Intelligent substation real-time monitoring system and method based on big data

Abstract

The invention discloses an intelligent substation real-time monitoring system and method based on big data, and particularly relates to the technical field of power equipment monitoring. Comprising a multi-source acquisition and processing module, a dynamic topology updating module, an equipment state monitoring module, a fault diagnosis and prediction module, an equipment life prediction module, an energy efficiency analysis and optimization module and a remote operation and maintenance scheduling module. According to the invention, high-precision fault detection and implicit association mining can be realized, the substation anomaly detection capability is improved, and the service life management of each power device of the substation is effectively optimized; reasonable distribution of maintenance resources can be ensured, equipment reliability is improved, comprehensive energy efficiency is improved, carbon emission is reduced, manual intervention is not needed to adjust an operation mode, optimization efficiency is improved, and intelligent operation of the transformer substation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a real-time monitoring system and method for a smart substation based on big data. Background Art

[0002] With the rapid development of energy Internet, the power grid has put forward higher requirements for intelligence, reliability and real-time performance. Traditional substation monitoring systems mainly rely on a single data source, and there are problems such as data islands, insufficient real-time performance, and low fault diagnosis accuracy. In recent years, with the breakthroughs in technologies such as big data, artificial intelligence, and the Internet of Things, smart substations have gradually become an important part of smart grids. As the core hub of the power grid, the stable operation of substations is directly related to the safety and economy of the power system. With the increase in the proportion of new energy grid connection, the uncertainty of power grid operation has increased, and traditional monitoring methods are difficult to meet the needs of modern smart grids for substation status perception, fault warning, and autonomous optimization. Therefore, how to build an efficient, accurate, and intelligent real-time monitoring system for substations has become a hot research issue.

[0003] The existing real-time monitoring systems and methods for smart substations are unable to perform high-precision fault detection and implicit association mining, which reduces the substation's abnormality detection capabilities; in addition, the existing real-time monitoring systems and methods for smart substations are unable to reasonably allocate maintenance resources, which reduces equipment reliability, and requires manual intervention to adjust the operating mode, which reduces optimization efficiency. To this end, we propose a real-time monitoring system and method for smart substations based on big data. Summary of the invention

[0004] The purpose of the present invention is to solve the defects existing in the prior art; therefore, a real-time monitoring system and method for a smart substation based on big data is proposed.

[0005] In the first aspect of the implementation of the present invention, a real-time monitoring system for a smart substation based on big data is first proposed, the system comprising: Multi-source acquisition and processing module, dynamic topology update module, equipment status monitoring module, fault diagnosis and prediction module, equipment life prediction module, energy efficiency analysis and optimization module, and remote operation and maintenance scheduling module; The multi-source acquisition and processing module is used to collect real-time detection data collected by different sensors deployed in various power equipment in the substation, and pre-process each group of collected detection data; The dynamic topology update module is used to establish a power equipment association map and dynamically update the power equipment association map based on the pre-processed detection data; The equipment status monitoring module monitors and analyzes the operating status of each power equipment in the substation based on the pre-processed detection data received in real time and the power equipment association map; The fault diagnosis and prediction module predicts potential faults of each power equipment in the substation based on the real-time analysis results of the operating status of each power equipment in the substation and the aging of the equipment; The equipment life prediction module is used to build a virtual model of the equipment, the real-time analysis results of the operating status of each power equipment in the substation and the fault prediction results, and analyze the aging of each equipment; The energy efficiency analysis and optimization module optimizes the operation mode of each power equipment in the substation based on the real-time analysis results of the operation status of each power equipment in the substation and the power equipment association map; The remote operation and maintenance scheduling module is used to optimize the substation maintenance plan by combining the fault detection data, operation mode and life prediction results of each power equipment in the substation.

[0006] As a further solution of the present invention, the specific steps of preprocessing each group of collected detection data by the multi-source acquisition processing module are as follows: S1.1: The multi-source acquisition processing module receives various detection data collected by different sensors, including temperature data, voltage data, humidity data and ultrasonic data, and groups the detection data of the same type into one group. Then, the sampling time step of each group of detection data is synchronized through the time window resampling method. After the synchronization is completed, the unit of each group of detection data is unified through unit standardization processing; S1.2: Arrange each group of test data from small to large according to the data value, and use the values ​​of the 25% and 75% positions of each group of test data as the first quartile Q1 and the third quartile Q3 of each group of data, and obtain the interquartile range IQR of each group of test data through Q3-Q1. Then, based on IQR, use Q1-1.5×IQR as the lower boundary and Q3+1.5×IQR as the upper boundary to draw the corresponding box plot, traverse each group of test data, and if there is a test data below the lower boundary or above the upper boundary, mark the test data as an outlier; S1.3: Traverse each group of test data and count the missing values ​​in each group of test data. Then fill the missing values ​​through linear interpolation, and then replace the outliers through mean filling. After the outlier processing is completed, recalculate Q1, Q3 and IQR, and draw a new box plot to perform outlier detection again to verify the quality of each group of test data. After the verification, normalize the test data of each group to the range of [0, 1].

