Photovoltaic power station fault detection method and system based on big data, and storage medium

Abstract

The invention discloses a photovoltaic power station fault detection method and system based on big data and a storage medium, and relates to the technical field of photovoltaic power station fault detection. Through communication connection with various sensors, electric power parameters and environmental parameters of each substring are collected, deep analysis is carried out on data according to the technical means of Fourier transform, trend change analysis and the like, and key indexes of harmonic distortion, a conversion monitoring value and a resistance monitoring value are extracted as three judgment elements; comprehensive analysis is carried out based on the three judgment elements, substrings with faults are recognized, and corresponding security strategies are executed accordingly; intelligent monitoring and preventive maintenance of the photovoltaic power station are realized, preventive measures can be taken before a fault occurs, the fault occurrence probability and the influence range are reduced, the service life of equipment is prolonged, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power station fault detection, and in particular to a photovoltaic power station fault detection method, system and storage medium based on big data. Background Art

[0002] Photovoltaic power stations are facilities that convert solar energy into electrical energy, especially centralized photovoltaic power stations, which are often built in areas with sufficient sunlight and open areas, such as deserts, mountains, and plains. Since photovoltaic power stations have the environmental advantages of reducing fossil energy consumption and carbon emissions, they play an important role in the promotion of clean energy. Therefore, fault detection of photovoltaic power stations is particularly important to ensure the safe operation of photovoltaic power stations and stable power output.

[0003] Centralized photovoltaic power stations usually occupy a very large area. Traditional photovoltaic power station fault detection often relies on a single detection method, such as manual inspection or a simple monitoring system, which makes the accuracy and efficiency of fault detection low. Especially when a fault occurs, it is impossible to perform maintenance and solve the fault in time, resulting in the spread of the problem or a decrease in power generation efficiency, affecting stable output. Summary of the invention

[0004] The present invention provides a photovoltaic power station fault detection method, system and storage medium based on big data, which are used to solve the above-mentioned technical problems.

[0005] A first aspect of the present invention provides a photovoltaic power station fault detection method based on big data, comprising the following steps:

[0006] Step 1: Divide the photovoltaic power station into several areas, each area is equipped with an inverter, and the photovoltaic panels in the area are divided into several sub-strings, and the photovoltaic panels of the sub-strings are connected in parallel; the photovoltaic panels in the sub-strings are connected in series; the photovoltaic panels of each sub-string are numbered and marked on the photovoltaic power station map;

[0007] Step 2: Communicate with various sensors installed in various areas to collect power parameters and environmental parameters of each substring in each area, and based on this, conduct in-depth analysis of the inverter working state, light energy conversion state, and resistance state of each substring in the area to obtain harmonic distortion, conversion monitoring value, and resistance monitoring value; thereby, the three elements of judging the photovoltaic panels of each substring in the area can be determined, and the specific three elements are: harmonic distortion, conversion monitoring value, and resistance monitoring value of the inverter; the power parameters include voltage signal V(t), current signal I(t), and power generation; the environmental parameters include ambient temperature, ambient humidity, and irradiance;

[0008] Step 3: Perform a comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panels with faults, and execute the corresponding safety strategy accordingly.

[0009] Optionally, the specific steps of comprehensively analyzing the three factors to identify the faulty substring photovoltaic panels are:

[0010] 3-1: Retrieve the three judgment factors of each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; if the harmonic distortion is greater than or equal to the set harmonic threshold, the photovoltaic panels in the area are controlled to stop working, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer; if the harmonic distortion is less than the set harmonic threshold, execute 3-2;

[0011] 3-2: Normalize the harmonic distortion VI, conversion monitoring value KZ and resistance monitoring value RD and take their values, and perform formula calculation and analysis on the values ​​to obtain the abnormal value CY; the specific Pythagorean theorem calculation formula is:

[0012]

[0013] Among them, λ1 and λ2 are respectively the set proportional constants;

[0014] 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the substring photovoltaic panel is recorded as a high-risk substring; when the abnormal value is in the set abnormal interval, the substring photovoltaic panel is recorded as a medium-risk substring; when the abnormal value is less than the minimum value in the set abnormal interval, the substring photovoltaic panel is recorded as a light-risk substring; count the number of high-risk substrings, medium-risk substrings and light-risk substrings in the area respectively, and record them as G1, G2 and G3 respectively; if G3≥G1+G2, the high-risk substring in the area is recorded as a faulty substring, the faulty substring is controlled to stop working, and the number and position of the faulty substring are output, and the number and position of the faulty substring are sent to the corresponding engineer; otherwise, the area is recorded as an abnormal area, and the abnormal area is controlled to stop working; and the number and position of the abnormal area are output, thereby integrating the number, position and abnormal values ​​of each substring photovoltaic panel into the abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer;

[0015] 3-4: Repeat the above steps 3-1 to 3-3 until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output.

