CN113379005A - Intelligent energy management system and method for power grid power equipment - Google Patents
Intelligent energy management system and method for power grid power equipment Download PDFInfo
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Abstract
The invention relates to a system and a method for intelligently managing power grid power equipment energy, wherein the system comprises: the data acquisition module is used for acquiring basic information of the power equipment; the device comprises an electric energy output quantity prediction module, a device fluctuation evaluation module and a power generation device, wherein the electric energy output quantity prediction module is used for training an output power prediction network and determining a corresponding training sample; other power equipment is used as secondary stable power generation equipment; and the equipment control module is used for selecting the primary stable power generation equipment to carry out grid-connected input control according to the demand of power consumption, and the secondary stable power generation equipment is used as standby equipment. The method can reasonably classify the power equipment, and specifically screen out the power equipment which is beneficial to the safety and the stability of the power grid, and preferentially adopts the primary power generation equipment to be put into use under the condition of limited power demand, and takes the secondary power generation equipment with relatively low safety as standby equipment, so that the method can be helpful for improving the safety and the stability of the power grid.
Description
Technical Field
The invention relates to the field of artificial intelligence and automatic management of power generation equipment in the power industry, in particular to a system and a method for intelligently managing energy of power grid power equipment.
Background
In the prior art, for monitoring the state of the power equipment of the power grid, indexes such as output voltage, current and power of the power equipment are generally monitored, taking power as an example, the monitored power is an actual value of power output in the running process of the equipment, the state quantity and the state variation of the power are continuously monitored and compared with a correspondingly set safety threshold, and if the monitored power exceeds the threshold, corresponding protection is started, for example, a line connecting the power equipment with the power grid is cut off, the power equipment is shut down, and then after-the-fact fault detection and maintenance processing are performed.
The monitoring and management mode of the power equipment has the disadvantages that the reason that the unstable fault of the power grid can occur is found out from the power equipment after the emergency treatment is carried out through the relay protection device on the line only when the unstable fault of the power grid line occurs, namely the unstable fault of the power grid line is finally caused due to the reasons of equipment aging, overlong running time and the like of the power equipment, so that the safety and the stability of the power grid are reduced by using the power equipment with the potential safety hazard.
Disclosure of Invention
The invention aims to provide a power grid power equipment energy intelligent management system and method, which are used for solving the problem that the safety and the stability of a power grid are reduced by the existing power equipment monitoring and management method.
Therefore, the adopted technical scheme is as follows:
the data acquisition module is used for acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and equipment running time; the device is also used for acquiring the actual output power of the equipment when the running time is t;
the electric energy output quantity prediction module is used for randomly selecting the basic information of a set number of electric power equipment in batches from the basic information of all the electric power equipment, using the basic information of all the batches of the electric power equipment as training samples, respectively using the training samples of all the batches as input, using the actual output power of the equipment in the training samples of all the batches when the operation time is t as output, respectively and independently training an output power prediction network, wherein the output power prediction network adopts a neural network, outputting the accuracy of each output power prediction network after training, and selecting a reference network according to the accuracy of each output power prediction network to be used as an optimal network for predicting the output power of the electric power equipment; the basic information of the power equipment is specifically equipment aging degree
Type of device
Length of operation of the apparatus
；
The equipment volatility evaluation module is used for selecting corresponding electric equipment in the training samples participating in the training of the optimal network as primary stable power generation equipment; selecting power equipment which does not participate in the optimal network training as secondary stable power generation equipment;
and the equipment control module is used for selecting the primary stable power generation equipment to carry out grid-connected input control according to the power requirement and a preset priority input sequence for each power generation equipment in all the primary stable power generation equipment determined by the equipment volatility evaluation module, and using the rest primary stable power generation equipment and the rest secondary stable power generation equipment as standby equipment.
Preferably, the specific construction process of the neural network is as follows:
collecting training samples including basic information of a plurality of electric power equipment to be screened, wherein the basic information is expressed as
Wherein, in the step (A),
is as follows
The device identification of the individual power device,
three symbols in the sequence are
Equipment aging degree, equipment model and equipment running time of each power equipment;
training output power prediction network, including power output quantity prediction encoder and full connection layer, network inputs basic information of electric power equipment, i.e.
