Management system and control method for wireless power load

Abstract

The invention discloses a wireless power load management system and a control method, and relates to the technical field of charge management, the system comprises a load identification module, a risk early warning module and a risk identification module, the load identification module collects waveform data through deploying a sensor on a distribution line, and the risk early warning module performs early warning on the load; a dynamic time warping algorithm is applied to accurately identify load requirements of new electric appliances, electric power is adjusted according to the load rate of a transformer and the like, a risk early warning module integrates meteorological data, charge state data and load use data, and a mathematical optimization model is established, so that distributed energy and load balance optimization scheduling is carried out, and the optimal scheduling efficiency is improved. And meanwhile, risk early warning is carried out on interaction data of distributed energy, load and energy storage, a risk identification module generates a maintenance scheme after early warning, and iteration is carried out on risk correction parameters according to fault identification related indexes, so that intelligent management of the power load is realized, and the stability and reliability of operation of a power system are improved.

Description

Technical Field

[0001] This invention relates to the field of charge management technology, and more specifically to a management system and control method for wireless power loads. Background Technology

[0002] With the growth in the number of electricity users and the diversification of electrical equipment, traditional power management models are unable to meet the needs of real-time and accurate load monitoring and control. In order to obtain data from various equipment in real time, analyze equipment status and power parameters, and make intelligent decisions, a wireless power load management system and control method are needed.

[0003] Existing technologies, such as the invention patent application CN118868426A, disclose a power energy management system based on the Internet of Things (IoT), relating to the field of energy management technology. This invention includes a data acquisition unit, an equipment status analysis unit, a power supply data analysis unit, a trend analysis unit, and a decision support unit. The data acquisition unit is used to acquire and integrate data from different devices and sensors, and perform preprocessing; it identifies the electrical equipment and sensors that need to be monitored and connects them physically or wirelessly to the data acquisition unit. This invention, by integrating IoT technology, significantly improves the efficiency and intelligence level of the power energy management system. Compared with traditional manual monitoring and periodic maintenance methods, this system can monitor the power grid status in real time, quickly respond to power grid anomalies, reduce response time, and provide in-depth insights using advanced data analysis technology, thereby optimizing energy consumption patterns and equipment maintenance strategies.

[0004] The above solution has the following technical problems: 1. The above solution focuses on the acquisition and analysis of existing equipment status data, and does not provide a detailed method for load identification when new electrical appliances are connected. It cannot accurately identify the load demand of new electrical appliances, and does not directly reflect the mechanism for real-time and flexible adjustment of power consumption after the connection of new electrical appliances based on real-time load changes.

[0005] 2. Although the above analysis addresses energy consumption patterns, it does not consider the interaction between distributed energy, energy storage, and load for balanced and optimized scheduling. It does not plan energy storage charging and discharging and main power supply start-up and shutdown schemes, which reduces the practicality of load balancing optimization. Furthermore, in terms of risk warning, the above scheme does not have tiered warnings, does not determine maintenance schemes based on fault types and fault data, and cannot iteratively optimize risk correction parameters, thus reducing the sophistication of risk warning and handling processes. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the present invention aims to provide a management system and control method for wireless power loads.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: On the one hand, the present invention provides a wireless power load management system, including the following modules: a load identification module, used to identify the load demand of new electrical appliances and incorporate them into the overall load management, and adjust the power consumption in real time.

[0008] The risk warning module is used to collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, it performs optimized scheduling for distributed energy and load balance optimization. Furthermore, it collects interactive data of distributed energy, load, and energy storage, identifies risks in the interactive data of distributed energy, load, and energy storage, identifies potential imbalance risks, and issues warnings.

[0009] The risk identification module is used to assess risks and generate maintenance plans when an imbalance risk warning is issued. It then collects risk maintenance data and iterates risk correction parameters based on the risk maintenance data.

[0010] Preferably, the generation process of the maintenance scheme is as follows: After issuing an early warning, data on changes in regional load rate, voltage deviation, frequency fluctuation, and energy storage state imbalance are collected. The regional load rate change data is substituted into the load risk index calculation formula to obtain the load risk index. The load risk index is multiplied by the load correction index to obtain the load failure assessment index. Similarly, based on the analysis process of the regional load rate change data, the data on changes in voltage deviation, frequency fluctuation, and energy storage state imbalance are analyzed to obtain the voltage deviation risk index, voltage deviation failure assessment index, frequency fluctuation risk index, frequency fluctuation failure assessment index, energy storage state risk index, and energy storage state failure assessment index.

