A new energy remote centralized control method and control system

Abstract

The present invention relates to the technical field of centralized control of new energy, and specifically discloses a remote centralized control method for new energy and its control system, the method comprising: obtaining geographic information data of a new energy station sensor network, constructing an adjacency matrix of the new energy station sensor network, and analyzing the path length and data correlation between each station sensor, screening and obtaining each associated station sensor of a core monitoring station sensor; based on the monitoring data of the core monitoring station sensor and each associated station sensor, comprehensively analyzing and obtaining a station equipment fault assessment index, and assessing the station equipment fault level. The present invention screens station sensors that need to be processed, reduces data redundancy, improves the reliability of fault data, and provides a scientific basis for fault level assessment, which helps to timely discover and handle equipment faults and improve the stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy centralized control, and in particular to a new energy remote centralized control method and a control system thereof. Background Art

[0002] With the rapid development of the new energy industry, the number of new energy power plants has gradually increased, and new energy remote centralized control methods have begun to be widely used in new energy power plants. By building a remote centralized control center, new energy power plants can achieve comprehensive management of new energy projects and improve management efficiency and application capabilities.

[0003] For example, the invention patent with announcement number CN107272643B is a single-machine equivalent method for new energy stations, which involves the technical field of power system simulation modeling. The application first equates all new energy units in the new energy station to one unit, obtains a single-machine equivalent system and runs it until the fault occurs at time t0; then, the active power of the new energy station during the fault period replaces the reference value of the active power of the active power control channel of the single-machine equivalent system at the corresponding time in the time period t0~tc, and continues to run the single-machine equivalent system until the fault is cleared at time tc; finally, the active power of the new energy station after the fault is cleared replaces the active power reference value of the active power control channel of the single-machine equivalent system after the fault is cleared, until the single-machine equivalent system runs to the steady-state time tn. It is used to eliminate the equivalent error of the single-machine equivalent model of the new energy station during the fault and after the fault is cleared.

[0004] For example, the invention patent with announcement number CN112015162B is a hardware-in-the-loop test system and method for reactive voltage control systems of new energy stations. The system includes: a simulation host, a simulation target machine, an SVG controller and an AVC controller; wherein the simulation host, the SVG controller and the AVC controller are all connected to the simulation target machine; the simulation host is configured to establish a digital model, including a power grid model, a photovoltaic power station model, an IGBT module and a transformer model, and the mathematical model is downloaded to the simulation target machine after code conversion; the simulation target machine is configured to simulate the reactive voltage control system of the new energy station based on the received model code and run in real time. This application can effectively verify the effectiveness of the AVC substation control strategy and the dynamic response performance between the devices under conditions close to the actual working conditions.

[0005] Based on the above solution, it is found that there are still some shortcomings in the centralized control of new energy, which is specifically reflected in the centralized transmission of the original data collected by the sensor network. The sensor network of new energy stations is usually composed of multiple sensor nodes. These nodes will continuously collect and transmit data, resulting in a rapid accumulation of data volume. A large amount of data transmission will occupy a large amount of network resources, causing network congestion and affecting the real-time and accuracy of the data. At the same time, since the collected original data contains a large amount of repeated, invalid or low-value information, centralized transmission will reduce the system's work efficiency and have an adverse effect on the judgment of station equipment faults. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a new energy remote centralized control method and a control system thereof, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a new energy remote centralized control method, including: obtaining geographic information data of the new energy station sensor network, analyzing and obtaining the actual communication distance of each station sensor, and thereby constructing an adjacency matrix of the new energy station sensor network.

[0008] The station sensors that directly indicate the fault status are marked as core monitoring station sensors. According to the adjacency matrix of the new energy station sensor network, the path length between each station sensor and the core monitoring station sensor is analyzed, and the data correlation analysis between the core monitoring station sensor and each station sensor is performed to screen out the associated station sensors of the core monitoring station sensor.

[0009] Based on the monitoring data of the core monitoring station sensors and the sensors of each associated station, a comprehensive analysis is conducted to obtain the station equipment fault assessment index. Based on the station equipment fault assessment index, the station equipment fault level is evaluated to obtain the evaluation results and provide feedback.

[0010] As a further method, the analysis obtains the actual communication distance of each station sensor. The specific analysis process is: based on the geographic information data of the new energy station sensor network, including the installation location of each station sensor, transmission power, working area of ​​the new energy station and the area occupied by buildings of each height level.

[0011] The transmission power of each station sensor is matched with the communication distance corresponding to each transmission power stored in the new energy station database to obtain the initial communication distance of each station sensor.

[0012] Deploy several environmental monitoring points to collect the environmental humidity of the working area of ​​the new energy station at each environmental monitoring point. According to the area of ​​the working area of ​​the new energy station, the floor area of ​​buildings at each height level and the environmental humidity, a comprehensive analysis is performed to obtain the station environmental interference value. The station environmental interference value is used to quantitatively evaluate the impact of environmental factors of the new energy station on the communication distance of the station sensor.

[0013] The station environmental interference value is matched with the communication attenuation distance corresponding to the environmental interference value interval of each station stored in the new energy station database to obtain the communication attenuation distance of the working area of ​​the new energy station. The initial communication distance of each station sensor is subtracted from the communication attenuation distance of the working area of ​​the new energy station to obtain the actual communication distance of each station sensor.

[0014] As a further method, the adjacency matrix of the new energy station sensor network is constructed. The specific process is: the relative position distance between each station sensor is obtained, and the distance matrix D of the new energy station sensor network can be constructed. The element D[i][j] in the i-th row and j-th column of the matrix D represents the distance between the i-th station sensor and the j-th station sensor. The mathematical expression of the matrix D is:

[0015]

[0016] In the formula, the value of element D[i][j] can be written as d ij , d ij represents the relative position distance between the i-th field station sensor and the j-th field station sensor, i represents the row number in the matrix, j represents the column number in the matrix, i=1,2,3,...,n, j=1,2,3,...,n, n represents the total number of field station sensors.

