Intelligent power supply control method of photovoltaic energy storage integrated equipment

Abstract

The invention provides an intelligent power supply control method for photovoltaic energy storage integrated equipment, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining a plurality of groups of AC load equipment and a plurality of groups of DC load equipment; obtaining a first AC power supply demand and a first DC power supply demand; reading first stored electric energy and first generating capacity; obtaining a load power supply decision and an energy storage charging and discharging decision; according to the method, power is supplied according to a load power supply decision and an energy storage charging and discharging decision, power supply abnormity identification is carried out, a first abnormity identification result is obtained, power supply on-off control is carried out according to the first abnormity identification result, and the problems that in the prior art, power supply equipment is damaged due to insufficient voltage transformation abnormity analysis in the power supply process, and power supply reliability is poor are solved. The technical problem of poor power supply control effect in the prior art is solved, the technical effect of improving the accuracy of power supply decision planning is achieved, and the technical effects of improving the power supply safety and ensuring the safe operation of equipment are achieved by performing abnormal identification on the power supply process.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and specifically to an intelligent power supply control method for photovoltaic energy storage integrated equipment. Background Art

[0002] Nowadays, new energy power generation technology is developing rapidly and gradually replacing traditional energy power generation. Among them, photovoltaic power generation is widely used in distributed buildings. Photovoltaic power generation devices are generally used in conjunction with energy storage devices to store electrical energy that cannot be consumed locally for subsequent use, forming a photovoltaic energy storage integrated device. Most of the existing photovoltaic power supply controls only distribute power based on power demand, but the power supply mode and power supply voltage need to be changed during the photovoltaic power supply process, which makes it easy for abnormalities to occur during voltage conversion, which in turn causes damage to the power supply equipment and poor power supply control.

[0003] In summary, the prior art has the technical problem that the power supply equipment is damaged and the power supply control effect is poor due to insufficient analysis of abnormal voltage conversion during the power supply process. Summary of the invention

[0004] The present application provides an intelligent power supply control method for photovoltaic energy storage integrated equipment, which is used to solve the technical problems in the prior art that the power supply equipment is damaged and the power supply control effect is poor due to insufficient analysis of abnormal voltage conversion during the power supply process.

[0005] According to a first aspect of the present application, an intelligent power supply control method for a photovoltaic energy storage integrated device is provided, comprising: obtaining a load device at a power supply end, and classifying and dividing the load device according to a power supply mode, a power supply time, and a power supply voltage to obtain a plurality of groups of AC load devices and a plurality of groups of DC load devices; performing a power supply demand analysis based on the plurality of groups of AC load devices and the plurality of groups of DC load devices within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand; reading a first stored electric energy for a first predetermined time through an energy storage device, and reading a first power generation for a first predetermined time through a photovoltaic power generation device; based on the first stored electric energy, the first power generation, the first AC power supply demand, and the first DC power supply demand, performing a power supply decision analysis on the photovoltaic power generation device and the energy storage device through a power supply control center to obtain a load power supply decision and an energy storage charging and discharging decision; supplying power to the plurality of groups of AC load devices, the plurality of groups of DC load devices, and the energy storage device with the load power supply decision and the energy storage charging and discharging decision, and performing power supply abnormality identification on the plurality of groups of AC load devices, the plurality of groups of DC load devices, and the energy storage device to obtain a first abnormality identification result, and performing power supply on-off control according to the first abnormality identification result.

[0006] According to a second aspect of the present application, an intelligent power supply control system for photovoltaic energy storage integrated equipment is provided, including: a load device classification module, the load device classification module is used to obtain the load device at the power supply end, and classify and divide the load device according to the power supply mode, power supply time and power supply voltage, to obtain multiple groups of AC load devices and multiple groups of DC load devices; a power supply demand analysis module, the power supply demand analysis module is used to perform power supply demand analysis based on the multiple groups of AC load devices and the multiple groups of DC load devices within a first predetermined time, to obtain a first AC power supply demand and a first DC power supply demand; an electric energy data reading module, the electric energy data reading module reads the first stored electric energy for the first predetermined time through the energy storage device, and reads the first predetermined time through the photovoltaic power generation device a first power generation amount; a power supply decision analysis module, the power supply decision analysis module is used to perform power supply decision analysis on the photovoltaic power generation device and the energy storage device through the power supply control center based on the first stored electric energy, the first power generation amount, the first AC power supply demand and the first DC power supply demand, and obtain load power supply decision and energy storage charging and discharging decision; a power supply anomaly analysis module, the power supply anomaly analysis module is used to power the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device with the load power supply decision and the energy storage charging and discharging decision, and perform power supply anomaly identification on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device, obtain a first anomaly identification result, and perform power supply on-off control according to the first anomaly identification result.

