Dynamic voltage restorer optimization method and system based on fuzzy control

Abstract

The invention discloses a dynamic voltage restorer optimization method and system based on fuzzy control, and relates to the technical field of power system control, and the method comprises the steps: collecting a bus voltage signal, calculating a voltage error and a change rate, and setting a load grade parameter according to a load precision grade to adjust the sensitivity of a controller; the parameters are input into a fuzzy controller after being fuzzified, and initial compensation voltage information is output through fuzzy reasoning; and after amplitude limiting, change rate limiting and waveform quality constraint processing are carried out on the initial compensation voltage information, the initial compensation voltage information is applied to a load side through an inverter and an injection transformer to realize voltage recovery. The technical problems that an existing dynamic voltage restorer is low in response speed, low in compensation precision and difficult to adapt to different load precision requirements under the complex voltage fluctuation condition are solved, and the technical effects of dynamic adjustment and precise limitation based on fuzzy control and optimization of response sensitivity, compensation precision and fitness in the voltage recovery process are achieved.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, specifically to a dynamic voltage restorer optimization method and system based on fuzzy control. Background Technology

[0002] With the continuous expansion of power systems, especially against the backdrop of large-scale new energy integration, diversified load demands, and increasingly complex grid structures, voltage fluctuation problems have become increasingly prominent. Traditional voltage recovery methods mainly rely on devices such as automatic voltage regulators (AVRs) or static synchronous compensators (STATCOMs). While these methods can regulate voltage to a certain extent, their response speed and accuracy often fail to meet requirements under conditions of large load fluctuations or frequent grid disturbances, easily leading to problems such as voltage overshoot, excessive fluctuations, or regulation lag. Furthermore, traditional methods struggle to flexibly adjust to the accuracy requirements of different loads, resulting in low control precision and an inability to effectively adapt to significant changes in the grid environment. Summary of the Invention

[0003] This application provides a method and system for optimizing dynamic voltage restorers based on fuzzy control, which solves the technical problems of existing dynamic voltage restorers having slow response speed, low compensation accuracy, and difficulty in adapting to different load accuracy requirements under complex voltage fluctuation conditions.

[0004] The first aspect of this application provides a dynamic voltage restorer optimization method based on fuzzy control. The method includes: acquiring the current bus voltage signal; calculating the voltage error value and error change rate based on the bus voltage signal and the target voltage; setting a load level parameter according to the accuracy level of the load to adjust the response sensitivity of the controller; inputting the voltage error value, error change rate, and load level parameter as input variables, performing fuzzification processing, and inputting them into a fuzzy controller; outputting a fuzzy control quantity through a fuzzy inference rule base to obtain initial compensation voltage information; applying amplitude limiting, change rate limiting, and waveform quality constraints to the initial compensation voltage information to obtain a target compensation voltage signal; and applying the target compensation voltage signal to the load side through an inverter and an injection transformer for voltage recovery.

[0005] A second aspect of this application provides a dynamic voltage restorer optimization system based on fuzzy control. The system includes: a voltage error calculation module, which acquires the current bus voltage signal and calculates the voltage error value and error change rate based on the bus voltage signal and the target voltage; a load level setting module, which sets load level parameters according to the load's accuracy level to adjust the controller's response sensitivity; an initial compensation voltage information acquisition module, which takes the voltage error value, error change rate, and load level parameters as input variables, performs fuzzification processing, inputs them to a fuzzy controller, outputs fuzzy control quantities through a fuzzy inference rule base, and obtains initial compensation voltage information; and a voltage recovery module, which limits the amplitude, rate of change, and waveform quality of the initial compensation voltage information to obtain a target compensation voltage signal, and applies the target compensation voltage signal to the load side through an inverter and an injection transformer for voltage recovery.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The fuzzy control-based dynamic voltage restorer optimization method and system provided in this application relate to the field of power system control technology. It calculates the error and rate of change by collecting bus voltage signals, adjusts the sensitivity based on load accuracy settings, inputs the fuzzy parameters into a fuzzy controller, and obtains the initial compensation voltage through inference. After constraint optimization of the initial compensation voltage, it is applied to the load side via an inverter and transformer to achieve voltage restoration. This solves the technical problems of existing dynamic voltage restorers, such as slow response speed, low compensation accuracy, and difficulty in adapting to different load accuracy requirements under complex voltage fluctuation conditions. It achieves dynamic adjustment and precise limitation based on fuzzy control, optimizing the response sensitivity, compensation accuracy, and adaptability of the voltage restoration process. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic flowchart of the dynamic voltage restorer optimization method based on fuzzy control provided in the embodiments of this application;

[0010] Figure 2 A schematic diagram of the structure of the dynamic voltage restorer optimization system based on fuzzy control provided in the embodiments of this application.

[0011] Explanation of reference numerals in the attached diagram: Voltage error calculation module 11, load level setting module 12, initial compensation voltage acquisition module 13, voltage recovery module 14. Detailed Implementation

[0012] This application provides a method and system for optimizing dynamic voltage restorers based on fuzzy control, which solves the technical problems of existing dynamic voltage restorers having slow response speed, low compensation accuracy, and difficulty in adapting to different load accuracy requirements under complex voltage fluctuation conditions.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a dynamic voltage restorer optimization method based on fuzzy control, which includes:

[0016] P10: Acquire the current bus voltage signal, and calculate the voltage error value and error change rate based on the bus voltage signal and the target voltage.

[0017] Specifically, the first step is to acquire the current bus voltage signal in real time using voltage sensors or power monitoring devices. The bus voltage signal refers to the voltage value of major voltage nodes in the power system (such as power transformers and distribution lines). This value can be acquired using high-precision sensors and converted into a digital signal before being input into the control system. To ensure signal accuracy, a preset sampling frequency must be used to avoid signal distortion or delay caused by an excessively low sampling frequency. For example, the sampling frequency should be set to at least twice the voltage signal frequency (satisfying the Nyquist sampling theorem).

