Power quality voltage regulation control method and control device for smart power station

Abstract

The present application relates to the technical field of power quality control, and specifically to a power quality voltage regulation control method and control device for smart power stations. The method includes: collecting corresponding data at each moment; determining the device hardware status based on the number of actions and reactive compensation amount, and determining a correction coefficient based on the quality of the device hardware status; predicting the load value and voltage value at future moments based on historical load values ​​and historical voltage values, and correcting the load value at the current moment based on the difference between the past load value and the predicted load value and the correction coefficient; constructing an objective function based on voltage deviation, reactive compensation amount, number of actions and future load trends, solving the objective function using dynamic inertia weights, and achieving precise voltage regulation. The present application improves voltage stability and avoids device conflicts.

Description

Technical Field

[0001] The present application relates to the technical field of power quality control, and in particular to a power quality voltage regulation control method and a control device for a smart power station. Background Art

[0002] With the large-scale integration of distributed energy resources and the rapid development of smart grids, power quality issues have become a key factor restricting the stable operation of power grids. Reactive power and voltage control are crucial components of power system operation. Their purpose is to maintain voltage stability in the power system by regulating the distribution and magnitude of reactive power, ensuring the normal operation of power equipment and ensuring power quality.

[0003] Existing technologies often use a single voltage regulation control device for control. However, the patent "CN118316135A: Real-time Voltage Control Method for Active Distribution Networks Based on Reactive Power Regulation of Photovoltaic Inverters" leverages the reactive power regulation capabilities of photovoltaic inverters and combines them with a multi-agent deep reinforcement learning algorithm (MATD3PG) to achieve real-time voltage control for active distribution networks. While this approach requires no additional equipment, it may not be able to cope with certain complex situations. For example, when photovoltaic output is insufficient or inverter capacity is limited, it may not provide sufficient reactive power compensation. Furthermore, controlling different devices for voltage regulation can lead to conflicting adjustments, resulting in insufficient voltage regulation, or simultaneous adjustments, resulting in excessive compensation. Summary of the Invention

[0004] In order to solve the technical problems of conflict and compensation transition caused by adjustments of different devices, this application provides a power quality voltage regulation control method and control device for a smart power station. The technical solutions adopted are as follows:

[0005] In a first aspect, the present application proposes a power quality voltage regulation control method for a smart power station, the method comprising the following steps:

[0006] Collect reactive power, active power, voltage, OLTC operation times, and SVG capacity utilization at each moment;

[0007] Calculate the reactive compensation capacity and, in combination with the capacity utilization rate, obtain the reactive compensation amount at the current moment; obtain the device hardware status at the current moment based on the difference between the reactive compensation amount and the reactive compensation capacity, and the difference between the current number of actions and the maximum number of actions; and combine the device hardware status with a preset initial correction coefficient to obtain the current correction coefficient.

[0008] Obtain the predicted load value and predicted voltage value at a preset future moment by using the historical load value and historical voltage value before the current moment respectively; correct the load value at the current moment based on the difference between the load value at the past moment and the predicted load value and the correction coefficient at the current moment to obtain the corrected load value at the current moment;

[0009] The objective function is constructed based on the current voltage deviation, reactive compensation amount, number of actions, and future load trends and voltage deviations. The preset inertia weight is adjusted based on the load change rate to obtain the dynamic inertia weight. The objective function is solved using the dynamic inertia weight to obtain the target compensation amount, and precise voltage regulation is achieved using the target compensation amount.

[0010] In the above-mentioned scheme, this application preemptively triggers reactive power compensation and tap adjustment based on load trend prediction, eliminating voltage overshoot associated with traditional hysteresis control. Error correction is also performed based on real-time load and voltage regulator status. When load growth is predicted, multi-objective optimization preemptively triggers the SVG to precharge reactive power and the OLTC to pre-adjust taps, effectively reducing voltage fluctuations during load growth and improving power quality. Furthermore, the combined use of feedforward and feedback coordinated regulation avoids frequent OLTC operation and extends equipment life. An adaptive strategy adapts to load fluctuations in real time, significantly improving voltage stability and enabling adjustments to be made sequentially across different devices to avoid conflicts.

[0011] In one embodiment, the load value is the sum of reactive power and active power at each moment.

