A micro-wind power generation and energy storage coupled off-grid and grid-connected switching system

Abstract

The present application relates to the field of power system and new energy power generation control, specifically to a kind of micro wind power generation and energy storage coupling off-grid switching system, comprising: multidimensional data perception module: micro wind and power grid characteristic data are collected;Multidimensional input data set is constructed;Edge intelligent computing module: micro wind energy trend prediction model is established;The probability of micro wind continuous energy supply and battery health loss are calculated;Energy confidence assessment module: construct micro energy load supply-demand elasticity index;Quantify the degree of dynamic balance of supply and demand;Adaptive control module: set high and low confidence threshold;The elasticity index is compared with threshold value;Flexible switching and power flow direction instruction are generated;The present application avoids blind off-grid switching, realizes the global optimization of energy utilization rate and equipment life.

Description

Technical Field

[0001] This invention relates to the field of power systems and new energy power generation control, specifically to an off-grid switching system that couples micro-wind power generation with energy storage. Background Technology

[0002] A micro-wind power generation and energy storage coupling system refers to a device that captures wind energy through a wind turbine and coordinates with an energy storage battery for power management and load supply in a low-wind-speed resource environment. Current micro-wind power generation off-grid and grid-connected switching technology mainly relies on rigid electrical parameter criteria, including: monitoring the instantaneous voltage amplitude of the point of common coupling, detecting the current flow direction, and setting a fixed power threshold to trigger the conversion between off-grid and grid-connected modes. However, when switching control is performed based on existing technologies, on the one hand, it is impossible to predict the random fluctuations of wind energy and the probability of continuous energy supply in the future based solely on instantaneous electrical data, which can easily cause the system to oscillate and switch frequently when the wind speed fluctuates critically; on the other hand, traditional strategies do not couple the health status loss of the battery with the economic value of the small amount of energy obtained, which often leads to the system performing unprofitable charging and discharging operations under ineffective wind energy, which not only reduces the overall utilization efficiency of wind energy, but also seriously affects the service life of power devices and energy storage batteries. Summary of the Invention

[0003] The purpose of this invention is to provide an off-grid switching system that couples micro-wind power generation with energy storage, and to solve the following technical problems: Abandoning the traditional rigid switching logic that relies solely on instantaneous voltage or current thresholds, this paper constructs a micro-load supply and demand elasticity index for flexible decision-making, avoiding blind on-grid switching and battery life loss caused by small wind power throughput, thereby achieving global optimization of energy utilization and equipment life in low-wind environments.

[0004] The objective of this invention can be achieved through the following technical solutions: The multi-dimensional data sensing module is used to collect environmental and operational data of the micro-wind power generation system, including micro-wind characteristic data, power grid characteristic data, load characteristic data and battery status data, and obtain a multi-dimensional input dataset; The edge intelligent computing module is used to process the collected multidimensional input dataset, build a wind energy trend prediction model using historical data, calculate the probability of wind continuing to provide energy in the future, and combine it with battery status data to calculate the health loss of the battery life caused by the current operation, and generate energy prediction and loss assessment results. The energy confidence assessment module is used to construct a micro-energy load supply and demand elasticity index based on energy prediction and loss assessment results. This index represents the degree of dynamic balance between the supply-side capacity composed of the current remaining battery power and predicted wind energy input and the demand-side pressure composed of the critical load floor value and predicted load demand. The adaptive control module is used to make off-grid / on-grid switching decisions based on the micro-load supply and demand elasticity index. It presets a high confidence threshold and a low confidence threshold, wherein the high confidence threshold is greater than the low confidence threshold. It compares the micro-load supply and demand elasticity index with the high confidence threshold and the low confidence threshold, and generates flexible switching commands and power flow control commands based on the comparison results to control the converter to switch between off-grid and on-grid modes and distribute energy.

[0005] As a further aspect of the present invention: the multidimensional data sensing module includes: The environmental feature acquisition unit is used to monitor wind speed, wind direction and wind energy capture curves over a set time period in real time, while also monitoring voltage fluctuation rate and power outage history frequency at the point of common coupling. The load and battery monitoring unit is used to identify the characteristics of users' electricity consumption periods, distinguish between peak load periods and off-peak load periods, and read the state of charge, health status and internal temperature data of the energy storage battery in real time. It synchronizes the environmental characteristic data with the load and battery data in time and stores them in a multidimensional input dataset.

[0006] As a further aspect of the present invention: the edge intelligent computing module includes: The trend prediction unit is used to calculate the probability that the wind speed will remain above the power generation threshold in the short term in the future by using historical capture curves in the light wind characteristic data and a probability model. When the wind speed is detected to be within the threshold but is on a downward trend, the prediction weight of wind energy input is reduced. The loss calculation unit is used to calculate the amount of battery health loss caused by the current switching action or charge-discharge cycle in real time based on battery status data, and to calculate the energy gain generated by the wind energy. When the energy gain is lower than the amount of battery health loss, an evaluation result of abandoning the absorption of the wind energy is generated.

[0007] As a further aspect of the present invention: the energy confidence assessment module includes: The index calculation unit is used to calculate the micro-energy load supply and demand elasticity index. The value of this index is proportional to the sum of the current remaining battery power and the weighted predicted wind energy input, and inversely proportional to the sum of the critical load floor value and the predicted load demand. The dynamic weighting unit is used to adjust the confidence coefficient of the predicted wind energy input based on the probability of continuous energy supply from light winds. When the probability of continuous energy supply from light winds is lower than the preset probability threshold, the confidence coefficient is reduced, thereby reducing the value of the micro-energy load supply and demand elasticity index and preventing the index from being artificially high due to wind speed fluctuations.