[0018] After acquiring the bus voltage signal, it needs to be compared with a pre-set target voltage. The target voltage is an ideal voltage value determined based on factors such as power system design, load demand, or grid regulations. The target voltage can be a constant value, but may be dynamically adjusted according to load changes in certain specific situations. At this point, the control system will calculate the voltage error between the bus voltage and the target voltage. The voltage error value can be obtained through a simple subtraction operation, as shown in the formula: Voltage Error Value = Bus Voltage - Target Voltage. This voltage error value reflects the degree of deviation between the current power system voltage and the expected target voltage. When the voltage error is large, it indicates that the system may have overvoltage or undervoltage phenomena, which may lead to unstable operation or even damage to the load equipment.

[0019] In addition, the voltage error rate of change needs to be calculated. The voltage error rate of change, which is the rate at which the voltage error changes over time, effectively describes the dynamic trend of voltage error change. It can be obtained by differential calculation of the voltage error value over time. By calculating the voltage error rate of change, the trend of voltage error change can be obtained, thereby determining the stability of the voltage system. If the voltage error rate of change is large, it indicates that the voltage fluctuation is relatively drastic, and the control system needs to respond quickly to avoid over-adjustment or lag, preventing overcompensation or recovery lag during voltage recovery. Conversely, if the rate of change is small, the system may have entered a steady state, and the controller can reduce the adjustment frequency or amplitude.

[0020] The key step in this process is comparing the bus voltage signal with the target voltage and calculating the error value and its rate of change in real time. In practical applications, digital signal processing techniques (such as filtering and denoising) can be used to improve the reliability and accuracy of the signal, avoiding the influence of noise or interference signals on the calculation results. Ultimately, the calculated voltage error value and rate of change will provide a basis for the input variables of the subsequent fuzzy controller, helping the control system to optimize and adjust according to the actual voltage deviation and trend, thereby achieving voltage recovery and system stability control.

[0021] P20: Set the load level parameter according to the accuracy level of the load to adjust the controller's response sensitivity.

[0022] Optionally, the load level parameter can be set according to the accuracy level of the load. This parameter plays a crucial role in adjusting the controller's response sensitivity.

[0023] The accuracy class of a load refers to the degree of voltage accuracy required by the load device. Different load devices have significantly different sensitivities to voltage fluctuations due to their different operating characteristics and application scenarios. For example, some high-precision industrial production equipment, medical equipment, and precision electronic instruments have extremely high requirements for voltage stability; even small voltage fluctuations can affect their normal operation or even lead to equipment failure. On the other hand, ordinary lighting equipment and some non-precision household appliances have relatively lower requirements for voltage accuracy and can tolerate voltage fluctuations within a certain range.

[0024] Based on these different accuracy levels of the load, this solution proposes the concept of setting a load level parameter. The load level parameter is a quantitative indicator used to characterize the load's voltage accuracy requirements. It can be divided into different levels according to the specific needs of the load device, such as high accuracy, medium accuracy, and low accuracy. For example, for high accuracy loads, the load level parameter can be set to a higher value, such as 3 or 4; for medium accuracy loads, it can be set to 2; and for low accuracy loads, it can be set to 1.

[0025] The load level parameter is input as one of the input variables to the fuzzy controller to adjust its response sensitivity. Response sensitivity refers to the controller's speed of reaction and adjustment accuracy to changes in the input signal. When the load level parameter is high, it indicates that the load has strict requirements for voltage accuracy. In this case, the controller needs to be more sensitive to changes in voltage error and error rate of change, making rapid adjustments to ensure that the compensation voltage can be output in a timely and accurate manner, maintaining the stability of the load voltage. Conversely, when the load level parameter is low, the controller's response sensitivity can be appropriately reduced to minimize unnecessary over-adjustment and improve system stability and economy.

[0026] By adjusting the controller's response sensitivity by setting load level parameters, personalized voltage recovery control for loads of different precision levels can be achieved, improving the adaptability and flexibility of the dynamic voltage restorer and enabling it to better meet the voltage quality requirements of load devices in various complex application scenarios.

[0027] P30: The voltage error value, error change rate, and load level parameters are used as input variables, and after fuzzification processing, they are input into the fuzzy controller. The fuzzy control quantity is output through the fuzzy inference rule base to obtain the initial compensation voltage information.

[0028] Furthermore, step P30 in this embodiment of the application also includes:

[0029] P31: Configure the voltage error value and error change rate as triangular membership function structures, and configure the load level parameter as a trapezoidal membership function structure; P32: Based on the numerical range and dynamic characteristics of each input variable, divide the voltage error value, error change rate, and load level parameter into multiple fuzzy language levels; P33: Using the membership functions of the voltage error value, error change rate, and load level parameter, perform fuzzification processing on the input variables based on the fuzzy language levels, and convert them into fuzzy language variables.

[0030] It should be understood that voltage error value, error change rate, and load level parameters are used as input variables. These variables are then fuzzified before being input into the fuzzy controller. The fuzzy controller performs inference based on the fuzzy inference rule base, outputs fuzzy control quantity, and thus obtains the initial compensation voltage information. This process is the core application of fuzzy control theory in the optimization of dynamic voltage restorers. Through fuzzification and fuzzy inference, it can effectively cope with the uncertainties in the voltage recovery process and achieve intelligent adjustment of the compensation voltage.

[0031] First, the voltage error value and error rate of change are configured as triangular membership functions. These membership functions effectively represent different degrees of membership between input variables, i.e., the degree of matching between the input variable value and a specific fuzzy linguistic level. Triangular membership functions offer good computational efficiency and smooth transitions, making them suitable for describing continuously changing quantities such as voltage error and error rate of change. For example, for the voltage error value, fuzzy linguistic levels can be defined as negative large (NL), negative small (NS), zero (Z), positive small (PS), and positive large (PL), with each level corresponding to a triangular membership function. By converting the voltage error value and error rate of change into triangular membership functions, the controller can be ensured to have high flexibility and responsiveness when processing these variables.

