Stable control method, system and device of intelligent light environment system under harmonic environment

Abstract

The application provides a stable control method, system and equipment of a smart light environment system in a harmonic environment, the method comprising: collecting electrical signals in real time based on an electric energy quality monitoring unit deployed at a key power distribution node of the smart light environment system, and extracting harmonic characteristic parameters based on the electrical signals; constructing a harmonic-light environment system dynamic coupling model based on the harmonic characteristic parameters; performing stability evaluation using a frequency domain impedance analysis method based on the harmonic-light environment system dynamic coupling model and the harmonic characteristic parameters, and obtaining a stability evaluation result; performing multi-objective optimization solving using a hierarchical optimization strategy based on the stability evaluation result, the harmonic characteristic parameters and a multi-objective optimization algorithm, and obtaining an optimal control decision; sending the optimal control decision to actuators of each lighting loop in the smart light environment system, and performing control mode switching or sending a treatment instruction to an active filter in the smart light environment system according to the stability evaluation result.

Description

Technical Field

[0001] This application belongs to the field of stability control technology, and relates to a stability control method, system and equipment for a smart lighting environment system under harmonic environment. Background Technology

[0002] With the development of smart manufacturing and green factories, intelligent lighting environment systems are increasingly being used in industrial settings such as glass factories due to their potential for energy saving and humanization. These systems integrate sensors, dimmable LED luminaires, and intelligent algorithms to achieve on-demand lighting and energy conservation.

[0003] However, glass factories, as typical high-energy-consuming and highly interference-prone industrial settings, generally suffer from severe harmonic pollution in their power distribution networks caused by nonlinear loads such as large electric arc furnaces and frequency converters. This specific environmental factor fundamentally contradicts the essential requirement of smart lighting systems to rely heavily on high-quality electricity, leading to the following three specific hazards:

[0004] Equipment-level damage: Harmonic voltage can easily cause LED driver power supply to overheat and electrolytic capacitors to age faster, resulting in lamp flickering, accelerated light decay, and even premature failure.

[0005] System-level instability: As a closed-loop control system, smart lighting is susceptible to harmonic disturbances that can distort the illuminance sensor signal or cause abnormal dimming commands. This can lead to system oscillations, dimming malfunctions, and communication interruptions, resulting in the complete loss of its "smart" function.

[0006] The paradox of energy efficiency and quality: In the context of harmonics, the system may be forced to increase its output in order to maintain stable surface illuminance, which in turn leads to an increase in actual energy consumption and a decrease in light quality, forming the paradox of "the harder you try, the more power you consume, and the worse the effect".

[0007] Currently, the approaches taken by industry and academia to address these issues are significantly limited and have failed to systematically resolve the fundamental contradictions.

[0008] Limitations of existing industrial lighting control solutions: Most publicly available smart lighting solutions for factories focus their innovation on high-level application strategies such as zoning, timing, light sensing, and linkage. Their core flaw lies in the fact that these solutions' control models are all based on the implicit assumption of "ideal power grid quality," completely failing to incorporate grid harmonics—a key physical variable affecting the system's underlying stability—into the design considerations. Therefore, in the harsh realities of a glass factory's power grid, their reliability and expected performance often cannot be achieved.

[0009] The isolation of traditional harmonic mitigation solutions: Harmonic mitigation devices such as active power filters (APFs), commonly used in factories, are typically configured and operated independently, aiming to provide broad compensation for distribution buses. They lack state awareness and collaborative closed-loop systems with intelligent lighting environments. APFs cannot know the real-time stability margin of the lighting system, and their compensation strategies are not optimized to ensure the dynamic stability of specific control loops. This is a costly but inefficient and reactive form of defense.

[0010] The one-sidedness of academic research models: At the theoretical research level, the field of power electronics has conducted in-depth analyses of the impedance interaction stability problem between converters and the power grid (such as the Middlebrook criterion). However, such research usually focuses on the interface between a single converter device and the power grid, while treating the entire smart optical environment system (including multiple devices, multiple control loops, and communication networks) as a whole and quantitatively studying the coupling mechanism, stability boundary, and collaborative optimization methods between its internal dynamics and external power grid harmonics remains an underexplored area. Summary of the Invention

[0011] This application provides a method, system, and device for stable control of a smart lighting environment system under harmonic environments, which is used to solve the problem of poor stability of smart lighting environment systems in high harmonic industrial environments in the prior art.

[0012] In a first aspect, this application provides a method for stable control of a smart lighting environment system under harmonic conditions. The method includes: real-time acquisition of electrical signals by a power quality monitoring unit deployed at a key power distribution node of the smart lighting environment system, and extraction of harmonic characteristic parameters based on the electrical signals; the harmonic characteristic parameters include, but are not limited to, total harmonic distortion rate and harmonic content of each order, amplitude and phase of key harmonics, and preliminary estimates of harmonic impedance; construction of a dynamic coupling model of the harmonic-lighting environment system based on the harmonic characteristic parameters; stability assessment using frequency domain impedance analysis based on the dynamic coupling model of the harmonic-lighting environment system and the harmonic characteristic parameters, and obtaining stability assessment results; multi-objective optimization solution using a hierarchical optimization strategy based on the stability assessment results, the harmonic characteristic parameters, and a multi-objective optimization algorithm, to obtain the optimal control decision; issuing the optimal control decision to the actuators of each lighting circuit in the smart lighting environment system, and switching the control mode or sending a control command to the active filter in the smart lighting environment system according to the stability assessment results.

[0013] In one implementation of the first aspect, harmonic feature parameters are extracted in real time based on the electrical signal using a fast Fourier transform algorithm; or harmonic feature parameters are extracted in real time based on the electrical signal using a wavelet transform algorithm.

[0014] In one implementation of the first aspect, constructing a dynamic coupling model of the harmonic-light environment system based on the harmonic characteristic parameters includes: constructing the dynamic coupling model of the harmonic-light environment system based on the LED driver, digital controller, and sensor of the lighting circuit in the smart light environment system; constructing a dynamic response model based on the LED driver and the harmonic characteristic parameters; the dynamic response model is the dynamic response model of the LED driver under the input voltage disturbance of the harmonic characteristic parameters; obtaining a luminous flux transfer function based on the dynamic response model and the LED driver as a device-level coupling model; the luminous flux transfer function is the transfer function of luminous flux to the harmonic voltage of the harmonic characteristic parameters, and the luminous flux is the flicker of the LED driver under the input voltage disturbance of the harmonic characteristic parameters; obtaining a closed-loop transfer function based on the device-level coupling model, the digital controller, and the sensor as a dynamic coupling model of the harmonic-light environment system; the closed-loop transfer function is the closed-loop transfer function of the grid-side harmonic disturbance to the key performance indicators of the smart light environment system, and the key performance indicators include at least the lighting error of the lighting circuit in the smart light environment system.

[0015] In one implementation of the first aspect, stability assessment is performed using frequency domain impedance analysis based on the dynamic coupling model of the harmonic-light environment system and the harmonic characteristic parameters. The stability assessment results include: calculating the equivalent output impedance of the lighting equipment at the common coupling point using frequency domain impedance analysis based on the electrical signal of the lighting equipment in the smart light environment system; calculating the equivalent harmonic source impedance of the power grid side at the common coupling point using frequency domain impedance analysis based on the fluctuations of harmonic voltage and current on the power grid side of the smart light environment system; and based on the... The loop gain is calculated using the effective output impedance and the equivalent harmonic source impedance, and the corresponding generalized Nyquist curve is obtained based on the loop gain. The magnitude margin is quantized based on the distance between the generalized Nyquist curve and a set critical point, and the quantized magnitude margin is obtained. The phase margin is quantized based on the phase difference of the generalized Nyquist curve at the phase crossing frequency, and the quantized phase margin is obtained. The stability margin includes the quantized magnitude margin and the quantized phase margin. The stability evaluation result is obtained by judging based on the quantized magnitude margin and a set magnitude margin threshold, and the quantized phase margin and a set phase margin threshold.

[0016] In one implementation of the first aspect, the set modulus margin threshold includes a set modulus margin warning threshold and a set modulus margin critical threshold, and the set phase margin threshold includes a set phase margin warning threshold and a set phase margin critical threshold. The stability evaluation result is obtained by judging based on the quantized modulus margin and the set modulus margin threshold, and the quantized phase margin and the set phase margin threshold. Specifically, when the quantized modulus margin is greater than or equal to the set modulus margin warning threshold, and the quantized phase margin is greater than or equal to the set phase margin warning threshold, the stability evaluation result is... The intelligent lighting environment system is in a stable state. When the quantized modulus margin is less than the set modulus margin warning threshold but greater than or equal to the set modulus margin critical threshold, or the quantized phase margin is less than the set phase margin warning threshold but greater than or equal to the set phase margin critical threshold, the stability assessment result indicates that the intelligent lighting environment system has entered a warning state. When the quantized modulus margin is less than the set modulus margin critical threshold, or the quantized phase margin is less than the set phase margin critical threshold, the stability assessment result indicates that the intelligent lighting environment system has entered a dangerous state.

