Low-voltage photovoltaic user anomaly detection method, device and equipment and storage medium

Abstract

The invention discloses a low-voltage photovoltaic user anomaly detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the three-phase zero value judgment of a current wiring according to a current load current and a user photovoltaic type, and obtaining a wiring anomaly detection result; based on a preset power generation normal coefficient, judging whether the current user is on the current day within nearly preset short-term days and the daily generating capacity per unit capacity of at least one day except the current day is smaller than the lower limit of a preset power generation interval or not; or whether the daily generating capacity per unit capacity exceeding the preset days is smaller than the lower limit of the preset power generation interval within the nearly preset long-term days or not exists, and if yes, it is judged that the current user equipment breaks down; and if the daily generating capacity per unit capacity of the current user in the nearly preset short-term days is greater than the upper limit of the preset generating interval, judging that the capacity of the current user is increased privately. The technical problem that in the prior art, photovoltaic anomaly detection mainly depends on a manual rechecking or parameter estimation mode, and the result is lack of accuracy and reliability can be solved.

Description

Technical Field

[0001] The present application relates to the field of distributed photovoltaic technology, and in particular to a method, device, equipment and storage medium for detecting abnormalities in low-voltage photovoltaic users. Background Art

[0002] In recent years, the installed capacity of distributed photovoltaics has been continuously increasing, but this has also led to the following problems. First, the installed electricity meters must be inspected and accepted before the photovoltaic power is turned on. However, since the current increase in installed capacity is far greater than the acceptance range of the acceptance personnel, it may not only cause delays in power supply, but also problems with the acceptance quality, which will damage the rights and interests of users. Second, there is the pressure of customer complaints caused by photovoltaic equipment failures. Although photovoltaic equipment is not an asset of the power supply company, users are accustomed to attributing the reduction in settled electricity due to photovoltaic equipment failures to the poor monitoring of the power supply company. Customer complaints not only have a great impact on the customer service indicators of the power supply company, but may also pose a risk of escalating public opinion. In addition, for users who enjoyed higher grid-connected subsidy price policies in early years, there is a phenomenon that some users have privately increased capacity to obtain power generation subsidies.

[0003] It can be seen that the abnormal state of distributed photovoltaics may damage the rights and interests of users, power supply departments, governments and other parties. At present, for the abnormal problems of distributed photovoltaics, either the abnormal situation can only be judged subjectively during the monthly manual review of electricity consumption, and the photovoltaic user acceptance is in a non-powered state, so the review and acceptance results lack accuracy. Or it is estimated based on some power generation related parameters, and it is impossible to accurately and reliably judge whether the user has installed photovoltaic panels privately. At the same time, compared with centralized photovoltaics, low-voltage distributed photovoltaics upload data to the grid side with lower frequency and fewer data types. It is very difficult to realize photovoltaic abnormality detection and it is difficult to adapt to the current development needs of distributed photovoltaics. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for detecting abnormalities in low-voltage photovoltaic users, which are used to solve the technical problem that the existing technology mainly relies on manual review or parameter estimation to perform photovoltaic abnormality detection, and the obtained results lack accuracy and reliability and cannot meet the current photovoltaic development needs.

[0005] In view of this, the first aspect of the present application provides a low-voltage photovoltaic user abnormality detection method, comprising:

[0006] Obtain the current load current of the target power grid system, the user photovoltaic type of the current user, and the daily power generation per unit capacity;

[0007] Perform three-phase zero value judgment analysis on the current user wiring condition according to the current load current and the user photovoltaic type to obtain a wiring abnormality detection result;

[0008] Based on the preset power generation normal coefficient, it is judged whether the current user has a daily power generation of the unit capacity less than the lower limit of the preset power generation interval on the current day and at least one day other than the current day within the recent preset short-term days, or whether there are more than a preset number of days within the recent preset long-term days where the daily power generation of the unit capacity is less than the lower limit of the preset power generation interval. If so, it is judged that the current user equipment is faulty;

[0009] If the daily power generation per unit capacity of the current user within the preset short-term days is greater than the upper limit of the preset power generation interval, it is determined that the current user has increased the capacity privately.