[0008] As a further aspect of the present invention: the adaptive control module includes: The surplus mode control unit is configured to: when the supply and demand elasticity index of micro-load is greater than or equal to the preset high confidence threshold, determine that the system has sufficient energy, generate a grid connection command, control the system to enter the grid connection mode, and allow power to be fed back to the grid or to supply power to high-power loads according to grid characteristic data; The defense mode control unit is configured to: when the supply and demand elasticity index of micro-load is less than or equal to the preset low confidence threshold, determine that the system is in short supply of energy, generate an off-grid preparation command or a power limiting command, prioritize charging the energy storage battery to retain a minimum charge, and cut off the power supply circuit of non-critical loads.

[0009] As a further aspect of the present invention, the adaptive control module further includes: The intermediate state maintenance unit is configured to maintain the current off-grid or grid-connected state of the system when the micro-energy load supply and demand elasticity index is greater than the preset low confidence threshold and less than the preset high confidence threshold. It uses the energy storage battery as a buffer to smooth out fluctuations and avoids the system from frequently switching between off-grid and grid-connected at the critical point.

[0010] As a further aspect of the present invention, the adaptive control module further includes: The micro-energy bypass unit is used to activate the bypass power supply logic when the power generated by the micro-wind is less than the battery charging polarization loss threshold. This controls the power flow to be directed directly from the wind turbine to the load, or to cut off the wind turbine input when the load demand is zero, thus preventing a small current from flowing into the energy storage battery and avoiding battery life loss caused by ineffective throughput.

[0011] As a further aspect of the present invention, the system also includes: The flexible execution module is used to respond to the flexible switching command generated by the adaptive control module. During the off-grid to grid-connected process, it performs pre-synchronization tracking based on the grid voltage phase. When both the phase difference and voltage difference are less than the allowable deviation value, the switch is closed. During the grid-connected to off-grid process, the switch is opened after adjusting the converter output power to the zero power point to achieve a smooth transition.

[0012] As a further aspect of the present invention, the system also includes: The user feedback interaction module is used to receive the micro-load supply and demand elasticity index and map the index to the power supply guarantee level. The current power supply guarantee status is displayed to the user through a visual interface. The power supply guarantee status includes worry-free use status, economical use status, and critical load-only status.

[0013] The beneficial effects of this invention are: 1) This invention establishes a micro-wind energy trend prediction model and calculates battery health loss through an edge intelligent computing module. It can identify in advance invalid operating conditions where the instantaneous wind speed meets the standard but the wind is about to stop, as well as unprofitable operating conditions where the energy gain is lower than the battery life loss. This overcomes the blindness of the existing technology that relies solely on instantaneous electrical parameters for rigid switching, avoids unnecessary losses to the energy storage battery caused by invalid wind energy, and significantly extends the service life of the core components of the system.

[0014] 2) This invention constructs a micro-energy load supply and demand elasticity index and uses an adaptive control module to set dual thresholds of high confidence and low confidence, and establishes an intermediate state maintenance unit between the two thresholds; it uses energy storage batteries as a buffer to smooth fluctuations, effectively preventing the system from frequently switching between grid connection and off-grid due to small wind speed disturbances at the critical point, solving the problems of mechanical switch wear and grid voltage flicker caused by traditional single threshold control, and improving the system's operational stability.

[0015] 3) This invention introduces a micro-energy bypass unit, which executes bypass power supply or cuts off input logic for the special scenario where the power generation of a light wind is less than the battery charging polarization loss threshold. This design ensures that only electrical energy with effective chemical energy conversion value is stored in the battery, avoiding battery polarization damage caused by long-term shallow charging with a small current, and improving the overall energy efficiency ratio of energy harvesting in light wind environment and the precision of battery health management.

[0016] 4) The flexible execution module of the present invention performs voltage phase pre-synchronization tracking when switching from off-grid to on-grid, and disconnects after adjusting the converter to zero power point when switching from on-grid to off-grid. This soft switching strategy eliminates the inrush current and voltage spikes generated during the traditional hard switching process, which not only protects the safety of sensitive electrical appliances on the user side, but also reduces the impact of distributed power supply access on the weak grid at the end, and ensures power quality.

[0017] 5) This invention synchronously collects environmental, power grid, load and battery status data through a multi-dimensional data sensing module, and combines it with the dynamic weighting mechanism of the energy confidence assessment module to achieve a quantitative assessment of the system's energy supply robustness. Based on this, the system can generate profits by backfeeding in surplus mode and cut off non-critical loads to ensure supply in defense mode, thus achieving optimal energy allocation and critical load survival guarantee under the dual uncertainties of micro-wind resource fluctuations and load demand changes. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a schematic diagram of the off-grid switching system for micro-wind power generation and energy storage coupling according to the present invention. Detailed Implementation

[0020] 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.

[0021] Please see Figure 1 As shown, a grid-connected switching system for micro-wind power generation and energy storage coupling includes: a multi-dimensional data sensing module, used to collect environmental and operational data of the micro-wind power generation system, including micro-wind characteristic data, grid characteristic data, load characteristic data and battery status data, and obtain a multi-dimensional input dataset; The edge intelligent computing module is used to process the collected multidimensional input dataset, build a wind energy trend prediction model using historical data, calculate the probability of wind continuing to provide energy in the future, and combine it with battery status data to calculate the health loss of the battery life caused by the current operation, and generate energy prediction and loss assessment results. The energy confidence assessment module is used to construct a micro-energy load supply and demand elasticity index based on energy prediction and loss assessment results. This index represents the degree of dynamic balance between the supply-side capacity composed of the current remaining battery power and predicted wind energy input and the demand-side pressure composed of the critical load floor value and predicted load demand. The adaptive control module is used to make off-grid and on-grid switching decisions based on the micro-load supply and demand elasticity index. It presets a high confidence threshold and a low confidence threshold, with the high confidence threshold being greater than the low confidence threshold. It compares the micro-load supply and demand elasticity index with the high confidence threshold and the low confidence threshold, and generates flexible switching commands and power flow control commands based on the comparison results to control the converter to switch between off-grid and on-grid modes and distribute energy.