[0032] The load level parameters are configured using a trapezoidal membership function structure. Compared to triangular membership functions, trapezoidal membership functions have gentler boundaries, better handling input variables with a wider range of variation. For example, load level parameters can be divided into fuzzy logic levels such as low precision (L), medium precision (M), and high precision (H), with each level corresponding to a trapezoidal membership function. Since load level parameters are typically related to voltage control accuracy and can vary over a wide range, the application of trapezoidal membership functions allows for a smooth transition within a more lenient range, avoiding over-adjustment or distortion.

[0033] Next, to better adapt to dynamically changing input variables, the voltage error value, error rate of change, and load level parameters need to be divided into multiple fuzzy linguistic levels based on their numerical range and dynamic characteristics. Fuzzy linguistic levels are a way of describing the state of input variables in fuzzy control. By dividing continuous numerical variables into several discrete fuzzy linguistic levels, control logic can be simplified, and the adaptability and flexibility of the control system can be improved. For example, the voltage error value can be divided into levels such as positive large, positive small, zero error, negative small, and negative large; the error rate of change can be divided into levels such as rapid rise, slow rise, stable, slow fall, and rapid fall; and the load level parameter can be divided into levels such as high precision, medium precision, and low precision. These fuzzy linguistic levels help the controller better understand the characteristics of the input variables and make corresponding control decisions based on different levels.

[0034] Finally, the input variables are fuzzified using the membership functions of the voltage error value, error change rate, and load level parameter, along with their corresponding fuzzy linguistic levels. Fuzzification is a key step in fuzzy control. Membership functions map the precise values ​​of the input variables to fuzzy linguistic levels, enabling the control system to process input information using fuzzy logic. For example, suppose the acquired voltage error value is -3V, the error change rate is 0.5V / s, and the load level parameter is 2 (medium precision). According to the membership functions, these values ​​can be mapped to their corresponding fuzzy linguistic levels: the voltage error value -3V might be mapped to the "negative small" (NS) level; the error change rate 0.5V / s might be mapped to the "positive small" (PS) level; and the load level parameter 2 might be mapped to the medium precision (M) level. After fuzzification, these fuzzy linguistic variables are input into the fuzzy controller. The fuzzy controller performs inference based on the fuzzy inference rule base, outputs fuzzy control quantities, and thus obtains the initial compensation voltage information, achieving optimized control of the dynamic voltage restorer.

[0035] Through the above steps, the fuzzy controller can generate a compensation voltage signal based on the fuzzy variables of voltage error, error change rate and load level parameters, combined with fuzzy inference rules, providing precise and stable regulation for the voltage recovery process.

[0036] Furthermore, after configuring the voltage error value and the error change rate as triangular membership function structures, this embodiment of the application further includes step P31a, which further includes:

[0037] P31-1a: Introduce a disturbance amplitude monitoring module into the fuzzy controller to obtain the absolute value of the voltage error in real time and set a disturbance intensity threshold; P31-2a: When the disturbance amplitude determined by the absolute value of the voltage error is greater than the disturbance intensity threshold, compress the width of the base of the triangle membership function; P31-3a: When the disturbance amplitude is not greater than the disturbance intensity threshold, restore the default base width.

[0038] Optionally, the behavior of the fuzzy controller can be further refined, especially by adjusting the response sensitivity of the fuzzy control rules when voltage error changes drastically.

[0039] Specifically, a disturbance amplitude monitoring module is first introduced to monitor the absolute value of the voltage error in real time and determine the intensity of the disturbance amplitude based on this error value. The disturbance amplitude monitoring module functions similarly to sensitivity adjustment for environmental changes, dynamically adjusting the controller's behavior according to the system's real-time status.

[0040] By introducing a disturbance amplitude monitoring module, the system can acquire the absolute value of the voltage error in real time. This absolute value effectively reflects the amplitude of the current voltage fluctuation in the power system, serving as a basis for determining whether the system is in a "disturbance" state. If the voltage error amplitude is large, i.e., a large voltage fluctuation occurs, it indicates that the stability of the power system is affected. In this case, the controller needs to respond more quickly and sensitively to minimize the adverse effects of excessive errors on the load.

[0041] Next, when the disturbance amplitude determined by the absolute value of the voltage error exceeds the set disturbance intensity threshold, the system adjusts the width of the base of the triangular membership function. The base width controls the sensitivity and adaptation range of the membership function. The wider the base, the greater the tolerance of the fuzzy controller to errors, but the slower the response; while the narrower the base, the more sensitive the controller is to changes in voltage error and the faster it can respond. When voltage fluctuations are large, compressing the width of the base of the triangular membership function makes the controller more sensitive to changes in voltage error, thus enabling it to identify drastic changes more quickly and take timely compensation measures. This "compression" is similar to human perception of "cold" or "hot": in stable weather, the perception range of "cold" is wider, but in cold weather, even slight temperature changes are more acutely perceived.

[0042] Conversely, if the disturbance amplitude does not reach the set threshold, meaning the voltage change is relatively stable, the controller will not overreact immediately. In this case, the system will revert to the default width of the triangular membership function base. That is, when the voltage fluctuation is small, the system will return to its original tolerance range, allowing the controller to continue making appropriate adjustments without needing to respond overly sensitively to small changes.

[0043] Through this series of adjustments, the fuzzy controller can dynamically adjust its response sensitivity according to the drastic changes in voltage. When voltage fluctuations are severe, the system reacts more quickly and accurately to changes in error, while when voltage fluctuations are small or stable, the system avoids over-response and maintains system stability. This dynamic adjustment mechanism can effectively improve the efficiency of voltage recovery, reduce unnecessary adjustments and energy waste, thereby improving the overall performance of the system.

[0044] Furthermore, the load level parameters are configured as a trapezoidal membership function structure. This embodiment of the application further includes step P31b, which includes:

[0045] P31-1b: Interact with the current load status and identify the instantaneous control accuracy requirement based on the current load status; P31-2b: Configure the accuracy within the range of the load level parameter according to the instantaneous control accuracy requirement to obtain an accuracy adjustment amount, which is the difference between the instantaneous control accuracy requirement and the accuracy level requirement of the load level parameter; P31-3b: Adjust the structure of the trapezoidal membership function according to the accuracy adjustment amount, wherein the trapezoidal membership function structure has adjustable transition width and slope parameters, and adjusts the transition width and slope parameters according to the direction and value of the change of the accuracy adjustment amount.

