Method for Evaluating Performance of Regional Renewable Energy Power Generation Equipment

Abstract

The present invention discloses a method and system for evaluating the performance of regional renewable energy power generation equipment. The evaluation method includes: the power dispatching center receives or generates the need for evaluating the performance of renewable energy equipment and plans the spatial area; according to the meteorological information collection range of the unmanned aerial vehicle (UAV), the spatial area is divided into several collection blocks; the UAV independently completes or multiple UAVs cooperate to complete the meteorological data collection task and the renewable energy equipment detection task; after completing the task, the UAV returns to the UAV base station and transmits the collected data to the power dispatching center; the power dispatching center predicts the future power generation situation in the spatial area according to the collected meteorological data and the renewable energy equipment detection data. The present invention has the advantages of faster data collection, lower communication requirements for distributed execution algorithms, and better flexibility in self-organizing network cooperation, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of detection of renewable energy power generation equipment and evaluation of power generation performance in a new power system, and specifically relates to a method for evaluating the performance of regional renewable energy power generation equipment based on meteorological factors and multi-unmanned aerial vehicle cooperation. Background Art

[0002] In a new power system, renewable energy power generation equipment plays an increasingly important role, and its normal operation is directly related to the supply of power generation within the region and the accurate prediction of power load. In order to detect the performance of renewable energy power generation equipment and then evaluate and predict the power generation within the region, it is mainly necessary to start from the following two aspects:

[0003] On the one hand, collecting the meteorological conditions around the equipment is a basic task for evaluating the utilization efficiency of renewable energy and predicting future power generation. However, currently, the collection of meteorological data mainly relies on fixed meteorological observation stations and remote sensing satellites, which limits the accurate evaluation of the meteorological conditions around the equipment.

[0004] On the other hand, it is also essential to detect the renewable energy power generation equipment itself. Traditional detection methods usually rely on manual workers to reach fixed detection nodes. However, when faced with renewable energy power generation equipment with complex models and wide distribution, a large number of experienced maintenance personnel are required. In addition, many equipment are located on the top of houses or high towers, and the risks and costs of manual maintenance are extremely high.

[0005] All in all, the power generation capacity of renewable energy highly depends on meteorological conditions such as wind speed, light intensity, precipitation, and temperature. These factors not only increase the volatility of the power generation capacity but also pose challenges to the safety of the equipment. Against the background of the continuous increase in the proportion of renewable energy and the increasing dispersion of distributed power generation equipment, the requirement for detecting the equipment performance is becoming more urgent. How to combine the meteorological condition field around the renewable energy power generation equipment to achieve the performance evaluation of the renewable energy power generation equipment, and then predict the future power generation capacity and regional power load, is of great significance to the power dispatching of the new power system. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the related technologies to some extent.

[0007] The purpose of the present invention is to provide a method for evaluating the performance of regional renewable energy power generation equipment, which combines unmanned aerial vehicle cooperation technology to evaluate the performance of regional renewable energy equipment, and provides a guarantee for scientifically evaluating the performance of renewable energy power generation equipment, predicting the future power generation capacity, and pre-dispatching the regional power load.

[0008] To achieve the above object, the present invention first provides a method for evaluating the performance of regional renewable energy power generation equipment, including:

[0009] S100. The power dispatching center receives or generates a demand for evaluating the performance of renewable energy equipment, and plans a spatial area that needs to collect meteorological data and includes all renewable energy equipment to be evaluated;

[0010] S200. The power dispatching center divides the spatial area into several acquisition blocks according to the meteorological information acquisition range of the unmanned aerial vehicle (UAV). The center of each acquisition block is the corresponding acquisition point. Among them, the acquisition point corresponding to the acquisition block containing the renewable energy equipment to be evaluated is the interest acquisition point, and the rest are ordinary acquisition points;

[0011] S300. The UAV dynamically obtains the information of the current acquisition point through self-organizing network, and then plans the flight path according to the information of the acquisition point and its own electric energy reserve situation, and independently completes or multiple UAVs cooperate to complete the task of collecting meteorological data at the acquisition point and the task of detecting the renewable energy equipment at the interest point; after completing all the detection tasks at the interest points and the acquisition tasks at the acquisition points, the UAV returns to the UAV base station and transmits the collected data to the power dispatching center;

[0012] S400. The power dispatching center evaluates the operating performance of each renewable energy equipment according to the collected meteorological data and the detected data of the renewable energy equipment, and then predicts the future power generation situation in the spatial area to guide the pre-scheduling of power load.

[0013] A further preferred technical solution of the present invention is that step S200 includes:

[0014] S210. The power dispatching center dispatches several UAVs, obtains the current base station positions of all UAVs, and the effective detection ranges of the sensors of each UAV for collecting meteorological data;

[0015] S220. Taking the minimum effective detection range among the sensors of all UAVs as the standard, the spatial area is divided into several acquisition blocks;

[0016] S230. Taking the center of each acquisition block as the acquisition point, the power dispatching center takes the acquisition point corresponding to the acquisition block containing the renewable energy equipment to be evaluated as the interest acquisition point according to the distribution of the renewable energy equipment in the acquisition block, and the rest are ordinary acquisition points, and sets the task types of each acquisition block accordingly; and according to the task type, the type of the renewable energy equipment in the acquisition block, and the distance between the renewable energy equipment and the acquisition point, sets the additional task reward and the shortest acquisition duration for the corresponding acquisition block.

[0017] Preferably, in step S220, the spatial area is divided into several acquisition blocks based on the minimum effective detection range among the sensors of all the drones. The specific method is as follows:

[0018] A sphere is constructed based on the minimum effective detection range among the sensors of all the drones, and the largest rhombic dodecahedron inscribed in the sphere is determined. The spatial area planned in step S100 is filled with the rhombic dodecahedron to obtain a spatial grid evenly distributed in the spatial area, and each spatial network serves as an acquisition block.

