A PR value monitoring system for photovoltaic stations based on drones

Abstract

The present application relates to the technical field of photovoltaic stations, and in particular to a PR value monitoring system for photovoltaic stations based on drones. It includes: a central control unit, which establishes multiple monitoring points according to the equipment parameters of the photovoltaic station; a monitoring unit, which includes multiple monitoring submodules, and the monitoring submodules are arranged at the monitoring points to collect the operating data of each monitoring point; a drone unit, which is used to collect the monitoring data of each monitoring point; by periodically monitoring the PR value of each photovoltaic component, accurate monitoring of the operating status of each photovoltaic component is achieved, and the performance status of the power station is monitored for a long time, providing data support for the later operation and maintenance of the project. Based on the drone module, the environmental and equipment monitoring data of each abnormally operating photovoltaic component are collected, and the real-time PR value is corrected, thereby eliminating the disturbance of the environmental parameters to the PR value, timely warning of the potential failure risk of each photovoltaic component, and improving the maintenance efficiency of the photovoltaic station.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic stations, and in particular to a photovoltaic station PR value monitoring system based on unmanned aerial vehicles. Background Art

[0002] The performance ratio (PR) of a photovoltaic power station refers to the ratio of photovoltaic equivalent utilization hours to peak sunshine hours. The PR value of a photovoltaic power station can indicate how efficient and reliable the power generation of the power station is. By measuring the PR value of a photovoltaic power station, the power output capacity of the photovoltaic power station can be evaluated and predicted. In addition, the performance status of the power station can be monitored over a long period of time, providing a reference for the later operation and maintenance of the project.

[0003] Existing technical means usually use sensors and monitoring equipment installed on photovoltaic modules to collect various data of photovoltaic stations in real time, combine weather data and the characteristics of photovoltaic modules for processing, calculate the actual power generation and theoretical power generation, and thus obtain the PR value; the system can set a reasonable PR range, and when the PR value exceeds the preset range, the system will promptly issue an alarm so that the operation and maintenance personnel can adjust the operating status of the photovoltaic station in time; but in actual application, when the rated efficiency of each photovoltaic module in the photovoltaic station is different, the calculation of the PR value is too complicated; secondly, by obtaining the PR value, only the efficiency of the photovoltaic station can be confirmed, and the information of abnormal photovoltaic modules cannot be obtained, which reduces the accuracy of maintenance and thus cannot reduce the workload of maintenance personnel. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a photovoltaic station PR value monitoring system based on drones, aiming to improve the monitoring accuracy of the PR value of photovoltaic modules, improve the operation and maintenance efficiency of photovoltaic modules, and reduce the overall operation and maintenance cost of photovoltaic power stations.

[0005] In some embodiments of the present application, by periodically monitoring the PR value of each photovoltaic module, accurate monitoring of the operating status of each photovoltaic module is achieved, the performance status of the power station is monitored over a long period of time, and data support is provided for the later operation and maintenance of the project, thereby improving the overall operation and maintenance efficiency of the photovoltaic site.

[0006] In some embodiments of the present application, based on the drone module, the environmental and equipment monitoring data of each abnormally operating photovoltaic component are collected, and the real-time PR value is corrected to eliminate the disturbance of the environmental parameters on the PR value, and timely warn of the potential failure risks of each photovoltaic component, thereby improving the maintenance efficiency of the photovoltaic site.

[0007] In some embodiments of the present application, a PV station PR value monitoring system based on a drone is provided, comprising:

[0008] The central control unit establishes multiple monitoring points according to the equipment parameters of the photovoltaic station;

[0009] A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at monitoring points and are used to collect operation data of each monitoring point;

[0010] UAV unit, used to collect monitoring data from each monitoring point;

[0011] The central control unit comprises:

[0012] The first processing module is used to generate a first-level PR value of each monitoring point according to a preset feedback time node;

[0013] The second processing module is used to determine the operating status of each monitoring point according to all the first-level PR values.

[0014] In some embodiments of the present application, the primary processing module is also used for:

[0015] Establish a monitoring point sequence A, A=(a 1 , a 2 …a i …an), where ai is the ith monitoring point and n is the number of monitoring points;

[0016] Set the i-th monitoring point as the target monitoring point in sequence;

[0017] Obtain the operating data of the target monitoring point at the current feedback time node and generate the first-level PR value of the target monitoring point;

[0018] Generate the first-level PR value of each monitoring point in turn;

[0019] Establish the first-level PR value sequence B of the current feedback time node, B = (b 1 , b 2 …b i …b n ), where bi is the first-level PR value of the i-th monitoring point at the current feedback time node;

[0020] Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B;

[0021] The time interval t between the current feedback time node and the next feedback time node is set according to the monitoring evaluation value j.

