Distributed photovoltaic power station intelligent operation and maintenance method and system based on Internet of Things

Abstract

The invention relates to the technical field of photovoltaic power stations, in particular to a distributed photovoltaic power station intelligent operation and maintenance method and system based on the Internet of Things. Comprising the following steps: setting a plurality of equipment nodes according to equipment parameters of the distributed photovoltaic power station; obtaining operation parameters of each equipment node according to a preset monitoring time node, and generating a performance evaluation value of each equipment node; selecting a plurality of to-be-monitored nodes according to all the performance evaluation values, and generating a risk evaluation value of each to-be-monitored node; and generating a maintenance plan according to all risk evaluation values, periodically collecting operation data of each equipment node by establishing the Internet of Things among the equipment nodes, thereby evaluating the operation state of each equipment node, and screening the equipment nodes running abnormally according to the evaluation result, thereby improving the maintenance efficiency. The monitoring parameters of each equipment node which operates abnormally are set, and the potential fault risk of the equipment node is diagnosed and early warned in time, so that the maintenance efficiency is improved, the operation and maintenance efficiency is improved, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic power stations, and in particular to an intelligent operation and maintenance method and system for distributed photovoltaic power stations based on the Internet of Things. Background Art

[0002] Distributed photovoltaic power stations are developing rapidly as an important application form of renewable energy. However, compared with centralized photovoltaic power stations, the operation and maintenance management of distributed photovoltaic power stations faces a series of challenges, including equipment fault detection, resource optimization and cost control, etc. Distributed photovoltaic power stations frequently experience problems such as equipment downtime, inefficient operation, and abnormal heat dissipation due to reasons such as changeable geographical locations, large fluctuations in the power grid, and weak overall operation and maintenance capabilities. The current Internet of Things technology only monitors the status of the equipment and does not integrate data analysis technology to perform intelligent diagnosis of the equipment, making it impossible to warn of potential risks to the equipment. Summary of the invention

[0003] The purpose of this application is: to solve the above technical problems, this application provides a distributed photovoltaic power station intelligent operation and maintenance method and system based on the Internet of Things, aiming to improve the operation and maintenance efficiency of distributed photovoltaic power stations and reduce the overall operation and maintenance costs.

[0004] In some embodiments of the present application, an Internet of Things is established between each device node, and the operating data of each device node is periodically collected to evaluate the operating status of each device node, providing data support for the management personnel's operation and maintenance decisions. The operation and maintenance personnel can view the status of each device and other key parameters in real time through the remote management interface, and adjust the real-time working parameters of each device node in time according to the real-time status, thereby improving the overall operation efficiency of the distributed photovoltaic power station.

[0005] In some embodiments of the present application, by establishing multi-level monitoring, the operating status of each device node is periodically evaluated, and the device nodes with abnormal operation are screened according to the evaluation results, and the monitoring parameters of each device node with abnormal operation are set, and the potential failure risks of the device nodes are diagnosed and warned in time, thereby improving the maintenance efficiency, ensuring the stable operation of the distributed photovoltaic power station, improving the operation and maintenance efficiency, and reducing the operation and maintenance costs.

[0006] In some embodiments of the present application, a distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things is provided, including: Set multiple device nodes according to the device parameters of the distributed photovoltaic power station; Obtain the operating parameters of each device node according to the preset monitoring time node, and generate the performance evaluation value of each device node; Select multiple nodes to be monitored according to all performance evaluation values, and generate risk evaluation values ​​for each node to be monitored; Generate an overhaul plan based on all risk evaluation values; Among them, when setting multiple equipment nodes, it includes: Establish an equipment node sequence A, A = (a1, a2…a i …a n ), where a i is the i-th equipment node; n is the number of equipment nodes.

[0007] In some embodiments of the present application, when generating the performance evaluation values of each equipment node, it includes: Set a i as the target equipment node in sequence according to the equipment node sequence A; Obtain the operating parameters of the target equipment node at the current monitoring time node; Generate the performance evaluation value b of the target equipment node; b = U * η i *(j i - j' i ); Among them, U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influence factor of the i-th performance evaluation indicator; j i is the reference value of the i-th performance evaluation indicator of the target equipment node at the current monitoring time node; j' i is the standard value of the i-th performance evaluation indicator; Generate the performance evaluation values of each equipment node in sequence; Establish a performance evaluation value sequence B at the current monitoring time node, B = (b1, b2…b i …b n ), where b i is the performance evaluation value of the i-th equipment node at the current monitoring time node.