[0011] This yields the fault assessment index and risk index for four types of faults. The fault type corresponding to the largest fault assessment index among the four types of faults is recorded as the actual fault type. Then, the risk index corresponding to the actual fault type is obtained. The maintenance plan corresponding to each risk index of the four types of faults is retrieved from the database. This gives the maintenance plan corresponding to the risk index of the actual fault type, which is recorded as the current maintenance plan.

[0012] On the other hand, the present invention provides a method for controlling wireless power loads, including the following steps: Step 1, load identification: identify the load demand of new electrical appliances and incorporate them into the overall load management, and adjust the power consumption in real time.

[0013] Step 2, Risk Warning: Collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, optimize the scheduling of distributed energy and load balance. Then, collect the interaction data of distributed energy, load, and energy storage, identify risks in the interaction data of distributed energy, load, and energy storage, identify potential imbalance risks, and issue warnings.

[0014] Step 3, Risk Identification: When an imbalance risk warning is issued, risk assessment is performed, a maintenance plan is generated, risk maintenance data is collected, and risk correction parameters are iterated based on the risk maintenance data.

[0015] The beneficial effects of this invention are as follows: 1. By deploying sensors on power distribution lines to collect waveform data, this invention accurately identifies the load demand of new electrical appliances using a dynamic time warping algorithm, and adjusts the power consumption based on transformer load rate, etc. The risk warning module integrates meteorological data, state of charge data, and load usage data to establish a mathematical optimization model, thereby performing optimized scheduling of distributed energy and load balance. At the same time, it provides risk warnings for the interaction data of distributed energy, load, and energy storage. After the warning, the risk identification module generates a maintenance plan and iterates the risk correction parameters based on fault identification-related indicators. This invention realizes intelligent management of power load and improves the stability and reliability of power system operation.

[0016] 2. This invention uses wavelet transform to extract high-frequency transient features and constructs feature vectors for electrical appliances. This multi-dimensional data acquisition and feature extraction method improves the accuracy of electrical appliance identification. Compared with single-dimensional data acquisition, it can more comprehensively reflect the characteristics of electrical appliances and greatly improve the accuracy of identification. At the same time, this invention can set adjustment priorities according to load changes, improve the scientific nature of power consumption adjustment, and ensure the real-time safety of power system operation.

[0017] 3. This invention achieves balanced and optimized scheduling by combining the interaction between distributed energy, energy storage, and load, and plans energy storage charging and discharging and main power supply start-up and shutdown schemes. It is more targeted and practical in the utilization of distributed energy and load balance optimization. At the same time, this invention provides tiered early warning during the early warning process, thereby determining the maintenance plan. Furthermore, it can iteratively optimize risk analysis parameters based on fault identification-related indicators, thereby improving the completeness and accuracy of the risk early warning and handling process. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0020] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] according to Figure 1 As shown, the present invention provides a management system for wireless power loads, including the following modules: a load identification module, a risk warning module, a risk identification module, and a database.

[0023] The risk warning module is connected to the load identification module and the risk identification module, respectively. The load identification module, the risk warning module, and the risk identification module are all connected to the database.

[0024] The load identification module is used to identify the load demand of new electrical appliances and incorporate them into the overall load management, adjusting the power consumption in real time.

[0025] In one specific embodiment, the identification of the load demand of new electrical appliances is carried out as follows: high-frequency current sensors and high-frequency voltage sensors are deployed on the power distribution line. When the fingerprint of the electrical appliance is entered, the current intensity and voltage intensity of each electrical appliance are collected. The waveform data of each electrical appliance is obtained through an oscilloscope. The corresponding power curve trajectory is plotted based on the waveform data of each electrical appliance. At the same time, high-frequency transient features are extracted through wavelet transform to construct the feature vector of each electrical appliance.

[0026] High-frequency current sensors and high-frequency voltage sensors are deployed at key nodes of the power distribution line. When electrical appliances are connected, the current intensity and voltage intensity of each key node are collected. The similarity of electrical appliances at each key node is calculated by a dynamic time warping algorithm. When the similarity of electrical appliances at a certain key node is greater than the preset basic similarity of electrical appliances, it indicates that an electrical appliance is connected to that key node.

