Solar street lamp power scheduling method based on energy consumption dynamic matching

Abstract

The present application relates to the technical field of industrial control system, disclose a kind of solar street lamp power scheduling method based on energy consumption dynamic matching, comprising: monitoring output load current fluctuation rate, injects pilot micro-pulse to determine main detection excitation depth;Collecting potential characterization parameter and response back-up slope, construct two-dimensional state vector and project to control phase plane;According to the deviation displacement of vector relative to safe energy consumption envelope line generates nonlinear damping coefficient, constrain next cycle power linear boundary, the present application identifies energy storage unit state evolution trend, so that power scheduling has endogenous damping characteristics;By pre-judging and absorbing polarization process nonlinear fluctuation, realize energy consumption output and electrochemical response feature accurate alignment, avoid critical operating condition control oscillation, ensure lighting continuous and extend energy storage element service cycle.

Description

Technical Field

[0001] This invention relates to a power scheduling method for solar streetlights based on dynamic energy consumption matching, belonging to the field of industrial control system technology. Background Technology

[0002] Current solar street light control systems utilize photovoltaic modules to acquire energy and store it in batteries. The controller executes power dispatch commands based on the real-time value of the battery terminal voltage. In conventional technical approaches, the controller maintains full-power lighting when there is sufficient power, and switches to energy-saving mode or performs protective shutdown when the voltage drops to a preset threshold. However, the control method based on static voltage thresholds has limitations in energy utilization under conditions such as continuous cloudy or rainy days. The battery terminal voltage and state of charge have a non-linear mapping, and are affected by the internal polarization effect caused by the discharge current, resulting in a false voltage drop that cannot reflect the actual energy reserve. When the street light load generates a large current, the instantaneous drop in terminal voltage often leads to misjudgment of energy depletion, causing the system to shut down when the battery has the potential to supply power or to produce logic oscillations and start-stop jumps in a critical state, which damages the lifespan of energy storage components and reduces lighting reliability.

[0003] Besides hardware-level limitations in charging and discharging efficiency, the sophistication of energy management determines the quality of lighting. Existing control strategies focus on simple logic switching, lacking an understanding of the internal dynamic characteristics of the battery. For example, the utility model patent CN201237139Y discloses a solar LED street light with automatic power adjustment. By setting different output power lighting terminals, it switches between rated power and low power according to the lighting duration or battery capacity. The segmented adjustment achieves jump-like management through static parameters. However, the control logic fails to decouple the instantaneous polarization response of the battery discharge process, making it difficult to identify high-current loads. The false voltage drop component caused by the load, the scheduling scheme that deviates from the electrochemical evolution trend, will misjudge the battery critical operating condition, resulting in a mismatch between the power command and the actual energy level of the energy storage unit, and cannot solve the problems of operational stability and energy utilization accuracy. Lowering the protection threshold to forcibly extend the lighting time will increase the risk of deep over-discharge of the battery. The introduction of electrochemical impedance monitoring equipment is limited by hardware cost and processor load, making it difficult to apply on a large scale. Under the pressure of load fluctuation and environmental randomness, the existing technology is difficult to decouple the instantaneous polarization response and the actual charge level, resulting in a mismatch between the power regulation command and the dynamic evolution trend of the energy storage unit.

[0004] Therefore, how to construct a power adaptive regulation mechanism that aligns with the electrochemical response characteristics of a battery, thereby extending the lighting duration and ensuring hardware stability under limited energy constraints, is the technical problem that this invention aims to solve. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A power scheduling method for solar streetlights based on dynamic energy consumption matching, executed by an industrial controller, includes the following steps: Step S101: Monitor the instantaneous fluctuation rate of the output load current in real time through the current sampling circuit; Step S102: If the instantaneous fluctuation rate is lower than the set steady-state threshold, then within the determined power scheduling cycle, a pilot micropulse with a duration of less than 50ms is injected into the energy storage unit, and the instantaneous potential drop slope triggered by the pilot micropulse is collected. Step S103: Based on the mapping relationship between the instantaneous potential drop slope and the safety voltage drop envelope determined by the steady-state potential of the energy storage unit, the excitation adjustment amount of the main detection excitation is dynamically set by adjusting the control signal weight of the output power, and the main detection excitation is injected into the energy storage unit. Step S104: Collect real-time potential characterization parameters of the energy storage unit during the main probe excitation injection. and the slope of the response recovery after the main probe excitation is eliminated. ; Step S105: Utilize real-time potential to characterize parameters With response recovery slope Construct a two-dimensional state vector and project it onto a control phase plane composed of a set of critical polarization feature points under different aging cycles; Step S106: Calculate the normal deviation distance of the two-dimensional state vector relative to the safety energy consumption envelope in the control phase plane. And based on the normal deviation distance Nonlinear damping coefficient for generating power output The calculation formula is: ,in, This is the system stability reference constant; Step S107, using the nonlinear damping coefficient Linear boundary constraints are applied to the initial output power of the next scheduling cycle to generate a power command that has been smoothed by the trajectory.