[0016] Optionally, the monitoring and analysis steps of the inverter working status are:

[0017] The voltage and current signals of the inverter are retrieved, and the sampling frequency is often much higher than the frequency of the output signal, such as several thousand hertz or higher, to ensure that high-order harmonics can be accurately captured; through Fourier transform (FFT)

[0018] The time domain signal is converted into a frequency domain signal, and the voltage harmonic expression and each current harmonic expression are obtained accordingly. The specific voltage harmonic expression is:

[0019] V(t)=V1·sin(ωt+θ1)+V2·sin(2ωt+θ2)+V3·sin(3ωt+θ3)+…+V J ·sin(Jωt+θ J )

[0020] Where j = 1, 2, 3, ..., J; J is a positive integer, J represents the highest order of voltage harmonics, j represents the number of one of the voltage harmonics; V1 is the voltage amplitude of the fundamental wave, V2, V3, ..., V J is the voltage amplitude of each harmonic, corresponding to the 2nd, 3rd, up to the Jth voltage harmonic; ω is the angular frequency of the fundamental frequency;

[0021] θ1,θ2,θ3,……,θ J is the phase angle of each voltage harmonic;

[0022] The specific current harmonic expression is:

[0023] I(t)=I1·sin(ωt+φ1)+I2·sin(2ωt+φ2)+I3·sin(3ωt+φ3)+…+I F ·sin(Fωt+φ F )

[0024] Where f = 1, 2, 3, ..., F; F is a positive integer, F represents the highest order of current harmonics, f represents the number of one of the current harmonics; I1 is the current amplitude of the fundamental wave, I2, I3, ..., I F is the current amplitude of each harmonic, corresponding to the 2nd, 3rd, up to the Fth current harmonic; ω is the angular frequency of the fundamental frequency; φφ1, φ2, φ3, ..., φ F is the phase angle of each current harmonic;

[0025] Extract the amplitude V of each harmonic based on the voltage harmonic expression and current harmonic expression j and I f , and perform formulaic calculation and analysis to obtain the harmonic distortion VI; the specific calculation formula is:

[0026]

[0027] where a V 、a I are respectively set proportional constants, and the specific values ​​are set by those skilled in the art according to actual needs. For example, a V The value is 0.35, a I The value is 0.65.

[0028] Optionally, the monitoring and analysis steps of the conversion status are:

[0029] Retrieve the irradiance and power generation corresponding to each substring in the region at each acquisition time; normalize the irradiance and power generation and take their values, divide the values ​​to calculate the conversion rate, and thus obtain the conversion rate corresponding to each acquisition time, recorded as Kb, where b = 1, 2, 3, ..., B; B is a positive integer, B represents the total number of acquisition times, and b represents the sequence number of any acquisition time; construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the conversion rate as the vertical coordinate, input the conversion rate into the coordinate axis according to its corresponding acquisition time, and record the position of the conversion rate in the coordinate axis as the conversion point; use a smooth curve to connect each conversion point in sequence to obtain a curve chart of the conversion rate changing with time; draw a tangent line at each conversion point, use data fitting to calculate the tangent slope and record it as the conversion slope; sum the conversion slopes greater than zero to obtain the conversion increase value, and sum the conversion slopes less than zero to take the absolute value to calculate the conversion decrease value;

[0030] The conversion rate Kb, conversion slope Zb, conversion increase value H1 and conversion decrease value H2 are normalized and their values ​​are taken, and the values ​​are analyzed and calculated by formula to obtain the conversion monitoring value KZ. The specific calculation formula is:

[0031]

[0032] Among them, σ1, σ2, and σ3 are the set proportional constants, is the mean of the conversion slopes at each conversion point.

[0033] Optionally, the monitoring and analysis steps of the resistance status are:

[0034] Each substring photovoltaic panel is set to correspond to a standard environmental parameter, wherein the standard environmental parameter includes a standard ambient temperature and a standard ambient humidity;

[0035] The insulation resistance Rb 绝缘 , series impedance Rb 阻抗 , ambient temperature Tb, ambient humidity Sb, standard ambient temperature AT and standard ambient humidity AS are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the resistance state value PR; the specific calculation formula is:

[0036]

[0037] Among them, β1, β2, β3, and β4 are respectively set proportional constants, from which the resistance state value PRb corresponding to each acquisition moment can be obtained;

[0038] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the resistance state value as the vertical coordinate, the resistance state value is input into the coordinate axis according to its corresponding acquisition time, and the position of the resistance state value in the coordinate axis is recorded as a resistance point, and a smooth curve is used to connect each resistance point in sequence to obtain a curve graph of the resistance state value changing with time; a tangent line is drawn at each resistance point, and the slope of the tangent line is obtained by data fitting and recorded as the resistance slope; the resistance slopes greater than zero are summed up to obtain a state increase value, and the resistance slopes less than zero are summed up and then the absolute value is taken to calculate a state decrease value;

[0039] The resistance state value PRb, resistance slope Db, state increase value H3 and state decrease value H4 at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the resistance monitoring value RD. The specific calculation formula is:

[0040]

[0041] Among them, α1, α2, and α3 are the set proportional constants respectively. is the average resistance slope of each resistance point.

