Microgrid operation supervision system based on artificial intelligence

Abstract

The invention discloses a micro-grid operation supervision system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a micro-grid structure, and acquiring a micro-grid structure data network according to the micro-grid structure; performing data acquisition on the micro-grid structure data network through a distributed power supply data unit, an energy storage data unit and a power utilization data unit, and establishing a micro-grid acquisition time group data network; secondly, acquiring energy conversion data by using an energy conversion unit and mapping the energy conversion data to a micro-grid acquisition time group data network; an energy processing unit is used for extracting a microgrid data set in the microgrid acquisition time set data network and processing the microgrid data set, a first energy conversion loss rate and a second energy scheduling data value are obtained, and then a microgrid operation state set is obtained; and finally, establishing an energy collaboration data monitoring model through the energy collaboration module, sending the energy collaboration data monitoring model to the monitoring module, obtaining an abnormal collaboration signal, sending the abnormal collaboration signal to the management module, and managing the micro-grid. And the stability of the micro-grid is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and specifically to a microgrid operation supervision system based on artificial intelligence. Background Art

[0002] A microgrid, also translated as a micro-network, refers to a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc.; the proposal of the microgrid aims to achieve the flexible and efficient application of distributed power sources and solve the problem of grid connection of a large number and diverse forms of distributed power sources; the development and extension of the microgrid can fully promote the large-scale access of distributed power sources and renewable energy, realize the highly reliable supply of various energy forms to loads, and is an effective way to realize an active distribution network, enabling the traditional power grid to transition to an intelligent power grid;

[0003] Among them, the energy conversion device is mainly responsible for converting different forms of electric energy to meet the needs of various load devices inside the microgrid; in the microgrid, the renewable energy output by distributed power sources such as photovoltaic power generation, wind power generation, fuel cells, gas turbines, etc. is stored by the storage device, and then the energy conversion device converts the DC electric energy into AC electric energy for use by households, enterprises, and industries, playing a bridging role between the distributed power source and the load; however, when the power supply load is overloaded or the automatic regulation is unbalanced, abnormal fluctuations occur during the conversion process, thus affecting the stability of the microgrid; therefore, a microgrid operation supervision system based on artificial intelligence is provided. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a microgrid operation supervision system based on artificial intelligence;

[0005] The object of the present invention can be achieved through the following technical solutions: A microgrid operation supervision system based on artificial intelligence, the system includes a status data acquisition module, a conversion processing module, an energy coordination module, a monitoring module, and an adjustment module;

[0006] The status data acquisition module includes a distributed power source data unit, an energy storage data unit, and a power consumption data unit: used to obtain the microgrid structure, and obtain the microgrid structure data network according to the microgrid structure; data collection is performed on the microgrid structure data network through the distributed power source data unit, the energy storage data unit, and the power consumption data unit to establish a microgrid acquisition time group data network;

[0007] The conversion processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it to the microgrid acquisition time group data network; the energy processing unit is used to extract the microgrid data group in the microgrid acquisition time group data network and perform processing to obtain the first energy conversion loss rate and the second energy scheduling data value, and further obtain the microgrid operation status group;

[0008] The energy coordination module is used to map the microgrid operation status group to the time group nodes corresponding to the microgrid acquisition time group data network to establish an energy coordination data monitoring model;

[0009] The monitoring module is used to extract the microgrid operation status group corresponding to a continuous number of time group nodes in the energy coordination data monitoring model to obtain an abnormal coordination signal;

[0010] The management module is used to manage the microgrid according to the abnormal coordination signal.

[0011] Further, the obtaining process of the microgrid structure data network includes:

[0012] The distributed power source, energy storage device, energy conversion device and load in the microgrid structure are respectively corresponding generated into a power measurement node, a storage device node, an energy conversion node and a user node; a power grid transmission channel is set, and the power measurement node, the storage device node and the user node are sequentially connected through the power grid transmission channel, and then the energy conversion nodes are distributed on each power grid transmission channel to obtain the microgrid structure data network.