[0008] In some embodiments of the present application, when selecting multiple nodes to be monitored according to all performance evaluation values, it includes: Preset a performance evaluation value threshold F1; If f i < F1, set the i-th equipment node at the current monitoring time node as the node to be monitored; Obtain all nodes to be monitored and establish a sequence of nodes to be monitored A1; A1 = (a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node.

[0009] In some embodiments of the present application, when generating the risk assessment value of each node to be monitored, it includes: According to the number of nodes to be monitored A1, set a 1i is the target node to be monitored; Generate a monitoring period of the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period and generate a time interval sequence T, T= (t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals in the monitoring period; Generate running deviation values ​​in each time interval; Establish the running deviation value sequence G of the target node to be monitored, G=(g1,g2…g i …g m ), where g i is the running deviation value in the i-th time interval; Generate the risk assessment value c of the target node to be monitored based on the running deviation value sequence G; Generate the risk assessment value of each monitoring point in turn; Establish a risk assessment value series C, C=(c1, c2…c i …c n1 ), where c i is the risk assessment value of the i-th node to be monitored.

[0010] In some embodiments of the present application, generating the running deviation value in each time interval includes: Establish a monitoring model for the target node to be monitored and generate multiple characteristic monitoring indicators; Generate the primary standard value of each characteristic monitoring indicator; Generate secondary standard values ​​of each characteristic monitoring indicator based on the operating parameters of all device nodes obtained at the current feedback time node; Set t according to the time interval sequence T i is the target time interval; Obtain feedback data of the target node to be monitored within the target time interval; Generate the running deviation value g of the target time interval; g=e1*Q1* µ i *(h i -h 1i ) 2 ]+e2*Q2* µ i *(h i-h 2i ) 2 ]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of characteristic monitoring indicators; µ i is the influencing factor of the i-th characteristic monitoring indicator; h i is the reference value of the i-th characteristic monitoring index of the target node to be monitored within the target time interval; 1i is the first-level standard value of the i-th characteristic monitoring indicator of the target node to be monitored; h 2i is the secondary standard value of the i-th characteristic monitoring indicator of the target node to be monitored; Generate the running deviation value of each time interval in turn.

[0011] In some embodiments of the present application, generating a risk assessment value c of a target node to be monitored includes: c=e3*Q3*[ (g i -∆g) 2 ]+e4*Q4*[ Y(i)*(g i -g') 2 ]; Wherein, 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; ∆g is the average value of all data in the running deviation value series G; g' is the running deviation value threshold; Y(i) is the selection coefficient; if (g i -g')>0,Y(i)=1; if (g i -g')<0,Y(i)=0.

[0012] In some embodiments of the present application, when a maintenance plan is generated according to all risk assessment values, it includes: Preset risk assessment value threshold C1; Determine each node to be monitored according to the risk assessment value sequence C; If ci>C1, set the i-th node to be monitored as the node to be repaired; Generate an initial maintenance strategy based on the location parameters of all nodes to be repaired, and iterate the initial maintenance strategy based on the risk assessment value of each node to be repaired; Generate a maintenance plan based on the iteration results.

[0013] In some embodiments of the present application, a distributed photovoltaic power station intelligent operation and maintenance system based on the Internet of Things is provided, including: The central control unit is used to set multiple device nodes according to the device parameters of the distributed photovoltaic power station; The monitoring unit includes multiple monitoring sub - modules, and the monitoring unit is used to collect the operation parameters of each device node; The central control unit includes: The first processing module is used to obtain the operation parameters of each device node according to the preset monitoring time node, and generate the performance evaluation value of each device node; The second processing module is used to select multiple nodes to be monitored according to all the performance evaluation values, and generate the risk evaluation value of each node to be monitored; The maintenance module is used to generate a maintenance plan according to all the risk evaluation values; The third processing module is used to establish a device node sequence A, A=(a1,a2…a i …a n ), where a i is the i - th device node; n is the number of device nodes.

[0014] In some embodiments of the present application, the first processing module is further used for: Sequentially set a i as the target device node according to the device node sequence A; Obtain the operation parameters of the target device node at the current monitoring time node; Generate the performance evaluation value b of the target device node; b = U* η i *(j i -j' i ); Where U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influence factor of the i - th performance evaluation indicator; j i is the reference value of the i - th performance evaluation indicator of the target device node at the current monitoring time node; j' i is the standard value of the i - th performance evaluation indicator; Sequentially generate the performance evaluation values of each device node; Establish a performance evaluation value sequence B at the current monitoring time node, B=(b1, b2…b i …b n ), where b i is the performance evaluation value of the i - th device node at the current monitoring time node.