[0007] As a further solution of the present invention, the dynamic topology update module collects each group of power equipment in the substation and uses it as a node. According to the electrical connection relationship between the equipment, edges between the nodes are established to connect the nodes. A power equipment association map is established according to the nodes and the edges between the nodes, and each node feature is initialized. The features include detection data, historical operating status of the equipment and fault records, and the power equipment association map is dynamically updated based on the groups of detection data collected in real time.

[0008] As a further solution of the present invention, the specific steps of the equipment status monitoring module to monitor and analyze the operating status of each power equipment in the substation are as follows: S2.1: The state monitoring module establishes a monitoring and analysis model based on the ST-GCN architecture. The monitoring and analysis model includes an input layer, a GCN layer, a TCN layer, and an output layer. Each group of data recorded in the power equipment association map established by the dynamic topology update module and each group of pre-processed real-time detection data are transmitted to the monitoring and analysis model as input data. Among them, each group of data recorded in the power equipment association map includes historical detection data, connection relationships of various power equipment, historical operation status of equipment, and fault records; S2.2: The monitoring and analysis model receives input data and performs forward propagation. The GCN layer establishes the adjacency matrix between each power device based on the input data, normalizes the adjacency matrix, and then performs graph convolution on the normalized adjacency matrix to extract the spatial characteristics of the state of each power device. Then, the 1D convolution kernel of the TCN layer is used to extract the temporal characteristics of each power device. S2.3: The spatial and temporal features of each power equipment are extracted layer by layer through multiple groups of GCN layers and TCN layers. The output layer receives the final extracted spatial and temporal features, and performs nonlinear activation processing on each feature data through the activation function, and outputs the fault correlation between each power equipment and predicts the probability of occurrence of each type of fault. At the same time, based on the fault correlation results and the probability of occurrence of each type of fault, the existing fault propagation path is warned in advance, and the parameters of the monitoring and analysis model are updated according to the actual power equipment fault situation.

[0009] As a further solution of the present invention, the specific calculation formula of the graph convolution processing in S2.2 is as follows:

[0010] In the formula, represents the normalized adjacency matrix; Degree matrix; Represents the adjacency matrix, where if the device With equipment If electrical connection exists, , otherwise 0; Representative The node spatial characteristics of the layer; represents the activation function; Representative The node spatial characteristics of the layer; Representative Layer training weights; The specific calculation formula for extracting the time characteristics of each power device using the 1D convolution kernel described in S2.2 is as follows:

[0011] In the formula, represent The temporal characteristics of the moment; Represents the temporal convolution kernel size; Representative Temporal convolution kernel weights; Represents the expansion rate and controls the sampling interval.

[0012] As a further solution of the present invention, the specific steps of the equipment life prediction module for analyzing the aging of each device are as follows: S3.1: The equipment life prediction module collects the physical parameters of each electrical equipment through actual measurements by staff or by referring to the equipment manufacturer's instruction manual, and builds an equipment simulation model based on the collected physical parameters. According to the internal structure, components and operation mechanism of the electrical equipment, the physical equations of the corresponding components are established, and the finite element method is used to mesh the electrical equipment. Then, combined with real-time sensor data, the physical equations of the simulation models of each equipment are solved to obtain the internal state distribution of the electrical equipment; S3.2: Based on the simulation model of each device, a corresponding aging prediction model is constructed, and the historical operation data of each power device is divided into a training set and a test set. The training set data is input into the aging prediction model. Through the forward propagation algorithm, each input training set data is processed layer by layer, and the final power equipment aging prediction value is output. After that, the aging prediction value is compared with the historical actual aging value, and the corresponding model error value is calculated by the mean square error; S3.3: If the model error value is higher than the preset threshold, the error value is input into the aging prediction model, and the aging prediction model parameters are adjusted through the back propagation algorithm. After each round of training, the test set data is input into the aging prediction model, and the error value of the trained aging prediction model is calculated. If the error value converges to the preset range, the training is stopped, otherwise, the model training continues; S3.4: Input the current operating status of each power equipment and the fault prediction results into the corresponding aging prediction model respectively, predict the aging trend residual of each power equipment through the forward propagation algorithm, and correct the remaining life of each power equipment based on the aging trend residual. At the same time, output the corrected remaining life of each power equipment, compare the latest life prediction result with the previous round of life prediction results, and if the difference between the power equipment life prediction values ​​exceeds the preset threshold, generate an early warning and formulate a maintenance plan.

[0013] As a further solution of the present invention, the specific calculation formula of the model error value in S3.2 is as follows:

[0014] In the formula, Represents the total number of power equipment in the substation; Represents the sensor's The real status value of each power device; Representative prediction The status value of an electrical device.