[0007] According to one or more technical solutions adopted in this application, the beneficial effects that can be achieved are as follows:

[0008] The load devices at the power supply end are obtained, and the load devices are classified and divided according to the power supply mode, power supply time and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices. Based on the multiple groups of AC load devices and multiple groups of DC load devices, power supply demand analysis is performed within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand. The first stored electric energy at the first predetermined time is read through the energy storage device, and the first power generation at the first predetermined time is read through the photovoltaic power generation device. Based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, a power supply decision analysis is performed on the photovoltaic power generation device and the energy storage device through the power supply control center to obtain the load power supply decision and the energy storage charging and discharging decision. The multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device are powered by the load power supply decision and the energy storage charging and discharging decision, and power supply anomaly identification is performed on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device to obtain a first anomaly identification result, and power supply on-off control is performed according to the first anomaly identification result. Therefore, by conducting a joint power supply decision analysis on the power generation, demand and storage capacity of the load equipment, photovoltaic power generation devices and energy storage devices, the technical effect of improving the accuracy of power supply decision planning can be achieved, and then abnormalities in the power supply process can be identified to achieve the technical effect of improving power supply safety and ensuring the safe operation of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. The drawings constituting a part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of an intelligent power supply control method for a photovoltaic energy storage integrated device provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the structure of an intelligent power supply control system for a photovoltaic energy storage integrated device provided in an embodiment of the present application.

[0012] Explanation of reference numerals: load device classification module 11 , power supply demand analysis module 12 , electric energy data reading module 13 , power supply decision analysis module 14 , power supply abnormality analysis module 15 . DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more obvious, the exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0014] The terms used in the specification are used to describe the embodiments, rather than to limit the present application. As used in the specification, the singular terms "a", "an" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. When used in the specification, the terms "comprise" and / or "include" specify the presence of steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other steps, operations, elements, components and / or groups thereof.

[0015] Unless otherwise defined, all terms (including technical and scientific terms) used in this specification shall have the same meaning as commonly understood by those skilled in the art to which this application belongs. Terms, such as those defined in commonly used dictionaries, should not be interpreted in an idealized or overly formal sense unless explicitly defined herein. Throughout the specification, the same reference numerals represent the same elements.

[0016] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0017] Embodiment 1

[0018] Figure 1 A diagram of an intelligent power supply control method for a photovoltaic energy storage integrated device provided in an embodiment of the present application, the method comprising:

[0019] Obtain load devices at the power supply end, and classify and divide the load devices according to power supply mode, power supply time, and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices;

[0020] The embodiment of the present application provides an intelligent power supply control method for photovoltaic energy storage integrated equipment, which is applied to an intelligent power supply control system for photovoltaic energy storage integrated equipment. The system is a system platform for executing any one of the steps in the method. The system includes a photovoltaic power generation device, a power supply control center and an energy storage device. The photovoltaic power generation device and the energy storage device together constitute the photovoltaic energy storage integrated equipment, that is, the electric energy produced by the photovoltaic power generation device can be directly input into the power supply end, or it can be stored in the energy storage device. The specific photovoltaic power generation device and energy storage device need to be determined in combination with the actual situation. The power supply control center is a control terminal used to control the photovoltaic power generation device and the energy storage device to supply power to the power supply end.

[0021] Obtain the load equipment at the power supply end. The load equipment is the electrical equipment that needs to be powered by photovoltaic power. It needs to be determined in combination with the actual situation, and the load equipment is classified and divided according to the power supply mode, power supply time and power supply voltage, and multiple groups of AC load equipment and multiple groups of DC load equipment are obtained. Among them, the power supply mode includes DC power supply mode and AC power supply mode. The power supply time can be determined according to the historical power supply record. The power supply voltage refers to the required voltage of the load equipment, such as DC 220V, AC 220V, etc. The user manual of the load equipment can be obtained. The specific classification process is detailed as follows.

[0022] In a preferred embodiment, it also includes:

[0023] The load devices are classified and identified based on the power supply time to obtain a first-level load identification device; the first-level load identification devices are classified and identified according to the power supply mode to obtain an AC power supply identification device and a DC power supply identification device; the AC power supply identification device and the DC power supply identification device are classified and identified according to the power supply voltage to obtain the multiple groups of AC load devices and the multiple groups of DC load devices.

[0024] Classifying and identifying the load devices based on the power supply time means marking the load devices with the same power supply time in the same way. For example, some load devices are powered 24 hours a day, and some load devices are powered from 1 to 3 pm, etc., and a first-level load identification device can be obtained. Further classifying and identifying the first-level load identification device according to the power supply mode, the power supply mode includes AC power supply mode and DC power supply mode, the AC power supply mode is marked in the same way, and the DC power supply mode is marked in the same way, and an AC power supply identification device and a DC power supply identification device can be obtained. Classifying and identifying the AC power supply identification device and the DC power supply identification device according to the power supply voltage, the power supply voltage of different load devices may be different, and the devices with the same power supply voltage are identified in the same way, so that each load device is identified 3 times, and the load devices with the same three identification results are divided into one group, that is, the load devices with the same power supply mode, power supply time and power supply voltage are divided into one group, and the multiple groups of AC load devices and the multiple groups of DC load devices can be obtained. By classifying and dividing the load devices, a basis is provided for subsequent power supply control.

