CN116780781B - Power management method for smart grid access - Google Patents
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Abstract
The invention relates to the technical field of smart power grids, in particular to a power management method for smart power grid access, which comprises the following steps: s1: collecting power value data of a smart grid; s2: extracting the characteristics of the acquired power value data of the intelligent power grid; s3: performing optimal feature interval division of the power value data of the intelligent power grid based on the feature extraction result; s4: and carrying out abnormality detection based on the optimal characteristic interval division result and reporting the abnormality detection result. The clustering performance and the accuracy of data analysis of the power value data are enhanced based on mining processing and normalization processing, the acquired characteristic data are subjected to characteristic selection processing through characteristic extraction processing and optimization classification processing, the optimal segmentation point is acquired through an improved ant colony algorithm, and the updated pheromone volatilization coefficient strategy is adopted, so that the convergence of the algorithm is enhanced, the classification performance of the power characteristic data is improved, and the optimal power characteristic data are acquired. The classification quality and the characterization capability of the feature data can be ensured.
Description
Technical Field
The invention relates to the technical field of smart grids, in particular to a power management method for smart grid access.
Background
The intelligent power grid is used for realizing energy substitution and compatible utilization, integrating data in a system and optimizing operation and management of the power grid. The method mainly forms instant connection network interaction between users and between the users and a power grid company through the terminal sensor, thereby realizing the effects of real-time, high-speed and bidirectional data reading and integrally improving the comprehensive efficiency of the power grid. The sensor can be used for carrying out real-time monitoring and data integration on the running conditions of key power equipment such as power generation, transmission, distribution, power supply and the like. The existing smart power grid is widely applied to various fields, so that a power management method for a power system connected to the smart power grid is needed to detect the power state of the smart power grid and guarantee the safe operation of the smart power grid.
Disclosure of Invention
The invention aims to solve the defects in the background technology by providing a power management method for smart grid access.
The technical scheme adopted by the invention is as follows:
the power management method for providing the smart grid access comprises the following steps:
s1: collecting power value data of a smart grid;
s2: extracting the characteristics of the acquired power value data of the intelligent power grid;
s3: performing optimal feature interval division of the power value data of the intelligent power grid based on the feature extraction result;
s4: and carrying out abnormality detection based on the optimal characteristic interval division result and reporting the abnormality detection result.
As a preferred technical scheme of the invention: the smart grid power value data in the S1 comprises smart grid node power value data, power value data of normal operation of the smart grid and maximum power data of the smart grid.
As a preferred technical scheme of the invention: in the step S2, the acquired power value data of the smart grid is based on an iterative mining functionPerforming iterative mining processing on power value data of the intelligent power grid:
wherein,for the iterative mining of the processing functions,as a function of the distance between cluster centers of the smart grid power value data,clustering center and iteration center for power value data of intelligent power gridThe distance between the two electrodes is equal to the distance between the two electrodes,for the amount of power value data of the smart grid for the clustering process,and (5) the iterative mining times of the power value data of the intelligent power grid.
As a preferred technical scheme of the invention: and in the step S2, normalization processing is further carried out on the power value data of the intelligent power grid obtained through the iterative mining processing.
As a preferred technical scheme of the invention: in the step S2, a power value data sequence is obtained based on the normalized smart grid power dataAnd carrying out feature extraction on the smart grid power value data sequence based on a feature extraction algorithm:
wherein,for the correlation value of the data in the smart grid power value data sequence,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,the number of the power value data sequences of the intelligent power grid is the number of the data sequences of the power value data sequences of the intelligent power grid;
screening power value data of the smart grid based on chi-square test:
wherein,is a parameter of the chi-square,for the power value data of the normal operation of the smart grid,the expected average value of the power value data of the intelligent power grid;
and carrying out feature extraction on the data based on the screening result:
wherein,the power characteristic data obtained for the characteristic extraction,for the previous period of the smart grid power value dataset,a smart grid power value dataset for the next cycle.
As a preferred technical scheme of the invention: in the step S3, data optimization classification processing is performed based on the acquired power characteristic data so as to extract optimal power characteristic data.