[0019] Preferably, step S300 includes:

[0020] S310. The power dispatching center initializes the map information, determines the information of each acquisition block, and synchronizes the task map with the drone base station. The drones take off from the base station and fly to the acquisition points to perform tasks;

[0021] S320. The drones perform meteorological data acquisition tasks and renewable energy equipment detection tasks according to the task types at the acquisition points and their own power reserves. After completing the tasks at the current acquisition points, the task map is updated, and the acquisition points where the tasks have been completed are marked;

[0022] S330. During the process of the drones performing tasks or flying to the next acquisition point, at regular intervals, the drones attempt to establish communication with the base station or other drones. If communication is established, they form a network with each other. The drones exchange their own task maps with the base station or other drones, and inform their location information and the acquisition points where they are working;

[0023] S340. The drones plan the next action route according to the updated task map, and loop through steps S320 and S330.

[0024] Preferably, in step S310, the grid point information in the task map includes the following:

[0025] The acquisition points of the grid Location ; Acquisition identifier , used to mark whether the point has been acquired. If it has been acquired, it is 0; if it has not been acquired, it is 1; Interest identifier , used to mark whether the block is an interest block. If it is not an interest block, it is 0; if it is an interest block, it is 1; The minimum acquisition time at the acquisition point ; The location table of renewable energy equipment in the grid , where is the three-dimensional coordinate of the th renewable energy generation equipment, is the three-dimensional coordinate of the last renewable energy generation equipment, is the number of renewable energy power generation devices within the grid; the shortest inspection schedule for the devices within the grid , where is the shortest inspection time of the th renewable energy power generation device, is the shortest inspection time of the last renewable energy power generation device; the timestamp is used to save the last time when the status of this point was updated; the meteorological data matrix is used to store the meteorological data within this grid, and the device data matrix is used to store the detection data of each device; the grid reward value is defined as:

[0026]

[0027] where, is the self - reward, is the derived reward, is the charging incentive factor, is the basic reward for all collection points, is the reward attached to this block, is the number of surrounding collection points, is the collection flag, used to identify whether this block has been collected; is the self - reward of the th surrounding block, is the collection flag of the th surrounding block; according to the partitioning method in step S230, is usually 12 and will be different at the boundary;

[0028] Meanwhile, for the drone base stations where drone batteries can be replaced , where , is the number of drone base stations, marking the positions of the base stations , and the charging reward value is defined as:

[0029]

[0030] where, is the self - reward, is the derived reward, is the charging incentive factor, is the basic reward for replacing the battery, is the distance from the current position of the drone to the base station , is for the i th drone at the momentt The position of is the position of is the number of collection points around the block where is located.

[0031] Preferably, in step S340, when the drone plans the next action route, it determines to select one from the collection points adjacent to the current position according to its own power reserve, or return to the base station to replenish power; and designs a reward function to encourage the drone to fully and economically use the power reserve while ensuring the lowest safe power reserve;

[0032] Specifically, design the following reward function to constrain the behavior of the drone:

[0033]

[0034] Among them, is the remaining power incentive factor, is the remaining power of the drone at moment, is the lower limit of the lowest power of the drone;

[0035]

[0036] Among them, is the battery surplus penalty factor, is the minimum distance of the drone from the battery replacement base station, is the maximum flight speed of the drone, is the maximum battery capacity of the drone, is the energy consumption per unit time of flight;

[0037]

[0038] Among them, is the power shortage penalty factor, is the shortest collection duration at the collection point , is the energy consumption per unit time of hovering collection;

[0039]

[0040] Among them, is the flight stability incentive factor, is the height of the drone at moment;

[0041] In summary, the reward function for the drone to select the next action block is:

[0042]

[0043]

[0044] The constraints are as follows:

[0045] Power constraint: for each drone at any time the remaining power shall not be lower than the minimum threshold ;

[0046] Position constraint: the positions of each drone must be continuous, starting from departing, and ending at ;

[0047] Boundary constraint: when performing data collection and detection tasks, the drone shall not exceed the scope of the area;

[0048] No-interruption constraint: the tasks for each collection point need to be completed at one time without interruption;

[0049] No-duplication constraint: the rewards for collection points in each grid can only be obtained once and cannot be repeatedly obtained by drones that subsequently visit the grid;

[0050] Shortest detection time constraint: for each detection point, the detection time shall not be less than ; ( is the shortest collection duration for collection point ).

[0051] Preferably, in step S320, when the current collection point is an ordinary collection point, the drone performs a meteorological data collection task, and the specific method is as follows:

[0052] After the drone reaches the collection point according to the planned route, it collects the meteorological data of the collection block through the sensors equipped on the drone;

[0053] When the current collection point is an interesting collection point, the drone performs a meteorological data collection task and a renewable energy device detection task, and the specific method is as follows:

[0054] The drone arrives at the collection point and first performs a meteorological data collection task. During the execution of the meteorological data collection task, it plans the action route between the collection point and the renewable energy device. After the meteorological data collection task is completed, it goes to the vicinity of the renewable energy device and uses its imaging device to detect the operating state of the renewable energy device, and the detection duration is not less than the shortest collection duration.

[0055] Preferably, before the UAV executes the detection task of renewable energy equipment, during the process of planning the action route between the collection point and the renewable energy equipment, it evaluates whether it has the ability to independently complete the detection task within the power reserve limit. The minimum power requirement for this detection sequence of the UAV is:

[0056]

[0057] Wherein, is the total flight time of the path planned by the UAV within the block ; is the energy consumption per unit flight time; is the minimum detection time for the th device; is the number of renewable energy power generation devices in the current grid; is the th shortest inspection time of the renewable energy equipment; is the energy consumption per unit time for hovering collection; is the distance from the current position of the UAV to the base station ; is the position of the i nd UAV at time t ; is 's position; is the maximum flight speed of the UAV;

[0058] If is satisfied, the UAV detects the operation status of the renewable energy power generation equipment one by one according to this route, and the detection time for each equipment is not less than ; is the remaining power of the UAV at time ; is the minimum power limit of the UAV; after completing a series of detection tasks, the UAV will continue to the next action point; if is satisfied, it will send a cooperation request to other UAVs within the direct communication range, and try to dispatch the detection task of the renewable energy equipment in this collection block to other UAVs to perform joint detection.