[0046] Optionally, the trapezoidal membership function structure of the load level parameters can be further optimized to enhance the adaptability of the fuzzy controller to different load accuracy requirements. Based on the immediate accuracy requirements of the load, the accuracy of the load level parameters is adjusted in real time, and the structure of the trapezoidal membership function is influenced by the accuracy adjustment, thereby optimizing the response speed and accuracy of voltage compensation.

[0047] First, by interacting with the load device, the actual operating status of the current load is obtained, and the real-time accuracy requirements of the control are identified. Load status may change over time, and each load has different requirements for voltage accuracy. For example, some loads may have a high tolerance for voltage fluctuations at certain times, while others may have very strict requirements for voltage accuracy. By identifying the current load status, the control system can accurately determine the load's voltage accuracy requirements at that moment, and thus decide how to adjust the controller's response accuracy.

[0048] Next, based on the immediate control accuracy requirements, the load level parameters are configured with accuracy within a range to obtain the accuracy adjustment amount. The accuracy adjustment amount is the difference between the immediate control accuracy requirements and the accuracy level requirements of the load level parameters, reflecting the difference between the current load accuracy requirement and the system's actual accuracy response. For example, if the load accuracy requirement is high but the current control accuracy is low, the accuracy adjustment amount is positive, indicating that the system response accuracy needs to be improved. Conversely, if the load accuracy requirement is low, the accuracy adjustment amount is negative, and the system response accuracy can be appropriately relaxed.

[0049] Next, the structure of the trapezoidal membership function is adjusted based on the accuracy adjustment. The trapezoidal membership function consists of two parts: a flat top region and two sloping transition regions. The system dynamically adjusts the width and slope parameters of the transition regions of the trapezoidal membership function according to the direction and value of the accuracy adjustment. Specifically, if the accuracy adjustment indicates a high accuracy requirement from the load, the system will compress the width of the transition regions of the trapezoidal membership function, making the membership function steeper, thereby improving the controller's sensitivity and response speed to voltage errors. Conversely, if the accuracy requirement is lower, the width of the transition regions will increase, the slope of the membership function will become gentler, and the system's response to voltage changes will be more relaxed.

[0050] This dynamic adjustment mechanism functions similarly to adjusting driving style according to passenger needs. If there are children or elderly passengers, more caution is needed, and the vehicle will start more slowly; however, if there are no passengers or only ordinary cargo, normal driving is possible. In a voltage compensation system, the load level parameter determines the sensitive range of the response. Just as a driver adjusts their driving style according to passenger needs, the system adjusts its control strategy based on the load's precision requirements. If the load requires high precision, it must respond more precisely and quickly to voltage fluctuations; conversely, if the load has lower precision requirements, the response requirements can be relaxed to avoid over-adjustment.

[0051] This method is particularly suitable for power grid environments with frequent fluctuations or limited resources, where voltage compensation for high-precision loads needs to be more sensitive. In a stable, slowly changing environment, even with high load accuracy requirements, the response requirements can be appropriately relaxed to improve system robustness and energy efficiency. By dynamically adjusting the membership function structure, the system can maintain efficient and accurate voltage control under different load conditions, improving the overall system performance and adaptability.

[0052] Furthermore, steps P31-3b in the embodiments of this application also include:

[0053] P31-31b: When the direction of change of the precision adjustment amount is from high to low, the width of the transition zone is widened according to the change value, and the edge slope parameter is reduced; P31-32b: When the direction of change of the precision adjustment amount is from low to high, the width of the transition zone is narrowed according to the change value, and the edge slope parameter is increased.

[0054] Specifically, in further refinement, the transition zone width and edge slope parameters of the trapezoidal membership function can be dynamically adjusted according to the direction of change in the accuracy adjustment amount, thereby optimizing the response sensitivity of the voltage compensation system.

[0055] First, when the accuracy adjustment changes from high to low, meaning the load's accuracy requirement decreases from high to low, the transition zone width is widened and the edge slope parameter is reduced based on the change in value. This adjustment method makes the transition region of the trapezoidal membership function smoother, thereby reducing the controller's sensitivity to voltage changes. For example, if the load changes from high-precision equipment (such as precision instruments) to low-precision equipment (such as general lighting), the system will adjust the membership function accordingly to make its response to voltage fluctuations more moderate, avoiding unnecessary over-adjustment and improving system stability and economy.

[0056] Conversely, when the accuracy adjustment changes from low to high, i.e., the load's accuracy requirement increases from low to high, the transition zone width is reduced and the edge slope parameter is increased based on the change in value. This adjustment method makes the transition region of the trapezoidal membership function steeper, thereby improving the controller's sensitivity to voltage changes. For example, if the load changes from a low-precision device to a high-precision device, the system will adjust the membership function accordingly, enabling it to respond to voltage fluctuations more quickly and accurately, ensuring the normal operation of the high-precision load.

[0057] This dynamic adjustment mechanism enables the voltage compensation system to adaptively adjust according to the actual load requirements, improving the system's flexibility, robustness, and energy efficiency, and ensuring that it can always maintain efficient and accurate voltage regulation under different working environments.

[0058] Furthermore, in the embodiment of this application, step P32 further includes dividing the language into multiple fuzzy levels:

[0059] P32-1: When the voltage error changes in the same direction for N consecutive sampling periods, calculate the disturbance trend factor to characterize the trend strength of the error change. The number of N corresponds to the duration of the sampling period, and N is a positive integer greater than 2. P32-2: Based on the disturbance trend factor, perform dynamic division and adjustment of the fuzzy language level.

[0060] Optionally, to further improve the fuzzy controller's response to voltage error changes, the classification of fuzzy language levels can be further refined under different disturbance trends.