Obtaining the characteristic vector after the coding by the power output quantity prediction coder, sending the characteristic vector into a full connection layer, wherein the running time of the network output equipment is as long astAnd the actual value of the output power of the hour is obtained through detection.
Preferably, the selecting a reference network according to the accuracy of each output power prediction network as the optimal network for predicting the output power of the power equipment includes:
(A) using a sample of basic information of the first batch of electrical equipment as a first training set to predict the output power of the network W1Training and judging the network W1Whether the predicted value of each output quantity is accurate or not is determined according to the network W1Outputting the number of accurate power output predicted values, and determining the network W after the training of the first training set1Accuracy A of1；
(B) In the amount of
Taking the second batch of samples as a second training set, and sending the second training set serving as a first verification set into the trained network W1And obtaining the verified network W of the first verification set1Accuracy of
(ii) a Then, the second training set is used as a training sample, and the network W is predicted according to the output power of the initial parameters2Training is carried out, and a second training set is obtained to train the network W2Accuracy of
；
(C) Obtaining a trained network W of a first training set1Accuracy of
Network W after verification with first verification set1Accuracy of
As a first accuracy deviation; obtaining a trained network W of a first training set1Accuracy of
Network W after training with the second training set2Accuracy of
As a second accuracy deviation; judging whether a set condition is met or not according to the first accuracy deviation and the second accuracy deviation, and determining a reference network;
(D) then the number is
Taking the next batch of samples as a new second training set, and taking the reference network W determined for the first time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the above steps (A) - (C) is repeated, and the reference network W is updated again1’；
(E) Repeating step (D) above until the last group is of quantity
Taking the last batch of samples as a new second training set, and taking the reference network W determined last time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the steps (A) - (C) is repeated, and the optimal reference network W is obtained by updating1' to this point.
Preferably, in the step (C), whether the set condition is satisfied is determined according to the magnitudes of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet the first set condition, the network is retrained again by using the second training set to obtain the reference network W1', the formula of the first setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
Preferably, in the step (C), whether the set condition is satisfied is determined according to the magnitudes of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation satisfy a second set condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the second setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
Preferably, in the step (C), whether the set condition is satisfied is determined according to the magnitudes of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet a third set condition, combining the first training set and the second training set, and training the output power prediction network of the initial parameters by using the combined training set to obtain a reference network W1', the formula of the third setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
Preferably, in the step (C), whether the set condition is satisfied is determined according to the magnitudes of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation satisfy a fourth setting condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the fourth setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
In a second aspect, the invention provides a method for intelligently managing energy of power grid power equipment, which comprises the following steps:
s1, acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and equipment running time; the actual output power of the equipment when the running time is t is also obtained;
s2, randomly selecting the basic information of a set number of electric power equipment in batches from the basic information of all the electric power equipment, using the basic information of all the electric power equipment in each batch as training samples, respectively using the training samples in each batch as input, using the actual output power of the equipment in the training samples in each batch when the operation time is t as output, respectively and independently training an output power prediction network, wherein the output power prediction network adopts a neural network, outputting the accuracy of each output power prediction network after training, and selecting a reference network according to the accuracy of each output power prediction network as an optimal network for predicting the output power of the electric power equipment; the basic information of the power equipment comprises equipment aging degree
Type of device
Length of operation of the apparatus
；
S3, selecting corresponding power equipment in the training samples participating in the optimal network training as primary stable power generation equipment; selecting power equipment which does not participate in the optimal network training as secondary stable power generation equipment;
and S4, selecting the first-stage stable power generation equipment to carry out grid-connected input control according to the power requirement and the preset priority input sequence for each power generation equipment in all the first-stage stable power generation equipment, and taking the rest first-stage stable power generation equipment and the rest second-stage stable power generation equipment as standby equipment.
