CN111881628B - Method and system for optimizing electrical parameters of square wave voltage source and storage medium - Google Patents
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Abstract
The invention discloses a method, a system and a storage medium for square wave voltage source electrical parameter optimization, wherein the method for square wave voltage source electrical parameter optimization comprises the following steps: constructing a mathematical model of a square wave voltage source voltage forming loop, determining electrical optimization parameters and optimization targets of the square wave voltage source voltage forming loop, and determining a value range of the electrical optimization parameters; and calculating to obtain a training sample set for training the network through the value range of the electrical optimization parameters, then training through the deep belief network to obtain the trained deep belief network, and finally obtaining a group of parameters with the minimum comprehensive optimization target, namely the electrical parameter optimal value. According to the method for optimizing the electrical parameters of the square wave voltage source, the number of times of tests and test time are reduced by using the deep belief network, and the deep belief network is trained firstly, namely the deep belief network is pre-trained, so that initial parameters of the network are more accurate.
Description
Technical Field
The invention relates to the field of high-voltage direct-current transmission, in particular to a method and a system for optimizing electrical parameters of a square wave voltage source and a storage medium.
Background
The direct current transformer is used as an important primary device in a high-voltage direct current transmission system, and the accuracy, reliability and transient characteristics of the direct current transformer directly influence the safe and stable operation of the high-voltage direct current transmission system. Particularly, with the wide application of the flexible direct current system, the control protection device of the flexible direct current system puts higher requirements on the rapidity and the accuracy of the transient response of the direct current transformer. However, due to the lack of technical equipment for correspondingly checking and calibrating transient characteristics, the direct current transformer is not reliably and effectively checked and calibrated all the time. According to actual operation and test results of direct current transformers in the current domestic converter stations, various direct current transformer devices have certain problems and hidden dangers, partial direct current transformers of the converter stations have faults for many times, and some faults directly cause monopole locking of a direct current system, so that great threats and hidden dangers are brought to stable and reliable power grid and economic operation. The relevant test results show that some performance defects can be found early through field calibration or periodic calibration, and some performance defects can be prevented and corrected through field tests. Therefore, the square wave voltage source for field test of the direct current measuring device is required to perform transient characteristic verification and calibration on the high-voltage direct current transformer, so that the safe operation of the direct current transmission system is ensured.
At present, methods for performing transient characteristic verification and calibration on a high-voltage direct-current transformer by applying a square-wave voltage source are an exhaustion method and a neural network method, and the exhaustion method requires many test times, so that the test time is long; neural network methods do not have pre-training and therefore do not have better initial parameters for the network.
Therefore, how to design a method for optimizing the electrical parameters of the square wave voltage source, which has a small number of tests and accurate initial parameters of the network, becomes an urgent problem to be solved.
Disclosure of Invention
The invention provides a method, a system and a storage medium for square wave voltage source electrical parameter optimization, which aim to solve the problems of multiple test times and inaccurate network initial parameters of the existing square wave voltage source electrical parameter optimization method.
In a first aspect, the present invention provides a method for electrical parameter optimization of a square wave voltage source, comprising:
s1: constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
s2: determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, and determining the value range of the electrical optimization parameters by combining a mapping method and a load voltage expression, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise the rise time, the duration and the maximum current of the square wave voltage, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
s3: calculating to obtain a training sample set for training the network according to the value range of the electrical optimization parameters;
s4: training through a deep belief network according to a training sample set, and calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement to obtain a trained deep belief network;
s5: unifying the three optimization targets into a comprehensive optimization target;
s6: in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is obtained through the parameters, and the group of parameters with the minimum comprehensive optimization target is the optimal value of the electrical parameter.
Optionally, the calculating, through the value range of the electrical optimization parameter, a training sample set for training the network includes:
s31: selecting an input sample within the value range of the electrical optimization parameter;
s32: output samples under input samples, namely a training sample set for training the network, are obtained through MATLAB/SIMULINK simulation.
Optionally, each input sample includes an energy storage capacitance value, a current limiting resistance value, and a pull-down resistance value.
Optionally, the three optimization targets are unified into a comprehensive optimization target, and the weight of the optimization target is set through an optimization target subjective method and an entropy weight method to obtain the comprehensive optimization target.