[0010] Preferably, the obtaining of the current load current of the target power grid system, the user photovoltaic type of the current user and the daily power generation per unit capacity includes:

[0011] Acquire the current load current of the target power grid system and the current user power generation parameters, wherein the current user power generation parameters include the user's daily power generation, the user's installed capacity and the user's photovoltaic type;

[0012] The daily power generation per unit capacity of the current user is calculated based on the daily power generation of the user and the installed capacity of the user.

[0013] Preferably, the method of judging based on the preset power generation normal coefficient whether the current user has a daily power generation per unit capacity less than the lower limit of the preset power generation interval on the current day and on at least one day other than the current day within the recent preset short-term days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than the preset number of days within the recent preset long-term days, and if so, judging that the current user equipment is faulty, further comprises:

[0014] Get the daily power generation per unit capacity of all users on the first day of the month, and get the historical daily power generation per unit capacity;

[0015] Based on the normal distribution algorithm and the gradient descent method, a maximum likelihood estimation analysis is performed according to the historical unit capacity daily power generation, the normal upper limit coefficient of power generation and the normal lower limit coefficient of power generation are determined, and a preset normal power generation coefficient is obtained.

[0016] Preferably, the method of judging based on the preset power generation normal coefficient whether the current user has a daily power generation per unit capacity less than the lower limit of the preset power generation interval on the current day and on at least one day other than the current day within the recent preset short-term days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than the preset number of days within the recent preset long-term days, and if so, judging that the current user equipment is faulty, further comprises:

[0017] Get the daily power generation per unit capacity of all users on any day, and get the daily power generation per unit capacity array of users;

[0018] Selecting the median power generation value from the user unit capacity daily power generation array;

[0019] The preset power generation range of all users on that day is calculated based on the preset power generation normal coefficient and the power generation median.

[0020] The second aspect of the present application provides a low-voltage photovoltaic user abnormality detection device, comprising:

[0021] A parameter acquisition unit, used to acquire the current load current of the target power grid system, the user photovoltaic type of the current user and the daily power generation per unit capacity;

[0022] A wiring detection unit, used to perform three-phase zero value judgment analysis on the current user wiring condition according to the current load current and the user photovoltaic type, and obtain a wiring abnormality detection result;

[0023] A fault analysis unit is used to determine, based on a preset power generation normal coefficient, whether the current user has a daily power generation per unit capacity less than the lower limit of a preset power generation interval on the current day and on at least one day other than the current day within a preset short-term number of days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than a preset number of days within a preset long-term number of days, and if so, determine that the current user equipment is faulty;

[0024] The capacity increase judgment unit is used to judge that the current user has increased the capacity privately if the daily power generation per unit capacity of the current user within the preset short-term days is greater than the upper limit of the preset power generation interval.

[0025] Preferably, the parameter acquisition unit is specifically used to:

[0026] Acquire the current load current of the target power grid system and the current user power generation parameters, wherein the current user power generation parameters include the user's daily power generation, the user's installed capacity and the user's photovoltaic type;

[0027] The daily power generation per unit capacity of the current user is calculated based on the daily power generation of the user and the installed capacity of the user.

[0028] Preferably, it also includes:

[0029] The first extraction unit is used to obtain the daily power generation per unit capacity of all users on the first day of the month to obtain the historical daily power generation per unit capacity;

[0030] The coefficient analysis unit is used to perform maximum likelihood estimation analysis based on the historical unit capacity daily power generation based on the normal distribution algorithm and the gradient descent method, determine the normal upper limit coefficient of power generation and the normal lower limit coefficient of power generation, and obtain a preset normal power generation coefficient.

[0031] Preferably, it also includes:

[0032] The second extraction unit is used to obtain the daily power generation per unit capacity of all users on any day, and obtain an array of the daily power generation per unit capacity of the users;

[0033] A median selection unit, used for selecting a median power generation value from the user unit capacity daily power generation array;

[0034] The interval calculation unit is used to calculate the preset power generation interval of all users on the day according to the preset power generation normal coefficient and the power generation median.

