CN112564098A - High-proportion photovoltaic power distribution network voltage prediction method based on time convolution neural network - Google Patents
High-proportion photovoltaic power distribution network voltage prediction method based on time convolution neural network Download PDFInfo
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Abstract
The invention discloses a high-proportion photovoltaic power distribution network voltage prediction method based on a time convolution neural network, which comprises the following steps of: step 1, carrying out data preprocessing on original load data: based on multiple time scales, performing normalization processing on the voltage time sequence data by adopting a maximum and minimum interval scaling method to obtain a complete voltage sequence; step 2, constructing an input feature vector set: performing feature screening based on an extreme gradient lifting tree algorithm of a decision tree, constructing a training sample set, outputting each feature weight, and screening out different feature subsets by combining the weight and the voltage prediction model condition; and 3, establishing a voltage prediction framework based on the photovoltaic power distribution network with the high proportion, training a time convolution network prediction model, and obtaining a voltage prediction result. The method combines the extracted characteristics with time and inputs the characteristics into different channels of the time convolution neural network model to obtain a prediction result, so that the aim of remarkably improving the voltage prediction precision of the power distribution network is fulfilled.
Description
Technical Field
The invention relates to the field of high-proportion photovoltaic power distribution network voltage prediction, in particular to a high-proportion photovoltaic power distribution network voltage prediction method based on a time convolution neural network.
Background
Under the dual pressure of increasingly worsening environmental problems and shortage of traditional energy, the grid-connected capacity of new photovoltaic energy is rapidly increased, the distributed photovoltaic access proportion of users of a part of low-voltage distribution networks is higher, the problems of voltage fluctuation, out-of-limit and the like are more serious, the fluctuation and the intermittent change of environmental factors such as solar radiation, weather, temperature and the like are obvious, a lot of problems are brought to large-scale grid connection of distributed photovoltaic, the risk of out-of-limit and fluctuation of the high-proportion distributed photovoltaic distribution networks is aggravated, and the problem is difficult to solve only by means of traditional voltage regulation. In addition, the node voltage fluctuation is more obvious due to the high-proportion distributed photovoltaic output sudden change, and the photovoltaic property rights of users belong to users, so that the uncertainty of the operation of the power grid is further increased.
Along with the improvement of the intelligent degree of the power grid, the electric meters with the communication function are used in a large number in the power distribution network, so that a large amount of data are accumulated in the running of the power distribution network. Considering that photovoltaic power generation has certain regularity, some potentially valuable information is hidden in the huge data volume, and the data value is not effectively utilized at present and needs to be explored by means of an artificial intelligence technology. Therefore, from the data driving perspective, the voltage variation trend is predicted, and a brand new idea is provided for solving the problem.
In conclusion, due to the fact that the historical operation data volume of the power grid is huge, and the voltage prediction belongs to the time series prediction category and has certain regularity, the potential key information of the global reactive voltage data can be deeply mined by preprocessing the big data according to the historical data formed in the power grid information operation process, and a reliable data set is provided for the research of the intelligent prediction technology of the high-proportion distributed photovoltaic distribution network voltage. The voltage prediction research is developed by an artificial intelligence deep learning theory from the data driving angle, a new power distribution network voltage prediction method with higher precision is explored, the technical problems occurring in the development process of a power distribution network and a distributed source are solved in time, and the competitiveness of the power distribution network in the aspects of operation, control, optimization and the like is improved.
However, due to the black box property of the deep learning model structure, the characteristics of voltage prediction need to be screened, the complexity of the deep learning model is reduced, overfitting is prevented, and a relatively ideal prediction result can be obtained. In addition, the voltage prediction data form must be analyzed in depth to find a prediction method that can satisfy the actual condition of the voltage data.
Disclosure of Invention
In order to make up for the defects in the prior art, the invention provides a voltage prediction method of a high-proportion photovoltaic power distribution network based on a time convolution neural network, so as to achieve the purpose of improving the reactive voltage prediction result of the photovoltaic power distribution network.