Preferably, in step S2, the selecting a reference network according to the accuracy of each output power prediction network as the optimal network for predicting the output power of the electrical power equipment includes:
(A) using a sample of basic information of the first batch of electrical equipment as a first training set to predict the output power of the network W1Training and judging the network W1Whether the predicted value of each output quantity is accurate or not is determined according to the network W1Outputting the number of accurate power output predicted values, and determining the network W after the training of the first training set1Accuracy A of1；
(B) In the amount of
Taking the second batch of samples as a second training set, and sending the second training set serving as a first verification set into the trained network W1And obtaining the verified network W of the first verification set1Accuracy of
(ii) a Then using the second training set asTraining samples, output Power prediction network W for initial parameters2Training is carried out, and a second training set is obtained to train the network W2Accuracy of
；
(C) Obtaining a trained network W of a first training set1Accuracy of
Network W after verification with first verification set1Accuracy of
As a first accuracy deviation; obtaining a trained network W of a first training set1Accuracy of
Network W after training with the second training set2Accuracy of
As a second accuracy deviation; judging whether a set condition is met or not according to the first accuracy deviation and the second accuracy deviation, and determining a reference network;
(D) then the number is
Taking the next batch of samples as a new second training set, and taking the reference network W determined for the first time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the above steps (A) - (C) is repeated, and the reference network W is updated again1’；
(E) Repeating step (D) above until the last group is of quantity
The last batch of samples of the individuals is used as a new second training set, and the last batch of samples is used as a previous training setReference network W of secondary determination1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the steps (A) - (C) is repeated, and the optimal reference network W is obtained by updating1' to this point.
Preferably, in the step (C), whether the set condition is satisfied is determined according to the magnitudes of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet the first set condition, the network is retrained again by using the second training set to obtain the reference network W1', the formula of the first setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation satisfy a second set condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the second setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation meet a third set condition, combining the first training set and the second training set, and training the output power prediction network of the initial parameters by using the combined training set to obtain a reference network W1', the formula of the third setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation satisfy a fourth setting condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the fourth setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
The invention has the following beneficial effects:
the invention discloses an intelligent management system and method for standby energy, which divide basic information of different power equipment into a plurality of batches, perform independent neural network training on equipment information of each batch, determine an optimal network according to the accuracy of each neural network, and finally utilize a sample participating in the optimal network training to reasonably classify the power equipment into primary power generation equipment with small volatility and secondary power generation equipment with relatively large volatility, namely the power equipment in the sample participating in the optimal network training is primary power generation equipment, and the power equipment in the sample not participating in the optimal network training is secondary power generation equipment.
Therefore, in the control process of the power equipment which is put into operation, the power equipment which is beneficial to the safety and the stability of the power grid can be screened in a targeted mode, the equipment is preferentially adopted to be put into use under the condition that the power demand is limited, the secondary power generation equipment with relatively low safety is used as standby equipment, and output power fluctuation is more likely to occur to the power generation equipment relative to the primary power generation equipment, so that the power grid is broken down. In addition, the intelligent automatic classification method can realize the intelligent automatic classification of the primary power generation equipment and the secondary power generation equipment, and the automatic classification speed is higher and more reasonable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of an intelligent energy management system for grid power equipment in embodiment 1 of the present invention;
fig. 2 is a flowchart of a method for intelligently managing energy of grid power equipment in embodiment 2 of the present invention.
Detailed Description
The embodiments provided by the invention are specifically described below with reference to the accompanying drawings.
Example 1:
the invention discloses an intelligent energy management system for power grid power equipment, which mainly aims to realize the following steps: the invention relates to efficient and quick-response intelligent management of power equipment, which aims at the following specific scenes: under the intelligent power grid scene, demand side demand electric quantity is known, does not consider transmission loss, carries out overall management to power equipment's switch.
Specifically, the system for intelligently managing the energy of the grid power equipment as shown in fig. 1 includes:
the data acquisition module is used for acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and current running time of the equipment.
The basic information of the above electric power equipment is known as the basic attribute of the electric power equipment, wherein the rated power of the equipment corresponding to the equipment can be obtained according to the model of the equipment.