[0013] Further, the establishing process of the microgrid acquisition time group data network includes:

[0014] The distributed power source data unit, the energy storage data unit and the power consumption data unit are respectively used to collect the corresponding power generation data, stored energy data and power consumption data according to the power measurement node, the storage device node and the user node;

[0015] Set the acquisition time node t i in the power measurement node, where i is the acquisition node number and takes positive integer values, and set the transmission time Δt in the power grid transmission channel; obtain the acquisition time nodes corresponding to the storage device node and the user node according to the transmission time, denoted as the storage time node and the user time node, which are (t i +Δt) and (t i +2Δt) respectively, and generate a data acquisition time group with the acquisition time node;

[0016] Furthermore, the distributed power source data unit, the energy storage data unit and the power consumption data unit collect the corresponding power generation data, stored energy data and power consumption data in real time according to the data acquisition time group and connect them to generate a microgrid data group;

[0017] Generate corresponding time - group nodes for several data - acquisition time groups, map the micro - grid data groups to the corresponding time - group nodes, and then connect the time - group nodes to establish a micro - grid acquisition time - group data network.

[0018] Furthermore, the process of obtaining the energy - conversion data includes:

[0019] Obtain the first energy - conversion acquisition time point and the second energy - conversion acquisition time point corresponding to the energy - conversion node according to the data - acquisition time group, denoted as and respectively, and then collect the energy - conversion data corresponding to the energy - conversion node and map it on the time - group node corresponding to the micro - grid acquisition time - group data network; among them, the energy - conversion data includes converted energy data, pre - stored energy data, and scheduled energy data.

[0020] Furthermore, the process of obtaining the micro - grid operation - state group includes:

[0021] Extract the micro - grid data group and energy - conversion data on the time - group node corresponding to the micro - grid acquisition time - group data network for processing, and obtain the first energy - conversion loss rate and the second energy - scheduling data value;

[0022] The specific formula for obtaining the first energy - conversion loss rate is:

[0023]

[0024] where represents the first energy - conversion loss rate; represents the generated - electricity data collected corresponding to the acquisition time node; represents the stored - energy data collected corresponding to the storage time node; represents the converted - energy data collected corresponding to the first energy - conversion acquisition time point, and

[0025] If , then , and then label the energy - conversion node corresponding to the first energy - conversion acquisition time point as a high - quality energy - conversion node;

[0026] If vice versa, when , then label the energy - conversion node corresponding to the first energy - conversion acquisition time point as an abnormal energy - conversion node, and then generate an energy - conversion loss state for the state corresponding to the energy - conversion node; vice versa, then generate a normal conversion node for the corresponding energy - conversion node.

[0027] Furthermore, the specific formula for obtaining the second energy - scheduling data value is:

[0028]

[0029] Among them, γ i is represented as the second energy scheduling data value; is represented as the scheduled energy data collected corresponding to the second energy conversion acquisition time point, and ; C is represented as the pre-stored energy data; is represented as the power consumption data collected corresponding to the user node;

[0030] If γ i > 0, then label the energy conversion node corresponding to the second energy conversion acquisition time point as a normal scheduling node; otherwise, label the corresponding energy conversion node as an abnormal scheduling node, and generate an energy supply shortage state for the state corresponding to the energy conversion node;

[0031] Generate a normal state for the states corresponding to the high-quality energy conversion nodes, normal conversion nodes, and normal scheduling nodes, and mark the energy conversion loss state and the energy supply shortage state as abnormal states, and then generate a microgrid operation state group for the obtained states.

[0032] Furthermore, the process by which the monitoring module obtains the abnormal collaboration signal includes:

[0033] Set a monitoring period group N, and N > 0. Extract the corresponding time group nodes according to the monitoring period group, obtain the corresponding microgrid operation state groups, and extract the states corresponding to the same energy conversion node in each microgrid operation state group. Furthermore, obtain the abnormal state ratio K of the energy conversion node in the monitoring period group, that is, the calculation formula is: , where M is the number of abnormal states;

[0034] If K > 0.3, then generate an abnormal collaboration signal for the corresponding energy conversion node and send it to the management module, otherwise do nothing.