[0035] A third aspect of the present application provides a low-voltage photovoltaic user abnormality detection device, the device comprising a processor and a memory;

[0036] The memory is used to store program code and transmit the program code to the processor;

[0037] The processor is used to execute the low-voltage photovoltaic user abnormality detection method described in the first aspect according to the instructions in the program code.

[0038] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the low-voltage photovoltaic user abnormality detection method described in the first aspect.

[0039] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0040] In the present application, a method for detecting abnormalities in low-voltage photovoltaic users is provided, including: obtaining the current load current of the target power grid system, the user photovoltaic type of the current user, and the daily power generation per unit capacity; performing a three-phase zero value judgment analysis on the current user's wiring condition according to the current load current and the user photovoltaic type to obtain a wiring abnormality detection result; judging based on a preset power generation normal coefficient whether the current user has a daily power generation per unit capacity of less than the lower limit of a preset power generation range on the current day and on at least one day other than the current day within a recent preset short-term number of days, or whether there are daily power generation per unit capacity of less than the lower limit of a preset power generation range for more than a preset number of days within a recent preset long-term number of days, and if so, judging that the current user's equipment is faulty; if the daily power generation per unit capacity of the current user within the recent preset short-term number of days is greater than the upper limit of the preset power generation range, judging that the current user has increased the capacity privately.

[0041] The low-voltage photovoltaic user abnormality detection method provided by this application uses different distributed photovoltaic data to design three different photovoltaic user abnormality detection methods; based on the load current and the user's photovoltaic type, the current user's wiring condition is quantitatively analyzed, so as to accurately determine whether the current user has abnormal wiring, without relying on manual review, which is more in line with the actual situation and can ensure the accuracy of the results; then, according to the preset normal power generation coefficient, it is determined whether the current user's unit capacity daily power generation is within the normal range. If not, the equipment failure can be detected in a variety of situations. The judgment mechanism and basis parameters of this process can better reflect the characteristics of the actual distributed photovoltaic system and ensure the reliability of the analysis results; finally, the problem of users' private capacity increase can be analyzed based on the unit capacity daily power generation. It is not an estimate or a manual judgment, but an accurate detection result; therefore, the above comprehensive photovoltaic abnormality detection scheme can better meet the current photovoltaic development needs. Therefore, this application can solve the technical problem that the existing technology mainly relies on human review or parameter estimation to detect photovoltaic abnormalities, and the results obtained lack accuracy and reliability and cannot meet the current photovoltaic development needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process for detecting abnormality of a low-voltage photovoltaic user provided in an embodiment of the present application;

[0043] Figure 2 A schematic structural diagram of a low-voltage photovoltaic user abnormality detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] For easier understanding, see Figure 1 , an embodiment of a low-voltage photovoltaic user abnormality detection method provided by the present application includes:

[0046] Step 101: Obtain the current load current of the target power grid system, the user photovoltaic type of the current user, and the daily power generation per unit capacity.

[0047] Further, in step 101, the current load current of the target power grid system and the current user power generation parameters are obtained, where the current user power generation parameters include the user's daily power generation, the user's installed capacity and the user's photovoltaic type;

[0048] Calculate the current user's daily power generation per unit capacity based on the user's daily power generation and the user's installed capacity.

[0049] It should be noted that the grid operation data can be queried in the metering automation system of the power grid company, and the query data includes but is not limited to meter code, load current, meter power, etc.; and the marketing management system of the power grid can extract user comprehensive file information, including but not limited to the installed capacity and photovoltaic type of photovoltaic users, among which the photovoltaic type of users refers to whether the user has surplus power or full power. The current user is the user currently undergoing abnormal analysis, and can also be limited according to actual conditions. The number of users can be large, and each user can obtain its corresponding data based on this method for subsequent abnormal analysis.