[0006] Preferably, the nonlinear damping coefficient is determined. Previously, it also included a self-calibration step using the characteristics of the recovery curve: when acquiring the slope of the response recovery... During the process, real-time potential characterization parameters are calculated synchronously using differential operators. The second-order rate of change of the recovery trajectory is determined, and the aging drift of the energy storage unit is determined based on the comparison between the second-order rate of change and the initial health state characteristic value of the storage. An aging correction factor is generated based on the aging drift, and this correction factor is used to adjust the nonlinear damping coefficient. The calculated parameters are used to perform real-time offset compensation to offset the differences in physical characteristics of the energy storage unit during different service cycles.

[0007] Preferably, step S101 further includes: if the instantaneous volatility exceeds the set steady-state threshold, then the main probe excitation is injected with a delay through timing shift logic until the output load current returns to steady state.

[0008] Preferably, in step S106, the nonlinear damping coefficient is generated. Previously, this also included: collecting real-time potential characterization parameters during the main probe excitation injection. The first slope of the initial rise phase and the second slope of the stabilizing rise phase are calculated; the ratio of the first slope to the second slope is calculated, and the nonlinear damping coefficient is adjusted based on this ratio. The base gain is adjusted.

[0009] Preferably, the self-calibration step further includes: extracting real-time potential characterization parameters using differential operators. Curvature characteristic value during the recovery process; based on the curvature characteristic value, identify the impedance offset of the energy storage unit caused by the ambient temperature; use the impedance offset to correct the gain operator of the two-dimensional state vector to perform thermal compensation on the power command.

[0010] Preferably, in step S102, the energy state factor is determined according to the two-dimensional state vector. Real-time adjustment of the triggering period of the main probe excitation .

[0011] Preferably, adjust the trigger cycle. Including: based on energy state factor The reduction in [the value] proportionally extends the time interval between two adjacent main detection excitations.

[0012] Preferably, if the energy state factor If the energy efficiency falls below the established warning threshold, the real-time potential characterization parameters will be monitored in real time. The rate of change of the descent slope is determined, and when the rate of change of the descent slope exceeds the set perturbation threshold, the next main probe excitation is triggered.

[0013] Preferably, the safe energy consumption envelope is composed of the critical polarization characteristic point set of the energy storage unit under different service durations.

[0014] Preferably, after step S107, the following action is further performed: monitoring the real-time potential characterization parameters of the energy storage unit after executing the power command. The rate of change decreases over time; if the rate of change exceeds a predetermined risk threshold, the industrial controller is switched to pulse sustain mode, which maintains the real-time potential characterization parameters by reducing the proportion of output power duration per unit time. The amplitude.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the power scheduling of solar street lights, pulse detection excitation is injected into the battery to transform the control process from simple terminal voltage monitoring to active dynamic feature identification; the voltage recovery slope after excitation elimination is used to characterize the internal polarization state of the battery, and the instantaneous voltage drop interference caused by high current discharge is eliminated; based on the active detection feedback mechanism, the controller identifies the real energy margin of the energy storage unit, avoids the protective shutdown logic misjudgment when the control system faces environmental fluctuations, and ensures the continuous operation of the lighting process under limited energy constraints.

[0016] 2. A two-dimensional logical phase plane is constructed using real-time terminal voltage and voltage rise slope. The current energy state vector is projected onto the preset safe energy consumption envelope, shifting from point-to-point linear adjustment to control trajectory prediction. A nonlinear damping operator is generated based on the deviation distance of the state vector from the envelope, dynamically suppressing power command mutations. The power decay trajectory evolves smoothly along the electrochemical characteristic tangent, absorbing nonlinear fluctuations in the battery polarization process at the software level and enhancing the control program's ability to operate under critical conditions.