[0012] In one embodiment, the method for obtaining the predicted load value is:

[0013] The load values ​​for the 24 hours before the current moment are extracted with a step length of 5 minutes, and the load values ​​for the next 5 minutes, 10 minutes, and 15 minutes from the current moment are obtained through time series analysis.

[0014] The voltage values ​​for the 24 hours before the current moment are extracted with a step size of 5 minutes, and the voltage values ​​for the next 5 minutes, 10 minutes, and 15 minutes from the current moment are obtained through time series analysis.

[0015] In one embodiment, the corrected load value is positively correlated with the difference between the load value at the past moment and the predicted load value, the correction coefficient at the current moment, and the load value at the current moment.

[0016] In one embodiment, the reactive compensation amount at the current moment is the product of the reactive compensation capacity and the capacity utilization rate of the SVG at the current moment.

[0017] In one embodiment, the method for obtaining the device hardware status at the current moment based on the difference between the reactive compensation amount at the current moment and the reactive compensation capacity, and the difference between the number of actions at the current moment and the maximum number of actions is:

[0018] , Indicates the reactive compensation amount at the current moment, Indicates reactive compensation capacity, Indicates the number of actions at the current moment, Indicates the maximum number of actions per day, 、 Respectively represent the preset weight coefficients, Indicates the device hardware status.

[0019] In one embodiment, the objective function is expressed as:

[0020] , represents the voltage deviation at time t, represents the reactive compensation amount of SVG at time t, represents the number of actions at time t, represents the corrected load value at the i-th moment after time t, It represents the voltage deviation at the i-th moment in the future at time t, 、 、 Indicates the preset weight values ​​of the three.

[0021] In one embodiment, the voltage deviation is the absolute value of the difference between the current voltage and the rated voltage.

[0022] In one embodiment, the method for adjusting the preset inertia weight based on the load change rate to obtain the dynamic inertia weight is:

[0023] , Indicates the preset initial inertia weight, Indicates the preset inertia weight adjustment range, Indicates the load change rate at the current moment, Indicates the preset change threshold, is a symbolic function, Represents the dynamic inertia weight.

[0024] In a second aspect, an embodiment of the present application also provides a power quality voltage regulation control device for a smart power station, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned power quality voltage regulation control methods for a smart power station.

[0025] The beneficial effects of this application are:

[0026] This application proactively triggers reactive power compensation and tap adjustment based on load trend prediction, eliminating voltage overshoot associated with traditional hysteresis control. Error correction is performed based on real-time load and voltage regulator status. When load growth is predicted, multi-objective optimization pre-triggers the SVG to precharge reactive power and the OLTC to pre-adjust taps, effectively reducing voltage fluctuations during load growth and improving power quality. Furthermore, the combined use of feedforward and feedback coordinated regulation avoids frequent OLTC activation and extends equipment life. An adaptive strategy adapts to load fluctuations in real time, significantly improving voltage stability and enabling sequential adjustments across various devices to avoid conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 This is a flow chart of a power quality voltage regulation control method for a smart power station provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of the power quality voltage regulation control method and control device for smart power stations proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0031] Embodiments of a power quality voltage regulation control method and control device for a smart power station:

[0032] The specific scheme of the power quality voltage regulation control method and control device for a smart power station provided by this application is described in detail below with reference to the accompanying drawings.

[0033] See also Figure 1 , which shows a flow chart of a method for controlling power quality voltage regulation in a smart power station provided by an embodiment of the present application. The method includes the following steps:

[0034] The voltage regulation device used in this application for smart power stations consists of an on-load tap-changing transformer (OLTC) and a static VAR generator (SVG). The OLTC achieves coarse voltage regulation by adjusting the transformer tap position, offering a wide adjustment range and suitable for handling long-term, stable voltage deviations. The SVG has fast dynamic response capabilities, enabling precise reactive power regulation within a short period of time, making it suitable for handling rapidly changing voltage fluctuations.

[0035] An OLTC is a device that changes the tap position while the transformer is under load. It operates by varying the turns ratio of the transformer windings to adjust the output voltage. When the grid voltage changes, the OLTC switches the tap position based on a control signal, thereby changing the transformer's ratio to maintain the output voltage within a specified range.