[0017] According to the actual communication distance of the sensors at each station, the direct communication capability between the sensors at each station is evaluated.

[0018] If d ij >max(L i ,L j ), it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station.

[0019] If max(L i ,L j )≥d ij >min(L i ,L j ), it means that the field sensors with long-range communication capability can send direct signals to the field sensors with short-range communication capability, and the field sensors with short-range communication capability cannot directly transmit signals back.

[0020] If d ij≤min(L i ,L j ), it means that the i-th station sensor and the j-th station sensor can communicate directly with each other.

[0021] Among them, L i represents the actual communication distance of the i-th station sensor, L j Represents the actual communication distance of the jth station sensor.

[0022] According to the evaluation results of the direct communication capability between the sensors of each station, the adjacency matrix A of the new energy station sensor network is constructed. The element A[i][j] in the i-th row and j-th column of the matrix A represents the direct communication capability between the i-th station sensor and the j-th station sensor. The mathematical expression of the matrix A is:

[0023]

[0024] In the formula, the value of element A[i][j] can be written as a ij , a ij ∈{0,1}.

[0025] If a ij = 0, it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station. ij =1, it indicates that the i-th station sensor can communicate directly with the j-th station sensor.

[0026] As a further method, the analysis obtains the path length between each station sensor and the core monitoring station sensor. The specific analysis process is: according to the adjacency matrix of the new energy station sensor network, the number of hops between each station sensor and the core monitoring station sensor is marked as the path length between each station sensor and the core monitoring station sensor.

[0027] As a further method, data correlation analysis is performed on the core monitoring station sensors and the sensors of each station. The specific analysis process is: historical monitoring data of the core monitoring station sensors and the sensors of each station are obtained from the new energy station database, and the time series function of the core monitoring station sensors and the sensors of each station are obtained through curve fitting.

[0028] The time series functions of the core monitoring station sensors and the sensors of each station are differentiated to obtain the derivative functions of the core monitoring station sensors and the sensors of each station, and the derivative function images of the core monitoring station sensors and the sensors of each station are extracted.

[0029] The derivative function images of the core monitoring station sensors and the sensors of each station are overlapped and compared, and the core monitoring data related evaluation values ​​of the sensors of each station are obtained through comprehensive analysis. The core monitoring data related evaluation values ​​of the sensors of each station are used to quantitatively evaluate the similarity of the data change trends of the historical monitoring data of the sensors of each station and the core monitoring station sensors in the time series.

[0030] As a further method, the screening obtains the associated site sensors of the core monitoring site sensors, and the specific analysis process is: based on the path length between each site sensor and the core monitoring site sensor and the core monitoring data related evaluation value of each site sensor, a comprehensive analysis is performed to obtain the data association analysis degree index of each site sensor, and the data association analysis degree index of each site sensor is used to quantitatively evaluate the recommended degree of association analysis between the monitoring data of each site sensor and the monitoring data of the core monitoring site sensor.

[0031] The data association analysis degree index of each station sensor is compared with the data association analysis degree index threshold stored in the new energy station database. If the data association analysis degree index of a station sensor is greater than or equal to the data association analysis degree index threshold, the station sensor is marked as an associated station sensor of the core monitoring station sensor, and the associated station sensors of the core monitoring station sensor are screened.

[0032] As a further method, the comprehensive analysis obtains the site equipment fault assessment index. The specific analysis process is: obtaining the monitoring data values ​​of the core monitoring site sensors and each associated site sensors, and extracting the monitoring data reference standard values ​​of the core monitoring site sensors and each associated site sensors from the new energy site database, and comprehensively analyzing to obtain the site equipment fault assessment index.

[0033] As a further method, the station equipment fault level is evaluated according to the station equipment fault evaluation index, and the evaluation result is obtained and fed back. The specific process is: the station equipment fault evaluation index is matched with the fault level corresponding to each station equipment fault evaluation index interval stored in the new energy station database to obtain the station equipment fault level.

[0034] Feedback the site equipment fault level to relevant personnel, and perform corresponding fault maintenance measures based on the site equipment fault level.

[0035] As a further method, the station environment interference value is specifically expressed as:

[0036]

[0037] Where β represents the environmental interference value of the station, W trepresents the ambient humidity at the tth environmental monitoring point, P r represents the area of ​​the r-th height level building complex, P0 represents the working area of ​​the new energy station, W0 represents the set reference standard ambient humidity, ψ1 represents the station environmental interference factor corresponding to the set ambient humidity, ψ2 represents the station environmental interference factor corresponding to the set building density, μ r It represents the site environmental interference impact factor corresponding to the area of ​​the building complex at the rth height level. t represents the number of each environmental monitoring point, t=1,2,3,...,s, s represents the total number of environmental monitoring points, r represents the number of each height level, r=1,2,3,...,h, h represents the total number of height levels.

[0038] The second aspect of the present invention provides a new energy remote centralized control system, including: a sensor network topology construction module, which is used to obtain geographic information data of the new energy station sensor network, analyze and obtain the actual communication distance of each station sensor, and thereby construct an adjacency matrix of the new energy station sensor network.

[0039] The sensor network association analysis module is used to mark the station sensors that directly indicate the fault status as core monitoring station sensors. According to the adjacency matrix of the new energy station sensor network, the path length between each station sensor and the core monitoring station sensor is analyzed, and the data correlation analysis between the core monitoring station sensor and each station sensor is performed to screen out the associated station sensors of the core monitoring station sensor.

[0040] The comprehensive assessment module for station equipment failures is used to obtain a station equipment failure assessment index based on the monitoring data of the core monitoring station sensors and the associated station sensors, evaluate the station equipment failure level based on the station equipment failure assessment index, obtain the assessment results and provide feedback.

[0041] The new energy station database is used to store data related to the centralized control of new energy stations, including geographic information data of the new energy station sensor network, communication distances corresponding to various transmission powers, communication attenuation distances corresponding to the environmental interference value intervals of each station, historical monitoring data of the core monitoring station sensors and each station sensor, data correlation analysis degree indicator threshold, monitoring data reference standard values ​​of the core monitoring station sensors and each associated station sensor, and fault levels corresponding to the fault assessment index intervals of each station equipment.