[0059] Preferably, if the UAV does not have the ability to independently complete the detection task within the power reserve limit, the specific method for performing joint detection is:

[0060] The UAVs in this collection block send cooperation requests to other UAVs within the direct communication range, and other UAVs in the self-organizing network return their current positions , the position of the next target point , the remaining battery power and its own cooperation ability , is expressed as:

[0061]

[0062] Among them, is the participation in other detection tasks. If it is currently participating in other detection tasks or currently performing data collection, then take the value between according to the completion status of the task; if this value is 1, it means that the collection of a certain point is just completed, or the cooperation just ends, or it is flying and there is no pending detection task; is the distance between the UAV and the collection point within the block to be collaborated ; is a ratio used to adjust the influence of the distance factor and the remaining power factor on the cooperation ability, is the distance between the current position of the UAV and the position of the block that needs to cooperate ;

[0063] The UAV in this collection block determines the best cooperative UAV according to the current position of other UAVs, the position of the next target point, the remaining power reserve, and the cooperation ability, and sends the detection points to be detected to the best cooperative UAV through the ad hoc network. The specific method is as follows:

[0064] According to sort from high to low, and use the swarm intelligence optimization algorithm to plan the detection point route for the UAVs after joining. The path of each UAV should start from the current position and end at the next target point . When the power is insufficient, the UAV should be planned to return to the nearby base station in time to replace the battery. The objective function of this algorithm is:

[0065]

[0066] Among them, , are the weights of the flight time and altitude change of the UAV in the plan respectively; is the flight time of the UAV ; is the altitude change value of the UAV used to encourage the UAV to fly smoothly; is the number of UAVs included in the local path planning. The constraint conditions are:

[0067] Power constraint, each UAV at any time remaining electric energy shall not be lower than the minimum threshold and when arriving at , i.e., the electric energy shall not be lower than when reaching the next originally scheduled destination;

[0068] Position constraint: The positions of each drone must be continuous, starting from departing and ending at ;

[0069] No interruption constraint: The tasks at each detection point need to be completed at one time without interruption;

[0070] No repetition constraint: Each detection point can only be detected by one drone once;

[0071] Full coverage constraint: All detection points within the block must be fully detected in this local path planning;

[0072] Shortest detection time constraint: For each detection point, the detection time shall not be less than ;

[0073] Obtain the objective value of the plan after adding a new drone , if it satisfies:

[0074]

[0075] then add the drone to the scheduling plan, where is the planning objective value before adding this drone, and is the importance ratio of time optimization and collaboration ability; repeat this step until this equation is not satisfied, and adopt the plan corresponding to the in the last planning, and send the detection points required to be detected by each drone to the corresponding drone through the ad hoc network;

[0076] The corresponding drone, after completing the task at the current collection point, goes to the position of the received detection point to execute the detection task.

[0077] Preferably, step S400 includes:

[0078] S410. The power dispatching center calls the expected power generation model, calculates the energy efficiency and equipment capacity factor according to the detection data of renewable energy devices collected by the drones, evaluates the equipment integrity and equipment cleanliness, and conducts a fuzzy comprehensive evaluation of each renewable energy power generation device accordingly;

[0079] S420. Use the collected meteorological data to perform meteorological forecasting on the spatial area planned in step S100. The power dispatching center combines the evaluation results obtained in step S410 with the meteorological forecasting results, and uses a neural network model to predict the total power generation capacity in the future spatial area and the power load in the future area, so as to achieve pre-scheduling of power load.

[0080] On the other hand, the present invention provides a system for evaluating the performance of regional renewable energy power generation equipment, including:

[0081] A power dispatching center, a plurality of UAV base stations, and a number of UAVs arranged at the UAV base stations;

[0082] The renewable energy power generation equipment is one or more of photovoltaic power generation equipment, wind power generation equipment, and hydraulic power generation equipment;

[0083] The power dispatching center accepts or generates a demand for evaluating the performance of renewable energy equipment, plans a spatial area that needs to collect meteorological data and includes all renewable energy equipment to be evaluated; and divides the spatial area into several collection blocks according to the meteorological information collection range of the UAVs. The center of each collection block is the corresponding collection point. The collection point corresponding to the collection block containing the renewable energy equipment to be evaluated is the interest collection point, and the rest are ordinary collection points;

[0084] The UAVs dynamically obtain the information of the current collection point through self-organizing networking, and then plan the flight path according to the information of the collection point and their own power reserves, and independently complete or multiple UAVs cooperate to complete the task of collecting meteorological data at the collection points and the task of detecting renewable energy equipment at the interest points; after completing all the detection tasks at the interest points and the collection tasks at the collection points, the UAVs return to the UAV base stations and transmit the collected data to the power dispatching center;

[0085] The power dispatching center evaluates the operating performance of each renewable energy equipment according to the collected meteorological data and renewable energy equipment detection data, and then predicts the future power generation situation in the spatial area to guide the pre-scheduling of power load.

[0086] Beneficial effects: The present invention provides a method for evaluating the performance of regional renewable energy equipment combined with UAV cooperation technology, which has the advantages of faster data collection, lower communication requirements for distributed execution algorithms, and better flexibility of self-organizing networking cooperation. It solves the problems of single data collection method for traditional renewable energy equipment performance evaluation, poor adaptability to multi-model distributed systems, and inflexible response of traditional multi-UAV detection scheduling.

[0087] The present invention adopts a multi-UAV cooperation method that simultaneously completes meteorological data collection and the detection of renewable energy power generation equipment. It can specifically collect the performance data of renewable energy power generation equipment while taking into account the meteorological data collection within the region, achieving a good balance between energy conservation and comprehensive collection, and providing scientific guarantees for the performance evaluation of renewable energy power generation equipment and the pre-scheduling of regional power loads.

[0088] The present invention proposes a strategy for formulating collection points by dividing the spatial region according to the rhombic dodecahedron filling theory. It can not only make full use of the collection range of UAVs, but also ensure a high degree of regularity in the distribution of collection points. Compared with the typical cubic division, when the effective collection radius of the UAV is the same, the number of required blocks will be reduced by approximately 23%.

[0089] Through a carefully designed reward function and constraint conditions, the present invention allows UAVs to perform self-organizing networking communication, enabling UAVs to complete meteorological field data collection under poor communication conditions and more interference, and performing collaborative detection of renewable energy power generation equipment through a local swarm intelligence optimization algorithm, greatly improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a flowchart of the method for evaluating the performance of regional renewable energy power generation equipment in Embodiment 1 of the present invention;

[0091] Figure 2 It is a schematic diagram of the filling method of the spatial region of the present invention;

[0092] Figure 3 It is a schematic framework diagram of the system for evaluating the performance of regional renewable energy power generation equipment in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0093] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them, and they should not be construed as limiting the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for the purpose of description and cannot be construed as indicating or implying relative importance.