[0042] A second aspect of the present invention provides a photovoltaic power station fault detection system based on big data, comprising: a server, a monitoring and analysis module, and a fault output module;

[0043] The server divides the photovoltaic power station into regions and collects information. The specific information collected is power parameters and environmental parameters. The power parameters include voltage signal V(t), current signal I(t), and power generation. The environmental parameters include ambient temperature, ambient humidity, and irradiance.

[0044] The monitoring and analysis module performs feature analysis on the inverter status in each area, the conversion status and the resistance status of each substring photovoltaic panel based on power parameters and environmental parameters to extract the three judgment elements; the specific three judgment elements are: the harmonic distortion of the inverter, the conversion monitoring value and the resistance monitoring value;

[0045] The fault output module conducts a comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panel with faults, and execute the corresponding safety strategy accordingly; specifically:

[0046] 3-1: Retrieve the three judgment factors of each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; if the harmonic distortion is greater than or equal to the set harmonic threshold, the photovoltaic panels in the area are controlled to stop working, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer; if the harmonic distortion is less than the set harmonic threshold, execute 3-2;

[0047] 3-2: Normalize the harmonic distortion VI, conversion monitoring value KZ and resistance monitoring value RD and take their values, and perform formula calculation and analysis on the values ​​to obtain the abnormal value CY; the specific Pythagorean theorem calculation formula is:

[0048]

[0049] Among them, λ1 and λ2 are respectively the set proportional constants;

[0050] 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the substring photovoltaic panel is recorded as a high-risk substring; when the abnormal value is in the set abnormal interval, the substring photovoltaic panel is recorded as a medium-risk substring; when the abnormal value is less than the minimum value in the set abnormal interval, the substring photovoltaic panel is recorded as a light-risk substring; count the number of high-risk substrings, medium-risk substrings and light-risk substrings in the area respectively, and record them as G1, G2 and G3 respectively; if G3≥G1+G2, the high-risk substring in the area is recorded as a faulty substring, the faulty substring is controlled to stop working, and the number and position of the faulty substring are output, and the number and position of the faulty substring are sent to the corresponding engineer; otherwise, the area is recorded as an abnormal area, and the abnormal area is controlled to stop working; and the number and position of the abnormal area are output, thereby integrating the number, position and abnormal values ​​of each substring photovoltaic panel into the abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer;

[0051] 3-4: Repeat the above steps 3-1 to 3-3 until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output.

[0052] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned photovoltaic power station fault detection method based on big data.

[0053] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0054] 1. By numbering each area and substring and marking the location of each substring on the map, it facilitates subsequent fault location and maintenance. When a substring photovoltaic panel fails, engineers can quickly locate the problem based on the number and location information, thereby shortening maintenance time and improving maintenance efficiency.

[0055] 2. Through communication connection with various sensors, the power parameters and environmental parameters of each substring are collected, and the data is deeply analyzed based on technical means such as Fourier transform and trend change analysis, and the key indicators of harmonic distortion, conversion monitoring value and resistance monitoring value are extracted as the three elements of judgment, which can realize accurate monitoring and analysis of the inverter status of the region and the conversion status and resistance status of the photovoltaic panel; this data-driven analysis method can more accurately reflect the working status of the photovoltaic panel, and provide strong support for subsequent fault diagnosis and preventive maintenance;

[0056] 3. Based on the comprehensive analysis of the three judgment factors of each sub-string photovoltaic panel, the health status of each sub-string can be accurately judged, the sub-string with faults can be identified, and the corresponding safety strategy can be executed accordingly; specifically: when the harmonic distortion is detected to exceed the set threshold, the photovoltaic panels in the relevant area can be immediately controlled to stop working to prevent the harmonics from exceeding the standard from causing further damage to the equipment and system; in addition, by classifying the risk levels of the sub-string photovoltaic panels, corresponding safety strategies can be adopted for the sub-string photovoltaic panels of different risk levels to further improve the safety of the system; realize intelligent monitoring and preventive maintenance of photovoltaic power stations, and take preventive measures before failure occurs, so as to reduce the probability and impact range of failure, thereby extending the service life of equipment and improving overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0058] Figure 1 It is a principle block diagram of the present invention;

[0059] Figure 2 This is a system module connection diagram of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention provide an energy scheduling management method, apparatus, device and storage medium. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In an embodiment of the present invention, a photovoltaic power station fault detection method based on big data includes the following steps:

[0062] Step 1: Since the centralized photovoltaic power station occupies a vast area and contains a huge number of photovoltaic panels, the entire photovoltaic power station is divided into several areas, each of which is numbered m, where m = 1, 2, 3, ..., M; M is a positive integer, M represents the total number of areas in the entire photovoltaic power station, and m represents the sequence number of any area. Each area is equipped with an inverter. Compared with configuring one or several high-power centralized inverters for the entire photovoltaic power station, this configuration can effectively disperse risks and reduce the impact of single-point failures; the photovoltaic panels in each area use multiple The photovoltaic panels in the area are first divided into several groups (also called substrings), and the photovoltaic panels of each group are connected in series to form a series group; thus, the photovoltaic panels in the area can be divided into several substrings, and each substring is numbered as n, where n = 1, 2, 3, ..., N; N is a positive integer, N represents the total number of substrings in the area, and n represents the sequence number of any substring; thus, the position of each substring is recorded as Lmn, and it is marked on the map of the photovoltaic power station, which is helpful for the rapid positioning and timely maintenance of faulty photovoltaic panels in the later stage;

[0063] By numbering each area and substring and marking the location of each substring on the map, it is convenient for subsequent fault location and maintenance. When a substring photovoltaic panel fails, engineers can quickly locate the problem based on the number and location information, thereby shortening maintenance time and improving maintenance efficiency.

