CN115579870A - Resource optimal configuration control method for power grid operation monitoring and source grid load storage - Google Patents
Resource optimal configuration control method for power grid operation monitoring and source grid load storage Download PDFInfo
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Abstract
The invention provides a resource optimal configuration control method for power grid operation monitoring and source grid load storage, which is used for solving the technical problems of serious power resource transportation loss, and low working efficiency of power grid regulating and controlling personnel and system operation efficiency. Acquiring power failure duration and failure times on power equipment to obtain a first stability index of a power grid operation state; acquiring frequency change and voltage change on the power system to obtain a second stability index of the running state of the power grid; calculating the overall operation state of the power grid based on the first stability index and the second stability index of the operation state of the power grid; the overall operation state of the power grid is predicted through a neural network, the future power load of the power grid is predicted, and the power load and the energy storage resources are intelligently analyzed through an ant colony algorithm, so that the optimal distribution of the energy storage resources is achieved. The scheme of the invention can realize the optimal distribution of energy storage resources based on the ant colony algorithm, and improve the working efficiency of power grid regulation and control personnel and the system operation efficiency.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a resource optimal configuration control method for power grid operation monitoring and source grid load storage.
Background
With the rapid development of national socioeconomic, the demand for electric energy is more and more, and the scale of a power grid in China is nearly doubled since 2010, in 2020, the national electricity consumption exceeds 7.5 trillion kilowatts, the highest electricity load exceeds 9 billion kilowatts, and the installed capacity is 22 billion kilowatts, which is the big data report of Chinese energy (2021.06.24).
At present, with the increasing expansion of the scale of a power grid, a smart power grid is rapidly developed, the scale and the complexity of the smart power grid are increased, and in order to accurately describe the operation state of the smart power grid, the state of the smart power grid needs to be estimated, and in the prior art: chinese patent invention with publication number of 2022.03.25 and publication number of CN107994570B discloses a state estimation method and system based on neural network, including: when a state estimation request for a target power grid is received, acquiring measurement data of the target power grid; dividing the measurement data according to areas to obtain a plurality of area measurement data; aiming at each region measurement data, distributing the measurement data to a preset neural network for forward calculation; and when a finishing instruction for performing forward calculation on the plurality of regional measurement data is detected, outputting a state vector associated with the target power grid.
In the invention patent, although the state of the target power grid can be monitored and analyzed, the current power grid state is only analyzed, the future power grid state is predicted according to the current power grid state, the power load of the power grid is predicted and judged based on the current power grid state, the power load is combined with distributed power energy storage resource points, the power resource transportation loss is reduced, an area which preferentially supplies power resources is found, intelligent prediction and optimal distribution are realized, the working efficiency of power grid regulating and controlling personnel and the system operation efficiency are improved, the cost for maintaining the power grid is reduced, and an idea is provided for realizing the scientization of power grid intelligent operation and management.
Disclosure of Invention
Aiming at the technical problems of serious power resource transportation loss, low working efficiency and system operation efficiency of power grid regulating and controlling personnel and high power grid maintenance cost, the invention provides a resource optimal configuration control method for power grid operation monitoring and source grid charge storage, and the aims of improving the working efficiency and system operation efficiency of the power grid regulating and controlling personnel, reducing the power resource transportation loss and reducing the power grid maintenance cost are fulfilled.
In order to achieve the above object, the technical solution of the present invention is a resource optimal allocation control method for power grid operation monitoring and source grid storage, which includes:
the method comprises the following steps: acquiring the power equipment power failure time length and the power equipment failure times within a period of time in a certain area by using a preset sampling frequency to obtain a power equipment power failure time length sequence and a power equipment failure time sequence; determining a first stability index of the power grid operation state according to the power failure duration of the power equipment and the failure frequency of the power equipment;
step two: acquiring frequency change of a power system and voltage change of the power system within a period of time in a certain area by using a preset sampling frequency to obtain a frequency sequence of the power system and a voltage sequence of the power system; determining a second stability index of the power grid operation state according to the frequency change of the power system and the voltage change of the power system;
step three: evaluating the overall operation state of the power grid based on the first stability index of the operation state of the power grid and the second stability index of the operation state of the power grid, and predicting the overall operation state of the power grid through a prediction neural network to obtain a prediction result;
step four: and obtaining that the overall operation states of future power grids in different areas are different and the future power loads are different based on the prediction results, combining the future power loads with distributed power energy storage resource points, and optimally distributing power resources through an ant colony algorithm to obtain the area which is supplied most preferentially and the minimum transportation loss.