[0027] When an appliance is connected to a critical node, the power curve trajectory of each appliance is retrieved from the database. A dynamic time warping algorithm is used to analyze the current and voltage intensity of the critical node to obtain the trajectory similarity of various types of appliances at that critical node. When the trajectory similarity of a certain type of appliance at the critical node is greater than a preset trajectory similarity, it is recorded as an effective type of appliance at that critical node. This process is repeated to obtain the effective types of appliances at the critical node. Feature vectors for each type of appliance are retrieved from the database. The dynamic time warping algorithm is then used to analyze the current and voltage intensity of the critical node to obtain the feature similarity of each effective type of appliance at that critical node. The effective type of appliance with the highest feature similarity is selected and recorded as a new appliance at that critical node. This process is repeated to identify the new appliance. Finally, the load demand of various types of appliances is retrieved from the database to obtain the load demand of the new appliance.

[0028] It should be noted that the preset trajectory similarity is the similarity of the normal matching process. When the trajectory similarity is greater than or equal to the preset trajectory similarity, it indicates that the new appliance is the corresponding type of appliance. When the trajectory similarity is less than the preset trajectory similarity, it indicates that the new appliance is not the corresponding type of appliance. The specific value is set by the staff.

[0029] In one specific embodiment, the adjustment of power consumption is carried out as follows: the current transformer load rate, the load demand of the new electrical appliance, and the usage time of each electrical appliance are collected. The current transformer load rate, the load demand of the new electrical appliance, and the usage time of each electrical appliance are substituted into the load state index calculation formula to obtain the current load state index. If the current load state index is greater than the preset standard load state index, the charging power of the electrical equipment is adjusted.

[0030] The system retrieves the usage frequency, duration, and load change frequency of each electrical appliance at the current transformer load rate from the database. These parameters are then substituted into the usage index calculation formula to obtain the usage index for each appliance. The appliances are then arranged in ascending order of their usage indices to determine their adjustment priorities. The system retrieves the adjustment power of each appliance from the database. Based on these priorities, the power consumption of each appliance is sequentially reduced by the corresponding adjustment power until the current load state index is less than or equal to the preset standard load state index.

[0031] It should be noted that the formula for calculating the exponent is as follows: ,in Let be the usage index of the currently used appliance b, and be the number of the current appliance b. The value of b is a positive integer. , and These represent the usage frequency, usage duration, and load change frequency of the current appliance b at the current transformer load rate b. , and These are the preset standard usage frequency, standard usage duration, and standard load change frequency, respectively. , and These are the preset weighting factors for usage frequency, usage duration, and load change frequency. , , , .

[0032] Standard usage frequency, standard usage duration, and standard load change frequency are the average usage frequency, average usage duration, and average load change frequency of a normal electrical appliance under a preset standard load, respectively. Specific values ​​were obtained through multiple experiments. 0.3 For 0.6 and It is 0.4. , and This represents the degree of influence of usage frequency, usage duration, and load change frequency on the analysis results. Specific values ​​are set by the staff, for example... 0.3 For 0.3 and It is 0.4.

[0033] The risk warning module is used to collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, it performs optimized scheduling for distributed energy and load balance optimization. Furthermore, it collects interactive data of distributed energy, load, and energy storage, identifies risks in the interactive data of distributed energy, load, and energy storage, identifies potential imbalance risks, and issues warnings.

[0034] In one specific embodiment, the collection process for meteorological data, state of charge data, and load usage data is as follows: meteorological data includes photovoltaic panel temperature, photovoltaic panel irradiance, and real-time wind speed. Photovoltaic panel temperature and photovoltaic panel irradiance are collected through temperature sensors, irradiance sensors, and power sensors on the photovoltaic panel, and real-time wind speed is collected through an anemometer on the wind turbine.

[0035] The state of charge (SOC) data includes charge / discharge current intensity, SOC, and battery pack temperature. Charge / discharge current intensity and battery pack temperature are acquired through current and temperature sensors. Charge / discharge current intensity and battery pack temperature at each time point are acquired through temperature and current sensors. SOC is acquired using the coulomb counting method.

