CN117498555B - Cloud-edge fusion-based intelligent operation and maintenance system for energy storage power station - Google Patents
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Abstract
The invention relates to the technical field of energy storage power stations, in particular to an intelligent operation and maintenance system of an energy storage power station based on cloud edge fusion, which comprises a monitoring center, wherein the monitoring center is in communication connection with an edge monitoring module, a data storage module, a data analysis module, an electricity consumption prediction module and an energy storage adjustment module; the edge monitoring module is used for setting edge monitoring points according to the flow characteristics and acquiring data monitoring indexes of the edge monitoring points; the data storage module is used for storing historical data information; the data analysis module is used for analyzing the data monitoring results of the edge monitoring points; the power consumption prediction module is used for constructing an assembly topological graph of each flow sub-sequence, and acquiring power consumption prediction values of each flow sub-sequence according to an assembly relation; the energy storage adjustment module is used for dynamically adjusting the energy storage information of the relevant load equipment of each node in the assembly topological graph, and improving the efficiency and accuracy of the load dynamic balance of the energy storage power station.
Description
Technical Field
The invention relates to the technical field of energy storage power stations, in particular to an intelligent operation and maintenance system of an energy storage power station based on cloud edge fusion.
Background
With the large-scale renewable energy source accessing the whole power system, especially the wind power and the photovoltaic have the characteristics of volatility, randomness, difficult prediction and the like, the stable operation and control of the power system are greatly challenged. The energy storage system can be used as a power supply and a load, and the flexible bidirectional interaction performance enables the energy storage system to play an important role in renewable energy power generation and absorption. Particularly, the electrochemical energy storage is realized, and the converter has the characteristics of high response speed and high precision, so that the converter has better regulation and control performance compared with other power supplies and loads. The power grid side electrochemical energy storage power station can provide various services such as peak shaving, frequency modulation, standby, black start, demand side response and the like for power grid operation, so that the power grid operation efficiency is improved, and the regional power supply load pressure is relieved.
The comparison file CN210380254U 'energy storage power supply and power grid dispatching coordination control system' can control the on-off of the converter according to the charge state of the energy storage power supply, the working voltage and the working current required by the power grid dispatching module through the charge state detection module, the coordination control module and the voltage and current detection module, can achieve the effect of stably supplying power to the power grid dispatching module in the operation process, and can effectively guarantee the service life of the energy storage power supply.
The contrast file CN113205190B is an energy storage safety early warning system of a smart power grid, a traditional energy storage operation and maintenance management system is essentially and iteratively optimized by utilizing an early warning platform, a new mode of omnibearing, multi-angle and whole-process operation and maintenance management is established, the management and control capacity of energy storage power station equipment is effectively enhanced, and the data value insight and intelligent operation and maintenance decision capacity of an energy storage power station are improved.
In the prior art, the energy storage power station in the smart power grid needs to be further strengthened in the aspects of mass data analysis and processing, safety risk analysis and control and the like, the prediction accuracy of the power grid is not high enough, particularly the electricity consumption and loss of each electric equipment are inconsistent due to various abnormal conditions, and when the electricity loss data are huge, great data deviation still exists when the related balance equipment of the energy storage power station balances the electric energy easily.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent operation and maintenance system of an energy storage power station based on cloud edge fusion, which comprises a monitoring center, wherein the monitoring center is in communication connection with an edge monitoring module, a data storage module, a data analysis module, an electricity consumption prediction module and an energy storage adjustment module;
The edge monitoring module is used for acquiring process flow characteristics of load equipment related to the energy storage power station, setting edge monitoring points according to the process flow characteristics, and acquiring data monitoring indexes of the edge monitoring points;
The data storage module is used for storing historical data information of relevant load equipment of the energy storage power station and each edge monitoring point;
the data analysis module is used for analyzing the data monitoring results of the edge monitoring points;
the power consumption prediction module is used for constructing an assembly topological graph of each flow sub-sequence, and acquiring power consumption prediction values of each flow sub-sequence according to an assembly relation;
The energy storage adjustment module is used for dynamically adjusting the energy storage information of the relevant load equipment of each node in the assembly topological graph.