[0022] In some embodiments of the present application, when generating the monitoring evaluation value j of the current feedback time node, it includes:

[0023] Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B;

[0024]

[0025] Among them, e1 is the preset first weight coefficient; Q1 is the preset first fixed coefficient; e2 is the preset second weight coefficient; Q2 is the preset second fixed coefficient; b' i is the standard first-level PR value of the i-th monitoring point at the current feedback time node; Y(i) is the selection coefficient; if (b i -b' i )>0,Y(i)=1 / (b i -b' i ); if (b i -b' i )<0, Y(i)=0; θ is the number of historical evaluation indicators; β i is the impact factor of the i-th historical evaluation index; p i is the reference value of the i-th historical evaluation indicator at the current feedback time node.

[0026] In some embodiments of the present application, the second processing module is further used to:

[0027] Obtain the first-level PR value sequence B of the current feedback time node, and generate the running deviation value of each monitoring point at the current feedback time node;

[0028] Establish the running deviation value sequence C of the current feedback time node, C = (c 1 , c 2 …c i …c n ), where c i is the running deviation value of the i-th monitoring point at the current feedback time node;

[0029] Preset a first running deviation value threshold C1;

[0030] If c i >C1, set the i-th monitoring point as the abnormal operation point;

[0031] Establish the abnormal operation point number sequence A1 of the current feedback time node, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where n 1i is the i-th abnormal operation point at the current feedback time node; n1 is the number of abnormal operation points at the current feedback time node.

[0032] In some embodiments of the present application, when establishing the running deviation value sequence C of the current feedback time node, it includes:

[0033] Set the i-th monitoring point as the target monitoring point in sequence;

[0034] Generate the running deviation value c of the target monitoring point at the current feedback time node;

[0035] c=e3*Q3*b i +e4*Q4*(b i -b' i );

[0036] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient;

[0037] Generate the operation deviation value of each monitoring point at the current feedback time node in turn.

[0038] In some embodiments of the present application, the drone unit includes:

[0039] A drone submodule, wherein there are multiple drone submodules, and the drone submodules are used to collect monitoring data of each monitoring point;

[0040] The first control module is used to set the control strategy of the drone submodule.

[0041] In some embodiments of the present application, the first control module is further used to:

[0042] Get the abnormal operation point number column A1 of the current feedback time node;

[0043] Generate multiple initial inspection strategies based on the location parameters of all abnormal operation points;

[0044] Establish the initial inspection strategy sequence D, D = (d 1 , d 2 …d i …d m ), where d i is the i-th initial inspection plan at the current feedback time node; m is the number of initial inspection plans at the current feedback time node;

[0045] Generate cost evaluation values ​​for each initial inspection plan;

[0046] Establish cost evaluation value series K, K = (k 1 , k 2 …k i …k m ), where k i is the cost evaluation value of the i-th initial inspection plan;

[0047] Set the maximum value k among the cost evaluation values ​​K max The corresponding initial inspection plan is the first-level inspection plan;

[0048] Generate the control strategy for the current feedback time node based on the first-level inspection plan.

[0049] In some embodiments of the present application, the central control unit further includes:

[0050] The correction module is used to obtain the monitoring data of each abnormal operation point and generate the secondary PR value of each secondary abnormal point based on all the monitoring data;

[0051] The early warning module is used to determine whether to generate an early warning instruction based on all secondary PR values.

[0052] In some embodiments of the present application, the correction module is also used to:

[0053] According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point;

[0054] Obtain monitoring data of target abnormal operation points;

[0055] Generate a disturbance evaluation value f of the target abnormal operation point;

[0056]

[0057] Wherein, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; Q5 is the preset fifth fixed coefficient; Q6 is the preset sixth fixed coefficient; r1 is the number of environmental disturbance indicators; η 1i is the influencing factor of the i-th environmental disturbance index; g 1i is the real-time reference value of the i-th environmental disturbance index of the target abnormal operation point generated based on the monitoring data; r2 is the number of equipment disturbance indexes; η 2i is the influencing factor of the disturbance index of the ith device; g 2i To generate a real-time reference value of the disturbance index of the i-th equipment at the target abnormal operation point based on the monitoring data;

