorithm is the same, resulting in poor initial search efficiency. By performing non-uniform distribution processing on the initial information concentration, that is, performing differential processing on the pheromone concentration of each path, the ant colony can clarify the search purpose in the initial stage, thereby improving the search efficiency of the algorithm. Therefore, the present invention can further improve the algorithm search efficiency.

[0091] 3. The transformer internal insulation life prediction method based on BP neural network described in the present invention controls the volatilization speed of pheromone through adaptive pheromone volatilization rules to improve the convergence speed and avoid falling into local optimum, thereby obtaining a global optimal solution. Therefore, the present invention can obtain a global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 The present invention is a flowchart of the method.

[0093] Figure 2 It is a schematic diagram of the structure of the system of the present invention.

[0094] Figure 3 It is a structural schematic diagram of the device described in the present invention.

[0095] Figure 4This is a flow chart of initial network parameter optimization and BP neural network training in step S2 of the method of the present invention. DETAILED DESCRIPTION

[0096] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0097] Embodiment 1:

[0098] See also Figure 1 , a transformer internal insulation life prediction method based on BP neural network, is carried out in the following steps:

[0099] S1. First, collect the historical operation data of the transformer, such as load rate K, harmonic voltage U, harmonic current I, coil temperature Tc, top oil temperature θ top 、Ambient temperatureθ a The principal components are extracted by the principal hierarchy analysis method. The principal component extraction method includes four steps, namely, data standardization, determination of principal components, calculation of principal component variance contribution rate and cumulative variance contribution rate, and selection of principal components. To avoid unnecessary information loss, the cumulative variance contribution rate G(l) ≥ 95% is used as the benchmark when determining the number of principal components, and the first l principal components are extracted. In order to make the data basically consistent in terms of discreteness, the standard principal component analysis method performs data standardization according to the following formula, so that the variance of each indicator of the processed sample data is equal and 1:

[0100]

[0101] In the above formula, x ij represents the sample data without data standardization; x′ ij Represents sample data after data standardization; δ Xj Respectively represent the mean and standard deviation of the sample data;

[0102] However, this method ignores the influence of the difference in the degree of discreteness between data on the selection of principal components, resulting in that the sample data after standardization cannot well contain most of the information of the original data when further extracting the principal components; therefore, averaging is used instead of data standardization. The formula for averaging is:

[0103]

[0104] S2. Use the pre-processed transformer historical operation data to train the BP neural network model; at the beginning of training, use the ant colony algorithm to optimize the initial network parameters of the BP neural network model, and substitute the optimal initial network parameters into the BP neural network to start training; the BP neural network model includes an input layer, a hidden layer and an output layer. The number of input layers and output layers is determined to be 1 layer, but the number of hidden layers is related to the global error size of the network. When the number of layers is large, the complexity of the network structure will increase, and it is easy to fall into the local minimum, which reduces the network training effect. Therefore, it is necessary to reasonably select the number of layers according to the actual problem. In the hot spot temperature prediction, when the number of hidden layers is selected as one, most of the function information can be processed, and the overall comprehensive evaluation effect of the network is better, so the BP neural network structure selects three layers; BP neural network The network training mainly includes two stages, namely, sample data forward transmission and error reverse transmission; in the process of sample data forward transmission, the input sample data is converted to the relationship between weights and thresholds and transmitted to the hidden layer nodes. Under the action of the activation function, the processed information is output to the output layer, and finally the output is obtained; if the final output does not match the expected output result and the error between the two is greater than or equal to the preset error threshold ε0, the error reverse transmission stage will begin to execute; the error reverse transmission stage uses the error information as the basis, readjusts the weights and thresholds of each layer according to the principle of gradient descent, and transmits the adjusted error information back to the hidden layer and input layer. When the error between the final output and the expected output result is less than the preset error threshold, the training is terminated and the trained BP neural network model is output;

[0105] See also Figure 4 The initial network parameter optimization and BP neural network training steps are specifically as follows:

[0106] S21, inputting the pre-processed historical operation data of the transformer;

[0107] S22, parameter initialization: The elements composed of the initial weights and thresholds generated by each node of the BP neural network are recorded as set I wi , assuming that the number of ants in the ant population is N, the path coordinates of the ants are the weights and thresholds of each node in the BP neural network, and the F clustering algorithm effect evaluation is used Measure Function is used as the fitness function, and path i→j is assumed to start from starting point e, pass through nodes i and j, and reach end point s;