[0036] Load usage data includes current trend load parameters, current seasonal load parameters, current date load parameters, and current load values. Load values ​​for each period are obtained from the database, fitted into curves, and the slope of the current period curve is obtained from the curves. Load parameters corresponding to each curve slope are obtained from the database to obtain the current trend load parameters. Historical load values ​​for the corresponding season of the current period are obtained from the database, and the average value is calculated to obtain the standard load value for the current season. Load parameters corresponding to each standard load value are obtained from the database to obtain the current seasonal load parameters. Historical load values ​​for the corresponding date of the current period are obtained from the database. Based on the analysis process of the current seasonal load parameters, the historical load values ​​for the corresponding date of the current period are analyzed to obtain the current date load parameters. Load values ​​at each time point of the last period are collected by a load cell.

[0037] It should be noted that the types of dates include, but are not limited to, weekdays, weekends, and various public holidays.

[0038] In a specific embodiment, the optimization scheduling of distributed energy and load balance optimization is carried out as follows: S1, analyze meteorological data to obtain power generation data, and then establish a distributed energy power generation model.

[0039] S2. Analyze the state of charge data to obtain charge and discharge efficiency data, and then establish a charge and discharge efficiency model.

[0040] S3. Analyze the load usage data to obtain load periodicity data, and then establish a load periodicity model.

[0041] S4. Based on the distributed energy generation model, charging and discharging efficiency model, and load periodic change model, establish a mathematical optimization model to obtain the charging time period, discharging time period, and supplementary power time period for each cycle, and then plan the energy storage charging and discharging and main power supply start-up and shutdown schemes.

[0042] In another specific embodiment, the distributed energy generation model expression is as follows: The distributed energy generation model expression includes a photovoltaic power generation model and a wind power generation model, wherein the photovoltaic power generation model is: ,in, The power generation capacity of the photovoltaic panel. and The solar panel's light intensity and solar panel's light temperature. , and The photovoltaic panel's rated power, light intensity, and battery temperature are set under preset standard test conditions. This is the preset power temperature coefficient.

[0043] It should be noted that the power temperature coefficient is the sensitivity of the photovoltaic panel's power generation to temperature changes, and it is generally a negative value. The specific value is obtained through multiple experiments. Similarly, the rated power of the photovoltaic panel, the light intensity, the battery temperature, and the power temperature coefficient under standard test conditions are all obtained through multiple experiments.

[0044] Wind power generation model: ,in, This refers to the power generation capacity of the wind turbine. For real-time wind speed, and These are the preset inlet and outlet air speeds, respectively. and These are the preset rated power and rated wind speed of the fan, respectively.

[0045] It should be noted that the rated wind speed is the wind speed at which the rated power is achieved, the cut-in wind speed is the minimum wind speed at which the wind turbine starts generating electricity, and the cut-out wind speed is the maximum wind speed for safe operation, protecting the wind turbine from excessive mechanical stress and electrical shock. The specific values ​​were obtained through multiple experiments. Similarly, the cut-in wind speed, cut-out wind speed, and rated power of the wind turbine were all obtained through multiple experiments.

[0046] In another specific embodiment, the expression for the charge / discharge efficiency model is: ,in, For charging and discharging efficiency, The charging and discharging efficiency is under standard conditions, and SOC is the state of charge. For standard state of charge, This is a correction factor for the state of charge. and These are the charging / discharging current intensity and the battery pack temperature, respectively. , and These are the preset charge / discharge rate, battery rated capacity, and standard ambient temperature, respectively. The preset standard charge and discharge efficiency correction factor, and These are the weighting factors for the preset charge / discharge rate and the weighting factor for the battery pack temperature, respectively. , , .

[0047] It should be noted that the charge / discharge rate refers to the ratio of the charge / discharge current to the battery's rated capacity. The standard state of charge, the correction factor for the state of charge, the charge / discharge rate, the battery's rated capacity, and the standard ambient temperature are all standard data obtained from multiple experimental tests. 0.6 0.89 0.56 For 45 and It is 21.

[0048] and These represent the influence of charge / discharge rate and battery pack temperature on charge / discharge efficiency, respectively. Specific values ​​were obtained from multiple experiments. For 0.3 and It is 0.7.

[0049] In another specific embodiment, the expression for the load periodic variation model is: ,in, This is the actual load value. and The current load value and The total load value of new electrical appliances within the time period, where g, s, and h are the load parameters for the current trend, current season, and current date, respectively. , and These are the preset standard trend load parameters, standard seasonal load parameters, and standard date load parameters. and These are the preset weighting factors for the current load value and the weighting factors for the new appliance load value, respectively. , , , , and These are the preset weighting factors for trend-related load parameters, seasonal load parameters, and date-related load parameters. , , , .