[0042] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0043] (1) The present invention provides a new energy remote centralized control method and a control system thereof, thereby screening the station sensors that need to be processed, reducing data redundancy, and improving the reliability of fault data. At the same time, it provides a scientific basis for fault level assessment, which helps to timely discover and handle equipment failures, reduce the impact of failures on the operation of new energy stations, and improve the stability and reliability of the system.

[0044] (2) The present invention analyzes the interference situation of the station environment and determines the actual communication distance of each station sensor, thereby ensuring the stability and reliability of data during transmission and providing more reliable data support for station management.

[0045] (3) By constructing the distance matrix and adjacency matrix of the new energy station sensor network, the present invention can intuitively present the topological structure and connection relationship of the sensor network, making the network status easier to understand and analyze.

[0046] (4) The present invention can evaluate and optimize the performance of the entire network by analyzing the path length between sensors at each station. Understanding the path length between sensors can help the network select the optimal routing path and avoid long-distance or low-quality links, thereby improving the efficiency and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0048] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0049] Figure 2 It is a schematic diagram of system module connection of the present invention;

[0050] Figure 3 It is a schematic diagram of the functional relationship between the monitoring data deviation value of the core monitoring station sensor and the station equipment fault assessment index involved in an embodiment of the present invention;

[0051] Figure 4 This is the automation system platform architecture involved in the embodiments of the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0053] Reference Figure 1 As shown, the first aspect of the present invention provides a new energy remote centralized control method, including: acquiring geographic information data of the new energy site sensor network, analyzing to obtain the actual communication distance of each site sensor, and thereby constructing an adjacency matrix of the new energy site sensor network.

[0054] Specifically, the actual communication distance of each station sensor is analyzed. The specific analysis process is: based on the geographic information data of the new energy station sensor network, including the installation location of each station sensor, transmission power, working area of ​​the new energy station and the area occupied by buildings at each height level.

[0055] The transmission power of each station sensor is matched with the communication distance corresponding to each transmission power stored in the new energy station database to obtain the initial communication distance of each station sensor.

[0056] It should be understood that the transmission power of the field station sensor in this embodiment can be obtained by querying the specification information of each field station sensor from the product manual, data sheet or technical specification book, or the transmission power of each field station sensor can be collected in real time by a power meter. The transmission power and communication distance are one-to-one corresponding. The size of the transmission power directly affects the intensity and range of signal transmission. The greater the transmission power, the farther the signal can be transmitted, and the corresponding communication distance is also greater. Conduct wireless communication experiments in an actual environment, and by measuring the communication distance under different transmission powers, the actual corresponding relationship between the transmission power and the communication distance can be obtained.

[0057] Deploy several environmental monitoring points to collect the environmental humidity of the working area of ​​the new energy station at each environmental monitoring point. According to the area of ​​the working area of ​​the new energy station, the floor area of ​​buildings at each height level and the environmental humidity, a comprehensive analysis is performed to obtain the station environmental interference value. The station environmental interference value is used to quantitatively evaluate the impact of environmental factors of the new energy station on the communication distance of the station sensor.

[0058] Furthermore, the specific numerical expression of the station environment interference value is:

[0059]

[0060] Where β represents the environmental interference value of the station, W t represents the ambient humidity at the tth environmental monitoring point, Pr represents the area of ​​the r-th height level building complex, P0 represents the working area of ​​the new energy station, W0 represents the set reference standard ambient humidity, ψ1 represents the station environmental interference factor corresponding to the set ambient humidity, ψ2 represents the station environmental interference factor corresponding to the set building density, μ r It represents the site environmental interference impact factor corresponding to the area of ​​the building complex at the rth height level. t represents the number of each environmental monitoring point, t=1,2,3,...,s, s represents the total number of environmental monitoring points, r represents the number of each height level, r=1,2,3,...,h, h represents the total number of height levels.

[0061] Table 1 Example of station environmental interference value data

[0062]

[0063] It can be seen from Table 1 that the station environmental interference value is jointly determined by the environmental humidity and the area occupied by the height-level building complex. The larger the values ​​of the environmental humidity and the area occupied by the height-level building complex, the larger the corresponding station environmental interference value, indicating that the communication distance of the station sensor is more susceptible to environmental interference.

[0064] This embodiment combines the impact of two key factors, environmental humidity and building density, on the station environment, and provides a comprehensive evaluation framework. By setting different weight factors, the degree of influence of different factors on the station environmental interference value can be flexibly adjusted, so that the evaluation results are more in line with the actual situation. The formula uses a quantitative calculation method to evaluate the station environmental interference value through specific numerical values ​​and proportions, making the evaluation results more objective and comparable. Through formulaic calculations, the environmental interference conditions of different stations or the same station at different time points can be quickly evaluated, which improves the efficiency and accuracy of the evaluation and can provide strong support for environmental management during station construction and operation.

[0065] It should be understood that the reference standard ambient humidity in this embodiment can be obtained based on the suitable working ambient humidity of each station sensor in the new energy station. The suitable working ambient humidity interval of each station sensor is extracted from the specification information of the instructions for use of each station sensor, and the suitable working ambient humidity interval of each station sensor is integrated to obtain a comprehensive suitable working ambient humidity interval, ensuring that the comprehensive suitable working ambient humidity interval covers the suitable working ambient humidity interval of all station sensors, and the interval range needs to be minimized. The median of the comprehensive suitable working ambient humidity interval is extracted and marked as the reference standard ambient humidity of the new energy station.

[0066] It should be understood that in this embodiment, the range of values ​​of the station environmental interference impact factor corresponding to the ambient humidity, building group density and the floor area of ​​each building group at each height level is between 0 and 1. The building group density is obtained by calculating the ratio of the sum of the floor area of ​​the building group and the area of ​​the working area of ​​the new energy station. In this embodiment, the specific values ​​of the station environmental interference value under different ambient humidity, building group density and building groups at each height level can be obtained based on historical data, thereby constructing a mapping set of ambient humidity, building group density and building groups at each height level and station environmental interference values, and obtaining the specific values ​​of the station environmental interference impact factor corresponding to the ambient humidity, building group density and the floor area of ​​each building group at each height level.