[0022] This embodiment elaborates on the core architecture and operating mechanism of the above system. The core logic of the system lies in abandoning the rigid logic of traditional switching that relies solely on instantaneous voltage or current thresholds, and instead adopting a flexible decision-making mechanism based on the elasticity of energy supply and demand. The multi-dimensional data sensing module, as the sensory nerve ending of the system, synchronously collects key data of the micro-wind power generation system through a high-frequency sensor interface to construct a multi-dimensional input dataset. ; The edge intelligent computing module is deployed in the local controller and uses time series analysis algorithms such as Markov chain-based probability transition matrices or discrete state lookup tables to establish a micro-wind energy trend prediction model. And calculate the probability of sustained energy supply from light breezes during the future set time period. Simultaneously, health loss is calculated by combining battery SOH data. The energy confidence assessment module calculates the micro-energy load supply and demand elasticity index. This index is a dimensionless value used to quantify how robust a system is in maintaining power supply continuity when faced with uncertain future wind energy input and deterministic load demand. The adaptive control module will calculate Compared with the preset high confidence threshold and low confidence threshold The comparison generates flexible switching commands to control the on / off state of the grid-connected switch, and generates power flow control commands to set the power points of the DC / DC and DC / AC converters. This embodiment introduces an energy confidence assessment mechanism to construct a forward-looking control strategy in the micro-wind power generation scenario. This strategy can identify complex operating conditions such as when the current wind speed meets the standard but the wind is about to stop, or when the grid voltage fluctuates but the battery is not suitable to intervene, thereby avoiding blind on / off grid switching and achieving global optimization of energy utilization and equipment life.

[0023] In a preferred embodiment of the present invention, the multidimensional data sensing module includes: an environmental feature acquisition unit, used to monitor wind speed, wind direction, and wind energy capture curves within a set time period in real time, while simultaneously monitoring voltage fluctuation rate and historical outage frequency at the point of common connection; and a load and battery monitoring unit, used to identify the user's electricity consumption period characteristics, distinguish between peak load periods and off-peak load periods, and read the state of charge, health status, and internal temperature data of the energy storage battery in real time, synchronizing the environmental feature data with the load and battery data in time, and storing them in a multidimensional input dataset.

[0024] This embodiment is a detailed design of the multi-dimensional data perception module in the previous embodiment, aiming to ensure the spatiotemporal consistency of the data. The environmental feature acquisition unit is equipped with an ultrasonic anemometer and a power quality analyzer. When acquiring wind energy data, it not only collects instantaneous values, but also records the wind energy capture curve over a set time period, such as the past 24 hours, using a sliding window algorithm. At the same time, it monitors the voltage fluctuation rate at the PCC point. and frequency of power outages These data are used to subsequently determine the background stability of the power grid; to ensure a unified quantitative standard for power grid quality assessment, this embodiment defines... The calculation formula is: , in, The voltage sample values ​​are from the past 10 minutes. Nominal voltage; The total number of sampling points is equal to the sampling time window length divided by the sampling interval; simultaneously, it is defined as follows: This refers to the total number of power outages lasting longer than 1 minute that occurred within the past 30 calendar days. The load and battery monitoring unit processes historical load data using the K-Means clustering algorithm through the smart circuit breaker and BMS communication interface; The specific steps are as follows: Select the load power data of the past 30 days and construct a two-dimensional feature vector set containing time index and power amplitude; In order to eliminate the interference of data dimension difference on Euclidean distance calculation, the system adopts the Min-Max standardization method to map the time index and power amplitude to the [0,1] interval respectively; Set the number of clusters The system uses Euclidean distance as a metric for iterative convergence, marking the cluster with the higher final centroid power value as the peak load period and the other cluster as the low load period. To address the issue of different sampling rates for millisecond-level wind speed data and second-level battery temperature data, this unit employs a linear interpolation alignment method to uniformly map all data onto the same timestamp axis and store it in the multidimensional input dataset. ; This embodiment incorporates a time-weighted dimension when calculating the energy index by differentiating between load periods and synchronizing multi-source data. For example, before the evening peak, even with high wind speeds, the system tends to adopt a more conservative strategy due to the recognition of peak characteristics, thus providing a high-quality data foundation with temporal correlation for subsequent accurate decision-making.

