Distributed photovoltaic power station assembly cleaning decision-making method, device and equipment

Abstract

The invention provides a distributed photovoltaic power station component cleaning decision-making method, device and equipment, which are used for solving the technical problem of inaccurate cleaning decision-making of a distributed photovoltaic power station. The method comprises the following steps: acquiring data of a distributed photovoltaic power station, calculating an inter-station deviation sigma 1 of equivalent utilization hours on that day, an equivalent utilization hour year-on-year deviation sigma 2 of the equivalent utilization hours on that day, an inter-station deviation sigma 3 of a maximum power generation power proportion on that day and an year-on-year deviation sigma 4 of the maximum power generation power proportion on that day on line, and then calculating a comprehensive deviation sigma; and calculating expected benefits after the power station is cleaned according to the comprehensive deviation and the natural attenuation rate of the components, and automatically giving decision information about whether the components of the power station are cleaned or not by the system according to the expected benefits and the cleaning cost. According to the invention, the timeliness and accuracy of photovoltaic power station cleaning decision can be improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and in particular to a distributed photovoltaic power station component cleaning decision method, device and equipment. Background Art

[0002] With the increasing number of small and medium-sized photovoltaic power stations such as distributed industrial and commercial, household power stations, many photovoltaic power station operators have a large number of photovoltaic power stations in their hands, and the installed capacity of each power station is less than 100kwp. How to judge whether a power station is operating normally and whether the power generation efficiency is high has become a problem that troubles distributed power station operators. How to use existing data for effective analysis and decision-making has become a rigid demand for distributed power station operators.

[0003] Usually, a regular cleaning program is adopted. However, the regular cleaning program is limited by the observation cycle, and sometimes the contamination of photovoltaic modules cannot be discovered in time, resulting in untimely and inaccurate cleaning plans, causing losses to the owner's power generation income. Summary of the invention

[0004] In view of this, the present invention provides a distributed photovoltaic power station component cleaning decision method, device and equipment, which are used to solve the technical problem of inaccurate cleaning decision of distributed photovoltaic power stations.

[0005] Based on one aspect of an embodiment of the present invention, the present invention provides a distributed photovoltaic power station component cleaning decision method, the method comprising:

[0006] Collect and store basic information and real-time data of distributed photovoltaic power plants;

[0007] Calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day, the equivalent utilization hours D1 of the first distributed photovoltaic power station on the same day, and the average daily equivalent utilization hours Y2 of the first station in the same month of the same period last year;

[0008] Use D1 and Y to calculate the inter-station deviation σ1 of the equivalent utilization hours of the first power station on that day;

[0009] Use D1 and Y2 to calculate the year-on-year deviation σ2 of equivalent utilization hours of the first power station on that day;

[0010] Calculate the maximum power generation ratio Z of the distributed photovoltaic power station as a whole on the day, the maximum power generation ratio P1 of the first power station on the day, and the maximum power generation ratio Z2 of the first power station in the same period of the month;

[0011] Use P1 and Z to calculate the inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day;

[0012] Use P1 and Z2 to calculate the year-on-year deviation σ4 of the maximum power generation ratio of the first power station on that day;

[0013] Calculating the comprehensive deviation σ of the σ1, σ2, σ3 and σ4;

[0014] The expected increase in revenue after the cleaning of the first power station is calculated by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate corresponding to the years of service of the components, and the comprehensive deviation σ;

[0015] When the estimated increase in revenue is greater than the cost of cleaning the power plant, decision information suggesting cleaning the power plant is output; otherwise, decision information suggesting not cleaning the power plant is output.

[0016] Furthermore, when calculating the σ1, σ2, σ3 and σ4, the step of using the installation deviation factor X to adjust the obtained result is also included, and when calculating the comprehensive deviation σ, the adjusted σ1, σ2, σ3 and σ4 are used for calculation;

[0017] The calculation method of the installation deviation factor X is:

[0018] The sum of the daily power generation of all power stations in the distributed photovoltaic power station is divided by the power station component capacity of all power stations to obtain the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day;

[0019] The equivalent utilization hours of the first power station on the first day of grid connection are obtained by dividing the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station;

[0020] The installation deviation factor X is obtained by dividing the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day.