In a second aspect, the present invention provides a system for optimizing electrical parameters of a square wave voltage source, including a square wave voltage source state expression obtaining module, an electrical optimization parameter-optimization target obtaining module, a training sample set obtaining module, a training module, a comprehensive optimization target obtaining module, and an electrical parameter optimal value obtaining module, wherein:
the square wave voltage source state expression obtaining module is used for constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
the electrical optimization parameter-optimization target acquisition module is used for determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, combining a mapping method and a load voltage expression to determine a value range of the electrical optimization parameters, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise square wave voltage rise time, duration and maximum current, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
the training sample set acquisition module is used for calculating a training sample set used for training the network according to the value range of the electrical optimization parameters;
the training module is used for training through a deep belief network according to a training sample set, calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement, and obtaining the trained deep belief network;
the comprehensive optimization target acquisition module is used for setting the weight of the optimization target through a subjective method and an entropy weight method and unifying the three optimization targets into a comprehensive optimization target;
the electrical parameter optimal value acquisition module is used for acquiring a group of parameters with the minimum comprehensive optimization target through the parameters in the trained deep belief network, and the group of parameters with the minimum comprehensive optimization target is the electrical parameter optimal value.
In a third aspect, the present invention provides a storage medium containing computer executable instructions which, when executed by a computer processor, implement the method of square wave voltage source electrical parameter optimization as described in the first aspect.
The invention provides a method, a system and a storage medium for square wave voltage source electrical parameter optimization, wherein the method for square wave voltage source electrical parameter optimization comprises the following steps: constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression; determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, and determining a value range of the electrical optimization parameters by combining a mapping method and a load voltage expression; calculating to obtain a training sample set for training the network according to the value range of the electrical optimization parameters; training through a deep belief network according to a training sample set, and calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement to obtain a trained deep belief network; unifying the optimization targets into a comprehensive optimization target; in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is obtained through the parameters, and the group of parameters with the minimum comprehensive optimization target is the optimal value of the electrical parameter. According to the square wave voltage source electrical parameter optimization method, the deep belief network is used, so that the test times and the test time are reduced, the deep belief network is trained firstly, namely the deep belief network is pre-trained, and the initial parameters of the network are more accurate.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any inventive exercise.
FIG. 1 is a flow chart of a method for electrical parameter optimization of a square wave voltage source according to the present invention;
FIG. 2 is a deep belief network training result of the method for optimizing electrical parameters of a square wave voltage source provided by the invention;
FIG. 3 is a schematic diagram of a voltage forming loop for a square wave voltage source according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited by the specific embodiments disclosed below.
Referring to fig. 1, in a first aspect, the present invention provides a method for electrical parameter optimization of a square wave voltage source, including:
s1: constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
the state expression of the square wave voltage source is as follows:
in the formula: u1 is direct current voltage source voltage, u2 is load voltage, R1 and R2 are respectively a current limiting resistor and a pull-down resistor, and C1 and C2 are respectively an energy storage capacitor and a load capacitor.
S2: determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, and determining the value range of the electrical optimization parameters by combining a mapping method and a load voltage expression, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise the rise time, the duration and the maximum current of the square wave voltage, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
the electrical optimization parameters of the square wave voltage forming loop are as follows:
C1,R1,R2
c1 is the energy storage capacitor of the square wave voltage source, and the value range is [ C 1min ,C 1max ]；
R1 is the current-limiting resistance of the square wave voltage source, and the value range is [ R 1min ,R 1max ]；
R2 is a pull-down resistor of a square wave voltage source, and the value range is [ R 2min ,R 2max ]；
The optimization target is as follows: tr, ts, imax
tr is the rise time of the square wave voltage source, and the smaller the value is, the better the value is;
ts is a voltage holding time, and the larger the value, the better;
imax is the loop current peak value, and the smaller the value, the better the value;
the electrical optimization value range is shown in table 1.
| Structural parameters | Range of variation |
| C1/μF | [0.3,1] |
| R1/Ω | [1500,3500] |
| R2/MΩ | [4,20] |
TABLE 1 electric optimization parameter variation Range interval
The experimental factors included: the capacitor comprises an energy storage capacitance value C1, a current limiting resistance value R1 and a pull-down resistance value R2.