[0050] Step 102: Perform three-phase zero value judgment analysis on the current user wiring condition according to the current load current and the user's photovoltaic type to obtain a wiring abnormality detection result.

[0051] The user photovoltaic type can divide different photovoltaic users into two types: surplus power grid-connected and full power grid-connected. Then, the current user's wiring situation can be analyzed according to different user photovoltaic types and different current load currents to obtain wiring anomaly detection results.

[0052] It should be noted that the meter wiring error is that the meter's incoming and outgoing lines are connected in reverse, resulting in the reverse load current of the meter and incorrect electricity settlement. The incoming line of the meter of a photovoltaic user who is fully connected to the grid is connected to the photovoltaic power generation on the user side, and the outgoing line is connected to the grid side. The load current is positive when generating electricity. The incoming line of the meter of a photovoltaic user who is connected to the grid side and the outgoing line is connected to the user side. The power consumption is positive and the power generation is reverse. The meter load current is the vector sum of the user's power consumption and the photovoltaic power generation current.

[0053] For the load current obtained from the metering system every day, the time range is set to 12:00, 20:00, 21:00, and 22:00 of the day. The three-phase load current of a single user at the four times can be expressed as , , ,in, , , Respectively The load current of phases A, B and C in hours, For the day hours, and =12, 20, 21, 22.

[0054] The three-phase load current of the current users who are connected to the grid with surplus power is judged one by one, mainly at night time, so the load current at 20:00, 21:00 and 22:00 is selected for analysis; if the load current of any phase at these three time points is less than zero, it can be judged that the meter wiring is abnormal; specifically expressed as: if , then it means Being together The load current at the moment is less than zero, and the current user meter wiring is abnormal; among them, , .

[0055] The three-phase load current of full-fee users is judged one by one, mainly based on daytime, so the three-phase current analysis at 12:00 is selected; if any phase load current at this time is less than zero, it is judged that the current user's meter wiring is abnormal. Specifically: , then it is judged that the meter wiring is abnormal, among which, .

[0056] This embodiment is aimed at full-fee users. Whether the metering connection is abnormal can be determined by the positive and negative current during the day. In order to simplify the judgment steps and calculation amount, a single data point at 12:00 noon is selected. If the current is negative, it indicates that the metering is reversed. For users who use surplus electricity to access the Internet, electricity may only be used at night. If a reverse current appears, it indicates that the meter is reversed. Select the data points of 20:00, 21:00, and 22:00, which are commonly used times for residents' lighting. If the current has a negative value, it indicates that the metered electricity is reversely connected to the power generation line.

[0057] Step 103, based on the preset power generation normal coefficient, determine whether the current user has a daily power generation per unit capacity that is less than the lower limit of the preset power generation range on the current day and at least one day other than the current day within the recent preset short-term days, or whether there are more than a preset number of days within the recent preset long-term days when the daily power generation per unit capacity is less than the lower limit of the preset power generation range. If so, determine that the current user's equipment is faulty.

[0058] Furthermore, step 103, before that, also includes:

[0059] Get the daily power generation per unit capacity of all users on the first day of the month, and get the historical daily power generation per unit capacity;

[0060] Based on the normal distribution algorithm and gradient descent method, maximum likelihood estimation analysis is performed according to the historical unit capacity daily power generation, the normal upper limit coefficient and the normal lower limit coefficient of power generation are determined, and the preset normal power generation coefficient is obtained.

[0061] Furthermore, step 103 also includes:

[0062] Get the daily power generation per unit capacity of all users on any day, and get the daily power generation per unit capacity array of users;

[0063] Select the median power generation from the user's unit capacity daily power generation array;

[0064] The preset power generation range for all users on that day is calculated based on the preset normal power generation coefficient and the median power generation.