[0015] In a second aspect of the present invention, a real-time monitoring method for a smart substation based on big data is proposed, the method comprising the following steps: Ⅰ. Collect and pre-process the operation data and environmental data of each power equipment in the substation; Ⅱ. Based on the collected data, a power equipment correlation map is constructed to analyze the hidden fault correlation between multiple devices and optimize the maintenance strategy of the substation; III. Manage data access rights in real time through zero-trust access control mechanism, and predict the life of power equipment in each substation based on real-time operating data; IV. Real-time analysis of various power equipment losses and renewable energy output factors, and dynamic adjustment of operating modes.

[0016] As a further solution of the present invention, the specific steps of optimizing the maintenance strategy of the substation in step II are as follows: S4.1: According to the operating status, detection data and maintenance resources of each power equipment, the corresponding equipment state space is constructed respectively, and different maintenance operations and the corresponding costs, risks and benefits of each operation are collected to construct the corresponding action space, and the reward function is set based on maintenance cost, reliability benefit and economic loss; S4.2: The current state of the power equipment is taken as the root node, and multiple groups of child nodes corresponding to the root node are generated based on each maintenance decision in the action space to establish an initial strategy tree. Then, starting from the root node, the upper confidence bound value of each child node is calculated, and based on the UCB selection strategy, the child node with the highest upper confidence bound value is selected layer by layer until a group of incompletely expanded child nodes is selected; S4.3: Select the unused maintenance operation of the child node in the action space and expand the new child node. Then randomly select any new child node based on the current child node, simulate the evolution of the health state of the corresponding power equipment under the current detection operation, calculate the strategy benefit through the reward function, and then send the simulation result back to the root node, and update the cumulative reward and visit count of each node; S4.4: Repeat the selection, expansion, simulation and backtracking until the cumulative reward of each node converges to the preset threshold in multiple rounds of iterations, then stop the iteration, select the node path with the highest cumulative reward, and build a complete maintenance strategy based on the detection operations corresponding to each node in the node path, and adjust the original maintenance strategy. At the same time, dynamically update the maintenance strategy based on the subsequent changes in the operating status of each power equipment, detection data and maintenance resources.

[0017] As a further solution of the present invention, the specific steps of dynamically adjusting the operation mode in step IV are as follows: S5.1: Collect the input carbon emissions, output carbon emissions, carbon emission reductions and carbon emission losses of the substation, and construct a carbon flow matrix to generate the corresponding substation carbon flow balance equation, and then establish a fitness function with three sets of optimization goals: minimizing the total carbon emissions, maximizing the comprehensive energy efficiency of the substation and reducing the operating cost; S5.2: Initialize a group of populations, each individual in the population represents a group of substation operating parameters, then initialize the position and speed of each individual, and calculate the fitness value of each group of individuals through the fitness function, then take the individual position with the highest fitness value as the global optimal position, and select the individual position with the highest historical fitness value of each individual as the local optimal position; S5.3: Update the speed of each individual according to the global optimal position and the local optimal position, and then update the position of each individual based on the updated speed value. After the position of each individual is updated, the best individual is selected through the NSGA-II algorithm, and the global optimal position update, local optimal position update, individual speed update and individual position update are performed again; S5.4: Repeatedly update and iterate until the individual fitness value of the global optimal position converges to the preset threshold, then stop the optimization, select the substation operation parameters corresponding to the optimal individual, and use it as the optimal operation mode. Based on the selected optimal operation mode, adjust the substation load distribution, energy storage charging and discharging, and equipment operation.

[0018] As a further solution of the present invention, the specific formula of the carbon flow matrix in S5.1 is as follows:

[0019] Carbon flow matrix based on construction To construct the following substation carbon flow balance equation:

[0020] In the above formulas, represents input carbon emissions; Represents carbon emissions per unit of electricity generated; represents the amount of electricity generated by fossil fuels; represents the output carbon emissions; represents the carbon emission factor; Represents the user load supplied by the substation; represents carbon emission reduction; stands for photovoltaic power generation; Represents the discharge of the energy storage system; Represents equipment loss; Represents transformer copper loss; represents transformer iron loss; represents the carbon flow balance value of the substation; The specific calculation formula for updating the position of each individual as described in S5.3 is as follows:

[0021] In the formula, Representative Individuals in speed at 1000 s; represents the inertia weight; Representative Individuals in speed at 1000 s; as well as They represent acceleration factors respectively; as well as Represent random numbers respectively; Representative The individual optimal position of each individual; represents the global optimal position; Representative The current location of each individual; Representative Individuals in The position at the time; Representative Individuals in The position at that time.