Further, the process flow characteristics of the load equipment related to the energy storage power station are obtained by the edge monitoring module, the edge monitoring points are set according to the process flow characteristics, and the process of obtaining the data monitoring index of each edge monitoring point comprises the following steps:
acquiring process flow characteristics of load equipment related to a current energy storage power station, extracting flow information according to the process flow characteristics, splitting the energy storage power station equipment according to the flow information, and dividing the energy storage power station equipment into a plurality of flow subsequences;
Selecting an evaluation index according to the characteristics of each process flow in each flow subsequence, setting index weight of the evaluation index according to historical data, and judging a membership matrix of each flow subsequence to a preset importance level through fuzzy comprehensive evaluation;
Acquiring importance levels of all the flow subsequences according to the membership matrix and the index weight, comparing the importance levels of the flow subsequences with preset importance levels, and setting edge monitoring points of the flow subsequences meeting the importance level standard;
and determining the number of the edge monitoring points and the data monitoring index of the edge monitoring points according to the importance evaluation grade and the evaluation index of the flow subsequence.
Further, the process of setting the index weight of the evaluation index by the edge monitoring module according to the historical data includes:
the method comprises the steps of obtaining historical data monitoring results of a plurality of historical acquisition periods of evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, obtaining abnormal accumulation times in the range that the historical data monitoring results do not accord with the corresponding historical evaluation index threshold ranges, and determining index weights of the evaluation indexes according to the abnormal accumulation times.
Further, the process of analyzing the data monitoring result of each edge monitoring point by the data analysis module includes:
acquiring data monitoring results of all index data of all edge monitoring points in a current acquisition period, marking acquisition time, setting a threshold range of all key index data, and judging whether the monitoring data of all the key index data of all the edge monitoring points are located in the corresponding threshold range;
if the monitoring data are in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a normal state;
If the monitoring data are not in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a state to be detected, acquiring the environment parameters of the edge monitoring point position, acquiring the corresponding environment compensation parameters of each edge monitoring point position under different environment parameters from a data storage module, matching the corresponding environment compensation parameters according to the current environment parameters of the edge monitoring point position, and carrying out compensation parameter adjustment on the data monitoring result;
If the monitoring data are in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as a normal state; and if the monitoring data are not in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as an abnormal state.
Further, the power consumption prediction module constructs an assembly topological graph of each flow sub-sequence, and the process of obtaining the power consumption prediction value of each flow sub-sequence according to the assembly relation comprises the following steps:
acquiring an assembly relation and a flow direction sequence among load devices in each flow subsequence, and constructing an assembly topological graph among the flow subsequences according to the assembly relation and the flow direction sequence among the flow subsequences;
Taking each flow subsequence as a node of the assembly topological graph, taking the assembly relation and the flow direction sequence among the flow subsequences as the connection relation among the nodes, calculating the operation influence degree of each node on other nodes according to the adjacency matrix of each node in the assembly topological graph, and setting an adjustment grade for the nodes according to the operation influence degree of each node on other nodes;
And taking index data monitoring results of the edge monitoring points corresponding to the flow subsequences as complementary nodes of the nodes, constructing an electric energy consumption prediction model based on the RBF neural network, learning the assembly topological graph, and outputting electric energy consumption prediction values of the flow subsequences.
Further, the power consumption prediction module builds a power consumption prediction model based on the RBF neural network, learns the assembly topological graph, and outputs the power consumption prediction amount of each flow subsequence, wherein the process comprises the following steps:
Acquiring historical electricity load and historical electricity generation load of each node in different time periods of a plurality of historical scheduling periods from a data storage module, and taking the historical electricity load and the historical electricity generation load as a test set and a training set;
The training set is input into the electric energy consumption prediction model for training until the loss function training is stable, model parameters are saved, the energy consumption model is tested through the testing set until the preset requirements are met, the electric energy consumption prediction model is output, the preset time information of the energy storage power station is obtained, and the preset time information of the energy storage power station is input into the electric energy consumption prediction model to obtain the preset electric energy consumption amount at the current moment.