[0108] When the traditional ant colony algorithm is used to improve the BP neural network, the initial pheromone concentration of the path is the same, which will lead to the problem of too long model training time. In order to solve this problem, the initial information concentration is processed with non-uniform distribution, that is, the pheromone concentration of each path is processed differently, so that the ant colony can clarify the search purpose in the initial stage and improve the search efficiency of the algorithm. The calculation formula for the non-uniform distribution processing is:

[0109]

[0110]

[0111] In the above formula, v ij is the pheromone concentration from node i to node j; d ij is the Euclidean distance between node i and node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration; the starting point and end point of the ant path in the ant colony algorithm are usually clearly specified by initializing variables to ensure that the algorithm can be optimized according to the established goals. The starting point e and end point s in this embodiment are determined by the initialization parameters;

[0112] S23, path selection: place all ants at the starting point and move them according to the following formula:

[0113]

[0114] In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration on the path from node i to node j at time t; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j;

[0115] S24. When all ants have traversed the elements in the set, the pheromone concentration on the path is updated. Since the pheromone update rule of the traditional ant colony algorithm is to update all the pheromones on all paths, it cannot form an obvious concentration difference and cannot highlight the shortest path or the shorter path. In order to solve this problem, the pheromone concentration of the shorter path is strengthened, and vice versa, the update is weakened to speed up the operation of the ant algorithm. The pheromone concentration update formula is as follows:

[0116] v ij (t+n)=(1-ρ)vij (t)+Δv ij (t,t+n)+(h×(LL i→j )) / L i→j ;

[0117] In the above formula, v ij (t+n) is the pheromone concentration on the path from node i to node j at time t+n; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; L i→j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient; after each iteration, when L>L i→j , it means that the path of this iteration is shorter than the optimal path of the previous iteration. Save the path and strengthen the pheromone update of this path. On the contrary, when L<L i→j , then the update is weakened;

[0118] If the value of ρ is too small, the pheromone will evaporate slowly, and a large amount of pheromone will accumulate on a better path, causing the search to fall into a local optimum. If the value of ρ is too large, the pheromone will evaporate too quickly, and the amount of information remaining on the path will not attract ants to search, slowing down the convergence speed. In order to solve this problem, the pheromone evaporation speed is controlled by an adaptive pheromone evaporation rule to improve the convergence speed, avoid falling into a local optimum, and obtain the global optimal solution. Specifically, the pheromone evaporation coefficient ρ is adjusted according to the following formula:

[0119]

[0120] In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1);

[0121] S25, calculating the fitness function value of each ant; in order to prevent the population from being premature, ants with fitness values ​​less than a preset threshold are formed into sub-populations, which are substituted into the genetic algorithm for crossover mutation, and the sub-populations after crossover mutation are combined with ants with fitness values ​​greater than or equal to the preset threshold to form a new ant population; the specific steps of crossover mutation include:

[0122] A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula:

[0123]

[0124] In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of newly generated individuals after the crossover;

[0125] In order to make the initial convergence speed faster and prevent premature mutation, the crossover probability calculation formula is improved; the improved crossover probability calculation formula is:

[0126]

[0127] In the above formula, b max , b min is the maximum and minimum value of the crossover probability; J max represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals undergoing crossover; J avg Represents the average fitness value of the sub-population;

[0128] A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is:

[0129]

[0130] In the above formula, P i is the probability of individual i being selected in the subpopulation; f i is the fitness function of individual i;

[0131] A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain the mutant individual; this reverse mutation method can increase the diversity of the population to improve the search efficiency and avoid falling into the local optimal solution; for example, the gene coding string of the original individual is: (231459867), two points are randomly selected: (231|4598|67), and the mutant individual is: (231|8954|67);

[0132] S26, return to step S23 to iterate and calculate until the maximum number of iterations is reached, and output the ant with the highest fitness value as the optimal solution; substitute the optimal solution into the BP neural network to calculate the network error between the expected result and the predicted result;

[0133] S27, determine whether the network error is less than the preset error threshold ε0; if so, the training ends; if not, return to step S21 to continue training.

[0134] S3. Predict the hot spot temperature information of the transformer based on the trained BP neural network model, and perform a denormalization operation on the prediction result to obtain the predicted value of the hot spot temperature information;

[0135] S4. Input the predicted value of the hot spot temperature information into an insulation life evaluation model to obtain an insulation life evaluation value; the insulation life evaluation model is:

[0136]

[0137] In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant, with a value of 9.34×10 5 θ H is the hot spot temperature value;

[0138] S5. In actual applications, due to changes in manufacturing processes, environmental conditions and operating conditions, device parameters will change, and the time required to reach failure will also vary accordingly. The life of components should usually be expressed using statistical values; therefore, a Monte Carlo simulation method is used to analyze 10,000 insulation life assessment values ​​obtained based on the insulation life assessment model within a 90% confidence interval, and a reliability assessment model at the transformer system level is established. The confidence level value is selected as the final insulation life prediction value.