[0017] 3. Extract the second-order rate of change characteristics of the voltage recovery curve to sense the degree of battery aging and environmental temperature deviation and compensate online; use the curvature component of the recovery trajectory to correct the damping coefficient and gain operator of the control system, offset the perception deviation caused by physical characteristic drift, and ensure that the power scheduling logic remains consistent throughout the battery's life cycle and cross-seasonal operating conditions, thus extending the service life of energy storage components; monitor the output load current fluctuation status and lock the injection phase of the detection window to ensure that the recovery slope acquisition is in a steady-state plateau period; coordinate the detection timing with the load fluctuation logic to improve the signal-to-noise ratio of the control signal, avoid the distortion of the recovery characteristics caused by external interference in dynamic lighting scenarios, solve the problem of internal sensing excitation and external random disturbance competition from the time domain dimension, and ensure the accuracy of energy efficiency inversion. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the street light power scheduling system architecture and signal interaction principle based on dynamic energy consumption matching of the present invention; Figure 2 This is a comparative trend chart of the control stability scores of the experimental group and the control group under different load fluctuation intensities according to the present invention; Figure 3 This is a timing flowchart of the synchronous sensing and detection window delay locking mechanism under the load change scenario of the present invention. Detailed Implementation

[0019] The following disclosure is intended to illustrate the technical solutions claimed in this invention in detail through specific examples, so that those skilled in the art can more clearly understand their technical essence and engineering implementation. It should be noted that the following embodiments are only used to explain and illustrate this invention, and are not intended to limit the scope of protection of this invention. In the absence of conflict, the various embodiments and features in the embodiments of this invention can be combined with each other.

[0020] This invention provides a power scheduling method for solar streetlights based on dynamic energy consumption matching, executed by an industrial controller. Its core architecture revolves around active dynamic characteristic recognition and response phase plane control. By real-time monitoring of the instantaneous fluctuation rate of the output load current, when the detection conditions are met, a combined detection sequence containing a pilot micropulse and a main detection excitation is injected into the energy storage unit. Real-time potential characterization parameters of the energy storage unit under the excitation are collected. and the slope of the response recovery after excitation removal Then, using the above parameters, a two-dimensional state vector is constructed and projected onto the control phase plane. The deviation distance of the vector from the normal vector relative to the safe energy consumption envelope is then used to determine the state vector. Generate nonlinear damping coefficient Finally, this coefficient is used to apply linear boundary constraints to the initial output power of the next scheduling cycle, thereby achieving dynamic alignment between the power scheduling command and the real polarization of the energy storage unit. To address the problem of random electromagnetic noise generated by external loads masking battery signal acquisition in complex intelligent lighting scenarios, this invention implements a load synchronization sensing and detection window locking procedure. The industrial controller continuously monitors the instantaneous fluctuation rate of the output load current through a current sampling circuit. When it exceeds a preset steady-state threshold, the timing shift logic automatically delays the injection time of the main detection excitation until the output load current returns to the steady-state plateau, thereby locking the signal-to-noise ratio of the voltage recovery slope. When the street light radar induction triggers, causing the load current to change from... Sudden rise to When the volatility calculation module detects that the current variance exceeds a preset threshold, the system will release the probe pulse originally scheduled for that moment. Extend forward by a random time step ,make sure The data acquisition environment is in an electrochemical steady state. This mechanism addresses the competition between internal sensing excitation and external random perturbation from a time-domain perspective, ensuring the accuracy of energy efficiency inversion.