[0050] It should be noted that the standard trend load parameter, standard seasonal load parameter, and standard date load parameter are the trend load parameter, seasonal load parameter, and date load parameter of the normal load. If the current trend load parameter is greater than the standard trend load parameter, it indicates that the current load is increasing over time. If the seasonal load parameter is greater than the standard seasonal load parameter, it indicates that the current seasonal load is greater than the normal load. Seasons are generally winter and summer, as air conditioning heating and cooling are required. If the current date load parameter is greater than the standard date load parameter, in the case of a residential system, the date is generally a holiday. The specific values ​​are set by the staff. 0.6 For 0.7 and It is 0.75.

[0051] , and These represent the degree of influence of trend-related load parameters, seasonal load parameters, and date-related load parameters on load changes. Specific values ​​are set by staff. 0.2 0.4 and It is 0.4. and These represent the degree of influence of the preset current load value and the new appliance load value on the predicted charge value, respectively. The larger the new appliance load value, the higher the predicted charge value. The larger the value, the smaller the additional electrical load. The larger the value, the more specific the value is set by the staff, for example... For 0.3 and It is 0.7.

[0052] In another specific embodiment, the mathematical optimization model expression is: Where E is the output result. The system is set to a preset baseline charging and discharging efficiency. When the output of the mathematical optimization model is 0, it is connected to the energy storage power supply. When the output of the mathematical optimization model is 1, it is connected to the main power supply. When the output is 2, it is connected to the energy storage charging. When the output is 3, it is connected to the main line and outputs current.

[0053] It should be noted that the preset baseline charge / discharge efficiency is the charge / discharge efficiency during normal charging. If the charge / discharge efficiency exceeds the baseline efficiency, it indicates that the current battery charge / discharge efficiency is low and energy loss is high, requiring connection to the main circuit for power supply or output. The specific values ​​are set by the operator. It is 0.67.

[0054] In one specific embodiment, the acquisition of interactive data from distributed energy sources, loads, and energy storage is carried out as follows: The interactive data from distributed energy sources, loads, and energy storage includes regional load rate, voltage deviation, frequency fluctuation rate, and energy storage state imbalance. Current voltage and current are acquired through sensors at key nodes of the power distribution line. Current power is obtained according to the power calculation formula. The current power is divided by a preset rated capacity to obtain the regional load rate. The difference between the current voltage and the preset rated voltage is divided by the preset rated voltage to obtain the voltage deviation. The maximum value, minimum value, and number of grid frequency changes within a preset time period are acquired through the synchronous phasor measurement unit at the distributed power source grid connection point. The difference between the maximum and minimum grid frequency within the preset time period is divided by the number of grid frequency changes to obtain the frequency fluctuation rate. The maximum, minimum, and average voltage of the energy storage device within a preset time period are acquired through voltage sensors. The difference between the maximum and minimum voltage within the preset time period is divided by the average voltage to obtain the energy storage state imbalance.

[0055] In one specific embodiment, the risk identification process for the interaction data of distributed energy resources, loads, and energy storage is as follows: Regional load factor, voltage deviation, frequency fluctuation rate, and energy storage status imbalance are input into the risk assessment model to obtain the output results. The numerical values ​​of the output results... This refers to the risk level. , , This is the highest risk level.

[0056] It should be noted that the risk assessment model expression is as follows: ,in, For the output results, R, U, W, and J represent the regional load factor, voltage deviation, frequency fluctuation rate, and energy storage state imbalance, respectively. , , and These are the preset standard area load rate, standard voltage deviation, standard frequency fluctuation rate, and standard energy storage state imbalance, respectively. , , and These are the preset weighting factors for regional load rate, voltage deviation, frequency fluctuation, and energy storage state imbalance. , , , , .

[0057] Standard area load factor, standard voltage deviation, standard frequency fluctuation, and standard energy storage state imbalance are threshold values ​​for these parameters during normal power usage. When the actual values ​​of these parameters exceed the preset thresholds, it indicates a potential power system fault. Specific values ​​are set by the operators. 0.8 0.76 For 0.42 and It is 0.36. , , and These represent the impact of regional load factor, voltage deviation, frequency fluctuation rate, and energy storage imbalance on power system faults. The higher the regional load factor, the more faults occur. The larger the voltage deviation, the more frequent the faults. The larger the frequency volatility, the more frequency-related failures there will be. The larger the value, the more faults related to energy storage state imbalance. The larger the value, the more specific the value is set by the staff, for example... 0.24 It is 0.27. For 0.23 and It is 0.26.