As a preferred technical scheme of the invention: the optimization classification process is specifically as follows:
acquiring maximum value of power characteristic data based on power characteristic data set acquired by characteristic extractionAnd minimum valueNumber of divided sectionsThe value range of each interval is:at this time, the characteristic interval is divided into:
wherein,is a divided characteristic interval.
As a preferred technical scheme of the invention: and the optimization classification process obtains the optimal dividing point of the power characteristic data based on the improved ant colony algorithm and performs power anomaly identification of the intelligent power grid by the optimal power characteristic data.
As a preferred technical scheme of the invention: the improved ant colony algorithm is specifically as follows:
simulating a feature data set of a divided section as an ant-selected edgeIntegration of pheromone concentrationHeuristic functionFor the firstPoint location of the firstOnly ants, whenNot belong toWhen it is, selectProbability of (2)The method comprises the following steps:
wherein,andrespectively represent a heuristic factor and a visibility factor on the basis of the pheromone,is antSlave nodeMoving to a nodeIs used for the information element strength of the (a),represent the firstFeasible nodes of the ant set only;
wherein,is a constant value of the pheromone,is antThe length of the path travelled;
wherein,represent the firstThe pheromone concentration of the ants alone,representing the volatilization coefficient of the pheromone;representing the first in the current cycleOnly ants are on the edgeThe increment to be presented in the above-mentioned,representing the number of ants;
wherein,is the minimum coefficient of volatilization,for the number of the maximum ants to be the same,is a weight coefficient.
As a preferred technical scheme of the invention: and S4, detecting and identifying abnormal power value data based on the SVM two classifiers.
As a preferred technical scheme of the invention: and the intelligent electric meter or the SCADA system is adopted for collecting the power value data of the intelligent electric network.
As a preferred technical scheme of the invention: and reporting and alarming the abnormal data according to the detection and identification result of the constant power value data.
Compared with the prior art, the intelligent power grid access power management method provided by the invention has the beneficial effects that:
the invention enhances the clustering property of the power value data and the accuracy of data analysis based on mining processing and normalization processing, eliminates the characteristic data with severe trend change through characteristic extraction processing, and constructs the power data characteristic of the intelligent power grid. And (3) performing further feature selection processing on the acquired feature data based on the optimized classification processing, and acquiring an optimal segmentation point by improving an ant colony algorithm, wherein in the improved ant colony algorithm, an updated pheromone volatilization coefficient strategy is adopted, so that the convergence of the algorithm is enhanced, the classification performance of the power feature data is improved, and the optimal power feature data is acquired. The classification quality and the characterization capability of the feature data can be ensured.
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FIG. 1 is a flow chart of a method of a preferred embodiment of the present invention.
Detailed Description
It should be noted that, under the condition of no conflict, the embodiments of the present embodiments and features in the embodiments may be combined with each other, and 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 obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a preferred embodiment of the present invention provides a power management method for smart grid access, including the steps of:
s1: collecting power value data of a smart power grid, wherein the power value data of the smart power grid is collected by a smart electric meter or an SCADA system, and the SCADA system is adopted in the embodiment;
s2: extracting the characteristics of the acquired power value data of the intelligent power grid;
s3: performing optimal feature interval division of the power value data of the intelligent power grid based on the feature extraction result;
s4: and carrying out abnormality detection based on the optimal characteristic interval division result and reporting the abnormality detection result.
The smart grid power value data in the S1 comprises smart grid node power value data, power value data of normal operation of the smart grid and maximum power data of the smart grid.