And (II) an electric energy output quantity prediction module, which is used for randomly selecting the basic information of a set number of electric power equipment in batches from the basic information of all the electric power equipment, using the basic information of all the batches of electric power equipment as training samples, respectively using the training samples of all the batches as input, using the actual output power of the equipment in the training samples of all the batches when the running time is t as output, respectively and independently training output power prediction networks, outputting the accuracy of all the output power prediction networks after training, and selecting a reference network according to the accuracy of all the output power prediction networks to be used as an optimal network for predicting the output power of the electric power equipment.
In the module, an output power prediction network for predicting theoretical output power is realized by adopting a neural network, and the specific construction process of the neural network is as follows:
collecting training samples including basic information of a plurality of power equipment to be screened (screening primary stable power generation equipment), specifically the aging degree of the equipment
Type of device
Length of operation of the apparatus
The basic information is expressed as
Wherein, in the step (A),
is as follows
The equipment identification of each power equipment is marked by an implementer, and all the equipment needs to be ensured to be marked and not to be repeatedly marked. In addition, the collected information also includes the operation time of each power equipment as
Actual output power of the time.
For a single device, based on its initial operating duration
Obtaining the long information of the running time of the equipment with continuous n periods, i.e.
，
N =1,2, … N for the sampling interval of the device basis information. Thus, basic information of different operation time lengths of a single device and corresponding actual output power can be collected.
The training output power prediction network comprises a power output quantity prediction coder and a full connection layer, and the specific training process is as follows: network input of basic information of electric power equipment, i.e.
Obtaining the characteristic direction after encoding by the power output quantity prediction encoderMeasuring, namely sending the characteristic vector into a full connection layer, wherein the network output is the actual output power when the equipment operation time is t; t is a fixed duration, set by the implementer. Taking t as six hours as an example, the output quantity is an output power detection value of the device which continuously operates for six hours.
Further, in order to improve the prediction accuracy of the output power prediction network, that is, according to the basic information of the input power device, the error between the theoretical output power output by the prediction network and the actual output power is smaller, a prediction network with good prediction performance needs to be determined as a reference network for predicting the theoretical output power of each power device.
The specific determination process of the reference network is as follows:
(A) using a sample of basic information of a first batch of equipment as a first training set to predict the output power of the network W1Training and judging the network W1Whether the predicted value of each output quantity is accurate or not is determined according to the network W1Outputting the number of accurate power output predicted values, and determining the network W after the training of the first training set1Accuracy A of1。
Understandably, the number of samples in a single batch in the training process is set as
Taking a single batch of samples as a first training set to perform network training, wherein a loss function of the network adopts a mean square error function, namely
Wherein, in the step (A),
is as follows
The device is at
Power transmission at every momentThe predicted value of the output quantity is,
is as follows
The device is at
Obtaining the actual value of the output power (namely the detected value of the output power) at each moment, and obtaining the network W after the training of the first training set1Accuracy of
。
The accuracy rate
Is defined as: when it comes to
The device is at
When the difference between the predicted value and the actual value of the output power at each moment satisfies the following setting conditions, the predicted value of the output power is judged to be accurate. The setting conditions are as follows:
in the formula (I), the compound is shown in the specification,
is as follows
The device is at
The predicted value of the power output quantity at each moment,
is as follows
The device is at
The actual value of the output power at each time instant, M is the number of samples of a single training batch, N is the sampling period of the power output, N =1,2, …, N is an integer.
Then, according to the above setting conditions, the number of the predicted values which are accurate in statistical judgment is counted and judged
I.e. the number of predicted values satisfying the above formula, the ratio of the number of accurate predicted values to the total number of predicted values
Accuracy as a first training set
(ii) a The higher the accuracy, the more uniform the distribution of the samples representing the selected training set, and the lower the fluctuation of the actual value of the corresponding output power of the selected training set, i.e. the closer to the predicted value of the power output.