The invention adopts the following technical scheme: a high-proportion photovoltaic power distribution network reactive voltage prediction method based on a time convolution neural network comprises the following steps:
and 3, establishing a voltage prediction framework based on the photovoltaic power distribution network with a high proportion, training a time convolution neural network TCN prediction model, connecting the extracted feature vector to a neural network with a full-connection hidden layer, and outputting a voltage prediction value.
Furthermore, in step 1, in order to eliminate the influence of data dimensions such as voltage and power, the raw data is subjected to non-dimensionalization processing so that the values are placed in a (0, 1) interval, and the normalization formula of the preprocessing is as follows:
in the formula (1), the reaction mixture is,v * (t) In order to normalize the processed reactive voltage data,v(t) In the form of a voltage time-series signal,v max and v min respectively the maximum value and the minimum value of the reactive voltage signal.
Further, the step 2 specifically includes three steps:
step 21: firstly, according to the data preprocessing in the step 1, obtaining complete and dimensionless node voltage and net power data of the power distribution network, sampling the data through a sliding time window to construct sample characteristics, and setting the length of the sliding window asHThe sliding step length is 1 time step length, and the node voltage characteristic vectors are respectively obtained according to the time sequenceV i And node net power eigenvectorP i And corresponding labely i The following were used:
in the formula (2), the reaction mixture is,tat some point in time (as determined by the selection of the entire data set),ifor the purpose of the sample number, the number,V i andP i is of dimension ofHThe feature vector of (1), each element in the vector being a front elementHThe historical data of the individual points in time,y i voltage data of a sample label with a value of 1 hour later;
step 22: after the discrete time variable is subjected to one-hot encoding processing, a time characteristic vector of a prediction time point is constructedT i (ii) a Wherein i is a sample number,T i represent byy i Time information of the corresponding predicted point;
step 23: node voltage and net power vector obtained by sliding windowV i 、P i And temporal feature vectorT i Go on to bunch
Combining to obtain the final input feature vector of the ith samplex i Comprises the following steps:
feature vectorx i And a labely i The training sample set which jointly forms the XGboost algorithm is as follows:
in the formula (4), the reaction mixture is,nthe number of training samples;
the specific expression of the loss function L of all tree models of the XGboost algorithm is as follows:
in the formula (5), n is the number of samples,y i for the value of the tag of the ith sample,
the predicted output value for the ith sample model is used, in a regression problem,l typically a squared error function; whereinΩ(b m ) The calculation formula of the complexity term of the tree model, namely the model regularization term, is as follows:
in the formula (6), T is the number of leaf nodes of a single tree model,wthe vector is output for the leaf node(s),γandλthe parameters for controlling the regularization term weight can be adjusted before algorithm training;
applying a forward distribution algorithm calculation idea, initializing a model, and performing M rounds of circular calculation, wherein a decision tree model is optimized each time, and the newly added tree model minimizes a loss function L; in the t-th round of calculation, the objective function to be minimizedObj t()The calculation formula is as follows:
in the formula (7), the reaction mixture is,b t is as followstThe tree model is trained in a round of training,
is frontt-1 round of obtaining additive model prediction output values,Cis a constant with a value of fronttThe sum of the complexity of the tree models obtained in 1 round, i.e.
，Ω(b t ) Is as followstComplexity of the tree model obtained in turn;
calculating the Gain when the tree model divides the nodes according to the formula (7), and determining the optimal division point which enables the Gain to be maximum by performing characteristic traversal, wherein the Gain calculation formula is as follows:
in the formula (8), the first two terms respectively represent gains generated after the left sub-tree and the right sub-tree are segmented for the node, and the last term is the gain when the node is not segmented; the tree model structure can be optimized through the gain, and then a learning model enabling the loss function to be smaller is obtained.