[0064] Step 2: Communicate with various sensors installed in various areas to collect power parameters and environmental parameters of each substring in each area, and based on this, conduct in-depth analysis of the inverter working state, light energy conversion state, and resistance state of each substring in the area to obtain harmonic distortion, conversion monitoring value, and resistance monitoring value; the power parameters include voltage signal V(t), current signal I(t), and power generation;

[0065] Environmental parameters include ambient temperature, ambient humidity and irradiance;

[0066] 2-1: Monitoring and analysis of inverter:

[0067] The voltage and current signals of the inverter are retrieved, and the sampling frequency is often much higher than the frequency of the output signal, such as several thousand hertz or higher, to ensure that high-order harmonics can be accurately captured; through Fourier transform (FFT)

[0068] The time domain signal is converted into a frequency domain signal, and the voltage harmonic expression and each current harmonic expression are obtained accordingly. The specific voltage harmonic expression is:

[0069] V(t)=V1·sin(ωt+θ1)+V2·sin(2ωt+θ2)+V3·sin(3ωt+θ3)+…+V J ·sin(Jωt+θ J )

[0070] Where j = 1, 2, 3, ..., J; J is a positive integer, J represents the highest order of voltage harmonics, j represents the number of one of the voltage harmonics; V1 is the voltage amplitude of the fundamental wave, V2, V3, ..., V J is the voltage amplitude of each harmonic, corresponding to the 2nd, 3rd, up to the Jth voltage harmonic; ω is the angular frequency of the fundamental frequency;

[0071] θ1,θ2,θ3,……,θ J is the phase angle of each voltage harmonic;

[0072] The specific current harmonic expression is:

[0073] I(t)=I1·sin(ωt+φ1)+I2·sin(2ωt+φ2)+I3·sin(3ωt+φ3)+…+I F ·sin(Fωt+φ F )

[0074] Where f = 1, 2, 3, ..., F; F is a positive integer, F represents the highest order of current harmonics, f represents the number of one of the current harmonics; I1 is the current amplitude of the fundamental wave, I2, I3, ..., I Fis the current amplitude of each harmonic, corresponding to the 2nd, 3rd, up to the Fth current harmonic; ω is the angular frequency of the fundamental frequency;

[0075] φφ1,φ2,φ3,…,φ F is the phase angle of each current harmonic;

[0076] Extract the amplitude V of each harmonic based on the voltage harmonic expression and current harmonic expression j and I f , and perform formulaic calculation and analysis to obtain the harmonic distortion VI; the specific calculation formula is:

[0077]

[0078] where a V 、a I are respectively set proportional constants, and the specific values ​​are set by those skilled in the art according to actual needs. For example, a V The value is 0.35, a I The value is 0.65;

[0079] It should be noted that abnormal harmonic values ​​usually indicate a fault in the inverter;

[0080] 2-2: Monitoring and analysis of sub-string photovoltaic panels:

[0081] Retrieve the irradiance and power generation corresponding to each substring in the area at each collection time; normalize the irradiance and power generation and take their values, divide the values ​​to calculate the conversion rate, and thus the conversion rate corresponding to each collection time is recorded as Kb, where b = 1, 2, 3, ..., B; B is a positive integer, B represents the total number of collection times, and b represents the sequence number of any collection time; construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the conversion rate as the vertical coordinate, input the conversion rate into the coordinate axis according to its corresponding collection time, and record the position of the conversion rate in the coordinate axis as the conversion point; use a smooth curve to connect each conversion point in turn to obtain the curve of the conversion rate changing with time. line graph; draw a tangent line at each conversion point, calculate the tangent slope by data fitting and record it as conversion slope Zb; it should be noted that when the conversion slope is greater than zero, it indicates that the conversion rate shows an increasing trend, and when the conversion slope is less than zero, it indicates that the conversion rate shows a decreasing trend; sum up the conversion slopes greater than zero to obtain a conversion increase value recorded as H1, and sum up the conversion slopes less than zero to obtain an absolute value to obtain a conversion reduction value recorded as H2; normalize the conversion rate Kb, the conversion slope Zb, the conversion increase value H1 and the conversion reduction value H2 and take their numerical values, perform formulaic calculation and analysis on the numerical values ​​to obtain the conversion monitoring value KZ, and the specific calculation formula is:

[0082]