[0058] In one specific embodiment, the warning process is as follows: obtain the risk level of each analysis from the database to obtain the current risk level and the current risk duration. If the current risk level is greater than the standard risk level, issue a warning. If the current risk level is greater than the baseline risk level but less than or equal to the standard risk level, and the current risk duration is greater than the preset risk duration, issue a warning.

[0059] The risk identification module is used to assess risks and generate maintenance plans when an imbalance risk warning is issued. It then collects risk maintenance data and iterates risk correction parameters based on the risk maintenance data.

[0060] In one specific embodiment, the generation process of the maintenance scheme is as follows: After issuing an early warning, data on changes in regional load rate, voltage deviation, frequency fluctuation, and energy storage state imbalance are collected. The regional load rate change data is substituted into the load risk index calculation formula to obtain the load risk index. The load risk index is multiplied by the load correction index to obtain the load fault assessment index. Similarly, based on the analysis process of the regional load rate change data, the data on changes in voltage deviation, frequency fluctuation, and energy storage state imbalance are analyzed to obtain the voltage deviation risk index, voltage deviation fault assessment index, frequency fluctuation risk index, frequency fluctuation fault assessment index, energy storage state risk index, and energy storage state fault assessment index.

[0061] It should be noted that the voltage deviation change data includes the total voltage deviation and the maximum single voltage deviation value within the change period; the frequency fluctuation change data includes the frequency fluctuation difference and the maximum single frequency fluctuation difference within the change period; and the energy storage state imbalance change data includes the energy storage state imbalance difference and the maximum single energy storage state imbalance difference within the change period.

[0062] The formula for calculating the load risk index is as follows: ,in The load risk index includes regional load rate change data, which includes the regional load rate difference and the maximum single fluctuation load rate difference within the change period. The difference in regional load rates is... The maximum load rate difference during a single fluctuation within the change cycle. This is the preset standard load rate change. and These are the preset weighting factors for regional load rate differences and the weighting factors for single-event load rate difference, respectively. , , .

[0063] The standard load rate change is the load change rate under normal load conditions. When the regional load rate difference is less than the standard load rate change, it indicates a low degree of load change. If the maximum load rate difference during a single fluctuation within the change period is less than the standard load rate change, it indicates stable load. Specific values ​​are set by staff, for example... It is 0.8. and These represent the impact of the total load rate change and the maximum load rate change on load risk, respectively. When the current load is high, the greater the degree of change, the greater the risk of load failure. Therefore, the higher the load, the greater the risk of load failure. The larger the value, the lower the current load. If the load is unstable, the greater the risk of load failure. Therefore, the smaller the load, the lower the risk of load failure. The larger the value, the more specific the value is set by the staff, for example... For 0.45 and It is 0.55.

[0064] This yields the fault assessment index and risk index for four types of faults. The fault type corresponding to the largest fault assessment index among the four types of faults is recorded as the actual fault type. Then, the risk index corresponding to the actual fault type is obtained. The maintenance plan corresponding to each risk index of the four types of faults is retrieved from the database. This gives the maintenance plan corresponding to the risk index of the actual fault type, which is recorded as the current maintenance plan.

[0065] It should be noted that the four types of faults refer to load faults, voltage deviation faults, frequency fluctuation faults, and energy storage status faults.

[0066] In one specific embodiment, the risk correction parameter iteration process is as follows: The fault identification rate, fault identification accuracy, and maximum fault identification frequency for four types of faults are obtained from the database. If the fault identification rate of a certain type of fault is less than the corresponding standard fault identification rate, the fault identification accuracy of that type of fault is less than the corresponding standard fault identification accuracy, or the fault identification frequency of that type of fault is less than the corresponding fault identification frequency, the correction index corresponding to that type of fault is increased by a preset unit amount. If the fault identification rate of a certain type of fault is greater than or equal to the corresponding standard fault identification rate, the fault identification accuracy of that type of fault is greater than or equal to the corresponding standard fault identification accuracy, and the fault identification frequency is greater than or equal to the corresponding fault identification frequency, the correction index corresponding to that type of fault is decreased by a preset unit amount.