[0139] Embodiment 2:

[0140] See also Figure 2 , a transformer internal insulation life prediction system based on improved BP neural network, including a data acquisition module, a model training module, a hot spot temperature prediction module, and an insulation life calculation module;

[0141] The data acquisition module is used to acquire the historical operation data of the transformer and pre-process it using the principal hierarchy analysis method;

[0142] The model training module is used to train the BP neural network model using the preprocessed historical operation data of the transformer. During the training, the ant colony algorithm combined with the genetic algorithm is used to optimize the initial network parameters of the BP neural network model, and then the BP neural network model is trained based on the optimal initial network parameters. The model training module specifically performs the optimization of the initial network parameters and the training of the BP neural network model according to the following steps:

[0143] S21, inputting the pre-processed historical operation data of the transformer;

[0144] S22, parameter initialization: The elements composed of the initial weights and thresholds generated by each node of the BP neural network are recorded as set I wi, let the number of ants in the ant population be N, the path coordinates of the ants be the weights and thresholds of each node in the BP neural network, let the path i→j be from the starting point e through the nodes i and j to the end point s, and obtain the pheromone concentration of the path i→j through the non-uniform distribution processing; the calculation formula for the non-uniform distribution processing is:

[0145]

[0146] In the above formula, v ij is the pheromone concentration from node i to node j; d ij is the Euclidean distance between node i and node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration;

[0147] S23, path selection: place all ants at the starting point and move them according to the following formula:

[0148]

[0149] In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration on the path from node i to node j at time t; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j;

[0150] S24. When all ants have traversed the elements in the set, the pheromone concentration on the path is updated according to the following formula:

[0151] v ij (t+n)=(1-ρ)v ij (t)+Δv ij (t,t+n)+(h×(LL i→j )) / L i→j ;

[0152] In the above formula, v ij (t+n) is the pheromone concentration on the path from node i to node j at time t+n; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; L i→j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient;

[0153] Adjust the pheromone volatility coefficient ρ according to the following formula:

[0154]

[0155] In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1);

[0156] S25, calculating the fitness function value of each ant, forming a sub-population with ants whose fitness value is less than a preset threshold, substituting into a genetic algorithm for crossover mutation, and forming a new ant population with the sub-population after crossover mutation and ants whose fitness value is greater than or equal to the preset threshold; the crossover mutation is performed according to the following steps:

[0157] A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula:

[0158]

[0159] In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of newly generated individuals after the crossover;

[0160] The calculation formula of the crossover probability is:

[0161]

[0162] In the above formula, b max , b min is the maximum and minimum value of the crossover probability; J max represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals undergoing crossover; J avg Represents the average fitness value of the sub-population;

[0163] A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is:

[0164]

[0165] In the above formula, P i is the probability of individual i being selected in the subpopulation; f i is the fitness function of individual i;

[0166] A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain a mutant individual;

[0167] S26, return to step S23 to iterate until the maximum number of iterations is reached, output the ant with the highest fitness value as the optimal solution, and substitute it into the BP neural network as the optimal initial network parameter;

[0168] S27, calculating the network error of the BP neural network;

[0169] S28, judging whether the network error of the BP neural network is less than the preset error threshold ε0; if so, the training ends; if not, the network parameters of the BP neural network are updated, and the training continues after returning to step S27;

[0170] The hot spot temperature prediction module is used to predict the hot spot temperature information of the transformer using the trained BP neural network model; the prediction result output by the BP neural network model is denormalized to obtain the hot spot temperature information prediction value;

[0171] The insulation life calculation module is used to first input the hot spot temperature information prediction value into the insulation life evaluation model to obtain the insulation life evaluation value, and then convert the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method; the insulation life evaluation model is:

[0172]

[0173] In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant; θ H is the hot spot temperature value.

[0174] Embodiment 3:

[0175] See also Figure 3, a transformer internal insulation life prediction device based on BP neural network, the prediction device includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the method described in Example 1 according to the instructions in the computer program code.

[0176] Embodiment 4:

[0177] A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described in Embodiment 1 when executed by a processor.