Further, the first stability index of the power grid operation state is determined by multiplying the variance of the power failure duration information of the power equipment in the region and the mean value of the number of times of faults of the power equipment after normalization.
Further, the second stability index of the grid operation state is determined by multiplying the normalized frequency variance of the power system in the region and the normalized voltage mean value of the power system.
Further, the method for evaluating the overall operation state of the power grid comprises the following steps:
wherein: f is a first stability index of the running state of the power grid; u is a second stability index of the power grid operation state; and K is the integral operation state of the power grid.
Further, the overall operation state of the power grid is predicted by adopting a neural network: and inputting the data sequences of the overall operation state of the power grid in each historical set time period into the trained LSTM prediction network to output a prediction result. The operation state of the power grid can be monitored and whether the power load exists or not can be judged by combining the prediction neural network, so that the analysis is more three-dimensional, the prediction is more accurate, the working efficiency and the system operation efficiency of power grid regulation and control personnel are effectively improved, and meanwhile, the maintenance cost of the power grid is saved.
Further, the training process of the LSTM prediction network model is:
constructing an LSTM prediction network;
acquiring continuous data indexes of the overall running state of the power grid in each historical set time period, using the data indexes as a training set, inputting the training set into an LSTM prediction network, training the LSTM prediction network, and obtaining an LSTM neural network model after training;
introducing an improved loss function during training, calculating the error between output data and input actual data of a training set through the improved loss function, and training the LSTM prediction network; the improved loss function is: and calculating the data confidence of the whole operation state of the power grid corresponding to each historical time period, and weighting the confidence to the mean square error loss function corresponding to the data of the whole operation state of the power grid in each time period.
Further, the loss function of the LSTM prediction network is: using confidence C i As a mass fraction and normalized to the sample weight C = { C added to one 1 ,C 2 ,C 3 ,....C j And:
Loss＝∑(Loss j *C j )
wherein: and C is a mass coefficient after normalization, which is used as a loss weight, loss is the loss of each sample, and the obtained sequence is the overall operation state data of the power grid.
Further, the ant colony algorithm takes the resource storage points as ants, N ants exist in N resource storage points, the area for supplying power is taken as a target path point, the optimal path with the minimum power loss is obtained by searching pheromones, the future power load is obtained according to the prediction result, and the power loads in different areas are taken as weights. And then, the method is combined with distributed power energy storage resource points, and a point with the highest priority for supply and the minimum transportation abrasion are found through an ant colony algorithm to obtain a configuration method with the optimal resources, so that the power resource transportation loss is reduced, and manpower and material resources are saved.
The invention has at least the following beneficial effects: acquiring the power failure duration of the power equipment and the failure times of the power equipment by using a sensor based on a specific sampling frequency, and analyzing a first stable index of the power grid operation state; acquiring frequency change of the power system and voltage change of the power system, and analyzing a second stability index of the running state of the power grid; combining a first stable index and a second stable index of the power grid operation state to obtain a final power grid overall operation state; firstly, judging whether the problem of power load exists or not through a first stable index, judging the integral state of the current power grid by combining with a second stable index, and combining the first stable index and the second stable index to predict a neural network, so that the running state of the power grid can be monitored and whether the condition of the power load exists or not can be judged, the analysis is more three-dimensional, the prediction is more accurate, the working efficiency and the system running efficiency of power grid regulating and controlling personnel are effectively improved, and meanwhile, the maintenance cost of the power grid is saved; and then, the method is combined with distributed power energy storage resource points, and a point with the highest priority for supply and the minimum transportation abrasion are found through an ant colony algorithm to obtain a configuration method with the optimal resources, so that the power resource transportation loss is reduced, and manpower and material resources are saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions 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 optimization method provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of 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 obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, a method for controlling optimal resource allocation of power grid operation monitoring and source grid load storage includes the following steps:
the method comprises the following steps: acquiring the power failure time length of the power equipment and the failure times of the power equipment in a certain area by using a preset sampling frequency to obtain a power failure time length sequence of the power equipment and a failure time sequence of the power equipment; determining a first stability index of the power grid operation state according to the power failure duration of the power equipment and the failure frequency of the power equipment;
step two: acquiring the frequency change of a power system and the voltage change of the power system within a period of time in a certain area by using a preset sampling frequency to obtain a frequency sequence of the power system and a voltage sequence of the power system; determining a second stability index of the power grid operation state according to the frequency change of the power system and the voltage change of the power system;
step three: evaluating the overall operation state of the power grid based on the first stability index of the operation state of the power grid and the second stability index of the operation state of the power grid, and predicting the overall operation state of the power grid through a prediction neural network to obtain a prediction result;
step four: and obtaining that the overall operation states of future power grids in different areas are different and the future power loads are different based on the prediction results, combining the future power loads with distributed power energy storage resource points, and optimally distributing power resources through an ant colony algorithm to obtain the area which is supplied most preferentially and the minimum transportation loss.