[0067] according to Figure 2 As shown, the present invention provides a method for controlling wireless power loads, including the following steps: Step 1, load identification: identify the load demand of new electrical appliances and incorporate them into the overall load management, and adjust the power consumption in real time.

[0068] Step 2, Risk Warning: Collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, optimize the scheduling of distributed energy and load balance. Then, collect the interaction data of distributed energy, load, and energy storage, identify risks in the interaction data of distributed energy, load, and energy storage, identify potential imbalance risks, and issue warnings.

[0069] Step 3, Risk Identification: When an imbalance risk warning is issued, risk assessment is performed, a maintenance plan is generated, risk maintenance data is collected, and risk correction parameters are iterated based on the risk maintenance data.

[0070] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A management system for wireless power loads, characterized in that, Includes the following modules: The load identification module is used to identify the load demand of new electrical appliances and incorporate them into the overall load management, adjusting the power consumption in real time. The risk warning module is used to collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, it performs optimized scheduling for distributed energy and load balance optimization. Furthermore, it collects interaction data of distributed energy, load, and energy storage, identifies risks in the interaction data of distributed energy, load, and energy storage, identifies potential imbalance risks, and issues warnings. The risk identification module is used to assess risks and generate maintenance plans when an imbalance risk warning is issued. It then collects risk maintenance data and iterates risk correction parameters based on the risk maintenance data.

2. The wireless power load management system according to claim 1, characterized in that, The specific process for identifying the load demand of new electrical appliances is as follows: High-frequency current sensors and high-frequency voltage sensors are deployed in the power distribution lines. When the fingerprint of electrical appliances is entered, the current intensity and voltage intensity of each electrical appliance are collected. The waveform data of each electrical appliance is obtained through an oscilloscope. The corresponding power curve trajectory is plotted based on the waveform data of each electrical appliance. At the same time, high-frequency transient features are extracted through wavelet transform to construct the feature vector of each electrical appliance. High-frequency current sensors and high-frequency voltage sensors are deployed at key nodes of the power distribution line. When electrical appliances are connected, the current intensity and voltage intensity of each key node are collected. The similarity of electrical appliances at each key node is calculated by dynamic time warping algorithm. When the similarity of electrical appliances at a certain key node is greater than the preset basic electrical appliance similarity, it indicates that an electrical appliance is connected to the key node. When an appliance is connected to a critical node, the power curve trajectory of each appliance is retrieved from the database. A dynamic time warping algorithm is used to analyze the current and voltage intensity of the critical node to obtain the trajectory similarity of various types of appliances at that critical node. When the trajectory similarity of a certain type of appliance at the critical node is greater than a preset trajectory similarity, it is recorded as an effective type of appliance at that critical node. This process is repeated to obtain the effective types of appliances at the critical node. Feature vectors for each type of appliance are retrieved from the database. The dynamic time warping algorithm is then used to analyze the current and voltage intensity of the critical node to obtain the feature similarity of each effective type of appliance at that critical node. The effective type of appliance with the highest feature similarity is selected and recorded as a new appliance at that critical node. This process is repeated to identify the new appliance. Finally, the load demand of various types of appliances is retrieved from the database to obtain the load demand of the new appliance.

3. The wireless power load management system according to claim 2, characterized in that, The specific adjustment process for adjusting the power consumption is as follows: Collect the current transformer load rate, the load demand of the new electrical appliances, and the usage time of each electrical appliance. Substitute the current transformer load rate, the load demand of the new electrical appliances, and the usage time of each electrical appliance into the load state index calculation formula to obtain the current load state index. If the current load state index is greater than the preset standard load state index, adjust the charging power of the electrical equipment. The system retrieves the usage frequency, duration, and load change frequency of each electrical appliance at the current transformer load rate from the database. These parameters are then substituted into the usage index calculation formula to obtain the usage index for each appliance. The appliances are then arranged in ascending order of their usage indices to determine their adjustment priorities. The system retrieves the adjustment power of each appliance from the database. Based on these priorities, the power consumption of each appliance is sequentially reduced by the corresponding adjustment power until the current load state index is less than or equal to the preset standard load state index.

