A Photovoltaic Grid-Connected Voltage Control System and Method Based on Edge Computing

Abstract

The present invention discloses a photovoltaic grid-connected voltage control system and method based on edge computing, belonging to the technical field of power grid control. The control system includes: a solar cell array, a photovoltaic inverter module, and a protocol conversion module; the photovoltaic inverter module includes a modulation module, a voltage sensor, and a communication module; the protocol conversion module includes a protocol conversion unit and a voltage regulation system. The protocol conversion unit, on the one hand, receives the AC voltage data sent by the communication module, and on the other hand, obtains the target grid-connected voltage data from the power grid. The voltage regulation system, on the one hand, obtains the AC voltage data output by the modulation module and the target grid-connected voltage data of the power grid from the protocol conversion unit, on the other hand, generates voltage regulation parameters based on model predictive control, and on the other hand, sends the voltage regulation parameters to the communication module. The present invention adds a voltage regulation system based on edge computing to the existing protocol converter to realize real-time voltage monitoring and regulation.

Description

Technical Field

[0001] The present invention relates to the field of power system control. Specifically, it relates to a photovoltaic grid-connected voltage control system and method based on edge computing. Background Art

[0002] The number of photovoltaic grid connections has been increasing year by year. After new photovoltaic users are installed in some photovoltaic regions, due to the quality reasons of photovoltaic inverters, the voltage rises above 260V, which seriously affects the residential electricity consumption and even poses potential safety hazards to the safe operation of the power grid. There are various types of photovoltaic inverters with different communication protocols, resulting in extremely high communication costs and equipment update costs during the grid connection process due to the inability to unify photovoltaic inverters.

[0003] In addition, the number of power grid users is large, and the number of photovoltaic installations is relatively large every year. Due to the widespread phenomenon of increased photovoltaic voltage, the line loss of the region increases after the photovoltaic voltage rises, resulting in an increase in the management cost of the region and a significant reduction in economic benefits. More seriously, when the voltage rises too high, there are potential safety hazards to the safe operation of the power grid. Summary of the Invention

[0004] In view of the problem that the grid-connected voltages of different photovoltaic inverters cannot be uniformly regulated in the prior art, the present invention provides a photovoltaic grid-connected voltage control system and method based on edge computing. The present invention is oriented to protocol terminals, and by independently controlling and calculating the output voltage of each modulation module, the edge computing method and the remote debugging function of the main station-side developed App are utilized to realize real-time control of the photovoltaic voltage, prevent the grid-connected voltage from being too high, and ensure the safe operation of the power grid.

[0005] The technical means adopted by the present invention are as follows:

[0006] A photovoltaic grid-connected voltage control system based on edge computing, comprising: a solar cell array, a photovoltaic inverter module, and a protocol conversion module; wherein:

[0007] The photovoltaic inverter module includes a modulation module, a voltage sensor, and a communication module. The modulation module is used to modulate the direct current output by the solar cell array into alternating current; the voltage sensor is used to detect the alternating current voltage data output by the modulation module; the communication module is used on the one hand to receive the voltage data collected by the voltage sensor, and on the other hand to send the received voltage adjustment parameter to the modulation module to control the alternating current voltage output by the modulation module;

[0008] The protocol conversion module includes a protocol conversion unit and a voltage regulation system. The protocol conversion unit receives the AC voltage data sent by the communication module on the one hand, and obtains the voltage allowed by the power grid during PV grid connection from the power grid on the other hand. The voltage regulation system obtains the AC voltage data output by the modulation module and the target grid connection voltage data of the power grid from the protocol conversion unit on the one hand, generates voltage regulation parameters based on model predictive control on the other hand, and sends the voltage regulation parameters to the communication module.

[0009] Generating voltage regulation parameters based on model predictive control includes:

[0010] Construct a voltage prediction model as follows:

[0011]

[0012] Where, is the predicted voltage output of the voltage regulation system at time t+1, is the system state variable at time t, is the control variable in the voltage regulation system, is the voltage prediction model based on the recurrent neural network, are the learnable parameters of the model;

[0013] Construct a predictive control objective function as follows:

[0014]

[0015] Where, is the predicted voltage output of the voltage regulation system, is the target value of the output voltage of the preset modulation module, is the length of the prediction time domain, is the length of the control time domain, , is the change in the control input at time t, which satisfies the constraint condition , is the lower limit of the control input, is the upper limit of the control input, and at the same time , is the upper limit of the control input change rate, is the lower limit of the control input change rate, is the weight parameter for balancing the tracking error and the change of the control input;

[0016] Solve the predictive control objective function to obtain the predicted value of the output voltage of the modulation module and the predicted value of the voltage regulation parameters.

[0017] Furthermore, solving the predictive control objective function to obtain the predicted value of the output voltage of the modulation module and the predicted value of the voltage regulation parameters includes:

[0018] Derive the predictive control objective function and iteratively solve for the optimal solution through the gradient descent method.

[0019] Furthermore, the voltage regulation system communicates with the relay service in real time and uploads the acquired information to the management terminal, facilitating information collection and control between the terminal and the master station.