[0058] Generate a compensation coefficient v of the target abnormal operation point according to the preset correction model and the disturbance evaluation value f;

[0059] Obtain the first-level PR value b of the target abnormal operation point at the current feedback time node;

[0060] Generate the secondary PR value h of the target abnormal operation point at the current feedback time node, h = v * b;

[0061] Abnormally generates the secondary PR value of each abnormal operating point;

[0062] Establish the secondary PR value sequence H of the current feedback time node, H = (h 1 ,h 2 …h i …h n1), where h i It is the secondary PR value of the i-th abnormal operation point at the current feedback time node.

[0063] In some embodiments of the present application, the early warning module is also used to:

[0064] Get the secondary PR value sequence H;

[0065] According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point;

[0066] Generate a risk assessment value k of the target abnormal operation point;

[0067] k=e7*Q7*h+e8*Q8*(h-h');

[0068] Among them, e7 is the preset seventh weight coefficient; e8 is the preset eighth weight coefficient; Q7 is the preset seventh fixed coefficient; Q8 is the preset eighth fixed coefficient; h' is the standard secondary PR value of the target abnormal operation point at the current feedback time node;

[0069] Preset a first risk assessment value threshold K1 and a second risk assessment value threshold K2;

[0070] If k<K1, the target abnormal operation point does not generate a warning instruction;

[0071] If K1<k<K2, the target abnormal operation point generates a first-level warning instruction at the current feedback time node;

[0072] If k>K2, the target abnormal operation point generates a secondary warning instruction at the current feedback time node.

[0073] Compared with the prior art, the photovoltaic station PR value monitoring system based on drone in the embodiment of the present application has the following beneficial effects:

[0074] By periodically monitoring the PR value of each PV module, the operating status of each PV module can be accurately monitored, the performance status of the power station can be monitored over a long period of time, and data support can be provided for the later operation and maintenance of the project, thereby improving the overall operation and maintenance efficiency of the PV site.

[0075] Based on the drone module, the environmental and equipment monitoring data of each abnormally operating PV module are collected, and the real-time PR value is corrected to eliminate the disturbance of environmental parameters on the PR value, and timely warn of the potential failure risks of each PV module, thereby improving the maintenance efficiency of PV sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a structural schematic diagram of a photovoltaic station PR value monitoring system based on a drone in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0077] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0078] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0079] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0080] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0081] like Figure 1 As shown, a photovoltaic station PR value monitoring system based on a drone in a preferred embodiment of the present application includes:

[0082] The central control unit establishes multiple monitoring points according to the equipment parameters of the photovoltaic station;

[0083] A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at monitoring points and are used to collect operation data of each monitoring point;

[0084] UAV unit, used to collect monitoring data from each monitoring point;

[0085] The central control unit includes:

[0086] The first processing module is used to generate a first-level PR value of each monitoring point according to a preset feedback time node;

[0087] The second processing module is used to determine the operating status of each monitoring point according to all the first-level PR values.

[0088] Specifically, a monitoring point series is established according to the number of photovoltaic modules, and a single monitoring point corresponds to one photovoltaic module.

[0089] Specifically, the monitoring submodule is arranged on each photovoltaic module, and is used to collect the operating parameters of each photovoltaic module, so as to monitor the PR value of each photovoltaic module in real time.

[0090] Specifically, the primary processing module is also used to:

[0091] Establish a monitoring point sequence A, A=(a 1 , a 2 …a i …a n ), where ai is the i-th monitoring point and n is the number of monitoring points;

[0092] Set the i-th monitoring point as the target monitoring point in sequence;

[0093] Obtain the operating data of the target monitoring point at the current feedback time node and generate the first-level PR value of the target monitoring point;

[0094] Generate the first-level PR value of each monitoring point in turn;

[0095] Establish the first-level PR value sequence B of the current feedback time node, B = (b 1 , b 2 …b i …b n ), where bi is the first-level PR value of the i-th monitoring point at the current feedback time node;

[0096] Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B;

[0097] The time interval t between the current feedback time node and the next feedback time node is set according to the monitoring evaluation value j.

[0098] Specifically, a monitoring point series is established according to the number of photovoltaic modules, and a single monitoring point corresponds to one photovoltaic module.