[0178] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0182] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for predicting the internal insulation life of a transformer based on a BP neural network, characterized in that: The prediction method comprises the following steps: S1. Obtain the historical operation data of the transformer and pre-process it using the principal hierarchy analysis method; S2. Using the pre-processed historical operation data of the transformer to train the BP neural network model. During the training, the ant colony algorithm combined with the genetic algorithm is first used to optimize the initial network parameters of the BP neural network model, and then the BP neural network model is trained based on the optimal initial network parameters. S3, predicting the hot spot temperature information of the transformer based on the trained BP neural network model; S4, inputting the predicted value of the hot spot temperature information into the insulation life assessment model to obtain an insulation life assessment value; S5. Convert the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method.

2. The transformer internal insulation life prediction method based on BP neural network according to claim 1 is characterized in that: The S2 comprises the following steps: S21, inputting the pre-processed historical operation data of the transformer; S22, parameter initialization: the elements composed of the initial weights and thresholds generated by each node of the BP neural network are recorded as a set Iwi, the number of ants in the ant population is set to N, the path coordinates of the ants are the weights and thresholds of each node of the BP neural network, and the path i→j is set from the starting point e through the nodes i and j to the end point s, and the pheromone concentration of the path i→j is obtained by non-uniform distribution processing; the calculation formula of the non-uniform distribution processing is: In the above formula, vij is the pheromone concentration from node i to node j; dij is the Euclidean distance from node i to node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration; S23, path selection: place all ants at the starting point and move them according to the following formula: In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration on the path from node i to node j at time t; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j; S24. When all ants have traversed all the elements in the set, the pheromone concentration on the path is updated according to the following formula: vij(t+n)=(1-ρ)vij(t)+Δvij(t,t+n)+(h×(L-Li → j)) / Li → j; In the above formula, v ij (t+n) is the pheromone concentration on the path from node i to node j at time t+n; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; Li → j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient; S25, calculating the fitness function value of each ant, forming a sub-population of ants whose fitness values ​​are less than a preset threshold, substituting them into a genetic algorithm for crossover mutation, and forming a new ant population with the sub-population after crossover mutation and the ants whose fitness values ​​are greater than or equal to the preset threshold; S26, return to step S23 to iterate and calculate until the maximum number of iterations is reached, output the ant with the highest fitness value as the optimal solution, and substitute it into the BP neural network as the optimal initial network parameter; S27, calculating the network error of the BP neural network; S28, determine whether the network error of the BP neural network is less than the preset error threshold ε0; if so, the training ends; if not, update the network parameters of the BP neural network and return to step S27 to continue training.

3. The transformer internal insulation life prediction method based on BP neural network according to claim 2 is characterized in that: In S24, the pheromone volatility coefficient ρ is adjusted according to the following formula: In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1).

4. The transformer internal insulation life prediction method based on BP neural network according to claim 2 is characterized in that: In step S25, the specific steps of crossover mutation include: A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula: In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of individuals newly generated after the crossover; The calculation formula of the crossover probability is: In the above formula, bmax and bmin are the maximum and minimum values ​​of the crossover probability; Jmax represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals that are crossed; Javg represents the average fitness value of the subpopulation; A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is: In the above formula, Pi is the probability of individual i being selected in the subpopulation; fi is the fitness function of individual i; A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain a mutant individual.

5. The method for predicting transformer internal insulation life based on BP neural network according to any one of claims 1 to 4, characterized in that: The insulation life assessment model is: In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant; θ H is the hot spot temperature value.

6. Transformer internal insulation life prediction system based on BP neural network, characterized by: The prediction system includes a data acquisition module, a model training module, a hot spot temperature prediction module, and an insulation life calculation module; The data acquisition module is used to acquire the historical operation data of the transformer and pre-process it using the principal hierarchy analysis method; The model training module is used to train the BP neural network model using the preprocessed historical operation data of the transformer. During the training, the initial network parameters of the BP neural network model are first optimized using an ant colony algorithm combined with a genetic algorithm, and then the BP neural network model is trained based on the optimal initial network parameters; The hot spot temperature prediction module is used to predict the hot spot temperature information of the transformer based on the trained BP neural network model; The insulation life calculation module is used to input the hot spot temperature information prediction value into the insulation life evaluation model to obtain the insulation life evaluation value, and convert the insulation life evaluation value into the final insulation life prediction value based on the Monte Carlo method.