[0067] It should be understood that in this embodiment, the ambient humidity is monitored by a humidity sensor. The ambient humidity will have a certain impact on the communication distance of the field station sensor. Humidity may indirectly affect the propagation of wireless signals by affecting the physical properties such as the dielectric constant of the propagation medium (such as air). In a high humidity environment, especially when there is water mist or raindrops, the wireless signal will encounter additional reflection and refraction, resulting in a multipath effect. The multipath effect may cause signal attenuation and phase shift, thereby reducing the signal quality at the receiving end and affecting the communication distance.

[0068] It should be understood that the height and density of the building complex in this embodiment will have an impact on the communication distance of the field station sensor. The ability of wireless signals to penetrate buildings decreases as the building height increases. The building structure of high-rise buildings will absorb or reflect wireless signals, causing signal attenuation, thereby affecting the communication distance. The higher the building height, the more obvious the blocking effect of the obstacle on the transmission of wireless signals, which may cause the communication distance to become shorter or unstable. The taller the building and the denser the building complex, the more likely the wireless signal will propagate multipath, that is, the signal reaches the receiving end through different paths, including direct paths and paths that are reflected and scattered, causing signal interference at the receiving end, affecting the signal reception quality and communication distance.

[0069] The station environmental interference value is matched with the communication attenuation distance corresponding to the environmental interference value interval of each station stored in the new energy station database to obtain the communication attenuation distance of the working area of ​​the new energy station. The initial communication distance of each station sensor is subtracted from the communication attenuation distance of the working area of ​​the new energy station to obtain the actual communication distance of each station sensor.

[0070] It should be understood that in this embodiment, there is a positive correlation between the station environment interference value and the communication attenuation distance. The larger the station environment interference value, the greater the impact of environmental factors on the station sensor communication, and the greater the communication attenuation distance. The corresponding relationship between the station environment interference value interval and the communication attenuation distance can be obtained by fitting the relationship curve based on historical data.

[0071] In a specific embodiment, when the communication distance is short or the signal transmission is unstable, data may be lost, bit-errored or delayed. In this embodiment, the actual communication distance of each station sensor is analyzed to ensure the stability and reliability of data during transmission, providing more reliable data support for station management.

[0072] It should be understood that in order to simplify the calculation model, in this embodiment, it is assumed that the environmental conditions in the entire new energy station working area have a consistent impact on communication attenuation. The actual communication attenuation distance may be affected by many factors, and different station sensors may be affected differently by communication attenuation under the same environmental conditions.

[0073] The station sensors that directly indicate the fault status are marked as core monitoring station sensors. According to the adjacency matrix of the new energy station sensor network, the path length between each station sensor and the core monitoring station sensor is analyzed, and the data correlation analysis between the core monitoring station sensor and each station sensor is performed to screen out the associated station sensors of the core monitoring station sensor.

[0074] Specifically, the adjacency matrix of the new energy station sensor network is constructed. The specific process is: the relative position distance between the sensors of each station is obtained, and the distance matrix D of the new energy station sensor network can be constructed. The element D[i][j] in the i-th row and j-th column of the matrix D represents the distance between the i-th station sensor and the j-th station sensor. The mathematical expression of the matrix D is:

[0075]

[0076] In the formula, the value of element D[i][j] can be written as d ij , d ij represents the relative position distance between the i-th field station sensor and the j-th field station sensor, i represents the row number in the matrix, j represents the column number in the matrix, i=1,2,3,...,n, j=1,2,3,...,n, n represents the total number of field station sensors.

[0077] It should be understood that the relative position distance between the sensors of each station can be obtained by using a rangefinder according to the installation position of the sensors of each station. In this embodiment, the distance matrix of the new energy station sensor network is symmetrical, that is, the distance from the i-th sensor to the j-th sensor is the same as the distance from the j-th sensor to the i-th sensor.

[0078] According to the actual communication distance of the sensors at each station, the direct communication capability between the sensors at each station is evaluated.

[0079] If d ij >max(L i ,Lj ), it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station.

[0080] In this embodiment, max(L i ,L j ) means taking L i and L j It is understandable that if L i >L j , then max(L i ,L j )=L i ; If L i <L j , then max(L i ,L j )=L j ; If L i =L j , then max(L i ,L j )=L i =L j .

[0081] If max(L i ,L j )≥d ij >min(L i ,L j ), it means that the field station sensor with a larger actual communication distance can send a direct communication signal to the field station sensor with a smaller actual communication distance, but the latter with weaker communication capability cannot directly communicate with the former in the reverse direction.

[0082] In this embodiment, min(L i ,L j ) means taking L i and L j It is understandable that if L i <L j , then min(L i ,L j )=L i ; If L i >L j , then min(L i ,L j )=L j ; If L i =L j , then min(L i ,L j )=L i =L j .

[0083] In a specific embodiment, if L i >L j , it means that the i-th station sensor can communicate directly with the j-th station sensor, but the j-th station sensor cannot communicate directly with the i-th station sensor.

[0084] If d ij ≤min(L i ,L j ), it means that the i-th station sensor and the j-th station sensor can communicate directly with each other.

[0085] Among them, L i represents the actual communication distance of the i-th station sensor, L j Represents the actual communication distance of the jth station sensor.

[0086] According to the evaluation results of the direct communication capability between the sensors of each station, the adjacency matrix A of the new energy station sensor network is constructed. The element A[i][j] in the i-th row and j-th column of the matrix A represents the direct communication capability between the i-th station sensor and the j-th station sensor. The mathematical expression of the matrix A is:

[0087]

[0088] In the formula, the value of element A[i][j] can be written as a ij , a ij ∈{0,1}.

[0089] If a ij = 0, it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station. ij =1, it indicates that the i-th station sensor can communicate directly with the j-th station sensor.