[0017] In one implementation of the first aspect, the optimal control decision is obtained by performing multi-objective optimization using a hierarchical optimization strategy based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm. This includes: using the quantized modulus margin being greater than or equal to a set modulus margin safety threshold and the quantized phase margin being greater than or equal to a set phase margin safety threshold as constraints; minimizing total energy consumption, illuminance deviation, and flicker index as optimization objectives; and performing multi-objective optimization using a hierarchical optimization strategy based on the constraints, the optimization objectives, and the multi-objective optimization algorithm to obtain the optimal control decision.

[0018] In one implementation of the first aspect, the multi-objective optimization algorithm is: Where w1 represents the weighting coefficient of the energy consumption term, w2 represents the weighting coefficient of the illuminance deviation term, w3 represents the weighting coefficient of the flicker suppression term, and P total (x) represents the total power consumption of the system, E i E represents the actual illuminance value at the i-th illuminance sensor measurement point. ref Indicates the illuminance setpoint (target illuminance), Σ|E i -E ref | represents the sum of the absolute values ​​of the illuminance deviations at all sensor points, Flicker index(x) This indicates the flicker index.

[0019] In one implementation of the first aspect, the optimal control decision includes the dimming setpoint, dimming speed, and controller parameters for each lighting circuit.

[0020] Secondly, this application provides a stable control system for a smart lighting environment system under harmonic conditions. The system includes: a harmonic sensing module configured to collect electrical signals in real time based on power quality monitoring units deployed at key power distribution nodes of the smart lighting environment system, and to extract harmonic characteristic parameters based on the electrical signals; the harmonic characteristic parameters include, but are not limited to, total harmonic distortion rate and harmonic content, amplitude and phase of key harmonics, and preliminary estimates of harmonic impedance; a coupling modeling module configured to construct a dynamic coupling model of the harmonic-lighting environment system based on the harmonic characteristic parameters; and a stability evaluation module configured to... The system is configured to perform stability assessment using frequency domain impedance analysis based on the dynamic coupling model of the harmonic-light environment system and the harmonic characteristic parameters, and obtain the stability assessment results; a multi-objective optimization module is configured to perform multi-objective optimization based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm using a hierarchical optimization strategy to obtain the optimal control decision; and an execution module is configured to issue the optimal control decision to the actuators of each lighting circuit in the smart light environment system, and switch the control mode or send governance commands to the active filters in the smart light environment system according to the stability assessment results.

[0021] Thirdly, this application provides an electronic device, the electronic device comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, so that the electronic device executes the above-described stable control method for a smart light environment system under harmonic conditions.

[0022] As described above, the stable control method, system, and equipment for a smart lighting environment system under harmonic conditions described in this application have the following beneficial effects:

[0023] This application, by establishing a harmonic-system coupling model and online stability assessment, can quantify the system's stability margin in real time and proactively issue warnings and adjust control strategies when the margin approaches the safety boundary, thus nipping instability risks in the bud and achieving a fundamental shift from "post-event remediation" to "pre-event prevention." It is the first to provide a quantitative stability analysis method for industrial intelligent lighting environment systems based on impedance ratio and the Nyquist criterion. This transforms the system's stable state from a vague concept into a real-time monitorable indicator with a clearly defined safety threshold, providing a scientific basis for the high-reliability design, operation, and maintenance of the system.

[0024] This application accurately senses the impact of harmonics on light output through a coupled model and utilizes MPC optimization control to quickly and smoothly compensate for or avoid them. This ensures that the light output ripple coefficient remains below the human sensitivity threshold under any operating condition, providing a healthy, comfortable, and flicker-free high-quality lighting environment. Multi-objective optimization under stability constraints ensures that the system can accurately track the illuminance setpoint while resisting harmonics, maintaining the uniformity and stability of illuminance in the working area. This directly improves the quality and efficiency of visual operations in glass factory processes such as sheet inspection and patrol inspection.

[0025] The optimization control in this application seeks the optimal energy efficiency solution under hard constraints of stability. This means that the system will never sacrifice stability in pursuit of extreme energy saving, but will dynamically seek the optimal energy-saving operating point within an absolutely safe operating range, thereby achieving a true balance between safety and energy saving. By maintaining system stability and reducing output ripple, this application fundamentally reduces the electrical and thermal stresses on lighting equipment in harsh power quality environments, significantly extending the service life of lamps and drivers and reducing total life-cycle maintenance costs.

[0026] This application provides precise harmonic mitigation recommendations based on stability requirements to mitigation equipment such as active power filters, achieving a leap from "local lighting control" to "plant-level power quality collaborative optimization" and improving overall mitigation efficiency. Attached Figure Description

[0027] Figure 1 The diagram shows a hardware scenario illustrating the stable control method of a smart light environment system under harmonic conditions as described in an embodiment of this application.

[0028] Figure 2 The diagram shown is a flowchart illustrating the stable control method of a smart lighting environment system under harmonic conditions as described in an embodiment of this application.

[0029] Figure 3 The diagram shown is a schematic representation of the coupling mechanism between harmonics and the optical environment system described in an embodiment of this application.

[0030] Figure 4 The diagram shown is a schematic of the system stability analysis model based on impedance ratio as described in the embodiments of this application.

[0031] Figure 5 The diagram shown is a block diagram illustrating the principle of multi-objective optimization control under stability constraints as described in the embodiments of this application.

[0032] Figure 6 The diagram shows the connection of the hardware-in-the-loop experimental verification platform described in this application embodiment.

[0033] Figure 7 This diagram illustrates a comparison of the effectiveness of the conventional solution and the solution described in this application in dealing with harmonic disturbances.

[0034] Figure 8 The diagram shown is a structural schematic of a stable control system for a smart lighting environment system under harmonic conditions, as described in an embodiment of this application.

[0035] Figure 9 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application. Detailed Implementation

[0036] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0037] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0038] The following embodiments of this application provide a stable control method, system and device for a smart lighting environment system under harmonic environments, which solves the problem of poor stability of smart lighting environment systems in high harmonic industrial environments in the prior art.

[0039] Existing smart lighting environment systems suffer from gaps in both "system-level cognition" and "collaborative design." The industry urgently needs a new method to fundamentally understand and resolve the inherent contradictions between harmonics and smart lighting environment systems. This method should not passively defend against or ignore harmonics, but rather proactively treat harmonic states as core internal variables, performing system-level coupling modeling, quantitative stability analysis, and proactive collaborative optimization. This will allow for the simultaneous achievement of three goals in real industrial power grids: stable operation of the smart lighting environment system, high-quality light output, and high energy efficiency. This application aims to fill this systemic technological gap.

[0040] To address the shortcomings of existing solutions in system-level cognition and collaborative design, this application proposes a closed-loop technical solution of "perception-modeling-evaluation-optimization-collaboration". Its core idea is to transform grid harmonics from external disturbance variables into internal system state variables. By establishing a precise coupled model, its impact boundary on system stability is quantitatively analyzed. Using this boundary as a constraint, multi-objective collaborative optimization of light environment quality and energy efficiency is achieved.

[0041] This solution first makes a breakthrough at the "cognition" level by constructing a coupled model of harmonics and light environment systems to reveal the intrinsic mechanism of action. Then, it innovates at the "control" level by upgrading traditional open-loop or simple feedback control to predictive optimization control with stability as a hard constraint, and actively linking with factory-level treatment equipment.

[0042] Step 1: Harmonic State Perception and Feature Extraction

[0043] The system deploys power quality monitoring units at key power distribution nodes of the smart lighting environment system (such as the beginning of lighting circuits in each workshop) to collect voltage and current signals in real time. Using Fast Fourier Transform or Wavelet Transform algorithms, the system extracts the following key characteristic parameters in real time: total harmonic distortion (THD) and harmonic content of each order, amplitude and phase of key harmonics, and preliminary estimates of harmonic impedance. These data constitute the system's current "harmonic situation map."

