Distributed sensing network and system for real-time monitoring of new energy power generation

Abstract

The invention provides a distributed sensing network and system for real-time monitoring of new energy power generation, and the network comprises sensor nodes which collect the operation parameters and environment data of all new energy power generation equipment in real time according to a preset sampling frequency, and carry out the preliminary data processing. The data aggregation nodes are used for collecting data from the sensor nodes and carrying out data fusion and primary processing, and the data center is used for receiving, storing, processing and analyzing the data from the data aggregation nodes; the sensor nodes, the data aggregation nodes and the data center are connected through a multi-level wireless communication network so as to achieve short-distance and low-power-consumption communication between the sensor nodes and the data aggregation nodes and long-distance and reliable communication between the data aggregation nodes and the data center. According to the invention, real-time monitoring and early warning can be carried out on new energy power generation, and stable operation of a power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation monitoring, and in particular to a distributed sensor network for real-time monitoring of renewable energy power generation and a renewable energy power generation real-time monitoring and early warning system based on the distributed sensor network. Background Art

[0002] Serving the development of clean energy and accelerating the transformation of clean and low-carbon energy are the fundamental ways to achieve sustainable energy development. Local governments attach great importance to it and require power grid companies to play their professional advantages in promoting the development of new energy, cooperating with air pollution control, and protecting water ecology. Actively serve the overall situation of regional economic and social development, promote clean, efficient, low-carbon and high-quality energy to gradually become the theme of energy transportation and consumption, form a green cultural atmosphere that promotes the consumption of clean energy, promote the construction of regional ecological civilization, and help the green development of the regional economy. Therefore, it is necessary to deepen the exploration of scientific and effective management measures, establish a rapid response process, and form a new energy flexible dispatching mechanism with multi-professional joint interaction and comprehensive measures to greatly increase the space for new energy consumption.

[0003] With the continuous development of new energy power generation technology and the continuous reduction of costs, the scale of new energy power generation has continued to expand and gradually become an important part of the power system. However, due to the intermittent, volatile and uncertain characteristics of new energy power generation, large-scale access to new energy power generation has brought greater challenges to the stable operation of the power system. Therefore, real-time monitoring and early warning of new energy power generation are needed to ensure the stable operation of the power system. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed sensor network for real-time monitoring of renewable energy power generation and a real-time monitoring and early warning system for renewable energy power generation based on the distributed sensor network in view of the deficiencies in the prior art.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] A distributed sensor network for real-time monitoring of renewable energy power generation comprises sensor nodes for real-time acquisition of operating parameters and environmental data of each renewable energy power generation device at a preset sampling frequency and preliminary data processing, data aggregation nodes for collecting data from the sensor nodes and performing data fusion and preliminary processing, and a data center for receiving, storing, processing and analyzing data from each data aggregation node; the sensor nodes, data aggregation nodes and data center are connected via a multi-level wireless communication network to achieve short-distance, low-power communication between the sensor nodes and the data aggregation nodes, and long-distance, reliable communication between the data aggregation nodes and the data center.

[0007] As a preferred technical solution of the present invention, the new energy power generation equipment includes a wind farm and a photovoltaic power farm; for a wind farm, its corresponding sensor nodes include strain sensors, vibration sensors and temperature sensors installed on wind turbine blades for monitoring the structural health and operating status of the blades, and acceleration sensors, rotation speed sensors and wind direction and speed sensors arranged in the wind turbine cabin for obtaining the dynamic operating parameters of the wind turbine; for a photovoltaic power farm, its corresponding sensor nodes include light intensity sensors and photovoltaic panel temperature sensors installed on photovoltaic panels, and current sensors and voltage sensors arranged at inverters.

[0008] As a preferred technical solution of the present invention, the sensor node is equipped with a wireless communication module, which supports ZigBee and LoRa communication technologies and has automatic networking, data encryption and verification functions, so that the sensor node can automatically join the network and make adaptive adjustments when the network topology changes; the data aggregation node is equipped with a high-performance processor and a large-capacity storage device to quickly process and store a large amount of data, and has a network management function to monitor the network status, manage sensor nodes and coordinate data transmission.

[0009] As a preferred technical solution of the present invention, the sensor nodes, data aggregation nodes and data centers use a dedicated data transmission protocol and data format to transmit data; the data transmission protocol includes data encryption, verification and retransmission, and uses data compression technology; the data format includes sensor type, collection time and data value, and is encapsulated in XML or JSON format.