[0099] Specifically, by collecting the operating parameters of the PV modules corresponding to each monitoring point, the first-level PR value of each monitoring point is generated, thereby realizing real-time monitoring of the performance status of each PV module in the corresponding PV station, which facilitates management personnel to make operation and maintenance decisions.

[0100] Specifically, when generating the monitoring evaluation value j of the current feedback time node, it includes:

[0101] Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B;

[0102]

[0103] Among them, e1 is the preset first weight coefficient; Q1 is the preset first fixed coefficient; e2 is the preset second weight coefficient; Q2 is the preset second fixed coefficient; b' i is the standard first-level PR value of the i-th monitoring point at the current feedback time node; Y(i) is the selection coefficient; if (b i -b' i )>0,Y(i)=1 / (b i -b' i ); if (b i -b' i )<0, Y(i)=0; θ is the number of historical evaluation indicators; β i is the impact factor of the i-th historical evaluation index; p i is the reference value of the i-th historical evaluation indicator at the current feedback time node.

[0104] Specifically, by presetting the first fixed coefficient and the second fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0105] Specifically, the larger the monitoring evaluation value is, the greater the internal operation fluctuation of the current photovoltaic station is, and the greater the possibility of fluctuation in the primary PR value of each photovoltaic module is. It is necessary to shorten the time interval between adjacent feedback time nodes in time to improve the monitoring efficiency of each photovoltaic module.

[0106] Specifically, as the life of the photovoltaic module decreases, the PR value will decrease, so b' i It needs to be set according to the cumulative operating time of the photovoltaic module corresponding to the i-th monitoring point, so as to eliminate the impact of life loss.

[0107] Specifically, the historical evaluation indicators include, but are not limited to, historical light intensity fluctuation values, historical temperature fluctuation values, the number of historical failures in the photovoltaic station and other parameters.

[0108] It can be understood that in the above embodiment, by periodically monitoring the PR value of each photovoltaic module, accurate monitoring of the operating status of each photovoltaic module is achieved, the performance status of the power station is monitored over a long period of time, and data support is provided for the later operation and maintenance of the project, thereby improving the overall operation and maintenance efficiency of the photovoltaic site.

[0109] In a preferred embodiment of the present application, the second processing module is also used for:

[0110] Obtain the first-level PR value sequence B of the current feedback time node, and generate the running deviation value of each monitoring point at the current feedback time node;

[0111] Establish the running deviation value sequence C of the current feedback time node, C = (c 1 , c 2 …c i …c n ), where c i is the running deviation value of the i-th monitoring point at the current feedback time node;

[0112] Preset a first running deviation value threshold C1;

[0113] If c i >C1, set the i-th monitoring point as the abnormal operation point;

[0114] Establish the abnormal operation point number sequence A1 of the current feedback time node, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where n 1i is the i-th abnormal operation point at the current feedback time node; n1 is the number of abnormal operation points at the current feedback time node.

[0115] Specifically, the first operation deviation value threshold may be set according to historical parameters. If the actual operation deviation value is greater than the first operation deviation value threshold, it indicates that the corresponding monitoring point has abnormal operation.

[0116] Specifically, when establishing the running deviation value sequence C of the current feedback time node, it includes:

[0117] Set the i-th monitoring point as the target monitoring point in sequence;

[0118] Generate the running deviation value c of the target monitoring point at the current feedback time node;

[0119] c=e3*Q3*b i +e4*Q4*(b i -b' i );

[0120] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient;

[0121] Generate the operation deviation value of each monitoring point at the current feedback time node in turn.

[0122] Specifically, by presetting the third fixed coefficient and the fourth fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0123] Specifically, the drone unit includes:

[0124] UAV submodule, there are multiple UAV submodules, and the UAV submodules are used to collect monitoring data of each monitoring point;

[0125] The first control module is used to set the control strategy of the drone submodule.

[0126] Specifically, the drone sub-module is preferentially a drone, and a single drone is equipped with a variety of sensors such as temperature sensors for collecting data on various environmental indicators, such as temperature, humidity, light intensity, light angle and other parameters. It also has image acquisition capabilities for collecting image data of each photovoltaic panel, thereby analyzing the shading conditions of each photovoltaic component.