4. The wireless power load management system according to claim 1, characterized in that, The optimized scheduling for distributed energy resources and load balancing is specifically optimized as follows: S1. Analyze meteorological data to obtain power generation data, and then establish a distributed energy generation model; S2. Analyze the state of charge data to obtain charge and discharge efficiency data, and then establish a charge and discharge efficiency model; S3. Analyze the load usage data to obtain load periodicity data, and then establish a load periodicity model; S4. Based on the distributed energy generation model, charging and discharging efficiency model, and load periodic change model, establish a mathematical optimization model to obtain the charging time period, discharging time period, and supplementary power time period for each cycle, and then plan the energy storage charging and discharging and main power supply start-up and shutdown schemes.

5. A wireless power load management system according to claim 1, characterized in that, The risk identification process for the interaction data of distributed energy resources, loads, and energy storage is as follows: Interactive data from distributed energy resources, loads, and energy storage includes regional load factor, voltage deviation, frequency fluctuation, and energy storage status imbalance. These data are input into the risk assessment model to obtain the output results, which represent the risk level.

6. A wireless power load management system according to claim 5, characterized in that, The warning process is as follows: The risk levels of each analysis are obtained from the database to obtain the current risk level and the current risk duration. If the current risk level is greater than the standard risk level, an early warning is issued. If the current risk level is greater than the baseline risk level but less than or equal to the standard risk level, and the current risk duration is greater than the preset risk duration, an early warning is issued.

7. A wireless power load management system according to claim 6, characterized in that, The specific process for generating the maintenance scheme is as follows: After issuing an early warning, data on changes in regional load rate, voltage deviation, frequency fluctuation, and energy storage status imbalance are collected. The regional load rate change data is substituted into the load risk index calculation formula to obtain the load risk index. The load risk index is multiplied by the load correction index to obtain the load failure assessment index. Similarly, based on the analysis process of regional load rate change data, the data on changes in voltage deviation, frequency fluctuation, and energy storage status imbalance are analyzed to obtain the voltage deviation risk index, voltage deviation failure assessment index, frequency fluctuation risk index, frequency fluctuation failure assessment index, energy storage status risk index, and energy storage status failure assessment index. This yields the fault assessment index and risk index for four types of faults. The fault type corresponding to the largest fault assessment index among the four types of faults is recorded as the actual fault type. Then, the risk index corresponding to the actual fault type is obtained. The maintenance plan corresponding to each risk index of the four types of faults is retrieved from the database. This gives the maintenance plan corresponding to the risk index of the actual fault type, which is recorded as the current maintenance plan.

8. A wireless power load management system according to claim 7, characterized in that, The risk correction parameter iteration process is as follows: The database retrieves the fault identification rate, fault identification accuracy, and maximum fault identification frequency for four types of faults. If the fault identification rate, fault identification accuracy, or maximum fault identification frequency for a certain type of fault is less than the corresponding standard fault identification rate, the corresponding correction index for that type of fault is increased by a preset unit amount. If the fault identification rate, fault identification accuracy, or maximum fault identification frequency for a certain type of fault is greater than or equal to the corresponding standard fault identification rate, the corresponding correction index for that type of fault is decreased by a preset unit amount.

9. A wireless power load management system according to claim 1, characterized in that, The database stores the power curve trajectory of each electrical appliance, the feature vector of each type of electrical appliance, the load demand of each type of electrical appliance, the usage frequency of each electrical appliance at the current transformer load rate, the usage duration of each electrical appliance at the current transformer load rate, the load change frequency of each electrical appliance at the current transformer load rate, the adjustment power of each electrical appliance, the risk level of each analysis, the maintenance plan corresponding to each risk index of the four types of faults, the fault identification rate of the four types of faults, the fault identification accuracy of the four types of faults, and the maximum fault identification frequency of the four types of faults.

10. A management method for a wireless power load management system according to any one of claims 1-9, characterized in that, Includes the following steps: Step 1: Load Identification: Identify the load demand of new electrical appliances and incorporate them into the overall load management, adjusting power consumption in real time; Step 2, Risk Warning: Collect meteorological data, state of charge data, and load usage data. Based on the meteorological data, state of charge data, and load usage data, optimize the scheduling of distributed energy and load balance. Then, collect the interaction data of distributed energy, load, and energy storage, identify risks in the interaction data of distributed energy, load, and energy storage, identify potential imbalance risks, and issue warnings. Step 3, Risk Identification: When an imbalance risk warning is issued, risk assessment is performed, a maintenance plan is generated, risk maintenance data is collected, and risk correction parameters are iterated based on the risk maintenance data.

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