[0021] Furthermore, the calculation method of σ1 and σ2 is:

[0022] Divide the difference between D1 and Y by Y to obtain the station deviation of equivalent utilization hours of the first power station on that day, and adjust it with the installation deviation factor X to obtain the adjusted station deviation σ1 of equivalent utilization hours of the first power station on that day;

[0023] The difference between D1 and Y2 is divided by Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on that day, and then the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X are adjusted to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on that day.

[0024] Furthermore, the calculation method of σ3 and σ4 is:

[0025] Divide the difference between P1 and Z by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on that day, and then adjust it with the installation deviation factor X to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day;

[0026] The difference between P1 and Z2 is divided by Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on that day, and then the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X are adjusted to obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on that day.

[0027] Furthermore, the method for calculating the comprehensive deviation σ of σ1, σ2, σ3 and σ4 is:

[0028] The comprehensive deviation σ is obtained by calculating the adjusted σ1, σ2, σ3 and σ4 in the form of standard deviation or weighted average.

[0029] Based on another aspect of the embodiment of the present invention, the present invention further provides a distributed photovoltaic power station component cleaning decision device, the device comprising:

[0030] The acquisition and storage module is used to acquire and store basic information and real-time data of distributed photovoltaic power stations;

[0031] The first calculation module is used to calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day, the equivalent utilization hours D1 of the first power station in the distributed photovoltaic power station on the same day, and the average daily equivalent utilization hours Y2 of the first power station in the same month of the same period last year; use D1 and Y to calculate the inter-station deviation σ1 of the equivalent utilization hours of the first power station on the same day; use D1 and Y2 to calculate the year-on-year deviation σ2 of the equivalent utilization hours of the first power station on the same day;

[0032] The second calculation module is used to calculate the overall daily maximum power generation ratio Z of the distributed photovoltaic power station, the daily maximum power generation ratio P1 of the first power station, and the historical maximum power generation ratio Z2 of the first power station in the same period of the month; P1 and Z are used to calculate the inter-station deviation σ3 of the daily maximum power generation ratio of the first power station; P1 and Z2 are used to calculate the year-on-year deviation σ4 of the daily maximum power generation ratio of the first power station;

[0033] A comprehensive deviation calculation module, used for calculating the comprehensive deviation σ of the σ1, σ2, σ3 and σ4;

[0034] The cleaning decision module is used to calculate the expected increase in revenue after the cleaning of the first power station by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate corresponding to the years of service of the components, and the comprehensive deviation σ; when the expected increase in revenue is greater than the cost of cleaning the power station, the decision information recommending cleaning the power station is output, otherwise the decision information not recommending cleaning the power station is output.

[0035] Furthermore, the device also includes:

[0036] The installation deviation calculation module is used to obtain the equivalent utilization hours of the first power station on the first day of grid connection by dividing the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station; and to obtain the installation deviation factor X by dividing the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day;

[0037] The first calculation module is further used to adjust the σ1 and σ2 using the installation deviation factor X;

[0038] The second calculation module is further used to adjust the σ3 and σ4 using the installation deviation factor X;

[0039] The comprehensive deviation calculation module calculates the comprehensive deviation σ based on the adjusted σ1, σ2, σ3 and σ4.

[0040] Furthermore, the first calculation module includes:

[0041] The first subunit is used to divide the difference between D1 and Y by Y to obtain the station deviation of the equivalent utilization hours of the first power station on that day, and adjust it with the installation deviation factor X to obtain the adjusted station deviation σ1 of the equivalent utilization hours of the first power station on that day;

[0042] The second subunit is used to divide Y2 by the difference between D1 and Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on that day, and then adjust it with the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on that day;

[0043] The second calculation module includes:

[0044] The third subunit is used to divide the difference between P1 and Z by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on that day, and then use the installation deviation factor X to adjust it to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day;

[0045] The fourth subunit is used to divide Z2 by the difference between P1 and Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on the day, and then adjust it with the natural attenuation rate corresponding to the years of use of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on the day.

[0046] Furthermore, the comprehensive deviation calculation module calculates the adjusted σ1, σ2, σ3 and σ4 by means of standard deviation or weighted average to obtain the comprehensive deviation σ.