Further, the first stable index of the power grid operation state firstly acquires the power failure time length in a certain area, analyzes whether the power grid operation state is stable or not from the power failure time length, and secondly acquires the failure times of the power equipment in the certain area, wherein the less the failure times of the power equipment are, the shorter the power failure time length is, and the more stable the power grid operation state is.
Further, a second stable index of the power grid operation state firstly acquires the frequency change of a power grid system and the voltage change of the power grid system in a certain area, and when the power grid operates in an emergency state, the frequency and the voltage of the power grid system are abnormal, so that whether the operation state of the power grid at the moment has a problem or not is judged.
Further, the overall operation state of the power grid is obtained by combining the first stability index of the operation state of the power grid and the second stability index of the operation state of the power grid; the combination of the two can monitor the running state of the power grid and judge whether the power load exists; and judging the future overall operation state of the power grid through the prediction neural network according to the overall operation state of the power grid at the moment, and measuring the overall power load of the power grid according to the overall operation state of the power grid.
Further, the time for collecting data is prolonged to 1 day as a sampling moment, and data within 1 month is collected to extract a characteristic value for analysis.
Further, the first stability index of the power grid operation state is determined by multiplying the variance of the power failure time length of the power equipment in the region and the mean value of the failure times of the power equipment after normalization.
Wherein: f is the first stability indicator of the grid operating condition,
the average deviation of the power failure time of the power equipment,
is the average and difference ratio of the number of power equipment failures.
Further, the second stability index of the grid operation state is determined by multiplying the normalized frequency variance of the power system in the region and the normalized voltage mean value of the power system.
Wherein: u is the second stable index of the power grid running state, Q is the number of times of power failure of the power equipment, and L is the power failure time of the power equipment.
Further, the method for evaluating the overall operation state of the power grid comprises the following steps:
wherein: f is a first stability index of the running state of the power grid; u is a second stability index of the power grid operation state; and K is the overall operation state of the power grid.
Further, the overall operation state of the power grid is predicted by adopting a neural network: inputting the data sequences of the whole operation state of the power grid in each historical set time period into a trained LSTM prediction network, outputting a prediction result, and predicting the future power load of the power grid in the region; based on the combination of future power load and distributed power energy storage resource points, the distribution is carried out through an ant colony algorithm.
Further, the training process of the LSTM prediction network model is as follows:
constructing an LSTM prediction network;
acquiring continuous data indexes of the overall running state of the power grid in each historical set time period, using the data indexes as a training set, inputting the training set into an LSTM prediction network, training the LSTM prediction network, and obtaining an LSTM neural network model after training;
introducing an improved loss function during training, calculating the error between output data and input actual data of a training set through the improved loss function, and training the LSTM prediction network; the improved loss function is: and calculating the data confidence of the whole operation state of the power grid corresponding to each historical time period, and weighting the confidence to the mean square error loss function corresponding to the data of the whole operation state of the power grid in each time period.
Further, the loss function of the LSTM prediction network is: using confidence C i As a mass fraction and normalized to the sample weight C = { C added to one 1 ,C 2 ,C 3 ,....C j And:
Loss＝∑(Loss j *C j )
wherein: and C is the normalized mass coefficient serving as a loss weight, loss is the loss of each sample, and the obtained sequence is the overall operation state data of the power grid.
Repeating the above process by analogy, and obtaining each corresponding prediction result in the sequence; and obtaining the predicted overall operation state index sequence of the power grid.
According to different concrete requirements of implementers and different actual scenes in use, for setting the threshold value in the prediction network, the threshold value is set according to historical experience in big data, and when the threshold value is reached, the prediction network automatically stops prediction.
Further, the ant colony algorithm takes the resource storage points as ants, N ants exist at the N resource storage points, the area for supplying power is taken as a target path point, the optimal path with the minimum power loss is obtained by searching pheromones, the future power load is obtained according to the prediction result, and the power loads in different areas are taken as weights. By combining the prediction result with the distributed power energy storage resource points, the most preferred supply point and the minimum transportation wear are found through the ant colony algorithm, the optimal resource allocation method is obtained, the power resource transportation loss is reduced, and manpower and material resources are saved.