(B) In the amount of
Taking the second batch of samples as a second training set, and sending the second training set serving as a first verification set into the trained network W1And obtaining the verified network W of the first verification set1Accuracy of
(ii) a Then, the second training set is used as a training sample, and the network W is predicted according to the output power of the initial parameters2Training is carried out, and a second training set is obtained to train the network W2To makeRate of determination
。
(C) Obtaining a trained network W of a first training set1Accuracy of
Network W after verification with first verification set1Accuracy of
As a first accuracy deviation; obtaining a trained network W of a first training set1Accuracy of
Network W after training with the second training set2Accuracy of
As a second accuracy deviation. And judging whether the set conditions are met or not according to the first accuracy deviation and the second accuracy deviation, and determining the reference network. The judgment process is as follows:
(1) when the first accuracy deviation and the second accuracy deviation meet the first set condition, the network is retrained again by using the second training set to obtain the reference network W1'. Wherein, the formula of the first setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
(2) When the first accuracy deviation and the second accuracy deviation satisfy a second set condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1'. Wherein the formula of the second setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
(3) When the first accuracy deviation and the second accuracy deviation meet a third set condition, combining the first training set and the second training set, and pre-correcting the output power of the initial parameter by using the combined training setTraining the testing network to obtain a reference network W1'. Wherein, the formula of the third setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
(4) When the first accuracy deviation and the second accuracy deviation satisfy a fourth setting condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1'. Wherein, the formula of the fourth setting condition is as follows:
in the formula (I), the compound is shown in the specification,
is as followsA deviation of the accuracy of the measurement of the measured value,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
In order to facilitate understanding of the meaning of the above four judgment conditions, a condition specific judgment process is provided, which specifically comprises the following steps:
first, it is judged
And a preset first accuracy threshold
In a relation of (1), if
The sample distribution of the first training set is considered to be consistent with the sample distribution of the second training set, and if the sample distribution of the first training set is consistent with the sample distribution of the second training set, the sample distribution of the second training set is considered to be consistent with the sample distribution of the first training set
，
If the actual value label fluctuation is the second accuracy threshold value, judging that the actual value label fluctuation corresponding to the first training set and the second training set is approximate, namely the fluctuation degrees of the actual values of the output power of the electric power equipment in the two training sets are similar, retraining the network trained by the first training set through the second training set, and taking the retrained network as a reference network; if it is
The fluctuation degree of the actual output power value of the electric power equipment in the two training sets is explainedAnd (4) the training network with higher accuracy is reserved as a reference network.
If it is
The distribution of the first training set is not uniform, if it is
The fluctuation degrees of the actual values of the output power of the electric power equipment in the two training sets are similar, and at the moment, the sample distribution of the first training set and the sample distribution of the second training set are judged to have a complementary relation, so that the first training set and the second training set are combined to be used as the training sets, the initial network is trained, and the trained network is used as a reference network; if it is
And keeping the trained network with higher accuracy as a reference network.
(D) Then the number is
Taking the next batch of samples as a new second training set, and taking the reference network W determined for the first time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the above steps (A) - (C) is repeated, and the reference network W is updated again1’。
(E) Repeating step (D) above until the last group is of quantity
Taking the last batch of samples as a new second training set, and taking the reference network W determined last time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the steps (A) - (C) is repeated, and the optimal reference network W is obtained by updating1' to this point.
Reference network W finally determined by the above steps1', theThe training data sets corresponding to the network are distributed uniformly, the actual value fluctuation is low, and the generalization performance of the network is good. Therefore, the optimal reference network is the optimal network, which is the basis for the device volatility evaluation module to be mentioned later to screen the primary stable power generation device.
The electric energy output quantity prediction module has the advantages that the accuracy of the network is utilized, the four set conditions are combined, some batches of samples participating in training are screened, the samples of each batch are traversed, the samples meeting the corresponding set conditions participate in network training, the purpose of continuously updating the reference network is achieved, the updated reference network can uniquely correspond to a certain batch or several batches of training samples each time, after all batches of samples are traversed, the finally updated reference network is used as the optimal network, and the corresponding samples participating in training can be determined according to the trained optimal network.
(III) a device volatility evaluation module for evaluating the volatility of the reference network W according to the finally determined reference network W1' i.e. an optimal network, selecting corresponding power equipment in a training sample participating in optimal network training as primary stable power generation equipment, i.e. power equipment with good stability and less volatility; and selecting corresponding power equipment in the sample which does not participate in the optimal network training as secondary stable power generation equipment, namely equipment with poor stability and large volatility.