Further, the process of dynamically adjusting the energy storage information of the relevant load equipment of each node in the assembly topological graph by the energy storage adjustment module comprises the following steps:
acquiring initial energy storage information and power consumption pre-measurement of a current acquisition period of relevant load equipment of each node in each assembly topological graph, acquiring supplementary node information of an abnormal state in each assembly topological graph, acquiring a data monitoring result of the supplementary node of the abnormal state and an adjustment grade of a node corresponding to the supplementary node, and acquiring relevant load equipment loss information corresponding to the node according to the data monitoring result;
Constructing a target function set according to the initial energy storage information of each node, the power consumption predicted amount corresponding to each node and the related load equipment loss information corresponding to each node, and solving the value of the target function based on a genetic algorithm;
when the energy storage information of each node, the power consumption predicted quantity of each node and the loss information of the related load equipment corresponding to each node reach dynamic balance, acquiring the energy storage adjustment data of the current acquisition period of each node;
The energy storage adjustment process of each node is ordered according to the adjustment level of each node, and the energy storage information of the relevant load equipment of each node is dynamically adjusted in sequence according to the energy storage adjustment data of the current acquisition period of each node and the ordering result.
Further, the dynamic balance condition is: the stored energy power is equal to the sum of the predicted amount of power consumption and the power consumption of the associated load device.
Compared with the prior art, the invention has the beneficial effects that: according to the process characteristics, edge monitoring points are set, data monitoring indexes of the edge monitoring points are obtained, comprehensive operation and performance optimization of the battery pack can be achieved through collection, analysis and management of the data, reliability and efficiency of a power station are improved, meanwhile, energy storage adjustment processes of the nodes are ordered according to adjustment grades of the nodes, energy storage information of relevant load equipment of each node is dynamically adjusted in sequence according to energy storage adjustment data and ordering results of the current collection period of the nodes, and efficiency and accuracy of load dynamic balance of the energy storage power station are improved.
Drawings
Fig. 1 is a schematic diagram of an intelligent operation and maintenance system of an energy storage power station based on cloud edge fusion according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, an intelligent operation and maintenance system of an energy storage power station based on cloud edge fusion comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module 1, and the intelligent operation and maintenance system of the energy storage power station based on cloud edge fusion comprises the monitoring center, and is characterized in that the monitoring center is in communication connection with an edge monitoring module, a data storage module, a data analysis module, an electricity consumption prediction module and an energy storage adjustment module;
The edge monitoring module is used for acquiring process flow characteristics of load equipment related to the energy storage power station, setting edge monitoring points according to the process flow characteristics, and acquiring data monitoring indexes of the edge monitoring points;
The data storage module is used for storing historical data information of relevant load equipment of the energy storage power station and each edge monitoring point;
the data analysis module is used for analyzing the data monitoring results of the edge monitoring points;
the power consumption prediction module is used for constructing an assembly topological graph of each flow sub-sequence, and acquiring power consumption prediction values of each flow sub-sequence according to an assembly relation;
The energy storage adjustment module is used for dynamically adjusting the energy storage information of the relevant load equipment of each node in the assembly topological graph.
It should be further noted that, in the implementation process, the process flow characteristics of the load equipment related to the energy storage power station are obtained by the edge monitoring module, the edge monitoring points are set according to the flow characteristics, and the process of obtaining the data monitoring index of each edge monitoring point comprises:
acquiring process flow characteristics of load equipment related to a current energy storage power station, extracting flow information according to the process flow characteristics, splitting the energy storage power station equipment according to the flow information, and dividing the energy storage power station equipment into a plurality of flow subsequences;
Selecting an evaluation index according to the characteristics of each process flow in each flow subsequence, setting index weight of the evaluation index according to historical data, and judging a membership matrix of each flow subsequence to a preset importance level through fuzzy comprehensive evaluation;
Acquiring importance levels of all the flow subsequences according to the membership matrix and the index weight, comparing the importance levels of the flow subsequences with preset importance levels, and setting edge monitoring points of the flow subsequences meeting the importance level standard;
and determining the number of the edge monitoring points and the data monitoring index of the edge monitoring points according to the importance evaluation grade and the evaluation index of the flow subsequence.