[0015] In some embodiments of the present application, the second processing module is further used for: Preset a performance evaluation value threshold F1; If f i <F1, set the i - th device node at the current monitoring time node as a node to be monitored; Get all nodes to be monitored and establish a node sequence A1 to be monitored; A1=(a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node; According to the number of nodes to be monitored A1, set a 1i is the target node to be monitored; Generate a monitoring period of the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period and generate a time interval sequence T, T= (t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals in the monitoring period; Generate running deviation values ​​in each time interval; Establish the running deviation value sequence G of the target node to be monitored, G=(g1,g2…g i …g m ), where g i is the running deviation value in the i-th time interval; Generate the risk assessment value c of the target node to be monitored based on the running deviation value sequence G; Generate the risk assessment value of each monitoring point in turn; Establish a risk assessment value series C, C=(c1, c2…c i …c n1 ), where c i is the risk assessment value of the i-th node to be monitored.

[0016] Compared with the prior art, the distributed photovoltaic power station intelligent operation and maintenance method and system based on the Internet of Things in the embodiment of the present application has the following beneficial effects: By establishing an Internet of Things between each device node, the operating data of each device node is periodically collected to evaluate the operating status of each device node, providing data support for the management personnel's operation and maintenance decisions. The operation and maintenance personnel can view the status of each device and other key parameters in real time through the remote management interface, and adjust the real-time working parameters of each device node in time according to the real-time status, thereby improving the overall operation efficiency of the distributed photovoltaic power station.

[0017] By establishing multi-level monitoring, the operating status of each device node is periodically evaluated, and the device nodes with abnormal operation are screened according to the evaluation results. The monitoring parameters of each device node with abnormal operation are set, and the potential failure risks of the device nodes are diagnosed and warned in time, so as to improve the maintenance efficiency, ensure the stable operation of distributed photovoltaic power stations, improve the operation and maintenance efficiency, and reduce the operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] like Figure 1 As shown, a distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things in a preferred embodiment of the present application is characterized by comprising: S101: Setting a plurality of device nodes according to device parameters of a distributed photovoltaic power station; S102: acquiring operating parameters of each device node according to a preset monitoring time node, and generating a performance evaluation value of each device node; S103: selecting a plurality of nodes to be monitored according to all performance evaluation values, and generating a risk evaluation value for each node to be monitored; S104: Generate a maintenance plan based on all risk assessment values; When setting multiple device nodes, it includes: Create a device node array A, A=(a1,a2…a i …a n ), where a i is the i-th device node; n is the number of device nodes.

[0024] Specifically, multiple device nodes are set according to the number of devices in the distributed photovoltaic power station. PV panels, inverters, combiner boxes and other devices can all be set as a single device node; Depending on the device category corresponding to each device node, its performance evaluation indicators and feature monitoring indicators are also different.

[0025] Specifically, when generating the performance evaluation value of each device node, it includes: Set a in sequence according to the device node sequence A i is the target device node; Get the operating parameters of the target device node at the current monitoring time node; Generate a performance evaluation value b of the target device node; b=U*[ η i *(j i -j' i )]; Among them, U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influencing factor of the i-th performance evaluation index; j i is the reference value of the i-th performance evaluation index of the target device node at the current monitoring time node; j' i is the standard value of the i-th performance evaluation index; Generate the performance evaluation value of each device node in turn; Establish the performance evaluation value series B of the current monitoring time node, B=(b1, b2…b i …b n ), where b i is the performance evaluation value of the i-th device node at the current monitoring time node.

[0026] Specifically, the performance evaluation value of each device node is normalized through the conversion coefficient.

[0027] Specifically, corresponding performance evaluation indicators are set according to the device category corresponding to the target device node, and standard values of each performance evaluation indicator are set according to historical parameters.

[0028] The performance evaluation indicators include but are not limited to failure frequency, volatility of each operating parameter, cumulative operating duration, load parameter and other parameters. The larger the performance evaluation value is, the better the overall operating state of the device corresponding to the current device node is.