[0065] It should be noted that common faults of photovoltaic equipment include component cracking, hot spots, short circuits and open circuits between arrays, which will all lead to a decrease in power generation. There is a correlation between different photovoltaic daily power generation in the same area, and they can be used as a reference for detection. The photovoltaic power generation per unit installed capacity can be expressed as:

[0066]

[0067] in, is the power generation, the unit is kW / h, is the installed capacity of the component, in kWp, is the total solar radiation on the horizontal surface, in units of , is the irradiance under standard conditions, is the comprehensive efficiency coefficient. Including but not limited to component type correction factor, photovoltaic array inclination, azimuth correction factor, photovoltaic power generation system availability, light utilization rate, inverter efficiency, collection line loss, step-up transformer loss, photovoltaic module surface contamination correction factor, photovoltaic module conversion efficiency correction factor.

[0068] For photovoltaic systems installed in the same geographical location, with the same solar radiation intensity and time, the difference in power generation mainly depends on the comprehensive efficiency coefficient ; According to the comprehensive efficiency coefficient The daily power generation of photovoltaic unit installed capacity in the same area is proportional, which is expressed as the comprehensive efficiency coefficient ratio.

[0069] This embodiment compares the daily power generation per unit capacity of photovoltaic systems in the same area horizontally. Devices with values ​​lower than the normal range may have faults. Calculate the median of the daily power generation per unit capacity of photovoltaic systems in the area, set the normal range threshold of daily power generation, and devices with values ​​lower than the threshold are in fault state. The power generation normal coefficient can be adjusted according to local meteorological conditions, actual installation conditions and monitoring needs.

[0070] Specifically, if there are N users with normal connection, the daily power generation sequence that can be obtained from the metering system is: ,in, is the user serial number, and The installed capacity sequence of the current user can be derived from the marketing system and expressed as Then, the daily power generation per unit installed capacity can be calculated based on these two parameters, that is, the daily power generation per unit capacity:

[0071]

[0072] in, The date serial number.

[0073] For each day, the user's unit capacity daily power generation array can be Select the corresponding median power generation ; Then according to the preset normal power generation coefficient , The user's preset power generation range for the day can be determined.

[0074] This embodiment proposes a specific solution for determining the preset power generation normal coefficient, based on the user data distribution on the first day of each month; the photovoltaic power generation data of different users presents a negative skewed distribution, that is, a left skewed distribution, so the log-normal distribution is used to determine the k value. Take the daily power generation data of the unit installed capacity on the 1st of each month in this region, calculate the parameters of the log-normal distribution of the data, and determine the k value for that month. , For Z users, the historical unit capacity daily power generation on the first day of the month is expressed as ,in, It represents the daily power generation per unit capacity of user z.

[0075] Lognormal distribution is the normal distribution after taking the logarithm of the original data:

[0076]

[0077] It conforms to the normal distribution, that is ;in, , are the mean and standard deviation of a normal distribution. Initial values ​​for the mean and standard deviation of :

[0078]

[0079]

[0080] in, is the initial value of the mean, is the initial value of the standard deviation.

[0081] Iterate the calculation from the initial value and use the gradient descent method to , Perform maximum likelihood estimation analysis and use the normal distribution Principle to determine the specific normal upper limit coefficient of power generation and normal offline coefficient of power generation , get the preset normal power generation coefficient; the specific expression is:

[0082]

[0083]

[0084] Then the preset power generation range determined based on the preset power generation normal coefficient can be expressed as .

[0085] Then, based on the preset power generation range The current user's daily power generation per unit capacity is judged and analyzed, and whether there is an equipment failure; the preset short-term days selected in this embodiment are 3 days, and the preset long-term days are 30 days. For example, first calculate the normal power generation range of the current user in the past 3 days, that is, the preset power generation range; if it is assumed , ; Then the calculated normal power generation range can be expressed as , ,......, .in, , , Represent the day before yesterday, yesterday and today respectively. If user n is the current user on the dth day, and its unit capacity daily power generation is less than the lower limit of the normal power generation range of the day, and, , If there is a day when the unit capacity daily power generation is also less than the lower limit of the normal power generation range of the corresponding day, it is judged that the equipment is faulty. Specifically, it can be expressed as:

[0086]

[0087] Next, if user n is the current user, calculate the preset power generation interval within 30 days, that is, the normal power generation interval, expressed as , ,......, If the daily power generation per unit capacity of user n is less than the lower limit of the preset power generation range for more than a preset number of days, this embodiment selects that the daily power generation per unit capacity is less than the lower limit of the normal power generation range of the corresponding day for more than 5 days, then it is determined that the current user equipment is faulty.