[0036] SVG converts DC power on the DC side into AC power by controlling the on / off switching of power electronic devices (such as IGBTs) and injecting it into the grid. Its basic principle is to rapidly adjust the output current magnitude and phase based on the grid's reactive power requirements, thereby dynamically compensating for reactive power. Specifically, SVG detects the grid's voltage and current signals, calculates the magnitude and direction of reactive power, and then controls the IGBT modules to generate the corresponding compensation current, which is injected into the grid to balance reactive power and regulate the grid voltage.

[0037] This application utilizes an OLTC and SVG in parallel, integrating them into an integrated design. The operational priorities of the SVG and OLTC are dynamically adjusted based on the grid's real-time operating conditions and voltage fluctuations. For example, during rapid voltage fluctuations, the SVG is prioritized for rapid reactive power compensation to mitigate voltage fluctuations. When voltage deviations are large but relatively stable, the OLTC is activated for tap adjustment to achieve long-term voltage stability. This dynamic priority allocation strategy leverages the respective strengths of the SVG and OLTC, improving the efficiency and accuracy of voltage regulation.

[0038] Step S001: Collect corresponding data at each moment.

[0039] First, data is collected based on the data collection device, and the collection frequency is set. In this embodiment, the collection frequency is 1s, that is, data is collected every 1s.

[0040] The data required includes active power, reactive power, and voltage, collected by power meters and voltmeters. An OLTC operation counter and SVG capacity monitoring device are installed on the OLTC and SVG, respectively, to collect the number of OLTC operations and the capacity utilization of the SVG. The OLTC operation counter only counts the number of operations per day and restarts at 0 each day.

[0041] At this point, the active power, reactive power, voltage, OLTC operation times, and SVG capacity utilization rate of each acquisition are obtained.

[0042] Step S002: determining the device hardware status according to the number of actions and the reactive compensation amount, and determining a correction coefficient according to the quality of the device hardware status.

[0043] The edge control device receives the collected data and analyzes the collected data to complete the power quality voltage regulation control.

[0044] When performing voltage regulation control, the current state of the voltage regulator must also be considered. When the SVG approaches its maximum reactive compensation capacity or the number of OLTC operations approaches its upper limit, the hardware is under high pressure or at high risk. Relying on historical load trends at this point can lead to results that are out of sync with actual demand. This can lead to the risk of SVG overload. Historical data may not reflect the current SVG capacity limitations, and predicted high load demands may require the SVG to output reactive power beyond its capabilities, causing equipment damage. Alternatively, if the OLTC operates frequently, trend forecasts may overlook the OLTC's remaining life margin, continuously triggering tap adjustments and accelerating contact wear. The reactive compensation capacity is calculated based on the "Reactive Compensation Capacity Calculation Coefficient Table." The reactive compensation capacity is the maximum reactive capacity. The current reactive compensation amount is calculated by multiplying the SVG's capacity utilization by its reactive compensation capacity.

[0045] Based on the difference between the reactive compensation amount and the number of operations at the current moment and the upper limit of the two, the state of the voltage regulating device is corrected to obtain the device hardware state.

[0046] The hardware status of the device is negatively correlated with the reactive compensation amount and its upper limit difference, and the number of actions and its upper limit difference.

[0047] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by actual application and this application does not impose any special restrictions.

[0048] Preferably, in this embodiment, the expression of the device hardware status is:

[0049] , Indicates the reactive compensation amount at the current moment, Indicates reactive compensation capacity, Indicates the number of actions at the current moment, Indicates the maximum number of actions per day, 、 Represent the weight coefficients, Indicates the device hardware status. 、 The values ​​of are 0.6 and 0.4 respectively.

[0050] Since SVG is responsible for fast response and the damage to the device when the reactive compensation amount of SVG exceeds the maximum is greater than that when the number of OLTC operations exceeds the maximum, its corresponding weight coefficient is larger.

[0051] The value range is The larger the HHI, the better the hardware status. When SVG capacity utilization is low and the number of OLTC operations is small, the HHI approaches 1; otherwise, it approaches 0.

[0052] The formula for dynamically adjusting the correction coefficient is: ,in Represents the correction coefficient at the current moment, norm() represents the normalization function, represents the initial correction coefficient. In this embodiment, the initial correction coefficient is 0.5.

