Electric power data acquisition method for photovoltaic energy storage system

Abstract

The invention relates to the field of data processing, and relates to an electric power data acquisition method for a photovoltaic energy storage system, which comprises the following steps that: intelligent electric energy meters are respectively arranged at the joints of all alternating current output ends of the photovoltaic energy storage system and a power grid to acquire and transmit electric power data, and all the intelligent electric energy meters are in communication connection; the transmission method comprises the following steps: acquiring historical power consumption data sequences of the intelligent electric energy meter and the electric energy meter connected with the intelligent electric energy meter, and calculating the period of each power consumption data sequence; obtaining a subsequence in each period, and obtaining a cluster in each period; calculating the comprehensive abnormal degree of each electric energy meter; and the electricity utilization data sequence of the electric energy meter with the highest comprehensive abnormal degree is transmitted through the network node with the highest communication quality. According to the method, transmission errors of the power utilization data of the abnormal electric energy meter can be avoided, and it is guaranteed that abnormal conditions of all connection points of the photovoltaic energy storage system and the power grid are found in time.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method for collecting power data for a photovoltaic energy storage system. Background Art

[0002] Photovoltaic energy storage system is a comprehensive energy solution that combines photovoltaic power generation with electric energy storage technology. Its core goal is to achieve efficient utilization and stable supply of clean energy. Photovoltaic energy storage system usually consists of photovoltaic array, inverter, energy storage equipment, energy storage converter and energy management system. The photovoltaic array is composed of multiple solar panels, which convert solar energy into direct current (DC). The inverter is used to convert the direct current generated by photovoltaic into alternating current (AC) for use in home or industrial equipment. The energy storage equipment is used to store the electric energy generated by the photovoltaic array. The energy storage equipment uses lithium batteries, lead-acid batteries or liquid flow batteries. The energy storage converter is used to control the charging and discharging process of the battery and optimize the energy flow; the energy management system is used to monitor the photovoltaic power generation, energy storage status, load demand and grid electricity price in real time.

[0003] In order to ensure the reliable operation of the power network connected to the photovoltaic energy storage system, it is necessary to collect power data of the photovoltaic energy storage system and the grid connection point in real time to monitor whether each connection point is operating normally. In the prior art, smart energy meters are usually used to collect power data of photovoltaic energy storage systems.

[0004] Smart energy meters are electronic energy meters that, in addition to accurately measuring electricity, also have multiple functions such as data storage, information transmission, remote power outage, and abnormal alarm. Smart energy meters can accurately measure forward, reverse, active, and reactive energy, and record key parameters such as maximum demand, voltage and current harmonics, and power factor. They have high accuracy, wide range, wide power factor, sensitive start-up, and long-term accuracy without the need for rotation. Smart energy meters have built-in data processing units that can process and analyze the collected electricity data in real time, generate electricity reports, trend charts, energy consumption comparisons, etc., provide more data support for power supply companies and users, and help optimize electricity management and the formulation of energy-saving measures.

[0005] With the continuous development of smart grid and Internet of Things technology, smart energy meters, as an important part of the power system, have been applied to various places such as homes, businesses and industries. Smart energy meters have two-way communication functions and can exchange data with other smart substations in the smart grid. Through the built-in communication module, it can remotely read the meter and transmit the power consumption data to the management system of the power supply company or the user, eliminating the tedious process of traditional manual meter reading and improving the accuracy and efficiency of the data.

[0006] like Figure 1As shown, in the prior art, the method of using smart energy meters to collect and transmit power data of photovoltaic energy storage systems is: smart energy meters are respectively installed at the connection points between each AC output terminal of the photovoltaic energy storage system and the power grid to collect power data and transmit it. During transmission, the power consumption data of multiple smart energy meters are uniformly transmitted through a main smart energy meter. However, under this data transmission method, since the power consumption data of each energy meter is transmitted indiscriminately during transmission and the amount of transmitted data is large, if a certain energy meter is abnormal, the power consumption data collected by the abnormal energy meter may not be transmitted in time, resulting in the failure to timely discover the abnormal situation of the power grid connection point corresponding to the abnormal energy meter. Summary of the invention

[0007] In order to solve the technical problem that the data transmission method of the electric energy meter in the prior art cannot timely detect the abnormal conditions of each connection point between the photovoltaic energy storage system and the power grid, the present invention provides solutions in the following aspects.