[0047] The present invention collects distributed photovoltaic power station data, calculates online the station deviation σ1 of the equivalent utilization hours of the power station, the year-on-year deviation σ2 of the equivalent utilization hours of the day, the station deviation σ3 of the maximum power generation ratio of the day, and the year-on-year deviation σ4 of the maximum power generation ratio of the day, and then calculates the comprehensive deviation σ, and then calculates the expected income after cleaning the power station according to the comprehensive deviation and the natural attenuation rate of the components, and the system automatically gives the decision information on whether to clean the power station components according to the expected income and the cleaning cost. Due to the real-time collection of power station data and online calculation, the present invention can effectively identify whether there is abnormal power generation or inefficient power generation in the components. At the same time, the present invention considers the cost and benefit to make a cleaning decision, does not need to arrange additional detection hardware, has low cost and good real-time performance, and can better eliminate the influence of various accidental factors such as weather, attenuation, installation conditions, etc., and improves the accuracy and scientificity of the photovoltaic power station cleaning decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments of the present invention or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings of the embodiments of the present invention.

[0049] Figure 1 A schematic flow chart of steps of a distributed photovoltaic power station cleaning decision method is provided for one embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the structure of a distributed photovoltaic power station component cleaning decision device provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the structure of an electronic device for implementing a distributed photovoltaic power station component cleaning decision method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, rather than limiting the embodiments of the present invention. The singular forms of "a", "said" and "the" used in the embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings. Although the embodiments of the present invention may be described in a first, second, or third manner, this description is only used to distinguish similar information, entities, or steps, rather than to describe a specific order or sequence. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. For another example, in some scenarios, the first information may refer to one information or to multiple information of the same type. In addition, the word "if" used may be interpreted as "at..." or "when..." or "in response to determination". The "and / or" in the present invention is only a kind of association relationship describing the associated objects, indicating that there may be three relationships, for example, A and / or B, which may represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B may be singular or plural. In addition, in the description of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0053] The main factors that affect whether the photovoltaic panels of regional distributed photovoltaic power stations need to be cleaned are as follows:

[0054] Geographical location factors: Different geographical locations have different rainfall, sandstorm conditions, air humidity and other factors. Therefore, geographical location factors will affect the degree of contamination of photovoltaic modules and thus affect the cleaning frequency.

[0055] Climate factors: Different climate zones have different climate conditions. In dry, windy or dusty areas, photovoltaic modules are more easily contaminated by dust, sand, etc., and the cleaning frequency is relatively higher.

[0056] Seasonal factors: Summer may see more rainfall, which is conducive to natural cleaning, while winter may see more dust and contaminants, requiring more frequent cleaning.

[0057] Equipment aging factors: The power generation efficiency of photovoltaic modules will change over time, and the trend of declining power generation efficiency will also affect the cleaning frequency.

[0058] Pollution factors: The degree of pollution on the surface of photovoltaic modules will also affect the cleaning frequency. When the pollution is serious, the cleaning frequency needs to be increased.

[0059] Based on the above factors, photovoltaic power stations usually choose to conduct centralized cleaning twice a year, in April to May in spring and August to September in autumn. In special weather conditions, such as heavy snowfall in winter or local sandstorms and haze that have a significant impact on power generation, construction personnel are organized to conduct targeted temporary cleaning of photovoltaic panels that affect power generation. However, this method of human decision-making is mainly based on experience, and it is impossible to accurately and scientifically decide when the cleaning time is most reasonable or when it can best reduce losses and obtain maximum benefits.

[0060] The cost of cleaning components in a photovoltaic power station mainly includes the following aspects:

[0061] Resource costs: Cleanup requires the use of clean water and energy, especially in arid areas or areas with water shortages. Cleanup will consume a large amount of precious water resources and clean energy.

[0062] Human resource cost: Cleaning requires human resources to operate, and the human resource cost is relatively high, especially in large-scale photovoltaic power stations, which require a lot of human resources.

[0063] Time cost: Cleaning the entire photovoltaic power station takes a lot of time, which will affect the normal operation of the photovoltaic power station and cause loss of power generation income.

[0064] If a regular cleaning plan is adopted, it is easy to be limited by the observation cycle. Sometimes the contamination of photovoltaic panels cannot be discovered in time, resulting in untimely and inaccurate cleaning plans, causing losses in power generation revenue for the owner. Too much cleaning frequency will cause waste of resources.