[0053] The HHI is used to perceive the hardware health status in real time, and the strategy is dynamically adjusted when the device approaches its limit to avoid overload risks. The smaller the HHI, the worse the hardware status, and the less reliance on historical trends (α decreases) should be used, and more reliance on real-time load data should be used to avoid device overload due to prediction errors. Conversely, the better the hardware status, the larger the correction factor should be.

[0054] At this point, the correction coefficient at the current moment is obtained.

[0055] Step S003: predicting the load value and voltage value at a future moment based on the historical load value and the historical voltage value, and correcting the load value at the current moment based on the difference between the past load value and the predicted load value and the correction coefficient.

[0056] Because load fluctuations directly affect voltage levels, voltage regulation is a key means of maintaining power supply quality. When load increases, increased line current leads to increased voltage losses, potentially causing local voltage drops. Conversely, a sudden load drop can cause voltage increases. By adjusting the voltage tap or reactive power compensation capacity through voltage regulators to compensate for voltage deviations caused by load fluctuations, the grid can ensure stable operation under various load scenarios. The reactive power compensation capacity mentioned here refers to the reactive power replenishment capacity.

[0057] This application uses time series analysis to analyze load trends and proactively capture load change trends, providing a basis for forward-looking decision-making for control devices. Specifically, load forecasting can help the system anticipate voltage fluctuation risks, optimize reactive power compensation strategies (such as preemptive reactive power injection by SVG) and OLTC tap adjustment timing, thereby completing control actions before sudden load changes occur and reducing voltage deviations. Furthermore, trend data can be used to dynamically adjust device action priorities, reducing OLTC mechanical wear and extending equipment life.

[0058] Since the power load has significant time periodicity and exhibits nonlinear and non-stationary characteristics, it is necessary to use time series analysis to extract key information of the power load in order to predict future data.

[0059] Specifically, for each moment, several historical load values ​​are extracted. These values ​​are input via the ARM processor's integrated floating-point unit (FPU), and time series analysis is used to output several future load values. In this embodiment, the extracted historical load values ​​represent 24 hours of load values, with an extraction step of 5 minutes. The output load values ​​are the load values ​​for the next 5, 10, and 15 minutes from the current moment. The load value at each moment is the sum of the reactive power and active power at that moment. Based on the same operation described above, several future voltage values ​​are output using historical voltage values.

[0060] It should be noted that the load value is based on the most recently entered value. That is, if the predicted values ​​at time t and time t+1 at the same time in the future are different, the value at time t+1 will prevail. For example, if the predicted values ​​10 minutes into the future at time t and 5 minutes into the future at time t+1 are different, the predicted value 5 minutes into the future at time t+1 will be used as the current predicted value.

[0061] Because time series analysis relies on the cyclical nature of historical load data, predicted values ​​can deviate from actual loads when the grid experiences extreme weather or equipment failures. By introducing a real-time feedback correction mechanism, predictions can be dynamically calibrated, reducing control strategy deviations caused by trend analysis lags. This ensures the accuracy of SVG and OLTC actuators, thereby improving voltage regulation stability and equipment lifespan.

[0062] Therefore, based on the difference between the load value at the past moment and the predicted load value and the correction coefficient at the current moment, the load value at the current moment is corrected to obtain the corrected load value at the current moment.

[0063] The corrected load value is positively correlated with the difference between the load value at the past moment and the predicted load value, the correction coefficient at the current moment, and the load value at the current moment.

[0064] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large; the specific relationship is determined by actual application and this application does not impose any special restrictions.

[0065] Preferably, in this embodiment, the expression for the modified load value is:

[0066] , represents the corrected load value at time t-1, represents the predicted load value at time t-1, represents the predicted load value at time t, Indicates the correction coefficient at the current moment, Represents the corrected load value at time t.

[0067] in, The value range of is [0,1]; the larger the value, the stronger the correction effect, and the smaller the value, the more the load change trend is retained to avoid excessive correction.

[0068] When the load changes steadily, the load value obtained by trend analysis should be closer to the actual value, so the correction factor needs to be increased so that the corrected load value is closer to the most recent actual value. When the load fluctuates greatly, the correction factor needs to be reduced to preserve the load change trend and avoid overcorrection.