[0010] As a preferred technical solution of the present invention, the data center is provided with a server, a storage device and a network device; the server has high computing power and storage capacity, and supports parallel computing and distributed storage; the server includes a data management module and a data analysis platform to realize real-time monitoring of renewable energy power generation data, historical data analysis and fault diagnosis; the server also has an external system interface to interact with the power grid dispatching system and the management system of the renewable energy power generation enterprise to realize information sharing and collaborative work.

[0011] A real-time monitoring and early warning system for renewable energy power generation based on the above-mentioned distributed sensor network comprises:

[0012] The data collection and collation module collects data on renewable energy power generation equipment monitored by the distributed sensor network, with a time span covering at least the next 72 hours of the forecast period and historical data for the same period; it also collects data on grid reserve capacity, power abandonment control target data, and historical renewable energy available power and electricity contribution rate data;

[0013] The new energy power forecast module uses a combination of multiple forecasting methods to forecast the power generation of new energy and obtain hourly power forecast values ​​for the next 72 hours;

[0014] The power grading module determines several power level intervals based on the historical data distribution of the available power and power contribution rate of new energy sources;

[0015] The early warning standard formulation module formulates early warning standards for the forecast results of renewable energy power generation based on factors such as power abandonment control targets and grid backup capacity;

[0016] The early warning response module judges the forecast results of renewable energy power generation obtained through real-time monitoring according to the determined power level and early warning standards, and triggers corresponding early warnings based on the judgment results; when an early warning is triggered, early warning information is sent to the power grid dispatching center, and the early warning information includes the early warning level, predicted power data and possible impact; for different early warning levels, the power grid dispatching center initiates corresponding emergency response measures.

[0017] As a preferred technical solution of the present invention, the new energy power generation prediction result provided by the new energy power prediction module is a weighted average or integration of the prediction results of different prediction methods; in the case of using more than three prediction methods, the errors between their prediction results and the actual values ​​are calculated respectively, and then the error distances between the two are calculated, and then based on these error distances, it is determined whether a triangle can be constructed, the triangle has three of the prediction methods as vertices and the corresponding three error distances as three sides; if such a triangle exists, the sine values ​​of the three internal angles of the triangle are calculated respectively according to the properties of trigonometric functions, and the three sine values ​​are used as the relative weights of the three prediction methods respectively, and the prediction results of the three prediction methods are weighted averaged to obtain the combined prediction result of the new energy power prediction module.

[0018] As a preferred technical solution of the present invention, the electricity grading module divides the electricity into three levels: high, medium and low. When the predicted electricity contribution rate is significantly higher than the historical average level and the available power is large, it is a high electricity level; when the predicted electricity contribution rate is close to the historical average level and the available power is in a medium range, it is a medium electricity level; when the predicted electricity contribution rate is lower than the historical average level and the available power is small, it is a low electricity level.

[0019] As a preferred technical solution of the present invention, the early warning standard formulation module, when the predicted power level is at a high level and exceeds the acceptable capacity of the power grid, is a red warning, indicating that emergency measures need to be taken to avoid excessive power abandonment; the emergency measures include adjusting the power generation plan and increasing the investment in energy storage equipment; when the predicted power level is at a medium level and close to the upper limit of the acceptable capacity of the power grid, is a yellow warning, indicating that the power grid dispatchers need to pay close attention to power changes and be prepared for adjustments; when the predicted power level is at a low level or may affect the stability of the power grid, is a blue warning, indicating that the dispatchers need to be reminded to plan the backup power supply investment or adjust the load distribution in advance.

[0020] As a preferred technical solution of the present invention, the early warning response module triggers a corresponding early warning according to preset early warning triggering thresholds and conditions.

[0021] The beneficial effects of the present invention are:

[0022] The distributed sensor network proposed in the present invention can monitor the power generation of new energy in real time, and the real-time monitoring and early warning system of new energy power generation based on the distributed sensor network can predict the power generation of new energy 72 hours in advance, formulate the power classification and early warning standards, and carry out the classification and early warning of new energy power prediction based on the characteristics of the available power-power contribution rate of new energy, combined with the power abandonment control target, power grid standby and other factors, so as to provide a basis for the flexible dispatch of power grid. The promotion and implementation of the present invention will have a positive impact on promoting the deepening reform of the national power market, promoting the healthy development of new energy, promoting the optimization of energy resource allocation, energy conservation and emission reduction, and protecting the environment nationwide. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural schematic diagram of the real-time monitoring and early warning system for renewable energy power generation of the present invention. DETAILED DESCRIPTION