[0025] Performing power supply demand analysis on the multiple groups of AC load devices and the multiple groups of DC load devices within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand;

[0026] The first scheduled time refers to any time period from now to the future. In order to ensure the accuracy of power supply control, the first scheduled time is generally a shorter time, such as no more than 24 hours, because there are many uncertain factors in photovoltaic power generation. At the first scheduled time, the power supply demand analysis of the multiple groups of AC load devices and the multiple groups of DC load devices is to obtain the electric energy required by the multiple groups of AC load devices and the multiple groups of DC load devices at the first scheduled time. Specifically, the electric energy used by the multiple groups of AC load devices and the multiple groups of DC load devices in the recent history of the same time period as the first scheduled time can be respectively retrieved as the first AC power supply demand and the first DC power supply demand. The first AC power supply demand is the electric energy required by the multiple groups of AC load devices, and the first DC power supply demand is the electric energy required by the multiple groups of DC load devices.

[0027] Reading a first stored electric energy for a first predetermined time through the energy storage device, and reading a first power generation amount for a first predetermined time through the photovoltaic power generation device;

[0028] The first stored electric energy of the first predetermined time is read through the energy storage device. The first predetermined time refers to any time period from now to the future. Therefore, the first stored electric energy is the remaining electric energy of the current energy storage device, and can be directly read by connecting the energy storage device. The first power generation of the first predetermined time is read through the photovoltaic power generation device. In simple terms, it is to predict the power generation of the photovoltaic power generation device within the first predetermined time. Photovoltaic power generation prediction is a common technical means used by technicians in this field. For example, the historical power generation record and historical meteorological condition record of the photovoltaic power generation device can be obtained, and then the first meteorological condition under the first predetermined time can be obtained through the existing meteorological forecast platform. The first meteorological condition is compared with the historical meteorological condition record to obtain the historical power generation record corresponding to the same historical meteorological condition record. The power generation per unit time is obtained according to the historical power generation record and multiplied by the first predetermined time to obtain the first power generation. The above is just an example. In actual applications, technicians in this field can obtain the first power generation through other methods.

[0029] Based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, a power supply decision analysis is performed on the photovoltaic power generation device and the energy storage device through the power supply control center to obtain a load power supply decision and an energy storage charging and discharging decision;

[0030] Based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, the power supply decision analysis of the photovoltaic power generation device and the energy storage device is performed through the power supply control center to obtain the load power supply decision and the energy storage charge and discharge decision. That is to say, the photovoltaic power generation device and the energy storage device can both supply power to the load equipment. At this time, the energy storage device is in a discharging state, and the photovoltaic power generation device can also supply power to the energy storage device, that is, charge the energy storage device. Therefore, it is necessary to first determine whether the first power generation can meet the first AC power supply demand and the first DC power supply demand based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand. If so, there is no need to supply power through the energy storage device, and the remaining power can be stored in the energy storage device, that is, the energy storage device is charged, thereby obtaining the load power supply decision and the energy storage charge and discharge decision. The specific acquisition process is described in detail as follows.

[0031] In a preferred embodiment, it also includes:

[0032] Calculate the sum of the power supply demands of the first AC power supply demand and the first DC power supply demand, and generate a first AC power supply decision, a first DC power supply decision and a first energy storage charging and discharging decision based on the sum of the power supply demands and the first power generation; compare and analyze the first energy storage charging and discharging decision and the first stored electric energy to obtain a first auxiliary power supply decision of a first auxiliary power supply device and a residual electric energy output decision of an external power grid; use the first AC power supply decision, the first DC power supply decision and the first auxiliary power supply decision as the load power supply decision; use the first energy storage charging and discharging decision and the residual electric energy output decision as the energy storage charging and discharging decision.

[0033] Specifically, the sum of the power supply demands of the first AC power supply demand and the first DC power supply demand is calculated, and a first AC power supply decision, a first DC power supply decision and a first energy storage charging and discharging decision are generated based on the sum of the power supply demands and the first power generation. Specifically, the sum of the power supply demands and the first power generation are first compared. If the first power generation is greater than or equal to the sum of the power supply demands, the power required by each load device in the first AC power supply demand and the first DC power supply demand is used as the first AC power supply decision and the first DC power supply decision, respectively. That is, the power generation of the photovoltaic power generation device can fully meet the power supply needs of all load devices, and power can be directly supplied to multiple groups of AC load devices and multiple groups of DC load devices through the photovoltaic power generation device; at the same time, the remaining electric energy obtained by subtracting the sum of the power supply demands from the first power generation is used as the input electric energy to the energy storage device, that is, the remaining electric energy is stored in the energy storage device by charging, which is used as the first energy storage charging and discharging decision. If the first power generation is less than the total power supply demand, all the electric energy produced by the photovoltaic power generation device needs to be input into the load equipment to obtain the first AC power supply decision and the first DC power supply decision, and according to the electric energy deviation between the first power generation and the total power supply demand, the electric energy deviation is compensated by the energy storage device, that is, the energy storage device discharges to the load equipment, which is used as the first energy storage charging and discharging decision.