[0065] Based on the problems existing in the current photovoltaic power station cleaning solutions, the present invention aims to provide a distributed photovoltaic power station cleaning decision-making solution. The specific implementation process of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the step flow shown in the accompanying drawings and embodiments can be executed in a computer system such as a set of computer executable instructions, and one or more of the exemplified method steps can be single or multiple or aggregated together and run in the computer system in the form of a software module or a virtual device. Moreover, although the exemplified step flow has time and sequence logic, the present invention is not limited to these steps. In certain cases, these steps can be executed in an order different from that here. Under the guidance of the purpose of the present invention, simple changes or combinations of the execution order of these steps should also fall within the scope of the disclosure of the embodiments of the present invention.

[0066] Figure 1 A schematic flow chart of the steps of a distributed photovoltaic power station cleaning decision-making method is provided for one embodiment of the present invention.

[0067] Step 101. Collect and store basic information and real-time data of distributed photovoltaic power stations;

[0068] Basic information includes basic information of inverters, basic information of photovoltaic modules and cleaning records of power stations; real-time data includes power generation, power generation and meteorological data;

[0069] The distributed photovoltaic power station described in the present invention refers to a photovoltaic power station system composed of multiple photovoltaic power stations deployed in the same geographical area, for example, a distributed photovoltaic power station composed of multiple photovoltaic power stations deployed in different areas of the same city and operated by the same operator.

[0070] The basic information of the inverter may include but is not limited to: brand, model, time of commissioning, etc.

[0071] The basic information of PV modules may include but is not limited to: brand, model, installation location, installation angle, time of commissioning, etc.

[0072] The power station cleaning record information may include but is not limited to: cleaning time information, cleaning cost information, etc.

[0073] The above basic information only needs to be collected and updated when the power station initially installs the equipment or when there are subsequent changes.

[0074] Real-time data refers to the relevant information of the photovoltaic power station that changes dynamically over time. These real-time data can be collected by the real-time acquisition module according to the preset collection cycle, and recorded in the historical database for subsequent statistical summary.

[0075] Real-time data includes but is not limited to: power generation, power generation, meteorological data, etc. Power generation and power generation can be obtained by collecting data from photovoltaic meters and inverters, and meteorological data can be obtained from the local meteorological bureau or collected by sensors installed in the power station.

[0076] For example, in one embodiment of the present invention, the power generation of the power station is collected every 30 seconds with a collection cycle of 30 seconds, and the maximum power generation of the day can be obtained based on the historical record of the power generation recorded on the day. For another example, the daily power generation of each power station is collected on a daily basis with a collection cycle of one day, and the average daily power generation of each power station in the same month during the same period in history can be calculated based on the daily power generation of each power station. The total daily power generation of the distributed photovoltaic power station can be obtained by summarizing the daily power generation of each power station.

[0077] Meteorological data include but are not limited to: solar radiation, weather (eg, cloudy, sunny, rainy, etc.), temperature, wind speed, etc.

[0078] In one embodiment of the present invention, the basic information and real-time data collected are stored in a large relational database, through which rapid query, aggregation and statistics of historical data can be realized.

[0079] Step 102: Calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day; calculate the equivalent utilization hours of the first power station on the first day of grid connection, and divide the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours of the distributed photovoltaic power station as a whole on that day to obtain the installation deviation factor X of the first power station;

[0080] Any photovoltaic power station in the distributed photovoltaic power station is referred to as the first power station. For each photovoltaic power station, the power generation situation can be compared from two dimensions: one is a horizontal comparison with other photovoltaic power stations in the region, and the other is a comparison with the same period last year.

[0081] Taking into account the different installation conditions of various power stations, different power stations in the same area may have different power generation effects even if they use the same inverter and the same components. Therefore, in one embodiment of the present invention, when making any comparison, the calculated deviations (σ1, σ2, σ3 and σ4) are adjusted by the installation deviation factor X, so as to more accurately calculate the expected increase in revenue. However, using X for deviation adjustment is an optional step. By using X for deviation adjustment, better cleaning decision effects can be further obtained.

[0082] In one embodiment of the present invention, the installation deviation factor is calculated as follows:

[0083] S001. First calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day;

[0084] The equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day can be obtained by dividing the sum of the power generation of all power stations on the same day by the power station component capacity of all power stations. The calculation formula is as follows:

[0085]

[0086] Among them, i is used to identify the i-th power station, and n is the number of photovoltaic power stations in the distributed photovoltaic power station.