S3: calculating to obtain a training sample set for training the network according to the value range of the electrical optimization parameters;
s4: training through a deep belief network according to a training sample set, and calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement to obtain a trained deep belief network;
s5: unifying the three optimization targets into a comprehensive optimization target;
s6: in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is obtained through the parameters, and the group of parameters with the minimum comprehensive optimization target is the optimal value of the electrical parameter.
Optionally, the calculating, through the value range of the electrical optimization parameter, a training sample set for training the network includes:
s31: selecting an input sample within the value range of the electrical optimization parameter;
s32: output samples under input samples, namely a training sample set for training the network, are obtained through MATLAB/SIMULINK simulation.
Optionally, each of the input samples includes an energy storage capacitance value, a current limiting resistance value and a pull-down resistance value.
Selecting a plurality of training samples in the value range of the electrical optimization parameters, and obtaining square wave voltage technical index values (including tr, ts and imax) corresponding to each group of electrical parameters by using MATLAB/SIMULINK simulation so as to construct a training sample set TRAIN (p 3);
the selection mode of the training sample is as follows: in [ C ] 1min ,C 1max ]Taking p points as energy storage capacitance parameters at [ R ] by inner average 1min ,R 1max ]Taking p points as current limiting resistance parameters in inner average at R 2min ,R 2max ]Taking p points as pull-down resistance parameters by inner averaging;
then, the p is selected out by permutation and combination 3 In a combinatorial manner, construct p 3 Each training sample constituting a training sample set TRAIN (p) 3 )；
Each training sample comprises an energy storage capacitance value, a current limiting resistance value and a pull-down resistance value, and technical indexes such as corresponding square wave voltage rise time tr, voltage holding time ts, loop current peak value imax and the like are obtained according to MATLAB/SIMULINK simulation.
The deep belief network training specifically comprises the following steps:
step 1, pre-training stage: the pre-training stage is a process of training a multi-layer Restricted Boltzmann Machine (RBM), and the pre-training is started from a bottom RBM and is sequentially trained from bottom to top.
The RBM is provided with a visible layer V and a hidden layer H; at a given kth RBM model parameter theta k ＝{W k ,a k ,b k -defining an energy function for this RBM:
wherein, V k ＝[v k,1 ,v k,2 ,...,v k,m ] T Is the state vector of the visible layer of the kth RBM Unit, v k,i Represents the state of the ith neuron of the kth RBM unit, i ∈ [1,m ∈ ]]M represents the number of visible layer neurons, k ∈ [1,L ]]L represents the number of RBM units;
A k ＝[a k,1 ,a k,2 ,...,a k,m ] T is the offset vector of the visible layer of the kth RBM unit, a k,i Denotes the bias of the ith neuron of the kth RBM unit, i ∈ [1,m]L represents the number of RBM units;
H k ＝[h k,1 ,h k,2 ,...,h k,n ] T is the state vector of the hidden layer of the kth RBM unit, h k,j Represents the state of the jth neuron of the kth RBM unit, j ∈ [1,n ∈]N represents the number of hidden layer neurons, and L represents the number of RBM units;
B k ＝[b k,1 ,b k,2 ,...,b k,n ] T is the bias vector of the hidden layer of the k-th RBM unit, b k,j Represents the bias of the jth neuron of the kth RBM unit, j is epsilon [1,n]L represents the number of RBM units;
W k ＝{w k,i,j }∈R L×m×n a weight matrix representing the kth RBM unit connecting neurons of a visible layer and neurons of a hidden layer;
defining a function
The index is used for judging the kth RBM training result, the larger the value of the index is, the more fit between the RBM pre-training result and the distribution of training samples is represented, and the correlation process is described by the following mathematical formula:
in the above formula, θ k ＝{W k ,a k ,b k S is a training sample set, n s In order to train the number of samples,
each sample in the set S has the same probability distribution and is independent of each other;
step 2, fine adjustment stage: after the RBM is pre-trained, the initial parameter theta after training is carried out k＝L ＝{W k＝L ,a k＝L ,b k＝L Assigning to the last layer of neural network; and a Levenberg Marquardt algorithm is adopted, a training sample is used as a supervision signal, and the whole network is optimized from top to bottom.