[0083] Among them, σ1, σ2, and σ3 are the set proportional constants, is the average conversion slope of each conversion point; from the formula, it can be seen that when the conversion increase value is larger and the conversion decrease value is smaller, the conversion monitoring value is larger; when the conversion rate is unstable, it means that the risk of failure of the photovoltaic panel is greater, and the conversion monitoring value is smaller;

[0084] 2-3: Resistance monitoring and analysis of sub-string photovoltaic panels:

[0085] The insulation resistance, series impedance, ambient temperature and ambient humidity corresponding to each acquisition time are respectively recorded as Rb 绝缘 and Rb 阻抗 , Tb and Sb; set each substring photovoltaic panel to correspond to a standard environmental parameter, where the standard environmental parameters include standard ambient temperature and standard ambient humidity, and record them as AT and AS respectively; it should be noted that the standard environmental parameters refer to the environmental conditions that have the least impact on the photovoltaic panel, which are specifically determined by the manufacturing process and manufacturing materials of the photovoltaic panel; the environment has a greater impact on the photovoltaic panel, especially the insulation resistance and series impedance are more sensitive to the environment, humidity penetration will reduce the insulation performance of the photovoltaic panel material and increase the risk of current leakage, especially in bad weather or high humidity environment; high temperature will aggravate the oxidation or corrosion of the metal connectors inside the photovoltaic panel, resulting in poor contact, thereby increasing the impedance and reducing the output power; long-term thermal expansion and contraction may also damage the welding points and wiring of the photovoltaic panel;

[0086] It should be noted that insulation resistance refers to the insulation level between the electrical parts of the photovoltaic module or system (such as wires, junction boxes, etc.) and the ground or grounding. The higher the insulation resistance, the better the insulation of the system, which can effectively prevent leakage, short circuit and other faults; series impedance refers to the total resistance of the circuit between the photovoltaic module and the cable, including the contact resistance and the internal resistance of the photovoltaic cell. The lower the series impedance, the smaller the current loss and the higher the power generation efficiency; but if the impedance increases (such as poor contact, welding point problems, etc.), it will increase the current loss of the system and reduce power output. Usually, by analyzing the changes in series impedance, local faults in components or connecting parts can be detected;

[0087] The insulation resistance Rb 绝缘 , series impedance Rb 阻抗 , ambient temperature Tb, ambient humidity Sb, standard ambient temperature AT and standard ambient humidity AS are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the resistance state value PR; the specific calculation formula is:

[0088]

[0089] Among them, β1, β2, β3, and β4 are respectively set proportional constants, from which the resistance state value PRb corresponding to each acquisition moment can be obtained; from the formula, it can be seen that when the insulation resistance is smaller and the series impedance is larger, the resistance state value is larger, which means that the risk of failure of the photovoltaic panel is greater; when the ambient temperature and ambient humidity deviate from the standard environmental parameters, it means that the environmental conditions at this time have a greater impact on the photovoltaic panel, and the resistance state value is larger;

[0090] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the resistance state value as the vertical coordinate. The resistance state value is input into the coordinate axis according to its corresponding acquisition time, and the position of the resistance state value in the coordinate axis is recorded as the resistance point. A smooth curve is used to connect each resistance point in sequence to obtain a curve graph of the resistance state value changing with time; a tangent line is drawn at each resistance point, and the slope of the tangent line is obtained by data fitting and recorded as the resistance slope Db; the resistance slopes greater than zero are summed to obtain a state increase value recorded as H3, and the resistance slopes less than zero are summed and then the absolute value is taken to calculate the state decrease value recorded as H4; the resistance state value PRb, the resistance slope Db, the state increase value H3 and the state decrease value H4 at each acquisition time are normalized and their values ​​are taken, and the values ​​are analyzed by formula calculation to obtain the resistance monitoring value RD. The specific calculation formula is:

[0091]

[0092] Among them, α1, α2, and α3 are the set proportional constants respectively. is the average value of the resistance slope of each resistance point; from the formula, it can be seen that when the resistance state value is larger, the increasing trend of the resistance state value is greater than the decreasing trend of the resistance state value, which means that the resistance state of the photovoltaic panel is worse and the risk of failure is greater; the larger the resistance monitoring value; when the resistance state value fluctuates more (i.e. The larger the value), the greater the resistance monitoring value;

[0093] 2-4: From this, the three elements of judging each sub-string photovoltaic panel in the area can be determined. The specific three elements are: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value;

[0094] By communicating with various sensors, the power parameters and environmental parameters of each substring are collected, and the data is deeply analyzed based on technical means such as Fourier transform and trend change analysis. The key indicators of harmonic distortion, conversion monitoring value and resistance monitoring value are extracted as the three elements of judgment. It can realize accurate monitoring and analysis of the inverter status of the region and the conversion status and resistance status of the photovoltaic panels. This data-driven analysis method can more accurately reflect the working status of the photovoltaic panels, and provide strong support for subsequent fault diagnosis and preventive maintenance.