[0022] Beneficial effects of the present invention: 1. The present invention establishes an associated graph of power equipment based on the electrical connection relationship between devices, initializes node features at the same time, and uses the ST-GCN architecture to build a monitoring and analysis model. The adjacency matrix between devices is normalized through the GCN layer of the model, and spatial features are extracted using graph convolution. The TCN layer extracts time features through a 1D convolution kernel. After multi-layer feature extraction, the hidden fault association between each power device is finally output, and the fault propagation path is predicted to achieve early warning, collect physical parameters of electrical equipment, build a device simulation model, and establish physical equations based on the internal structure and components of the equipment. The finite element method is used Grid division is performed, and the internal state distribution of the equipment is solved by combining real-time sensor data. An aging prediction model is constructed, and the historical operation data is divided into a training set and a test set. The parameters of the aging prediction model are trained and optimized, and the aging rate and crack propagation rate are corrected by combining the Arrhenius equation and the Paris formula. Real-time equipment data is input, the aging trend of the equipment is predicted, and the remaining life is calculated. If the difference between the two rounds of prediction results exceeds the threshold, an early warning is generated in advance, and a maintenance plan is formulated. This can achieve high-precision fault detection and implicit association mining, improve the abnormality detection capability of the substation, and effectively optimize the life management of various power equipment in the substation.

[0023] 2. The present invention constructs a maintenance decision space according to the operating status, environmental conditions and maintenance resources of the power equipment, collects the costs, risks and benefits of different decisions, establishes a corresponding action space, takes the current equipment status as the root node, expands the sub-nodes according to different maintenance decisions, and screens the optimal sub-nodes layer by layer through the upper confidence limit selection strategy until the sub-nodes that are not fully expanded, then simulates the impact of the maintenance strategy on the future health status of the equipment, calculates the benefits based on the reward function, backtracks and updates the cumulative rewards and access times of each node, and continuously iterates the process until the reward value converges, and finally selects the maintenance strategy corresponding to the path with the highest cumulative reward, optimizes the maintenance plan, and then constructs the carbon flow matrix, and optimizes the maintenance plan with the most With the goal of minimizing carbon emissions, maximizing comprehensive energy efficiency, and reducing operating costs, a fitness function is established, the population is initialized, each individual represents a set of substation operating parameters, and the fitness value is calculated to determine the global and local optimal positions. Subsequently, the individual speed and position are adjusted according to the optimal position, and the NSGA-II algorithm is used to screen the optimal individual. Continuous optimization is carried out until the fitness value converges. Finally, the substation operating parameters corresponding to the optimal individual are selected to optimize the load distribution, energy storage charging and discharging strategies, and equipment operating parameters. This can ensure the reasonable allocation of maintenance resources, improve equipment reliability, enhance comprehensive energy efficiency, reduce carbon emissions, and adjust the operating mode without manual intervention, thereby improving optimization efficiency and realizing intelligent operation of substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] Figure 1A system block diagram of a smart substation real-time monitoring system based on big data provided by an embodiment of the present invention; Figure 2 A flowchart of a real-time monitoring method for a smart substation based on big data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0028] The embodiment of the present invention provides a real-time monitoring system for smart substations based on big data. Figure 1 , Figure 1 A system block diagram of a real-time monitoring system for a smart substation based on big data provided by an embodiment of the present invention. The system includes the following modules: a multi-source acquisition and processing module, a dynamic topology update module, an equipment status monitoring module, a fault diagnosis and prediction module, an equipment life prediction module, an energy efficiency analysis and optimization module, and a remote operation and maintenance scheduling module.

[0029] The multi-source acquisition and processing module is used to collect real-time detection data collected by different sensors deployed in various power equipment in the substation, and pre-process each group of collected detection data.

[0030] Specifically, the multi-source acquisition processing module receives various detection data collected by different sensors, including temperature data, voltage data, humidity data and ultrasonic data, and divides the detection data of the same type into a group. Then, the sampling time step of each group of detection data is synchronized through the time window resampling method. After the synchronization is completed, the units of each group of detection data are unified through unit standardization processing, and each group of detection data is arranged from small to large according to the data value, and the values ​​of the 25% position and 75% position of each group of detection data are respectively used as the first quartile Q1 and the third quartile Q3 of each group of data, and the interquartile range IQR of each group of detection data is obtained through Q3-Q1. Then, based on IQR, Q1−1.5×IQR is used as the lower boundary and Q3+1.5×IQR is used as the upper boundary to draw the corresponding box plot. Each group of test data is traversed. If there is a test data below the lower boundary or above the upper boundary, the test data is marked as an outlier. Each group of test data is traversed, and the missing values ​​in each group of test data are counted. Then, the missing values ​​are filled by linear interpolation, and then the outliers are replaced by mean filling. After the outlier processing is completed, Q1, Q3 and IQR are recalculated, and a new box plot is drawn to perform anomaly detection again to verify the quality of each group of test data. After the verification, the test data of each group are unified to the range of [0, 1] through normalization.

[0031] The dynamic topology update module is used to establish a power equipment association map and dynamically update the power equipment association map based on the pre-processed detection data.