[0008] In a first aspect, the present invention provides a method for collecting power data for a photovoltaic energy storage system, comprising: installing smart power meters at the connection points between each AC output terminal of the photovoltaic energy storage system and the power grid to collect power data and transmit it, each smart power meter is connected to each other in communication, and the transmission method comprises: obtaining a historical power data sequence of the smart power meter and the power meter connected thereto and calculating the period of each power data sequence; According to multiple cycles, multiple power consumption data sequences are cut off by the length of one cycle to obtain multiple subsequences, and multiple subsequences corresponding to each cycle are clustered to obtain cluster clusters under each cycle, and the length of the subsequence is equal to the corresponding cycle; for each electric energy meter, the first abnormality degree under each cycle is calculated, and a weighted sum is performed to obtain the comprehensive abnormality degree; and the power consumption data sequence of the electric energy meter with the highest comprehensive abnormality degree is transmitted through the network node with the highest communication quality; the expression of the first abnormality degree is: ; In the formula, Indicates the abnormality of the sth electric energy meter in the tth cycle, represents the hyperbolic tangent function, represents the current power consumption data of the sth electric energy meter, Represents the probability density function of the cluster to which the subsequence of the s-th electric energy meter in the t-th period belongs.

[0009] Its beneficial effects are as follows: before transmitting the power consumption data sequences of multiple power meters, the power consumption data collection method for the photovoltaic energy storage system of the present invention first calculates the abnormality of each power meter based on the power consumption data sequence, and uses the network node with the highest communication quality to transmit the power consumption data sequence of the power meter with the highest abnormality, thereby achieving faster acquisition of abnormal conditions of the power meter, avoiding transmission errors in the power consumption data of abnormal power meters, and ensuring timely discovery of abnormal conditions at various connection points between the photovoltaic energy storage system and the power grid. In addition, when calculating the abnormality of the power meter, the periodicity of the power consumption data sequence is taken into account, and the power consumption data sequence is intercepted to obtain a subsequence according to the length of each cycle of the power consumption data sequence, and the subsequence under each cycle is used to calculate the abnormality of the power meter under each cycle. Compared with using the entire power consumption data sequence to calculate the abnormality of the power meter, the amount of data processing is greatly reduced, and the efficiency of calculating the abnormality of the power meter is improved.

[0010] Furthermore, when calculating the degree of abnormality in a certain period, the subsequences in the period are first clustered to obtain various cluster clusters, taking into account that the smaller the difference between the current electricity consumption data of the electric energy meter and the center of the distribution, the greater the proximity between the two, the greater the possibility that the current electricity consumption data belongs to the corresponding probability distribution, and the smaller the possibility that the electricity consumption data sequence of the electric energy meter is abnormal, the abnormality degree of the electric energy meter in a certain period is positively correlated with the difference, thereby more accurately calculating the abnormality degree of the electric energy meter in a certain period.

[0011] Preferably, the calculation expression of the comprehensive abnormality degree is: ; In the formula, Indicates the comprehensive abnormality degree of the sth electric energy meter, It represents the probability that the power consumption data of the cluster where the subsequence of the sth electric energy meter in the tth period belongs to follows the normal distribution, represents the mean of the intra-class distances of the subsequences of the s-th electric energy meter in the t-th period, and T represents the total number of the periods.

[0012] Its beneficial effects are as follows: the present invention calculates the comprehensive abnormality degree by weighted summing the first abnormality degrees under different periods. Under normal circumstances, the greater the probability that the electricity consumption data in the cluster corresponding to the electric energy meter obeys the normal distribution, the greater the credibility of the calculated first abnormality degree, and the weight of the first abnormality degree should also be greater when weighted summing; the smaller the intra-class distance of the cluster in which the electric energy meter is located under different periods, the greater the similarity of the cluster, and the greater the weight of the first abnormality degree calculated by the cluster. Therefore, when weighted summing the first abnormality degree under different periods, let the corresponding weight be positively correlated with the probability that the electricity consumption data in the cluster obeys the normal distribution, and inversely proportional to the intra-class distance of the cluster in which the electric energy meter is located under the corresponding period, so that the comprehensive abnormality degree of the electric energy meter can be calculated more accurately.