[0024] To facilitate understanding of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood by those skilled in the art that the embodiments are only intended to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0025] In the description of the following embodiments, specific details such as specific system structures, technologies, etc. are provided for illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0027] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0028] In addition, in the description of the present application specification and the attached claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference to "one embodiment" or "some embodiments" described in the present application specification means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in the different parts of this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] Example 1

[0030] The present invention provides a distributed sensor network for real-time monitoring of renewable energy power generation, comprising sensor nodes for real-time collection of operating parameters and environmental data of each renewable energy power generation device at a preset sampling frequency and preliminary data processing, data aggregation nodes for collecting data from the sensor nodes and performing data fusion and preliminary processing, and a data center for receiving, storing, processing and analyzing data from each data aggregation node; the sensor nodes, the data aggregation nodes and the data center are connected through a multi-level wireless communication network to achieve short-distance, low-power communication between the sensor nodes and the data aggregation nodes, and long-distance, reliable communication between the data aggregation nodes and the data center.

[0031] Example 2

[0032] New energy power generation equipment includes wind farms and photovoltaic power farms; for wind farms, the corresponding sensor nodes include strain sensors, vibration sensors and temperature sensors installed on wind turbine blades for monitoring the structural health and operating status of the blades, as well as acceleration sensors, speed sensors and wind direction and speed sensors set in the wind turbine nacelle for obtaining the dynamic operating parameters of the wind turbine; for photovoltaic power farms, the corresponding sensor nodes include light intensity sensors and photovoltaic panel temperature sensors installed on photovoltaic panels, as well as current sensors and voltage sensors set at inverters.

[0033] Example 3

[0034] The deployment of sensor nodes and data aggregation nodes needs to comprehensively consider factors such as the monitoring needs of renewable energy power generation, environmental conditions, communication coverage, power supply, and data processing capabilities, so as to ensure that the distributed sensor network can operate efficiently and stably, and provide accurate data support for real-time monitoring and prediction of renewable energy power generation.

[0035] Regarding the deployment of sensor nodes. According to the characteristics of renewable energy power generation and the monitoring objectives, the parameters that need to be monitored include temperature, humidity, light intensity, wind speed, wind direction, etc. Different parameters may require different types of sensor nodes. For photovoltaic power generation systems, light intensity sensor nodes need to be deployed to monitor the intensity of solar radiation; for wind power generation systems, wind speed and wind direction sensor nodes need to be deployed to monitor wind conditions. However, no matter what kind of renewable energy power generation is used, sensor nodes need to be deployed as evenly as possible in the site to be able to fully monitor the conditions of the entire area. At the same time, for key equipment, parts prone to failure, or areas that have an important impact on power generation efficiency, the density of sensor nodes should be appropriately increased to provide key coverage.

[0036] The deployment of sensor nodes also needs to consider the environmental conditions of the renewable energy power generation site, including terrain, climate, and electromagnetic interference. It is necessary to select a suitable installation location and method to ensure that the sensor nodes can work normally and accurately collect data. For sensor nodes installed outdoors, take protective measures such as waterproofing, dustproofing, and lightning protection. And install sensor nodes in easily accessible locations for easy maintenance and replacement. Sensor nodes can use a pluggable design so that they can be quickly replaced in the event of a failure. It is generally necessary to consider setting up a special maintenance channel on the wind power tower to facilitate the installation of sensor nodes or consider using drones for maintenance in advance during installation.

[0037] Example 4

[0038] Regarding the layout of data aggregation nodes. According to the distribution and communication mode of sensor nodes, data aggregation nodes are usually deployed in the center of the new energy power generation site or close to the center of the network, so as to efficiently collect data from various sensor nodes.

[0039] Each sensor node is connected to the data aggregation node through wireless communication. Therefore, according to the communication coverage of the data aggregation node, if the communication distance between the sensor node and the data aggregation node is far or there are obstacles, relay nodes can be added or the position of the data aggregation node can be adjusted to ensure that all sensor nodes can be covered.

[0040] The data aggregation node can be powered by the grid, solar energy or backup batteries to ensure that it can work normally under any circumstances. In addition, multiple data aggregation nodes can be set up to back up each other, or backup data aggregation nodes can be set up in key locations to improve the reliability of the system.