[0032] It should be further explained that the dynamic topology update module collects each group of power equipment in the substation and uses it as a node. According to the electrical connection relationship between the equipment, edges are established between the nodes to connect the nodes. According to the nodes and the edges between the nodes, a power equipment association map is established, and each node feature is initialized. The features include detection data, historical equipment operating status and fault records, and the power equipment association map is dynamically updated based on the detection data collected in real time.

[0033] The equipment status monitoring module monitors and analyzes the operating status of each power equipment in the substation based on the pre-processed detection data received in real time and the power equipment association map.

[0034] Specifically, the status monitoring module establishes a monitoring and analysis model based on the ST-GCN architecture, and the monitoring and analysis model includes an input layer, a GCN layer, a TCN layer and an output layer. The groups of data recorded in the power equipment association map established by the dynamic topology update module and the groups of real-time detection data after preprocessing are transmitted to the monitoring and analysis model as input data. Among them, the groups of data recorded in the power equipment association map include historical detection data, connection relationships of various power equipment, historical operation status of equipment and fault records. The monitoring and analysis model receives the input data and performs forward propagation. The GCN layer establishes the adjacency matrix between each power equipment based on the input data, and normalizes the adjacency matrix, and then The normalized adjacency matrix is ​​subjected to graph convolution processing to extract the spatial characteristics of the state of each power equipment. Then, the 1D convolution kernel of the TCN layer is used to extract the temporal characteristics of each power equipment. The spatial and temporal characteristics of each power equipment are extracted layer by layer through multiple groups of GCN layers and TCN layers. The output layer receives the final extracted spatial and temporal characteristics, and performs nonlinear activation processing on each feature data through the activation function. The fault association between each power equipment and the predicted probability of each type of fault are output, and the existing fault propagation path is warned in advance based on the fault association results and the probability of each type of fault, and the parameters of the monitoring and analysis model are updated according to the actual power equipment fault situation.

[0035] It should be further explained that the specific calculation formula for graph convolution processing is as follows:

[0036] In the formula, represents the normalized adjacency matrix; Degree matrix; Represents the adjacency matrix, where if the device With equipment If electrical connection exists, , otherwise 0; Representative The node spatial characteristics of the layer; represents the activation function; Representative The node spatial characteristics of the layer; Representative Layer training weights; The specific calculation formula for extracting the time characteristics of each power equipment using the 1D convolution kernel is as follows:

[0037] In the formula, represent The temporal characteristics of the moment; Represents the temporal convolution kernel size; Representative Temporal convolution kernel weights; Represents the expansion rate and controls the sampling interval.

[0038] The fault diagnosis and prediction module predicts the potential faults of each power equipment in the substation based on the real-time analysis results of the operating status of each power equipment in the substation and the aging of the equipment; The equipment life prediction module is used to build a virtual model of the equipment, the real-time analysis results of the operating status of each power equipment in the substation and the fault prediction results, and analyze the aging status of each equipment.

[0039] Specifically, the equipment life prediction module collects the physical parameters of each electrical equipment through actual measurements by staff or by referring to the instruction manual of the equipment manufacturer, and builds an equipment simulation model based on the collected physical parameters. According to the internal structure, components and operation mechanism of the electrical equipment, the physical equations of the corresponding components are established, and the electrical equipment is meshed using the finite element method. Then, combined with real-time sensor data, the physical equations of each equipment simulation model are solved to obtain the internal state distribution of the electrical equipment. Based on the simulation model of each equipment, a corresponding aging prediction model is built, and the historical operation data of each power equipment is divided into a training set and a test set. The training set data is input into the aging prediction model, and the input training set data is processed layer by layer through the forward propagation algorithm, and the final power equipment aging prediction value is output. Then, the aging prediction value is compared with the historical actual aging value, and the mean square error is calculated. The error calculation corresponds to the model error value. If the model error value is higher than the preset threshold, the error value is input into the aging prediction model, and the aging prediction model parameters are adjusted through the back propagation algorithm. After each round of training, the test set data is input into the aging prediction model, and the error value of the trained aging prediction model is calculated. If the error value converges to the preset range, the training is stopped. Otherwise, the model is continued to be trained, and the current operating status of each power equipment and the fault prediction results are respectively input into the corresponding aging prediction model. The aging trend residual of each power equipment is predicted through the forward propagation algorithm, and the remaining life of each power equipment after correction based on the aging trend residual is output at the same time. The latest life prediction result is compared with the previous round of life prediction results. If the difference in the predicted value of the power equipment life exceeds the preset threshold, an early warning is generated in advance and a maintenance plan is formulated.

[0040] In this embodiment, the specific calculation formula of the model error value is as follows:

[0041] In the formula, Represents the total number of power equipment in the substation; Represents the sensor's The real status value of each power device; Representative prediction The status value of an electrical device.