[0034] Further compare and analyze the first energy storage charge and discharge decision and the first stored electric energy to obtain the first auxiliary power supply decision of the first auxiliary power supply device and the residual electric energy output decision of the external power grid. The first auxiliary power supply device refers to other power supply devices other than the photovoltaic power supply device and the energy storage device, such as an external power grid or other power generation equipment. That is, if the electric energy that needs to be provided to the load device in the first energy storage charge and discharge decision is greater than the first stored electric energy, it is necessary to provide additional power through the first auxiliary power supply device to make up for the difference between the electric energy that needs to be provided to the load device in the first energy storage charge and discharge decision and the first stored electric energy, and use the difference as the electric energy that needs to be output by the first auxiliary power supply device, which is used as the first auxiliary power supply decision. Similarly, if the charging energy in the first energy storage charge and discharge decision, that is, the electric energy input by the photovoltaic power generation device to the energy storage device is greater than the remaining capacity of the energy storage device (the capacity of the energy storage device minus the first stored electric energy), it is necessary to transmit the excess electric energy to the external power grid, which is used as the residual electric energy output decision. Finally, the first AC power supply decision, the first DC power supply decision, and the first auxiliary power supply decision are used as the load power supply decision, and the first energy storage charging and discharging decision and the remaining electric energy output decision are used as the energy storage charging and discharging decision, thereby realizing the power supply decision analysis of the photovoltaic power generation device and the energy storage device and improving the accuracy of power supply.

[0035] The multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device are powered by the load power supply decision and the energy storage charging and discharging decision, and power supply abnormality identification is performed on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device to obtain a first abnormality identification result, and power supply on-off control is performed according to the first abnormality identification result.

[0036] The load power supply decision and the energy storage charge and discharge decision are used to control the power supply of the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device. During the power supply process, power supply abnormalities are identified for the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device to obtain a first abnormality identification result, and power supply on-off control is performed according to the first abnormality identification result. That is, when power is supplied to the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device, it is generally necessary to perform voltage conversion through a transformer (such as an inverter). Abnormalities may occur during the voltage conversion. At this time, power-off control needs to be performed in time, otherwise the equipment may be damaged. The specific process is described in detail as follows.

[0037] In a preferred embodiment, it also includes:

[0038] Acquire the transformer devices connected to the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device, and collect and acquire multiple groups of historical transformer record data of the transformer devices; perform transformer gear adjustment stability analysis on the transformer devices according to the multiple groups of historical transformer record data to acquire a transformer gear adjustment stability coefficient; construct M transformer abnormality detection channels for the transformer devices based on the transformer gear adjustment stability coefficient, where M is an integer greater than 1; acquire the transformer devices corresponding to the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device, perform matching analysis in the M transformer abnormality detection channels, and acquire matching transformer abnormality detection channels; perform power supply abnormality identification with the matching transformer abnormality detection channels, and output the first abnormality identification result.

[0039] In a preferred embodiment, it also includes:

[0040] Extracting first historical voltage transformation record data based on the multiple groups of historical voltage transformation record data; performing cluster analysis on the first historical voltage transformation record data with the voltage transformation gear as a cluster feature to obtain multiple gear adjustment record data groups; performing gear adjustment stability analysis based on the multiple gear adjustment record data groups to obtain the voltage transformation gear adjustment stability coefficient; wherein the expression for obtaining the voltage transformation gear adjustment stability coefficient is:

[0041]

[0042] Wherein, Γ is the voltage-changing gear-adjusting stability coefficient, Vx i(t) is the voltage variation amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; i (t) is the current change amplitude that reaches the voltage transformation target within the time process t after the gear adjustment in the i-th gear adjustment record data group; Rz i (t) The impedance change amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; β1, β2 and β3 are weight coefficients, and β1+β2+β3=1, β1=β2=β3; n is the total number of multiple adjustment record data groups.

[0043] In a preferred embodiment, it also includes:

[0044] According to the transformer gear adjustment stability coefficient, M identified transformer devices that are greater than or equal to the predetermined transformer gear adjustment stability coefficient are extracted; based on the historical transformer record data corresponding to the M identified transformer devices, M transformer anomaly detection channels are constructed through training; unidentified transformer devices are obtained, and based on the historical transformer record data corresponding to the unidentified transformer devices, calibrated transformation voltage, calibrated transformation current, and calibrated transformation impedance are extracted; abnormal power supply of the unidentified transformer devices is identified according to the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance.

[0045] Obtain the transformer equipment (such as inverters and other devices for voltage conversion) connected to the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device, and collect and obtain multiple groups of historical transformer record data of the transformer equipment. The multiple groups of historical transformer record data are transformer records of the transformer equipment in the past period of time, including data such as voltage, current, impedance, etc. after transformer transformation. It should be noted that the multiple groups of historical transformer record data refer to data with abnormal values ​​removed and missing values ​​supplemented. Perform transformer gear adjustment stability analysis on the transformer equipment based on the multiple groups of historical transformer record data to obtain the transformer gear adjustment stability coefficient. The transformer gear adjustment stability coefficient is used to reflect the severity of changes in voltage, current, impedance, etc. in the power supply circuit after transformer transformation. The specific acquisition process is described in detail below.