[0094] The present invention relates to the performance detection of regional renewable energy power generation equipment based on meteorological factors and multi-UAV cooperation, aiming to solve the problem of complex performance detection of renewable energy power generation equipment in a new power system.

[0095] Combining the meteorological condition field around renewable energy power generation equipment to evaluate the performance of renewable energy power generation equipment, and then predicting future power generation and regional power load, is of great significance for the power dispatching of new power systems. The implementation of this work relies on the collection of a large amount of data. The special location of renewable energy power generation equipment and its sensitivity to meteorological conditions make drones an ideal choice for this data collection work, capable of effectively collecting meteorological information. In addition, many equipment are located on the top of houses or high towers. Using drones for regular inspections (such as extraction of maintenance node information, cleanliness detection, thermal imaging scanning, etc.) is obviously a more effective solution.

[0096] Currently, the solutions for multi-drone meteorological data collection and multi-drone collaborative fault detection of renewable energy power generation equipment are mostly separate. The former tends to collect overall meteorological data, which is a supplement to the work of meteorological stations and remote sensing satellites; the latter focuses on maintenance after a fault occurs.

[0097] The present invention precisely aims at the problems existing in the performance evaluation of traditional renewable energy equipment, such as single data collection method, poor adaptability to multi-model distributed systems, and inflexible response of traditional multi-drone detection and dispatching. The proposed method and system for evaluating the performance of regional renewable energy power generation equipment can efficiently collect the meteorological field in a certain period around the renewable energy power generation equipment through the collaborative path planning of multiple drones, and conduct regular inspections on the self-condition of the renewable energy power generation equipment, realizing the efficient inspection of regional renewable energy power generation equipment. It provides a guarantee for scientifically evaluating the performance of renewable energy power generation equipment, predicting future power generation capacity and pre-dispatching of regional power load, and can also effectively cope with the challenges brought by emergencies.

[0098] Next, in combination with Figures 1 to 3 Describe the method and system for evaluating the performance of regional renewable energy power generation equipment provided by the present invention.

[0099] Embodiment 1: This embodiment provides a method for evaluating the performance of regional renewable energy power generation equipment.

[0100] Next, the evaluation method of the present invention will be described in detail. As Figure 1 shown, the specific steps are as follows:

[0101] S100. The power dispatching center accepts or generates the need for evaluating the performance of renewable energy equipment and plans the spatial area where meteorological data needs to be collected . Set the spatial area with the power dispatching area of the new power system as the division standard.

[0102] S200. The power dispatching center determines the number of drones required according to the size of the area where meteorological information needs to be collected and the meteorological information collection range of the drones , such asFigure 2 As shown, the space is filled in the way of rhombic dodecahedron packing, and the collection points are planned accordingly. The specific process includes:

[0103] S210. The power dispatching center dispatches unmanned aerial vehicles (UAVs) from two base stations, and obtains the base station positions where the UAVs are currently located , where ; , where . Collect the sensor parameters of all UAVs . For meteorological information collection, the UAVs should carry at least lidar, temperature and humidity sensors, wind speed and direction sensors, barometric pressure sensors, and radiation sensors to measure the necessary meteorological data. The minimum value of the effective detection range of these sensors is defined as .

[0104] S220. With a sphere as the radius, determine the largest rhombic dodecahedron inscribed in the sphere. The side length of this rhombic dodecahedron is . The UAVs can effectively collect the detailed meteorological data within this rhombic dodecahedron.

[0105] S230. Use this rhombic dodecahedron to pack and fill the space, and a series of uniformly distributed spatial grids can be obtained. The center of each grid is the collection point. For example, if the coordinates of a collection point are , then the coordinates of the twelve collection points around it (if not out of the boundary) are respectively:

[0106]

[0107]

[0108]

[0109]

[0110] Therefore, the actual distance between every two collection points is meters. Through this principle, the task space can be divided into a series of uniformly distributed collection blocks. In this embodiment, assuming that a collection point is located at , there are 231 collection points in this space.

[0111] S240. The power dispatching center selects the collection blocks where the renewable energy power generation equipment is located and the corresponding collection points according to the regional characteristics and the distribution of renewable energy equipment within the region As the region of interest and points of interest for this task. Each collection point should include its three-dimensional coordinates , the type of task to which the point belongs (interest collection point, ordinary collection point), as an additional task reward for the point of interest , and the shortest collection duration at each point is .

[0112] In this embodiment, the specific information on the distribution of renewable energy power generation equipment in the region is shown in Table 1:

[0113] Table 1 Distribution information of renewable energy power generation equipment

[0114]

[0115] The additional task reward for the point of interest and the shortest collection duration can be determined by the type of equipment included in the collection point , the positional difference from the collection point, and the minimum detection duration. For example, for a collection point with coordinates (-400, 2000, 100), it can be calculated by the method of unit basic reward * minimum detection duration + d (detection point, collection point), that is ; it can be calculated by the method of basic collection duration + equipment type parameter * minimum equipment detection duration, that is .

[0116] S300. The drone dynamically obtains the information of the points to be collected through self-organizing networking, collects meteorological data through the designed algorithm, and returns to the base station to replace the battery in a timely manner according to its own power reserve. After completing the sensing and collecting meteorological data in all regions of interest, the data is sent to the base station and transmitted to the power dispatching center. If the collection task is completed, it returns to its own base station. If there are devices to be detected in the region of interest, the devices to be detected are detected. If the ability is insufficient, multi-drone collaborative detection is carried out autonomously. The specific process includes:

[0117] S310. The power dispatching center initializes the map information, determines the information of each grid, and then the power dispatching center is networked with the drone base station to synchronize the task map. The drone takes off from the base station to execute the meteorological data collection task.