[0044] Step 2: Dynamic coupling modeling of harmonic-optical environment system

[0045] This forms the theoretical basis of this application. We establish the following coupling model:

[0046] Device-level model: Establish a frequency domain small-signal model of the LED driver that includes harmonic voltage input, derive the transfer function of its output luminous flux to a specific harmonic voltage, and quantify the light output fluctuation caused by harmonics.

[0047] System-level closed-loop model: The above model is embedded into a microprocessor-based digital control closed loop (including an illuminance sensor, dimming controller, and communication network) to construct a complete system control block diagram. This model can characterize how a harmonic voltage disturbance is amplified or attenuated through the control loop.

[0048] Step 3: Online stability assessment and margin calculation based on impedance ratio

[0049] The system model established in the second step is converted into equivalent output impedance and input admittance within the frequency band of interest. The impedance ratio criterion is applied to calculate the system's stability margin. The core objective is to solve for and monitor the distance between the generalized Nyquist curve and the critical point in real time, or to directly calculate the phase margin and gain margin based on the model. Output: Generate a "stability index" or directly determine whether the system has entered a preset "stability warning zone".

[0050] Step 4: Multi-objective optimization control decision under stability constraints

[0051] The core control algorithm of this application is executed in this step. Its optimization problem is formulated as follows:

[0052] Optimization objective: Min [Total energy consumption, illuminance deviation, flicker index]

[0053] Decision variables: dimming setpoint, dimming speed, controller parameters, etc. for each lighting circuit.

[0054] Core constraint: System stability margin > safety threshold (this threshold is determined by dynamic calculation or offline simulation in step three).

[0055] Solution Method: A hierarchical optimization strategy is adopted. The upper layer uses offline calculations, such as genetic algorithms, to calculate the optimal control parameter set under different harmonic scenarios, forming a strategy library; the lower layer uses Model Predictive Control (MPC) for rolling optimization during online operation. The predictive model of MPC includes harmonic disturbance prediction and stability constraints, ensuring that all future control sequences solved by it meet the stability requirements.

[0056] Step 5: Command Execution and Proactive Collaborative Governance

[0057] The optimized controller output commands are sent to the actuators of each lighting circuit. Simultaneously, the system uploads critical information to the factory's energy management system.

[0058] If local harmonics exceed the standard and are close to the stability boundary, the system can send a priority treatment command to the active filter in that area to suppress the subharmonics that have the greatest impact on stability.

[0059] The system can automatically switch control modes based on stability assessment results, such as switching from "fine dimming mode" to "high stability anti-interference mode".

[0060] In some embodiments, this application provides a smart lighting environment system for implementing the above-described method. The smart lighting environment system includes, but is not limited to: a distributed harmonic sensing module deployed on-site, responsible for data acquisition and feature extraction; an edge computing controller responsible for rapid calculation of the local coupled model, real-time stability assessment, and simple local optimization control; a central optimization decision-making platform that aggregates plant-wide data, runs complex multi-objective optimization algorithms, manages the control strategy library, and generates collaborative governance instructions; high-immunity execution devices employing LED driver power supplies with a wider input voltage range and stronger filtering capabilities; and a plant-level collaborative interface using standard protocol interfaces for communication with the APF and energy management system.

[0061] Figure 1 This diagram illustrates a hardware scenario for the stable control method of a smart lighting environment system under harmonic conditions, as described in an embodiment of this application. Figure 1 As shown, the intelligent light environment system includes a sensing actuator (on-site), an edge controller (workshop level), and a cloud platform / collaboration layer, clearly depicting the three-layer distributed architecture of "sensing execution, edge control, and cloud collaboration" proposed in this application.

[0062] In some embodiments, the perception and execution layer consists of a smart lighting gateway with harmonic monitoring and harmonic-resistant LED luminaires. The gateway integrates an energy quality metering chip and a local microprocessor, responsible for data acquisition, local rapid stability assessment, and instruction execution. The edge control layer is deployed at the workshop level, running complex coupling models and MPC optimization algorithms, and is responsible for the coordinated control of the workshop. The cloud management / collaboration layer performs plant-wide data aggregation, strategy library management, and big data analysis, and is responsible for cross-system collaboration with the plant's APF system and energy management platform.

[0063] In some embodiments, this three-tier distributed architecture provides a top-down control flow: optimization strategies and control commands are sent from the cloud platform through the edge controller to the execution devices on site.

[0064] This three-tiered distributed architecture provides a bottom-up data flow: on-site data such as harmonics, illuminance, and personnel status are collected and uploaded to the edge controller and cloud platform as the basis for analysis and decision-making.

[0065] This three-tier distributed architecture also provides horizontal collaborative flow: the cloud platform interacts with the factory's existing energy management system and active filters for information exchange and command coordination.

[0066] In some embodiments, the cloud platform / collaboration layer acts as the "intelligent brain," aggregating and analyzing data from the entire plant, optimizing long-term strategies, and enabling cross-system collaboration with other key energy systems in the plant. This is the key to achieving global optimization in this application.

[0067] In some embodiments, the edge control layer, acting as a "regional neural center," is deployed in each workshop. It carries the three core algorithm modules of this application (coupled model, stability assessment, and MPC optimizer), and is responsible for high real-time computation, control, and rapid decision-making in its region.

[0068] In some embodiments, the sensing and execution layer, acting as "senses and limbs," consists of high-precision sensors and actuators with strong anti-interference capabilities, ensuring the accuracy of data and the reliable execution of instructions in harsh electromagnetic environments.

[0069] In some embodiments, the anti-interference communication protocol adopted in this application is as follows: enhancement measures are taken at the physical layer and link layer, such as using shielded twisted-pair cables and differential signal transmission. The application layer protocol adopts a retransmission and acknowledgment mechanism, and adds timestamps and sequence numbers to data packets to cope with transient communication interference that may be caused by harmonics.

[0070] This hardware architecture diagram provides a closed-loop and collaborative solution: it demonstrates a complete closed loop from perception to execution, and then to collaboration with the APF, reflecting a systematic solution. Edge intelligence: by pushing the core model and algorithms down to the edge controller, it ensures the system's rapid response capability and operational reliability under complex harmonic interference. Clear hierarchy: the responsibilities of each layer are clearly defined, which is conducive to the actual deployment, expansion, and maintenance of the system.

[0071] In some embodiments, the hardware structure diagram of the intelligent lighting environment system described in this application provides the following closed-loop paths:

[0072] 1. Sensor (illuminance / personnel) → Edge controller (MPC optimization) → LED lighting → Environment / work surface → Sensor.

[0073] 2. Harmonic monitoring unit → Edge controller (stability assessment) → Edge controller (command adjustment) → LED lamps → Power grid side harmonic impedance → Harmonic monitoring unit.

[0074] 3. Edge controller → Active power filter (APF) → Grid side harmonic impedance → Harmonic monitoring unit → Edge controller.

[0075] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0076] like Figure 2 As shown, this embodiment provides a stable control method for a smart light environment system under harmonic conditions, the method including the following steps S201 to S205.

[0077] Step S201: Based on the power quality monitoring unit deployed at the key power distribution nodes of the smart light environment system, electrical signals are collected in real time, and harmonic characteristic parameters are extracted based on the electrical signals. The harmonic characteristic parameters include, but are not limited to, total harmonic distortion rate and harmonic content, amplitude and phase of key harmonics, and preliminary estimates of harmonic impedance.

[0078] The electrical signals include, but are not limited to, voltage signals and current signals.

[0079] Step S202: Construct a dynamic coupling model of the harmonic-optical environment system based on the harmonic characteristic parameters.

[0080] Step S203: Based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters, the stability is evaluated using frequency domain impedance analysis to obtain the stability evaluation results.

[0081] Step S204: Based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm, a hierarchical optimization strategy is used to solve the multi-objective optimization problem and obtain the optimal control decision.

[0082] Step S205: Issue the optimal control decision to the actuators of each lighting circuit in the smart lighting environment system, and switch the control mode or send a control command to the active filter in the smart lighting environment system according to the stability evaluation results.

[0083] In some embodiments, the total harmonic distortion (THD) and harmonic content of each harmonic serve as macroscopic judgments, trigger thresholds, and risk identification, applied in model initiation, stability assessment triggering, and collaborative governance prioritization. The amplitude and phase of key harmonics serve as quantitative inputs, determining flicker amplitude and stability margin, applied in device-level model input, system-side equivalent output impedance calculation, grid-side equivalent harmonic source impedance estimation, loop gain calculation, flicker prediction, and precise APF commands. The grid-side equivalent harmonic source impedance estimate reflects the dynamic characteristics of the power grid, determines the stability boundary, and is applied in system-level model verification, loop gain calculation, stability criterion input, and optimization constraint boundaries. In other words, the THD serves as a macroscopic judgment, trigger threshold, and verification of estimation validity; the harmonic content of each harmonic serves as a quantitative input, determining the amplitude and phase of impedance calculation and loop gain; the amplitude and phase of key harmonics serve as dominant frequency identification and risk source location; and the preliminary estimate of harmonic impedance reflects the dynamic characteristics of the power grid and determines the loop gain denominator.