[0041] The sensor nodes are equipped with wireless communication modules that support ZigBee and LoRa communication technologies and have automatic networking, data encryption and verification functions, so that the sensor nodes can automatically join the network and make adaptive adjustments when the network topology changes.

[0042] Sensor nodes, data aggregation nodes and data centers use dedicated data transmission protocols and data formats to transmit data; the data transmission protocol includes data encryption, verification and retransmission, and uses data compression technology; the data format includes sensor type, collection time and data value, and is encapsulated in XML or JSON format.

[0043] The data aggregation node performs preliminary processing on the received data to remove redundant information; uses data fusion algorithms to conduct comprehensive analysis on the data of multiple sensor nodes; uses cache technology to temporarily store data when communication is interrupted or the data center is busy, and transmits it after the network is restored. The data aggregation node is equipped with a high-performance processor and a large-capacity storage device to quickly process and store large amounts of data, and has network management functions to monitor network status, manage sensor nodes, and coordinate data transmission.

[0044] Example 5

[0045] The data center is equipped with servers, storage devices and network equipment; the servers have high computing power and storage capacity, and support parallel computing and distributed storage; the servers include data management modules and data analysis platforms to achieve real-time monitoring of renewable energy power generation data, historical data analysis and fault diagnosis; the servers also have external system interfaces to interact with the power grid dispatching system and the management system of renewable energy power generation enterprises to achieve information sharing and collaborative work.

[0046] Example 6

[0047] like Figure 1 As shown, the present invention also provides a new energy power generation real-time monitoring and early warning system based on the above-mentioned distributed sensor network, including a data collection and organization module, a new energy power forecasting module, a power classification module, an early warning standard formulation module and an early warning response module.

[0048] Example 7

[0049] The data collection and collation module collects data on renewable energy power generation equipment monitored by the distributed sensor network, including but not limited to parameters such as light intensity, wind speed, temperature, current, voltage, etc. The time span covers at least the forecast period of the next 72 hours and historical data for the same period; it organizes grid backup capacity data, power abandonment control target data, and historical renewable energy available power and electricity contribution rate data.

[0050] Example 8

[0051] The new energy power forecasting module uses a combination of multiple forecasting methods to forecast the power generation of new energy and obtain the future hourly power forecast value. In the prior art, the available forecasting methods include time series analysis, support vector regression, random forest and other relatively mature machine learning algorithms. The forecast result of new energy power generation provided by the new energy power forecasting module is a weighted average or integration of the forecast results of different forecasting methods, and the forecast results are combined. In the case of using more than three forecasting methods, the errors between their forecast results and actual values ​​are calculated respectively, and then the error distances between them are calculated. Then, based on these error distances, it is determined whether a triangle can be constructed, which uses three of the forecasting methods as vertices and the corresponding three error distances as three sides; if such a triangle exists, the sine values ​​of the three internal angles of the triangle are calculated respectively according to the properties of trigonometric functions, and the three sine values ​​are used as the relative weights of the three forecasting methods respectively. The forecast results of the three forecasting methods are weighted averaged to obtain the combined forecast result of the new energy power forecasting module.

[0052] According to the properties of triangles, the sum of any two sides must be greater than the third side to form a triangle. The three sides of the triangle represent the differences between the three methods or some distance measurement. The difference between the prediction results of two methods can be measured by indicators such as mean square error (MSE) or mean absolute error (MAE), which is used as the length of the side.

[0053] For example, prediction methods A, B, and C form such a triangle. For prediction methods A, B, and C, the errors between the predicted values ​​and the actual values ​​are recorded as E A 、EB and E C The three sides of the triangle are side AB, side BC and side CA. The length of side AB is: d AB =|E A -E B |; The length of side BC is: d BC =|E B -E C |; The length of side CA is: d CA =|E C -E A |.

[0054] The three internal angles of the triangle are ∠A, ∠B, and ∠C. According to the definition of trigonometric functions, we can get:

[0055]

[0056] The three sine values ​​are used as the relative weights of the three prediction methods A, B, and C, that is, the relative weights of prediction methods A, B, and C are: ω A = sin∠A,ω B = sin∠B,ω C = sin∠C. The prediction results of prediction methods A, B, and C are P A , P B , P C , then the combined prediction result is: Pcom=ω A P A +ω B P B +ω C P C It can be seen that using this method to determine weights can avoid extreme values ​​of weights and improve the balance of weights.