[0025] In a preferred embodiment of the present invention, the edge intelligent computing module includes: a trend prediction unit, which is used to calculate the probability that the wind speed will remain above the power generation threshold in the future short period of time by using the historical capture curve in the light wind feature data and a probability model; when the wind speed is detected to be within the threshold but in a downward trend, the prediction weight of wind energy input is reduced. The loss calculation unit is used to calculate the amount of battery health loss caused by the current switching action or charge-discharge cycle in real time based on battery status data, and to calculate the energy gain generated by the wind energy. When the energy gain is lower than the amount of battery health loss, an assessment result of abandoning the absorption of the wind energy is generated. This embodiment further specifies the internal logic of the edge intelligent computing module in the previous embodiment, focusing on addressing the question of whether a breeze is worth utilizing. The trend prediction unit employs probability weight-based prediction logic, and to more accurately reflect the instability of the breeze, a probability of continuous energy supply from the breeze is defined. The calculation logic is as follows: , in, The wind speed is a random variable representing future moments; Derived from a preset constant, its physical meaning is the minimum threshold for wind turbine to start generating electricity, and its unit is m / s; Indicated at the current wind speed Wind speed at the previous sampling time Under the given conditions, the conditional probability that the future wind speed will meet the threshold; to ensure the feasibility of this probabilistic model, this embodiment uses the two-dimensional state transition matrix method for specific calculation: The wind speed is discretized into several intervals, and a wind speed state index is defined, the calculation formula of which is: , in, Specifically refers to the width of the wind speed discretization interval; in this embodiment, the value is set to 0.5 m / s to balance calculation accuracy and storage overhead. The total number of states is 50 in this embodiment, corresponding to a wind speed range of 0-25 m / s. To address the issue of ambiguous boundary value classification, this embodiment specifically stipulates that: when When it is exactly equal to the maximum range value, such as 25 m / s, a forced command is executed. This ensures that the state falls within a valid range [24.5, 25], preventing index out-of-bounds errors or probabilistic lookup failures. Simultaneously define the trend state index Here, we introduce the real-time change in wind speed. To distinguish the aforementioned interval width This is specifically noted here as The calculation logic is: if but That is, rising, if but That is, stable, if but That is, a decrease, of which The trend dead zone threshold is, for example, 0.1 m / s; To construct this probability lookup table, this embodiment discloses specific offline statistical and construction steps: selecting historical wind speed data sequences for at least one complete year in the past at the site, with a sampling interval of 1 minute; Traversing every point in time in the historical sequence According to the wind speed at that moment and trend calculation status index Again, check each After In this embodiment, wind speed data is set within a 10-minute time window. If the wind speed values ​​at all sampling points within this window are greater than or equal to the power generation threshold, the data will be used. If the power supply is successful, the success count for this state will be counted. Add 1, and also the total number of occurrences of this state. Add 1; Calculate the base probability ; Regarding the extremely low frequency of certain extreme states in historical data, i.e. This situation leads to probability distortion. In this embodiment, the Laplace smoothing method is used for correction. The correction formula is: , If a certain state has never appeared in historical data, that is When the wind speed is at that time, the adjacent wind speed state is taken. and The average of the corresponding probabilities is used as the default value; the above calculation results are stored as... A static probability lookup table; during real-time calculation, the index is directly based on the current state. Find the probability value in the matrix. ; The value is derived from real-time wind speed derivative calculations and its physical meaning is a trend correction factor. To overcome the ambiguity in describing a downward trend, this embodiment defines... The specific calculation formula is as follows: , in, This refers to the wind speed change as defined above. For example, take the preset attenuation coefficient. This formula ensures that the probability of energy supply remains constant when wind speed drops sharply. It will be quickly suppressed; The loss calculation unit introduces an economic criterion to calculate the energy gain in real time. and battery health status SOH loss To prevent logical errors caused by inconsistent dimensions and to accommodate the discrete computing characteristics of the digital controller, this embodiment calculates the wind energy output within the prediction period. The unit is kWh. Converting continuous integrals to discrete summation is as follows: , in, To predict wind speed sequences; to obtain refined time series for integration calculations. In this embodiment, the K-nearest neighbor waveform matching method is used; the historical database is defined as a set of continuous wind speed sampling sequences stored in local non-volatile memory. This set contains all historical wind speed data points sampled and stored minute-by-minute within the past year, representing past operating cycles of the system, and the data format is key-value pairs of <timestamp, wind speed value>. The specific matching steps are as follows: First, construct the feature vector. With sampling interval In this embodiment, the time is set to 60 seconds, for the past In this embodiment, wind speed is sampled over a 10-minute period, resulting in the following vector: , in, Let be the feature dimension, and its calculation formula is: In this embodiment Take 10 minutes. Take 60 seconds, therefore The second step is to traverse the historical database. Extract all elements of length using a sliding window method. Historical fragments as candidate vectors The filtering criteria require that there must be a segment with a length of at least [length missing] after the end time of the segment. The third step is to calculate the Euclidean distance using the following formula: (The text abruptly ends here, so the translation stops as well.) , Fourth step, select the one that makes Minimal historical fragment index Locate the segment in End time index in directly from Read from in order to Data points, among which Predicted sequences can be constructed without resampling. This embodiment clearly defines the prediction period. One hour, or 3600 seconds, is sufficient to cover a typical recharge cycle of a micro-wind system; Other parameters are defined as follows: The discrete sampling time step is set to 60 seconds. To predict the number of steps; The conversion factor for converting joules (J) to kilowatt-hours (kWh); Let the air density be denoted as . , The swept area of ​​the wind turbine impeller, in units , The wind energy utilization coefficient is taken as 0.4. The total system conversion efficiency; based on this, the energy gain. The unit is yuan, and the calculation is as follows: , in, The unit is yuan / kWh, representing the comprehensive value electricity price. To overcome the logical flaw of solely basing economic comparisons on a fixed electricity price in scenarios of off-grid energy shortages potentially leading to power outages, this embodiment dynamically adjusts the electricity price based on system status. The calculation formula is as follows: , in, This is the time-of-use electricity price constant pre-stored in the controller, for example, 1.0 yuan during peak hours and 0.3 yuan during off-peak hours; This is a scarcity function based on the battery's state of charge, and to avoid conflicting with the trend correction factor in the aforementioned formula... To avoid terminological confusion and ensure the independence of parameter naming, the following approach is adopted here. As the exponential decay coefficient, i.e. ,For example ; when At lower levels, the survival value of wind energy is amplified, making... This will greatly improve To avoid [the system] being in dire need of energy when it is most needed. And mistakenly abandon life-saving energy; when At higher levels, System regression is based on The purely economic decision; correspondingly, the state of health (SOH) loss of the battery. The unit is yuan, and the calculation is performed using a throughput-based discounting model: , in, This refers to the battery's rated capacity, expressed in kWh. This characterizes the proportion of energy throughput from the current breeze to the total battery capacity. For battery pack replacement cost, Standard cycle life; The accelerated aging factor, based on the current depth of discharge (DOD), is fitted using the following formula: , For example ;in, Defined as It should be noted that, Specifically refers to the nominal cycle life of a battery under standard test conditions, namely 25°C, 100% DOD, and 0.5C charge / discharge, such as 3000 cycles; and The empirical coefficients obtained by fitting data from accelerated battery aging experiments characterize the basic aging factor and the deep aging index factor, respectively. This correction term... This solves the problem of excessive losses when directly using the nominal life calculation under light wind, shallow charge and shallow discharge conditions; This computational logic ensures and Both are at the same minute level of economic value, thus enabling effective comparison; based on this, the system executes the following decision logic: in response to The system generates an assessment result of abandoning absorption and controls the converter to stop charging the battery or only supply power to the immediate load.