[0087] In the embodiment of the present invention, the "day" can be set by the administrator through the system configuration interface. For example, the period from the sunset time of the previous day to the sunset time of the current day can be set as a day, or the period from 3 am of the previous day to 3 am of the current day can be set as a day. The present invention does not make specific limitations and can be configured according to the actual situation of the user.

[0088] The power station component capacity is a fixed value determined when the power station is built. It is determined by the number of photovoltaic panels in the power station. The power generation on the day can be obtained by collecting data from the inverter or photovoltaic meter.

[0089] S002. Calculate the equivalent utilization hours of the first power station on the first day of grid connection, that is, divide the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station;

[0090] S003. Divide the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the entire distributed photovoltaic power station on that day to obtain the installation deviation factor X.

[0091]

[0092] In one embodiment of the present invention, the installation deviation factor X of the photovoltaic power station is updated based on the above steps on the first full day after each cleaning of the photovoltaic power station, and the installation deviation factor X remains unchanged before the next cleaning.

[0093] Step 110, calculating the equivalent utilization hours D1 of the first power station on the current day and the average daily equivalent utilization hours Y2 of the first power station in the same month of the same period last year;

[0094]

[0095] Step 111, dividing the difference between D1 and the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day by Y to obtain the station deviation of the equivalent utilization hours of the first power station on that day, and adjusting it with the installation deviation factor X to obtain the adjusted station deviation σ1 of the equivalent utilization hours of the first power station on that day;

[0096]

[0097] Step 112: Divide the difference between the equivalent utilization hours D1 of the first power station on the same day and the average daily equivalent utilization hours Y2 of the first power station in the same month of the same period last year by Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on the same day, and then adjust it by the natural attenuation rate of the component in the Nth year (that is, the natural attenuation rate corresponding to the service life of the component) and the installation deviation factor X to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on the same day;

[0098]

[0099] Step 120, calculating the maximum power generation ratio Z of the distributed photovoltaic power station as a whole on the day, the maximum power generation ratio P1 of the first power station on the day, and the maximum power generation ratio Z2 of the first power station in the same period of history;

[0100] The calculation method of the maximum power generation ratio Z of the distributed photovoltaic power station on the day is:

[0101]

[0102] Where n is the number of photovoltaic power stations in the distributed photovoltaic power station, and Z is the maximum value of the ratio of the maximum power generation power of all power stations in the distributed photovoltaic power station on that day to the component capacity of the power station.

[0103] The calculation method of the maximum power generation ratio P1 of the first power station on that day is:

[0104]

[0105] The calculation method of the maximum power generation ratio Z2 of the first power station in the same period of history is:

[0106]

[0107] Among them, the maximum power generation capacity of the first power station in the same month of the same period last year can be read from the historical database.

[0108] Step 121: Divide the difference between the maximum power generation ratio P1 of the first power station on the day and the maximum power generation ratio Z of the distributed photovoltaic power station on the day by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on the day, and then adjust it with the installation deviation factor X to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on the day;

[0109]

[0110] Step 122: Divide the difference between the maximum power generation ratio P1 of the first power station on the day and the maximum power generation ratio Z2 of the first power station in the same period of the month by Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on the day, and then use the natural attenuation rate of the component in the Nth year (that is, the natural attenuation rate corresponding to the service life of the component) and the installation deviation factor X to adjust and obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on the day;

[0111]

[0112] Step 130, calculating the standard deviation of the adjusted daily equivalent utilization hours inter-station deviation σ1, the daily equivalent utilization hours year-on-year deviation σ2, the daily maximum power generation ratio inter-station deviation σ3 and the daily maximum power generation ratio year-on-year deviation σ4 of the first power station to obtain a comprehensive deviation σ;

[0113] In one embodiment of the present invention, the standard deviations of four deviations, namely σ1, σ2, σ3, and σ4, are calculated based on the standard deviation formula to obtain a comprehensive deviation. The calculation method is exemplified as follows:

[0114]

[0115] In other embodiments of the present invention, other methods for calculating the comprehensive deviation may also be used. For example, a weight coefficient may be set for each deviation, and the comprehensive deviation σ may be calculated by weighted averaging.

[0116] Step 140, calculate the expected increase in revenue after cleaning of the first power station by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate of the components in the Nth year (i.e., the natural attenuation rate corresponding to the number of years the components have been used), and the comprehensive deviation σ;

[0117] The calculation method for calculating the expected increase in revenue after cleaning the first power station is:

[0118] Expected increase in revenue after cleaning = revenue in the same month of last year * natural attenuation rate of the component in the Nth year * σ

[0119] Among them, N is the number of years the component has been in use in the current year, for example, the difference between the year corresponding to the component production date and the year corresponding to the current date. The annual natural attenuation rate of the component is provided by the manufacturer and can also be obtained through self-testing.