[0118] The grid point information in the task map includes the following content:

[0119] The collection point of the grid position ; the collection identifier used to mark whether the point has been collected (if it has been collected, it is 0; if it has not been collected, it is 1); the interest identifier Used to mark whether a block is an interesting block (0 if it is not an interesting block, 1 if it is an interesting block); the minimum collection time at the collection point ; Location table of renewable energy devices within the grid , where is the three-dimensional coordinate of the th renewable energy power generation device, is the three-dimensional coordinate of the last renewable energy power generation device, is the number of renewable energy power generation devices within the grid; Shortest inspection time table of devices within the grid , where is the shortest inspection time of the th renewable energy power generation device, is the shortest inspection time of the last renewable energy power generation device; Timestamp Used to save the last time when the status of this point was updated; Meteorological data matrix Used to store the meteorological data within this grid, Device data matrix Used to store the detection data of each device; And, considering the spatial continuity of meteorological data, the blocks around each interesting block should also be given a certain reward. Therefore, the grid reward value depends on the base reward, the self-reward composed of additional rewards as interesting points and the derived reward brought by the surrounding points to be collected , defined as:

[0120]

[0121] where is the self-reward, is the derived reward, is the charging incentive factor, is the base reward of all collection points, is the reward attached to this block, is the number of surrounding collection points, is the collection identifier, used to identify whether this block has been collected. According to the partitioning method in step S230, is usually 12 and will be different at the boundary. This derived reward can effectively attract drones to the blocks around the interesting blocks and encourage drones to form cooperation through ad hoc networking.

[0122] At the same time, for the drone base station where drones can replace their batteries, , is the number of drone base stations, Mark the location of the base station , the value of charging reward depends on the distance the drone needs to fly to replace the battery here and the derived reward brought by the collection points around the base station , defined as:

[0123]

[0124] where is the charging reward, is the derived reward, is the charging incentive factor, is the basic reward for battery replacement, is the distance from the current position of the drone to the base station , is the number of collection points around the block where it is located. This formula aims to encourage the drone to find a base station closer to itself and with higher-value collection points around when the power is insufficient for battery replacement.

[0125] S320. Every once in a while, the drone tries to form an ad-hoc network with the base station or other drones to exchange map information and collaboration information. If there are no other drones or ground base stations within the communication range, it directly executes step S330. If communication is established, after the network formation is completed, the drone exchanges the collection points in its own map with other drones and the ground base station of , and informs its own position information and the working collection points . Finally, update the of all nodes in the map based on the communication result.

[0126] To prevent two drones from going to the same collection point and even colliding, the requirements for the communication interval time are as follows:

[0127]

[0128] where is the maximum flight speed of the drone, is the maximum communication range of the drone. That is to say, the communication interval not exceeding can ensure that two drones do not go to the same node, and the communication interval not exceeding can ensure that two drones do not collide. Assuming the of the drone, , then . In actual use, this value should be about half of 14s.

[0129] At this step, the power dispatching center can send new environmental data to the UAV through the base station to adjust map information such as points of interest.

[0130] S330. The UAV generates the next action using the distributed execution mode of the multi-agent reinforcement learning algorithm according to the updated local map. During each round of decision-making, the multi-agent reinforcement learning algorithm outputs the next action based on the current state. The action space of the next action is: one can be selected from the collection points adjacent to the current position, or return to the base station for battery replacement. After the execution is completed, the state is updated to , and the next action is iteratively generated until all of which are updated to 0, that is, all interest blocks have been collected.

[0131] To encourage the UAV to perform power-saving actions when the collection is completed, the following reward function is designed to constrain the behavior of the UAV:

[0132]

[0133] Among them, is the remaining power incentive factor, is the remaining power of the UAV at state , is the minimum power lower limit of the UAV. This reward function aims to encourage the UAV to continue the task when there is sufficient power.

[0134]

[0135] Among them, is the battery surplus penalty factor, is the minimum distance of the UAV from the battery replacement base station, is the maximum battery capacity of the UAV, is the energy consumption per unit flight time. This reward aims to encourage the UAV to make full use of the battery as much as possible without returning to the base station for battery replacement too early.

[0136]

[0137] Among them, is the power shortage penalty factor, is the energy consumption per unit time for hovering collection. This reward aims to prompt the UAV to ensure that there is enough power for replenishment. should be much greater than and increase as increases.

[0138]

[0139] Among them, is the flight stability excitation factor, is the altitude of the UAV at moment. Because although the straight-line distance between two adjacent collection points is the same, the change in altitude will cause the UAV to consume additional electrical energy. This reward aims to encourage the UAV to reduce altitude changes and fly smoothly. should not be set too large.

[0140] In summary, the reward function for the UAV to select the next action block is:

[0141]

[0142]

[0143] The constraint conditions are:

[0144] · Power constraint. The remaining power of each UAV at any moment shall not be lower than the minimum threshold . .

[0145] · Position constraint. The position of each UAV must be continuous, starting from and ending at .

[0146] · Boundary constraint. The UAV should not exceed the range of the area when performing collection and detection tasks.

[0147] · No-interruption constraint. The task of each collection point needs to be completed at one time without interruption.

[0148] · No-duplication constraint. The reward of the collection point in each grid can only be obtained once and cannot be repeatedly obtained by the UAVs that subsequently visit the grid.

[0149] · Shortest detection time constraint. For each detection point, the detection time shall not be less than .

[0150] S340. After the UAV determines the action, it will fly to the target point according to the plan. If it is a collection point, after arriving at the collection point, it will use temperature and humidity sensors, wind speed and direction sensors, barometric sensors, radiation sensors, and precipitation sensors to collect meteorological data. During the collection process, if the in the current block is not empty, then step S350 is executed.

[0151] The S350 drone obtains the next action point according to the multi-agent reinforcement learning algorithm, takes the current position as the starting point and the next action point as the ending point, and calls the heuristic algorithm to plan a detection sequence. According to this detection sequence, the drone needs to evaluate whether it has the ability to independently complete this detection sequence within the power limit. The minimum power requirement for this detection sequence is:

[0152]

[0153] where, is the total flight time of the path planned by the drone in block ; is the minimum detection time for the th device. If is satisfied, the drone will use devices such as high-definition cameras and thermal imagers to detect the operation status of renewable energy power generation equipment one by one according to this route (such as the structural damage of wind turbines, the cleanliness of solar panels, and the thermal conditions of components), and the detection time for each device is not less than . After completing a series of detection tasks, the drone will continue to move to the next action point. If is not satisfied, then step S360 is executed.