[0026] In a preferred embodiment of the present invention, the energy confidence assessment module includes: an index calculation unit, used to calculate the micro-energy load supply and demand elasticity index, the value of which is proportional to the sum of the current remaining battery power and the weighted predicted wind energy input, and inversely proportional to the sum of the critical load floor value and the predicted load demand; The dynamic weighting unit is used to adjust the confidence coefficient of the predicted wind energy input based on the probability of continuous energy supply from light winds. When the probability of continuous energy supply from light winds is lower than the preset probability threshold, the confidence coefficient is reduced, thereby reducing the value of the micro-energy load supply and demand elasticity index and preventing the index from being artificially high due to wind speed fluctuations.

[0027] This embodiment is a further specification of the core algorithm of the energy confidence assessment module in the previous embodiment; in order to quantify the energy supply elasticity of the system, the index calculation unit constructs a micro-load supply and demand elasticity index. The calculation formula is as follows: , in, Sourced from real-time BMS data, its physical meaning is the current remaining battery capacity, expressed in kWh. Derived from dynamic weighted unit calculation, its physical meaning is the confidence coefficient of wind energy input, with a value range of [0,1]. Derived from a prediction model, its physical meaning is the total amount of wind energy input within a future specified time period, and the unit is kWh; Specifically, this embodiment sets The value is directly taken from the wind energy output calculated by the loss calculation unit in the embodiment. To ensure consistency in energy assessment; Derived from a preset constant, its physical meaning is the critical load floor value that maintains the user's minimum survival needs, and the unit is kWh; Derived from load forecasting, its physical meaning is the regular load demand within a set future period, measured in kWh. To ensure the accuracy and calculability of this forecast value, and to prevent negative or infinite forecast values ​​due to zero denominator or sudden load changes, this embodiment uses a dynamic gradient correction method with saturation constraints for calculation: , in, The average power calculated based on load records for the same period over the past 7 days is multiplied by the predicted period length. The obtained baseline energy value, note: here This refers to the prediction period defined in the examples, to maintain consistent terminology throughout the text; The energy value for the current period, converted from the load power at the current moment. , This is the energy value for the time period converted from the load power at the previous sampling time. This is the load trend sensitivity coefficient, for example, 2.0, used to adjust the response weight of the prediction model to the current load change gradient; To prevent tiny quantities with a denominator of zero, take ; This is a cutoff function used to limit the magnitude of gradient correction. To adjust the upper limit, we take 0.5, which is the maximum allowable adjustment. ; The energy value corresponding to the minimum standby power consumption of the system is taken as 0.01 kWh. This formula ensures that... It is always positive and within a physically reasonable range; The dynamic weighting unit is responsible for adjusting based on the uncertainty of the breeze. The value is adjusted according to the following logic: , in, Derived from the trend prediction unit, its physical meaning is the probability of a sustained energy supply from a light breeze; It originates from system presets and its physical meaning is a preset probability threshold; To avoid system misjudgment due to the arbitrariness of this threshold setting, this embodiment clarifies that... Acquisition method: When the system is running for the first time and the accumulated historical data is less than 30 days, the default value is used. Once the system has accumulated 30 days of historical prediction data, it constructs a prediction-measured confusion matrix based on this data, plots a precision-recall curve, and selects the probability value corresponding to a precision rate of 90% as the updated value. This setting ensures that wind energy inputs are fully trusted and included in supply-side capacity only when the model has extremely high confidence; otherwise, they are linearly reduced. This embodiment uses a dynamic weighting algorithm and a threshold setting based on statistical verification to enable... The value can dynamically reflect the safety margin of the system; when the wind forecast is unreliable, the system automatically reduces its expectation of dependence on wind energy, prompting the system to enter the defense mode earlier, preventing power outages at critical moments due to blind optimism about unstable wind sources, and improving the reliability of the system's power supply.

[0028] In a preferred embodiment of the present invention, the adaptive control module includes: a surplus mode control unit configured to: determine that the system has sufficient energy when the micro-load supply-demand elasticity index is greater than or equal to a preset high confidence threshold, generate a grid connection command, control the system to enter grid connection mode, and allow power to be fed back to the grid or supplied to high-power loads based on grid characteristic data; and a defense mode control unit configured to: determine that the system has a shortage of energy when the micro-load supply-demand elasticity index is less than or equal to a preset low confidence threshold, generate an off-grid preparation command or a power limiting command, prioritize charging the energy storage battery to retain a minimum charge, and disconnect the power supply circuit for non-critical loads.