The evaluation index of the deep belief network training is the average absolute percentage error e MAPE ，e MAPE Is expressed as
In the formula, y i And
outputting the actual value and the predicted value of the optimization target for the predicted point, e MAPE The smaller the value, the more accurate the result prediction.
And training by utilizing Matlab software and a deep belief network method according to the obtained training samples. Wherein, 3 layers of RBMs are selected for pre-training, and the number of hidden layer units is 20, 10 and 5 from bottom to top in sequence. And assigning the initial parameters after training to the last layer of BP neural network, and training by using an LM (Levenberg-Marquardt) algorithm. And fitting the relation between each structural parameter of the grading ring and the optimization target to obtain a trained network.
Setting the weight of the optimization target by using a subjective method and an entropy weight method, unifying the three optimization targets into a comprehensive optimization target, wherein the weights of tr, ts and imas are respectively 0.2521, 0.4243 and 0.3244; in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is selected from the parameters throughout the process, and the parameters are the optimal values of the electrical parameters.
Optionally, the three optimization targets are unified into one comprehensive optimization target, and the weight of the optimization target is set by an optimization target subjective method and an entropy weight method to obtain the comprehensive optimization target.
Referring to fig. 2 and 3, the best scheme of the grading ring structure obtained by using the trained network is as follows: c1=0.42 μ F, R1=1.8k Ω, R2=5.3M, and at this time, the square wave voltage index tr =11.4 μ s, ts =21ms, imax =51a.
In a second aspect, the present invention provides a system for optimizing electrical parameters of a square wave voltage source, including a square wave voltage source state expression obtaining module, an electrical optimization parameter-optimization target obtaining module, a training sample set obtaining module, a training module, a comprehensive optimization target obtaining module, and an electrical parameter optimal value obtaining module, wherein:
the square wave voltage source state expression obtaining module is used for constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
the electrical optimization parameter-optimization target acquisition module is used for determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, combining a mapping method and a load voltage expression to determine a value range of the electrical optimization parameters, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise square wave voltage rise time, duration and maximum current, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
the training sample set acquisition module is used for calculating a training sample set used for training the network according to the value range of the electrical optimization parameters;
the training module is used for training through a deep belief network according to a training sample set, calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement, and obtaining the trained deep belief network;
the comprehensive optimization target acquisition module is used for setting the weight of the optimization target through a subjective method and an entropy weight method and unifying the three optimization targets into a comprehensive optimization target;
the electrical parameter optimal value acquisition module is used for acquiring a group of parameters with the minimum comprehensive optimization target through parameters in a trained deep belief network, wherein the group of parameters with the minimum comprehensive optimization target is the electrical parameter optimal value.
In a third aspect, the present invention provides a storage medium containing computer executable instructions which, when executed by a computer processor, implement the method of square wave voltage source electrical parameter optimization as described in the first aspect.
The invention provides a method, a system and a storage medium for optimizing electrical parameters of a square wave voltage source, wherein the method for optimizing the electrical parameters of the square wave voltage source comprises the following steps: constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression; determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, and determining a value range of the electrical optimization parameters by combining a mapping method and a load voltage expression; calculating to obtain a training sample set for training the network according to the value range of the electrical optimization parameters; training through a deep belief network according to a training sample set, and calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement to obtain a trained deep belief network; unifying the optimization targets into a comprehensive optimization target; in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is obtained through the parameters, and the group of parameters with the minimum comprehensive optimization target is the optimal value of the electrical parameter. According to the square wave voltage source electrical parameter optimization method, the deep belief network is used, so that the test times and the test time are reduced, the deep belief network is trained firstly, namely the deep belief network is pre-trained, and the initial parameters of the network are more accurate.
The foregoing is merely a detailed description of the invention, and it should be noted that modifications and adaptations by those skilled in the art may be made without departing from the principles of the invention, and should be considered as within the scope of the invention.