[0035] Furthermore, the process by which the management module manages the microgrid includes:

[0036] The management module is connected to a management terminal, which is used to receive the abnormal collaboration signal, obtain the energy conversion node corresponding to the abnormal collaboration signal, and further obtain the corresponding abnormal state;

[0037] If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired;

[0038] If it is an energy supply shortage state, it means that the power supply is insufficient, and energy scheduling needs to be performed on the energy storage device corresponding to the storage device node connected to the energy conversion node.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention obtains the microgrid structure and acquires the microgrid structure data network according to the microgrid structure; data collection is performed on the microgrid structure data network through the distributed power data unit, the energy storage data unit, and the power consumption data unit to establish a microgrid acquisition time group data network; secondly, the energy conversion unit is used to collect energy conversion data and map it on the microgrid acquisition time group data network; the energy processing unit is used to extract the microgrid data group in the microgrid acquisition time group data network and perform processing to obtain the first energy conversion loss rate and the second energy scheduling data value, and further obtain the microgrid operation status group; finally, an energy cooperation data monitoring model is established through the energy cooperation module and sent to the monitoring module, and an abnormal cooperation signal is obtained and sent to the management module to manage the microgrid; effectively improving the stability of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0041] Figure 1 It is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0043] Embodiment 1

[0044] As Figure 1 shown, a microgrid operation supervision system based on artificial intelligence, the system includes a state data acquisition module, a conversion processing module, an energy cooperation module, a monitoring module, and an adjustment module;

[0045] The state data acquisition module includes a distributed power data unit, an energy storage data unit, and a power consumption data unit: used to obtain the microgrid structure, acquire the microgrid structure data network according to the microgrid structure; perform data collection on the microgrid structure data network through the distributed power data unit, the energy storage data unit, and the power consumption data unit to establish a microgrid acquisition time group data network;

[0046] The conversion processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it to the microgrid acquisition time group data network; the energy processing unit is used to extract the microgrid data group in the microgrid acquisition time group data network and perform processing to obtain the first energy conversion loss rate and the second energy scheduling data value, and further obtain the microgrid operation state group;

[0047] The energy coordination module is used to map the microgrid operation state group to the time group nodes corresponding to the microgrid acquisition time group data network to establish an energy coordination data monitoring model;

[0048] The monitoring module is used to extract the microgrid operation state groups corresponding to several consecutive time group nodes in the energy coordination data monitoring model to obtain an abnormal coordination signal;

[0049] The management module is used to manage the microgrid according to the abnormal coordination signal.

[0050] Embodiment 2

[0051] This embodiment is a further limitation of Embodiment 1. The process of obtaining the microgrid structure data network includes:

[0052] The distributed power source, energy storage device, energy conversion device, and load in the microgrid structure are respectively corresponding to generate a power measurement node, a storage device node, an energy conversion node, and a user node; a power grid transmission channel is set, and the power measurement node, the storage device node, and the user node are sequentially connected through the power grid transmission channel, and then the energy conversion nodes are distributed on each power grid transmission channel to obtain the microgrid structure data network;

[0053] The process of establishing the microgrid acquisition time group data network includes:

[0054] The distributed power source data unit, the energy storage data unit, and the power consumption data unit are respectively used to collect corresponding power generation data, stored energy data, and power consumption data according to the power measurement node, the storage device node, and the user node;

[0055] Set the acquisition time node t i at the power measurement node, where i is the acquisition node number and takes positive integer values. Set the transmission time Δt on the power grid transmission channel; obtain the acquisition time nodes corresponding to the storage device node and the user node according to the transmission time, denoted as the storage time node and the user time node, which are (t i + Δt) and (t i + 2Δt) respectively, and generate a data acquisition time group with the acquisition time node;

[0056] Furthermore, the distributed power data unit, the energy storage data unit, and the power consumption data unit collect corresponding power generation data, stored energy data, and power consumption data in real time according to the data acquisition time group, and are connected to generate a microgrid data group;