[0057] Example 9

[0058] The power grading module determines several power level intervals based on the historical data distribution of the available power and power contribution rate of new energy. The power grading module divides the power into three levels: high, medium and low. When the predicted power contribution rate is significantly higher than the historical average and the available power is large, it is a high power level; when the predicted power contribution rate is close to the historical average and the available power is in the medium range, it is a medium power level; when the predicted power contribution rate is lower than the historical average and the available power is small, it is a low power level.

[0059] Example 10

[0060] The early warning standard formulation module formulates early warning standards for the forecast results of renewable energy power generation based on factors such as power abandonment control targets and grid backup capacity. When the predicted power is at a high level and exceeds the grid's acceptable capacity (considering the power abandonment control target), it is a red warning, indicating that emergency measures need to be taken to avoid excessive power abandonment; emergency measures include adjusting the power generation plan and increasing the investment in energy storage equipment; when the predicted power is at a medium level and close to the upper limit of the grid's acceptable capacity, it is a yellow warning, indicating that the grid dispatchers need to pay close attention to power changes and be prepared for adjustments; when the predicted power is at a low level or may affect the stability of the grid, it is a blue warning, indicating that the dispatchers need to be reminded to plan the investment in backup power sources or adjust the load distribution in advance.

[0061] Example

[0062] The early warning response module judges the forecast results of renewable energy power generation obtained through real-time monitoring according to the determined power level and early warning standards, and triggers corresponding early warnings based on the judgment results; when an early warning is triggered, early warning information is sent to the power grid dispatching center, and the early warning information includes the early warning level, predicted power data and possible impact; for different early warning levels, the power grid dispatching center initiates corresponding emergency response measures.

[0063] The early warning response module triggers a corresponding early warning based on the preset early warning trigger thresholds and conditions when the predicted power level is within a specific level range for several consecutive hours or the power level change rate exceeds a certain range.

[0064] For different warning levels, the power grid dispatching center initiates corresponding emergency response measures, including: Red warning: immediately organize experts to conduct analysis, formulate detailed dispatching adjustment plans, coordinate power generation plans with new energy power generation companies, and optimize the power grid operation mode to ensure the safety and stability of the power grid; Yellow warning: strengthen the monitoring of the power grid operation status, prepare dispatching plans in advance, communicate and coordinate with relevant departments, and be prepared for possible changes; Blue warning: evaluate the availability of the power grid's backup capacity, adjust load forecasts, and reasonably arrange the startup sequence of backup power sources.

[0065] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0066] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0067] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0068] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0069] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and may also instruct the relevant hardware to complete it through a computer program. The computer program may be stored in a computer-readable storage medium, and the computer program may implement the steps of the above-mentioned method embodiments when executed by the processor. . Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0070] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed sensor network for real-time monitoring of renewable energy generation, characterized in that: It includes sensor nodes for collecting operating parameters and environmental data of each new energy power generation equipment in real time according to a preset sampling frequency and performing preliminary data processing, data aggregation nodes for collecting data from the sensor nodes and performing data fusion and preliminary processing, and a data center for receiving, storing, processing and analyzing data from each data aggregation node; the sensor nodes, data aggregation nodes and data center are connected through a multi-level wireless communication network to achieve short-distance, low-power communication between sensor nodes and data aggregation nodes, and long-distance, reliable communication between data aggregation nodes and data centers.

2. The distributed sensor network for real-time monitoring of renewable energy power generation according to claim 1, characterized in that: The new energy power generation equipment includes wind farms and photovoltaic power farms; for wind farms, the corresponding sensor nodes include strain sensors, vibration sensors and temperature sensors installed on wind turbine blades for monitoring the structural health and operating status of the blades, as well as acceleration sensors, rotation speed sensors and wind direction and speed sensors arranged in the wind turbine cabin for obtaining the dynamic operating parameters of the wind turbine; for photovoltaic power farms, the corresponding sensor nodes include light intensity sensors and photovoltaic panel temperature sensors installed on photovoltaic panels, as well as current sensors and voltage sensors arranged at inverters.

3. The distributed sensor network for real-time monitoring of renewable energy power generation according to claim 1, characterized in that: The sensor node is equipped with a wireless communication module, which supports ZigBee and LoRa communication technologies and has automatic networking, data encryption and verification functions, so that the sensor node can automatically join the network and make adaptive adjustments when the network topology changes; the data aggregation node is equipped with a high-performance processor and a large-capacity storage device to quickly process and store large amounts of data, and has a network management function to monitor network status, manage sensor nodes and coordinate data transmission.