Further, the step 3 specifically includes three steps:
constructing a voltage prediction model based on TCN, wherein the voltage prediction model comprises an input layer, an output layer and a residual error module;
step 31: constructing an input feature set of voltage prediction: the extracted feature vectors obtained from step 2: a node vector, a net power, and a time vector; wherein, in order to extract the historical data characteristics, the historical voltage sequences with the same sliding window length are usedvNode net power eigenvector sequencepCombining the two layers into an H-2 dimensional matrix from top to bottom, wherein H is the length of a moving time window, inputting the H into a double-channel convolution layer of the TCN, and then inputting the H into a residual error module of the TCN to perform feature extraction operation;
step 32: each residual module is used for replacing a convolutional layer, namely, the characteristic extraction work of historical data is carried out through the stacking of the residual modules; wherein, the receptive field of TCN is the depth of networkNSize of convolution kernelkAnd rate of expansiondDetermining TCNNThe residual modules are stacked in an input-output connection mode, each module comprises two one-dimensional expansion causal convolution layers with the same expansion rate and convolution kernel size, and the expansion rate is increased along with the deepening of the residual modulesdIncreasing an exponential function with the base 2, and fully extracting historical input features;
step 33: the output extracted in step 32 is a feature matrix, which is set as E, and the dimension of the feature matrix is the length of the input sequence and the number of convolution kernels; aiming at the voltage point prediction problem, data of the last time T of the time sequence is output, the last time point value of the sequence extracted by each convolution kernel is taken as a feature vector F (the dimensionality is the number of the convolution kernels), and the feature vector and the time feature extracted by a TCN network are taken as the feature vectorTSerially connected to a neural network with a fully-connected hidden layer for output, the output layer dimension is 1, and the output voltage predicted value is obtainedy i 。
Compared with the closest prior art, the invention has the following beneficial effects:
1. according to the photovoltaic voltage regulation-taking-into-account power distribution network reactive voltage prediction model, the power quality is improved, the safety and stability of power grid operation are improved, energy is saved, loss is reduced, and the operation economy and reliability are improved in a reactive compensation mode and the like;
2. on the basis of constructing a high-proportion photovoltaic prediction model, the method uses a time convolution neural network to train the model, adopts a structure of causal convolution and expansion convolution, effectively solves the problem of insufficient extraction of historical data features, has the inherent advantages of parallel computing capacity, small occupied memory, difficulty in gradient disappearance or explosion and the like, adopts a network structure connected by residual errors to deepen the network, adopts a form of expansion convolution, enables the network to feel farther historical information, improves the extraction capability of the network on deep historical information features, and further improves the voltage prediction precision.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a simulation system architecture employed in an application example of the present invention;
FIG. 3 is a diagram of a time convolutional neural network used in the present invention;
FIG. 4 is an exploded view of the XGboost algorithm feature screening applied to the present invention;
FIG. 5 is a voltage diagram of the prediction node 16 at a time scale of 3h in an application example of the present invention;
fig. 6 is a voltage diagram of the prediction node 16 on a 6h time scale in an application example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the flow of the high-proportion photovoltaic power distribution network reactive voltage prediction method based on the time convolution neural network, which is disclosed by the invention, is shown in fig. 1, and specifically comprises the following steps:
step 1: data preprocessing of raw load data
The step 1 is as follows:
selecting historical operation data of key nodes of the power distribution network in the last year, predicting the voltage time scale to be 1h, namely s =1, and constructing a training feature set by adopting a rolling prediction mode. In order to facilitate the training of a time convolution neural network prediction model and the feature extraction, the voltage time sequence data is subjected to maximum and minimum normalization processing, so that the original data is positioned in a [0,1] interval, and the normalization processing formula is as shown in formula (1):
in the formula (1), the reaction mixture is,v * (t) In order to normalize the processed reactive voltage data,v(t) In the form of a voltage time-series signal,v max and v min are respectively reactive powerThe maximum and minimum values of the voltage signal;
step 2: constructing a set of input feature vectors
The step 2 comprises the following three steps:
step 21: firstly, according to the data preprocessing in the step 1, obtaining complete and dimensionless node voltage and net power data of the power distribution network, sampling the data through a sliding time window to construct sample characteristics, and setting the length of the sliding window asHThe sliding step length is 1 time step length, and the node voltage characteristic vectors are respectively obtained according to the time sequenceV i And node net power eigenvectorP i And corresponding labely i The following were used:
in the formula (2), the reaction mixture is,tat some point in time (as determined by the selection of the entire data set),ifor the purpose of the sample number, the number,V i andP i is of dimension ofHThe feature vector of (1), each element in the vector being a front elementHThe historical data of the individual points in time,y i voltage data of a sample label with a value of 1 hour later;
step 22: after the discrete time variable is subjected to one-hot encoding processing, a time characteristic vector of a prediction time point is constructedT i (ii) a Wherein i is a sample number,T i represent byy i Time information of the corresponding predicted point;
step 23: node voltage and net power vector obtained by sliding windowV i 、P i And temporal feature vectorT i Go on to bunch
Combining to obtain the final input feature vector of the ith samplex i Comprises the following steps:
feature vectorx i And a labely i The training sample set which jointly forms the XGboost algorithm is as follows:
in the formula (4), the reaction mixture is,nthe number of training samples;
the specific expression of the loss function L of all tree models of the XGboost algorithm is as follows:
in the formula (5), n is the number of samples,y i for the value of the tag of the ith sample,
the predicted output value for the ith sample model is used, in a regression problem,l typically a squared error function; whereinΩ(b m ) The calculation formula of the complexity term of the tree model, namely the model regularization term, is as follows:
in the formula (6), T is the number of leaf nodes of a single tree model,wthe vector is output for the leaf node(s),γandλthe parameters for controlling the regularization term weight can be adjusted before algorithm training;
applying a forward distribution algorithm calculation idea, initializing a model, and performing M rounds of circular calculation, wherein a decision tree model is optimized each time, and the newly added tree model minimizes a loss function L; in the t-th round of calculation, the objective function to be minimizedObj t()The calculation formula is as follows:
in the formula (7), the reaction mixture is,b t is as followstThe tree model is trained in a round of training,
is frontt-1 round of obtaining additive model prediction output values,Cis a constant with a value of fronttThe sum of the complexity of the tree models obtained in 1 round, i.e.
，Ω(b t ) Is as followstComplexity of the tree model obtained in turn;
calculating the Gain when the tree model divides the nodes according to the formula (7), and determining the optimal division point which enables the Gain to be maximum by performing characteristic traversal, wherein the Gain calculation formula is as follows:
in the equation (8), the first two terms represent gains generated after the left sub-tree and the right sub-tree are segmented for the node, respectively, and the last term represents a gain when the node is not segmented. The tree model structure can be optimized through the gain, and then a learning model enabling the loss function to be smaller is obtained. Finally, obtaining a screened feature vector subset: a node vector, a net power, and a time vector;
and step 3: establishment of voltage prediction framework based on photovoltaic power distribution network with high proportion
The step 3 specifically comprises three steps:
constructing a voltage prediction model based on TCN, wherein the voltage prediction model comprises an input layer, an output layer and a residual error module;
step 31: constructing an input feature set of voltage prediction: the extracted feature vectors obtained from step 2: a node vector, a net power, and a time vector; wherein, in order to extract the historical data characteristics, the historical voltage sequences with the same sliding window length are usedvNode net power eigenvector sequencepCombined one above the other into a matrix of H x 2 dimensions, where H isThe length of the dynamic time window is input into a double-channel convolution layer of the TCN and then input into a residual error module of the TCN for feature extraction operation;
step 32: each residual module is used for replacing a convolutional layer, namely, the characteristic extraction work of historical data is carried out through the stacking of the residual modules; wherein, the receptive field of TCN is the depth of networkNSize of convolution kernelkAnd rate of expansiondDetermining TCNNThe residual modules are stacked in an input-output connection mode, each module comprises two one-dimensional expansion causal convolution layers with the same expansion rate and convolution kernel size, and the expansion rate is increased along with the deepening of the residual modulesdIncreasing an exponential function with the base 2, and fully extracting historical input features;
step 33: the output extracted in step 32 is a feature matrix, which is set as E, and the dimension of the feature matrix is the length of the input sequence and the number of convolution kernels; aiming at the voltage point prediction problem, data of the last time T of the time sequence is output, the last time point value of the sequence extracted by each convolution kernel is taken as a feature vector F (the dimensionality is the number of the convolution kernels), and the feature vector and the time feature extracted by a TCN network are taken as the feature vectorTSerially connected to a neural network with a fully-connected hidden layer for output, the output layer dimension is 1, and the output voltage predicted value is obtainedy i 。
Example 2
1) Building network model containing high-proportion photovoltaic power distribution network
Figure 2 of the accompanying drawings is an IEEE33 node power distribution system, which comprises 3 photovoltaic power sources, wherein the nodes 5,14 and 28 are provided with photovoltaic power sources, and the capacity of the photovoltaic power sources is shown in table 1.