[0057] Generate corresponding time group nodes for several data acquisition time groups, map the microgrid data group to the corresponding time group nodes, and then connect the time group nodes to establish a microgrid acquisition time group data network;

[0058] Embodiment 3

[0059] This embodiment is a further limitation of Embodiment 1. The process of obtaining the energy conversion data includes:

[0060] Obtain the first energy conversion acquisition time point and the second energy conversion acquisition time point corresponding to the energy conversion node according to the data acquisition time group, denoted as and respectively, and then collect the energy conversion data corresponding to the energy conversion node and map it on the time group node corresponding to the microgrid acquisition time group data network; wherein, the energy conversion data includes converted energy data, pre-stored energy data, and scheduled energy data;

[0061] The process of obtaining the microgrid operation state group includes:

[0062] Extract the microgrid data group and the energy conversion data on the time group node corresponding to the microgrid acquisition time group data network for processing, and obtain the first energy conversion loss rate and the second energy scheduling data value;

[0063] The specific formula for obtaining the first energy conversion loss rate is:

[0064]

[0065] Wherein, represents the first energy conversion loss rate; represents the power generation data collected corresponding to the acquisition time node; represents the stored energy data collected corresponding to the storage time node; represents the converted energy data collected corresponding to the first energy conversion acquisition time point, and

[0066] If , then , and then label the energy conversion node corresponding to the first energy conversion acquisition time point as a high-quality energy conversion node;

[0067] If vice versa, when If so, mark the energy conversion node corresponding to the first energy conversion acquisition time point as an abnormal energy conversion node, and then generate an energy conversion loss state for the state corresponding to the energy conversion node; otherwise, generate a normal conversion node for the corresponding energy conversion node;

[0068] It should be further noted that in a specific embodiment, the energy lost during the conversion and transmission of power generation data through the power grid transmission channel is not considered. Therefore, the loss data during the conversion process can be obtained based on the power generation data, energy conversion data, and stored energy data, and then it can be determined whether the corresponding energy conversion device is abnormal;

[0069] The specific formula for obtaining the second energy scheduling data value is:

[0070]

[0071] where γ i represents the second energy scheduling data value; represents the scheduled energy data collected corresponding to the second energy conversion acquisition time point, and ; C represents the pre-stored energy data; represents the power consumption data collected corresponding to the user node;

[0072] If γ i > 0, mark the energy conversion node corresponding to the second energy conversion acquisition time point as a normal scheduling node; otherwise, mark the corresponding energy conversion node as an abnormal scheduling node, and generate an energy supply shortage state for the state corresponding to the energy conversion node;

[0073] Generate a normal state for the states corresponding to the high-quality energy conversion nodes, normal conversion nodes, and normal scheduling nodes, mark the energy conversion loss state and the energy supply shortage state as abnormal states, and then generate a microgrid operation state group for the obtained states;

[0074] In the above embodiment, it should be further noted that the energy conversion device plays a major role in the microgrid, including energy conversion, energy storage, and energy release, etc.;

[0075] Embodiment 4

[0076] This embodiment is a further limitation of Embodiment 1. The process by which the monitoring module obtains an abnormal cooperation signal includes:

[0077] Set a monitoring period group N, and N > 0. Extract the corresponding time group nodes according to the monitoring period group, obtain the corresponding microgrid operation state groups, and extract the states corresponding to the same energy conversion node in each microgrid operation state group. Then obtain the abnormal state ratio K of the energy conversion node in the monitoring period group, that is, the calculation formula is: , where M is the number of abnormal states;

[0078] If K > 0.3, an abnormal collaboration signal will be generated for the corresponding energy conversion node and sent to the management module; otherwise, no action will be taken.

[0079] In the above embodiment, it should be further noted that based on the energy collaboration data monitoring model, the nodes corresponding to the microgrid structure are collected in real time for the microgrid acquisition time group data network, and the collected data is generated into a data group, and all are stored and extracted by the time group nodes, which improves the data transmission speed. By processing the data group, the microgrid operation state group is obtained, and the monitoring cycle group is set to analyze the microgrid operation data group to avoid the contingency of the state. Furthermore, the stability of the microgrid is monitored in real time through the energy collaboration data monitoring model; the balance state of the microgrid is improved.