[0021] To address the technical bottleneck of misjudgment of energy reserves caused by the pseudo-voltage drop component due to the discharge rate during battery discharge, this invention employs an active detection and pilot-operated closed-loop regulation mechanism. Within a defined power scheduling cycle, the controller first injects energy into the energy storage unit for a duration less than [a certain value]. The system uses a voltage sampling circuit to obtain the instantaneous potential drop slope triggered by the pilot micropulse. This slope is then mapped to the safety voltage drop envelope determined by the current steady-state potential of the energy storage unit to calculate the excitation adjustment of the main detection excitation. If the voltage drop trajectory caused by the pilot micropulse indicates that the main pulse will reach the undervoltage protection threshold, the system proportionally reduces the PWM duty cycle of the main detection excitation; otherwise, the excitation depth is gradually increased to ensure a smooth response recovery slope after the main detection excitation is eliminated. It can accurately reflect the effective state of charge (SOC) value of the energy storage unit. Under this procedure, the system no longer passively waits for the voltage to drop to a threshold, but instead predicts the strength of the energy reserve by sensing the elasticity of the voltage recovery, eliminating the instantaneous voltage misjudgment caused by high current discharge and ensuring lighting continuity under extreme power depletion conditions. To overcome the control oscillation problem caused by the lack of evolution trend prediction in traditional control logic when facing the complex nonlinear polarization curve of the battery, this invention introduces trajectory constraint logic based on the dynamic response phase plane. The industrial controller obtains real-time potential characterization parameters. The x-axis represents the slope of the response recovery. Using the vertical axis as the ordinate, a control phase plane is established, consisting of a set of critical polarization feature points under different aging cycles. This is achieved by calculating the current state vector. Normal deviation distance relative to the safe energy consumption envelope Substitute into the formula Generate nonlinear damping coefficient , The real-time potential characterization parameters of the energy storage unit during the main probe excitation injection period, The slope of the response recovery after the main probe excitation is eliminated. This represents the normal deviation distance of the two-dimensional state vector relative to the safety energy consumption envelope in the control phase plane. This is a preset system stability reference constant. To generate the nonlinear damping coefficient for power output, the control phase plane feature point set is extracted based on a multi-rate gradient discharge calibration experiment of the same specification energy storage unit under a standard temperature field of 25℃. This involves collecting 20 sets of polarization steady-state feature points [Vi,Rvi] within the range of 90% to 10% state of charge. A cubic spline interpolation algorithm is used to fit and generate the safe energy consumption envelope equation f(V,R)=0. The industrial controller extracts the current two-dimensional state vector in real time, performs normal projection, and calculates the minimum Euclidean distance from the vector point to the envelope tangent vector to determine the normal deviation distance. When the state point is located in the safe zone below the envelope. A positive value is taken, and a negative value is taken to trigger the forced power compression logic. The logic calculation process is accelerated by a floating-point arithmetic unit. Trajectory point regression verification is performed every scheduling cycle to ensure that the physical boundary of the envelope is corrected in real time with the aging compensation factor.

[0022] Taking a specific set of engineering values ​​as an example, let's say the currently sampled values ​​are... Calculated If the normal deviation distance of the projection of the coordinate point onto the safe energy consumption envelope is... for And the preset system stability benchmark constant When, the calculated nonlinear damping coefficient Approximately This coefficient affects the initial output power of the next cycle. To generate power commands after trajectory smoothing This process transforms the control logic from point mapping to trend prediction, enabling power dispatch commands to adapt to physical characteristics. It also absorbs nonlinear fluctuations during polarization at the software level and uses the nonlinear damping coefficient ξ to constrain the initial output power. A first-order dynamic smoothing algorithm is used to target the power command. The calculation formula is ,in As the normal deviation distance Dbias decreases, ξ decays rapidly according to an S-shaped function, limiting the upper limit of the power command step increment. This ensures that the power change rate is linearly aligned with the electrochemical recovery rate Rv of the energy storage unit. After the power command is generated, it is verified by an amplitude limiter to ensure that the instantaneous output power does not exceed the critical discharge threshold determined by the real-time potential Vnow.

[0023] Based on the evolution of battery physical characteristics with aging and environmental temperature drift over service life, this invention configures a self-calibration and compensation procedure based on recovery trajectory characteristics; it acquires the response recovery slope... During the process, the controller synchronously calculates the real-time potential characterization parameters through the differential operator. The second-order rate of change of the recovery trajectory is calculated and compared with the initial health state characteristic value of the storage unit to determine the aging drift of the energy storage unit. Simultaneously, curvature characteristic values ​​during the recovery process are extracted to identify impedance offset caused by ambient temperature. The system generates an aging correction factor and a thermal compensation operator to adjust the nonlinear damping coefficient. The calculation parameters and the gain of the two-dimensional state vector are compensated for in real time to counteract the perception deviation caused by physical characteristic drift. At the end execution level of the system, to ensure the reliability of the power scheduling scheme under extreme energy deficit conditions, the invention also includes forced switching logic for pulse sustaining mode. The controller continuously monitors the real-time potential characterization parameters of the energy storage unit after the power command is executed. If the rate of change decreases over time and exceeds a preset risk threshold, it indicates that the energy storage unit has entered a deep polarization region. At this time, the controller forcibly switches to pulse sustain mode, locking the potential amplitude by reducing the duty cycle ratio of the PWM signal per unit time. This step utilizes the persistence of vision of the human eye to maintain a basic sense of lighting continuity while reducing actual power consumption, avoiding unexpected lamp-out accidents induced by detection excitation or load fluctuations, and realizing the self-organized smooth operation of the system at the energy extreme value boundary.