[0061] First, when the voltage error changes in the same direction over N consecutive sampling periods, the system calculates a disturbance trend factor to characterize the strength of the error change trend. Here, N represents the number of consecutive sampling periods, and N is greater than 2, meaning the system will determine the trend of voltage error change based on data from multiple consecutive sampling periods. If the voltage error changes in the same direction over multiple consecutive periods (e.g., the error is increasing or decreasing for several consecutive periods), it indicates a relatively obvious trend in voltage fluctuation. The disturbance trend factor is used to quantify this trend strength, and its calculation method can be based on parameters such as the cumulative value of the error change rate and the rate of change, obtained through weighted averaging or integration.

[0062] This disturbance trend factor is a key dynamic adjustment parameter that reflects the changing trend and intensity of voltage error, thereby helping the system determine whether it needs to increase its sensitivity to voltage changes. For example, when the voltage fluctuation trend is obvious, the system can strengthen its response to voltage error in order to take compensation measures quickly; while when the voltage change is relatively gentle, over-adjustment can be reduced, thereby improving the system's energy efficiency.

[0063] Next, the fuzzy language level is dynamically adjusted based on the disturbance trend factor. When the disturbance trend factor is large, meaning the voltage error change trend is drastic, the system will dynamically adjust the fuzzy language level classification according to this trend factor. For example, the originally broad fuzzy levels (such as small positive, medium negative) will become more detailed and precise, and the response range will be compressed to ensure that the system can more quickly identify voltage changes and make accurate compensations. If the disturbance trend factor is small, meaning the voltage fluctuation is relatively stable, the fuzzy language level classification can be appropriately relaxed, and the response range becomes wider to avoid overreaction.

[0064] By introducing a perturbation trend factor, the fuzzy controller can improve response sensitivity when voltage changes drastically, and relax response requirements when voltage fluctuations are small or stable, thereby achieving more efficient and accurate voltage compensation.

[0065] Furthermore, step P30 in this embodiment of the application also includes:

[0066] P30c: Before fuzzifying the voltage error value and the error change rate, an adaptive normalization model is used to perform normalization preprocessing on the input variables. The adaptive normalization model dynamically adjusts the normalization scale based on the mean and variance of historical disturbance amplitudes, and the current input variable is divided by the normalization scale to obtain the normalized variable.

[0067] In one possible embodiment of this application, an adaptive normalization model is introduced to perform normalization preprocessing on input variables such as voltage error value and error change rate, so as to ensure that the fuzzy controller can maintain stable performance and efficient response capability under different working environments.

[0068] Specifically, before fuzzifying the voltage error value and error change rate, the input variables need to be preprocessed by normalization. The purpose of normalization is to standardize input data of different magnitudes and units, placing them within a uniform range so that the fuzzy controller can process them effectively. Using an adaptive normalization model, the normalization scale can be automatically adjusted based on the dynamic characteristics and historical disturbances of the input data, thereby ensuring that the system's processing of voltage error and error change rate remains efficient and accurate.

[0069] The key to adaptive normalization models lies in dynamically adjusting the normalization scale based on the mean and variance of historical disturbance amplitudes. In practical applications, voltage errors and their rate of change in power systems are affected by various factors, exhibiting a certain degree of volatility. To adapt to these fluctuations, the model automatically adjusts the normalization scale based on historical data (including the mean and variance of voltage errors). Specifically, the mean and variance of historical disturbance amplitudes reflect the magnitude and trend of voltage error fluctuations. When the system experiences large fluctuations, the normalization scale is appropriately increased; conversely, when fluctuations are small or the system tends to stabilize, the normalization scale is correspondingly decreased.

[0070] The normalization process involves dividing the current input variable by an adaptively adjusted normalization scale to obtain the normalized variable. This operation eliminates dimensional differences between different input variables, allowing input variables such as voltage error and error change rate to be processed on the same scale. Through this normalization process, the fuzzy controller can more accurately fuzzify the input data, avoiding inconsistent controller responses or calculation errors caused by differences in the magnitude of the input data. This not only improves system stability but also enhances the system's robustness and response speed in the face of changing power environments.

[0071] Furthermore, the embodiments of this application also include step P30d, which further includes:

[0072] P31d: Obtain the historical stability factor based on the historical fluctuation amplitude and rate of change of the input variable within a preset time window; P32d: Obtain the trend rate of change factor based on the speed and direction of change of the current variable; P33d: Calculate the confidence coefficient by weighting the historical stability factor and the trend rate of change factor according to the configured weights; P34d: When fuzzifying the voltage error value and the error change rate, introduce the confidence coefficient into the membership function to correct the membership value corresponding to the fuzzy linguistic variable.

[0073] Specifically, the voltage error processing can be further refined by introducing historical stability factors, trend change rate factors, and confidence coefficients to enhance the judgment ability of the fuzzy controller and avoid making incorrect compensation decisions due to noise or rapid fluctuations.

[0074] First, the historical stability factor is calculated based on the historical fluctuation amplitude and rate of change of the input variables (voltage error value and error change rate) within a preset time window. The preset time window is a fixed period used to collect and analyze historical data of the input variables. Historical fluctuation amplitude reflects the difference between the maximum and minimum values ​​of the input variable over a past period, while the rate of change reflects how quickly the input variable changes over time. By analyzing this data, the stability of the input variable over the historical period can be assessed. For example, if the input variable fluctuates little and has a low rate of change over a past period, the historical stability factor is high, indicating that the input variable is relatively stable; conversely, if the fluctuation is large and the rate of change is high, the historical stability factor is low, indicating that the input variable is relatively unstable. For example, the formula for calculating the historical stability factor is: Among them, S t μ is the historical stability factor at the current moment. t ΔV is the average value over the past N periods. i Let N be the voltage change in the i-th cycle, N be the number of past cycles, and t be the current moment or time point.