[0127] Specifically, the first control module is further used for:

[0128] Get the abnormal operation point number column A1 of the current feedback time node;

[0129] Generate multiple initial inspection strategies based on the location parameters of all abnormal operation points;

[0130] Establish the initial inspection strategy sequence D, D = (d 1 , d 2 …d i …d m ), where d i is the i-th initial inspection plan at the current feedback time node; m is the number of initial inspection plans at the current feedback time node;

[0131] Generate cost evaluation values ​​for each initial inspection plan;

[0132] Establish cost evaluation value series K, K = (k 1 , k 2 …k i …k m ), where k i is the cost evaluation value of the i-th initial inspection plan;

[0133] Set the maximum value k among the cost evaluation values ​​K max The corresponding initial inspection plan is the first-level inspection plan;

[0134] Generate the control strategy for the current feedback time node based on the first-level inspection plan.

[0135] Specifically, a constraint model is established based on the current number of drones that can perform inspections, the flight distance of each drone, and the location parameters of each abnormal operating point, and multiple initial inspection strategies are generated based on the feasible solutions of the constraint model.

[0136] Specifically, a single inspection strategy includes the number of drones that need to be dispatched and the flight paths of each dispatched drone.

[0137] Specifically, the cost evaluation value can be generated according to the operation and maintenance cost, time cost and other parameters of each initial inspection strategy. The smaller the cost evaluation value is, the higher the feasibility of the current initial inspection strategy is.

[0138] It can be understood that in the above embodiment, based on the drone module, the environmental and equipment monitoring data of each abnormally operating photovoltaic component are collected, thereby eliminating the disturbance of environmental parameters to the PR value, improving the inspection efficiency of each photovoltaic component, and timely issuing early warnings for the potential failure risks of each photovoltaic component, thereby improving the maintenance efficiency of photovoltaic sites.

[0139] In a preferred embodiment of the present application, the central control unit further includes:

[0140] The correction module is used to obtain the monitoring data of each abnormal operation point and generate the secondary PR value of each secondary abnormal point based on all the monitoring data;

[0141] The early warning module is used to determine whether to generate an early warning instruction based on all secondary PR values.

[0142] Specifically, the correction module is also used to:

[0143] According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point;

[0144] Obtain monitoring data of target abnormal operation points;

[0145] Generate a disturbance evaluation value f of the target abnormal operation point;

[0146]

[0147] Wherein, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; Q5 is the preset fifth fixed coefficient; Q6 is the preset sixth fixed coefficient; r1 is the number of environmental disturbance indicators; η 1i is the influencing factor of the i-th environmental disturbance index; g 1i is the real-time reference value of the i-th environmental disturbance index of the target abnormal operation point generated based on the monitoring data; r2 is the number of equipment disturbance indexes; η 2i is the influencing factor of the disturbance index of the ith device; g 2i To generate a real-time reference value of the disturbance index of the i-th equipment at the target abnormal operation point based on the monitoring data;

[0148] Generate a compensation coefficient v of the target abnormal operation point according to the preset correction model and the disturbance evaluation value f;

[0149] Obtain the first-level PR value b of the target abnormal operation point at the current feedback time node;

[0150] Generate the secondary PR value h of the target abnormal operation point at the current feedback time node, h = v * b;

[0151] Abnormally generates the secondary PR value of each abnormal operating point;

[0152] Establish the secondary PR value sequence H of the current feedback time node, H = (h 1 ,h 2 …h i …h n1 ), where h i It is the secondary PR value of the i-th abnormal operation point at the current feedback time node.

[0153] Specifically, all parameters in the model are normalized by presetting the fifth fixed coefficient and the sixth fixed coefficient, so that each parameter is within the same value range.

[0154] Specifically, environmental disturbance indicators include but are not limited to temperature, humidity, light intensity, light angle and other parameters that affect the PR value. Equipment disturbance indicators include but are not limited to the degree of dust accumulation on photovoltaic panels, shadow occlusion area and other parameters that affect the PR value.

[0155] Specifically, the early warning module is also used to:

[0156] Get the secondary PR value sequence H;

[0157] According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point;

[0158] Generate a risk assessment value k of the target abnormal operation point;

[0159] k=e7*Q7*h+e8*Q8*(h-h');

[0160] Among them, e7 is the preset seventh weight coefficient; e8 is the preset eighth weight coefficient; Q7 is the preset seventh fixed coefficient; Q8 is the preset eighth fixed coefficient; h' is the standard secondary PR value of the target abnormal operation point at the current feedback time node;

[0161] Preset a first risk assessment value threshold K1 and a second risk assessment value threshold K2;

[0162] If k<K1, the target abnormal operation point does not generate a warning instruction;

[0163] If K1<k<K2, the target abnormal operation point generates a first-level warning instruction at the current feedback time node;

[0164] If k>K2, the target abnormal operation point generates a secondary warning instruction at the current feedback time node.