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 (8)
1. A resource optimal configuration control method for power grid operation monitoring and source grid loading and storage is characterized by comprising the following steps:
the method comprises the following steps: acquiring the power failure time length of the power equipment and the failure times of the power equipment in a certain area by using a preset sampling frequency to obtain a power failure time length sequence of the power equipment and a failure time sequence of the power equipment; determining a first stability index of the power grid operation state according to the power failure duration of the power equipment and the failure frequency of the power equipment;
step two: acquiring frequency change of a power system and voltage change of the power system within a period of time in a certain area by using a preset sampling frequency to obtain a frequency sequence of the power system and a voltage sequence of the power system; determining a second stability index of the power grid operation state according to the frequency change of the power system and the voltage change of the power system;
step three: evaluating the overall operation state of the power grid based on the first stability index of the operation state of the power grid and the second stability index of the operation state of the power grid, and predicting the overall operation state of the power grid through a prediction neural network to obtain a prediction result;
step four: and based on the prediction result, the fact that the overall operation states of future power grids in different areas are different and the future power loads are different is known, the future power loads are combined with distributed power energy storage resource points, the power resources are optimally distributed through an ant colony algorithm, and the area which is supplied most preferentially and the minimum transportation loss are obtained.
2. The method according to claim 1, wherein the first stable indicator of the grid operating state is determined by multiplying a variance of a power outage duration of the power equipment in the area and a mean value of a number of times of failure of the power equipment after normalization.
3. The method as claimed in claim 2, wherein the second stable index of the grid operating state is determined by multiplying the normalized frequency variance of the power system in the region and the normalized voltage mean of the power system.
4. The method for controlling the optimal resource allocation of the power grid operation monitoring and the source grid load storage according to any one of claims 1 to 3, wherein the method for evaluating the overall operation state of the power grid comprises the following steps:
wherein: f is a first stability index of the running state of the power grid; u is a second stability index of the power grid operation state; and K is the integral operation state of the power grid.
5. The method for controlling optimal allocation of resources for power grid operation monitoring and source grid storage according to claim 4, wherein the overall operation state of the power grid is predicted by adopting a neural network: and inputting the data sequences of the whole operation state of the power grid in each historical set time period into the trained LSTM prediction network to output a prediction result.
6. The method for controlling optimal allocation of resources for power grid operation monitoring and source grid charging and storage according to claim 5, wherein the training process of the LSTM predictive network model comprises:
constructing an LSTM prediction network;
acquiring continuous data indexes of the overall running state of the power grid in each historical set time period, using the data indexes as a training set, inputting the training set into an LSTM prediction network, training the LSTM prediction network, and obtaining an LSTM neural network model after training;
an improved loss function is introduced during training, the error between the output data and the actual data of the input training set is calculated through the improved loss function, and the LSTM prediction network is trained; the improved loss function is: and calculating the data confidence coefficient of the whole operation state of the power grid corresponding to each historical time period, and weighting the confidence coefficient to the mean square error loss function corresponding to the data of the whole operation state of the power grid in each time period.
7. The method of claim 6, wherein the LSTM prediction network loss function is: using confidence C i As a mass fraction and normalized to the sample weight C = { C added to one 1 ,C 2 ,C 3 ,....C j And:
Loss＝∑(Loss j *C j )
wherein: and C is the normalized mass coefficient serving as a loss weight, loss is the loss of each sample, and the obtained sequence is the overall operation state data of the power grid.
8. The method as claimed in claim 5, wherein the ant colony algorithm uses resource storage points as ants, N resource storage points include N ants, an area for supplying power is used as a target path point, an optimal path with minimum power loss is obtained by searching for pheromones, future power loads are obtained according to the prediction result, and the sizes of the power loads in different areas are used as weights.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116739202A (en) * | 2023-08-15 | 2023-09-12 | 深圳华越南方电子技术有限公司 | Power routing method, system, equipment and storage medium |
| CN117134507A (en) * | 2023-10-27 | 2023-11-28 | 南京中鑫智电科技有限公司 | Online monitoring method and system for full-station capacitive equipment based on intelligent group association |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116739202A (en) * | 2023-08-15 | 2023-09-12 | 深圳华越南方电子技术有限公司 | Power routing method, system, equipment and storage medium |
| CN116739202B (en) * | 2023-08-15 | 2024-01-23 | 深圳华越南方电子技术有限公司 | Power routing method, system, equipment and storage medium |
| CN117134507A (en) * | 2023-10-27 | 2023-11-28 | 南京中鑫智电科技有限公司 | Online monitoring method and system for full-station capacitive equipment based on intelligent group association |
| CN117134507B (en) * | 2023-10-27 | 2024-01-02 | 南京中鑫智电科技有限公司 | Online monitoring method and system for full-station capacitive equipment based on intelligent group association |
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