[0046] The process of performing a voltage adjustment stability analysis on the voltage transformer equipment according to the multiple groups of historical voltage transformation record data and obtaining the voltage adjustment stability coefficient is as follows: extract any group of historical voltage transformation record data based on the multiple groups of historical voltage transformation record data and record it as the first historical voltage transformation record data. Perform a cluster analysis on the first historical voltage transformation record data using the voltage transformation gear as a clustering feature, wherein the voltage transformation gear refers to the size of the voltage transformation, such as converting a voltage of 300V to 220V, that is, dividing the data with the same voltage transformation value into one category, thereby obtaining multiple gear adjustment record data groups. Further, perform a gear adjustment stability analysis based on the multiple gear adjustment record data groups to obtain the voltage transformation stability coefficient, wherein the expression for obtaining the voltage transformation stability coefficient is:

[0047]

[0048] Wherein, Γ is the voltage-changing gear-adjusting stability coefficient, Vx i (t) is the voltage change amplitude to reach the transformation target within the time process t after the adjustment in the ith adjustment record data group. The ith adjustment record data group belongs to multiple adjustment record data groups. The transformation target refers to the voltage value that is expected to be achieved through the transformation equipment. For example, in the process of converting 300V to 220V, the voltage will change drastically before it stabilizes at 220V. It is necessary to calculate the voltage change amplitude when it reaches the stable state, that is, the voltage change amplitude in the time process t, which can be calculated based on the voltage record in the ith adjustment record data group. The time process t is the time required for the voltage to reach the transformation target; Iy i (t) is the current change amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group, that is, the current change amplitude of the time process t; Rz i (t) The impedance change amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group, that is, the impedance change amplitude of the time process t; β1, β2 and β3 are weight coefficients, and β1+β2+β3=1, β1=β2=β3; n is the total number of multiple adjustment record data groups.

[0049] The above expression can be used to calculate the voltage transformer speed adjustment stability coefficient, which provides support for subsequent power supply anomaly identification, facilitates timely detection of power supply anomalies, performs power-off control, and protects equipment safety.

[0050] Further based on the voltage transformer gear adjustment stability coefficient, M voltage transformer abnormality detection channels are constructed for the voltage transformer equipment, where M is an integer greater than 1, and the specific construction process is as follows: according to the voltage transformer gear adjustment stability coefficient, M identified voltage transformer equipment greater than or equal to the predetermined voltage transformer gear adjustment stability coefficient is extracted, wherein the predetermined voltage transformer gear adjustment stability coefficient is set by technical personnel in this field based on their experience, that is, the higher the voltage transformer gear adjustment stability coefficient, the faster the change amplitude of the voltage, current and impedance after the voltage is converted by the voltage transformer equipment, and the more drastic the change, then the higher the probability of abnormality or failure. Therefore, the voltage transformer equipment greater than or equal to the predetermined voltage transformer gear adjustment stability coefficient is marked to obtain M identified voltage transformer equipment. Based on the historical transformer record data corresponding to the M identified transformer devices, M transformer anomaly detection channels are trained and constructed. The transformer anomaly detection channels are trained and constructed based on existing machine learning models (such as neural network models). The historical transformer record data are marked by existing technologies to obtain corresponding anomaly types, such as voltage change anomaly, current change anomaly, impedance change anomaly, no anomaly, etc. Then, the historical transformer record data is used as input data and the corresponding anomaly type is used as output data to train the transformer anomaly detection channel to a convergence state. The training of machine learning models is a common technical means for those skilled in the art, so it will not be expanded here. Unidentified transformer devices are obtained. Unidentified transformer devices are transformer devices other than the M identified transformer devices. The changes in voltage, current, and impedance of unidentified transformer devices are relatively stable. Based on the historical transformer record data corresponding to the unidentified transformer devices, calibrated transformation voltage, calibrated transformation current, and calibrated transformation impedance can be extracted. The calibrated transformation voltage, calibrated transformation current, and calibrated transformation impedance are the average voltage, average current, and average impedance after voltage transformation in the historical transformer record data. According to the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance, the abnormal power supply of the unidentified transformer equipment is identified, that is, the deviation of the voltage, current, and impedance output by the unidentified transformer equipment is compared with the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance. If the deviation is within the predetermined deviation range, the power supply is normal, otherwise, the power supply is abnormal. The predetermined deviation range is set by technicians in this field based on historical experience. Based on this, abnormal identification is performed after the transformer equipment performs the transformation, and the power supply circuit is cut off in time to prevent equipment damage.

[0051] Further obtain the transformer devices corresponding to the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device, and perform matching analysis in the M transformer abnormality detection channels. That is, different transformer devices have their own corresponding transformer abnormality detection channels, and obtain the matching transformer abnormality detection channel. If there is no channel matching it in the M transformer abnormality detection channels, it means that the transformer device is an unidentified device, and the abnormality is identified through the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance of the unidentified device. The power supply abnormality is identified by matching the transformer abnormality detection channel, and the first abnormality identification result is output. The power supply on-off control is performed according to the first abnormality identification result. That is, if the first abnormality identification result shows that the power supply is abnormal, the power supply circuit corresponding to the transformer device is disconnected to protect the safety of the equipment.

[0052] In a preferred embodiment, it also includes:

[0053] The energy storage device is charged and discharged to monitor the charge and discharge and obtain the charge and discharge monitoring data; the overcharge and overdischarge are identified by using the charge and discharge monitoring data to generate abnormal charge and discharge monitoring results; the power supply on and off control of the energy storage device is performed by using the abnormal charge and discharge monitoring results.