[0084] This application internalizes, models, and controls the harmonic problem. Traditional solutions treat harmonics as "noise" to be filtered out, while this solution treats them as perceptible, assessable, and usable system state variables. By constructing a closed-loop dynamic model of "harmonic-driver-controller-sensor," it achieves for the first time quantitative analysis and proactive defense of the stability of intelligent lighting environment systems under harmonic backgrounds, and performs global optimization under this constraint.

[0085] In one embodiment of this application, harmonic feature parameters are extracted in real time based on the electrical signal using a fast Fourier transform algorithm; or harmonic feature parameters are extracted in real time based on the electrical signal using a wavelet transform algorithm.

[0086] In one embodiment of this application, constructing a dynamic coupling model of the harmonic-optical environment system based on the harmonic characteristic parameters includes the following steps S301 to S304.

[0087] Step S301: Construct a dynamic coupling model of the harmonic-light environment system based on the LED driver, digital controller and sensor of the lighting circuit in the intelligent light environment system.

[0088] Step S302: Construct a dynamic response model based on the LED driver and the harmonic characteristic parameters; the dynamic response model is the dynamic response model of the LED driver under the input voltage disturbance of the harmonic characteristic parameters.

[0089] Step S303: Obtain the luminous flux transfer function based on the dynamic response model and the LED driver, as a device-level coupling model; the luminous flux transfer function is the transfer function of luminous flux to the harmonic voltage of the harmonic characteristic parameter, and the luminous flux is the flicker of the LED driver under the input voltage disturbance of the harmonic characteristic parameter.

[0090] Step S304: Based on the device-level coupling model, the digital controller, and the sensor, obtain the closed-loop transfer function as the dynamic coupling model of the harmonic-light environment system; the closed-loop transfer function is the closed-loop transfer function of the key performance indicators of the smart light environment system from the grid-side harmonic disturbance, and the key performance indicators include at least the lighting error of the lighting circuit in the smart light environment system.

[0091] Figure 3 This diagram illustrates the coupling mechanism between harmonics and the optical environment system as described in an embodiment of this application. Figure 3 As shown in the figure, this diagram clearly illustrates the "complex coupling path" through which harmonics pose a threat to smart lighting environment systems, which is precisely the key issue that traditional solutions overlook.

[0092] Conducted interference in the main path (solid arrow): This is the most direct path. Distorted grid voltage is directly input to the LED driver. Due to the nonlinear characteristics of the power conversion circuit inside the driver, voltage harmonics will cause low-frequency ripple in the output luminous flux, which is the "flicker" that can be perceived by the human eye.

[0093] Latent radiated interference (dashed arrow): This is the more insidious and highly dangerous coupling path highlighted in this application. High-order harmonics in the power grid generate electromagnetic radiation, thus "polluting" the entire electrical environment.

[0094] Interference sensors: Radiated noise may cause the readings of the illuminance sensor to jump or deviate, feeding back incorrect environmental information to the controller.

[0095] Interference with communication: Radiated noise may intrude into the communication link, causing dimming commands to be erroneous, delayed, or lost during transmission.

[0096] like Figure 3 As shown, the formation of the uncontrolled closed loop is as follows: When harmonics act simultaneously through the aforementioned multiple paths, the controller receives an erroneous signal from the interfered sensor. Based on this, it issues an erroneous dimming command, which may itself be affected by interference. This command drives the driver, already affected by power supply harmonics, producing a more unstable light output. This unstable light output is then detected by a misaligned sensor, forming a vicious cycle of "distortion-misjudgment-uncontrolled operation," ultimately causing the entire closed-loop system to oscillate or malfunction.

[0097] In some embodiments, harmonic-optical environment dynamic coupling modeling includes two levels: a device-level coupling model (LED driver) and a system-level closed-loop coupling model. This model forms the mathematical foundation for all subsequent analysis and optimization.

[0098] Device-level coupling model (LED driver): Establishing a dynamic response model of the driver's output luminous flux under harmonic voltage disturbances. Assume the input voltage includes fundamental and harmonic components: By analyzing the equivalent small-signal model of the driver's power stage circuit (such as flyback, Buck, etc. topologies) at harmonic frequencies, the luminous flux is derived. For harmonic voltage U h transfer function , See formula (1) below for details. This model can predict the extent of optical output ripple (i.e., flicker) caused by a specific subharmonic.

[0099] in, U1 represents the instantaneous input voltage, i.e., the actual mains voltage waveform injected into the LED driver. U1 represents the fundamental voltage amplitude, i.e., the peak value of the 50Hz (or 60Hz) mains frequency component. U represents the fundamental angular frequency. h Indicates the amplitude of the h-th harmonic voltage. Represents the h-th harmonic angular frequency. This represents the initial phase of the h-th harmonic.

[0100] Formula (1)

[0101] It represents the transfer function of luminous flux to harmonic voltage, and describes the dynamic characteristics of luminous flux ripple caused by the h-th harmonic voltage.

[0102] The Laplace transform of the output luminous flux fluctuation component caused by the h-th harmonic voltage represents the luminous flux ripple caused by the h-th harmonic voltage.

[0103] Let represent the h-th harmonic voltage, and the Laplace transform of the h-th harmonic voltage;

[0104] K represents the static gain, which is the amplitude of steady-state luminous flux fluctuation caused by unit harmonic voltage.

[0105] τ represents the time constant, which reflects the driver's response speed to harmonic disturbances; the smaller the value, the faster the response.

[0106] This represents the pure delay time, the lag time between the change in harmonic voltage and the luminous flux response;

[0107] This represents the damping coefficient, which determines the sharpness of the resonance peak; the smaller the value, the more pronounced the resonance peak.

[0108] This represents the natural angular frequency, the center frequency of the resonant peak;

[0109] s represents the Laplace operator, a complex frequency variable.

[0110] System-level closed-loop coupling model: placing the device model within a framework that includes the digital controller C(z), the sensor H(s), and communication delays. In the complete control loop, the closed-loop transfer function T from the grid harmonic disturbance d(s) to the key system performance indicators (such as illuminance error e(s)) is finally obtained. ed (s), T ed (s) as shown in formula (2). This model reveals how harmonics are amplified through the control loop, which is a direct basis for stability analysis. For example, through the phase lag of the control loop, the control command is out of phase with the actual demand, forming positive feedback and aggravating fluctuations. Under PI control, the 150Hz harmonic has a total delay of about 8ms, resulting in a command lag of 1.2 cycles, which aggravates the oscillations due to the out-of-phase effect. That is, the system-level closed-loop coupling model is specifically as follows: the device-level model is embedded into the control closed loop to form a complete feedback system description. The interaction stability between the system and the power grid is quantified through impedance analysis, providing quantitative criteria for real-time evaluation, providing the ability to predict stability margin for MPC, and enabling optimized control to be carried out under the premise of ensuring safety. This realizes a leap from the perspective of "local equipment" to "overall system".

[0111] Formula (2)

[0112] T ed (s) represents the closed-loop transfer function, which is the closed-loop transfer function from grid harmonic disturbances to actual illuminance;

[0113] It represents the transfer function of luminous flux to harmonic voltage, a device-level coupling model that describes the mapping from harmonic voltage to luminous flux ripple;

[0114] This indicates the communication and sampling delay components, describing the total delay of sensor sampling, AD conversion, and communication transmission;

[0115] This represents the total delay time, the total lag time from sensor sampling to controller reception;

[0116] H(s) represents the transfer function of the illuminance sensor, which is the dynamic characteristic that converts actual illuminance into an electrical signal;

[0117] C(z) represents the discrete transfer function of the digital controller and the dynamic characteristics of the controller running the MPC optimization algorithm;

[0118] G c (s) represents the driver's response transfer function to control commands, a mapping from control voltage to luminous flux output;

[0119] z represents the transformed variable, a complex variable in a discrete-time system;

[0120] T s Indicates the sampling period, the sampling interval of the digital controller;

[0121] s represents the Laplace operator, the complex frequency variable of a continuous-time system.

[0122] In one embodiment of this application, stability assessment is performed using frequency domain impedance analysis based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters. The stability assessment results are obtained by the following steps S401 to S406.