[0075] Next, based on the rate and direction of change of the current input variables (such as voltage error value and error change rate), a trend rate of change factor is calculated. This factor quantifies the trend strength of voltage error and error change. Specifically, the trend rate of change factor reflects the speed of change of voltage error and error change rate, as well as their direction (e.g., increase or decrease). If the current voltage error changes very rapidly or changes direction significantly, the trend rate of change factor will be high, indicating that the system is experiencing rapid disturbances; if the voltage change is slow and tends to be stable, the trend rate of change factor will be low, indicating that the system is not changing much. This factor helps the controller determine whether it is currently in a state of severe fluctuation, thereby deciding whether to increase the response speed or enhance compensation. For example, the formula for calculating the trend rate of change factor is: Among them, T t The rate of change factor is ΔV, where ∈ is a small constant used to avoid the denominator being zero. t Let ΔV be the voltage change at the current moment. t-1 This represents the voltage change at the previous moment.

[0076] Then, the historical stability factor and the trend change rate factor are weighted according to the configured weights to obtain the confidence coefficient. The configured weights are pre-set weights based on the system's emphasis on historical stability and trend change rate. Through weighted calculation, the historical stability and current trend of the input variable can be comprehensively considered, thus obtaining a confidence coefficient that reflects the reliability of the input variable. For example, if the historical stability factor is high and the trend change rate factor is low, the confidence coefficient is high, indicating that the input variable is relatively reliable; conversely, if the historical stability factor is low and the trend change rate factor is high, the confidence coefficient is low, indicating that the input variable may be affected by noise or rapid fluctuations. In this case, the level of reliance should be reduced to avoid the controller making inappropriate compensations due to erroneous information. For example, the formula for calculating the confidence coefficient is: C t =w1·S t +w2·(1-T t ); where C t The confidence coefficient is w1 + w2 = 1, and the empirical weights are usually taken as w1 = 0.6 and w2 = 0.4.

[0077] Finally, when fuzzifying the voltage error value and the rate of change of error, a confidence coefficient is introduced into the membership function to correct the membership values ​​corresponding to the fuzzy linguistic variables. In this way, the fuzzy controller can dynamically adjust its response strategy to the input variables based on their reliability and stability. When the input variables are relatively reliable, the controller can respond more actively; while when the input variables may be affected by noise or rapid fluctuations, the controller will be more cautious to avoid overcompensation or malfunctions caused by erroneous information. This dynamic adjustment mechanism not only improves the system's adaptability and flexibility but also achieves optimal voltage compensation under different voltage variation conditions, ensuring the stable operation of the load equipment and improving the overall performance and economy of the system.

[0078] P40: The initial compensation voltage information is limited in amplitude, rate of change and waveform quality to obtain the target compensation voltage signal. The target compensation voltage signal is applied to the load side through the inverter and injection transformer for voltage recovery.

[0079] Furthermore, step P40 in this embodiment of the application also includes:

[0080] P41: Configure compensation amplitude limit conditions based on the inverter's rated output voltage and the load's tolerance to voltage overshoot; P42: Configure rate of change limit conditions based on the load's fluctuation response sensitivity and the inverter's maximum supported adjustment step size; P43: Set the high-frequency limiting component and suppress the frequency component of the compensation voltage to determine the waveform quality requirements; P44: Use the compensation amplitude limit conditions, rate of change limit conditions, and waveform quality requirements as constraints to perform voltage signal constraint processing on the initial compensation voltage information to obtain the target compensation voltage signal.

[0081] It should be understood that further processing of the initial compensation voltage information, by introducing constraints such as compensation amplitude limits, rate of change limits, and waveform quality constraints, ensures that the voltage signal during the voltage recovery process meets system requirements while avoiding overshoot, excessively fast fluctuations, and undesirable waveforms, thereby optimizing the quality and control accuracy of the compensation voltage.

[0082] Specifically, when processing the initial compensation voltage information, a compensation amplitude limit condition is first configured based on the inverter's rated output voltage and the load's tolerance to voltage overshoot. This condition is set to ensure that the compensation voltage does not exceed the inverter's safe output range, while also preventing damage to the load due to excessively high compensation voltage. For example, if the inverter's rated output voltage is 230V, and the maximum voltage overshoot the load can withstand is 240V, then the compensation amplitude limit condition can be set to not exceed 240V to ensure the safe operation of the system and the load.

[0083] Next, based on the load's fluctuation response sensitivity and the inverter's maximum adjustable step size, a rate of change limit is configured. The load's fluctuation response sensitivity reflects its responsiveness to voltage changes, while the inverter's maximum adjustable step size limits the maximum magnitude of change in the compensation voltage per unit time. For example, if the load is highly sensitive to voltage changes and the inverter's maximum adjustable step size is 10V per second, the rate of change limit can be set to ensure that the compensation voltage's rate of change does not exceed 10V per second, preventing excessively rapid voltage changes from adversely affecting the load.

[0084] In addition, it is necessary to set the high-frequency limiting components and suppressed frequency components of the compensation voltage to determine the waveform quality requirements. The purpose of this step is to ensure that the waveform quality of the compensation voltage meets power system standards and to avoid interference to the system caused by the presence of high-frequency components or specific frequency components. For example, the high-frequency components of the compensation voltage can be limited to below 5kHz, and specific interference frequency components, such as 60Hz harmonics, can be suppressed to improve the purity and stability of the compensation voltage.

[0085] Finally, the compensation amplitude limit, rate of change limit, and waveform quality requirements are used as constraints to perform voltage signal constraint processing on the initial compensation voltage information. Through this processing, the initial compensation voltage information is adjusted and optimized to meet all the above constraints, thereby obtaining the final target compensation voltage signal. This target compensation voltage signal is applied to the load side through the inverter and injection transformer to achieve voltage recovery, ensuring that the load equipment can operate normally under stable and reliable voltage conditions.

[0086] The introduction of these constraints effectively improves the accuracy and stability of the system, ensuring that the compensation voltage during the voltage recovery process is neither overcompensated nor too slow, maintaining an appropriate response speed and waveform quality, thereby providing stable and reliable power support for the load equipment.