[0013] Preferably, for a certain cluster, the method for obtaining the probability that its power consumption data obeys the normal distribution includes: calculating the mean and variance of the electric energy meter readings in the cluster, and then using AD test to obtain the probability that the power consumption data in the cluster obeys the normal distribution.

[0014] The beneficial effects are: the AD test can better implement the normality test, can sensitively reveal the potential asymmetry of the data, and make the final probability of obeying the normal distribution more accurate.

[0015] Preferably, for a certain electric energy meter, the comprehensive abnormality degree is equal to the average value of the abnormality degrees in each cycle thereof.

[0016] Preferably, the method used to calculate the period of the electricity consumption data sequence is an autocorrelation function.

[0017] The beneficial effects are as follows: the autocorrelation function can detect the periodic components in the signal. When there are periodic features in the signal (such as the power consumption data sequence), the autocorrelation function will have peaks at the corresponding lag time. The positions of these peaks directly correspond to the period of the signal, thereby accurately identifying and analyzing the periodic features of the power consumption data sequence. In addition. The power consumption data sequence is often interfered by various noises, which may mask the true periodic features of the signal. Through the autocorrelation function, these random noises can be filtered out and the true periodic information of the signal can be extracted. Therefore, using the autocorrelation function to calculate the period of the power consumption data sequence can make the period calculation result more accurate.

[0018] Preferably, when clustering subsequences in a certain period, the calculation expression of the clustering distance is: ; In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the mutual correlation between the i-th subsequence and the j-th subsequence.

[0019] The beneficial effect is that the greater the mutual correlation between the two subsequences, the more similar the two subsequences are, and the smaller the clustering distance between the two subsequences. By making the clustering distance negatively correlated with the mutual correlation between the two subsequences, the clustering distance corresponding to the two subsequences can be calculated more accurately.

[0020] Preferably, when clustering subsequences in a certain period, the calculation expression of the clustering distance is: ; In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the i-th subsequence, represents the jth subsequence, Indicates the correspondence between the i-th subsequence and the j-th subsequence Norm.

[0021] The beneficial effect is that in multidimensional space, the L2 norm can accurately reflect the absolute distance between vectors. In addition, when calculating the distance, the L2 norm amplifies the influence of large differences by the square sum, but also performs a certain degree of smoothing on small differences. This characteristic makes the L2 norm have a certain robustness to noise when calculating the distance. Therefore, in the present invention, by using the L2 norm to calculate the distance between two subsequences, the calculation result of the cluster distance is made more accurate.

[0022] Preferably, when intercepting the power usage data sequence, interception starts from the end of the power usage data sequence.

[0023] The beneficial effect is that the abnormal condition of the electric energy meter may change over time. Therefore, the abnormal condition of the electric energy meter is detected based on the power consumption data collected by the electric energy meter in a time period close to the current time, which can ensure high accuracy. The present invention obtains a subsequence by intercepting from the tail of the power consumption data sequence, thereby further improving the accuracy of the calculation of the abnormal degree of the electric energy meter.

[0024] Preferably, a k-means clustering algorithm is used when clustering subsequences.

[0025] Preferably, if the transmission volume of the network node with the highest communication quality reaches saturation, the electricity consumption data sequence of the electric energy meter with the highest comprehensive abnormality level is transmitted through the network node with the second highest communication quality.

[0026] In summary, the beneficial effects of the present invention are: the method of the present invention can avoid transmission errors in the power consumption data of the abnormal power meter, obtain the abnormal situation of the power meter in time, and have high transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It schematically illustrates a data transmission method of a smart electric energy meter in the prior art; Figure 2 is a schematic structural diagram of a method for collecting power data for a photovoltaic energy storage system according to an embodiment of the present invention; Figure 3 FIG. 4 is a flow chart schematically showing a method for transmitting power consumption data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Embodiment of the power data collection method for photovoltaic energy storage system: The power data collection method for a photovoltaic energy storage system of the present invention comprises: installing smart power meters at the connection points between each AC output terminal of the photovoltaic energy storage system and the power grid to collect power data and transmit it, and each smart power meter is communicatively connected with each other.

[0030] like Figure 2 As shown, the smart electric energy meter includes: an electricity consumption data acquisition unit, a communication unit and a controller, wherein the communication unit is connected to multiple electric energy meters and multiple network nodes; the controller is used to transmit the electricity consumption data of the smart electric energy meter and the electric energy meters connected to it through the communication unit.