[0095] Step 3: Perform comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panels with faults, and execute corresponding safety strategies accordingly to achieve efficient intelligent monitoring and preventive maintenance; specifically:

[0096] 3-1: Retrieve the three elements for judging each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; compare and analyze the harmonic distortion with the set harmonic threshold. If the harmonic distortion is greater than or equal to the set harmonic threshold, it means that the power quality of the system has exceeded the safety range, and there is a great risk of causing equipment damage or efficiency reduction. Therefore, in order to ensure safety and equipment reliability, the photovoltaic panels in the area are controlled to stop working to prevent further impact caused by excessive harmonics, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer for timely maintenance; if the harmonic distortion is less than the set harmonic threshold, execute 3-2;

[0097] 3-2: Normalize the harmonic distortion VI, conversion monitoring value KZ and resistance monitoring value RD and take their values, and perform formula calculation and analysis on the values ​​to obtain the abnormal value CY; the specific Pythagorean theorem calculation formula is:

[0098]

[0099] Among them, λ1 and λ2 are respectively the set proportional constants;

[0100] 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the substring photovoltaic panel is recorded as a high-risk substring; when the abnormal value is in the set abnormal interval, the substring photovoltaic panel is recorded as a medium-risk substring; when the abnormal value is less than the minimum value in the set abnormal interval, the substring photovoltaic panel is recorded as a light-risk substring; count the number of high-risk substrings, medium-risk substrings and light-risk substrings in the area respectively, and record them as G1, G2 and G3 respectively; if G3≥G1+G2, the area The high-risk substrings in the domain are recorded as faulty substrings, the faulty substrings are controlled to stop working, and the number and position of the faulty substring are output, and the number and position of the faulty substring are sent to the corresponding engineer, so that the abnormal substrings can be repaired in time; otherwise, the area is recorded as an abnormal area, and the abnormal area is controlled to stop working; and the number and position of the abnormal area are output, thereby integrating the number, position, and abnormal values ​​of the photovoltaic panels of each substring into the abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer, so that each substring in the abnormal area can be maintained in time;

[0101] 3-4: Repeat the above steps until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output;

[0102] Based on the comprehensive analysis of the three judgment factors of each sub-string photovoltaic panel, the health status of each sub-string can be accurately judged, the sub-string with faults can be identified, and the corresponding safety strategy can be executed accordingly; specifically: when the harmonic distortion is detected to exceed the set threshold, the photovoltaic panels in the relevant area can be immediately controlled to stop working to prevent the harmonics from exceeding the standard from causing further damage to the equipment and system; in addition, by classifying the risk levels of the sub-string photovoltaic panels, corresponding safety strategies can be adopted for the sub-string photovoltaic panels of different risk levels to further improve the safety of the system; realize intelligent monitoring and preventive maintenance of photovoltaic power stations, and take preventive measures before failure occurs, so as to reduce the probability and impact range of failure, thereby extending the service life of equipment and improving overall operational efficiency.

[0103] See also Figure 2 In an embodiment of the present invention, a photovoltaic power station fault detection system based on big data includes: a server, a monitoring and analysis module and a fault output module;

[0104] The server divides the photovoltaic power station into regions and collects information, specifically:

[0105] Since the centralized photovoltaic power station occupies a vast area and contains a huge number of photovoltaic panels, the entire photovoltaic power station is divided into several areas, each of which is numbered m, where m = 1, 2, 3, ..., M; M is a positive integer, M represents the total number of areas in the entire photovoltaic power station, and m represents the serial number of any area. Each area is equipped with an inverter. Compared with the configuration of one or several high-power centralized inverters for the entire photovoltaic power station, this configuration can effectively disperse risks and reduce the impact of single-point failures; the photovoltaic panels in each area are connected in series using multiple groups of The photovoltaic panels in the area are first divided into several groups (also called substrings), and the photovoltaic panels of each group are connected in series to form a series group; thus, the photovoltaic panels in the area can be divided into several substrings, and each substring is numbered as n, where n = 1, 2, 3, ..., N; N is a positive integer, N represents the total number of substrings in the area, and n represents the sequence number of any substring; thus, the position of each substring is recorded as Lmn, and it is marked on the map of the photovoltaic power station, which is helpful for the rapid positioning and timely maintenance of faulty photovoltaic panels in the later stage;

[0106] By communicating with various sensors installed in various areas, the power parameters and environmental parameters of each substring in each area are collected, where the power parameters include voltage signal V(t), current signal I(t), and power generation; the environmental parameters include ambient temperature, ambient humidity, and irradiance.

[0107] The monitoring and analysis module performs characteristic analysis on the inverter status in each area, the conversion status and the resistance status of each substring photovoltaic panel based on power parameters and environmental parameters to extract the three judgment elements; the specific three judgment elements are: the harmonic distortion of the inverter, the conversion monitoring value and the resistance monitoring value.