[0123] Step S401: Based on the electrical signal of the lighting equipment at the common coupling point of the intelligent light environment system, the equivalent output impedance of the lighting equipment at the common coupling point is calculated using the frequency domain impedance analysis method.

[0124] Step S402: Based on the fluctuation of harmonic voltage and current at the common coupling point on the power grid side of the smart optical environment system, the equivalent harmonic source impedance at the common coupling point on the power grid side is calculated using the frequency domain impedance analysis method.

[0125] Step S403: Calculate the loop gain based on the equivalent output impedance and the equivalent harmonic source impedance, and obtain the corresponding generalized Nyquist curve based on the loop gain.

[0126] Step S404: Based on the distance between the generalized Nyquist curve and the set critical point, quantize the modulus margin and obtain the quantized modulus margin.

[0127] Step S405: Quantize the phase margin based on the phase difference of the generalized Nyquist curve at the phase crossover frequency to obtain the quantized phase margin; the stability margin includes the quantized modulus margin and the quantized phase margin.

[0128] Step S406: Based on the quantized modulus margin and the set modulus margin threshold, the quantized phase margin and the set phase margin threshold, a judgment is made to obtain the stability evaluation result.

[0129] In one embodiment of this application, the setting of the modulus margin threshold includes setting a modulus margin warning threshold and setting a modulus margin critical threshold, and the setting of the phase margin threshold includes setting a phase margin warning threshold and setting a phase margin critical threshold.

[0130] In one embodiment of this application, the determination of stability evaluation results based on the quantized modulus margin and the set modulus margin threshold, the quantized phase margin and the set phase margin threshold includes the following steps S501 to S503.

[0131] Step S501: When the quantized modulus margin is greater than or equal to the set modulus margin warning threshold, and the quantized phase margin is greater than or equal to the set phase margin warning threshold, the stability evaluation result is that the smart light environment system is in a stable state.

[0132] Step S502: When the quantized modulus margin is less than the set modulus margin warning threshold and greater than or equal to the set modulus margin critical threshold, or the quantized phase margin is less than the set phase margin warning threshold and greater than or equal to the set phase margin critical threshold, the stability evaluation result is that the smart light environment system enters a warning state.

[0133] Step S503: When the quantized modulus margin is less than the set modulus margin critical threshold, or the quantized phase margin is less than the set phase margin critical threshold, the stability assessment result is that the smart light environment system has entered a dangerous state.

[0134] Figure 4 The diagram shown is a schematic representation of the system stability analysis model based on impedance ratio described in an embodiment of this application. Figure 4 As shown, this application decomposes the core of system stability analysis—the impedance ratio criterion—into three intuitive parts for presentation:

[0135] Port Impedance Interaction Physical Model (Upper): This section abstracts the real-world scenario into a standard circuit model. The left-side power grid is equivalent to a harmonic voltage source and its internal resistance Z. grid The series connection is the source of the interference. The right-side lighting system is treated as a single load impedance Z. sys .

[0136] The two converge at the point of common coupling (PCC), where the voltage quality V pcc (t) directly determines the input conditions of the lighting equipment. The figure compares the ideal sine wave with the distorted waveform, visually illustrating the results caused by harmonic interaction.

[0137] Stability Criterion Mathematical Transformation (Middle Section): The key step is to transform physical quantities into analyzable mathematical quantities. Define loop gain. The stability of the system depends on the characteristics of this frequency function.

[0138] Nyquist Stability Criterion Diagram (Part 2): This is the ultimate tool for stability analysis. It relates to loop gain. With frequency The trajectory of change is plotted on the complex plane, yielding the Nyquist curve. The core criterion is: if the curve does not encircle the critical point (-1, j0), the system is stable; if it does, the system is unstable. Quantitative assessment: The distance 'd' in the graph represents the closest distance of the curve to the critical point, i.e., the stability margin. A larger margin indicates a stronger ability of the system to resist harmonic disturbances. This provides a direct quantitative indicator for achieving "early warning" and "optimization" in this application (e.g., setting 'd' to be greater than a certain threshold).

[0139] In some embodiments, the stability assessment and active early warning mechanism provided in this application is based on the above-mentioned coupling model and uses frequency domain impedance analysis to perform online stability assessment, as follows:

[0140] System port impedance division and calculation: Treating the smart lighting system as a "load" connected to the factory power grid, calculate its equivalent output impedance at the point of common coupling. , See formula (3) below for details. Simultaneously, the equivalent harmonic source resistance at that point on the grid side is estimated online using real-time monitored harmonic voltage / current data. , See formula (4) below for details.

[0141] Formula (3)

[0142] Formula (4)

[0143] Where jw represents the product of the imaginary unit and the angular frequency, the complex frequency variable in frequency domain analysis;

[0144] V PCC (jw) represents the frequency domain phasor of the voltage at the common coupling point, which is the complex representation of the voltage at the common coupling point in the frequency domain.

[0145] I in (jw) represents the frequency domain phasor of the input current, which is the complex representation of the current flowing into the lighting system in the frequency domain.

[0146] The frequency domain phasor represents the voltage change at the common coupling point, and the frequency domain representation of the voltage fluctuation between adjacent time windows;

[0147] The frequency domain phasor represents the change in current at the common coupling point, and the frequency domain representation of the current fluctuation in adjacent time windows.

[0148] Stability margin quantization based on the generalized Nyquist criterion: Calculating the loop gain .draw The generalized Nyquist curve is used. The stability margin of the system is quantified by the shortest distance between the curve and the critical point (-1, j0) (modulus margin) and the phase difference at the phase crossing frequency (phase margin). The system calculates these two margin values ​​in real time. Here, j0 indicates that the ordinate of the critical point is 0 (i.e., the imaginary part is zero).

[0149] Proactive early warning: Two threshold levels are set: an early warning threshold (e.g., phase margin > 30°) and a critical threshold (e.g., phase margin > 15°). When the margin value enters the early warning zone, the system triggers the optimizer to intervene and adjust in advance to prevent problems from occurring.

[0150] In one embodiment of this application, the optimal control decision is obtained by performing multi-objective optimization based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm using a hierarchical optimization strategy, including the following steps S601 to S603.

[0151] Step S601: The quantized modulus margin being greater than or equal to a set modulus margin safety threshold and the quantized phase margin being greater than or equal to a set phase margin safety threshold are used as constraints.

[0152] Step S602: Minimize total energy consumption, illuminance deviation, and flicker index as the optimization objective.

[0153] Step S603: Based on the constraints, the optimization objective, and the multi-objective optimization algorithm, a hierarchical optimization strategy is used to solve the multi-objective optimization problem and obtain the optimal control decision.

[0154] In one embodiment of this application, the multi-objective optimization algorithm is:

[0155]

[0156] Where w1 represents the weighting coefficient of the energy consumption term, w2 represents the weighting coefficient of the illuminance deviation term, w3 represents the weighting coefficient of the flicker suppression term, and P total (x) represents the total power consumption of the system, E i E represents the actual illuminance value at the i-th illuminance sensor measurement point. ref Indicates the illuminance setpoint (target illuminance), Σ|E i -E ref | represents the sum of the absolute values ​​of the illuminance deviations at all sensor points, Flicker index(x) This indicates the flicker index.

[0157] In one embodiment of this application, the optimal control decision includes the dimming setpoint, dimming speed, and controller parameters for each lighting circuit.

[0158] Figure 5The diagram shown is a block diagram illustrating the principle of multi-objective optimization control under stability constraints as described in an embodiment of this application. Figure 5 As shown,

[0159] This diagram clearly illustrates the core of patent control strategy decision-making, reflecting a fundamental shift from "passive response" to "proactive prediction and optimization."

[0160] First, regarding input and multi-objective settings (left side), the controller receives four main types of input, clearly defining the boundaries of the optimization proposition:

[0161] Real-time status: From Figure 1 , Figure 2 The harmonic state and illuminance feedback are the basis for dynamic optimization.

[0162] Core innovation constraints: stability constraints (such as PM) min This constraint is directly input as a hard constraint, which is the key difference between this application and all traditional lighting control. It ensures that any optimization results prioritize system stability.

[0163] Traditional constraints, such as process illumination requirements and equipment power limits, together constitute a complete set of constraints.

[0164] Secondly, optimize the core and mathematical model (middle section).

[0165] Predictive model: Built in Figure 3 , Figure 4 The harmonic-optical environment coupling model and stability assessment model established in this paper can accurately predict how the system's illuminance, energy consumption, flicker, and most critically, system stability margin will change under different control decisions over a period of time in the future.