[0090] It should be understood that the communication between the sensors at the stations in this embodiment is directional, and the adjacency matrix A is usually not symmetrical, that is, a ij and a ji The value of may be different, representing the communication capability from the i-th sensor to the j-th sensor and from the j-th sensor to the i-th sensor, respectively.

[0091] In a specific embodiment, the adjacency matrix is ​​a two-dimensional matrix, in which rows and columns represent nodes in the sensor network. The elements in the matrix represent the connection relationship between the nodes. This representation method can intuitively reflect whether there is a connection between any two nodes in the network, and can intuitively present the topological structure and connection relationship of the sensor network, making the network status easier to understand and analyze.

[0092] Specifically, the path length between each station sensor and the core monitoring station sensor is analyzed and obtained. The specific analysis process is: according to the adjacency matrix of the new energy station sensor network, the number of hops between each station sensor and the core monitoring station sensor is marked as the path length between each station sensor and the core monitoring station sensor.

[0093] It should be understood that the number of hops between each site sensor and the core monitoring site sensor in this embodiment is the number of intermediate sensors that need to be passed from each site sensor to the core monitoring site sensor when each site sensor communicates directly or indirectly with the core monitoring site sensor plus 1 to include the source sensor and the target sensor itself. In an undirected unweighted graph, it is equivalent to the number of edges in the path.

[0094] It should be understood that the path length between each site sensor that can communicate directly and the core monitoring site sensor is 1, and each site sensor that can communicate directly or indirectly with the core monitoring site sensor is extracted, and the path length between each site sensor that cannot communicate directly or indirectly and the core monitoring site sensor is marked as infinity.

[0095] It should be understood that in this embodiment, if a certain station sensor can communicate directly or indirectly with the core monitoring station sensor, it means that the core monitoring station sensor can directly receive or indirectly receive the transmission signal of the station sensor through other station sensors.

[0096] In this embodiment, the path length between each station sensor and the core monitoring station sensor can also be obtained by the Dijkstra algorithm. The Dijkstra algorithm accepts an adjacency matrix and a starting sensor number as input, and returns a dictionary, where the key in the dictionary is the sensor number and the value is the shortest path length from the starting sensor to the core monitoring station sensor.

[0097] In a specific embodiment, path length is one of the important indicators for evaluating the performance of a sensor network. A shorter path length means a lower delay in data transmission, thereby improving the real-time performance and response speed of the network. By obtaining the path length between sensors, the performance of the entire network can be evaluated and optimized. Knowing the path length between sensors can help the network select the optimal routing path and avoid long-distance or low-quality links, thereby improving the efficiency and reliability of data transmission.

[0098] Specifically, a data correlation analysis is performed on the core monitoring station sensors and the sensors of each station. The specific analysis process is: the historical monitoring data of the core monitoring station sensors and the sensors of each station are obtained from the new energy station database, and the time series function of the core monitoring station sensors and the sensors of each station are obtained through curve fitting.

[0099] It should be understood that in this embodiment, curve fitting is performed on historical monitoring data. First, a scatter plot is drawn based on the historical monitoring data, and a suitable curve type is selected to describe the distribution and trend of the data. Then, an appropriate function expression (such as a linear model, a nonlinear model) is selected as the fitting model. The goodness of fit is evaluated by using appropriate standards (such as minimizing the sum of squared residuals) to ensure that the fitting curve can be as close to or fit the known data as possible.

[0100] The time series functions of the core monitoring station sensors and the sensors of each station are differentiated to obtain the derivative functions of the core monitoring station sensors and the sensors of each station, and the derivative function images of the core monitoring station sensors and the sensors of each station are extracted.

[0101] The derivative function images of the core monitoring station sensors and the sensors of each station are overlapped and compared, and the core monitoring data related evaluation values ​​of the sensors of each station are obtained through comprehensive analysis. The core monitoring data related evaluation values ​​of the sensors of each station are used to quantitatively evaluate the similarity of the data change trends of the historical monitoring data of the sensors of each station and the core monitoring station sensors in the time series.

[0102] In a specific embodiment, the core monitoring data related evaluation value of each station sensor is obtained in the set detection cycle, and the numerical expression is:

[0103]

[0104] In the formula, χ q represents the core monitoring data related evaluation value of the qth station sensor, T1′ q (k) represents the historical monitoring data value of the qth site sensor over time k, T0′(k) represents the historical monitoring data value of the core monitoring site sensor over time k, k represents the time variable, k∈[a,b], a represents the starting time point of the detection cycle, b represents the ending time point of the detection cycle, q represents the number of the sensor at each site, q=1,2,3,...,n, n represents the total number of site sensors.

[0105] It should be understood that the time unit of the detection period set in this embodiment can be days, months, years, etc. The longer the detection period, the more reliable the data correlation analysis result.

[0106] In this embodiment, by calculating the difference between the two integral terms, the similarity between the sensors of each station and the sensors of the core monitoring station in historical monitoring data can be quantitatively evaluated. This quantitative evaluation method is more accurate and objective than traditional qualitative analysis. Since the integral operation takes into account the data within the entire detection cycle, the formula can reflect the overall change trend of the data in the time series, which can help analyze sensor performance, detect abnormal data or predict future trends. A and b in the formula can be set according to actual needs, so as to flexibly adjust the length of the detection cycle, so that the formula can be applied to the evaluation needs of different scenarios and time scales. By comparing the evaluation values ​​related to the core monitoring data of sensors at different stations, the station sensors with the most similar data change trends to the core monitoring station sensors can be identified, which provides strong support for subsequent decision-making, such as optimizing sensor layout, improving the accuracy and reliability of the monitoring system, etc.

[0107] Specifically, the associated site sensors of the core monitoring site sensors are screened, and the specific analysis process is: according to the path length between each site sensor and the core monitoring site sensor and the core monitoring data related evaluation value of each site sensor, a comprehensive analysis is performed to obtain the data association analysis degree index of each site sensor, and the data association analysis degree index of each site sensor is used to quantitatively evaluate the recommended degree of association analysis between the monitoring data of each site sensor and the monitoring data of the core monitoring site sensor.