[0031] The electricity consumption data collection unit includes a metering unit, a voltage sampling unit and a current sampling unit.

[0032] In this embodiment, the structure of the controller of the smart energy meter is conventional prior art, and the structure of the controller is the same as the controller structure in the Chinese patent with authorization announcement number CN113130235B.

[0033] The communication unit is connected to the user management system of the monitoring center through a plurality of network nodes.

[0034] like Figure 3 As shown, the transmission method includes: S101, obtaining historical electricity consumption data sequences of the smart electricity meter and the electricity meters connected thereto and calculating the period of each electricity consumption data sequence; In this embodiment, the historical electricity consumption data sequence of the electric energy meter refers to the data sequence composed of the electricity consumption data collected by the electric energy meter at historical moments. Since the distribution network has peak and valley periods of electricity consumption, and the electricity consumption of the loads connected to the distribution network is different in different time periods within a day, the electricity consumption data collected by the electric energy meter has a certain periodicity.

[0035] S102, obtaining subsequences in each cycle, specifically: truncating the length of one cycle from the multiple power consumption data sequences according to the multiple cycles, thereby obtaining multiple subsequences, clustering the multiple subsequences corresponding to each cycle, thereby obtaining cluster clusters in each cycle, wherein the length of the subsequence is equal to the corresponding cycle; For example, assuming that the cycles calculated in step S101 are 2, 3, and 5, and there are three power consumption data sequences, the three power consumption data sequences are truncated with 2, 3, and 5 as data lengths. After truncation, the lengths of the three subsequences corresponding to cycle 2 are all 2; the lengths of the three subsequences corresponding to cycle 3 are all 3; and the lengths of the three subsequences corresponding to cycle 5 are all 5.

[0036] In this embodiment, the k-means clustering algorithm is used to cluster the subsequences. In other embodiments, a suitable clustering algorithm such as hierarchical clustering, DBSCAN clustering algorithm, K-medoids clustering algorithm, etc. may also be used.

[0037] S103, obtaining the comprehensive abnormality degree of each electric energy meter, specifically: for each electric energy meter, calculating its first abnormality degree in each cycle, and performing weighted summation to obtain the comprehensive abnormality degree; the calculation expression of the first abnormality degree is: ; In the formula, Indicates the abnormality of the sth electric energy meter in the tth cycle, represents the hyperbolic tangent function, represents the current power consumption data of the sth electric energy meter, Represents the probability density function of the cluster to which the subsequence of the s-th electric energy meter in the t-th period belongs.

[0038] The current electricity consumption data of the electric energy meter refers to the electricity consumption data collected by the electric energy meter at the current moment.

[0039] In the present embodiment, there are two methods for obtaining the probability density function. The first one is the histogram method, which is a non-parametric method and does not require the assumption of the distribution type of X. The value range of X is divided into several small intervals, and then the frequency or frequency density in each interval is calculated according to the statistical results of X, and finally represented by a bar graph. The shape of the histogram can reflect the distribution characteristics of X, such as whether it is symmetrical, whether it is unimodal, whether it is skewed, etc. Another method is the parameter estimation method, which is a parameter method and requires the assumption of the distribution type of X, such as normal distribution, exponential distribution, uniform distribution, etc. The basic idea of ​​the parameter estimation method is to estimate the distribution parameters of X, such as mean, variance, shape parameters, etc., based on the statistical results of X using the maximum likelihood method, moment method or Bayesian method. Then substitute the estimated parameters into the distribution function of X to obtain the probability density distribution function f(x) of X.

[0040] represents the position of the current power consumption data of the sth electric energy meter in the probability density function. The closer the position is to the center, the greater the possibility that it belongs to the probability distribution corresponding to the probability density function. The difference between the current power consumption data of the sth power meter and the central power consumption data of the distribution is represented. The smaller the difference is, the closer the current power consumption data of the sth power meter is to the center of the distribution, the greater the possibility that the current power consumption data belongs to the distribution, and the smaller the possibility that it is abnormal. Therefore, the calculation expression of the first abnormality degree of this embodiment can more accurately calculate the abnormality degree of the power meter in a certain period.