[0029] It can be understood that in the above embodiments, by establishing the Internet of Things between each device node, the operating data of each device node is periodically collected, so as to evaluate the operating state of each device node, provide data support for the operation and maintenance decision-making of management personnel, and the operation and maintenance personnel can view key parameters such as the status of each device in real time through the remote management interface, and adjust the real-time working parameters of each device node in time according to the real-time status, so as to improve the overall operating efficiency of the distributed photovoltaic power station.

[0030] In the preferred embodiment of the embodiment of the present application, when selecting multiple nodes to be monitored according to all performance evaluation values, it includes: Presetting a performance evaluation value threshold F1; If f i < F1, set the i-th device node as the node to be monitored at the current monitoring time node; Obtain all nodes to be monitored, and establish a sequence A1 of nodes to be monitored; A1 = (a 11 , a 12 … a 1i … a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node.

[0031] Specifically, the performance evaluation value threshold can be set according to historical parameters. If the performance evaluation value of a single device node is less than the performance evaluation value threshold, it indicates that there is an abnormal operation situation for the corresponding device node.

[0032] Specifically, when generating the risk evaluation value of each node to be monitored, it includes: Set a 1i as the target node to be monitored in sequence according to the sequence A1 of nodes to be monitored; Generate the monitoring period of the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period, and generate a time interval sequence T, T = (t1, t2… t i … t m ), where ti is the i-th time interval; m is the number of time intervals in the monitoring period; Generate running deviation values ​​in each time interval; Establish the running deviation value sequence G of the target node to be monitored, G=(g1,g2…g i …g m ), where g i is the running deviation value in the i-th time interval; Generate the risk assessment value c of the target node to be monitored based on the running deviation value sequence G; Generate the risk assessment value of each monitoring point in turn; Establish a risk assessment value series C, C=(c1, c2…c i …c n1 ), where c i is the risk assessment value of the i-th node to be monitored.

[0033] Specifically, the operating status of each device node is evaluated periodically, and device nodes with abnormal operation are screened out based on the evaluation results. Device nodes with abnormal operation are set as nodes to be monitored, and the corresponding monitoring data collection frequency and collection volume are set according to the performance evaluation value of the node to be monitored, thereby setting the corresponding monitoring period.

[0034] Specifically, according to the data collection amount of the target node to be monitored, multiple time intervals are set within the monitoring cycle, and feedback data is collected once within a single time interval. The feedback data is the real-time parameters of each characteristic monitoring indicator corresponding to the target node to be monitored.

[0035] Specifically, by dynamically adjusting the monitoring parameters of the target nodes to be monitored, the monitoring efficiency of the target nodes to be monitored is improved, and the potential failure risks of the nodes to be monitored are warned in a timely manner. Specifically, the running deviation values ​​in each time interval are generated, including: Establish a monitoring model for the target node to be monitored and generate multiple characteristic monitoring indicators; Generate the primary standard value of each characteristic monitoring indicator; Generate secondary standard values ​​of each characteristic monitoring indicator based on the operating parameters of all device nodes obtained at the current feedback time node; Set t according to the time interval sequence T i is the target time interval; Obtain feedback data of the target node to be monitored within the target time interval; Generate the running deviation value g of the target time interval; g=e1*Q1* µ i *(h i-h 1i ) 2 ]+e2*Q2* µ i *(h i -h 2i ) 2 ]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of characteristic monitoring indicators; µ i is the influencing factor of the i-th characteristic monitoring indicator; h i is the reference value of the i-th characteristic monitoring index of the target node to be monitored within the target time interval; 1i is the first-level standard value of the i-th characteristic monitoring indicator of the target node to be monitored; h 2i is the secondary standard value of the i-th characteristic monitoring indicator of the target node to be monitored; Generate the running deviation value of each time interval in turn.

[0036] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter in the model is within the same value range.

[0037] Specifically, by obtaining the operating parameters of all device nodes, the primary standard value of each characteristic monitoring indicator is corrected, thereby eliminating environmental factors and mutual interference between various devices, and improving the diagnostic accuracy of potential faults of the nodes to be monitored.

[0038] Specifically, the larger the operation deviation value is, the greater the possibility that the current target monitored node is operating abnormally.

[0039] Specifically, the risk assessment value c of the target node to be monitored is generated, including: c=e3*Q3*[ (g i -∆g) 2 ]+e4*Q4*[ Y(i)*(g i -g') 2 ]; Wherein, 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; ∆g is the average value of all data in the running deviation value series G; g' is the running deviation value threshold; Y(i) is the selection coefficient; if (g i -g')>0,Y(i)=1; if (g i -g')<0,Y(i)=0.