[0080] Embodiment 5

[0081] This embodiment is a further limitation of Embodiment 1. The process of the management module managing the microgrid includes:

[0082] The management module is connected to a management terminal, which is used to receive the abnormal collaboration signal, obtain the energy conversion node corresponding to the abnormal collaboration signal, and then obtain the corresponding abnormal state;

[0083] If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired to ensure the stability of energy conversion;

[0084] If it is an energy supply shortage state, it means that the power supply is insufficient, and it is necessary to perform energy scheduling on the energy storage device corresponding to the storage device node connected to the energy conversion node and reduce the load corresponding to the user node to ensure the stability of energy scheduling.

[0085] Working principle: The present invention obtains the microgrid structure and obtains the microgrid structure data network according to the microgrid structure; collects data from the microgrid structure data network through the distributed power data unit, energy storage data unit, and power consumption data unit to establish a microgrid acquisition time group data network; then uses the energy conversion unit to collect energy conversion data and map it on the microgrid acquisition time group data network; uses the energy processing unit to extract and process the microgrid data group in the microgrid acquisition time group data network to obtain the first energy conversion loss rate and the second energy scheduling data value, and then obtains the microgrid operation state group; finally, through the energy collaboration module, an energy collaboration data monitoring model is established and sent to the monitoring module, and an abnormal collaboration signal is obtained and sent to the management module to manage the microgrid; effectively improving the stability of the microgrid.

[0086] The features and exemplary embodiments of various aspects of the present application will be described in detail below. For the purpose of making the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments; it should be understood that the specific embodiments described herein are only intended to explain the present application rather than limit the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0087] The above embodiments are only used to illustrate the technical methods of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical methods of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A microgrid operation supervision system based on artificial intelligence, characterized in that: The system includes a state data acquisition module, a conversion processing module, an energy coordination module, a monitoring module and a regulation module; The state data acquisition module includes a distributed power data unit, an energy storage data unit and a power consumption data unit: used to obtain the microgrid structure, and obtain the microgrid structure data network according to the microgrid structure; collect data from the microgrid structure data network through the distributed power data unit, the energy storage data unit and the power consumption data unit, and establish a microgrid collection time group data network; The conversion processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it in the microgrid collection time group data network; the energy processing unit is used to extract the microgrid data group in the microgrid collection time group data network and process it to obtain the first energy conversion loss rate and the second energy scheduling data value, and then obtain the microgrid operation status group; The energy coordination module is used to map the microgrid operation status group to the time group node corresponding to the microgrid collection time group data network to establish an energy coordination data monitoring model; The monitoring module is used to extract the microgrid operation status groups corresponding to a number of consecutive time group nodes in the energy coordination data monitoring model and obtain abnormal coordination signals; The management module is used to manage the microgrid according to the abnormal coordination signal.

2. According to the microgrid operation supervision system based on artificial intelligence according to claim 1, it is characterized in that: The acquisition process of the microgrid structure data network includes: The distributed power sources, energy storage devices, energy conversion devices and loads in the microgrid structure are correspondingly generated into power measurement nodes, storage device nodes, energy conversion nodes and user nodes; a power grid transmission channel is set, and the power measurement nodes, storage device nodes and user nodes are connected in sequence through the power grid transmission channel, and then the energy conversion nodes are distributed on each power grid transmission channel to obtain the microgrid structure data network.

3. According to the microgrid operation supervision system based on artificial intelligence in claim 2, it is characterized in that: The process of establishing the microgrid collection time group data network includes: The distributed power data unit, energy storage data unit and power consumption data unit are used to collect corresponding power generation data, storage energy data and power consumption data according to power measurement nodes, storage device nodes and user nodes respectively; Set the acquisition time node t at the power measurement node i , where i is the acquisition node number and is a positive integer. The transmission time Δt is set in the power grid transmission channel. The acquisition time nodes corresponding to the storage device node and the user node are obtained according to the transmission time, which are recorded as the storage time node and the user time node, respectively (t i +Δt) and (t i +2Δt), and generate a data collection time group with the collection time node; Then, the distributed power data unit, energy storage data unit and power consumption data unit collect corresponding power generation data, storage energy data and power consumption data in real time according to the data collection time group, and connect to generate a microgrid data group; Corresponding time group nodes are generated from a number of data collection time groups, and microgrid data groups are mapped to corresponding time group nodes, and then the time group nodes are interconnected to establish a microgrid collection time group data network.