That is, by continuously searching for a more optimized reference network process in the electric energy output quantity prediction module, samples participating in training are continuously searched and expanded in a step length manner of batch samples, and the training samples corresponding to the finally determined reference network are a set of a plurality of batch samples (i.e., a set formed by selected samples) screened from all batches of samples (basic information of all to-be-selected electric power devices), and compared with the remaining electric power devices corresponding to the electric power devices not participating in network training, the electric power devices in the set have better stability and smaller volatility of output power, so that the classification of the electric power devices can be reasonably realized.
And the equipment control module is used for selecting a set number of primary power generation equipment from all the primary stable power generation equipment determined by the equipment volatility evaluation module according to the power requirement and a preset priority input sequence for each power generation equipment, carrying out grid-connected input control, and taking the rest primary stable power generation equipment and the rest secondary stable power generation equipment as standby equipment.
It can be understood that, assuming that a total of 10 primary stable power generation devices are determined at present, on the basis of presetting a priority input sequence for each power generation device in advance, and according to the power demand condition in the power grid, it is determined that only 2 power generation devices with set rated power are needed, then 2 of the 10 primary stable power generation devices can be selected to be operated according to the priority input sequence and the power model of the power generation device. The invention is concerned with recommending the primary stable power generation equipment with higher priority level under the condition of limited quantity of the input operation equipment, and can ensure that the operation environment of the whole power grid is safer after the primary stable power generation equipment is selected for use than after the secondary stable power generation equipment is selected for use.
Preferably, in the system, the power equipment used for classification is solar household power generation equipment, equipment meeting the power grid requirement can be selected, meanwhile, stable power grid equipment is guaranteed to be connected to the power grid, and rapid identification of the power equipment which is beneficial to stable operation of the power grid is achieved. As another embodiment, the power plant for classification may be a large power plant, such as a photovoltaic power plant, a wind power plant, or the like.
Example 2:
the embodiment provides an intelligent energy management method for power grid power equipment, as shown in fig. 2, including the following steps:
s1, acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and equipment running time; the actual output power of the equipment when the running time is t is also obtained;
s2, selecting the basic information of the electric power equipment with set number in batch from the basic information of all the electric power equipmentBasic information of each batch of electric power equipment is used as training samples, each batch of training samples are respectively used as input, actual output power of the equipment in each batch of training samples when the running time is t is used as output, output power prediction networks are respectively and independently trained, the output power prediction networks adopt neural networks, the accuracy of each output power prediction network is output after training, and a reference network is selected according to the accuracy of each output power prediction network and used as an optimal network for predicting the output power of the electric power equipment; the basic information of the power equipment comprises equipment aging degree
Type of device
Length of operation of the apparatus
；
S3, selecting corresponding power equipment in the training samples participating in the optimal network training as primary stable power generation equipment; selecting power equipment which does not participate in the optimal network training as secondary stable power generation equipment;
and S4, selecting the first-stage stable power generation equipment to carry out grid-connected input control according to the power requirement and the preset priority input sequence for each power generation equipment in all the first-stage stable power generation equipment, and taking the rest first-stage stable power generation equipment and the rest second-stage stable power generation equipment as standby equipment.
The intelligent management method for device energy in this embodiment corresponds to the management method in the intelligent management system in embodiment 1, and the specific implementation process refers to the relevant records in embodiment 1, which is not described in detail in this embodiment.