[0040] 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.

[0041] Specifically, the operation deviation value threshold can be set according to the historical operation parameters. When the operation deviation value is greater than the operation deviation value threshold, it indicates that the equipment corresponding to the target monitored node in the corresponding time interval has abnormal operation.

[0042] Specifically, the larger the operation risk value is, the greater the possibility that the target monitored node has a potential failure risk.

[0043] It can be understood that in the above embodiments, by dynamically adjusting the monitoring parameters of each abnormally operating device node, the potential failure risks of the device node can be diagnosed and warned in a timely manner, thereby improving the maintenance efficiency, ensuring the stable operation of the distributed photovoltaic power station, improving the operation and maintenance efficiency, and reducing the operation and maintenance costs.

[0044] In a preferred embodiment of the present application, when a maintenance plan is generated according to all risk evaluation values, it includes: Preset risk assessment value threshold C1; Determine each node to be monitored according to the risk assessment value sequence C; If ci>C1, set the i-th node to be monitored as the node to be repaired; Generate an initial maintenance strategy based on the location parameters of all nodes to be repaired, and iterate the initial maintenance strategy based on the risk assessment value of each node to be repaired; Generate a maintenance plan based on the iteration results.

[0045] Specifically, an initial maintenance strategy is generated according to the location parameters of each node to be maintained and the maintenance cost optimization principle. The initial maintenance strategy includes generating multiple maintenance paths, and setting corresponding maintenance times according to the sequence of nodes to be maintained on each maintenance path.

[0046] Specifically, according to the different risk assessment values ​​corresponding to each node to be repaired, the maintenance priority level of each node to be repaired is set, thereby optimizing the maintenance path in the initial maintenance strategy, greatly reducing the attendance of operation and maintenance personnel and reducing the overall operation and maintenance costs.

[0047] Based on another preferred embodiment of a distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things in any of the above preferred embodiments, this preferred embodiment provides a distributed photovoltaic power station intelligent operation and maintenance system based on the Internet of Things, including: The central control unit is used to set multiple device nodes according to the device parameters of the distributed photovoltaic power station; A monitoring unit, including a plurality of monitoring submodules, wherein the monitoring unit is used to collect operating parameters of each device node; The central control unit comprises: The first processing module is used to obtain the operating parameters of each device node according to the preset monitoring time node and generate the performance evaluation value of each device node; The second processing module is used to select multiple nodes to be monitored according to all performance evaluation values, and generate a risk evaluation value for each node to be monitored; Maintenance module, used to generate maintenance plans based on all risk assessment values; The third processing module is used to establish the device node sequence A, A=(a1, a2…a i …a n ), where a i is the i-th device node; n is the number of device nodes.

[0048] Specifically, the monitoring submodule is set at each device node to collect the operating parameters and monitoring data of each device node; the monitoring submodule is preferably various types of sensors, which can be set according to the type of data to be collected by the corresponding device node. The monitoring submodule can collect data including but not limited to current, weather, geographic location, light resources, etc.

[0049] Specifically, the first processing module is further used for: Set a in sequence according to the device node sequence A i is the target device node; Get the operating parameters of the target device node at the current monitoring time node; Generate a performance evaluation value b of the target device node; b=U*[ η i *(j i -j' i )]; Among them, U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influencing factor of the i-th performance evaluation index; j i is the reference value of the i-th performance evaluation index of the target device node at the current monitoring time node; j' i is the standard value of the i-th performance evaluation index; Generate the performance evaluation value of each device node in turn; Establish the performance evaluation value series B of the current monitoring time node, B=(b1, b2…b i …b n ), where b i is the performance evaluation value of the i-th device node at the current monitoring time node.

[0050] Specifically, the second processing module is further configured to: Preset a performance evaluation value threshold F1; If f i < F1, set the i-th device node as a node to be monitored at the current monitoring time node; Obtain all the nodes to be monitored and establish a sequence A1 of nodes to be monitored; A1 = (a 11 , a 12 … a 1i … a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node; 依次设定a 1i 为目标待监测节点; Generate a monitoring period for the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period and generate a sequence T of time intervals, T = (t1, t2… t i … t m ), where t i is the i-th time interval; m is the number of time intervals within the monitoring period; Generate the operation deviation values within each time interval; Establish a sequence G of operation deviation values for the target node to be monitored, G = (g1, g2… g i … g m ), where g i is the operation deviation value within the i-th time interval; Generate a risk evaluation value c for the target node to be monitored according to the sequence G of operation deviation values; Generate the risk evaluation values of each monitoring point in sequence; Establish a sequence C of risk evaluation values, C = (c1, c2… c i … c n1 ), where c i is the risk evaluation value of the i-th node to be monitored.