[0166] The optimization solver solves a rigorous mathematical optimization problem in each control cycle: finding a future control sequence that minimizes the multi-objective function J while satisfying all constraints (especially hard stability constraints). The objective function comprehensively balances energy consumption and light quality (illuminance deviation and flicker).

[0167] Secondly, the rolling optimization mechanism (on the right) is the essence of Model Predictive Control (MPC). Figure 5 The timeline is used to visually represent the process:

[0168] Prediction and Planning: Based on the current state, the solver plans the optimal control sequence (u(k), u(k+1), ...) from the current time k to the next k+H steps and the corresponding prediction output.

[0169] Rolling execution: The system only executes the optimal control quantity u(k) at the current time k. At the next time k+1, the system collects the new actual state (such as the harmonics may have changed), and uses the new state as the starting point to re-predict, optimize and execute, thus "rolling" forward.

[0170] Core advantage: This mechanism enables the system to be forward-looking and adaptive, proactively avoiding potential instability risks in the future and dynamically tracking the optimal operating point.

[0171] In some embodiments, this application provides a multi-objective optimization control strategy under stability constraints, which can achieve a dynamic balance between energy efficiency and light quality under stability constraints. This is a typical constrained multi-objective optimization problem. The mathematical description of the optimization problem is as follows:

[0172] Decision variables , where P i K is the dimming power setting value for the i-th loop. p K i These are optional controller parameters that can be adjusted.

[0173] Objective function: That is, to simultaneously minimize total power consumption, illuminance deviation and flicker index.

[0174] Here, MinF(x) represents the decision variable vector, which contains all controllable parameters.

[0175] P total (x) represents the total power consumption of the system, in watts (W) or kilowatt-hours (kWh), calculated from the sum of the power of all lighting circuits: This indicator directly reflects the system's energy consumption and is an important target for energy conservation optimization. Among them, P... i (x) represents the actual power consumption of the i-th lighting circuit.

[0176] w1 represents the weighting coefficient of the energy consumption term, used to adjust the relative importance of the energy-saving target in the overall optimization. The larger w1 is, the more the system tends to choose a low-power control scheme, possibly at the cost of sacrificing illuminance accuracy or increasing flicker.

[0177] E i This represents the actual illuminance value at the i-th illuminance sensor measurement point, measured in lux, and is determined by both the system's light output and environmental factors.

[0178] E ref This indicates the illuminance setpoint (target illuminance), measured in lux. It is usually preset according to process requirements or operating standards, such as 1000 lux for the quality inspection area of ​​a glass factory.

[0179] Σ|Ei -E ref | represents the sum of the absolute values ​​of the illuminance deviations at all sensor points, measured in lux. This metric measures the accuracy with which the system tracks the set illuminance; a smaller value indicates more precise illuminance control.

[0180] w2 represents the weighting coefficient of the illuminance deviation term, controlling the importance of the illuminance accuracy target. The larger w2 is, the more the system prioritizes ensuring that the illuminance is stable near the set value.

[0181] Flicker index(x) The flicker index, a dimensionless value, is typically defined as the relative amplitude of light output fluctuations. It can be calculated based on the short-time flicker index or flicker percentage in the IEEE 1789-2015 standard. A smaller value indicates smoother light output and less impact on visual comfort.

[0182] w3 represents the weighting coefficient of the flicker suppression term, which is dimensionless and adjusts the importance of the light quality target. The larger the w3, the more the system tends to choose control strategies that reduce flicker (such as avoiding deep PWM dimming, using smooth dimming curves, etc.).

[0183] Constraints: Stability hard constraints: Illuminance soft constraint: This meets the minimum illumination requirements for the working surface. Equipment physical constraints: .

[0184] Where x represents the decision variable vector; Z grid It represents the equivalent harmonic impedance on the grid side, which is expressed in the frequency domain as the complex impedance looking from the common coupling point to the grid; Indicates phase margin; PM min This represents the minimum permissible phase margin, a lower threshold set to ensure the system has sufficient damping and robustness.

[0185] E i E represents the actual illuminance value measured by the i-th illuminance sensor, which is determined by both the system light output and environmental factors, and depends on the control variable x and the current disturbance (such as harmonics, changes in natural light); work This indicates the minimum illuminance requirement for the working surface, which is the lower limit set according to production processes, operational safety, or national standards.

[0186] Hierarchical rolling optimization solution mainly includes two steps: offline policy library generation and online model predictive control, as detailed below:

[0187] Offline strategy library generation: For typical harmonic scenarios (such as furnace start-up and shutdown, and operation of a large number of frequency converters), the non-dominated sorting genetic algorithm (NSGA-II) is used to solve offline to obtain a series of Pareto optimal control parameter sets that take into account both energy efficiency and light quality, and these are stored in the strategy library.

[0188] Online Model Predictive Control: Predictive Model: Employs a simplified discrete state-space form of a system-level closed-loop coupled model to predict the system's behavior under candidate control sequences over a future period. Rolling Optimization: In each control cycle, using the current harmonic state as input, the nearest optimal solution is selected from the policy library as the initial value. The MPC solver, while satisfying hard stability constraints, continuously solves for the optimal control sequence over several future time domains, executing only the first control variable. This process fully considers the time-varying characteristics of harmonics. For example, if a linearized model is used, the online MPC optimization can be reduced to the following quadratic programming problem:

[0189]

[0190]

[0191]

[0192] in, This indicates the control increment decision variable.

[0193] This represents the Hessian matrix.

[0194] These are represented as coefficient matrices for inequality and equality constraints, respectively.

[0195] These are respectively represented as constraint boundary vectors.

[0196] Figure 6 The diagram shows the connection schematic of the hardware-in-the-loop experimental verification platform described in the embodiments of this application. Figure 6 As shown in the figure, this diagram clearly constructs a closed-loop test environment that connects the virtual simulation world with the real physical hardware, which is the key to verifying the solution of this application.

[0197] 1. Real-time simulator (left side - virtual environment), this part acts as a "digital twin" of the factory's power environment.

[0198] Power grid and harmonic source model: High-precision simulation of the power distribution network of a glass factory, and can freely inject characteristic harmonics of different amplitudes, frequencies and phases (such as the 5th and 7th harmonics) to simulate complex operating conditions such as furnace start-up and shutdown.

[0199] Detailed load model: Establish a mathematical model that is consistent with the physical characteristics of the controlled object (LED driver), which can accurately respond to control commands and output real electrical feedback signals.

[0200] Core value: It can safely and repeatedly generate extreme harmonic scenarios that are difficult to reproduce or destructive in real factories, providing rich and demanding input conditions for testing.

[0201] 2. Signal conversion interface (middle - bridge): This part is the key bridge connecting the "virtual" and the "real", ensuring that the signal is transmitted between the two worlds without loss and in real time.

[0202] Power amplifier: Amplifies the weak voltage and current signals output by the simulator into standard electrical signals (such as 0-10V) sufficient to drive the input circuit of the real controller.

[0203] Data acquisition card: Real-time acquisition of physical control signals (such as PWM pulses) emitted by the real controller, and conversion of them into digital signals to feed back to the simulator, forming a closed loop.

[0204] Core value: It solves the problem of signal mismatch between the simulation environment and hardware devices, and realizes millisecond-level real-time interaction, which is the foundation of HIL test credibility.

[0205] 3. The actual hardware (right side - the object under test), which is the core entity of this application - the intelligent lighting controller.

[0206] It is a real product to be verified, which internally runs the coupling model, stability assessment algorithm and MPC optimization program described in this application.

[0207] In the test, it "thought" that it was connected to a real factory power grid and lighting fixtures, and made control decisions accordingly.

[0208] Core value: The successful testing on this platform strongly demonstrates the ability of the proposed solution to handle complex harmonic problems on real hardware, and its persuasiveness is far greater than that of pure digital simulation.

[0209] Figure 7 This diagram illustrates a comparison of the effectiveness of the conventional solution and the solution described in this application in addressing harmonic disturbances, as illustrated in the embodiments of this application. Figure 7 As shown, the same harmonic disturbance is applied at t=2s. Figure 7 (a) shows that the traditional scheme exhibits severe illuminance instability, while the scheme in this application provides stable output. Figure 7 (b) It is further revealed that the stability margin of the traditional scheme has fallen below the safety threshold (0.3), and the system is on the verge of collapse; this application actively maintains the margin within the safety range through predictive optimization, which fundamentally ensures the reliability of the system.