[0069] At this point, the correction of the load value at each moment is completed.

[0070] Step S004: construct an objective function based on voltage deviation, reactive compensation amount, number of actions and future load trend, and solve the objective function using dynamic inertia weight to achieve precise voltage regulation.

[0071] In smart power station voltage regulation, single-objective control strategies often fail to meet complex practical requirements. For example, focusing solely on voltage stability may lead to frequent equipment activation, shortening equipment lifespan; conversely, solely considering equipment lifespan may not effectively maintain voltage quality. Therefore, multi-objective optimization is employed to balance multiple conflicting objectives to achieve optimal overall system performance. The algorithm's primary goal is to minimize equipment activation and reduce active power losses while ensuring voltage quality. At the same time, it incorporates forward-looking information provided by load trend analysis to proactively develop more appropriate control strategies.

[0072] Based on the above analysis, the objective function is constructed by the current voltage deviation, reactive compensation amount, number of actions and future load trends.

[0073] Preferably, in this embodiment, the objective function is:

[0074] , represents the voltage deviation at time t, represents the reactive compensation amount of SVG at time t, represents the number of actions at time t, represents the corrected load value at the i-th moment after time t, It represents the voltage deviation at the i-th moment in the future at time t, 、 、 Indicates the weights of the three.

[0075] The voltage deviation is the absolute difference between the current voltage and the rated voltage, reflecting the extent to which the grid voltage deviates from the rated value. Excessive voltage deviation can affect the normal operation of power equipment and may even cause damage. Each OLTC operation causes wear on its contacts, and frequent operation shortens their service life. Limiting the number of OLTC operations can reduce equipment maintenance costs and replacement frequency. The inclusion of predicted load values ​​makes the control strategy more proactive and allows for proactive response to load changes.

[0076] In this embodiment, the weights are 0.5, 0.3, and 0.2, respectively. Voltage deviation is given a higher weight because ensuring voltage quality is the primary priority for grid operation. Equipment life is also important. Appropriately reducing the number of OLTC operations can extend equipment life and reduce operation and maintenance costs. Considering future loads and voltage deviations has a relatively small weight, but it is important for developing a reasonable control strategy in advance.

[0077] After determining the objective function, in order to ensure the feasibility and safety of the final output control strategy, constraints must also be met. The constraints of this application are as follows:

[0078] , Indicates rated voltage, Indicates the reactive power compensation capacity of SVG, which is 30 in this embodiment. Exceeding the reactive power compensation capacity may damage the SVG equipment, impacting its normal operation. The OLTC's operation frequency is limited to no more than 200 times per day to ensure equipment life. Frequent tap adjustment accelerates contact wear and increases the risk of equipment failure. Voltage deviation is controlled within 1% to meet grid voltage quality standards and ensure the normal operation of power equipment.

[0079] This application solves the objective function through an improved particle swarm algorithm. The inertia weight controls the global and local search capabilities of the particles. The larger the value, the more inclined to global search, and vice versa. The traditional particle swarm algorithm uses a fixed inertia weight, and load fluctuations have a significant impact in the smart power station voltage regulation scenario. When the load fluctuates, such as during the evening peak, the load changes greatly, and the traditional fixed inertia weight may not be able to adapt in time, resulting in falling into the local optimum. In stable scenarios, such as at night, the load changes little, and the traditional fixed inertia weight may cause oscillations and reduce the convergence accuracy.

[0080] Therefore, it is necessary to dynamically adjust the inertia weight based on the load change rate at the current moment. In this embodiment, the expression of the dynamic inertia weight is:

[0081] , represents the initial inertia weight, Indicates the inertia weight adjustment amplitude, Indicates the load change rate at the current moment, represents the change threshold, is a symbolic function, The dynamic inertia weight is set to 0.8, the inertia weight adjustment range is 0.3, and the change threshold is 0.1. The load change rate is the difference between the corrected load value at the current moment and the corrected load value at the previous moment.

[0082] After updating the particle positions by the dynamic inertia weight, it is necessary to check whether the position of each particle meets the constraints. If the position of a particle does not meet the constraints, it is adjusted to meet the constraints. For example, if Exceeded its maximum capacity , then adjust it to .