[0029] This embodiment further specifies the control logic of the adaptive control module in the embodiment under extreme conditions; the surplus mode control unit continuously monitors the supply and demand elasticity index of micro-energy load. ; in response The system determines that there is sufficient energy, generates a grid connection command to close the PCC point switch, and controls the bidirectional converter to allow the battery and wind turbine to simultaneously supply power to the grid or open the power supply circuit for high-power loads. Meanwhile, the defense mode control unit monitors The downward trend; in response to The system determines that there is a shortage of energy and generates an off-grid preparation command. During this period, if there are signs of fluctuation in the power grid, the system immediately switches to off-grid mode and executes load shedding logic to cut off the power supply circuits of non-critical loads. The system executes forced charging logic, prioritizing the storage of all power generated by the fan into the battery until the battery has sufficient remaining charge. Restore to a safe water level; this embodiment clearly defines the logic for setting the safe water level: The water level is set within the 30%-40% range of the battery's SOC. This range is higher than the cutoff voltage point for deep battery discharge and can meet the power supply requirements of critical loads for at least 4 hours in off-grid conditions. The specific value is dynamically calculated by the system based on the total power of the currently connected critical loads. To guide those skilled in the art in implementing this dynamic calculation process, this embodiment discloses a safe water level. The specific calculation formula is as follows: , in, This embodiment sets the minimum permissible physical depth of discharge for the battery at 10%. This represents the real-time total power of currently connected critical loads, in kW. The preset offline battery life guarantee time is set to 4 hours in this embodiment; This refers to the nominal total energy of the battery pack, expressed in kWh. The conversion efficiency of the off-grid inverter is, for example, 0.92. This formula ensures that the calculated safety level can reliably support the operation of critical loads in windless and grid-free conditions. Hour; To enable those skilled in the art to accurately implement the above-described judgment logic, this embodiment specifies a high confidence threshold. With low confidence threshold Specific numerical quantification was defined: considering This is a dimensionless supply-demand ratio, which will be used in this embodiment. Setting it in the range of 1.2 to 1.5, the physical meaning of this value is that the supply-side capacity must exceed the demand-side pressure by at least 20% before the system is allowed to enter a high-energy-consuming or reverse-feeding mode, in order to reserve sufficient fluctuation margin. Will The threshold is set in the range of 0.6 to 0.8. The physical meaning of this value is that when the supply-side capacity drops to only be able to meet 60% to 80% of the demand, the system must immediately activate the defense mechanism. The logic for setting this threshold is based on the statistical results of the energy fluctuation variance of the micro-wind power generation system in actual operation. This embodiment achieves adaptive adjustment of the system under different energy states through a dual-threshold control strategy and provides a clear threshold setting range. When energy is sufficient, it maximizes economic benefits by reverse power supply or high-load operation, while when energy is scarce, it maximizes survival by cutting load to protect the battery. This demonstrates the system's deep adaptability to energy fluctuations in rural micro-wind scenarios.

[0030] In a preferred embodiment of the present invention, the adaptive control module further includes an intermediate state maintenance unit, configured to maintain the current off-grid or grid-connected state of the system unchanged when the micro-energy load supply and demand elasticity index is greater than a preset low confidence threshold and less than a preset high confidence threshold, using the energy storage battery as a buffer to smooth fluctuations and avoid the system from frequently switching between off-grid and grid-connected at critical points.

[0031] This embodiment supplements the control logic of the adaptive control module in the intermediate state in the previous embodiment; this state is defined as a hysteresis buffer; the intermediate state maintenance unit monitors the supply and demand elasticity index of micro-energy load. Is it in Within the range; responding to Within this range, the system maintains its operational logic, meaning it does not actively change its current off-grid or grid-connected state; for example, if the system was previously in a grid-connected state, even if the wind speed decreases... It is falling, as long as it does not break through... The system remains connected to the grid; Based on this, the system utilizes energy storage batteries as a buffer. When the wind turbine power fluctuates, the batteries absorb and distribute the differential power to maintain the stability of the bus voltage. This embodiment constructs a control logic similar to a Schmitt trigger; this design effectively prevents the system from... The frequent oscillations caused by minute changes in wind speed near the critical point avoid wear and tear on mechanical switches and flicker in the power grid, thereby improving the operational stability of the micro-wind power generation system under critical conditions. In a preferred embodiment of the present invention, the adaptive control module further includes a micro-energy bypass unit, which is used to activate the bypass power supply logic when the micro-wind power generation is less than the battery charging polarization loss threshold, control the power flow to be directly from the wind turbine to the load, or cut off the wind turbine input when the load demand is zero, and prevent the flow of a small current into the energy storage battery, so as to avoid the battery life loss caused by ineffective throughput.