[0024] Example 1: During the operation of the intelligent street light cluster deployed in a coastal high-humidity environment using the technical solution of this invention, when the system faces three consecutive days of rainy weather and the energy storage unit is at a potential of approximately During the discharge edge condition, if the microwave radar sensing module equipped with the street light detects a pedestrian passing by and triggers the lighting power to be reduced... Sudden rise to The output load current generates a sudden, large-rate drawdown. Due to the intensified polarization effect inside the energy storage unit, a pseudo-voltage drop component is generated at its terminal voltage. At this time, the industrial controller executes the load synchronization sensing and detection window locking procedure, and collects the instantaneous fluctuation rate of the load current in real time through the current sampling circuit. Using the comparison result of the identified current variance and the preset steady-state threshold value, the timing shift logic extends the detection pulse sequence forward. The step size is adjusted until the load current waveform returns to the steady-state plateau, thereby avoiding crosstalk from external load fluctuations to the extraction of electrochemical response characteristics and ensuring the slope of the acquired response recovery. It is in a steady electrochemical state.

[0025] Within the aforementioned detection window, the industrial controller injects energy into the energy storage unit for a duration of [duration missing]. The pilot micropulse uses a voltage sampling circuit to obtain the instantaneous potential drop slope, and based on the mapping relationship between this slope and the current steady-state potential of the energy storage unit, the PWM duty cycle reduction of the main probe excitation is reduced to a preset weight. To prevent the detection excitation from directly reaching the battery undervoltage shutdown threshold, after the main detection excitation is eliminated, the sampling circuit obtains the response recovery slope. for This value reflects the charge mobilization capability of the energy storage unit under the current polarization state. The industrial controller will use the real-time potential characterization parameter. With response recovery slope The constructed two-dimensional state vector is projected onto the control phase plane, and the normal deviation of this vector relative to the safe energy consumption envelope is calculated. The nonlinear damping coefficient for the next scheduling cycle is obtained. , The real-time potential characterization parameters of the energy storage unit during the main probe excitation injection period, The slope of the response recovery after the main probe excitation is eliminated. This represents the normal deviation distance of the two-dimensional state vector relative to the safety energy consumption envelope in the control phase plane. This is the system stability reference constant. The nonlinear damping coefficient for the generated power output; nonlinear damping coefficient Initial output power acting on the next cycle To generate power commands When the calculation is obtained for and for When generated Approximately This coefficient, by constraining the linear boundary of output power, limits the risk of potential collapse caused by deep battery discharge, resolving the contradiction between lighting duration requirements and energy storage unit protection. It transforms the traditional static voltage point mapping into a dynamic trajectory constraint of response characteristics, enabling power dispatch commands to adapt to physical characteristics. After the radar induction trigger ends and the load current returns to its quiescent level, the system's self-calibration procedure based on the recovery trajectory characteristics analyzes... The second-order rate of change identifies impedance offsets caused by the environment and generates a compensation operator to perform offset correction on the control gain.

[0026] Example 2: In verifying the adaptability of the power dispatching method under dynamic load conditions, the test group was deployed on a physical verification platform consisting of a lead-acid battery pack and a programmable electronic load. Data was extracted from the real-time sampling port of the industrial controller. The platform's voltage acquisition channel has... static resolution and Hardware synchronous sampling frequency; regarding the key parameter sampling period The technical trade-off in this setting lies in balancing the capture of high-frequency polarization dynamics with maintaining low-overhead processor operation, when the external load switching frequency is at... to In the interval, to ensure that the instantaneous potential drop trajectory triggered by the leader micropulse is not distorted due to sparse sampling, the following will be implemented: Set as This setting, while satisfying the Nyquist sampling criterion, provides basic data for calculating the second-order rate of change of the subsequent recovery trajectory characteristics.

[0027] To simulate the complex electromagnetic background of an industrial environment, the experiment actively injected a signal-to-noise ratio of [value missing] into the electronic load circuit. Gaussian white noise interference was used, and an experimental group containing the complete technical solution and a control group B with the leader micropulse feature removed were set up, with the leader micropulse duration set to [value missing]. An out-of-range control group; the experiment constructed a control group by adjusting the current jump slope of the programmable load. to A load fluctuation intensity gradient system was used to observe the power regulation stability of energy storage units in deeply polarized regions. The original input data was the sampling potential, and the intermediate characteristic value was the calculated response recovery slope. The output is the target power command. See Table 1.