[0108] The fault output module conducts a comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panel with faults, and execute the corresponding safety strategy accordingly; specifically:

[0109] 3-1: Retrieve the three judgment factors of each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; if the harmonic distortion is greater than or equal to the set harmonic threshold, the photovoltaic panels in the area are controlled to stop working, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer; if the harmonic distortion is less than the set harmonic threshold, execute 3-2;

[0110] 3-2: Normalize the harmonic distortion VI, conversion monitoring value KZ and resistance monitoring value RD and take their values, and perform formula calculation and analysis on the values ​​to obtain the abnormal value CY; the specific Pythagorean theorem calculation formula is:

[0111]

[0112] Among them, λ1 and λ2 are respectively the set proportional constants;

[0113] 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the substring photovoltaic panel is recorded as a high-risk substring; when the abnormal value is in the set abnormal interval, the substring photovoltaic panel is recorded as a medium-risk substring; when the abnormal value is less than the minimum value in the set abnormal interval, the substring photovoltaic panel is recorded as a light-risk substring; count the number of high-risk substrings, medium-risk substrings and light-risk substrings in the area respectively, and record them as G1, G2 and G3 respectively; if G3≥G1+G2, the high-risk substring in the area is recorded as a faulty substring, the faulty substring is controlled to stop working, and the number and position of the faulty substring are output, and the number and position of the faulty substring are sent to the corresponding engineer; otherwise, the area is recorded as an abnormal area, and the abnormal area is controlled to stop working; and the number and position of the abnormal area are output, thereby integrating the number, position and abnormal values ​​of each substring photovoltaic panel into the abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer;

[0114] 3-4: Repeat the above steps 3-1 to 3-3 until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output.

[0115] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the photovoltaic power station fault detection method based on big data.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] 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 invention 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 several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. 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 invention.

Claims

1. A photovoltaic power station fault detection method based on big data, characterized in that: The following steps are involved: Step 1: Divide the photovoltaic power station into several areas, each area is equipped with an inverter, and the photovoltaic panels in the area are divided into several sub-strings, and the photovoltaic panels of the sub-strings are connected in parallel; the photovoltaic panels in the sub-strings are connected in series; the photovoltaic panels of each sub-string are numbered and marked on the photovoltaic power station map; Step 2: Communicate with various sensors installed in various areas to collect power parameters and environmental parameters of each substring in each area, and based on this, conduct in-depth analysis of the inverter working state, light energy conversion state, and resistance state of each substring in the area to obtain harmonic distortion, conversion monitoring value, and resistance monitoring value; thereby, the three elements of judging the photovoltaic panels of each substring in the area can be determined, and the specific three elements are: harmonic distortion, conversion monitoring value, and resistance monitoring value of the inverter; the power parameters include voltage signal V(t), current signal I(t), and power generation; the environmental parameters include ambient temperature, ambient humidity, and irradiance; Step 3: Perform a comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panels with faults, and execute the corresponding safety strategy accordingly.

2. The photovoltaic power station fault detection method based on big data according to claim 1 is characterized in that: The specific steps of comprehensively analyzing the three factors to identify the faulty substring photovoltaic panels are as follows: 3-1: Retrieve the three judgment factors of each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; if the harmonic distortion is greater than or equal to the set harmonic threshold, the photovoltaic panels in the area are controlled to stop working, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer; if the harmonic distortion is less than the set harmonic threshold, execute 3-2; 3-2: Normalize the harmonic distortion, conversion monitoring value and resistance monitoring value and take their values, and perform formula calculation and analysis on the values ​​to obtain abnormal values; 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the photovoltaic panel of the substring is recorded as a high-risk substring; when the abnormal value is within the set abnormal interval, the photovoltaic panel of the substring is recorded as a moderate-risk substring; When the abnormal value is less than the minimum value in the set abnormal interval, the photovoltaic panel of the substring is recorded as a mild risk substring; the number of high risk substrings, medium risk substrings and mild risk substrings in the area are counted respectively, and they are recorded as G1, G2 and G3 respectively; if G3≥G1+G2, the high risk substring in the area is recorded as a faulty substring, the faulty substring is controlled to stop working, and the number and location of the faulty substring are output, and the number and location of the faulty substring are sent to the corresponding engineer; Otherwise, the area is recorded as an abnormal area, and the control abnormal area stops working; and the number and position of the abnormal area are output, thereby integrating the number, position, and abnormal values ​​of each substring photovoltaic panel of the abnormal area into abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer; 3-4: Repeat the above steps 3-1 to 3-3 until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output.

3. The photovoltaic power station fault detection method based on big data according to claim 1 is characterized in that: The monitoring and analysis steps of the inverter working status are as follows: The voltage signal V(t) and current signal I(t) of the inverter are retrieved, and the time domain signal is converted into a frequency domain signal through Fourier transform, and the voltage harmonic expression and each subharmonic expression of the current are obtained accordingly; Extract the amplitude V of each harmonic based on the voltage harmonic expression and current harmonic expression j and I f , and the harmonic distortion will be obtained by formulating and analyzing.