[0108] In a specific embodiment, the weighted sum of the path length and the core monitoring data related evaluation value can be used to obtain the data association analysis degree index of each station sensor. The weights of the path length and the core monitoring data related evaluation value corresponding to different station sensors are in the range of 0 to 1. The influence of the path length and the core monitoring data related evaluation value on the data association analysis degree index can be obtained by linear fitting the historical data, and the calculation weights of the data association analysis degree index corresponding to the path length and the core monitoring data related evaluation value can be determined.

[0109] The data association analysis degree index of each station sensor is compared with the data association analysis degree index threshold stored in the new energy station database. If the data association analysis degree index of a station sensor is greater than or equal to the data association analysis degree index threshold, the station sensor is marked as an associated station sensor of the core monitoring station sensor, and the associated station sensors of the core monitoring station sensor are screened.

[0110] In a specific embodiment, the associated site sensors of the core monitoring site sensors are screened to provide information directly related to the core monitoring site sensors, making data collection more targeted, reducing the collection and processing of redundant data, and thus improving the efficiency and quality of data integration.

[0111] Based on the monitoring data of the core monitoring station sensors and the sensors of each associated station, a comprehensive analysis is conducted to obtain the station equipment fault assessment index. Based on the station equipment fault assessment index, the station equipment fault level is evaluated to obtain the evaluation results and provide feedback.

[0112] Specifically, a comprehensive analysis is performed to obtain the site equipment fault assessment index. The specific analysis process is as follows: the monitoring data values ​​of the core monitoring site sensors and the associated site sensors are obtained, and the monitoring data reference standard values ​​of the core monitoring site sensors and the associated site sensors are extracted from the new energy site database, and the site equipment fault assessment index is obtained through comprehensive analysis.

[0113] It should be understood that the site equipment failure assessment index in this embodiment is used to quantitatively assess the failure risk level of the site equipment.

[0114] In a specific embodiment, the numerical expression of the field station equipment fault assessment index is:

[0115]

[0116] In the formula, represents the station equipment failure assessment index, B G→1 Indicates the monitoring data value of the core monitoring station sensor, B G→0 Indicates the reference standard value of the monitoring data of the core monitoring station sensor, ΔB G Indicates the allowable deviation value of the monitoring data of the core monitoring station sensor, B p→1 represents the monitoring data value of the pth associated station sensor, B p→0 Indicates the reference standard value of the monitoring data of the pth associated station sensor, ΔB p represents the allowable deviation value of the monitoring data of the pth associated station sensor, χ p represents the core monitoring data related evaluation value of the pth associated station sensor, p represents the number of each associated station sensor, p=1,2,3,...,f, f represents the total number of associated station sensors.

[0117] In this embodiment, the allowable deviation value of the monitoring data of the core monitoring station sensor and each associated station sensor can be obtained by acquiring the specification information of the station sensor.

[0118] like Figure 3 As shown, if B p→0The value is 10, ΔB p The value is 1, when B p→1 =9.9, the functional relationship between the monitoring data deviation value of the core monitoring station sensor and the station equipment fault assessment index is shown in curve a; when B p→1 =10, the functional relationship between the monitoring data deviation value of the core monitoring station sensor and the station equipment fault assessment index is shown in curve b; when B p→1 =10.2, the functional relationship between the monitoring data deviation value of the core monitoring station sensor and the station equipment fault assessment index is shown in curve c. The monitoring data deviation value can be obtained by taking the absolute value of the difference between the monitoring data value and the reference standard value of the monitoring data. The value of the station equipment fault assessment index is jointly determined by the monitoring data deviation value of the core monitoring station sensor and the monitoring data deviation value of the associated station sensor. The larger the monitoring data deviation value of the core monitoring station sensor and the monitoring data deviation value of the associated station sensor, the larger the corresponding station equipment fault assessment index, indicating that the risk of station equipment failure is higher.

[0119] In this embodiment, not only the data of the core monitoring station is considered, but also the data of all associated station sensors are integrated, so that the fault status of the equipment can be evaluated more comprehensively. By calculating the ratio of the core monitoring data-related evaluation value of each associated station sensor to the sum of the core monitoring data-related evaluation values, different weights can be assigned to each associated station sensor according to its importance or influence, so that the evaluation results are more in line with the actual situation. The ratio of the absolute difference to the allowable deviation value is used for standardization, so that the data between different sensors are comparable, reducing the complexity of data processing. Using the tanh function as the output, the evaluation index The value range of is limited to between -1 and 1, which is easy to understand and apply. This formula can more accurately evaluate the fault status of station equipment and provide strong support for subsequent maintenance and management.

[0120] Specifically, the station equipment fault level is evaluated according to the station equipment fault evaluation index, and the evaluation result is obtained and fed back. The specific process is: the station equipment fault evaluation index is matched with the fault level corresponding to each station equipment fault evaluation index interval stored in the new energy station database to obtain the station equipment fault level.

[0121] It should be understood that in this embodiment, there is a one-to-one correspondence between the site equipment fault assessment index interval and the fault level, and the specific correspondence can be obtained by establishing a mapping table of the site equipment fault assessment index interval and the fault level based on historical data.

[0122] Feedback the site equipment fault level to relevant personnel, and perform corresponding fault maintenance measures based on the site equipment fault level.

[0123] It should be understood that in this embodiment, feedback can be provided through email, work order system, online chat tool or professional fault reporting platform, and the feedback report needs to provide a detailed fault description, the degree of impact on system operation and possible guesses on the cause of the fault.

[0124] According to the equipment fault classification standards, such as level one fault, level two fault, level three fault, and level four fault. Level one fault is a serious fault, and the operation of the faulty equipment should be stopped immediately to prevent the fault from further expanding, and professional technicians should be organized to troubleshoot and repair the fault; level two faults are moderate faults, and the operation of the faulty equipment should be suspended to reduce its impact on the system, and maintenance personnel should be organized to handle the fault as soon as possible, and the problem can be solved by simple adjustments or replacement of some parts; level three faults are minor faults, and the faulty equipment should be continuously monitored and observed, and the fault phenomenon and changes should be recorded. At the next planned maintenance, the faulty equipment should be inspected and repaired in detail to solve the fault problem; level four faults are minor faults, and the fault phenomenon and the operating status of the equipment should be recorded for subsequent analysis and improvement. At the next maintenance of the equipment, pay attention to checking related components to prevent similar faults from happening again.