[0165] Specifically, all parameters in the model are normalized by presetting the seventh fixed coefficient and the eighth fixed coefficient, so that each parameter is within the same value range.

[0166] Specifically, the greater the risk assessment value of the abnormal operation point, the greater the possibility that the photovoltaic module at the current abnormal operation point has a potential operation failure.

[0167] Specifically, the standard level 1 PR value and the standard level 2 PR value of a single monitoring point at the same time are the same, both of which are set according to the life loss of the photovoltaic module itself.

[0168] Specifically, the first-level warning instruction refers to using drones to clean the photovoltaic modules at the target abnormal operation points, and judging whether to perform maintenance based on the PR value of the photovoltaic modules after cleaning. The second-level warning instruction refers to repairing the photovoltaic modules at the target abnormal operation points, timely eliminating potential fault risks, and ensuring the stable operation of the photovoltaic modules. Improve the maintenance efficiency of photovoltaic stations.

[0169] According to the first concept of the present application, by periodically monitoring the PR value of each photovoltaic module, accurate monitoring of the operating status of each photovoltaic module can be achieved, the performance status of the power station can be monitored over a long period of time, and data support can be provided for the later operation and maintenance of the project, thereby improving the overall operation and maintenance efficiency of the photovoltaic site.

[0170] According to the second concept of the present application, based on the drone module, the environmental and equipment monitoring data of each abnormally operating photovoltaic component are collected, and the real-time PR value is corrected to eliminate the disturbance of environmental parameters on the PR value, and timely warn of the potential failure risks of each photovoltaic component, thereby improving the maintenance efficiency of photovoltaic sites.

[0171] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A PV station PR value monitoring system based on drones, characterized in that: include: The central control unit establishes multiple monitoring points according to the equipment parameters of the photovoltaic station; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at monitoring points and are used to collect operation data of each monitoring point; UAV unit, used to collect monitoring data from each monitoring point; The central control unit comprises: The first processing module is used to generate a first-level PR value of each monitoring point according to a preset feedback time node; The second processing module is used to determine the operating status of each monitoring point according to all the first-level PR values.

2. The PR value monitoring system for photovoltaic stations based on drones according to claim 1, characterized in that: The primary processing module is also used for: Establish a monitoring point sequence A, A = (a1, a2…a i …a n ), where ai is the i-th monitoring point and n is the number of monitoring points; Set the i-th monitoring point as the target monitoring point in sequence; Obtain the operating data of the target monitoring point at the current feedback time node and generate the first-level PR value of the target monitoring point; Generate the first-level PR value of each monitoring point in turn; Establish the first-level PR value sequence B of the current feedback time node, B = (b1, b2…b i …b n ), where bi is the first-level PR value of the i-th monitoring point at the current feedback time node; Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B; The time interval t between the current feedback time node and the next feedback time node is set according to the monitoring evaluation value j.

3. The PR value monitoring system for photovoltaic stations based on drones according to claim 2 is characterized in that: When generating the monitoring evaluation value j of the current feedback time node, it includes: Generate the monitoring evaluation value j of the current feedback time node according to the first-level PR value sequence B; Among them, e1 is the preset first weight coefficient; Q1 is the preset first fixed coefficient; e2 is the preset second weight coefficient; Q2 is the preset second fixed coefficient; b' i is the standard first-level PR value of the i-th monitoring point at the current feedback time node; Y(i) is the selection coefficient; if (b i -b' i )>0,Y(i)=1 / (b i -b' i ); if (b i -b' i )<0, Y(i)=0; θ is the number of historical evaluation indicators; β i is the impact factor of the i-th historical evaluation index; p i is the reference value of the i-th historical evaluation indicator at the current feedback time node.

4. The PR value monitoring system for photovoltaic stations based on drones as claimed in claim 3 is characterized in that: The second processing module is also used for: Obtain the first-level PR value sequence B of the current feedback time node, and generate the running deviation value of each monitoring point at the current feedback time node; Establish the running deviation value sequence C of the current feedback time node, C = (c1, c2...c i …c n ), where c i is the running deviation value of the i-th monitoring point at the current feedback time node; Preset a first running deviation value threshold C1; If c i >C1, set the i-th monitoring point as the abnormal operation point; Establish the abnormal operation point number sequence A1 of the current feedback time node, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where n 1i is the i-th abnormal operation point at the current feedback time node; n1 is the number of abnormal operation points at the current feedback time node.