In the step S2, the acquired power value data of the smart grid is based on an iterative mining functionPerforming iterative mining processing on power value data of the intelligent power grid:
wherein,for the iterative mining of the processing functions,as a function of the distance between cluster centers of the smart grid power value data,clustering center and iteration center for power value data of intelligent power gridThe distance between the two electrodes is equal to the distance between the two electrodes,for the amount of power value data of the smart grid for the clustering process,and (5) the iterative mining times of the power value data of the intelligent power grid. Specifically, distance functionThe Manhattan function is adopted, and specifically comprises the following steps:wherein the coordinates of the two clustering centers are respectivelyAnd。
in order to ensure better clustering effect, the embodiment adopts an adaptive mode to determine the optimal iterative mining times：
To adaptively determine the optimal iterative mining timesIt is necessary to use the contour coefficient as an evaluation index. First, a calculation method of a contour coefficient is defined:
wherein, among them,representing a sampleAverage distance to other samples in the same cluster (average distance to other samples in the cluster where it resides),representing a sampleAverage distance to other clusters samples (average distance from nearest other clusters samples);
for the distance between sample i and sample j,representing a collectionThe number of samples in (a) is,is the data set of the cluster in which the sample is located,representing a collectionThe number of samples in (a) is,data sets for clusters other than the cluster in which the sample is located that are closest to the sample:
1. performing data clustering and iterative mining on the original data set to obtainAndresults of iterative mining;
2. sample classification is carried out by using the clustering result to obtain labels of different clusters;
3. for each sample i, calculate itAnd；
4. calculating the contour coefficient of each sample；
5. Calculating the average value of the profile coefficients of all the samples as the average profile coefficientAs the currentClustering effect under the value;
6. repeating steps 1-5, sequentially trying differentA value;
7. selecting average profile coefficientsMaximum ofThe value is used as the optimal iteration mining frequency;
wherein the average profile factorThe calculation method of (1) is as follows:。
and in the step S2, normalization processing is further carried out on the power value data of the intelligent power grid obtained through the iterative mining processing.
In the step S2, a power value data sequence is obtained based on the normalized smart grid power dataAnd carrying out feature extraction on the smart grid power value data sequence based on a feature extraction algorithm:
wherein,for the correlation value of the data in the smart grid power value data sequence,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,the number of the power value data sequences of the intelligent power grid is the number of the data sequences of the power value data sequences of the intelligent power grid;
screening power value data of the smart grid based on chi-square test:
wherein,is a parameter of the chi-square,for the power value data of the normal operation of the smart grid,the expected average value of the power value data of the intelligent power grid;
and carrying out feature extraction on the data based on the screening result:
wherein,the power characteristic data obtained for the characteristic extraction,for the previous period of the smart grid power value dataset,a smart grid power value dataset for the next cycle.
In the step S3, data optimization classification processing is performed based on the acquired power characteristic data so as to extract optimal power characteristic data.
The optimization classification process is specifically as follows:
acquiring maximum value of power characteristic data based on power characteristic data set acquired by characteristic extractionAnd minimum valueNumber of divided sectionsThe value range of each interval is:at this time, the characteristic interval is divided into:
wherein,is a divided characteristic interval.
And the optimization classification process obtains the optimal dividing point of the power characteristic data based on the improved ant colony algorithm and performs power anomaly identification of the intelligent power grid by the optimal power characteristic data.
The improved ant colony algorithm is specifically as follows:
simulating a feature data set of a divided section as an ant-selected edgeIntegration of pheromone concentrationHeuristic functionFor the firstPoint location of the firstOnly ants, whenNot belong toWhen it is, selectProbability of (2)The method comprises the following steps:
wherein,andrespectively represent a heuristic factor and a visibility factor on the basis of the pheromone,is antSlave nodeMoving to a nodeIs used for the information element strength of the (a),represent the firstFeasible nodes of the ant set only;
wherein,is a constant value of the pheromone,is antThe length of the path travelled;
wherein,represent the firstThe pheromone concentration of the ants alone,representing the volatilization coefficient of the pheromone;representing the first in the current cycleOnly ants are on the edgeThe increment to be presented in the above-mentioned,representing the number of ants;
wherein,is the minimum coefficient of volatilization,for the number of the maximum ants to be the same,is a weight coefficient.
And S4, detecting and identifying abnormal power value data based on the SVM two classifiers.
And S4, generating a data log based on the detection and identification result of the abnormal power value data and storing the data log.
In the step S4, reporting processing and alarm processing of the abnormal data are performed based on the detection and identification result of the abnormal power value data.
In this embodiment, power value data including power value data of nodes of the smart grid during access of the smart grid is collected,Power value data of normal operation of the smart grid, maximum power data of the smart grid and the like. Based on iterative mining function for collected power value data of intelligent power gridPerforming iterative mining processing on power value data of the intelligent power grid:
wherein,for the iterative mining of the processing functions,as a function of the distance between cluster centers of the smart grid power value data,clustering center and iteration center for power value data of intelligent power gridThe distance between the intelligent power grid power value data quantity of the clustering processing is 1000, and the iterative mining times of the intelligent power grid power value data are 15 times. And normalizing the power value data of the intelligent power grid obtained by the mining processing.