[0087] In summary, the embodiments of this application have at least the following technical effects:

[0088] This application achieves rapid response and high-precision compensation for complex and variable voltage fluctuations by utilizing a fuzzy inference rule base to output fuzzy control quantities. It adjusts the controller's response sensitivity by setting load level parameters based on the load accuracy level, meeting the personalized control needs of loads with different accuracy requirements. An disturbance amplitude monitoring module is introduced to dynamically adjust the membership function structure, enhancing the controller's sensitivity to sudden voltage changes and different load accuracy requirements. The fuzzy language level is dynamically divided and adjusted according to the disturbance trend factor, enabling the controller to flexibly adjust its response strategy based on the voltage error change trend. An adaptive normalization model is used to preprocess the input variables, improving the controller's adaptability to disturbances of different amplitudes. Calculating confidence coefficients and introducing membership functions to correct membership values ​​enhances the controller's ability to judge the reliability of input variables. Amplitude limiting, rate-of-change limitation, and waveform quality constraint processing are applied to the initial compensation voltage information to ensure that the target compensation voltage signal meets the requirements of practical applications and guarantees the safe and stable operation of the system.

[0089] It achieves the technical effect of dynamic adjustment and precise limitation based on fuzzy control, optimizing the response sensitivity, compensation accuracy and adaptability of the voltage recovery process.

[0090] Example 2 is based on the same inventive concept as the fuzzy control-based dynamic voltage restorer optimization method in the previous examples, such as... Figure 2 As shown, this application provides a dynamic voltage restorer optimization system based on fuzzy control. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0091] Voltage error calculation module 11 is used to collect the current bus voltage signal and calculate the voltage error value and error change rate based on the bus voltage signal and the target voltage.

[0092] The load level setting module 12 is used to set load level parameters according to the accuracy level of the load, so as to adjust the response sensitivity of the controller.

[0093] The initial compensation voltage acquisition module 13 is used to take the voltage error value, error change rate, and load level parameters as input variables, input them to the fuzzy controller after fuzzification processing, and output fuzzy control quantity through the fuzzy inference rule base to obtain the initial compensation voltage information.

[0094] The voltage recovery module 14 is used to limit the amplitude, limit the rate of change, and constrain the waveform quality of the initial compensation voltage information to obtain the target compensation voltage signal. The target compensation voltage signal is then applied to the load side through the inverter and the injection transformer for voltage recovery.

[0095] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0096] The voltage error value and error change rate are configured as triangular membership function structures, and the load level parameter is configured as a trapezoidal membership function structure. Based on the numerical range and dynamic characteristics of each input variable, multiple fuzzy language levels are defined for the voltage error value, error change rate, and load level parameter. Using the membership functions of the voltage error value, error change rate, and load level parameter, the input variables are fuzzified based on the fuzzy language levels and converted into fuzzy language variables.

[0097] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0098] A disturbance amplitude monitoring module is introduced into the fuzzy controller to obtain the absolute value of voltage error in real time and set a disturbance intensity threshold. When the disturbance amplitude determined by the absolute value of voltage error is greater than the disturbance intensity threshold, the width of the base of the triangle membership function is compressed. When the disturbance amplitude is not greater than the disturbance intensity threshold, the default base width is restored.

[0099] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0100] The system interacts with the current load status and identifies the instantaneous control accuracy requirement based on the current load status. Based on the instantaneous control accuracy requirement, it configures the accuracy within a range for the load level parameters to obtain an accuracy adjustment amount, which is the difference between the instantaneous control accuracy requirement and the accuracy level requirement of the load level parameters. The system adjusts the structure of the trapezoidal membership function based on the accuracy adjustment amount, wherein the trapezoidal membership function structure has adjustable transition width and slope parameters, and the transition width and slope parameters are adjusted accordingly based on the direction and value of the change in the accuracy adjustment amount.

[0101] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0102] When the direction of change of the precision adjustment amount is from high to low, the width of the transition zone is widened according to the change value, and the edge slope parameter is reduced; when the direction of change of the precision adjustment amount is from low to high, the width of the transition zone is narrowed according to the change value, and the edge slope parameter is increased.

[0103] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0104] When the voltage error changes in the same direction for N consecutive sampling periods, a disturbance trend factor is calculated to characterize the trend strength of the error change. The number of N corresponds to the duration of the sampling period, and N is a positive integer greater than 2. Based on the disturbance trend factor, the dynamic division and adjustment of the fuzzy language level are performed.

[0105] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0106] Before fuzzifying the voltage error value and the rate of change of error, an adaptive normalization model is used to perform normalization preprocessing on the input variables. The adaptive normalization model dynamically adjusts the normalization scale based on the mean and variance of historical disturbance amplitudes, and the current input variable is divided by the normalization scale to obtain the normalized variable.

[0107] Furthermore, the initial compensation voltage acquisition module 13 is also used to perform the following steps:

[0108] Based on the historical fluctuation amplitude and rate of change of the input variable within a preset time window, a historical stability factor is obtained; based on the speed and direction of change of the current variable, a trend rate of change factor is obtained; the historical stability factor and the trend rate of change factor are weighted according to the configured weights to obtain a confidence coefficient; when fuzzifying the voltage error value and the error change rate, the confidence coefficient is introduced into the membership function to correct the membership value corresponding to the fuzzy linguistic variable.

[0109] Furthermore, the voltage recovery module 14 is also used to perform the following steps:

[0110] Based on the inverter's rated output voltage and the load's tolerance to voltage overshoot, configure compensation amplitude limit conditions; based on the load's fluctuation response sensitivity and the inverter's maximum supported adjustment step size, configure rate of change limit conditions; set high-frequency limiting components and suppressed frequency components for the compensation voltage to determine waveform quality requirements; use the compensation amplitude limit conditions, rate of change limit conditions, and waveform quality requirements as constraints to perform voltage signal constraint processing on the initial compensation voltage information to obtain the target compensation voltage signal.