[0028] Table 1: Comparison of dynamic response data for different groups under multi-dimensional load gradients

[0029] As can be seen from the data in Table 1, with the increase in the intensity of load fluctuations in the core issue variable... In addition, the experimental group identified the polarization-sensitive region by using a pilot micropulse and adjusted the main probe excitation depth to improve the slope of the acquired response recovery. Maintaining a positive correlation with the residual charge level, power command This resulted in a smooth, nonlinear, restricted distribution, without any protective shutdown induced by the spurious voltage drop; in contrast, control group B exhibited... Greater than Subsequently, due to excessive detection excitation depth, the battery entered instantaneous deep polarization. The value decays to the corresponding operating condition value of the test group. The following causes frequent oscillations in the power command; and in the case of an out-of-range group where the pilot micropulse duration is too long, the long-cycle low-power pulse directly pulls the terminal voltage down below the undervoltage protection threshold, causing the lighting operation to be interrupted. Load current fluctuation intensity, in units of , This represents the real-time potential of the energy storage unit, in units of... , In response to the recovery slope, the unit is , Power dispatch instructions, in units of .

[0030] Example 3: This example combines Figures 1 to 3 The method for power scheduling of solar streetlights based on dynamic energy consumption matching is explained, as follows: Figure 1As shown, the controller integrates a core power scheduling algorithm, active dynamic feature recognition function, and load synchronization sensing logic. The industrial controller receives energy input from photovoltaic modules as energy harvesting input, and simultaneously receives trigger signals generated by the microwave radar sensing module when capturing pedestrians or vehicles. It also performs high-frequency acquisition of current and voltage through electrical sampling circuit to obtain real-time fluctuation rate feedback. Based on the above input signals, the industrial controller sends power drive commands to the street light load containing the dimmable lighting module and interacts bidirectionally with the energy storage unit battery, including performing probe pulse injection and charge / discharge management on the energy storage unit, and receiving the potential recovery characteristics after receiving the probe pulse injection from the energy storage unit, thereby realizing energy storage and release control.

[0031] like Figure 2 As shown in the figure, the different load fluctuation intensities are illustrated. The graph shows the trend of control stability scores for the experimental group, control group B, and out-of-range group, with units of A / s. The horizontal axis represents the intensity of load fluctuation. The values ​​range from 0.2 to 4.5. The vertical axis represents the control stability score, ranging from 0 to 10. The solid line represents the control stability score of the experimental group, which decreases gradually with increasing load fluctuation intensity but remains at a relatively high level. The dashed line represents the control stability score of control group B, which shows a downward trend. The dotted line represents the control stability score of the out-of-range group, which decreases sharply with increasing load fluctuation intensity and approaches zero in the high fluctuation range. Figure 3 As shown, the system's timing logic begins with the radar sensing module detecting a pedestrian and triggering the lighting, causing a sudden change in the load current of the lighting load. The current sampling circuit then reports the current fluctuation data to the industrial controller, which calculates the current variance. If the variance exceeds the steady-state threshold, the system triggers a delay logic, which uses a timing shift logic to postpone the detection pulse timing. The system then enters a waiting-for-steady-state loop, during which the current is continuously monitored and the variance is recalculated until the load is confirmed to have returned to steady state. After this, the system unlocks the detection window and the industrial controller initiates the detection excitation sequence.

[0032] Example 4: In an engineering scenario for power dispatch parameter calibration, the industrial controller, facing control accuracy deviations caused by individual differences in energy storage units, constrains power commands by determining a quantization benchmark of the safety voltage drop envelope. The system enters an initialization self-test state, collects the terminal voltage of the energy storage unit under no-load conditions to determine the static potential benchmark, and applies a voltage to the energy storage unit for a duration of... And the current amplitude is The standard discharge pulse is used to record the voltage drop amplitude at the end of the pulse, which is defined as the reference origin for polarization depth. The calibration procedure defines the safety boundary of potential fluctuation by establishing a safety voltage drop envelope, which is composed of critical voltage drop points based on different states of charge. When the initial steady-state potential of the energy storage unit is... At that time, the corresponding upper limit of safe voltage drop Satisfying linear functions , This is the upper limit of safe voltage drop, in units of... , The initial steady-state potential of the energy storage unit is given by [value]. , The first system constant obtained by fitting the discharge characteristics is... The second system constant is used in this embodiment. Values , Values .