4. According to the microgrid operation supervision system based on artificial intelligence in claim 3, it is characterized in that: The process of acquiring the energy conversion data includes: According to the data acquisition time group, the first energy conversion acquisition time point and the second energy conversion acquisition time point corresponding to the energy conversion node are obtained and recorded as and , and then collect the energy conversion data corresponding to the energy conversion node and map it on the time group node corresponding to the microgrid collection time group data network; wherein the energy conversion data includes conversion energy data, pre-stored energy data and scheduling energy data.

5. According to claim 4, a microgrid operation supervision system based on artificial intelligence is characterized in that: The process of obtaining the microgrid operation status group includes: Extract the microgrid data group and energy conversion data on the corresponding time group node of the microgrid acquisition time group data network for processing to obtain the first energy conversion loss rate and the second energy scheduling data value; The specific formula for obtaining the first energy conversion loss rate is: in, Expressed as the first energy conversion loss rate; It represents the power generation data collected at the collection time node; It is represented by the storage energy data collected corresponding to the storage time node; is represented by the conversion energy data collected corresponding to the first energy conversion collection time point, and like ,but , and then marking the energy conversion node corresponding to the first energy conversion collection time point as a high-quality energy conversion node; If on the contrary, , the energy conversion node corresponding to the first energy conversion acquisition time point is marked as an abnormal energy conversion node, and then the state corresponding to the energy conversion node is generated as an energy conversion loss state; otherwise, the corresponding energy conversion node is generated as a normal conversion node.

6. The microgrid operation supervision system based on artificial intelligence according to claim 5 is characterized in that: The specific formula for obtaining the second energy scheduling data value is: Among them, γ i represented as a second energy scheduling data value; is represented by the scheduling energy data collected corresponding to the second energy conversion collection time point, and ; C represents pre-stored energy data; It represents the power consumption data collected corresponding to the user node; If γ i >0, the energy conversion node corresponding to the second energy conversion acquisition time point is marked as a normal scheduling node; otherwise, the corresponding energy conversion node is marked as an abnormal scheduling node, and the state corresponding to the energy conversion node is generated as an energy supply shortage state; The states corresponding to high-quality energy conversion nodes, normal conversion nodes and normal scheduling nodes are generated into normal states, and the energy conversion loss state and energy supply shortage state are marked as abnormal states, and then the acquired states are generated into a microgrid operation state group.

7. The microgrid operation supervision system based on artificial intelligence according to claim 6 is characterized in that: The process of the monitoring module acquiring the abnormal coordination signal includes: Set the monitoring cycle group N, and N>0, extract the corresponding time group node according to the monitoring cycle group, obtain the corresponding microgrid operation state group, and extract the state corresponding to the same energy conversion node in each microgrid operation state group, and then obtain the abnormal state ratio K of the energy conversion node in the monitoring cycle group, that is, the calculation formula is: Where, M is the number of abnormal states; If K>0.3, the corresponding energy conversion node will generate an abnormal coordination signal and send it to the management module, otherwise no processing will be performed.

8. The microgrid operation supervision system based on artificial intelligence according to claim 7 is characterized in that: The process of the management module managing the microgrid includes: The management module is connected to a management terminal, and is used to receive an abnormal coordination signal, obtain an energy conversion node corresponding to the abnormal coordination signal, and then obtain a corresponding abnormal state; If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired; If the energy supply is insufficient, it means that the power supply is insufficient, and energy scheduling needs to be performed on the energy storage device corresponding to the storage device node connected to the energy conversion node.

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