[0210] In some embodiments, this application takes the tin bath section of a glass factory as an example to describe a complete dynamic adjustment process, as follows:

[0211] Event: A group of inverters from a nearby edge-pulling machine suddenly started up, injecting significant 5th and 11th harmonics into the power grid.

[0212] Sensing: The smart gateway at the beginning of the lighting circuit in the quality inspection area detected a sudden change in the harmonic spectrum, THD. v It rose from 3% to 12%, with the 5th harmonic being the dominant factor.

[0213] Assessment: The edge controller receives real-time data and inputs it into the coupled model. Model calculations show that, at the current high dimming rate of 95%, the new harmonic state will cause the system phase margin to drop rapidly to 25° (entering the warning zone).

[0214] Decision: The MPC optimizer is triggered. It uses the current illuminance and the status of the personnel sensor (quality inspector is present) as initial conditions, and performs rolling optimization under the dual constraints of "ensuring illuminance is not less than 750 Lux" and "phase margin is restored to above 35°".

[0215] Execution and Collaboration: Local Execution: The MPC outputs the optimal instruction: smoothly reduce the dimming rate of the main lighting in the quality inspection area from 95% to 82%. This minute brightness adjustment (almost imperceptible to the human eye) provides sufficient stability margin, eliminating the risk of flicker. Active Collaboration: Simultaneously, the edge controller sends a targeted compensation suggestion to the factory's APF system via the collaboration interface: "South area of ​​the tin bath, prioritize suppressing the 5th harmonic current, with a target value below X amperes." This assists the APF in precise management.

[0216] Recovery and Learning: Once the edge-pulling machine stabilizes or stops, the harmonic level decreases. The system detects sufficient margin, and the MPC optimizer automatically and smoothly adjusts the illuminance back to the optimal setpoint. All data from this event (harmonic spectrum, control actions, and effects) is recorded and uploaded for subsequent iterative updates to the optimization strategy library.

[0217] The stability control method for intelligent optical environment systems under harmonic environments described in this application can fundamentally improve system stability and reliability, transforming passive stability assurance into proactive stability assurance: Traditional solutions passively respond after harmonic disturbances occur, often leading to instability due to delayed or inappropriate responses. This application, by establishing a harmonic-system coupling model and online stability assessment, can quantify the system stability margin in real time and proactively warn and adjust the control strategy when the margin approaches the safety boundary, nipping instability risks in the bud and achieving a fundamental shift from "post-event remediation" to "pre-event prevention." It provides quantitative stability criteria: This application, for the first time, provides a quantitative stability analysis method for industrial intelligent optical environment systems based on impedance ratio and the Nyquist criterion. This makes the system's stable state no longer a vague concept, but an indicator that can be monitored in real time with a clear safety threshold, providing a scientific basis for the high-reliability design, operation, and maintenance of the system.

[0218] The stable control method for a smart lighting environment system under harmonic conditions described in this application can achieve high-quality lighting environment output. It completely suppresses harmonic flicker: Traditional lighting under harmonic conditions easily leads to increased output ripple in LED drivers, producing flicker that is perceptible to the human eye. This application accurately senses the impact of harmonics on light output through a coupling model and uses MPC optimized control to quickly and smoothly compensate for or avoid them, ensuring that the light output ripple coefficient is below the human sensitivity threshold under any operating condition, providing a healthy, comfortable, and flicker-free high-quality lighting environment. It ensures accurate and uniform illuminance: Multi-objective optimization under stability constraints ensures that while resisting harmonics, the system can still accurately track the illuminance setpoint, maintaining the uniformity and stability of illuminance in the working area, directly improving the visual operation quality and efficiency of processes such as glass inspection and patrol in glass factories.

[0219] The stable control method for a smart lighting environment system under harmonic conditions described in this application achieves synergistic optimization of energy saving and equipment lifespan. Optimal energy saving within safe boundaries: The optimized control in this application seeks the optimal energy efficiency solution under hard stability constraints. This means that the system will never sacrifice stability in pursuit of extreme energy saving, but will dynamically seek the optimal energy-saving operating point within an absolutely safe operating range, thus achieving a true unity of safety and energy saving. Significantly extending the lifespan of critical equipment: Harmonics are the main cause of overheating damage to components such as LED power supplies and electrolytic capacitors. By maintaining system stability and reducing output ripple, this application fundamentally reduces the electrical and thermal stresses on lighting equipment under harsh power quality environments, significantly extending the lifespan of lamps and drivers, and reducing total lifespan maintenance costs.

[0220] The stable control method for a smart lighting environment system under harmonic conditions described in this application can form a cross-system collaborative governance capability. Intelligent linkage with the factory power grid governance system: This application is not only an independent control system, but also a distributed harmonic sensing node and a collaborative governance initiator. By providing precise harmonic governance suggestions based on stability requirements to governance equipment such as active filters, it achieves a leap from "local lighting control" to "plant-level power quality collaborative optimization," improving overall governance efficiency.

[0221] The scope of protection for the stable control method of the intelligent light environment system under harmonic environment described in the embodiments of this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0222] This application also provides a stable control system for a smart lighting environment system under harmonic conditions. The stable control system for the smart lighting environment system under harmonic conditions can realize the stable control method for the smart lighting environment system under harmonic conditions described in this application. However, the implementation device for the stable control method for the smart lighting environment system under harmonic conditions described in this application includes, but is not limited to, the structure of the stable control system for the smart lighting environment system under harmonic conditions listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0223] Figure 8 The diagram shown is a structural schematic of a stable control system for a smart lighting environment system under harmonic conditions, as described in an embodiment of this application. Figure 8 As shown, this embodiment provides a stable control system for a smart light environment system under harmonic conditions. The system 800 includes: a harmonic sensing module 801, a coupling modeling module 802, a stability evaluation module 803, a multi-objective optimization module 804, and an execution module 805.

[0224] The harmonic sensing module 801 is configured to collect electrical signals in real time based on the power quality monitoring unit deployed at the key power distribution node of the smart light environment system, and extract harmonic characteristic parameters based on the electrical signals; the harmonic characteristic parameters include, but are not limited to, the total harmonic distortion rate of voltage and the content of each harmonic, the amplitude and phase of the key harmonic, and the preliminary estimate of the harmonic impedance.

[0225] The coupling modeling module 802 is configured to construct a dynamic coupling model of the harmonic-optical environment system based on the harmonic characteristic parameters.

[0226] The stability assessment module 803 is configured to perform stability assessment using frequency domain impedance analysis based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters, and obtain the stability assessment results.

[0227] The multi-objective optimization module 804 is configured to perform multi-objective optimization based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm using a hierarchical optimization strategy to obtain the optimal control decision.

[0228] The execution module 805 is configured to issue the optimal control decision to the actuators of each lighting circuit in the smart lighting environment system, and to switch the control mode or send a control command to the active filter in the smart lighting environment system based on the stability assessment results.

[0229] It should be noted that the functions or operations of the harmonic sensing module 801, coupling modeling module 802, stability evaluation module 803, multi-objective optimization module 804, and execution module 805 described in this embodiment correspond one-to-one with the steps in the stability control method of the intelligent light environment system under harmonic environment described above, and therefore will not be repeated here.

[0230] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0231] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0232] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0233] Figure 9 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application. Figure 9 As shown, this embodiment provides an electronic device, the electronic device 900 including:

[0234] Memory 901, wherein memory 901 is used to store computer programs;

[0235] The processor 902 is used to execute the computer program stored in the memory 901 so that the electronic device 900 performs the above-described stable control method for the intelligent light environment system under harmonic environment.

[0236] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0237] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0238] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0239] In summary, the stable control method, system, and equipment for a smart lighting environment system under harmonic conditions described in this application have the following beneficial effects:

[0240] This application, by establishing a harmonic-system coupling model and online stability assessment, can quantify the system's stability margin in real time and proactively issue warnings and adjust control strategies when the margin approaches the safety boundary, thus nipping instability risks in the bud and achieving a fundamental shift from "post-event remediation" to "pre-event prevention." It is the first to provide a quantitative stability analysis method for industrial intelligent lighting environment systems based on impedance ratio and the Nyquist criterion. This transforms the system's stable state from a vague concept into a real-time monitorable indicator with a clearly defined safety threshold, providing a scientific basis for the high-reliability design, operation, and maintenance of the system.

[0241] This application accurately senses the impact of harmonics on light output through a coupled model and utilizes MPC optimization control to quickly and smoothly compensate for or avoid them. This ensures that the light output ripple coefficient remains below the human sensitivity threshold under any operating condition, providing a healthy, comfortable, and flicker-free high-quality lighting environment. Multi-objective optimization under stability constraints ensures that the system can accurately track the illuminance setpoint while resisting harmonics, maintaining the uniformity and stability of illuminance in the working area. This directly improves the quality and efficiency of visual operations in glass factory processes such as sheet inspection and patrol inspection.