[0088] Since transient faults can be self-recovered, such as being blocked by hanging objects, it may be manifested as a reduction in power generation on the day, and return to normal the next day. In order to eliminate the impact of transient faults and meet the requirements of timeliness and accuracy of detection, this embodiment sets a dual judgment mechanism of preset short-term days and preset long-term days; and the preset short-term days, preset long-term days and preset days can be set according to actual conditions. This embodiment is only an example and not limited.

[0089] Specifically, in order to reduce the false alarm rate and meet the timeliness requirements, a 3-day detection window is set. If the value is lower than the threshold twice in the last three days and has not recovered on the last day, it can be considered that the photovoltaic user has an irreversible fault. In order to reduce the missed alarm rate, a 30-day detection window is set. If there are 5 or more days below the threshold in the last 30 days, it can be considered that the photovoltaic user has an irreversible fault.

[0090] Step 104: If the daily power generation per unit capacity of the current user within the preset short-term number of days is greater than the upper limit of the preset power generation range, it is determined that the current user has increased the capacity privately.

[0091] If the current user's daily power generation per unit capacity within the preset short-term days on the same day exceeds the upper limit of the preset power generation interval, it is judged that the current user has increased the capacity privately. That is, when the preset short-term days are 3 days, the preset power generation interval is , ,......, ; If it appears:

[0092]

[0093] Unauthorized capacity increase refers to the increase of photovoltaic installed capacity and grid connection without reporting to the power supply department. The daily power generation per unit capacity of the unauthorized capacity increase user is greater than the theoretical value, which can be detected by horizontal comparison in the same area. This embodiment is based on the detection method of unauthorized photovoltaic capacity increase in the normal range of the area; if the upper limit value is exceeded every day within three days, it is considered to be a user who has increased capacity unauthorizedly.

[0094] This embodiment can detect three abnormal conditions of photovoltaic users. For users with incorrect meter wiring and users who have increased the capacity without permission, personnel can be sent to conduct on-site inspections. For users with photovoltaic equipment failures, it is necessary to communicate with the users in a timely manner to detect major situations and remind operation and maintenance.

[0095] The low-voltage photovoltaic user abnormality detection method provided in the embodiment of the present application uses different distributed photovoltaic data to design three different photovoltaic user abnormality detection methods; based on the load current and the user's photovoltaic type, the current user's wiring condition is quantitatively analyzed, so as to accurately determine whether the current user has abnormal wiring, without relying on manual review, which is more in line with the actual situation and can ensure the accuracy of the result; then, according to the preset normal power generation coefficient, it is determined whether the current user's unit capacity daily power generation is within the normal range. If not, the equipment failure can be detected in a variety of situations. The judgment mechanism and basis parameters of this process can better reflect the characteristics of the actual distributed photovoltaic system and ensure the reliability of the analysis results; finally, the problem of users' private capacity increase can be analyzed based on the unit capacity daily power generation. It is not an estimate or a manual judgment, but an accurate detection result; therefore, the above comprehensive photovoltaic abnormality detection scheme can better meet the current photovoltaic development needs. Therefore, the embodiment of the present application can solve the technical problem that the existing technology mainly relies on human review or parameter estimation to perform photovoltaic abnormality detection, and the obtained results lack accuracy and reliability and cannot meet the current photovoltaic development needs.