Based on the mining processing and the normalization processing, clustering performance of the power value data and accuracy of data analysis can be enhanced, and characterization capability of the feature data extracted later can be improved.
Power value data sequence obtained based on normalized smart grid power dataAnd carrying out feature extraction on the smart grid power value data sequence based on a feature extraction algorithm:
wherein,for the correlation value of the data in the smart grid power value data sequence,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,is the first power value data sequence of the intelligent power gridPersonal dataIs used to determine the expected data of the (c) data,the number of the power value data sequences of the intelligent power grid is the number of the data sequences of the power value data sequences of the intelligent power grid;
in order to enhance the accuracy of feature extraction, chi-square test is used to screen the data that produces interference:
wherein,is a parameter of the chi-square,for the power value data of the normal operation of the smart grid,the expected average value of the power value data of the intelligent power grid;
and carrying out feature extraction on the data based on the screening result:
wherein,the power characteristic data obtained for the characteristic extraction,for the previous period of the smart grid power value dataset,a smart grid power value dataset for the next cycle.
Through the feature extraction processing, feature data with severe trend change can be removed, and power data features of the intelligent power grid are constructed.
And carrying out data optimization classification processing based on the acquired power characteristic data so as to extract optimal power characteristic data:
acquiring maximum value of power characteristic data based on power characteristic data set acquired by characteristic extractionAnd minimum valueNumber of divided sectionsThe value range of each interval is:at this time, the characteristic interval is divided into:
wherein,is a divided characteristic interval.
Acquiring an optimal dividing point of the power characteristic data based on an improved ant colony algorithm, and performing power anomaly identification of the intelligent power grid by the optimal power characteristic data:
simulating a feature data set of a divided section as an ant-selected edgeIntegration of pheromone concentrationHeuristic functionFor the firstPoint location of the firstOnly ants, whenNot belong toWhen it is, selectProbability of (2)The method comprises the following steps:
wherein,andrespectively represent a heuristic factor and a visibility factor on the basis of the pheromone,is antSlave nodeMoving to a nodeIs used for the information element strength of the (a),represent the firstFeasible nodes of the ant set only;
ants leave pheromones during their travel to provide directional information for other ants. Under the positive feedback mechanism of ant colony, the pheromone concentration intensity on the path is in direct proportion to the number of attracted ants, and the updating rule is as follows:
wherein,is a constant value of the pheromone,is antThe length of the path travelled;
wherein,represent the firstThe pheromone concentration of the ants alone,representing the volatilization coefficient of the pheromone;representing the first in the current cycleOnly ants are on the edgeThe increment to be presented in the above-mentioned,representing the number of ants;
wherein,is the minimum coefficient of volatilization,for the number of the maximum ants to be the same,is a weight coefficient.