[0054] To monitor the charge and discharge of the energy storage device is to obtain the charge and discharge monitoring data through the existing monitoring instrument (such as a voltage detector). The charge and discharge monitoring data refers to the real-time charging voltage or real-time discharging voltage monitored by the monitoring instrument. Overcharging and overdischarging are identified by the charge and discharge monitoring data to generate abnormal charge and discharge monitoring results. Specifically, the charge cut-off voltage and the discharge cut-off voltage are read according to the user manual of the energy storage device. When the real-time charge voltage exceeds the charge cut-off voltage, or the real-time discharge voltage exceeds the discharge cut-off voltage, it indicates that there is overcharging or overdischarging, which is used as an abnormal charge and discharge monitoring result. The energy storage device is controlled to be powered on and off by the abnormal charge and discharge monitoring result. That is, when there is overcharging or overdischarging, the power supply charging circuit between the energy storage device and the photovoltaic power generation device is disconnected, or the power supply discharging circuit between the energy storage device and the load equipment is disconnected to protect the energy storage device and extend the service life of the energy storage device.

[0055] Based on the above analysis, it can be seen that one or more technical solutions provided in this application can achieve the following beneficial effects:

[0056] The load devices at the power supply end are obtained, and the load devices are classified and divided according to the power supply mode, power supply time and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices. Based on the multiple groups of AC load devices and multiple groups of DC load devices, power supply demand analysis is performed within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand. The first stored electric energy at the first predetermined time is read through the energy storage device, and the first power generation at the first predetermined time is read through the photovoltaic power generation device. Based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, a power supply decision analysis is performed on the photovoltaic power generation device and the energy storage device through the power supply control center to obtain the load power supply decision and the energy storage charging and discharging decision. The multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device are powered by the load power supply decision and the energy storage charging and discharging decision, and power supply anomaly identification is performed on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device to obtain a first anomaly identification result, and power supply on-off control is performed according to the first anomaly identification result. Therefore, by conducting a joint power supply decision analysis on the power generation, demand and storage capacity of the load equipment, photovoltaic power generation devices and energy storage devices, the technical effect of improving the accuracy of power supply decision planning can be achieved, and then abnormalities in the power supply process can be identified to achieve the technical effect of improving power supply safety and ensuring the safe operation of equipment.

[0057] Embodiment 2

[0058] Based on the same inventive concept as the intelligent power supply control method of the photovoltaic energy storage integrated device in the aforementioned embodiment, Figure 2 As shown, the present application also provides an intelligent power supply control system for photovoltaic energy storage integrated equipment, the system comprising:

[0059] A load device classification module 11, the load device classification module 11 is used to obtain the load devices at the power supply end, and classify the load devices according to the power supply mode, power supply time and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices;

[0060] A power supply demand analysis module 12, the power supply demand analysis module 12 is used to perform power supply demand analysis based on the multiple groups of AC load devices and the multiple groups of DC load devices within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand;

[0061] An electric energy data reading module 13, wherein the electric energy data reading module 13 reads a first stored electric energy at a first predetermined time through an energy storage device, and reads a first power generation at a first predetermined time through a photovoltaic power generation device;

[0062] A power supply decision analysis module 14, the power supply decision analysis module 14 is used to perform power supply decision analysis on the photovoltaic power generation device and the energy storage device through the power supply control center based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, to obtain a load power supply decision and an energy storage charging and discharging decision;

[0063] The power supply anomaly analysis module 15 is used to power the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device based on the load power supply decision and the energy storage charging and discharging decision, and to identify power supply anomalies of the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device, obtain a first anomaly identification result, and perform power supply on-off control according to the first anomaly identification result.

[0064] Furthermore, the load device classification module 11 also includes:

[0065] Classify and identify the load device based on the power supply demand duration to obtain a first-level load identification device;

[0066] Classify and identify the first-level load identification devices according to the power supply mode to obtain AC power supply identification devices and DC power supply identification devices;

[0067] The AC power supply identification devices and the DC power supply identification devices are classified and identified according to the power supply voltage to obtain the multiple groups of AC load devices and the multiple groups of DC load devices.

[0068] Furthermore, the power supply decision analysis module 14 also includes:

[0069] Calculating the sum of the power supply demands of the first AC power supply demand and the first DC power supply demand, and generating a first AC power supply decision, a first DC power supply decision, and a first energy storage charging and discharging decision based on the sum of the power supply demands and the first power generation;

[0070] Comparing and analyzing the first energy storage charging and discharging decision and the first stored electric energy, obtaining a first auxiliary power supply decision of the first auxiliary power supply device and a residual electric energy output decision of the external power grid;

[0071] Taking the first AC power supply decision, the first DC power supply decision, and the first auxiliary power supply decision as the load power supply decision;

[0072] The first energy storage charging and discharging decision and the remaining electric energy output decision are used as the energy storage charging and discharging decision.

[0073] Furthermore, the power supply abnormality analysis module 15 also includes:

[0074] Acquire the multiple groups of AC load devices, the multiple groups of DC load devices, and the transformer devices connected to the energy storage device, and collect and acquire multiple groups of historical transformer record data of the transformer devices;

[0075] Performing a voltage transformation and adjustment stability analysis on the voltage transformation equipment according to the multiple groups of historical voltage transformation record data to obtain a voltage transformation and adjustment stability coefficient;

[0076] Based on the voltage transformation gear adjustment stability coefficient, M voltage transformation abnormality detection channels are constructed for the voltage transformation equipment, where M is an integer greater than 1;

[0077] Obtaining the transformer devices corresponding to the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device, performing matching analysis in the M transformer abnormality detection channels, and obtaining matching transformer abnormality detection channels;

[0078] Power supply anomaly identification is performed by matching the voltage transformation anomaly detection channel, and the first anomaly identification result is output.