It should be further noted that, in the implementation process, the process flow characteristics of the load devices related to the energy storage power station include: the battery pack related equipment, a core component of the energy storage power station, is responsible for storing and releasing electric energy; inverter-related devices converting direct current electric energy into alternating current electric energy for outputting the stored energy to a power grid or supplying to a user; the charge and discharge related equipment comprises a charger and a discharger, and is used for controlling the charge and discharge processes of the battery pack, so that the high efficiency and the safety of energy conversion are ensured; the temperature control related equipment is used for monitoring and adjusting the temperature of the battery module and preventing performance degradation or safety problems caused by overheat or supercooling.
It should be further noted that, in the implementation process, the data monitoring indexes of the edge monitoring points include real-time state information such as electric quantity, voltage, current, temperature and energy conversion efficiency of the relevant load devices.
It should be further noted that, in the implementation process, the process of obtaining the fuzzy comprehensive evaluation result according to the membership matrix and the index weight includes:
the weight index matrix and the membership matrix of each index data of the flow subsequence are fused through the following formula to obtain a fuzzy comprehensive evaluation result matrix of the flow subsequence, and a fuzzy comprehensive evaluation result is obtained according to the fuzzy comprehensive evaluation result matrix;
wherein, the formula is:
M＝αM1+βM2；
Wherein M is a fuzzy comprehensive evaluation result matrix of the flow subsequence, M 1 is a weight index matrix of the index data, M 2 is the membership matrix, "+" represents addition of elements at positions corresponding to the weight index matrix of the index data and the membership matrix, and α and β are weighting parameters for controlling balance between the weight index matrix of the index data and the membership matrix in the fuzzy comprehensive evaluation result matrix of the flow subsequence.
It should be further noted that, in the implementation process, the process of setting the index weight of the evaluation index by the edge monitoring module according to the historical data includes:
the method comprises the steps of obtaining historical data monitoring results of a plurality of historical acquisition periods of evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, obtaining abnormal accumulation times in the range that the historical data monitoring results do not accord with the corresponding historical evaluation index threshold ranges, and determining index weights of the evaluation indexes according to the abnormal accumulation times.
It should be further noted that, in the implementation process, the process of the data analysis module for analyzing the data monitoring result of each edge monitoring point location includes:
acquiring data monitoring results of all index data of all edge monitoring points in a current acquisition period, marking acquisition time, setting a threshold range of all key index data, and judging whether the monitoring data of all the key index data of all the edge monitoring points are located in the corresponding threshold range;
if the monitoring data are in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a normal state;
If the monitoring data are not in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a state to be detected, acquiring the environment parameters of the edge monitoring point position, acquiring the corresponding environment compensation parameters of each edge monitoring point position under different environment parameters from a data storage module, matching the corresponding environment compensation parameters according to the current environment parameters of the edge monitoring point position, and carrying out compensation parameter adjustment on the data monitoring result;
If the monitoring data are in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as a normal state; and if the monitoring data are not in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as an abnormal state.
It should be further noted that, in the implementation process, the electricity consumption prediction module constructs an assembly topology diagram of each flow sub-sequence, and the process of obtaining the electricity consumption prediction value of each flow sub-sequence according to the assembly relationship includes:
acquiring an assembly relation and a flow direction sequence among load devices in each flow subsequence, and constructing an assembly topological graph among the flow subsequences according to the assembly relation and the flow direction sequence among the flow subsequences;
Taking each flow subsequence as a node of the assembly topological graph, taking the assembly relation and the flow direction sequence among the flow subsequences as the connection relation among the nodes, calculating the operation influence degree of each node on other nodes according to the adjacency matrix of each node in the assembly topological graph, and setting an adjustment grade for the nodes according to the operation influence degree of each node on other nodes;
And taking index data monitoring results of the edge monitoring points corresponding to the flow subsequences as complementary nodes of the nodes, constructing an electric energy consumption prediction model based on the RBF neural network, learning the assembly topological graph, and outputting electric energy consumption prediction values of the flow subsequences.