[0083] The final output is an optimal control sequence, which defines the priority of each actuator's action and the corresponding target compensation. Specifically, the actuator action priority determines the order in which different actuators should be activated under the current circumstances. The target compensation specifies a specific reactive power for each actuator. For example, an SVG may need to inject 10Mvar of reactive power, or an OLTC may need to adjust the tap to a specific position to achieve precise voltage regulation.

[0084] At this point, voltage regulation of power quality is achieved.

[0085] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a power quality voltage regulation control device for a smart power station, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned power quality voltage regulation control methods for a smart power station are implemented.

[0086] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A power quality voltage regulation control method for a smart power station, characterized in that: The method comprises the following steps: Collect reactive power, active power, voltage, OLTC operation times, and SVG capacity utilization at each moment; Calculate the reactive compensation capacity and, in combination with the capacity utilization rate, obtain the reactive compensation amount at the current moment; obtain the device hardware status at the current moment based on the difference between the reactive compensation amount and the reactive compensation capacity, and the difference between the current number of actions and the maximum number of actions; and combine the device hardware status with a preset initial correction coefficient to obtain the current correction coefficient. Obtain the predicted load value and predicted voltage value at a preset future moment by using the historical load value and historical voltage value before the current moment respectively; correct the load value at the current moment based on the difference between the load value at the past moment and the predicted load value and the correction coefficient at the current moment to obtain the corrected load value at the current moment; The objective function is constructed based on the current voltage deviation, reactive power compensation amount, number of operations, and future load trends and voltage deviations. The preset inertia weight is adjusted based on the load change rate to obtain the dynamic inertia weight. The objective function is then solved using the dynamic inertia weight to obtain the target compensation amount, which is used to achieve precise voltage regulation. The method for obtaining the device hardware status at the current moment based on the difference between the reactive compensation amount at the current moment and the reactive compensation capacity, and the difference between the number of actions at the current moment and the maximum number of actions is: , Indicates the reactive compensation amount at the current moment, Indicates reactive compensation capacity, Indicates the number of actions at the current moment, Indicates the maximum number of actions per day, 、 Represent the preset weight coefficients, Indicates the device hardware status; The expression of the objective function is: , represents the voltage deviation at time t, represents the reactive compensation amount of SVG at time t, represents the number of actions at time t, represents the corrected load value at the i-th moment after time t, It represents the voltage deviation at the i-th moment in the future at time t, 、 、 Indicates the preset weight values ​​of the three; The method for adjusting the preset inertia weight based on the load change rate to obtain the dynamic inertia weight is: , Indicates the preset initial inertia weight, Indicates the preset inertia weight adjustment range, Indicates the load change rate at the current moment, Indicates the preset change threshold, is a symbolic function, Represents the dynamic inertia weight.

2. The power quality voltage regulation control method for a smart power station according to claim 1, characterized in that: The load value is the sum of reactive power and active power at each moment.

3. The power quality voltage regulation control method for a smart power station according to claim 1, characterized in that: The method for obtaining the predicted load value and the predicted voltage value is: The load values ​​for the 24 hours before the current moment are extracted with a step length of 5 minutes, and the load values ​​for the next 5 minutes, 10 minutes, and 15 minutes from the current moment are obtained through time series analysis. The voltage values ​​for the 24 hours before the current moment are extracted with a step size of 5 minutes, and the voltage values ​​for the next 5 minutes, 10 minutes, and 15 minutes from the current moment are obtained through time series analysis.

4. The power quality voltage regulation control method for a smart power station according to claim 1, characterized in that: The corrected load value is positively correlated with the difference between the load value at the past moment and the predicted load value, the correction coefficient at the current moment, and the load value at the current moment.

5. The power quality voltage regulation control method for a smart power station according to claim 1, characterized in that: The reactive compensation amount at the current moment is the product of the reactive compensation capacity and the capacity utilization rate of the SVG at the current moment.

6. The power quality voltage regulation control method for a smart power station according to claim 1, characterized in that: The voltage deviation is the absolute value of the difference between the current voltage and the rated voltage.

7. A power quality voltage regulation control device for a smart power station, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the power quality voltage regulation control method for a smart power station as described in any one of claims 1 to 6 are implemented.

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