[0041] Since the greater the abnormality of the electric energy meter in each cycle, the greater the possibility that the electric energy meter is abnormal, the comprehensive abnormality of the electric energy meter needs to be positively correlated with the abnormality of each cycle when calculating the comprehensive abnormality of the electric energy meter. For example, the average of the abnormality of the electric energy meter in each cycle can be used as the comprehensive abnormality of the electric energy meter.

[0042] S104. Transmitting the electricity consumption data sequence of the electric energy meter with the highest comprehensive abnormality through the network node with the highest communication quality.

[0043] The method for collecting electricity data for a photovoltaic energy storage system of the present embodiment first calculates the degree of abnormality of each electricity meter based on the electricity data sequence before transmitting the electricity data sequence of multiple electricity meters, and uses the network node with the highest communication quality to transmit the electricity data sequence of the electricity meter with the highest abnormality, thereby achieving faster acquisition of abnormal conditions of the electricity meters, avoiding transmission errors of electricity data of abnormal electricity meters, and ensuring timely discovery of abnormal conditions at various connection points between the photovoltaic energy storage system and the power grid. In addition, when calculating the degree of abnormality of the electric energy meter, the periodicity of the electric energy data sequence is taken into account. The electric energy data sequence is intercepted according to the length of each cycle of the electric energy data sequence to obtain a subsequence, and the subsequence under each cycle is used to calculate the degree of abnormality of the electric energy meter in each cycle. Compared with using the entire electric energy data sequence to calculate the degree of abnormality of the electric energy meter, the amount of data processing is greatly reduced, the efficiency of calculating the degree of abnormality of the electric energy meter is improved, and the transmission efficiency of the electric energy data is further improved; furthermore, when calculating the degree of abnormality in a certain cycle, the subsequences under the cycle are first clustered to obtain each clustering cluster, considering that the smaller the difference between the current electric energy meter's electric energy data and the center of the distribution, the greater the degree of proximity between the two, the greater the possibility that the current electric energy data belongs to the corresponding probability distribution, and the smaller the possibility that the electric energy meter's electric energy data sequence is abnormal, the degree of abnormality of the electric energy meter in a certain cycle is positively correlated with the difference, thereby more accurately calculating the degree of abnormality of the electric energy meter in a certain cycle.

[0044] In the above embodiment, the abnormality degree of the sth electric energy meter in the tth period is calculated based on the probability density function of the cluster to which it belongs. In another embodiment, the calculation expression of the abnormality degree of the sth electric energy meter in the tth period is: ; In the formula, Indicates the abnormality of the sth electric energy meter in the tth cycle, represents the current power consumption data of the sth electric energy meter, represents the mean value of the electricity consumption data in the cluster where the subsequence of the sth electric energy meter in the tth period belongs, Represents the standard deviation of the electricity consumption data in the cluster where the subsequence of the s-th electric energy meter in the t-th period belongs.

[0045] The greater the difference between the reading of the electric energy meter and the mean value of the reading of the electric energy meter in the cluster corresponding to the electric energy meter, the greater the degree of abnormality of the electric energy data collected by the electric energy meter; when the current electric energy data of two electric energy meters are the same but the clusters corresponding to the two electric energy meters are different, the different degrees of discreteness of the electric energy data in different clusters are taken into consideration (that is, the standard deviations of the electric energy data in different clusters are different). By taking the ratio of the mean difference between the reading of the electric energy meter and the reading of the electric energy meter in the cluster corresponding to the electric energy meter and the standard deviation of the electric energy data in the corresponding cluster as the degree of abnormality of the electric energy meter in a certain period, it is ensured that when the current electric energy data of the two electric energy meters are the same but the clusters corresponding to the two electric energy meters are different, the calculated first abnormality degrees of the two electric energy meters are different, so that the calculated first abnormality degree is more accurate.

[0046] In one embodiment, the calculation expression of the comprehensive abnormality degree is: ; In the formula, Indicates the comprehensive abnormality degree of the sth electric energy meter, It represents the probability that the power consumption data of the cluster where the subsequence of the sth electric energy meter in the tth period belongs to follows the normal distribution, represents the mean of the intra-class distance of the subsequence of the sth electric energy meter in the tth period, T represents the total number of the periods, Indicates the abnormality degree of the sth electric energy meter in the tth cycle.