[0111] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0112] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0113] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A dynamic voltage restorer optimization method based on fuzzy control, characterized in that, include: Collect the current bus voltage signal, and calculate the voltage error value and error change rate based on the bus voltage signal and the target voltage; Based on the accuracy level of the load, set the load level parameter to adjust the controller's response sensitivity; The voltage error value, error change rate, and load level parameters are used as input variables, and after fuzzification processing, they are input into the fuzzy controller. The fuzzy control quantity is output through the fuzzy inference rule base to obtain the initial compensation voltage information. The initial compensation voltage information is subjected to amplitude limiting, rate of change limitation, and waveform quality constraint to obtain the target compensation voltage signal. The target compensation voltage signal is then applied to the load side through an inverter and an injection transformer for voltage recovery.

2. The dynamic voltage restorer optimization method based on fuzzy control according to claim 1, characterized in that, The voltage error value, error change rate, and load level parameters are used as input variables, and after fuzzification processing, the following are included: The voltage error value and the error change rate are configured as triangular membership function structures, and the load level parameters are configured as trapezoidal membership function structures. Based on the numerical range and dynamic characteristics of each input variable, multiple fuzzy language levels are defined for the voltage error value, error change rate, and load level parameter, respectively. Using the membership functions of the voltage error value, error change rate, and load level parameters, the input variables are fuzzified based on the fuzzy language level and converted into fuzzy language variables.

3. The dynamic voltage restorer optimization method based on fuzzy control according to claim 2, characterized in that, The voltage error value and the rate of change of error are configured as triangular membership function structures, and then the following is also included: A disturbance amplitude monitoring module is introduced into the fuzzy controller to obtain the absolute value of voltage error in real time and set the disturbance intensity threshold. When the disturbance amplitude determined by the absolute value of the voltage error is greater than the disturbance intensity threshold, the width of the base of the triangle membership function is compressed; When the disturbance amplitude is not greater than the disturbance intensity threshold, the default bottom width is restored.

4. The dynamic voltage restorer optimization method based on fuzzy control according to claim 2, characterized in that, The load level parameters are configured as a trapezoidal membership function structure, and then the following is also included: Interact with the current load status and identify the real-time accuracy requirements for control based on the current load status; Based on the real-time control accuracy requirement, the load level parameter is configured with accuracy within a range to obtain an accuracy adjustment amount, which is the difference between the real-time control accuracy requirement and the accuracy level requirement of the load level parameter. The structure of the trapezoidal membership function is adjusted according to the precision adjustment amount, wherein the trapezoidal membership function structure has an adjustable transition zone width and slope parameter, and the transition zone width and slope parameter are adjusted accordingly based on the direction and value of the change of the precision adjustment amount.

5. The dynamic voltage restorer optimization method based on fuzzy control according to claim 4, characterized in that, Adjusting the transition zone width and slope parameters according to the direction and value of the change in the precision adjustment amount includes: When the direction of change of the precision adjustment amount is from high to low, the width of the transition zone is widened according to the change value, and the edge slope parameter is reduced. When the direction of change of the precision adjustment amount is from low to high, the width of the transition zone is reduced according to the change value, and the edge slope parameter is increased.

6. The dynamic voltage restorer optimization method based on fuzzy control according to claim 2, characterized in that, The classification of multiple fuzzy language levels also includes: When the voltage error changes in the same direction for N consecutive sampling periods, a disturbance trend factor is calculated to characterize the trend strength of the error change. The number of N corresponds to the duration of the sampling period, and N is a positive integer greater than 2. Based on the aforementioned disturbance trend factor, the fuzzy language level is dynamically divided and adjusted.

7. The optimization method for dynamic voltage restorer based on fuzzy control according to claim 2, characterized in that, Also includes: Before fuzzifying the voltage error value and the rate of change of error, an adaptive normalization model is used to perform normalization preprocessing on the input variables. The adaptive normalization model dynamically adjusts the normalization scale based on the mean and variance of historical disturbance amplitudes, and the current input variable is divided by the normalization scale to obtain the normalized variable.

8. The dynamic voltage restorer optimization method based on fuzzy control according to claim 2, characterized in that, Also includes: The historical stability factor is obtained based on the historical fluctuation amplitude and rate of change of the input variable within a preset time window; The trend change rate factor is obtained based on the speed and direction of change of the current variable. The confidence coefficient is obtained by weighting the historical stability factor and the trend change rate factor according to the configuration weights. When fuzzifying the voltage error value and the error change rate, the confidence coefficient is introduced into the membership function to correct the membership value corresponding to the fuzzy linguistic variable.

9. The dynamic voltage restorer optimization method based on fuzzy control according to claim 1, characterized in that, The initial compensation voltage information is subjected to amplitude limiting, rate of change limiting, and waveform quality constraints to obtain the target compensation voltage signal, including: Configure compensation amplitude limit conditions based on the inverter's rated output voltage and the load's tolerance to voltage overshoot. Configure the rate of change limit based on the load fluctuation response sensitivity value and the inverter's maximum supported adjustment step size; Set the high-frequency limiting component and suppress the frequency component of the compensation voltage to determine the waveform quality requirements; Using the compensation amplitude limit, rate of change limit, and waveform quality requirements as constraints, voltage signal constraint processing is performed on the initial compensation voltage information to obtain the target compensation voltage signal.

10. A dynamic voltage restorer optimization system based on fuzzy control, characterized in that, The system includes: A voltage error calculation module is used to acquire the current bus voltage signal and calculate the voltage error value and error change rate based on the bus voltage signal and the target voltage. A load level setting module is used to set load level parameters according to the accuracy level of the load, which is used to adjust the response sensitivity of the controller. An initial compensation voltage acquisition module is used to take the voltage error value, error change rate, and load level parameters as input variables, input them to a fuzzy controller after fuzzification processing, and output fuzzy control quantities through a fuzzy inference rule base to obtain initial compensation voltage information. The voltage recovery module is used to limit the amplitude, rate of change, and waveform quality of the initial compensation voltage information to obtain a target compensation voltage signal. The target compensation voltage signal is then applied to the load side through the inverter and injection transformer for voltage recovery.

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