[0120] Step 150, compare the estimated profit increase after cleaning the first power station with the cost of cleaning the power station. If the estimated profit increase is greater than the cost of cleaning the power station, output decision information that recommends cleaning the power station; otherwise, output decision information that does not recommend cleaning the power station.

[0121] The cost of the cleaning power station can be configured by the user in the system, or read from other business systems. For example, dedicated operation and maintenance personnel can dynamically maintain the costs of water, electricity, equipment, manpower and other resources used in the cleaning power station and record them in the database, which can be directly read for comparison.

[0122] Figure 2 A schematic diagram of a distributed photovoltaic power station component cleaning decision-making device provided by an embodiment of the present invention, the device 200 includes a collection and storage module 201,

[0123] The collection and storage module 201 is used to collect and store basic information and real-time data of distributed photovoltaic power stations;

[0124] The first calculation module 202 is used to calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day, the equivalent utilization hours D1 of the first power station in the distributed photovoltaic power station on the same day, and the average daily equivalent utilization hours Y2 of the first power station in the same month of the same period last year; use D1 and Y to calculate the station-to-station deviation σ1 of the equivalent utilization hours of the first power station on the same day; use D1 and Y2 to calculate the year-on-year deviation σ2 of the equivalent utilization hours of the first power station on the same day;

[0125] The second calculation module 203 is used to calculate the overall daily maximum power generation ratio Z of the distributed photovoltaic power station, the daily maximum power generation ratio P1 of the first power station, and the historical maximum power generation ratio Z2 of the first power station in the same period of the month; use P1 and Z to calculate the inter-station deviation σ3 of the daily maximum power generation ratio of the first power station; use P1 and Z2 to calculate the year-on-year deviation σ4 of the daily maximum power generation ratio of the first power station;

[0126] A comprehensive deviation calculation module 204, used to calculate the comprehensive deviation σ of σ1, σ2, σ3 and σ4;

[0127] The cleaning decision module 205 is used to calculate the expected increase in revenue after the cleaning of the first power station by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate corresponding to the years of service of the components, and the comprehensive deviation σ; when the expected increase in revenue is greater than the cost of cleaning the power station, the decision information of recommending cleaning the power station is output, otherwise the decision information of not recommending cleaning the power station is output.

[0128] Furthermore, the device 200 further includes:

[0129] The installation deviation calculation module 206 is used to obtain the equivalent utilization hours of the first power station on the first day of grid connection by dividing the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station; and to obtain the installation deviation factor X by dividing the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day;

[0130] The first calculation module 202 is further used to adjust the σ1 and σ2 using the installation deviation factor X;

[0131] The second calculation module 203 is further used to adjust the σ3 and σ4 using the installation deviation factor X;

[0132] The comprehensive deviation calculation module 204 calculates the comprehensive deviation σ based on the adjusted σ1 , σ2 , σ3 , and σ4 .

[0133] Furthermore, the first calculation module 202 includes:

[0134] The first subunit is used to divide the difference between D1 and Y by Y to obtain the station deviation of the equivalent utilization hours of the first power station on that day, and adjust it with the installation deviation factor X to obtain the adjusted station deviation σ1 of the equivalent utilization hours of the first power station on that day;

[0135] The second subunit is used to divide Y2 by the difference between D1 and Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on that day, and then adjust it with the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on that day;

[0136] The second calculation module 203 includes:

[0137] The third subunit is used to divide the difference between P1 and Z by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on that day, and then use the installation deviation factor X to adjust it to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day;

[0138] The fourth subunit is used to divide Z2 by the difference between P1 and Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on the day, and then adjust it with the natural attenuation rate corresponding to the years of use of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on the day.

[0139] Figure 3 The electronic device structure diagram for implementing the distributed photovoltaic power station component cleaning decision method provided by one embodiment of the present invention is shown in FIG. 300. The device 300 includes: a processor 310 such as a central processing unit (CPU), a communication bus 320, a communication interface 340, and a memory 330. The processor 310 and the memory 330 can communicate with each other through the communication bus 320. The memory 330 stores a computer program, and when the computer program is executed by the processor 310, the function of one or more steps in the distributed photovoltaic power station component cleaning decision method provided by the present invention can be implemented.