[0042] The energy efficiency analysis and optimization module optimizes the operation mode of each power equipment in the substation based on the real-time analysis results of the operation status of each power equipment in the substation and the power equipment association map; The remote operation and maintenance scheduling module is used to optimize the substation maintenance plan by combining the fault detection data, operation mode and life prediction results of each power equipment in the substation.

[0043] The embodiment of the present invention also provides a real-time monitoring method for a smart substation based on big data, such as Figure 2 As shown, the method comprises the following steps: Collect and pre-process the operating data and environmental data of each power equipment in the substation.

[0044] Based on the collected data sets, a power equipment correlation map is constructed to analyze the hidden fault correlation between multiple devices, and the substation maintenance strategy is optimized.

[0045] Specifically, according to the operating status, detection data and maintenance resources of each power equipment, the corresponding equipment state space is constructed respectively, and different maintenance operations and the different costs, risks and benefits corresponding to each operation are collected to construct the corresponding action space. The reward function is set based on the maintenance cost, reliability benefit and economic loss. The current state of the power equipment is taken as the root node. Based on each maintenance decision in the action space, multiple groups of child nodes corresponding to the root node are generated to establish an initial policy tree. Then, starting from the root node, the upper confidence bound value of each child node is calculated, and based on the UCB selection strategy, the child node with the highest upper confidence bound value is selected layer by layer until a group of incompletely expanded child nodes is selected. The unused maintenance operations of the child node in the action space are selected, and the new strategy tree is expanded. 's child node, and then randomly selects any new child node based on the current child node, and simulates the evolution of the health status of the corresponding power equipment under the current detection operation, and calculates the strategy benefit through the reward function, and then transmits the simulation result back to the root node, and updates the cumulative reward and visit times of each node, repeats the selection, expansion, simulation and backtracking until the cumulative reward of each node converges to the preset threshold in multiple rounds of iterations, then stops the iteration, and then selects the node path with the highest cumulative reward, and builds a complete maintenance strategy based on the detection operations corresponding to each node in the node path, and adjusts the original maintenance strategy, and dynamically updates the maintenance strategy according to the subsequent changes in the operating status, detection data and maintenance resources of each power equipment.

[0046] Data access rights are managed in real time through a zero-trust access control mechanism, and the life of power equipment in each substation is predicted based on real-time operating data.

[0047] Analyze the losses of various power equipment and renewable energy output factors in real time, and dynamically adjust the operation mode.

[0048] Specifically, the input carbon emissions, output carbon emissions, carbon emission reductions and carbon emission losses of the substation are collected, and a carbon flow matrix is ​​constructed to generate the corresponding carbon flow balance equation of the substation. Then, the fitness function is established with three sets of optimization objectives: minimizing the total carbon emissions, maximizing the comprehensive energy efficiency of the substation and reducing the operating cost. A group of populations is initialized, and each individual in the population represents a group of substation operating parameters. Then, the position and speed of each individual are initialized, and the fitness value of each group of individuals is calculated by the fitness function. Then, the individual position with the highest fitness value is taken as the global optimal position, and the individual position with the highest historical fitness value of each individual is selected as the local optimal position. The optimal position of each individual is determined, and the speed of each individual is updated according to the global optimal position and the local optimal position. Then, based on the updated speed value, the position of each individual is updated. After the position of each individual is updated, the optimal individual is selected through the NSGA-II algorithm, and the global optimal position update, local optimal position update, individual speed update and individual position update are performed again. The update iteration is repeated until the individual fitness value of the global optimal position converges to the preset threshold. Then the optimization is stopped, and the substation operation parameters corresponding to the optimal individual are selected as the optimal operation mode. The substation load distribution, energy storage charging and discharging, and equipment operation are adjusted based on the selected optimal operation mode.

[0049] It should be further explained that the specific formula of the carbon flow matrix is ​​as follows:

[0050] Carbon flow matrix based on construction To construct the following substation carbon flow balance equation:

[0051] In the above formulas, represents input carbon emissions; Represents carbon emissions per unit of electricity generated; represents the amount of electricity generated by fossil fuels; represents the output carbon emissions; represents the carbon emission factor; Represents the user load supplied by the substation; represents carbon emission reduction; stands for photovoltaic power generation; Represents the discharge of the energy storage system; Represents equipment loss; Represents transformer copper loss; represents transformer iron loss; represents the carbon flow balance value of the substation; The specific calculation formula for updating the position of each individual is as follows:

[0052] In the formula, Representative Individuals in speed at 1000 s; represents the inertia weight; Representative Individuals in speed at 1000 s; as well as They represent acceleration factors respectively; as well as Represent random numbers respectively; Representative The individual optimal position of each individual; represents the global optimal position; Representative The current location of each individual; Representative Individuals in The position at the time; Representative Individuals in The position at that time.