5. The photovoltaic station PR value monitoring system based on drone according to claim 4 is characterized in that: When establishing the running deviation value sequence C of the current feedback time node, it includes: Set the i-th monitoring point as the target monitoring point in sequence; Generate the running deviation value c of the target monitoring point at the current feedback time node; c=e3*Q3*b i +e4*Q4*(b i -b' i ); Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; Generate the operation deviation value of each monitoring point at the current feedback time node in turn.

6. The PR value monitoring system for photovoltaic stations based on drones as claimed in claim 5 is characterized in that: The drone unit comprises: A drone submodule, wherein there are multiple drone submodules, and each drone submodule is used to collect monitoring data of each monitoring point; The first control module is used to set the control strategy of the drone submodule.

7. The PR value monitoring system for photovoltaic stations based on drones according to claim 6, characterized in that: The first control module is also used for: Get the abnormal operation point number column A1 of the current feedback time node; Generate multiple initial inspection strategies based on the location parameters of all abnormal operation points; Establish the initial inspection strategy sequence D, D = (d1, d2…d i …d m ), where d i is the i-th initial inspection plan at the current feedback time node; m is the number of initial inspection plans at the current feedback time node; Generate cost evaluation values ​​for each initial inspection plan; Establish cost evaluation value series K, K = (k1, k2…k i …k m ), where k i is the cost evaluation value of the i-th initial inspection plan; Set the maximum value k among the cost evaluation values ​​K max The corresponding initial inspection plan is the first-level inspection plan; Generate the control strategy for the current feedback time node based on the first-level inspection plan.

8. The PR value monitoring system for photovoltaic stations based on drones according to claim 6 is characterized in that: The central control unit also includes: The correction module is used to obtain the monitoring data of each abnormal operation point and generate the secondary PR value of each secondary abnormal point based on all the monitoring data; The early warning module is used to determine whether to generate an early warning instruction based on all secondary PR values.

9. The PR value monitoring system for photovoltaic stations based on drones according to claim 8, characterized in that: The correction module is also used for: According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point; Obtain monitoring data of target abnormal operation points; Generate a disturbance evaluation value f of the target abnormal operation point; Wherein, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; Q5 is the preset fifth fixed coefficient; Q6 is the preset sixth fixed coefficient; r1 is the number of environmental disturbance indicators; η 1i is the influencing factor of the i-th environmental disturbance index; g 1i is the real-time reference value of the i-th environmental disturbance index of the target abnormal operation point generated based on the monitoring data; r2 is the number of equipment disturbance indexes; η 2i is the influencing factor of the disturbance index of the ith device; g 2i A real-time reference value of the disturbance index of the i-th equipment at the target abnormal operation point is generated based on the monitoring data; Generate a compensation coefficient v of the target abnormal operation point according to the preset correction model and the disturbance evaluation value f; Obtain the first-level PR value b of the target abnormal operation point at the current feedback time node; Generate the secondary PR value h of the target abnormal operation point at the current feedback time node, h = v * b; Abnormally generates the secondary PR value of each abnormal operating point; Establish the secondary PR value sequence H of the current feedback time node, H = (h1, h2…h i …h n1 ), where h i It is the secondary PR value of the i-th abnormal operation point at the current feedback time node.

10. The PR value monitoring system for photovoltaic stations based on drones according to claim 9, characterized in that: The early warning module is also used for: Get the secondary PR value sequence H; According to the abnormal operation point sequence A1, the i-th abnormal operation point is sequentially set as the target abnormal operation point; Generate a risk assessment value k of the target abnormal operation point; k=e7*Q7*h+e8*Q8*(h-h'); Among them, e7 is the preset seventh weight coefficient; e8 is the preset eighth weight coefficient; Q7 is the preset seventh fixed coefficient; Q8 is the preset eighth fixed coefficient; h' is the standard secondary PR value of the target abnormal operation point at the current feedback time node; Preset a first risk assessment value threshold K1 and a second risk assessment value threshold K2; If k<K1, the target abnormal operation point does not generate a warning instruction; If K1<k<K2, the target abnormal operation point generates a first-level warning instruction at the current feedback time node; If k>K2, the target abnormal operation point generates a secondary warning instruction at the current feedback time node.

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