[0032] This embodiment is an optimized design for the specific operating conditions of micro-wind power generation described in the previous embodiment; the micro-energy bypass unit monitors the wind turbine output power in real time. This parameter characterizes the real-time DC output power of the wind turbine after rectification, in watts (W); and sets the battery charging polarization loss threshold. This parameter characterizes the minimum power limit required to overcome the internal electrochemical polarization resistance of the battery and initiate an effective chemical reaction, and is expressed in W. To ensure the feasibility of determining this threshold, this embodiment defines... The specific calculation formula is as follows: , in, This is the current open-circuit voltage of the battery. This represents the real-time internal impedance of the battery. This is a preset minimum effective charging current threshold, for example, 0.05C. Here, C represents the rated capacity of the battery. That is, if the rated capacity of the battery is QAh, then 1C = QA, so 0.05C corresponds to a current value of... Ampere, this formula clarifies that the power must be sufficient to overcome internal resistance losses and establish electrochemical potential energy; In order to obtain accurate The system does not use a fixed constant, but instead relies on real-time monitoring of the battery temperature. and state of charge The system queries a pre-installed three-dimensional mapping table of temperature, state of charge (SOC), and internal resistance on the controller. This table is generated based on the battery's HPPC pulse test data. For cases where the real-time temperature and SOC fall between the data points in the table, this embodiment employs a bilinear interpolation algorithm to calculate the accurate... value; To ensure the accuracy of the mapping table data, this embodiment discloses a specific formula for calculating the internal resistance based on HPPC data: at a specific temperature and SOC point, the applied duration is... For example, a 10-second discharge pulse current Record the open-circuit voltage before the pulse. Load voltage at the end of the pulse The internal resistance under this operating condition is Calculated as By traversing the temperature range of -20°C to 45°C with a step size of 5°C and the SOC range of 0% to 100% with a step size of 5%, a complete three-dimensional mapping table is generated; the system executes the following control logic: , In response to the above conditions being met, the system activates the bypass power supply logic; this is to prevent the fan power from exceeding the load power when the battery is disconnected. This leads to DC bus voltage runaway. In this embodiment, a voltage clamping power follower strategy is adopted: the DC / DC converter is controlled to switch from maximum power point tracking (MPPT) mode to constant voltage output (CV) mode, and the output voltage reference value is set to the rated operating voltage of the load. It should be noted that this CV mode is effective only if... If the system detects If the wind energy is insufficient to independently support the load, the system immediately activates the battery support mode, supplementing the load with battery output power. The gap, provided that If the battery is depleted, load cut-off logic must be executed to prevent voltage drop. In CV mode, the DC / DC converter automatically limits the input current, forcing the wind turbine's operating point to deviate from the optimal tip speed ratio. Utilizing the aerodynamic stall characteristics of the wind turbine blades, specifically, the controller sends commands to drive the pitch motor to adjust the blade angle of attack to near the 90-degree stall zone, or a mechanical braking device is activated. Specifically, the controller outputs a high-level trigger signal to close the electromagnetic brake relay, consuming excess wind energy and thus forcing the turbine to stop. At the same time, disconnect the battery's charging circuit to prevent even a tiny current from flowing into the battery. In response to the zero load demand at this time, the system directly cuts off the fan input to prevent the converter from being lost under no-load conditions; this embodiment proposes a solution to the common problem of ineffective charging in low-wind scenarios; By using bypass power supply logic, the battery health status (SOH) loss caused by the battery being in a low-rate shallow charging state for a long time is avoided, ensuring that every bit of energy entering the battery is effective and economical, thus improving the overall energy efficiency ratio of micro-energy harvesting.

[0033] In a preferred embodiment of the present invention, the system further includes: a flexible execution module, used to respond to the flexible switching command generated by the adaptive control module; during the off-grid to grid-connected process, pre-synchronization tracking is performed based on the grid voltage phase; when both the phase difference and voltage difference are less than the allowable deviation value, the switch is closed; during the grid-connected to off-grid process, the switch is opened after adjusting the converter output power to the zero power point to achieve a smooth transition.

[0034] This embodiment details the flexible control method for the actuator; the flexible actuator module executes a smooth transition procedure after receiving a switching command; during the off-grid to grid-connected transition, a pre-synchronization tracking algorithm is initiated to collect the grid voltage. That is, the real-time voltage vector at the grid-side point of common coupling and the inverter output voltage. That is, the real-time voltage vector on the output side of the converter; Calculate phase difference ,Right now Frequency difference ,Right now and amplitude difference ,Right now And adjust the inverter output to gradually bring the above deviation to zero; in response to and When the solid-state switch or contactor is closed, , The system's preset allowable grid-connected phase deviation threshold, The system's preset allowable grid-connected voltage amplitude deviation threshold, The system is preset with a permissible grid connection frequency deviation threshold; during the grid connection to off-grid transition process, the system executes a soft offload control strategy; Specifically, the controller will use the active current reference value in the converter inner loop control. and reactive current reference value According to a preset slope, such as linearly decreasing at 10A / s; the system monitors the switching power of the PCC point in real time. When the absolute values ​​of active power and reactive power are both less than the preset small threshold, such as 1% of the rated power, it is determined that the zero power point has been reached, and at this time, a command to disconnect the grid-connected switch is issued. This embodiment eliminates the inrush current and voltage spikes during switching by using soft switching technology. This design not only protects sensitive electrical appliances on the user side, but also reduces the impact on the weak power grid, ensuring the power quality and safety of the micro-wind power generation system when it is connected to the grid.

[0035] In a preferred embodiment of the present invention, the system further includes: a user feedback interaction module, used to receive the micro-load supply and demand elasticity index, map the index to the power supply guarantee level, and display the current power supply guarantee status to the user through a visual interface. The power supply guarantee status includes worry-free use status, economical use status, and critical load-only status.

[0036] This embodiment details the implementation logic of the human-computer interaction interface; the user feedback interaction module receives the calculated micro-energy load supply and demand elasticity index. And map it to a power supply protection level that is easy for users to understand; The specific mapping logic is as follows: in response to The system displays a worry-free usage status, usually indicated by a green icon, suggesting that the user can freely use high-power appliances; in response to The system displays a power-saving status, usually indicated by a yellow indicator, prompting users to disable unnecessary loads; in response to The system displays a critical load-only status, usually marked in red, to alert the user that the system is about to enter a backup mode, maintaining only lighting and communication. This embodiment establishes a friendly interaction between the power source, grid, and load through an intuitive visual feedback mechanism. This design guides users to actively adjust their electricity consumption behavior according to the system's energy status, thereby alleviating the energy supply pressure caused by the uncertainty of wind power generation on the user side and further improving the system's actual endurance in a light wind environment.