[0125] Reference Figure 2 As shown, the second aspect of the present invention provides a new energy remote centralized control system, including: a sensor network topology construction module, used to obtain geographic information data of the new energy station sensor network, analyze and obtain the actual communication distance of each station sensor, and thereby construct an adjacency matrix of the new energy station sensor network.

[0126] The sensor network association analysis module is used to mark the station sensors that directly indicate the fault status as core monitoring station sensors. According to the adjacency matrix of the new energy station sensor network, the path length between each station sensor and the core monitoring station sensor is analyzed, and the data correlation analysis between the core monitoring station sensor and each station sensor is performed to screen out the associated station sensors of the core monitoring station sensor.

[0127] The comprehensive assessment module for station equipment failures is used to obtain a station equipment failure assessment index based on the monitoring data of the core monitoring station sensors and the associated station sensors, evaluate the station equipment failure level based on the station equipment failure assessment index, obtain the assessment results and provide feedback.

[0128] The new energy station database is used to store data related to the centralized control of new energy stations, including geographic information data of the new energy station sensor network, communication distances corresponding to various transmission powers, communication attenuation distances corresponding to the environmental interference value intervals of each station, historical monitoring data of the core monitoring station sensors and each station sensor, data correlation analysis degree indicator threshold, monitoring data reference standard values ​​of the core monitoring station sensors and each associated station sensor, and fault levels corresponding to the fault assessment index intervals of each station equipment.

[0129] It should be understood that the data in the new energy station database in this embodiment is obtained by collecting experimental data, and can also be obtained by consulting relevant professional literature.

[0130] In a specific embodiment, Figure 4 As shown, a new energy remote centralized control system also provides an automation system platform. The automation system platform provides a two-layer architecture of system platform and application platform with the support of computing infrastructure consisting of computer hardware, operating system and general database management system. The system platform shields the basic software functions such as network communication and data persistence from the upper-layer software, and provides the upper-layer with basic functions such as hierarchical distributed real-time data access, file transfer and synchronization, real-time status information synchronization, historical data access, application log, etc. The upper-layer software can transparently access all these functions and then create system management, human-machine interface, information release and various data collection and monitoring control system applications including events / alarms, reports, and statistics, thereby building a comprehensive general monitoring system.

[0131] In a specific embodiment, the present invention provides a new energy remote centralized control method and a control system thereof, which screens site sensors that require data processing, reduces data redundancy, and improves the reliability of fault data. At the same time, it provides a scientific basis for fault level assessment, helps to promptly discover and handle equipment failures, reduce the impact of failures on the operation of new energy sites, and improve the stability and reliability of the system.

[0132] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A new energy remote centralized control method, characterized in that: include: Obtain geographic information data of the new energy station sensor network, analyze and obtain the actual communication distance of each station sensor, and thus construct the adjacency matrix of the new energy station sensor network; The station sensors that directly indicate the fault status are marked as core monitoring station sensors. According to the adjacency matrix of the new energy station sensor network, the path length between each station sensor and the core monitoring station sensor is analyzed, and the data correlation analysis between the core monitoring station sensor and each station sensor is performed to screen out the associated station sensors of the core monitoring station sensor. Based on the monitoring data of the core monitoring station sensors and the sensors of each associated station, a comprehensive analysis is conducted to obtain the station equipment fault assessment index. Based on the station equipment fault assessment index, the station equipment fault level is assessed, and the assessment results are obtained and fed back. The analysis obtains the actual communication distance of each station sensor, and the specific analysis process is: according to the geographic information data of the new energy station sensor network, including the installation location of each station sensor, the transmission power, the working area of ​​the new energy station and the area occupied by the building complex at each height level; Match the transmission power of each station sensor with the communication distance corresponding to each transmission power stored in the new energy station database to obtain the initial communication distance of each station sensor; Deploy several environmental monitoring points to collect the environmental humidity of the working area of ​​the new energy station at each environmental monitoring point. According to the area of ​​the working area of ​​the new energy station, the floor area of ​​the buildings at each height level and the environmental humidity, a comprehensive analysis is performed to obtain the environmental interference value of the station. The environmental interference value of the station is used to quantitatively evaluate the impact of environmental factors of the new energy station on the communication distance of the station sensor. Match the station environmental interference value with the communication attenuation distance corresponding to the environmental interference value interval of each station stored in the new energy station database to obtain the communication attenuation distance of the working area of ​​the new energy station, and perform a difference operation between the initial communication distance of each station sensor and the communication attenuation distance of the working area of ​​the new energy station to obtain the actual communication distance of each station sensor; The specific process of constructing the adjacency matrix of the new energy station sensor network is as follows: obtaining the relative position distances between the sensors of each station, constructing the distance matrix D of the new energy station sensor network, the element D[i][j] in the i-th row and j-th column of the matrix D represents the distance between the i-th station sensor and the j-th station sensor, and the mathematical expression of the matrix D is: In the formula, the value of element D[i][j] can be written as d ij , d ij represents the relative position distance between the i-th field station sensor and the j-th field station sensor, i represents the row number in the matrix, j represents the column number in the matrix, i=1, 2, 3, ..., n, j=1, 2, 3, ..., n, n represents the total number of field station sensors; According to the actual communication distance of sensors at each station, the direct communication capability between sensors at each station is evaluated; If d ij >max(L i , L j ), it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station; If max(L i , L j )≥d ij >min(L i , L j ), it means that the long-range communication capability site sensor can send direct signals to the short-range communication capability site sensor, and the short-range communication capability site sensor cannot directly return signals; If d ij ≤min(L i , L j ), it means that the sensor at the i-th station and the sensor at the j-th station can communicate directly with each other; Among them, L i represents the actual communication distance of the i-th station sensor, L j represents the actual communication distance of the jth station sensor; According to the evaluation results of the direct communication capability between the sensors of each station, the adjacency matrix A of the new energy station sensor network is constructed. The element A[i][j] in the i-th row and j-th column of the matrix A represents the direct communication capability between the i-th station sensor and the j-th station sensor. The mathematical expression of the matrix A is: In the formula, the value of element A[i][j] can be written as a ij , a ij ∈{0,1}; If a ij = 0, it means that the sensor at the i-th station cannot communicate directly with the sensor at the j-th station. ij =1, it means that the sensor at the i-th station can communicate directly with the sensor at the j-th station; The analysis obtains the path length between each station sensor and the core monitoring station sensor. The specific analysis process is as follows: According to the adjacency matrix of the new energy station sensor network, the number of hops between each station sensor and the core monitoring station sensor is marked as the path length between each station sensor and the core monitoring station sensor; The number of hops between each station sensor and the core monitoring station sensor is the number of intermediate sensors required to be passed from each station sensor to the core monitoring station sensor plus 1 when each station sensor communicates directly or indirectly with the core monitoring station sensor.