4. The photovoltaic power station fault detection method based on big data according to claim 3 is characterized in that: The steps for monitoring and analyzing the conversion status are as follows: Retrieve the irradiance and power generation corresponding to each substring in the region at each acquisition time; normalize the irradiance and power generation and take their values, divide the values ​​to calculate the conversion rate, and thus obtain the conversion rate corresponding to each acquisition time; construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the conversion rate as the vertical coordinate, input the conversion rate into the coordinate axis according to its corresponding acquisition time, and record the position of the conversion rate in the coordinate axis as the conversion point; use a smooth curve to connect each conversion point in sequence to obtain a curve chart of the conversion rate changing with time; draw a tangent line at each conversion point, use data fitting to calculate the tangent slope and record it as the conversion slope; sum the conversion slopes greater than zero to obtain the conversion increase value, and sum the conversion slopes less than zero to take the absolute value to calculate the conversion decrease value; The conversion rate, conversion slope, conversion increase value and conversion decrease value are normalized and their numerical values ​​are taken, and the numerical values ​​are subjected to formulaic calculation and analysis to obtain the conversion monitoring value.

5. The photovoltaic power station fault detection method based on big data according to claim 4 is characterized in that: The monitoring and analysis steps of the resistance status are as follows: Each substring photovoltaic panel is set to correspond to a standard environmental parameter, wherein the standard environmental parameter includes a standard ambient temperature and a standard ambient humidity; The insulation resistance, series impedance, ambient temperature and ambient humidity corresponding to each acquisition moment are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the resistance state value; A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the resistance state value as the vertical coordinate, the resistance state value is input into the coordinate axis according to its corresponding acquisition time, and the position of the resistance state value in the coordinate axis is recorded as a resistance point, and a smooth curve is used to connect each resistance point in sequence to obtain a curve graph of the resistance state value changing with time; a tangent line is drawn at each resistance point, and the slope of the tangent line is obtained by data fitting and recorded as the resistance slope; the resistance slopes greater than zero are summed up to obtain a state increase value, and the resistance slopes less than zero are summed up and then the absolute value is taken to calculate a state decrease value; The resistance state value, resistance slope, state increase value and state decrease value at each acquisition moment are normalized and their numerical values ​​are taken, and the numerical values ​​are calculated and analyzed by formula to obtain the resistance monitoring value.

6. Photovoltaic power station fault detection system based on big data, characterized in that: Applied to the photovoltaic power station fault detection method based on big data as claimed in any one of claims 1 to 6, the system comprises: a server, a monitoring and analysis module and a fault output module; The server divides the photovoltaic power station into regions and collects information. The specific information collected is power parameters and environmental parameters. The power parameters include voltage signal V(t), current signal I(t), and power generation. The environmental parameters include ambient temperature, ambient humidity, and irradiance. The monitoring and analysis module performs feature analysis on the inverter status in each area, the conversion status and the resistance status of each substring photovoltaic panel based on power parameters and environmental parameters to extract the three judgment elements; the specific three judgment elements are: the harmonic distortion of the inverter, the conversion monitoring value and the resistance monitoring value; The fault output module conducts a comprehensive analysis based on the three judgment factors of each sub-string photovoltaic panel in the area to accurately judge the health status of each sub-string photovoltaic panel, identify the sub-string photovoltaic panel with faults, and execute the corresponding safety strategy accordingly; specifically: 3-1: Retrieve the three judgment factors of each substring photovoltaic panel in the area: harmonic distortion of the inverter, conversion monitoring value and resistance monitoring value; if the harmonic distortion is greater than or equal to the set harmonic threshold, the photovoltaic panels in the area are controlled to stop working, and the area is recorded as a risk area, and the number and location of the risk area are output; the number and location of the risk area are sent to the corresponding engineer; if the harmonic distortion is less than the set harmonic threshold, execute 3-2; 3-2: Normalize the harmonic distortion, conversion monitoring value and resistance monitoring value and take their values, and perform formula calculation and analysis on the values ​​to obtain abnormal values; 3-3: Compare and analyze the abnormal value with the set abnormal interval. When the abnormal value is greater than the maximum value in the set abnormal interval, the substring photovoltaic panel is recorded as a high-risk substring; when the abnormal value is in the set abnormal interval, the substring photovoltaic panel is recorded as a medium-risk substring; when the abnormal value is less than the minimum value in the set abnormal interval, the substring photovoltaic panel is recorded as a light-risk substring; count the number of high-risk substrings, medium-risk substrings and light-risk substrings in the area respectively, and record them as G1, G2 and G3 respectively; if G3≥G1+G2, the high-risk substring in the area is recorded as a faulty substring, the faulty substring is controlled to stop working, and the number and position of the faulty substring are output, and the number and position of the faulty substring are sent to the corresponding engineer; otherwise, the area is recorded as an abnormal area, and the abnormal area is controlled to stop working; and the number and position of the abnormal area are output, thereby integrating the number, position and abnormal values ​​of each substring photovoltaic panel into the abnormal information of the abnormal area, and the abnormal information is sent to the corresponding engineer; 3-4: Repeat the above steps 3-1 to 3-3 until the fault judgment of all areas of the photovoltaic power station is completed and the corresponding safety strategy is output.

7. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the photovoltaic power station fault detection method based on big data as described in any one of claims 1 to 5 is implemented.

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