[0037] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A grid-connected switching system for micro-wind power generation and energy storage coupling, characterized in that, include: The multi-dimensional data sensing module is used to collect environmental and operational data of the micro-wind power generation system, including micro-wind characteristic data, power grid characteristic data, load characteristic data and battery status data, and obtain a multi-dimensional input dataset; The edge intelligent computing module is used to process the collected multidimensional input dataset, build a wind energy trend prediction model using historical data, calculate the probability of wind continuing to provide energy in the future, and combine it with battery status data to calculate the health loss of the battery life caused by the current operation, and generate energy prediction and loss assessment results. The energy confidence assessment module is used to construct a micro-energy load supply and demand elasticity index based on energy prediction and loss assessment results. This index represents the degree of dynamic balance between the supply-side capacity composed of the current remaining battery power and predicted wind energy input and the demand-side pressure composed of the critical load floor value and predicted load demand. The adaptive control module is used to make off-grid / on-grid switching decisions based on the micro-load supply and demand elasticity index. It presets a high confidence threshold and a low confidence threshold, wherein the high confidence threshold is greater than the low confidence threshold. It compares the micro-load supply and demand elasticity index with the high confidence threshold and the low confidence threshold, and generates flexible switching commands and power flow control commands based on the comparison results to control the converter to switch between off-grid and on-grid modes and distribute energy.

2. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The multidimensional data perception module includes: The environmental feature acquisition unit is used to monitor wind speed, wind direction and wind energy capture curves over a set time period in real time, while also monitoring voltage fluctuation rate and power outage history frequency at the point of common coupling. The load and battery monitoring unit is used to identify the characteristics of users' electricity consumption periods, distinguish between peak load periods and off-peak load periods, and read the state of charge, health status and internal temperature data of the energy storage battery in real time. It synchronizes the environmental characteristic data with the load and battery data in time and stores them in a multidimensional input dataset.

3. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The edge intelligent computing module includes: The trend prediction unit is used to calculate the probability that the wind speed will remain above the power generation threshold in the short term in the future by using historical capture curves in the light wind characteristic data and a probability model. When the wind speed is detected to be within the threshold but is on a downward trend, the prediction weight of wind energy input is reduced. The loss calculation unit is used to calculate the amount of battery health loss caused by the current switching action or charge-discharge cycle in real time based on battery status data, and to calculate the energy gain generated by the wind energy. When the energy gain is lower than the amount of battery health loss, an evaluation result of abandoning the absorption of the wind energy is generated.

4. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The energy confidence assessment module includes: The index calculation unit is used to calculate the micro-energy load supply and demand elasticity index. The value of this index is proportional to the sum of the current remaining battery power and the weighted predicted wind energy input, and inversely proportional to the sum of the critical load floor value and the predicted load demand. The dynamic weighting unit is used to adjust the confidence coefficient of the predicted wind energy input based on the probability of continuous energy supply from light winds. When the probability of continuous energy supply from light winds is lower than the preset probability threshold, the confidence coefficient is reduced, thereby reducing the value of the micro-energy load supply and demand elasticity index and preventing the index from being artificially high due to wind speed fluctuations.

5. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The adaptive control module includes: The surplus mode control unit is configured to: when the supply and demand elasticity index of micro-load is greater than or equal to the preset high confidence threshold, determine that the system has sufficient energy, generate a grid connection command, control the system to enter the grid connection mode, and allow power to be fed back to the grid or to supply power to high-power loads according to grid characteristic data; The defense mode control unit is configured to: when the supply and demand elasticity index of micro-load is less than or equal to the preset low confidence threshold, determine that the system is in short supply of energy, generate an off-grid preparation command or a power limiting command, prioritize charging the energy storage battery to retain a minimum charge, and cut off the power supply circuit of non-critical loads.

6. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 5, characterized in that, The adaptive control module also includes: The intermediate state maintenance unit is configured to maintain the current off-grid or grid-connected state of the system when the micro-energy load supply and demand elasticity index is greater than the preset low confidence threshold and less than the preset high confidence threshold. It uses the energy storage battery as a buffer to smooth out fluctuations and avoids the system from frequently switching between off-grid and grid-connected at the critical point.

7. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The adaptive control module also includes: The micro-energy bypass unit is used to activate the bypass power supply logic when the power generated by the micro-wind is less than the battery charging polarization loss threshold. This controls the power flow to be directed directly from the wind turbine to the load, or to cut off the wind turbine input when the load demand is zero, thus preventing a small current from flowing into the energy storage battery and avoiding battery life loss caused by ineffective throughput.

8. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The system also includes: The flexible execution module is used to respond to the flexible switching command generated by the adaptive control module. During the off-grid to grid-connected process, it performs pre-synchronization tracking based on the grid voltage phase. When both the phase difference and voltage difference are less than the allowable deviation value, the switch is closed. During the grid-connected to off-grid process, the switch is opened after adjusting the converter output power to the zero power point to achieve a smooth transition.

9. The off-grid switching system for coupled micro-wind power generation and energy storage according to claim 1, characterized in that, The system also includes: The user feedback interaction module is used to receive the micro-load supply and demand elasticity index and map the index to the power supply guarantee level. The current power supply guarantee status is displayed to the user through a visual interface. The power supply guarantee status includes worry-free use status, economical use status, and critical load-only status.

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