[0154] S360: The drone sends a cooperation request to other drones within the directly communicable range, attempts to assign the detection points to be detected in this block to other drones, and then performs joint detection. The specific process is as follows:

[0155] S361: The drone in this block sends a cooperation request to other drones within the directly communicable range. Other drones in the ad-hoc network return the current position , the position of the next target point , the remaining power and its own cooperation ability . can be expressed as:

[0156]

[0157] where, is the participation status of other detection tasks. If it is currently participating in other detection tasks or currently performing data collection, take a value between according to the completion status of the task; if this value is 1, it means that the collection of a certain point has just been completed, or the cooperation has just ended, or it is flying and there are no pending detection tasks; is the distance between the drone and the collection point in the block to be cooperated; It is a ratio used to adjust the influence of the distance factor and the remaining power factor on the cooperation ability. If there is a UAV base station in the network, the UAV base station executes step S362; if not, the UAV in this block executes step S362.

[0158] S362. According to Sort from high to low, and use the swarm intelligence optimization algorithm to plan the detection point route for the added UAVs Among them, the path of each UAV Should start from the current position And end until the next target point When the power is insufficient, the UAV should be planned to return to the nearby base station in time to replace the battery. The objective function of this algorithm is:

[0159]

[0160] Among them, , Are respectively the weights of the flight time and altitude change of the UAVs in the plan; Is the flight time of the UAV ; Is the altitude change value of the UAV Used to encourage the UAV to fly smoothly; Is the number of UAVs included in the local path planning. The constraint conditions are:

[0161] · Power constraint. The remaining power of each UAV At any time Cannot be lower than the minimum threshold , and the power when arriving at Cannot be lower than .

[0162] · Position constraint. The position of each UAV must be continuous, starting from And ending at .

[0163] · No-interruption constraint. The task of each detection point needs to be completed at one time without interruption.

[0164] · No-duplication constraint. Each detection point can only be detected by one UAV once.

[0165] · Full-coverage constraint. All detection points in this block must be fully detected in this local path planning.

[0166] · Shortest detection time constraint. For each detection point, the detection time cannot be less than .

[0167] Obtain the target value of the solution after adding the new drone , if the following conditions are met:

[0168]

[0169] Then add the drone to the scheduling plan. Among them, is the planned target value before adding this drone, is the importance ratio of time optimization and collaboration ability. Repeat this step until the equation is not satisfied, and adopt the solution corresponding to the planned last time, and send the detection points to be detected by each drone to the corresponding drone through the ad hoc network.

[0170] S363. The drone that receives the path point starts from its own position. If it is in the acquisition state or detection state, it will start after completing the current work. After the drone arrives at the detection point according to the received path, it uses detection devices such as high-definition cameras and thermal imagers to detect the operation status of renewable energy power generation equipment one by one. According to this path planning, the drone will complete the task and successfully reach the next original destination .

[0171] S400. The power dispatching center completes the collection of regional meteorological data and the observation data of renewable energy power generation equipment in the region through the drone base station, evaluates the operation performance of each equipment, and then predicts the future power generation situation in the region to guide the pre-scheduling of power load.

[0172] S410. The power dispatching center calls the expected power generation model, calculates the expected power generation of the power generation equipment during the data collection period according to the information collected by the drone , and retrieves the actual power generation of this equipment, and calculates the power efficiency ; calculate the capacity factor according to the installed capacity of the equipment ; evaluate the integrity of the equipment (0 means the equipment is completely damaged, 1 means the equipment is intact) and the cleanliness of the equipment (0 means the equipment is severely soiled, 1 means the equipment is clean), and conduct a fuzzy comprehensive evaluation of each renewable energy power generation equipment based on these factors to scientifically evaluate the operation status of each equipment;

[0173] S420. The power dispatching center retrieves the regional weather forecast from the weather station, and combines the evaluation results of step S410 to predict the total future power generation capacity in the future region, the future power load in the region Predict through a neural network model to guide the pre-scheduling of power load.

[0174] The evaluation method of this embodiment adopts multi-UAV collaboration to collect targeted meteorological data and conduct efficient and accurate detection of renewable energy power generation equipment. The block division is carried out using the principle of filling with rhombic dodecahedrons, making full use of the meteorological data collection range of UAVs. By using the distributed execution of the multi-agent reinforcement learning strategy, UAVs can effectively dynamically plan their flight routes even under poor communication conditions. A UAV ad-hoc network mechanism is established, and a swarm intelligence optimization algorithm is introduced. The networking enhanced by combining timing and broadcast requests improves the decision-making and task allocation capabilities of UAVs, enabling multi-UAVs to achieve effective collaboration in detecting renewable energy power generation equipment and meeting the detection requirements of regional renewable energy power generation equipment under complex conditions. Combining multiple factors of the detection results, fuzzy comprehensive evaluation is used to evaluate the performance of power generation equipment. At the same time, the total future power generation capacity in the region and the future regional power load are predicted through a neural network model to guide the pre-scheduling of power load. This method provides an efficient and reliable solution for the detection of regional renewable energy power generation equipment and the pre-scheduling of power load, effectively improving the detection efficiency of regional renewable energy power generation equipment and the accuracy of power load pre-scheduling.

[0175] Embodiment 2: This embodiment provides a system for evaluating the performance of regional renewable energy power generation equipment, as Figure 3 shown, including:

[0176] A power dispatching center, multiple UAV bases, and several UAVs arranged at the UAV bases;

[0177] The renewable energy power generation equipment includes photovoltaic power generation equipment and wind power generation equipment.

[0178] The power dispatching center accepts or generates the need for evaluating the performance of renewable energy equipment, plans the spatial area that needs to collect meteorological data and includes all renewable energy equipment to be evaluated; and divides the spatial area into several collection blocks according to the meteorological information collection range of UAVs. The center of each collection block is the corresponding collection point. The collection point corresponding to the collection block containing the renewable energy equipment to be evaluated is the interest collection point, and the rest are ordinary collection points;

[0179] The UAV dynamically obtains the information of the current collection point through ad-hoc networking, and then plans the flight path according to the information of the collection point and its own electric energy reserve, and independently completes or multiple UAVs collaborate to complete the task of collecting meteorological data at the collection point and the task of detecting renewable energy equipment at the interest point; after completing all the detection tasks at the interest points and the collection tasks at the collection points, the UAVs return to the UAV base and transmit the collected data to the power dispatching center;

[0180] According to the collected meteorological data and the detection data of renewable energy equipment, the power dispatching center evaluates the operating performance of each renewable energy equipment, and then predicts the future power generation situation in the spatial area to guide the pre-dispatching of power load.