[0047] This embodiment calculates the comprehensive abnormality degree by weighted summing the first abnormality degrees under different periods. Under normal circumstances, the greater the probability that the electricity consumption data in the cluster corresponding to the electric energy meter obeys the normal distribution, the greater the credibility of the calculated first abnormality degree, and the weight of the first abnormality degree should also be greater when weighted summing; the smaller the intra-class distance of the cluster in which the electric energy meter is located under different periods, the greater the similarity of the cluster, and the greater the weight of the first abnormality degree calculated by the cluster. Therefore, when weighted summing the first abnormality degree under different periods, let the corresponding weight be positively correlated with the probability that the electricity consumption data in the cluster obeys the normal distribution, and inversely proportional to the intra-class distance of the cluster in which the electric energy meter is located under the corresponding period, so that the comprehensive abnormality degree of the electric energy meter can be calculated more accurately.

[0048] In one embodiment, for a certain cluster, a method for obtaining the probability that its power consumption data follows a normal distribution includes: calculating the mean and variance of the electric energy meter readings in the cluster, and then using an AD test to obtain the probability that the power consumption data in the cluster follows a normal distribution.

[0049] The AD test can better implement the normality test and can sensitively reveal the potential asymmetry of the data, making the final probability of obeying the normal distribution more accurate.

[0050] In one embodiment, for a certain electric energy meter, the comprehensive abnormality degree is equal to the average value of the abnormality degrees in each cycle of the electric energy meter.

[0051] In one embodiment, the method used to calculate the period of the power consumption data sequence is an autocorrelation function.

[0052] The autocorrelation function can detect the periodic components in the signal. When there are periodic features in the signal (such as the power consumption data sequence), the autocorrelation function will have peaks at the corresponding lag time. The positions of these peaks directly correspond to the period of the signal, so as to accurately identify and analyze the periodic characteristics of the power consumption data sequence. In addition, the power consumption data sequence is often interfered by various noises, which may mask the true periodic characteristics of the signal. Through the autocorrelation function, these random noises can be filtered out and the true periodic information of the signal can be extracted. Therefore, using the autocorrelation function to calculate the period of the power consumption data sequence can make the period calculation result more accurate.

[0053] In one embodiment, when clustering subsequences in a certain period, the calculation expression of the clustering distance is: ; In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the mutual correlation between the i-th subsequence and the j-th subsequence.

[0054] The greater the mutual correlation between two subsequences, the more similar the two subsequences are, and the smaller the clustering distance between the two subsequences. By making the clustering distance negatively correlated with the mutual correlation between the two subsequences, the clustering distance corresponding to the two subsequences can be calculated more accurately.

[0055] In one embodiment, when clustering subsequences in a certain period, the calculation expression of the clustering distance is: ; In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the i-th subsequence, represents the jth subsequence, Indicates the correspondence between the i-th subsequence and the j-th subsequence Norm.

[0056] The L2 norm, also known as the Euclidean norm, represents the straight-line distance from the origin to the end point of the vector, that is, the length of the vector in the Euclidean space. This property makes the L2 norm intuitive and has a clear geometric meaning when measuring the distance between vectors. In multidimensional space, the L2 norm can accurately reflect the absolute distance between vectors. In addition, when calculating the distance, the L2 norm amplifies the impact of larger differences by the sum of squares, but also performs a certain degree of smoothing on smaller differences. This characteristic makes the L2 norm have a certain robustness to noise when calculating the distance. In this embodiment, the L2 norm is used to calculate the distance between two subsequences, so that the calculation result of the cluster distance is more accurate.

[0057] In one embodiment, when intercepting the power usage data sequence, the interception starts from the end of the power usage data sequence.

[0058] For example: Assume that the calculated cycles in step S101 of the above embodiment are 2, 3, and 4, and there are three power consumption data sequences. The first power consumption data sequence is [a, b, c, d, e], the second power consumption data sequence is [f, g, h, i, j], and the third power consumption data sequence is [i, j, k, l, m]. When the cycle is 2, the subsequences obtained after interception are: [d, e], [i, j], [l, m]. When the cycle is 3, the subsequences obtained after interception are: [c, d, e], [h, i, j], [k, l, m]. When the cycle is 4, the subsequences obtained after interception are: [b, c, d, e], [g, h, i, j], [j, k, l, m].