It should be further noted that, in the implementation process, a calculation formula for calculating the influence degree K i on the operation of other nodes according to the adjacency matrix of each node in the assembly topological graph is as follows:
Wherein K i represents the influence degree of the ith node on the operation of other nodes, The related coefficient of the ith node and the (i-1) th node is represented, and the specific numerical value of the related coefficient is obtained through a node adjacency matrix; θ represents an error coefficient, (L T)' represents a state transition matrix, W represents an n×1 matrix with all elements of 1, and n represents the number of nodes.
It should be further noted that, in the implementation process, the power consumption prediction module builds a power consumption prediction model based on the RBF neural network, learns the assembly topology map, and the process of outputting the power consumption prediction value of each flow sub-sequence includes:
Acquiring historical electricity load and historical electricity generation load of each node in different time periods of a plurality of historical scheduling periods from a data storage module, and taking the historical electricity load and the historical electricity generation load as a test set and a training set;
The training set is input into the electric energy consumption prediction model for training until the loss function training is stable, model parameters are saved, the energy consumption model is tested through the testing set until the preset requirements are met, the electric energy consumption prediction model is output, the preset time information of the energy storage power station is obtained, and the preset time information of the energy storage power station is input into the electric energy consumption prediction model to obtain the preset electric energy consumption amount at the current moment.
It should be further noted that, in the implementation process, the process of dynamically adjusting the energy storage information of the relevant load devices of each node in the assembly topology graph by the energy storage adjustment module includes:
acquiring initial energy storage information and power consumption pre-measurement of a current acquisition period of relevant load equipment of each node in each assembly topological graph, acquiring supplementary node information of an abnormal state in each assembly topological graph, acquiring a data monitoring result of the supplementary node of the abnormal state and an adjustment grade of a node corresponding to the supplementary node, and acquiring relevant load equipment loss information corresponding to the node according to the data monitoring result;
Constructing a target function set according to the initial energy storage information of each node, the power consumption predicted amount corresponding to each node and the related load equipment loss information corresponding to each node, and solving the value of the target function based on a genetic algorithm;
when the energy storage information of each node, the power consumption predicted quantity of each node and the loss information of the related load equipment corresponding to each node reach dynamic balance, acquiring the energy storage adjustment data of the current acquisition period of each node;
The energy storage adjustment process of each node is ordered according to the adjustment level of each node, and the energy storage information of the relevant load equipment of each node is dynamically adjusted in sequence according to the energy storage adjustment data of the current acquisition period of each node and the ordering result.
It should be further noted that, in the implementation process, the dynamic balance condition is: the stored energy power is equal to the sum of the predicted amount of power consumption and the power consumption of the associated load device.
It should be further noted that, in the implementation process, in order to make the relevant load device achieve the best performance and maintain the continuous stability in the power supply process, when the energy storage information of each node, the power consumption predicted amount of each node and the relevant load device loss information corresponding to each node achieve dynamic balance, the distributed power grid device maintains stability in the operation process, where the relation of the target function set is as follows:
Q＝nZi
Wherein, Q is the adjustment data of the objective function, G i is the controllable maximum electric energy of the current relevant load device, Y i is the predicted electric energy consumption of the current relevant load device, V i is the electric energy consumption of the relevant load device of the current relevant load device, and Z i is the adjustment data of each node after solving the objective function.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (7)
1. The intelligent operation and maintenance system of the energy storage power station based on cloud edge fusion comprises a monitoring center and is characterized in that the monitoring center is in communication connection with an edge monitoring module, a data storage module, a data analysis module, an electricity consumption prediction module and an energy storage adjustment module;
The edge monitoring module is used for acquiring process flow characteristics of load equipment related to the energy storage power station, setting edge monitoring points according to the process flow characteristics, and acquiring data monitoring indexes of the edge monitoring points;
The data storage module is used for storing historical data information of relevant load equipment of the energy storage power station and each edge monitoring point;
the data analysis module is used for analyzing the data monitoring results of the edge monitoring points;
the power consumption prediction module is used for constructing an assembly topological graph of each flow sub-sequence, and acquiring power consumption prediction values of each flow sub-sequence according to an assembly relation;
The energy storage adjustment module is used for dynamically adjusting energy storage information of relevant load equipment of each node in the assembly topological graph;
the process for acquiring the data monitoring index of each edge monitoring point position by the edge monitoring module comprises the following steps of:
acquiring process flow characteristics of load equipment related to a current energy storage power station, extracting flow information according to the process flow characteristics, splitting the energy storage power station equipment according to the flow information, and dividing the energy storage power station equipment into a plurality of flow subsequences;
And selecting an evaluation index according to the characteristics of each process flow in each flow subsequence, setting index weight of the evaluation index according to historical data, and judging a membership matrix of each flow subsequence to a preset importance level through fuzzy comprehensive evaluation.