[0096] For easier understanding, see Figure 2 , the present application provides an embodiment of a low-voltage photovoltaic user abnormality detection device, comprising:

[0097] The parameter acquisition unit 201 is used to acquire the current load current of the target power grid system, the user photovoltaic type of the current user and the daily power generation per unit capacity;

[0098] The wiring detection unit 202 is used to perform three-phase zero value judgment analysis on the current user wiring situation according to the current load current and the user's photovoltaic type to obtain a wiring abnormality detection result;

[0099] The fault analysis unit 203 is used to determine, based on a preset power generation normal coefficient, whether the current user has a daily power generation per unit capacity of less than the lower limit of a preset power generation interval on the current day and at least one day other than the current day within a preset short-term number of days, or whether there are more than a preset number of days within a preset long-term number of days where the daily power generation per unit capacity is less than the lower limit of the preset power generation interval. If so, determine that the current user equipment is faulty;

[0100] The capacity increase judgment unit 204 is used to judge that the current user has increased the capacity privately if the daily power generation per unit capacity of the current user within the recent preset short-term days is greater than the upper limit of the preset power generation interval.

[0101] Furthermore, the parameter acquisition unit 201 is specifically used for:

[0102] Obtain the current load current of the target power grid system and the current user power generation parameters, which include the user's daily power generation, the user's installed capacity and the user's photovoltaic type;

[0103] Calculate the current user's daily power generation per unit capacity based on the user's daily power generation and the user's installed capacity.

[0104] Furthermore, it also includes:

[0105] The first extraction unit 205 is used to obtain the daily power generation per unit capacity of all users on the first day of the month to obtain the historical daily power generation per unit capacity;

[0106] The coefficient analysis unit 206 is used to perform maximum likelihood estimation analysis based on the normal distribution algorithm and the gradient descent method according to the historical unit capacity daily power generation, determine the normal upper limit coefficient of power generation and the normal lower limit coefficient of power generation, and obtain a preset normal power generation coefficient.

[0107] Furthermore, it also includes:

[0108] The second extraction unit 207 is used to obtain the daily power generation per unit capacity of all users on any day, and obtain an array of the daily power generation per unit capacity of the users;

[0109] The median selection unit 208 is used to select the median power generation in the array of daily power generation per unit capacity of the user;

[0110] The interval calculation unit 209 is used to calculate the preset power generation interval of all users on the day according to the preset power generation normal coefficient and the power generation median.

[0111] A third aspect of the present application provides a low-voltage photovoltaic user abnormality detection device, the device comprising a processor and a memory;

[0112] The memory is used to store the program code and transmit the program code to the processor;

[0113] The processor is used to execute the low-voltage photovoltaic user abnormality detection method of the first aspect according to the instructions in the program code.

[0114] A fourth aspect of the present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the low-voltage photovoltaic user abnormality detection method of the first aspect.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0119] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting abnormality of low-voltage photovoltaic users, characterized in that: include: Obtain the current load current of the target power grid system, the user photovoltaic type of the current user, and the daily power generation per unit capacity; Perform three-phase zero value judgment analysis on the current user wiring condition according to the current load current and the user photovoltaic type to obtain a wiring abnormality detection result; Based on the preset power generation normal coefficient, it is judged whether the current user has a daily power generation of the unit capacity less than the lower limit of the preset power generation interval on the current day and at least one day other than the current day within the recent preset short-term days, or whether there are more than a preset number of days within the recent preset long-term days where the daily power generation of the unit capacity is less than the lower limit of the preset power generation interval. If so, it is judged that the current user equipment is faulty; If the daily power generation per unit capacity of the current user within the preset short-term days is greater than the upper limit of the preset power generation interval, it is determined that the current user has increased the capacity privately.

2. The low-voltage photovoltaic user abnormality detection method according to claim 1 is characterized in that: The obtaining of the current load current of the target power grid system, the user photovoltaic type of the current user and the daily power generation per unit capacity includes: Acquire the current load current of the target power grid system and the current user power generation parameters, wherein the current user power generation parameters include the user's daily power generation, the user's installed capacity and the user's photovoltaic type; The daily power generation per unit capacity of the current user is calculated based on the daily power generation of the user and the installed capacity of the user.