[0059] Since the abnormality of the electric energy meter may change over time, for example, the electric energy meter is in a normal state at the beginning, and the abnormality occurs after a period of time. Therefore, the abnormality of the electric energy meter is detected based on the power consumption data collected in the time period close to the current time, which can ensure higher accuracy. This embodiment obtains a subsequence by intercepting from the tail of the power consumption data sequence, thereby further improving the accuracy of the calculation of the abnormality degree of the electric energy meter.

[0060] In one embodiment, if the transmission volume of the network node with the highest communication quality reaches saturation, the power consumption data sequence of the electric energy meter with the highest comprehensive abnormality level is transmitted through the network node with the second highest communication quality.

[0061] If the transmission volume of the network node with the second highest communication quality also reaches saturation, the network node with the highest communication quality and unsaturated transmission volume is selected from the remaining network nodes for transmission.

[0062] The method for transmitting the electricity consumption data sequence of this embodiment can effectively and timely transmit the electricity consumption data sequence of the electric energy meter with the highest comprehensive abnormality level when the transmission volume of the network node with the highest communication quality reaches saturation.

[0063] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for collecting power data for a photovoltaic energy storage system, characterized in that: include: Smart energy meters are installed at the connection points between each AC output terminal of the photovoltaic energy storage system and the power grid to collect power data and transmit it. Each smart energy meter is connected to each other in communication. The transmission method includes: obtaining the historical power consumption data sequence of the smart energy meter and the power meter connected to it and calculating the period of each power consumption data sequence; According to multiple cycles, multiple power consumption data sequences are cut off by the length of one cycle to obtain multiple subsequences, and multiple subsequences corresponding to each cycle are clustered to obtain cluster clusters under each cycle, and the length of the subsequence is equal to the corresponding cycle; for each electric energy meter, the first abnormality degree under each cycle is calculated, and a weighted sum is performed to obtain the comprehensive abnormality degree; and the power consumption data sequence of the electric energy meter with the highest comprehensive abnormality degree is transmitted through the network node with the highest communication quality; the expression of the first abnormality degree is: ； In the formula, Indicates the abnormality of the sth electric energy meter in the tth cycle, represents the hyperbolic tangent function, represents the current power consumption data of the sth electric energy meter, Represents the probability density function of the cluster to which the subsequence of the s-th electric energy meter in the t-th period belongs.

2. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: The calculation expression of the comprehensive abnormality degree is: ； In the formula, Indicates the comprehensive abnormality degree of the sth electric energy meter, It represents the probability that the power consumption data of the cluster where the subsequence of the sth electric energy meter in the tth period belongs to follows the normal distribution, represents the mean of the intra-class distances of the subsequences of the s-th electric energy meter in the t-th period, and T represents the total number of the periods.

3. The method for collecting power data for a photovoltaic energy storage system according to claim 2, characterized in that: For a certain cluster, the method for obtaining the probability that its electricity consumption data obeys the normal distribution includes: calculating the mean and variance of the electric energy meter readings in the cluster, and then using AD test to obtain the probability that the electricity consumption data in the cluster obeys the normal distribution.

4. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: For a certain electric energy meter, the comprehensive abnormality degree is equal to the average value of the abnormality degrees in each cycle of the meter.

5. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: The method used to calculate the period of the electricity consumption data series is the autocorrelation function.

6. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: When clustering subsequences under a certain period, the calculation expression of clustering distance is: ； In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the mutual correlation between the i-th subsequence and the j-th subsequence.

7. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: When clustering subsequences under a certain period, the calculation expression of clustering distance is: ； In the formula, represents the clustering distance between the ith subsequence and the jth subsequence, represents the i-th subsequence, represents the jth subsequence, Indicates the correspondence between the i-th subsequence and the j-th subsequence Norm.

8. The method for collecting power data for a photovoltaic energy storage system according to claim 1, characterized in that: When intercepting the power consumption data sequence, interception starts from the end of the power consumption data sequence.

9. The method for collecting power data for a photovoltaic energy storage system according to any one of claims 1 to 8, characterized in that: The k-means clustering algorithm is used to cluster subsequences.

10. The method for collecting power data for a photovoltaic energy storage system according to any one of claims 1 to 8, characterized in that: If the transmission volume of the network node with the highest communication quality reaches saturation, the electricity consumption data sequence of the electric energy meter with the highest comprehensive abnormality will be transmitted through the network node with the second highest communication quality.

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