It should be noted that: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An intelligent management system for power grid power equipment energy, the system comprising:
the data acquisition module is used for acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and equipment running time; the device is also used for acquiring the actual output power of the equipment when the running time is t;
the electric energy output quantity prediction module is used for randomly selecting the basic information of a set number of electric power equipment in batches from the basic information of all the electric power equipment, using the basic information of all the batches of the electric power equipment as training samples, respectively using the training samples of all the batches as input, using the actual output power of the equipment in the training samples of all the batches when the operation time is t as output, respectively and independently training an output power prediction network, wherein the output power prediction network adopts a neural network, outputting the accuracy of each output power prediction network after training, and selecting a reference network according to the accuracy of each output power prediction network to be used as an optimal network for predicting the output power of the electric power equipment; the basic information of the power equipment is specifically equipment aging degree
Type of device
Length of operation of the apparatus
；
The equipment volatility evaluation module is used for selecting corresponding electric equipment in the training samples participating in the training of the optimal network as primary stable power generation equipment; selecting power equipment which does not participate in the optimal network training as secondary stable power generation equipment;
and the equipment control module is used for selecting the primary stable power generation equipment to carry out grid-connected input control according to the power requirement and a preset priority input sequence for each power generation equipment in all the primary stable power generation equipment determined by the equipment volatility evaluation module, and using the rest primary stable power generation equipment and the rest secondary stable power generation equipment as standby equipment.
2. A grid power equipment energy intelligent management system according to claim 1, wherein the neural network is constructed by the following specific process:
collecting training samples including basic information of a plurality of electric power equipment to be screened, wherein the basic information is expressed as
Wherein, in the step (A),
is as follows
The device identification of the individual power device,
three symbols in the sequence are
Equipment aging degree, equipment model and equipment running time of each power equipment;
training output power prediction network, including power output quantity prediction encoder and full connection layer, network inputs basic information of electric power equipment, i.e.
Obtaining the characteristic vector after the coding by the power output quantity prediction coder, sending the characteristic vector into a full connection layer, wherein the running time of the network output equipment is as long astAnd the actual value of the output power of the hour is obtained through detection.
3. The grid power equipment energy intelligent management system according to claim 1 or 2, wherein the reference network is selected according to the accuracy of each output power prediction network, and the selection as the optimal network for predicting the output power of the power equipment comprises:
(A) using a sample of basic information of the first batch of electrical equipment as a first training set to predict the output power of the network W1Training and judging the network W1Whether the predicted value of each output quantity is accurate or not is determined according to the network W1Outputting the number of accurate power output predicted values, and determining the network W after the training of the first training set1Accuracy A of1；
(B) In the amount of
Taking the second batch of samples as a second training set, and sending the second training set serving as a first verification set into the trained network W1And obtaining the verified network W of the first verification set1Accuracy of
(ii) a Then, the second training set is used as a training sample, and the network W is predicted according to the output power of the initial parameters2Training is carried out, and a second training set is obtained to train the network W2Accuracy of
；
(C) Obtaining a trained network W of a first training set1Accuracy of
Network W after verification with first verification set1Accuracy of
As a first accuracy deviation; obtaining a first training setTrained network W1Accuracy of
Network W after training with the second training set2Accuracy of
As a second accuracy deviation; judging whether a set condition is met or not according to the first accuracy deviation and the second accuracy deviation, and determining a reference network;
(D) then the number is
Taking the next batch of samples as a new second training set, and taking the reference network W determined for the first time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the above steps (A) - (C) is repeated, and the reference network W is updated again1’；
(E) Repeating step (D) above until the last group is of quantity
Taking the last batch of samples as a new second training set, and taking the reference network W determined last time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the steps (A) - (C) is repeated, and the optimal reference network W is obtained by updating1' to this point.