7. The transformer internal insulation life prediction system based on BP neural network according to claim 6 is characterized in that: The model training module is used to perform training according to the following steps: S21, inputting the pre-processed historical operation data of the transformer; S22, parameter initialization: Set the elements composed of the initial weights and thresholds generated by each node of the BP neural network as set Iwi, set the number of ants in the ant population as N, set the path coordinates of the ants as the weights and thresholds of each node of the BP neural network, set the path i→j as starting from the starting point e through the nodes i and j to the end point s, and obtain the pheromone concentration of the path i→j through the non-uniform distribution processing; the calculation formula of the non-uniform distribution processing is: In the above formula, vij is the pheromone concentration from node i to node j; dij is the Euclidean distance from node i to node j; (x i ,y i )、(x j ,y j )、(x e ,y e )、(x s ,y s ) are the coordinates of node i, node j, starting point e, and end point s respectively; d se is the Euclidean distance from the starting point e to the end point s; d ie is the Euclidean distance between node i and starting point e; d sj is the Euclidean distance from node j to end point s; v0 is the initial pheromone concentration; S23, path selection: place all ants at the starting point and move them according to the following formula: In the above formula, represents the probability of the kth ant transferring from node i to node j at time t; α is the inspiration factor; β is the expectation factor; v ij (t) is the pheromone concentration of node i and node j; allowed k is the set of next nodes to which the kth ant can move; is a heuristic function, which indicates the expected degree of ant transfer from node i to node j; S24. When all ants have traversed the elements in the set, the pheromone concentration on the path is updated according to the following formula: vij(t+n)=(1-ρ)vij(t)+Δvij(t,t+n)+(h×(L-Li → j)) / Li → j; In the above formula, v ij (t) is the pheromone concentration on the path from node i to node j at time t; ρ is the pheromone volatility coefficient; Δv ij (t, t+n) is the pheromone left by the ant on the path from node i to node j from time t to time t+n; L is the optimal path length of the previous iteration; Li → j is the path length from node i to node j in the current iteration; h is the reinforcement coefficient; S25, calculating the fitness function value of each ant, forming a sub-population of ants whose fitness values ​​are less than a preset threshold, substituting them into a genetic algorithm for crossover mutation, and forming a new ant population with the sub-population after crossover mutation and the ants whose fitness values ​​are greater than or equal to the preset threshold; S26, return to step S23 to iterate and calculate until the maximum number of iterations is reached, output the ant with the highest fitness value as the optimal solution, and substitute it into the BP neural network as the optimal initial network parameter; S27, calculating the network error of the BP neural network; S28, determine whether the network error of the BP neural network is less than the preset error threshold ε0; if so, the training ends; if not, update the network parameters of the BP neural network and return to step S27 to continue training.

8. The transformer internal insulation life prediction system based on BP neural network according to claim 7 is characterized in that: The model training module adjusts the pheromone volatility coefficient ρ according to the following formula: In the above formula, ρ(t) is the pheromone volatility coefficient at time t; ρ min is the minimum value of the pheromone volatility coefficient; η is the global pheromone decrease factor, η∈(0,1).

9. The transformer internal insulation life prediction system based on BP neural network according to claim 7 is characterized in that: The model training module performs crossover mutation according to the following steps: A1. The ants in the sub-population are randomly paired, and the crossover probability is calculated by combining Sigmoid and Gaussian distributions. Each group of individuals is crossovered to generate a new pair of individuals. The crossover is performed according to the following formula: In the above formula, a1 and a2 represent a group of paired individuals; b represents the crossover probability; c1 and c2 represent a group of individuals newly generated after the crossover; The calculation formula of the crossover probability is: In the above formula, bmax and bmin are the maximum and minimum values ​​of the crossover probability; Jmax represents the maximum fitness value in the subpopulation; k1 is the curve smoothing parameter; J′ represents the larger fitness value between the two individuals that are crossed; Javg represents the average fitness value of the subpopulation; A2. Use the roulette rule to select the individual to perform mutation. The probability of an individual being selected is: In the above formula, Pi is the probability of individual i being selected in the subpopulation; fi is the fitness function of individual i; A3. Randomly generate two natural numbers m1 and m2 greater than 1. M is the total number of genes in the gene coding string of the individual; the genes between m1 and m2 in the gene coding string of the individual selected by A2 are arranged in reverse order to obtain a mutant individual.

10. The transformer internal insulation life prediction system based on BP neural network according to any one of claims 6 to 9, characterized in that: The insulation life assessment model is: In the above formula, L is the remaining life of transformer insulation; m is the mass fraction of the initial moisture content in the oil-paper; DP is the current degree of polymerization of the oil-paper; DP end is the specified value of the degree of polymerization at the end of life; A is a constant; θ H is the hot spot temperature value.

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