[0079] Furthermore, the power supply abnormality analysis module 15 also includes:

[0080] Extracting first historical voltage transformation record data based on the multiple groups of historical voltage transformation record data;

[0081] Performing cluster analysis on the first historical voltage transformation record data by taking the voltage transformation gear as a cluster feature to obtain a plurality of gear adjustment record data groups;

[0082] Performing a gear shift stability analysis based on the plurality of gear shift record data groups to obtain the voltage transformer gear shift stability coefficient;

[0083] The expression for the voltage conversion and gear shifting stability coefficient is:

[0084]

[0085] Wherein, Γ is the voltage-changing gear-adjusting stability coefficient, Vx i (t) is the voltage variation amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; i (t) is the current change amplitude that reaches the voltage transformation target within the time process t after the gear adjustment in the i-th gear adjustment record data group; Rz i (t) The impedance change amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; β1, β2 and β3 are weight coefficients, and β1+β2+β3=1, β1=β2=β3; n is the total number of multiple adjustment record data groups.

[0086] Furthermore, the power supply abnormality analysis module 15 also includes:

[0087] According to the voltage transformation and gear adjustment stability coefficient, M identified voltage transformation devices having a voltage transformation and gear adjustment stability coefficient greater than or equal to a predetermined voltage transformation and gear adjustment stability coefficient are extracted;

[0088] Based on the historical transformer record data corresponding to the M identified transformer devices, M transformer anomaly detection channels are constructed;

[0089] Acquire an unidentified transformer device, and extract a calibrated transformation voltage, a calibrated transformation current, and a calibrated transformation impedance based on historical transformation record data corresponding to the unidentified transformer device;

[0090] Abnormal power supply of unidentified transformer equipment is identified based on the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance.

[0091] Furthermore, the system further includes a charge and discharge monitoring module, and the charge and discharge monitoring module includes:

[0092] Performing charge and discharge monitoring on the energy storage device to obtain charge and discharge monitoring data;

[0093] Using the charge and discharge monitoring data to identify overcharge and overdischarge, and generate abnormal charge and discharge monitoring results;

[0094] The power supply on and off of the energy storage device is controlled based on the abnormal charge and discharge monitoring result.

[0095] The specific example of the intelligent power supply control method for the photovoltaic energy storage integrated device in the aforementioned embodiment 1 is also applicable to the intelligent power supply control system of the photovoltaic energy storage integrated device in this embodiment. Through the aforementioned detailed description of the intelligent power supply control method for the photovoltaic energy storage integrated device, those skilled in the art can clearly understand the intelligent power supply control system of the photovoltaic energy storage integrated device in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0096] It should be understood that various forms of the processes shown above can be used to reorder, add or delete steps, as long as the expected results of the technical solutions disclosed in this application can be achieved, and this document does not limit this.

[0097] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. An intelligent power supply control method for photovoltaic energy storage integrated equipment, characterized in that: An intelligent power supply control system applied to photovoltaic energy storage integrated equipment, the system includes a photovoltaic power generation device, a power supply control center and an energy storage device, and the method includes: Obtain load devices at the power supply end, and classify and divide the load devices according to the power supply mode, power supply time, and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices; Performing power supply demand analysis on the multiple groups of AC load devices and the multiple groups of DC load devices within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand; Reading a first stored electric energy for a first predetermined time through the energy storage device, and reading a first power generation amount for a first predetermined time through the photovoltaic power generation device; Based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, a power supply decision analysis is performed on the photovoltaic power generation device and the energy storage device through the power supply control center to obtain a load power supply decision and an energy storage charging and discharging decision; The multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device are powered by the load power supply decision and the energy storage charging and discharging decision, and power supply abnormality identification is performed on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device to obtain a first abnormality identification result, and power supply on-off control is performed according to the first abnormality identification result.

2. The method according to claim 1, characterized in that The method of acquiring load devices at the power supply end and classifying the load devices according to the power supply mode, power supply time and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices includes: Classify and identify the load device based on the power supply time to obtain a first-level load identification device; Classify and identify the first-level load identification devices according to the power supply mode to obtain AC power supply identification devices and DC power supply identification devices; The AC power supply identification devices and the DC power supply identification devices are classified and identified according to the power supply voltage to obtain the multiple groups of AC load devices and the multiple groups of DC load devices.

3. The method according to claim 2, characterized in that The power supply decision analysis is performed by the power supply control center based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand to obtain a load power supply decision and an energy storage charging and discharging decision, including: Calculating the sum of the power supply demands of the first AC power supply demand and the first DC power supply demand, and generating a first AC power supply decision, a first DC power supply decision, and a first energy storage charging and discharging decision based on the sum of the power supply demands and the first power generation; Comparing and analyzing the first energy storage charging and discharging decision and the first stored electric energy, obtaining a first auxiliary power supply decision of the first auxiliary power supply device and a residual electric energy output decision of the external power grid; Taking the first AC power supply decision, the first DC power supply decision, and the first auxiliary power supply decision as the load power supply decision; The first energy storage charging and discharging decision and the remaining electric energy output decision are used as the energy storage charging and discharging decision.