2. A new energy remote centralized control method according to claim 1, characterized in that: The data correlation analysis of the core monitoring station sensors and the sensors of each station is performed as follows: Obtain the historical monitoring data of the core monitoring station sensors and the sensors of each station from the new energy station database, and obtain the time series function of the core monitoring station sensors and the sensors of each station through curve fitting; The time series functions of the core monitoring station sensors and the sensors of each station are derived to obtain the derivative functions of the core monitoring station sensors and the sensors of each station, and the derivative function images of the core monitoring station sensors and the sensors of each station are extracted at the same time; The derivative function images of the core monitoring station sensors and the sensors of each station are overlapped and compared, and the core monitoring data related evaluation values ​​of the sensors of each station are obtained through comprehensive analysis. The core monitoring data related evaluation values ​​of the sensors of each station are used to quantitatively evaluate the similarity of the data change trends of the historical monitoring data of the sensors of each station and the core monitoring station sensors in the time series.

3. A new energy remote centralized control method according to claim 2, characterized in that: The screening obtains the associated station sensors of the core monitoring station sensors, and the specific analysis process is as follows: According to the path length between each station sensor and the core monitoring station sensor and the core monitoring data related evaluation value of each station sensor, a comprehensive analysis is performed to obtain the data association analysis degree index of each station sensor, which is used to quantitatively evaluate the recommended degree of association analysis between the monitoring data of each station sensor and the monitoring data of the core monitoring station sensor; The data association analysis degree index of each station sensor is compared with the data association analysis degree index threshold stored in the new energy station database. If the data association analysis degree index of a station sensor is greater than or equal to the data association analysis degree index threshold, the station sensor is marked as an associated station sensor of the core monitoring station sensor, and the associated station sensors of the core monitoring station sensor are screened.

4. A new energy remote centralized control method according to claim 3, characterized in that: The comprehensive analysis results in the site equipment fault assessment index, and the specific analysis process is as follows: The monitoring data values ​​of the core monitoring station sensors and the sensors of each associated station are obtained, and the reference standard values ​​of the monitoring data of the core monitoring station sensors and the sensors of each associated station are extracted from the new energy station database, and the station equipment fault assessment index is obtained through comprehensive analysis.

5. A new energy remote centralized control method according to claim 4, characterized in that: The specific process of evaluating the fault level of the field equipment according to the field equipment fault evaluation index, obtaining the evaluation result and providing feedback is as follows: Matching the station equipment fault assessment index with the fault level corresponding to each station equipment fault assessment index interval stored in the new energy station database to obtain the station equipment fault level; Feedback the site equipment fault level to relevant personnel, and perform corresponding fault maintenance measures based on the site equipment fault level.

6. A new energy remote centralized control method according to claim 1, characterized in that: The specific numerical expression of the station environment interference value is: Where β represents the environmental interference value of the station, W t represents the ambient humidity at the tth environmental monitoring point, P r represents the area of ​​the r-th height level building complex, P0 represents the working area of ​​the new energy station, W0 represents the set reference standard ambient humidity, ψ1 represents the station environmental interference factor corresponding to the set ambient humidity, ψ2 represents the station environmental interference factor corresponding to the set building density, μ r It represents the site environmental interference impact factor corresponding to the area of ​​the building complex at the rth height level. t represents the number of each environmental monitoring point, t=1, 2, 3, ..., s, s represents the total number of environmental monitoring points, r represents the number of each height level, r=1, 2, 3, ..., h, h represents the total number of height levels.

7. A system using a new energy remote centralized control method according to any one of claims 1 to 6, characterized in that: include: The sensor network topology construction module is used to obtain the geographic information data of the new energy station sensor network, analyze the actual communication distance of each station sensor, and thus construct the adjacency matrix of the new energy station sensor network; The sensor network association analysis module is used to mark the station sensors that directly indicate the fault status as core monitoring station sensors, analyze the path length between each station sensor and the core monitoring station sensor according to the adjacency matrix of the new energy station sensor network, and perform data correlation analysis between the core monitoring station sensor and each station sensor to screen out the associated station sensors of the core monitoring station sensor; The comprehensive evaluation module for station equipment failure is used to obtain the station equipment failure evaluation index based on the monitoring data of the core monitoring station sensor and each associated station sensor, evaluate the station equipment failure level based on the station equipment failure evaluation index, obtain the evaluation result and provide feedback; The new energy station database is used to store data related to the centralized control of new energy stations, including geographic information data of the new energy station sensor network, communication distances corresponding to various transmission powers, communication attenuation distances corresponding to the environmental interference value intervals of each station, historical monitoring data of the core monitoring station sensors and each station sensor, data correlation analysis degree indicator threshold, monitoring data reference standard values ​​of the core monitoring station sensors and each associated station sensor, and fault levels corresponding to the fault assessment index intervals of each station equipment.

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