Claims (6)
1. A method of electrical parameter optimization of a square wave voltage source, the method comprising:
s1: constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
s2: determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, and determining the value range of the electrical optimization parameters by combining a mapping method and a load voltage expression, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise the rise time, the duration and the maximum current of the square wave voltage, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
s3: calculating to obtain a training sample set for training the network through the value range of the electrical optimization parameters;
s4: training through a deep belief network according to a training sample set, and calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement to obtain a trained deep belief network;
s5: unifying the three optimization targets into a comprehensive optimization target;
s6: in the trained deep belief network, a group of parameters with the minimum comprehensive optimization target is obtained through the parameters, and the group of parameters with the minimum comprehensive optimization target is the optimal value of the electrical parameter.
2. The method according to claim 1, wherein the step of calculating a training sample set for training a network according to the value range of the electrical optimization parameter comprises:
s31: selecting an input sample within the value range of the electrical optimization parameter;
s32: output samples under input samples, namely a training sample set for training the network, are obtained through MATLAB/SIMULINK simulation.
3. The method for electrical parameter optimization of a square wave voltage source of claim 2, wherein each of the input samples comprises an energy storage capacitance value, a current limiting resistance value, and a pull-down resistance value.
4. The method for electrical parameter optimization of a square wave voltage source according to claim 1, wherein the three optimization objectives are unified into one comprehensive optimization objective, and the comprehensive optimization objective is obtained by setting the weight of the optimization objective through an optimization objective subjective method and an entropy weight method.
5. The system for optimizing the electrical parameters of the square wave voltage source is characterized by comprising a square wave voltage source state expression obtaining module, an electrical optimization parameter-optimization target obtaining module, a training sample set obtaining module, a training module, a comprehensive optimization target obtaining module and an electrical parameter optimal value obtaining module, wherein:
the square wave voltage source state expression obtaining module is used for constructing a mathematical model of a square wave voltage source voltage forming loop, and solving through MATLAB to obtain a square wave voltage source state expression, wherein the square wave voltage source state expression comprises a direct current voltage source voltage expression, a load voltage expression, an energy storage capacitor expression and a load capacitor expression;
the electrical optimization parameter-optimization target acquisition module is used for determining electrical optimization parameters and optimization targets of a square wave voltage source voltage forming loop according to a square wave voltage source state expression, combining a mapping method and a load voltage expression to determine a value range of the electrical optimization parameters, wherein the electrical optimization parameters comprise an energy storage capacitor, a current limiting resistor and a pull-down resistor, the optimization targets comprise square wave voltage rise time, duration and maximum current, and the value range of the electrical optimization parameters comprises the value range of the energy storage capacitor, the value range of the current limiting resistor and the value range of the pull-down resistor;
the training sample set acquisition module is used for calculating a training sample set used for training the network according to the value range of the electrical optimization parameters;
the training module is used for training through a deep belief network according to a training sample set, calculating the relation between each electrical parameter of a square wave voltage source voltage forming loop and an optimization target until the deep belief network meets the error requirement, and obtaining the trained deep belief network;
the comprehensive optimization target obtaining module is used for setting the weight of the optimization target through a subjective method and an entropy weight method, and unifying the three optimization targets into one comprehensive optimization target;
the electrical parameter optimal value acquisition module is used for acquiring a group of parameters with the minimum comprehensive optimization target through parameters in a trained deep belief network, wherein the group of parameters with the minimum comprehensive optimization target is the electrical parameter optimal value.
6. A storage medium containing computer executable instructions which, when executed by a computer processor, implement a method of square wave voltage source electrical parameter optimization according to any of claims 1-4.
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| CN110414718A (en) * | 2019-07-04 | 2019-11-05 | 上海工程技术大学 | A kind of distribution network reliability index optimization method under deep learning |
| CN110737998A (en) * | 2019-09-25 | 2020-01-31 | 中国电力科学研究院有限公司 | equalizing ring optimization design method based on finite element and depth belief network |
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| CN110414718A (en) * | 2019-07-04 | 2019-11-05 | 上海工程技术大学 | A kind of distribution network reliability index optimization method under deep learning |
| CN110737998A (en) * | 2019-09-25 | 2020-01-31 | 中国电力科学研究院有限公司 | equalizing ring optimization design method based on finite element and depth belief network |
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| 基于改进深度信念网络的电力变压器故障诊断研究;李辉等;《软件导刊》;20180626(第07期);全文 * |
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