[0181] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0182] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A method for evaluating the performance of regional renewable energy power generation equipment, characterized in that: include: S100, the power dispatching center accepts or generates a demand for renewable energy equipment performance evaluation, and plans a spatial area that needs to collect meteorological data and includes all renewable energy equipment to be evaluated; S200, the power dispatching center divides the spatial area into several collection blocks according to the meteorological information collection range of the UAV, the center of each collection block is the corresponding collection point, the collection point corresponding to the collection block containing the renewable energy equipment to be evaluated is the interest collection point, and the rest are common collection points; S300, the drone dynamically obtains the information of the current point to be collected through the self-organizing network, and then plans the flight path according to the information of the point to be collected and its own power reserve, and independently completes or multiple drones cooperate to complete the meteorological data collection task of the collection point, as well as the renewable energy equipment detection task of the point of interest; After completing all the POI detection tasks and collection tasks at the collection points, the drone returns to the drone base station and transmits the collected data to the power dispatch center; Step S300 includes: S310, the power dispatching center initializes the map information, determines the information of each collection block, and synchronizes the mission map with the UAV base station. The UAV takes off from the base station and goes to the collection point to perform the mission; In step S310, the grid point information in the task map includes the following contents: The collection point p of the grid j Position(x j ,y j ,z j );Collection ID collect j ∈{0,1}, used to mark whether the point has been collected. If it has been collected, it is 0, if it has not been collected, it is 1; interest identifier interest j ∈{0,1}, used to mark whether the block is an interest block. If it is not an interest block, it is 0, and if it is an interest block, it is 1; the minimum acquisition time at the acquisition point Table of locations of renewable energy devices within the grid in, is the three-dimensional coordinate of the nth renewable energy power generation device, is the 3D coordinate of the last renewable energy generation equipment, N is the number of renewable energy generation equipment in the grid; the shortest inspection schedule of the equipment in the grid in is the shortest inspection time of the nth renewable energy power generation equipment, is the shortest inspection time of the last renewable energy power generation equipment; timestamp Used to save the last time the point status was updated; meteorological data matrix W j Used to store meteorological data within the grid, equipment data matrix D j Used to store the detection data of each device; grid reward value Defined as: in, Reward yourself, To derive rewards, R0 is the basic reward for all collection points. is the reward attached to the block, num j Yes j The number of surrounding collection points, collect j It is the collection mark, which is used to identify whether the block has been collected; is the reward of the surrounding a-th block, collect a is the collection mark of the surrounding a-th block; num j Usually 12, but can vary at zone boundaries; At the same time, the drone base station B that can be used to replace the drone battery k , where k = 1, 2, ..., K, K is the number of drone base stations, and the locations of the base stations are marked (x k ,y k ,z k ), charging reward value Defined as: in, For charging rewards, is the derived reward, δ1 is the charging incentive factor, R re It is the basic reward for replacing the battery. is from the current position of the drone to base station B k The distance is the position of the i-th UAV at time t, L k It is B k The position of k It is B k The number of collection points around the block; S320, the UAV performs the meteorological data collection task and the renewable energy equipment detection task according to the task type of the collection point and its own power reserve. After completing the task of the current collection point, the UAV updates the task map and marks the collection point that has completed the task; S330, when the drone is performing a task or heading to the next collection point, the drone attempts to establish communication with the base station or other drones at regular intervals. If communication is established, the drone and the base station or other drones are networked with each other, and the drone exchanges its own task map with the base station or other drones, and informs its own location information and the collection point it is working on; S340, the drone plans the next action route according to the updated mission map, and loops through steps S320 and S330; S400. The power dispatching center evaluates the operating performance of each renewable energy device based on the collected meteorological data and renewable energy device detection data, and then predicts the future power generation situation in the spatial area to guide the pre-dispatching of power loads.

2. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 1, characterized in that: Step S200 includes: S210, the power dispatching center dispatches a number of drones, and obtains the current base station locations of all drones, and the effective detection range of the sensors used by each drone to collect meteorological data; S220, the minimum effective detection range R of all drone sensors u As a standard, the spatial area is divided into several acquisition blocks; S230. Taking the center of each collection block as the collection point, the power dispatching center sets the collection points corresponding to the collection blocks containing the renewable energy equipment to be evaluated as the collection points of interest according to the distribution of renewable energy equipment in the collection blocks, and the rest as ordinary collection points. The task type of each collection block is set accordingly; and according to the task type, the type of renewable energy equipment in the collection block, and the distance between the renewable energy equipment and the collection point, the additional task reward and the shortest collection time of the corresponding collection block are set.

3. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 2, characterized in that: In step S220, the minimum effective detection range of all drone sensors is used as a standard to divide the space area into several acquisition blocks. The specific method is as follows: A sphere is constructed with the minimum effective detection range of all drone sensors, and the largest rhombic dodecahedron inscribed in the sphere is determined. The rhombic dodecahedron is used to fill the spatial area planned in step S100 to obtain a spatial grid evenly distributed in the spatial area, and each spatial network is used as a collection block.

4. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 1, characterized in that: In step S340, when the drone plans the next action route, it determines to select one of the collection points adjacent to the current position according to its own power reserve, or returns to the base station to replenish power; And design a reward function to encourage drones to fully and economically use power reserves while ensuring a minimum safe power reserve; Specifically design the following reward function to constrain the behavior of the drone: R2=δ2(E i (t)-E min ); Among them, δ2 is the residual energy excitation factor, E i (t) is the remaining power of the drone at time t, E min It is the minimum power limit of the drone; Among them, δ3 is the battery surplus penalty factor, min k∈K d(l i (t),L k ) is the minimum distance between the drone and the battery replacement base station, v max is the maximum flight speed of the drone, E max is the maximum battery capacity of the drone, c f is the energy consumption per unit time of flight; Among them, δ4 is the power shortage penalty factor, At the collection point p j The shortest acquisition time, c h is the energy consumption per unit time of hover collection; R5=-δ5|z t -With t-1 |; Among them, δ5 is the flight stability excitation factor, z t is the altitude of the drone at time t; In summary, the reward function for the drone to select the next action block is: R=R1+R2+R3+R4+R5; The constraints are: Power constraints, each drone U i The remaining electrical energy E at any time t i (t) cannot be lower than the minimum threshold E min ; Position constraint: the position of each drone must be continuous. Departure, arrival Finish; Boundary constraints: drones should not go beyond the scope of the area when performing collection and detection tasks; No interruption constraints. The tasks of each collection point must be completed at one time without interruption. There is no duplication constraint. The reward of each collection point in a grid can only be obtained once and cannot be obtained repeatedly by subsequent drones that visit the grid. The shortest detection time constraint: for each detection point, the detection time must not be less than is the collection point p j The shortest collection time.

5. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 1, characterized in that: In step S320, when the current collection point is a common collection point, the UAV performs the meteorological data collection task, and the specific method is as follows: After the drone arrives at the collection point according to the planned route, the sensors installed on the drone collect the meteorological data of the collection area; When the current collection point is the collection point of interest, the drone performs the meteorological data collection task and the renewable energy equipment detection task. The specific method is as follows: When the drone arrives at the collection point, it first performs the meteorological data collection task. During the execution of the meteorological data collection task, it plans the route between the collection point and the renewable energy equipment. After completing the meteorological data collection task, it goes to the vicinity of the renewable energy equipment and uses its own imaging equipment to detect the operating status of the renewable energy equipment. The detection time is not less than the shortest collection time.

6. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 5, characterized in that: Before the UAV performs the renewable energy equipment inspection task, it plans the route between the collection point and the renewable energy equipment and evaluates whether it has the ability to independently complete the inspection task within the power reserve limit. The minimum power requirement of the UAV for this inspection sequence is: Among them, t i,j is the total flight time of the UAV’s planned path in block j, c f is the energy consumption per unit time of flight, is the minimum detection time for the nth device, N is the number of renewable energy generation devices in the current grid, is the shortest inspection time of the nth renewable energy device, c h is the energy consumption per unit time of hover collection, is from the current position of the drone to base station B k The distance is the position of the i-th UAV at time t, L k It is B k The position of v max is the maximum flight speed of the drone; If satisfied The drone will test the operation of renewable energy power generation equipment one by one along this route, and the testing time for each device shall not be less than E i (t) is the remaining power of the drone at time t, E min is the minimum power limit of the drone; after completing a series of detection tasks, the drone will continue to the next action point; if It then issues a collaboration request to other drones within the direct communication range, and tries to dispatch the detection task of renewable energy equipment in this collection block to other drones to perform joint detection.

7. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 6, characterized in that: If the UAV does not have the ability to independently complete the inspection task within the power reserve limit, the specific method for performing joint inspection is as follows: The drone in this collection block sends a collaboration request to other drones within the direct communication range, and other drones in the ad hoc network return to their current locations. The location of the next target point Remaining power E i (t) and own cooperation ability C i , C i It is expressed as: Among them, ω i ∈[0,1] is the participation in other detection tasks. If it is participating in other detection tasks or is currently performing data collection, it takes a value between [0,1) according to the completion of the task; if the value is 1, it means that the collection of a certain point has just been completed, or the collaboration has just ended, or it is flying and has no detection tasks to be completed; d i,j It's a drone i and the collection point p in the block to be coordinated j distance; ε is a ratio used to adjust the influence of distance factor and residual power factor on cooperation ability, d i,j is the current position of the drone and the block p that needs to be cooperated j Position(x j ,y j ,z j ) between the two sides; The drone in this collection block determines the best cooperative drone based on the current position of other drones, the position of the next target point, the remaining power reserve, and the cooperation ability, and sends the required detection points to the best cooperative drone through the self-organizing network. The specific method is as follows: According to C i Sort from high to low, and use swarm intelligence optimization algorithm to join the UAV i The detection point route is planned after each UAV U i The path should be from the current location Set off until the next destination When the power is insufficient, the drone should be planned to return to the nearby base station in time to replace the battery; the objective function of the algorithm is: Among them, μ1 and μ2 are the weights of the UAV flight time and altitude change in the scheme respectively; t i For UAV i Flight time; ΔH i For UAV i The height change value is used to encourage the UAV to fly smoothly; I is the number of UAVs included in the local path planning; the constraints are: Power constraints, each drone U i The remaining electrical energy E at any time t i (t) cannot be lower than the minimum threshold E min , and upon arrival That is, the power consumption at the next original destination shall not be less than Position constraint: the position of each drone must be continuous. Departure, arrival Finish; No interruption constraints. The tasks of each detection point must be completed at one time without interruption. There is no duplication constraint, each detection point can only be detected once by a drone; Full coverage constraint: all detection points in the block must be fully detected in the local path planning; The shortest detection time constraint: for each detection point, the detection time must not be less than Get the target value F of the solution after adding the new drone after , if it satisfies: Then the drone U i Add to the scheduling plan, where F before is the planning target value before adding the drone, and λ is the importance ratio of time optimization to collaboration ability; repeat this step until the equation is not satisfied, and use the last planned F before The corresponding scheme is used, and the detection points required for each drone are sent to the corresponding drone through the self-organizing network; After completing the current collection point task, the corresponding drone goes to the received detection point location to perform the detection task.

8. The method for evaluating the performance of regional renewable energy power generation equipment according to claim 1, characterized in that: Step S400 includes: S410, the power dispatching center calls the expected power generation model, calculates the energy efficiency and equipment capacity factor based on the renewable energy equipment detection data collected by the drone, evaluates the equipment integrity and equipment cleanliness, and performs a fuzzy comprehensive evaluation on each renewable energy power generation equipment; S420. Use the collected meteorological data to make a weather forecast for the spatial area planned in step S100. The power dispatching center predicts the total power generation capacity and the future power load in the spatial area through a neural network model based on the meteorological forecast results and the evaluation results obtained in step S410, thereby realizing power load pre-dispatching.

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