[0140] Memory refers to a device based on a certain storage medium for storing computer programs and / or data. It can be a volatile memory (VM, often called memory) or a non-volatile memory (NVM). Memory refers to an internal memory that directly exchanges data with the processor. It can read and write data at any time and at a very fast speed. It is used as a storage medium for temporary data of the operating system and other running programs. Memory can be synchronous dynamic random access memory (SDRAM), dynamic random access memory (DRAM), etc. Non-volatile memory refers to a memory that uses a persistent storage medium. It has the characteristics of large capacity and persistent data storage. It can be a storage class memory (SCM), a solid state disk (SSD), a NAND flash memory, a disk, etc. SCM is the industry's general term for new storage media between memory and flash memory. It is a composite storage technology that combines persistent storage characteristics and memory characteristics. Its access speed is slower than DRAM and faster than SSD hard disks.

[0141] The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] It should be appreciated that embodiments of the present invention may be implemented or implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory (or non-persistent) memory. The method may be implemented in a computer program using standard programming techniques, including a non-transitory storage medium configured with a computer program, wherein the storage medium configured in this way causes the computer to operate in a specific and predefined manner. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if necessary, the program may be implemented in an assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed ASIC for this purpose. In addition, the operation of the process described in the present invention may be performed in any suitable order, unless the present invention otherwise indicates or otherwise clearly contradicts the context. The process described in the present invention (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as a code (e.g., executable instructions, one or more computer programs, or one or more applications) executed on one or more processors in common, by hardware or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0143] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described in the present invention includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0144] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed photovoltaic power station component cleaning decision method, characterized in that: The method includes: Collect and store basic information and real-time data of distributed photovoltaic power plants; Calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day, the equivalent utilization hours D1 of the first distributed photovoltaic power station on the same day, and the average daily equivalent utilization hours Y2 of the first station in the same month of the same period last year; Use D1 and Y to calculate the inter-station deviation σ1 of the equivalent utilization hours of the first power station on that day; Use D1 and Y2 to calculate the year-on-year deviation σ2 of equivalent utilization hours of the first power station on that day; Calculate the maximum power generation ratio Z of the distributed photovoltaic power station as a whole on the day, the maximum power generation ratio P1 of the first power station on the day, and the maximum power generation ratio Z2 of the first power station in the same period of the month; Use P1 and Z to calculate the inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day; Use P1 and Z2 to calculate the year-on-year deviation σ4 of the maximum power generation ratio of the first power station on that day; Calculating the comprehensive deviation σ of the σ1, σ2, σ3 and σ4; The expected increase in revenue after the cleaning of the first power station is calculated by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate corresponding to the years of service of the components, and the comprehensive deviation σ; When the estimated increase in revenue is greater than the cost of cleaning the power plant, decision information suggesting cleaning the power plant is output; otherwise, decision information suggesting not cleaning the power plant is output.

2. The method according to claim 1, characterized in that When calculating the σ1, σ2, σ3 and σ4, the step of using the installation deviation factor X to adjust the obtained result is also included, and when calculating the comprehensive deviation σ, the adjusted σ1, σ2, σ3 and σ4 are used for calculation; The calculation method of the installation deviation factor X is: The sum of the daily power generation of all power stations in the distributed photovoltaic power station is divided by the power station component capacity of all power stations to obtain the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day; The equivalent utilization hours of the first power station on the first day of grid connection are obtained by dividing the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station; The installation deviation factor X is obtained by dividing the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day.

3. The method according to claim 2, characterized in that The calculation method of σ1 and σ2 is: Divide the difference between D1 and Y by Y to obtain the station deviation of equivalent utilization hours of the first power station on that day, and adjust it with the installation deviation factor X to obtain the adjusted station deviation σ1 of equivalent utilization hours of the first power station on that day; The difference between D1 and Y2 is divided by Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on that day, and then the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X are adjusted to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on that day.

4. The method according to claim 2, characterized in that: The calculation method of σ3 and σ4 is: Divide the difference between P1 and Z by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on that day, and then adjust it with the installation deviation factor X to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day; The difference between P1 and Z2 is divided by Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on that day, and then the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X are adjusted to obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on that day.