[0242] The optimization control in this application seeks the optimal energy efficiency solution under hard constraints of stability. This means that the system will never sacrifice stability in pursuit of extreme energy saving, but will dynamically seek the optimal energy-saving operating point within an absolutely safe operating range, thereby achieving a true balance between safety and energy saving. By maintaining system stability and reducing output ripple, this application fundamentally reduces the electrical and thermal stresses on lighting equipment in harsh power quality environments, significantly extending the service life of lamps and drivers and reducing total life-cycle maintenance costs.

[0243] This application provides precise harmonic mitigation recommendations based on stability requirements to mitigation equipment such as active power filters, achieving a leap from "local lighting control" to "plant-level power quality collaborative optimization" and improving overall mitigation efficiency.

[0244] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0245] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for stable control of a smart lighting environment system under harmonic conditions, characterized in that, include: Power quality monitoring units deployed at key power distribution nodes of a smart lighting environment system collect electrical signals in real time and extract harmonic characteristic parameters based on the electrical signals. The harmonic characteristic parameters include, but are not limited to, total harmonic distortion rate and harmonic content, amplitude and phase of key harmonics, and preliminary estimates of harmonic impedance. A dynamic coupling model of the harmonic-optical environment system is constructed based on the aforementioned harmonic characteristic parameters; Based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters, the stability is evaluated using frequency domain impedance analysis to obtain the stability evaluation results. Based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm, a hierarchical optimization strategy is used to solve the multi-objective optimization problem and obtain the optimal control decision. The optimal control decision is issued to the actuators of each lighting circuit in the smart lighting environment system, and the control mode is switched or a control command is sent to the active filter in the smart lighting environment system based on the stability assessment results.

2. The stable control method for a smart lighting environment system under harmonic conditions according to claim 1, characterized in that, Harmonic characteristic parameters are extracted in real time based on the electrical signal using a Fast Fourier Transform algorithm; or Harmonic feature parameters are extracted in real time based on the electrical signal using a wavelet transform algorithm.

3. The stable control method for a smart lighting environment system under harmonic conditions according to claim 1, characterized in that, The dynamic coupling model of the harmonic-optical environment system based on the aforementioned harmonic characteristic parameters includes: A dynamic coupling model of the harmonic-light environment system is constructed based on the LED driver, digital controller, and sensors of the lighting circuit in the intelligent light environment system. A dynamic response model is constructed based on the LED driver and the harmonic characteristic parameters; the dynamic response model is the dynamic response model of the LED driver under the input voltage disturbance of the harmonic characteristic parameters. The luminous flux transfer function is obtained based on the dynamic response model and the LED driver, serving as a device-level coupling model. The luminous flux transfer function is the transfer function of luminous flux to the harmonic voltage of the harmonic characteristic parameter, and the luminous flux is the flicker of the LED driver under the input voltage disturbance of the harmonic characteristic parameter. Based on the device-level coupling model, the digital controller, and the sensor, a closed-loop transfer function is obtained as the dynamic coupling model of the harmonic-light environment system. The closed-loop transfer function is the closed-loop transfer function of the key performance indicators of the smart light environment system from the grid-side harmonic disturbance. The key performance indicators include at least the lighting error of the lighting circuit in the smart light environment system.

4. The stable control method for a smart lighting environment system under harmonic conditions according to claim 1, characterized in that, Based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters, a stability assessment was performed using frequency domain impedance analysis. The stability assessment results include: The equivalent output impedance of the lighting equipment at the common coupling point is calculated using frequency domain impedance analysis based on the electrical signal of the lighting equipment in the intelligent light environment system. The equivalent harmonic source impedance at the common coupling point of the power grid side based on the fluctuation of harmonic voltage and current on the power grid side of the smart optical environment system is obtained by frequency domain impedance analysis. The loop gain is calculated based on the equivalent output impedance and the equivalent harmonic source impedance, and the corresponding generalized Nyquist curve is obtained based on the loop gain. Based on the generalized Nyquist curve and the distance quantization modulus margin of the set critical point, the quantization modulus margin is obtained. The phase margin is quantized based on the phase difference at the phase crossover frequency of the generalized Nyquist curve to obtain the quantized phase margin; the stability margin includes the quantized modulus margin and the quantized phase margin. Based on the quantized modulus margin and the set modulus margin threshold, the quantized phase margin and the set phase margin threshold are used to make a judgment and obtain the stability evaluation result.

5. The stable control method for a smart lighting environment system under harmonic conditions according to claim 4, characterized in that, The set modulus margin threshold includes a set modulus margin warning threshold and a set modulus margin critical threshold; the set phase margin threshold includes a set phase margin warning threshold and a set phase margin critical threshold. Based on the quantized modulus margin and a set modulus margin threshold, and the quantized phase margin and a set phase margin threshold, the stability assessment results are obtained, including: When the quantized modulus margin is greater than or equal to the set modulus margin warning threshold, and the quantized phase margin is greater than or equal to the set phase margin warning threshold, the stability assessment result is that the smart light environment system is in a stable state. When the quantized modulus margin is less than the set modulus margin warning threshold and greater than or equal to the set modulus margin critical threshold, or when the quantized phase margin is less than the set phase margin warning threshold and greater than or equal to the set phase margin critical threshold, the stability assessment result is that the smart light environment system enters a warning state. When the quantized modulus margin is less than the set modulus margin critical threshold, or the quantized phase margin is less than the set phase margin critical threshold, the stability assessment result indicates that the smart light environment system has entered a dangerous state.

6. The stable control method for a smart lighting environment system under harmonic conditions according to claim 5, characterized in that, Based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm, a hierarchical optimization strategy is used to solve the multi-objective optimization problem to obtain the optimal control decision, including: The constraints are that the quantized modulus margin is greater than or equal to a set modulus margin safety threshold and the quantized phase margin is greater than or equal to a set phase margin safety threshold. The optimization objectives are to minimize total energy consumption, illuminance deviation, and flicker index. Based on the constraints, the optimization objective, and the multi-objective optimization algorithm, a hierarchical optimization strategy is used to solve the multi-objective optimization problem and obtain the optimal control decision.

7. The stable control method for a smart lighting environment system under harmonic conditions according to claim 1, characterized in that, The multi-objective optimization algorithm is as follows: Where w1 represents the weighting coefficient of the energy consumption term, w2 represents the weighting coefficient of the illuminance deviation term, w3 represents the weighting coefficient of the flicker suppression term, and P total (x) represents the total power consumption of the system, E i E represents the actual illuminance value at the i-th illuminance sensor measurement point. ref Indicates the illuminance setpoint (target illuminance), Σ|E i -E ref | represents the sum of the absolute values ​​of the illuminance deviations at all sensor points, Flicker index(x) This indicates the flicker index.

8. The stable control method for a smart lighting environment system under harmonic conditions according to claim 1, characterized in that, The optimal control decision includes the dimming setpoint, dimming speed, and controller parameters for each lighting circuit.

9. A stable control system for a smart lighting environment system under harmonic conditions, characterized in that, include: The harmonic sensing module is configured to collect electrical signals in real time based on the power quality monitoring unit deployed at the key power distribution node of the smart light environment system, and extract harmonic characteristic parameters based on the electrical signals; the harmonic characteristic parameters include, but are not limited to, the total harmonic distortion rate of voltage and the content of each harmonic, the amplitude and phase of the key harmonic, and the preliminary estimate of the harmonic impedance. The coupling modeling module is configured to construct a dynamic coupling model of the harmonic-optical environment system based on the harmonic characteristic parameters. The stability assessment module is configured to perform stability assessment based on the dynamic coupling model of the harmonic-optical environment system and the harmonic characteristic parameters using frequency domain impedance analysis to obtain the stability assessment results. The multi-objective optimization module is configured to perform multi-objective optimization based on the stability assessment results, the harmonic characteristic parameters, and the multi-objective optimization algorithm using a hierarchical optimization strategy to obtain the optimal control decision. The execution module is configured to issue the optimal control decision to the actuators of each lighting circuit in the smart lighting environment system, and to switch the control mode or send governance commands to the active filters in the smart lighting environment system based on the stability assessment results.

10. An electronic device, characterized in that, include: The memory is used to store computer programs; A processor for executing a computer program stored in the memory to cause the electronic device to perform the stable control method of the intelligent light environment system under harmonic environment as described in any one of claims 1 to 8.

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