4. The distributed sensor network for real-time monitoring of renewable energy generation according to claim 1, characterized in that: The sensor nodes, data aggregation nodes and data centers use a dedicated data transmission protocol and data format to transmit data; the data transmission protocol includes data encryption, verification and retransmission, and uses data compression technology; the data format includes sensor type, collection time and data value, and is encapsulated in XML or JSON format.

5. The distributed sensor network for real-time monitoring of renewable energy generation according to claim 1, characterized in that: The data center is equipped with servers, storage devices and network devices; the servers have high computing power and storage capacity, and support parallel computing and distributed storage; the servers include data management modules and data analysis platforms to achieve real-time monitoring of renewable energy power generation data, historical data analysis and fault diagnosis; the servers also have external system interfaces to interact with power grid dispatching systems and management systems of renewable energy power generation enterprises to achieve information sharing and collaborative work.

6. A new energy power generation real-time monitoring and early warning system based on the distributed sensor network according to any one of claims 1 to 5, characterized in that: include: The data collection and collation module collects data on renewable energy power generation equipment monitored by the distributed sensor network, with a time span covering at least the next 72 hours of forecast period and historical data for the same period; it collates data on grid reserve capacity, power abandonment control target data, and historical renewable energy available power and electricity contribution rate data; The new energy power forecast module uses a combination of multiple forecasting methods to forecast the power generation of new energy and obtain hourly power forecast values ​​for the next 72 hours; The power grading module determines several power level intervals based on the historical data distribution of the available power and power contribution rate of new energy sources; The early warning standard formulation module formulates early warning standards for the forecast results of renewable energy power generation based on factors such as power abandonment control targets and grid backup capacity; The early warning response module judges the forecast results of renewable energy power generation obtained through real-time monitoring according to the determined power level and early warning standards, and triggers corresponding early warnings according to the judgment results; When an early warning is triggered, early warning information is sent to the power grid dispatching center, which includes the warning level, predicted power data and possible impact; for different warning levels, the power grid dispatching center initiates corresponding emergency response measures.

7. The new energy power generation real-time monitoring and early warning system according to claim 6 is characterized in that: The new energy power generation prediction result provided by the new energy power prediction module is a weighted average or integration of the prediction results of different prediction methods; in the case of using more than three prediction methods, the errors between their prediction results and actual values ​​are calculated respectively, and then the error distances between them are calculated, and then based on these error distances, it is determined whether a triangle can be constructed, in which the three prediction methods are used as vertices and the corresponding three error distances are used as three sides; if such a triangle exists, the sine values ​​of the three internal angles of the triangle are calculated respectively according to the properties of trigonometric functions, and the three sine values ​​are used as the relative weights of the three prediction methods respectively, and the prediction results of the three prediction methods are weighted averaged to obtain the combined prediction result of the new energy power prediction module.

8. The new energy power generation real-time monitoring and early warning system according to claim 6 is characterized in that: The electricity grading module divides electricity into three levels: high, medium and low. When the predicted electricity contribution rate is significantly higher than the historical average level and the available power is large, it is a high electricity level; when the predicted electricity contribution rate is close to the historical average level and the available power is in a medium range, it is a medium electricity level; when the predicted electricity contribution rate is lower than the historical average level and the available power is small, it is a low electricity level.

9. The new energy power generation real-time monitoring and early warning system according to claim 8 is characterized in that: The warning standard formulation module, when the predicted power level is at a high level and exceeds the acceptable capacity of the power grid, is a red warning, indicating that emergency measures need to be taken to avoid excessive power abandonment; the emergency measures include adjusting the power generation plan and increasing the investment in energy storage equipment; When the predicted power level is at a medium level and close to the upper limit of the grid's acceptable capacity, it is a yellow warning, indicating that the grid dispatchers need to pay close attention to changes in power and be prepared for adjustments; when the predicted power level is at a low level or may have an impact on the stability of the grid, it is a blue warning, indicating that the dispatchers need to be reminded to plan the backup power supply or adjust the load distribution in advance.

10. The new energy power generation real-time monitoring and early warning system according to claim 6, characterized in that: The warning response module triggers corresponding warnings according to preset warning triggering thresholds and conditions.

Images