5. The method according to claim 1, characterized in that The method for calculating the comprehensive deviation σ of σ1, σ2, σ3 and σ4 is: The comprehensive deviation σ is obtained by calculating the adjusted σ1, σ2, σ3 and σ4 in the form of standard deviation or weighted average.

6. A distributed photovoltaic power station component cleaning decision device, characterized in that: The device includes: The acquisition and storage module is used to acquire and store basic information and real-time data of distributed photovoltaic power stations; The first calculation module is used to calculate the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on the same day, the equivalent utilization hours D1 of the first power station in the distributed photovoltaic power station on the same day, and the average daily equivalent utilization hours Y2 of the first power station in the same month of the same period last year; use D1 and Y to calculate the inter-station deviation σ1 of the equivalent utilization hours of the first power station on the same day; use D1 and Y2 to calculate the year-on-year deviation σ2 of the equivalent utilization hours of the first power station on the same day; The second calculation module is used to calculate the overall daily maximum power generation ratio Z of the distributed photovoltaic power station, the daily maximum power generation ratio P1 of the first power station, and the historical maximum power generation ratio Z2 of the first power station in the same period of the month; P1 and Z are used to calculate the inter-station deviation σ3 of the daily maximum power generation ratio of the first power station; P1 and Z2 are used to calculate the year-on-year deviation σ4 of the daily maximum power generation ratio of the first power station; A comprehensive deviation calculation module, used for calculating the comprehensive deviation σ of the σ1, σ2, σ3 and σ4; The cleaning decision module is used to calculate the expected increase in revenue after the cleaning of the first power station by multiplying the revenue of the first power station in the same month last year, the natural attenuation rate corresponding to the years of service of the components, and the comprehensive deviation σ; when the expected increase in revenue is greater than the cost of cleaning the power station, the decision information recommending cleaning the power station is output, otherwise the decision information not recommending cleaning the power station is output.

7. The device according to claim 6, characterized in that The device also includes: The installation deviation calculation module is used to obtain the equivalent utilization hours of the first power station on the first day of grid connection by dividing the power generation of the first power station on the first day of grid connection by the power station component capacity of the first power station; and to obtain the installation deviation factor X by dividing the equivalent utilization hours of the first power station on the first day of grid connection by the equivalent utilization hours Y of the distributed photovoltaic power station as a whole on that day; The first calculation module is further used to adjust the σ1 and σ2 using the installation deviation factor X; The second calculation module is further used to adjust the σ3 and σ4 using the installation deviation factor X; The comprehensive deviation calculation module calculates the comprehensive deviation σ based on the adjusted σ1, σ2, σ3 and σ4.

8. The device according to claim 7, characterized in that The first calculation module includes: The first subunit is used to divide the difference between D1 and Y by Y to obtain the station deviation of the equivalent utilization hours of the first power station on that day, and adjust it with the installation deviation factor X to obtain the adjusted station deviation σ1 of the equivalent utilization hours of the first power station on that day; The second subunit is used to divide Y2 by the difference between D1 and Y2 to obtain the year-on-year deviation of the equivalent utilization hours of the first power station on that day, and then adjust it with the natural attenuation rate corresponding to the service life of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ2 of the equivalent utilization hours of the first power station on that day; The second calculation module includes: The third subunit is used to divide the difference between P1 and Z by Z to obtain the inter-station deviation of the maximum power generation ratio of the first power station on that day, and then use the installation deviation factor X to adjust it to obtain the adjusted inter-station deviation σ3 of the maximum power generation ratio of the first power station on that day; The fourth subunit is used to divide Z2 by the difference between P1 and Z2 to obtain the year-on-year deviation of the maximum power generation ratio of the first power station on the day, and then adjust it with the natural attenuation rate corresponding to the years of use of the component and the installation deviation factor X to obtain the adjusted year-on-year deviation σ4 of the maximum power generation ratio of the first power station on the day.

9. The device according to claim 6, characterized in that The comprehensive deviation calculation module calculates the adjusted σ1, σ2, σ3 and σ4 by means of standard deviation or weighted average to obtain the comprehensive deviation σ.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a storage medium and a communication bus, wherein the processor, the communication interface and the storage medium communicate with each other via the communication bus; Storage medium for storing computer programs; A processor, for implementing the method according to any one of claims 1 to 5 when executing a computer program stored on a storage medium.

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