[0053] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A real-time monitoring system for smart substations based on big data, characterized in that: include: Multi-source acquisition and processing module, dynamic topology update module, equipment status monitoring module, fault diagnosis and prediction module, equipment life prediction module, energy efficiency analysis and optimization module, and remote operation and maintenance scheduling module; The multi-source acquisition and processing module is used to collect real-time detection data collected by different sensors deployed in various power equipment in the substation, and pre-process each group of collected detection data; The dynamic topology update module is used to establish a power equipment association map and dynamically update the power equipment association map based on the pre-processed detection data; The equipment status monitoring module monitors and analyzes the operating status of each power equipment in the substation based on the pre-processed detection data received in real time and the power equipment association map; The fault diagnosis and prediction module predicts potential faults of each power equipment in the substation based on the real-time analysis results of the operating status of each power equipment in the substation and the aging of the equipment; The equipment life prediction module is used to build a virtual model of the equipment, the real-time analysis results of the operating status of each power equipment in the substation and the fault prediction results, and analyze the aging of each equipment; The energy efficiency analysis and optimization module optimizes the operation mode of each power equipment in the substation based on the real-time analysis results of the operation status of each power equipment in the substation and the power equipment association map; The remote operation and maintenance scheduling module is used to optimize the substation maintenance plan by combining the fault detection data, operation mode and life prediction results of each power equipment in the substation.

2. According to the big data-based smart substation real-time monitoring system of claim 1, it is characterized in that: The specific steps of preprocessing each group of collected detection data by the multi-source acquisition processing module are as follows: S1.1: The multi-source acquisition processing module receives various detection data collected by different sensors, including temperature data, voltage data, humidity data and ultrasonic data, and groups the detection data of the same type into one group. Then, the sampling time step of each group of detection data is synchronized through the time window resampling method. After the synchronization is completed, the unit of each group of detection data is unified through unit standardization processing; S1.2: Arrange each group of test data from small to large according to the data value, and use the values ​​of the 25% and 75% positions of each group of test data as the first quartile Q1 and the third quartile Q3 of each group of data, and obtain the interquartile range IQR of each group of test data through Q3-Q1. Then, based on IQR, use Q1-1.5×IQR as the lower boundary and Q3+1.5×IQR as the upper boundary to draw the corresponding box plot, traverse each group of test data, and if there is a test data below the lower boundary or above the upper boundary, mark the test data as an outlier; S1.3: Traverse each group of test data and count the missing values ​​in each group of test data. Then fill the missing values ​​through linear interpolation, and then replace the outliers through mean filling. After the outlier processing is completed, recalculate Q1, Q3 and IQR, and draw a new box plot to perform outlier detection again to verify the quality of each group of test data. After the verification, normalize the test data of each group to the range of [0, 1].

3. A real-time monitoring system for smart substations based on big data according to claim 2, characterized in that: The specific steps of the equipment status monitoring module to monitor and analyze the operating status of each power equipment in the substation are as follows: S2.1: The state monitoring module establishes a monitoring and analysis model based on the ST-GCN architecture. The monitoring and analysis model includes an input layer, a GCN layer, a TCN layer, and an output layer. Each group of data recorded in the power equipment association map established by the dynamic topology update module and each group of pre-processed real-time detection data are transmitted to the monitoring and analysis model as input data. Among them, each group of data recorded in the power equipment association map includes historical detection data, connection relationships of various power equipment, historical operation status of equipment, and fault records; S2.2: The monitoring and analysis model receives input data and performs forward propagation. The GCN layer establishes the adjacency matrix between each power device based on the input data, normalizes the adjacency matrix, and then performs graph convolution on the normalized adjacency matrix to extract the spatial characteristics of the state of each power device. Then, the 1D convolution kernel of the TCN layer is used to extract the temporal characteristics of each power device. S2.3: The spatial and temporal features of each power equipment are extracted layer by layer through multiple groups of GCN layers and TCN layers. The output layer receives the final extracted spatial and temporal features, and performs nonlinear activation processing on each feature data through the activation function, and outputs the fault correlation between each power equipment and predicts the probability of occurrence of each type of fault. At the same time, based on the fault correlation results and the probability of occurrence of each type of fault, the existing fault propagation path is warned in advance, and the parameters of the monitoring and analysis model are updated according to the actual power equipment fault situation.