[0051] According to the first concept of the present application, by establishing the Internet of Things between each device node, collecting the operation data of each device node periodically, the operation state of each device node can be evaluated, providing data support for the operation and maintenance decision-making of the management personnel. The operation and maintenance personnel can view the key parameters such as the status of each device in real time through the remote management interface, and adjust the real-time working parameters of each device node in time according to the real-time status, improving the overall operation efficiency of the distributed photovoltaic power station.

[0052] According to the second concept of the present application, by establishing multi-level monitoring, the operating status of each device node is periodically evaluated, and the device nodes with abnormal operation are screened according to the evaluation results, and the monitoring parameters of each device node with abnormal operation are set, and the potential failure risks of the device nodes are diagnosed and warned in time, thereby improving the maintenance efficiency, ensuring the stable operation of the distributed photovoltaic power station, improving the operation and maintenance efficiency, and reducing the operation and maintenance costs.

[0053] 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 distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things, characterized in that: include: Set multiple device nodes according to the device parameters of the distributed photovoltaic power station; Obtain the operating parameters of each device node according to the preset monitoring time node, and generate the performance evaluation value of each device node; Select multiple nodes to be monitored according to all performance evaluation values, and generate risk evaluation values ​​for each node to be monitored; Generate maintenance plan based on all risk assessment values; When setting multiple device nodes, it includes: Create a device node array A, A=(a1,a2…a i …a n ), where a i is the i-th device node; n is the number of device nodes.

2. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things according to claim 1, characterized in that: When generating the performance evaluation value of each device node, it includes: Set a in sequence according to the device node sequence A i is the target device node; Get the operating parameters of the target device node at the current monitoring time node; Generate a performance evaluation value b of the target device node; b=U*[ η i *(j i -j' i )]; Among them, U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influencing factor of the i-th performance evaluation index; j i is the reference value of the i-th performance evaluation index of the target device node at the current monitoring time node; j' i is the standard value of the i-th performance evaluation index; Generate the performance evaluation value of each device node in turn; Establish the performance evaluation value series B of the current monitoring time node, B=(b1, b2…b i …b n ), where b i is the performance evaluation value of the i-th device node at the current monitoring time node.

3. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things as claimed in claim 2, characterized in that: When multiple nodes to be monitored are selected based on all performance evaluation values, it includes: Preset performance evaluation value threshold F1; If f i <F1, set the i-th device node as the node to be monitored at the current monitoring time node; Get all nodes to be monitored and establish a node sequence A1 to be monitored; A1=(a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node.

4. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things as claimed in claim 3 is characterized in that: When generating the risk assessment value of each node to be monitored, it includes: According to the number of nodes to be monitored A1, set a 1i is the target node to be monitored; Generate a monitoring period of the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period and generate a time interval sequence T, T= (t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals in the monitoring period; Generate running deviation values ​​in each time interval; Establish the running deviation value sequence G of the target node to be monitored, G=(g1,g2…g i …g m ), where g i is the running deviation value in the i-th time interval; Generate the risk assessment value c of the target node to be monitored based on the running deviation value sequence G; Generate the risk assessment value of each monitoring point in turn; Establish a risk assessment value series C, C=(c1, c2…c i …c n1 ), where c i is the risk assessment value of the i-th node to be monitored.

5. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things as claimed in claim 4, characterized in that: Generates running deviation values ​​for various time intervals, including: Establish a monitoring model for the target node to be monitored and generate multiple characteristic monitoring indicators; Generate the primary standard value of each characteristic monitoring indicator; Generate secondary standard values ​​of each characteristic monitoring indicator based on the operating parameters of all device nodes obtained at the current feedback time node; Set t according to the time interval sequence T i is the target time interval; Obtain feedback data of the target node to be monitored within the target time interval; Generate the running deviation value g of the target time interval; g=e1*Q1* µ i *(h i -h 1i ) 2 ]+e2*Q2* µ i *(h i -h 2i ) 2 ]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of characteristic monitoring indicators; µ i is the influencing factor of the i-th characteristic monitoring indicator; h i is the reference value of the i-th characteristic monitoring index of the target node to be monitored within the target time interval; 1i is the first-level standard value of the i-th characteristic monitoring indicator of the target node to be monitored; h 2i is the secondary standard value of the i-th characteristic monitoring indicator of the target node to be monitored; Generate the running deviation value of each time interval in turn.

6. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things according to claim 5, characterized in that: Generate the risk assessment value c of the target node to be monitored, including: c=e3*Q3*[ (g i -∆g) 2 ]+e4*Q4*[ Y(i)*(g i -g') 2 ]; Wherein, 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; ∆g is the average value of all data in the running deviation value series G; g' is the running deviation value threshold; Y(i) is the selection coefficient; if (g i -g')>0,Y(i)=1; if (g i -g')<0,Y(i)=0.

7. The distributed photovoltaic power station intelligent operation and maintenance method based on the Internet of Things according to claim 6, characterized in that: When generating a maintenance plan based on all risk assessment values, include: Preset risk assessment value threshold C1; Determine each node to be monitored according to the risk assessment value sequence C; If ci>C1, set the i-th node to be monitored as the node to be repaired; Generate an initial maintenance strategy based on the location parameters of all nodes to be repaired, and iterate the initial maintenance strategy based on the risk assessment value of each node to be repaired; Generate a maintenance plan based on the iteration results.

8. An intelligent operation and maintenance system for distributed photovoltaic power stations based on the Internet of Things, using the intelligent operation and maintenance method for distributed photovoltaic power stations based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: include: The central control unit is used to set multiple device nodes according to the device parameters of the distributed photovoltaic power station; A monitoring unit, including a plurality of monitoring submodules, wherein the monitoring unit is used to collect operating parameters of each device node; The central control unit comprises: The first processing module is used to obtain the operating parameters of each device node according to the preset monitoring time node and generate the performance evaluation value of each device node; The second processing module is used to select multiple nodes to be monitored according to all performance evaluation values, and generate a risk evaluation value for each node to be monitored; Maintenance module, used to generate maintenance plans based on all risk assessment values; The third processing module is used to establish the device node sequence A, A=(a1, a2…a i …a n ), where a i is the i-th device node; n is the number of device nodes.

9. The distributed photovoltaic power station intelligent operation and maintenance system based on the Internet of Things as claimed in claim 8, characterized in that: The first processing module is also used for: Set a in sequence according to the device node sequence A i is the target device node; Get the operating parameters of the target device node at the current monitoring time node; Generate a performance evaluation value b of the target device node; b=U*[ η i *(j i -j' i )]; Among them, U is the conversion coefficient; θ is the number of performance evaluation indicators; η i is the influencing factor of the i-th performance evaluation index; j i is the reference value of the i-th performance evaluation index of the target device node at the current monitoring time node; j' i is the standard value of the i-th performance evaluation index; Generate the performance evaluation value of each device node in turn; Establish the performance evaluation value series B of the current monitoring time node, B=(b1, b2…b i …b n ), where b i is the performance evaluation value of the i-th device node at the current monitoring time node.

10. The distributed photovoltaic power station intelligent operation and maintenance system based on the Internet of Things according to claim 9, characterized in that: The second processing module is also used for: Preset performance evaluation value threshold F1; If f i <F1, set the i-th device node as the node to be monitored at the current monitoring time node; Get all nodes to be monitored and establish a node sequence A1 to be monitored; A1=(a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th node to be monitored; n1 is the number of nodes to be monitored at the current monitoring time node; According to the number of nodes to be monitored A1, set a 1i is the target node to be monitored; Generate a monitoring period of the target node to be monitored according to the performance evaluation value of the target node to be monitored at the current feedback time node; Construct multiple time intervals within the monitoring period and generate a time interval sequence T, T= (t1, t2…t i …t m ), where t i is the i-th time interval; m is the number of time intervals in the monitoring period; Generate running deviation values ​​in each time interval; Establish the running deviation value sequence G of the target node to be monitored, G=(g1,g2…g i …g m ), where g i is the running deviation value in the i-th time interval; Generate the risk assessment value c of the target node to be monitored based on the running deviation value sequence G; Generate the risk assessment value of each monitoring point in turn; Establish a risk assessment value series C, C=(c1, c2…c i …c n1 ), where c i is the risk assessment value of the i-th node to be monitored.

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