TABLE 1 nodal photovoltaic Power supply parameters
| Mounting location | 5 | 14 | 28 |
| Active power/ |
25 | 16 | 45 |
| Reactive power/ |
8 | 5 | 5 |
2) Distribution network historical voltage data analysis
The bus 1 of the system is taken as a balance node, the node voltage approximately fluctuates in the range of 220-240V, a preprocessed voltage sequence of the node 16 in about 10 days is obtained according to the formula (1), then the preprocessed data is subjected to characteristic analysis of the XGboost algorithm, and the frequency proportion of each characteristic used for splitting the decision tree, namely the characteristic weight (the sum of all weights is 1), is output, as shown in FIG. 4.
3) Voltage prediction for photovoltaic power distribution network with high proportion
Voltage feature vector equalizing sliding window lengthsvNode net power eigenvectorpRespectively placing the two channels in the convolutional layer, inputting the two channels into a residual error module of the TCN, fully extracting the characteristics, and outputting the characteristics matrix and the time characteristics through extraction by the residual error module of the TCNTAnd serially connecting the neural networks with a fully-connected hidden layer for output to obtain a final predicted value. In order to show the prediction effect more clearly, the result is compared with the prediction effect of the LSTM, BPNN and SVM models, and the voltage variation curve of the prediction node 16 is obtained 3 hours before and 6 hours before under different time scales of different models, as shown in fig. 5 and 6.
Claims (4)
1. The high-proportion photovoltaic power distribution network voltage prediction method based on the time convolution neural network is characterized by comprising the following steps of:
step 1, performing data preprocessing on original load data: based on multiple time scales, performing normalization processing on the voltage time sequence data by adopting a maximum and minimum interval scaling method to obtain a complete voltage sequence;
step 2, constructing an input feature vector set: performing feature screening based on an XGboost algorithm of a decision tree, constructing a training sample set, outputting each feature weight, and screening out different feature subsets by combining the weight and the voltage prediction model condition;
step 3, establishing a voltage prediction framework based on the photovoltaic power distribution network with high proportion: and training a time convolution neural network TCN prediction model, connecting the extracted feature vector to a neural network with a fully-connected hidden layer, and outputting a voltage prediction value.
2. The method for predicting the voltage of the high-proportion photovoltaic power distribution network based on the time convolution neural network according to claim 1, wherein in the step 1, in order to eliminate the influence of data dimensions such as voltage and power, the raw data is subjected to non-dimensionalization processing, so that the values are placed in a (0, 1) interval, and the normalization formula of the preprocessing is as follows:
in the formula (1), the reaction mixture is,v * (t) In order to normalize the processed reactive voltage data,v(t) In the form of a voltage time-series signal,v max andv min respectively the maximum value and the minimum value of the reactive voltage signal.