4. The grid power equipment energy intelligent management system according to claim 3, wherein in the step (C), whether the set condition is met is judged according to the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet the first set condition, the network is retrained by the second training set again to obtain a reference networkW1', the formula of the first setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
5. The grid power equipment energy intelligent management system according to claim 3, wherein in the step (C), whether the set condition is met is judged according to the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation satisfy a second set condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the second setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
6. The grid power equipment energy intelligent management system according to claim 3, wherein in the step (C), whether the set condition is met is judged according to the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet a third set condition, combining the first training set and the second training set, and training the output power prediction network of the initial parameters by using the combined training set to obtain a reference network W1', the formula of the third setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
7. The grid power equipment energy intelligent management system according to claim 3, wherein in the step (C), whether the set condition is met is judged according to the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation satisfy a fourth setting condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the fourth setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
8. An intelligent energy management method for power grid power equipment is characterized by comprising the following steps:
s1, acquiring basic information of the power equipment, wherein the basic information comprises equipment aging degree, equipment model and equipment running time; the actual output power of the equipment when the running time is t is also obtained;
s2, randomly selecting the basic information of a set number of electric power equipment in batches from the basic information of all the electric power equipment, using the basic information of all the electric power equipment in each batch as training samples, respectively using the training samples in each batch as input, using the actual output power of the equipment in the training samples in each batch when the operation time is t as output, respectively and independently training an output power prediction network, wherein the output power prediction network adopts a neural network, outputting the accuracy of each output power prediction network after training, and selecting a reference network according to the accuracy of each output power prediction network as an optimal network for predicting the output power of the electric power equipment; the basic information of the power equipment comprises equipment aging degree
Type of device
Length of operation of the apparatus
；
S3, selecting corresponding power equipment in the training samples participating in the optimal network training as primary stable power generation equipment; selecting power equipment which does not participate in the optimal network training as secondary stable power generation equipment;
and S4, selecting the first-stage stable power generation equipment to carry out grid-connected input control according to the power requirement and the preset priority input sequence for each power generation equipment in all the first-stage stable power generation equipment, and taking the rest first-stage stable power generation equipment and the rest second-stage stable power generation equipment as standby equipment.
9. The grid power equipment energy intelligent management method according to claim 8, wherein in step S2, the step of selecting a reference network according to the accuracy of each output power prediction network as the optimal network for predicting the output power of the power equipment comprises:
(A) using a sample of basic information of the first batch of electrical equipment as a first training set to predict the output power of the network W1Training and judging the network W1Whether the predicted value of each output quantity is accurate or not is determined according to the network W1Outputting the number of accurate power output predicted values, and determining the network W after the training of the first training set1Accuracy A of1；
(B) In the amount of
Taking the second batch of samples as a second training set, and sending the second training set serving as a first verification set into the trained network W1And obtaining the verified network W of the first verification set1Accuracy of
(ii) a Then, the second training set is used as a training sample, and the network W is predicted according to the output power of the initial parameters2Training is carried out, and a second training set is obtained to train the network W2Accuracy of
；
(C) Obtaining a trained network W of a first training set1Accuracy of
Network W after verification with first verification set1Accuracy of
As a first accuracy deviation; obtaining a trained network W of a first training set1Accuracy of
Network W after training with the second training set2Accuracy of
As a second accuracy deviation; judging whether a set condition is met or not according to the first accuracy deviation and the second accuracy deviation, and determining a reference network;
(D) then the number is
Taking the next batch of samples as a new second training set, and taking the reference network W determined for the first time1' the corresponding training set is used as a new first training set, the processing of the first training set and the second training set in the above steps (A) - (C) is repeated, and the reference network W is updated again1’；
(E) Repeating step (D) above until the last group is of quantity
Taking the last batch of samples as a new second training set, and taking the reference network W determined last time1' the corresponding training set is used as the new first training set, heavyAnd (4) processing the first training set and the second training set in the steps (A) - (C) to update to obtain the optimal reference network W1' to this point.
10. The grid power equipment energy intelligent management method according to claim 9, wherein in the step (C), whether the set condition is satisfied is judged according to the magnitude of the first accuracy deviation and the second accuracy deviation, and the process of determining the reference network is as follows:
when the first accuracy deviation and the second accuracy deviation meet the first set condition, the network is retrained again by using the second training set to obtain the reference network W1', the formula of the first setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation satisfy a second set condition, the network W is compared1And W2The accuracy of (2) selecting the net with higher accuracyNetwork W1Or W2As a reference network W1', the formula of the second setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation meet a third set condition, combining the first training set and the second training set, and training the output power prediction network of the initial parameters by using the combined training set to obtain a reference network W1', the formula of the third setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset;
when the first accuracy deviation and the second accuracy deviation satisfy a fourth setting condition, the network W is compared1And W2The accuracy of (2) selecting the network W with higher accuracy1Or W2As a reference network W1', the formula of the fourth setting condition is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the first deviation of the accuracy,
for the second accuracy, the first accuracy is biased,
in order to preset the first accuracy threshold,
a second accuracy threshold is preset.
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