[0033] During the operation of the industrial controller, the load synchronization sensing module collects the output load current. Calculate continuous The variance of each sampling point is used to characterize the instantaneous volatility. When the time-shift logic identifies that the variance exceeds... When the steady-state threshold is reached, the injection time of the main probe excitation is automatically delayed. One scheduling cycle, until the variance reverts to... Within the following stable range, the system will use real-time potential characterization parameters. With response recovery slope Projected onto the vertical axis With the horizontal axis The constructed control phase plane is obtained by calculating the state vector points. The shortest Euclidean distance to the safe energy consumption envelope is used to determine the normal deviation distance. This is then passed as an input to the nonlinear damping coefficient generation logic.

[0034] Example 5: In the initial deployment of the solar street light control system, the industrial controller executes the calibration procedure of the energy storage unit's dynamic reference to determine the safe energy consumption envelope in the control phase plane. The system is under control Under certain conditions, a gradient discharge is performed on a fully charged energy storage unit via an electronic load, with sampling points covering the state of charge from... to The range of measurement, in each At the power step point, the industrial controller executes a single operation with a duration of [duration missing]. The main probe excitation and corresponding real-time potential characterization parameters are collected and recorded. With response recovery slope The resulting set of polarization feature points is fitted using the least squares method to generate a nonlinear feature curve. This process provides a physical boundary benchmark for energy storage units of different capacity specifications or batches, ensuring the subsequent normal deviation distance. The calculations have a physical reference system.

[0035] When the system faces operating conditions with fluctuating output load current and complex electromagnetic environment, the industrial controller adjusts the system stability reference constant. The value is used to match the nonlinear damping coefficient. The response speed is determined by a constant that follows a calculation logic based on the load ripple coefficient, and the system acquires the output load current. exist The current ripple rate is calculated from the peak-to-peak value and the mean value within the window. Substitute the ripple rate into the relation Update in the middle, The basic stability coefficient of the energy storage unit is determined under static testing, and its value is [value missing]. , This is a correction factor, with a value of [value missing]. , For the dimensionless load ripple rate, this procedure generates a nonlinear damping coefficient. The steepness of the power constraint is adjusted according to the intensity of environmental disturbances, and the power dispatch command automatically avoids the polarization risk of the energy storage unit by controlling the smooth evolution of the state vector trajectory in the phase plane.

[0036] Example 6: When the energy storage unit enters a low-temperature operating environment, the system monitors real-time potential characterization parameters. The descent slope over time is used to identify polarization risk; when the descent slope is greater than... And the duration exceeds During each power scheduling cycle, the industrial controller determines that the forced switching conditions are met, and the logic output changes from continuous lighting mode to pulse sustain mode; in pulse sustain mode, the system adjusts the reference duty cycle of the pulse width modulation signal. With polarization depth correction coefficient Convolution generates the final execution pulse width, and the polarization depth correction coefficient. The values ​​are derived from a preset polarization depth mapping table, which is obtained by performing an offline calibration procedure before system deployment. to Within a temperature gradient, a constant current electronic load is used to perform step discharge tests on different batches of energy storage units. The critical sustaining current corresponding to each steady-state potential point is recorded and converted into a duty cycle coefficient. This locks the voltage amplitude before the potential drops to the undervoltage protection threshold, enabling the industrial lighting terminal to maintain low luminous flux output under energy shortage conditions. The real-time potential characterization parameters of the energy storage unit during the main probe excitation injection period, The reference duty cycle for the pulse width modulation signal. These are the polarization depth correction coefficients determined by offline calibration.

[0037] In industrial environments facing electromagnetic interference from frequency converters, the load synchronization sensing module executes a multi-level sliding window filtering procedure to correct the calculated instantaneous fluctuation rate. The sampling circuit uses... Get load current for step size And store length is The system periodically calculates the standard deviation of the current data within the data buffer. And compare it with the steady-state threshold value. If the values ​​within two consecutive calculation periods are... All greater than The timing shift logic outputs a detection inhibit signal, while simultaneously adjusting the power weight of the main detection excitation to maintain the illumination intensity until the load current returns to a value less than the standard deviation. Within the stable range, this procedure addresses the problem of sensing feature failure caused by impulse interference. It achieves the adaptation of the sensing closed loop to the electromagnetic environment by defining the buffer update frequency and decision step, ensuring the reliability of the system's power output under non-stable load impacts. To collect the output load current in real time, This represents the standard deviation of the current data within the buffer.