2. The cloud edge fusion-based intelligent operation and maintenance system of an energy storage power station according to claim 1, wherein the process of obtaining the data monitoring index of each edge monitoring point location further comprises:
Acquiring importance levels of all the flow subsequences according to the membership matrix and the index weight, comparing the importance levels of the flow subsequences with preset importance levels, and setting edge monitoring points of the flow subsequences meeting the importance level standard;
and determining the number of the edge monitoring points and the data monitoring index of the edge monitoring points according to the importance evaluation grade and the evaluation index of the flow subsequence.
3. The cloud-edge fusion-based intelligent operation and maintenance system for the energy storage power station according to claim 2, wherein the process of setting the index weight of the evaluation index by the edge monitoring module according to the historical data comprises the following steps:
the method comprises the steps of obtaining historical data monitoring results of a plurality of historical acquisition periods of evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, obtaining abnormal accumulation times in the range that the historical data monitoring results do not accord with the corresponding historical evaluation index threshold ranges, and determining index weights of the evaluation indexes according to the abnormal accumulation times.
4. The cloud edge fusion-based intelligent operation and maintenance system for the energy storage power station according to claim 3, wherein the process of analyzing the data monitoring results of each edge monitoring point location by the data analysis module comprises the following steps:
acquiring data monitoring results of all index data of all edge monitoring points in a current acquisition period, marking acquisition time, setting a threshold range of all key index data, and judging whether the monitoring data of all the key index data of all the edge monitoring points are located in the corresponding threshold range;
and if the monitoring data is in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a normal state.
5. The intelligent operation and maintenance system of the energy storage power station based on cloud edge fusion according to claim 4, wherein,
If the monitoring data are not in the threshold range, marking the data monitoring result corresponding to the edge monitoring point position as a state to be detected, acquiring the environment parameters of the edge monitoring point position, acquiring the corresponding environment compensation parameters of each edge monitoring point position under different environment parameters from a data storage module, matching the corresponding environment compensation parameters according to the current environment parameters of the edge monitoring point position, and carrying out compensation parameter adjustment on the data monitoring result;
If the monitoring data are in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as a normal state; and if the monitoring data are not in the threshold range after the compensation parameter adjustment, marking the data monitoring result corresponding to the edge monitoring point position as an abnormal state.
6. The cloud-edge fusion-based intelligent operation and maintenance system for the energy storage power station, according to claim 5, wherein the electricity consumption prediction module constructs an assembly topological graph of each flow sub-sequence, and the process of obtaining the electricity consumption prediction value of each flow sub-sequence according to the assembly relation comprises the following steps:
and acquiring the assembly relation and the flow direction sequence among the load devices in each flow subsequence, and constructing an assembly topological graph among the flow subsequences according to the assembly relation and the flow direction sequence among the flow subsequences.
7. The cloud edge fusion-based intelligent operation and maintenance system for an energy storage power station as claimed in claim 6, further comprising
Taking each flow subsequence as a node of the assembly topological graph, taking the assembly relation and the flow direction sequence among the flow subsequences as the connection relation among the nodes, calculating the operation influence degree of each node on other nodes according to the adjacency matrix of each node in the assembly topological graph, and setting an adjustment grade for the nodes according to the operation influence degree of each node on other nodes;
And taking index data monitoring results of the edge monitoring points corresponding to the flow subsequences as complementary nodes of the nodes, constructing an electric energy consumption prediction model based on the RBF neural network, learning the assembly topological graph, and outputting electric energy consumption prediction values of the flow subsequences.
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