3. The low-voltage photovoltaic user abnormality detection method according to claim 1 is characterized in that: The method of judging based on the preset power generation normal coefficient whether the current user has a daily power generation per unit capacity less than the lower limit of the preset power generation interval on the current day and at least one day other than the current day within the recent preset short-term days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than the preset number of days within the recent preset long-term days, and if so, judging that the current user equipment is faulty, further includes: Get the daily power generation per unit capacity of all users on the first day of the month, and get the historical daily power generation per unit capacity; Based on the normal distribution algorithm and the gradient descent method, a maximum likelihood estimation analysis is performed according to the historical unit capacity daily power generation, the normal upper limit coefficient of power generation and the normal lower limit coefficient of power generation are determined, and a preset normal power generation coefficient is obtained.

4. The low-voltage photovoltaic user abnormality detection method according to claim 1 is characterized in that: The method of judging based on the preset power generation normal coefficient whether the current user has a daily power generation per unit capacity less than the lower limit of the preset power generation interval on the current day and at least one day other than the current day within the recent preset short-term days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than the preset number of days within the recent preset long-term days, and if so, judging that the current user equipment is faulty, further includes: Get the daily power generation per unit capacity of all users on any day, and get the daily power generation per unit capacity array of users; Selecting the median power generation value from the user unit capacity daily power generation array; The preset power generation range of all users on that day is calculated based on the preset power generation normal coefficient and the power generation median.

5. A low-voltage photovoltaic user abnormality detection device, characterized in that: include: A parameter acquisition unit, used to acquire the current load current of the target power grid system, the user photovoltaic type of the current user and the daily power generation per unit capacity; A wiring detection unit, used to perform three-phase zero value judgment analysis on the current user wiring condition according to the current load current and the user photovoltaic type, and obtain a wiring abnormality detection result; A fault analysis unit is used to determine, based on a preset power generation normal coefficient, whether the current user has a daily power generation per unit capacity less than the lower limit of a preset power generation interval on the current day and on at least one day other than the current day within a preset short-term number of days, or whether the daily power generation per unit capacity is less than the lower limit of the preset power generation interval for more than a preset number of days within a preset long-term number of days, and if so, determine that the current user equipment is faulty; The capacity increase judgment unit is used to judge that the current user has increased the capacity privately if the daily power generation per unit capacity of the current user within the preset short-term days is greater than the upper limit of the preset power generation interval.

6. The low-voltage photovoltaic user abnormality detection device according to claim 5 is characterized in that: The parameter acquisition unit is specifically used for: Acquire the current load current of the target power grid system and the current user power generation parameters, wherein the current user power generation parameters include the user's daily power generation, the user's installed capacity and the user's photovoltaic type; The daily power generation per unit capacity of the current user is calculated based on the daily power generation of the user and the installed capacity of the user.

7. The low-voltage photovoltaic user abnormality detection device according to claim 5 is characterized in that: Also includes: The first extraction unit is used to obtain the daily power generation per unit capacity of all users on the first day of the month to obtain the historical daily power generation per unit capacity; The coefficient analysis unit is used to perform maximum likelihood estimation analysis based on the historical unit capacity daily power generation based on the normal distribution algorithm and the gradient descent method, determine the normal upper limit coefficient of power generation and the normal lower limit coefficient of power generation, and obtain a preset normal power generation coefficient.

8. The low-voltage photovoltaic user abnormality detection device according to claim 5 is characterized in that: Also includes: The second extraction unit is used to obtain the daily power generation per unit capacity of all users on any day, and obtain an array of the daily power generation per unit capacity of the users; A median selection unit, used for selecting a median power generation value from the user unit capacity daily power generation array; The interval calculation unit is used to calculate the preset power generation interval of all users on the day according to the preset power generation normal coefficient and the power generation median.

9. A low-voltage photovoltaic user abnormality detection device, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the low-voltage photovoltaic user abnormality detection method described in any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the low-voltage photovoltaic user abnormality detection method according to any one of claims 1-4.

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