[0038] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A power scheduling method for solar streetlights based on dynamic energy consumption matching, characterized in that, Performed by the industrial controller, the following steps are included: Step S101: Monitor the instantaneous fluctuation rate of the output load current in real time through the current sampling circuit; Step S102: If the instantaneous fluctuation rate is lower than the set steady-state threshold, then within the determined power scheduling cycle, a pilot micropulse with a duration of less than 50ms is injected into the energy storage unit, and the instantaneous potential drop slope triggered by the pilot micropulse is collected. Step S103: Based on the mapping relationship between the instantaneous potential drop slope and the safety voltage drop envelope determined by the steady-state potential of the energy storage unit, the excitation adjustment amount of the main detection excitation is dynamically set by adjusting the control signal weight of the output power, and the main detection excitation is injected into the energy storage unit. Step S104: Collect real-time potential characterization parameters of the energy storage unit during the main probe excitation injection. and the slope of the response recovery after the main probe excitation is eliminated. ; Step S105: Utilize real-time potential characterization parameters With response recovery slope Construct a two-dimensional state vector and project it onto a control phase plane composed of a set of critical polarization feature points under different aging cycles; Step S106: Calculate the normal deviation distance of the two-dimensional state vector relative to the safety energy consumption envelope in the control phase plane. And based on the normal deviation distance Nonlinear damping coefficient for generating power output The calculation formula is: ,in, This serves as the system stability reference constant. Step S107, using the nonlinear damping coefficient Linear boundary constraints are applied to the initial output power of the next scheduling cycle to generate a power command that has been smoothed by the trajectory.

2. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, Determine the nonlinear damping coefficient Previously, it also included a self-calibration step using the characteristics of the recovery curve: when acquiring the slope of the response recovery... During the process, real-time potential characterization parameters are calculated synchronously using differential operators. The second-order rate of change of the recovery trajectory is determined, and the aging drift of the energy storage unit is determined based on the comparison between the second-order rate of change and the initial health state characteristic value of the storage. An aging correction factor is generated based on the aging drift, and this correction factor is used to adjust the nonlinear damping coefficient. The calculated parameters are used to perform real-time offset compensation.

3. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, Step S101 further includes: if the instantaneous volatility exceeds the set steady-state threshold, the main probe excitation is injected with a delay through timing shift logic until the output load current returns to steady state.

4. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, In step S106, the nonlinear damping coefficient is generated. Previously, this also included: collecting real-time potential characterization parameters during the main probe excitation injection. The first slope of the initial rise phase and the second slope of the stabilizing rise phase are calculated; the ratio of the first slope to the second slope is calculated, and the nonlinear damping coefficient is adjusted based on this ratio. The base gain is adjusted.

5. A solar street light power scheduling method based on dynamic energy consumption matching according to claim 2, characterized in that, The self-calibration step also includes: extracting real-time potential characterization parameters using differential operators. Curvature characteristic value during the recovery process; based on the curvature characteristic value, identify the impedance offset of the energy storage unit caused by the ambient temperature; use the impedance offset to correct the gain operator of the two-dimensional state vector to perform thermal compensation on the power command.

6. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, In step S102, the energy state factor is represented by the two-dimensional state vector. Real-time adjustment of the triggering period of the main probe excitation .

7. A solar street light power scheduling method based on dynamic energy consumption matching according to claim 6, characterized in that, Adjust trigger period Including: based on energy state factor The reduction in [the value] proportionally extends the time interval between two adjacent main detection excitations.

8. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 7, characterized in that, If the energy state factor If the energy efficiency falls below the established warning threshold, the real-time potential characterization parameters will be monitored in real time. The rate of change of the descent slope is determined, and when the rate of change of the descent slope exceeds the set perturbation threshold, the next main probe excitation is triggered.

9. The solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, The safe energy consumption envelope is composed of the set of critical polarization characteristic points of the energy storage unit under different service durations.

10. A solar street light power scheduling method based on dynamic energy consumption matching according to claim 1, characterized in that, Step S107 is followed by the following action: monitoring the real-time potential characterization parameters of the energy storage unit after the power command is executed. The rate of change decreasing over time; If the rate of change exceeds a predetermined risk threshold, the industrial controller will switch to pulse sustain mode, reducing the duration of output power per unit time to maintain the real-time potential characterization parameters. The amplitude.

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