4. The method according to claim 1, characterized in that The performing power supply anomaly identification on the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device to obtain a first anomaly identification result includes: Acquire the multiple groups of AC load devices, the multiple groups of DC load devices, and the transformer devices connected to the energy storage device, and collect and acquire multiple groups of historical transformer record data of the transformer devices; Performing a voltage transformation and adjustment stability analysis on the voltage transformation equipment according to the multiple groups of historical voltage transformation record data to obtain a voltage transformation and adjustment stability coefficient; Based on the voltage transformation gear adjustment stability coefficient, M voltage transformation abnormality detection channels are constructed for the voltage transformation equipment, where M is an integer greater than 1; Obtaining the transformer devices corresponding to the multiple groups of AC load devices, the multiple groups of DC load devices, and the energy storage device, performing matching analysis in the M transformer abnormality detection channels, and obtaining matching transformer abnormality detection channels; Power supply anomaly identification is performed by matching the voltage transformation anomaly detection channel, and the first anomaly identification result is output.

5. The method according to claim 4, characterized in that The step of performing a voltage transformation and adjustment stability analysis on the voltage transformation equipment according to the plurality of groups of historical voltage transformation record data to obtain a voltage transformation and adjustment stability coefficient includes: Extracting first historical voltage transformation record data based on the multiple groups of historical voltage transformation record data; Performing cluster analysis on the first historical voltage transformation record data by taking the voltage transformation gear as a cluster feature to obtain a plurality of gear adjustment record data groups; Performing a gear shift stability analysis based on the multiple gear shift record data groups to obtain the voltage transformer gear shift stability coefficient; The expression for the voltage conversion and gear shifting stability coefficient is: Wherein, Γ is the voltage-changing gear-adjusting stability coefficient, Vx i (t) is the voltage variation amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; i (t) is the current change amplitude that reaches the voltage transformation target within the time process t after the gear adjustment in the i-th gear adjustment record data group; Rz i (t) The impedance change amplitude that reaches the voltage transformation target within the time process t after the adjustment in the i-th adjustment record data group; β1, β2 and β3 are weight coefficients, and β1+β2+β3=1, β1=β2=β3; n is the total number of multiple adjustment record data groups.

6. The method according to claim 5, characterized in that The method of constructing M voltage transformation abnormality detection channels for the voltage transformation equipment based on the voltage transformation gear adjustment stability coefficient includes: According to the voltage transformation and gear adjustment stability coefficient, M identified voltage transformation devices having a voltage transformation and gear adjustment stability coefficient greater than or equal to a predetermined voltage transformation and gear adjustment stability coefficient are extracted; Based on the historical transformer record data corresponding to the M identified transformer devices, M transformer anomaly detection channels are constructed; Acquire an unidentified transformer device, and extract a calibrated transformation voltage, a calibrated transformation current, and a calibrated transformation impedance based on historical transformation record data corresponding to the unidentified transformer device; Abnormal power supply of unidentified transformer equipment is identified based on the calibrated transformation voltage, the calibrated transformation current, and the calibrated transformation impedance.

7. The method according to claim 1, characterized in that After the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device are powered by the load power supply decision and the energy storage charge and discharge decision, the method further includes: Performing charge and discharge monitoring on the energy storage device to obtain charge and discharge monitoring data; Using the charge and discharge monitoring data to identify overcharge and overdischarge, and generate abnormal charge and discharge monitoring results; The power supply on and off of the energy storage device is controlled based on the abnormal charge and discharge monitoring result.

8. An intelligent power supply control system for photovoltaic energy storage integrated equipment, characterized in that: The system is used to perform any one of the steps of the intelligent power supply control method for photovoltaic energy storage integrated equipment according to claims 1 to 7, wherein the system comprises a photovoltaic power generation device, a power supply control center and an energy storage device, and the system comprises: A load device classification module, which is used to obtain load devices at the power supply end, and classify the load devices according to the power supply mode, power supply time and power supply voltage to obtain multiple groups of AC load devices and multiple groups of DC load devices; A power supply demand analysis module, the power supply demand analysis module is used to perform power supply demand analysis based on the multiple groups of AC load devices and the multiple groups of DC load devices within a first predetermined time to obtain a first AC power supply demand and a first DC power supply demand; An electric energy data reading module, wherein the electric energy data reading module reads a first stored electric energy at a first predetermined time through an energy storage device, and reads a first power generation at a first predetermined time through a photovoltaic power generation device; A power supply decision analysis module, which is used to perform power supply decision analysis on the photovoltaic power generation device and the energy storage device through the power supply control center based on the first stored electric energy, the first power generation, the first AC power supply demand and the first DC power supply demand, to obtain a load power supply decision and an energy storage charging and discharging decision; A power supply anomaly analysis module, the power supply anomaly analysis module is used to power the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device based on the load power supply decision and the energy storage charging and discharging decision, and perform power supply anomaly identification on the multiple groups of AC load devices, the multiple groups of DC load devices and the energy storage device, obtain a first anomaly identification result, and perform power supply on-off control according to the first anomaly identification result.

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