Based on the optimized classification processing, further feature selection processing is carried out on the obtained feature data, and the optimal segmentation points are obtained by improving an ant colony algorithm, wherein in the improved ant colony algorithm, an updated pheromone volatilization coefficient strategy is adopted, and in the early stage of ant colony search, the pheromone volatilization coefficient is increased, so that the random search capability can be enhanced, and the search range is enlarged; and in the later stage, the volatilization coefficient of the pheromone is reduced, the search range gradually contracts to the optimal solution range, the convergence of the algorithm is enhanced, the classification performance of the power characteristic data is improved, and the optimal power characteristic data is obtained. The classification quality and the characterization capability of the feature data can be ensured. And finally, detecting, identifying and reporting the abnormal power data value based on the SVM two-classifier.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (8)
1. The power management method for smart grid access is characterized by comprising the following steps of: the method comprises the following steps:
s1: collecting power value data of a smart grid;
s2: extracting the characteristics of the acquired power value data of the intelligent power grid;
s3: performing optimal feature interval division of the power value data of the intelligent power grid based on the feature extraction result;
s4: performing abnormality detection based on the optimal characteristic interval division result and reporting the abnormality detection result;
the power value data of the intelligent power grid in the S1 comprises node power value data of the intelligent power grid, power value data of normal operation of the intelligent power grid and maximum power data of the intelligent power grid;
in the step S2, iterative mining processing is performed on the collected power value data of the smart grid based on the iterative mining function delta, wherein the power value data of the smart grid is:
wherein ρ is an iterative mining processing function, δ i Delta (c) is a distance function between clustering centers of smart grid power value data j ) Clustering center and iteration center c for power value data of intelligent power grid j The distance between the two power values is N, wherein N is the power value data quantity of the intelligent power grid subjected to clustering processing, and M is the iterative mining times of the power value data of the intelligent power grid;
in the step S2, normalization processing is further performed on the power value data of the intelligent power grid, which is obtained through the iterative mining processing; in S2, a power value data sequence w= { W is obtained based on the normalized smart grid power data r R=1, …, R }, performing feature extraction on the smart grid power value data sequence based on a feature extraction algorithm:
wherein cov (W) e ,W r ) For correlation values of data in a smart grid power value data sequence, E (W e ) For the e-th data W in the power value data sequence of the intelligent power grid e Is the expected data of E (W) r ) For the (r) data W in the power value data sequence of the intelligent power grid r R is the number of data of the power value data sequence of the intelligent power grid;
screening power value data of the smart grid based on chi-square test:
k is a chi-square parameter, Q is power value data of normal operation of the intelligent power grid, and E is an expected average value of the power value data of the intelligent power grid;
and carrying out feature extraction on the data based on the screening result:
wherein z is power characteristic data obtained by characteristic extraction, X a-1 For the previous period of the smart grid power value data set, X a+1 A smart grid power value dataset for the next cycle.
2. The smart grid accessed power management method of claim 1, wherein: in the step S3, data optimization classification processing is performed based on the acquired power characteristic data so as to extract optimal power characteristic data.
3. The smart grid accessed power management method of claim 2, wherein: the optimization classification process is specifically as follows:
acquiring a maximum value z of power characteristic data based on the power characteristic data set acquired by characteristic extraction max And a minimum value z min Dividing the number k of intervals, wherein the value range of each interval is as follows:the characteristic interval is divided into:
…
wherein Z is 0 ,Z 1 ,…,Z k-1 Is a divided characteristic interval.
4. A smart grid accessed power management method as defined in claim 3, wherein: and the optimization classification process obtains the optimal dividing point of the power characteristic data based on the improved ant colony algorithm and performs power anomaly identification of the intelligent power grid by the optimal power characteristic data.
5. The smart grid accessed power management method of claim 4, wherein: the improved ant colony algorithm is specifically as follows:
simulating the characteristic data set of the divided sections into ant-selected sides (u, v), and integrating the ant-selected sides into the pheromone concentration r uv Heuristic function h uv For the s-th ant at the position of the u-th point, when u does not belong to y t When then choose probability p of v uv (s) is:
wherein alpha and beta respectively represent a heuristic factor and a visibility factor under the basis of pheromone, and r ul (s) is the intensity of pheromone, y of ants s moving from node u to node l s A feasible node representing the s-th ant set;
wherein F is a pheromone constant, L s The path length travelled by ants s;
wherein r is uv (s+1) represents the pheromone concentration of the s+1st ant, and μ represents the pheromone volatilization coefficient;representing the increment of the s-th ant on the edge (u, v) in the current cycle, c representing the number of ants;
wherein mu min D is the maximum ant number, and ω is the weight coefficient.
6. The smart grid accessed power management method of claim 5, wherein: and S4, detecting and identifying abnormal power value data based on the SVM two classifiers.
7. The smart grid accessed power management method of claim 6, wherein: and the intelligent electric meter or the SCADA system is adopted for collecting the power value data of the intelligent electric network.
8. The smart grid accessed power management method of claim 7, wherein: and reporting and alarming the abnormal data according to the detection and identification result of the constant power value data.
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| JP2022129334A (en) * | 2021-02-24 | 2022-09-05 | 合同会社ポルテロ | Power supply system, management device, power supply device, program, and system |
| CN113017650A (en) * | 2021-03-12 | 2021-06-25 | 南昌航空大学 | Electroencephalogram feature extraction method and system based on power spectral density image |
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