3. The method for predicting the voltage of the high-proportion photovoltaic power distribution network based on the combined cyclic neural network according to claim 1, wherein the step 2 specifically comprises three steps:
step 21: firstly, according to the data preprocessing in the step 1, obtaining complete and dimensionless node voltage and net power data of the power distribution network, sampling the data through a sliding time window to construct sample characteristics, and setting the length of the sliding window asHThe sliding step length is 1 time step length, and the node voltage characteristic vectors are respectively obtained according to the time sequenceV i And node net power eigenvectorP i And corresponding labely i The following were used:
in the formula (2), the reaction mixture is,tat some point in time (as determined by the selection of the entire data set),ifor the purpose of the sample number, the number,V i andP i is of dimension ofHThe feature vector of (1), each element in the vector being a front elementHThe historical data of the individual points in time,y i voltage data of a sample label with a value of 1 hour later;
step 22: after the discrete time variable is subjected to one-hot encoding processing, a time characteristic vector of a prediction time point is constructedT i (ii) a Wherein i is a sample number,T i represent byy i Time information of the corresponding predicted point;
step 23: node voltage and net power vector obtained by sliding windowV i 、P i And temporal feature vectorT i Go on to bunch
Combining to obtain the final input feature vector of the ith samplex i Comprises the following steps:
feature vectorx i And a labely i Training samples jointly forming XGboost algorithmThe collection is as follows:
in the formula (4), the reaction mixture is,nthe number of training samples;
the specific expression of the loss function L of all tree models of the XGboost algorithm is as follows:
in the formula (5), n is the number of samples,y i for the value of the tag of the ith sample,
the predicted output value for the ith sample model is used, in a regression problem,l typically a squared error function; whereinΩ(b m ) The calculation formula of the complexity term of the tree model, namely the model regularization term, is as follows:
in the formula (6), T is the number of leaf nodes of a single tree model,wthe vector is output for the leaf node(s),γandλthe parameters for controlling the regularization term weight can be adjusted before algorithm training;
applying a forward distribution algorithm calculation idea, initializing a model, and performing M rounds of circular calculation, wherein a decision tree model is optimized each time, and the newly added tree model minimizes a loss function L; in the t-th round of calculation, the objective function to be minimizedObj t()The calculation formula is as follows:
in the formula (7)，b t Is as followstThe tree model is trained in a round of training,
is frontt-1 round of obtaining additive model prediction output values,Cis a constant with a value of fronttThe sum of the complexity of the tree models obtained in 1 round, i.e.
，Ω(b t ) Is as followstComplexity of the tree model obtained in turn;
calculating the Gain when the tree model divides the nodes according to the formula (7), and determining the optimal division point which enables the Gain to be maximum by performing characteristic traversal, wherein the Gain calculation formula is as follows:
in the formula (8), the first two terms respectively represent gains generated after the left sub-tree and the right sub-tree are segmented for the node, the last term is the gain when the node is not segmented, the tree model structure can be optimized through the gains, and then the learning model enabling the loss function to be smaller is obtained.
4. The method for predicting the voltage of the high-proportion photovoltaic power distribution network based on the time convolution neural network as claimed in claim 1, wherein the step 3 specifically comprises three steps:
constructing a voltage prediction model based on TCN, wherein the voltage prediction model comprises an input layer, an output layer and a residual error module;
step 31: constructing an input feature set of voltage prediction: the extracted feature vectors obtained from step 2: a node vector, a net power, and a time vector; wherein, in order to extract the historical data characteristics, the historical voltage sequences with the same sliding window length are usedvNode net power eigenvector sequencepCombining the two into H x 2 dimensional matrix, wherein H is the length of the moving time window, and inputting intoInputting the TCN double-channel convolution layer into a residual error module of the TCN for feature extraction;
step 32: each residual module is used for replacing a convolutional layer, namely, the characteristic extraction work of historical data is carried out through the stacking of the residual modules; wherein, the receptive field of TCN is the depth of networkNSize of convolution kernelkAnd rate of expansiondDetermining TCNNThe residual modules are stacked in an input-output connection mode, each module comprises two one-dimensional expansion causal convolution layers with the same expansion rate and convolution kernel size, and the expansion rate is increased along with the deepening of the residual modulesdIncreasing an exponential function with the base 2, and fully extracting historical input features;
step 33: the output extracted in step 32 is a feature matrix, which is set as E, and the dimension of the feature matrix is the length of the input sequence and the number of convolution kernels; aiming at the voltage point prediction problem, data of the last time T of the time sequence is output, the last time point value of the sequence extracted by each convolution kernel is taken as a feature vector F (the dimensionality is the number of the convolution kernels), and the feature vector and the time feature extracted by a TCN network are taken as the feature vectorTSerially connected to a neural network with a fully-connected hidden layer for output, the output layer dimension is 1, and the output voltage predicted value is obtainedy i 。
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