4. A real-time monitoring system for smart substations based on big data according to claim 3, characterized in that: The specific steps for the equipment life prediction module to analyze the aging of each device are as follows: S3.1: The equipment life prediction module collects the physical parameters of each electrical equipment through actual measurements by staff or by referring to the equipment manufacturer's instruction manual, and builds an equipment simulation model based on the collected physical parameters. According to the internal structure, components and operation mechanism of the electrical equipment, the physical equations of the corresponding components are established, and the finite element method is used to mesh the electrical equipment. Then, combined with real-time sensor data, the physical equations of the simulation models of each equipment are solved to obtain the internal state distribution of the electrical equipment; S3.2: Based on the simulation model of each device, a corresponding aging prediction model is constructed, and the historical operation data of each power device is divided into a training set and a test set. The training set data is input into the aging prediction model. Through the forward propagation algorithm, each input training set data is processed layer by layer, and the final power equipment aging prediction value is output. After that, the aging prediction value is compared with the historical actual aging value, and the corresponding model error value is calculated by the mean square error; S3.3: If the model error value is higher than the preset threshold, the error value is input into the aging prediction model, and the aging prediction model parameters are adjusted through the back propagation algorithm. After each round of training, the test set data is input into the aging prediction model, and the error value of the trained aging prediction model is calculated. If the error value converges to the preset range, the training is stopped, otherwise, the model training continues; S3.4: Input the current operating status of each power equipment and the fault prediction results into the corresponding aging prediction model respectively, predict the aging trend residual of each power equipment through the forward propagation algorithm, and correct the remaining life of each power equipment based on the aging trend residual. At the same time, output the corrected remaining life of each power equipment, compare the latest life prediction result with the previous round of life prediction results, and if the difference between the power equipment life prediction values ​​exceeds the preset threshold, generate an early warning and formulate a maintenance plan.

5. A real-time monitoring method for a smart substation based on big data, used to implement the function of a real-time monitoring system for a smart substation based on big data as described in any one of claims 1-4, characterized in that: The following steps are involved: Ⅰ. Collect and pre-process the operation data and environmental data of each power equipment in the substation; Ⅱ. Based on the collected data, a power equipment correlation map is constructed to analyze the hidden fault correlation between multiple devices and optimize the maintenance strategy of the substation; III. Manage data access rights in real time through zero-trust access control mechanism, and predict the life of power equipment in each substation based on real-time operating data; IV. Real-time analysis of various power equipment losses and renewable energy output factors, and dynamic adjustment of operating modes.

6. A real-time monitoring method for smart substations based on big data according to claim 5, characterized in that: The specific steps of optimizing the maintenance strategy of the substation described in step II are as follows: S4.1: According to the operating status, detection data and maintenance resources of each power equipment, the corresponding equipment state space is constructed respectively, and different maintenance operations and the corresponding costs, risks and benefits of each operation are collected to construct the corresponding action space, and the reward function is set based on maintenance cost, reliability benefit and economic loss; S4.2: The current state of the power equipment is taken as the root node, and multiple groups of child nodes corresponding to the root node are generated based on each maintenance decision in the action space to establish an initial strategy tree. Then, starting from the root node, the upper confidence bound value of each child node is calculated, and based on the UCB selection strategy, the child node with the highest upper confidence bound value is selected layer by layer until a group of incompletely expanded child nodes is selected; S4.3: Select the unused maintenance operation of the child node in the action space and expand the new child node. Then randomly select any new child node based on the current child node, simulate the evolution of the health state of the corresponding power equipment under the current detection operation, calculate the strategy benefit through the reward function, and then send the simulation result back to the root node, and update the cumulative reward and visit count of each node; S4.4: Repeat the selection, expansion, simulation and backtracking until the cumulative reward of each node converges to the preset threshold in multiple rounds of iterations, then stop the iteration, select the node path with the highest cumulative reward, and build a complete maintenance strategy based on the detection operations corresponding to each node in the node path, and adjust the original maintenance strategy. At the same time, dynamically update the maintenance strategy based on the subsequent changes in the operating status of each power equipment, detection data and maintenance resources.

7. A real-time monitoring method for smart substations based on big data according to claim 5, characterized in that: The specific steps of dynamically adjusting the operation mode described in step IV are as follows: S5.1: Collect the input carbon emissions, output carbon emissions, carbon emission reductions and carbon emission losses of the substation, and construct a carbon flow matrix to generate the corresponding substation carbon flow balance equation, and then establish a fitness function with three sets of optimization goals: minimizing the total carbon emissions, maximizing the comprehensive energy efficiency of the substation and reducing the operating cost; S5.2: Initialize a group of populations, each individual in the population represents a group of substation operating parameters, then initialize the position and speed of each individual, and calculate the fitness value of each group of individuals through the fitness function, then take the individual position with the highest fitness value as the global optimal position, and select the individual position with the highest historical fitness value of each individual as the local optimal position; S5.3: Update the speed of each individual according to the global optimal position and the local optimal position, and then update the position of each individual based on the updated speed value. After the position of each individual is updated, the best individual is selected through the NSGA-II algorithm, and the global optimal position update, local optimal position update, individual speed update and individual position update are performed again; S5.4: Repeatedly update and iterate until the individual fitness value of the global optimal position converges to the preset threshold, then stop the optimization, select the substation operation parameters corresponding to the optimal individual, and use it as the optimal operation mode. Based on the selected optimal operation mode, adjust the substation load distribution, energy storage charging and discharging, and equipment operation.

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