[0020] Furthermore, the voltage control system further includes a remote control terminal, which can run an APP program to achieve 7x24 system monitoring and manual adjustment.

[0021] The present invention also discloses a photovoltaic grid-connected voltage control method based on edge computing, which is implemented based on the control system described in any one of the above. It is characterized by including the following steps:

[0022] S1. Obtain the output voltage of the current photovoltaic inverter module through a voltage sensor, and send the collected output voltage information to the protocol conversion unit of the protocol conversion module through the communication module.

[0023] S2. The protocol conversion unit sends the output voltage information to the voltage regulation system on the one hand and sends the voltage information to the power grid on the other hand.

[0024] S3. The voltage regulation system predicts the future output voltage based on historical data and the current state, sets a target voltage and an objective function for minimizing energy loss for optimal control, and the optimal control uses PID control.

[0025] S4. Send the output voltage predicted by the voltage regulation system back to the modulation module of the photovoltaic inverter module, and the modulation module adjusts the output voltage of the photovoltaic inverter module according to the predicted output voltage.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] 1. The present invention adds a voltage regulation system based on edge computing to the existing protocol converter to achieve real-time voltage monitoring and regulation. Edge computing uses a mainstream real-time operating system (RTOS) to improve the response speed and ensure the real-time performance of the control algorithm.

[0028] 2. In the present invention, the communication module of the voltage system regulation system uses the CAT4 protocol to improve the communication stability and prevent packet loss problems.

[0029] 3. The edge computing module of the present invention establishes a dynamic model of the photovoltaic inverter and its connection to the power grid, and sets a target voltage and an objective function for minimizing energy loss to control the most appropriate voltage. Description of the Drawings

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 This is the architecture diagram of a photovoltaic grid-connected voltage control system based on edge computing according to the present invention.

[0032] Figure 2 This is the flowchart of a photovoltaic grid-connected voltage control method based on edge computing according to the present invention.

[0033] Figure 3 This is the schematic diagram of the fluctuation trends of voltage V, reactive power Q, active power P, and voltage set value V in the embodiments of the present invention.

[0034] Figure 4 This is the schematic diagram of the simulation results of voltage regulation in the embodiments of the present invention. Detailed implementation manners

[0035] To enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] As Figure 1 shown, the present invention provides a photovoltaic grid-connected voltage control system based on edge computing, including: a solar cell array, a photovoltaic inverter module, and a protocol conversion module. Among them:

[0037] The photovoltaic inverter module includes a modulation module, a voltage sensor, and a communication module. The modulation module is used to modulate the direct current output by the solar cell array into alternating current; the voltage sensor is used to detect the alternating current voltage data output by the modulation module; the communication module is used on the one hand to receive the voltage data collected by the voltage sensor, and on the other hand to send the received voltage regulation parameters to the modulation module to control the alternating current voltage output by the modulation module.

[0038] Specifically, the modulation module is one of the core components of the photovoltaic inverter module, which can convert direct current into alternating current energy through modulation technology. Its structure mainly includes a switching circuit, a filtering circuit, and a controller unit. During operation, the controller unit generates a PWM signal to drive the switching device, and finally outputs high-quality alternating current through filtering.

[0039] The protocol conversion module includes a protocol conversion unit and a voltage regulation system. On the one hand, the protocol conversion unit receives the AC voltage data output by the modulation module sent by the communication module, and on the other hand, obtains the voltage range allowed by the power grid during PV grid connection from the power grid. The voltage regulation system, on the one hand, obtains the AC voltage data output by the modulation module and the target grid connection voltage data of the power grid from the protocol conversion unit, on the other hand, generates voltage regulation parameters based on model predictive control, and on the other hand, sends the voltage regulation parameters to the communication module.

[0040] Specifically, the voltage regulation system communicates with the PV inverter module, calculates the current appropriate voltage through model predictive control and control algorithms according to the voltage output by the modulation module collected in real time by the voltage sensor, and feeds it back to the modulation module of the PV inverter module to change the output voltage to achieve voltage control.

[0041] The protocol conversion unit is a bridge for the PV inverter module to achieve communication and interoperability. Its core function is to solve the problem of incompatible communication protocols between different devices, ensure that data can be correctly transmitted and instructions can be accurately executed. The grid connection voltage output by the PV manufacturer system collected through the inverter converter is obtained, and at the same time, the allowed voltage range of the main grid is obtained, and the target value of voltage regulation is selected within the allowed voltage range. Through the protocol conversion unit, data interaction between the PV inverter module and the voltage regulation system is realized, and the closed-loop control of real-time voltage acquisition, analysis, feedback and execution is completed.

[0042] Furthermore, the voltage regulation system uses means such as filtering, correction, multi-parameter analysis, and prediction optimization to process the collected voltage information to ensure accurate control instructions are obtained. In this embodiment, the control algorithm of MPC (Model Predictive Control) is preferably adopted. According to the collected real-time voltage information, combined with historical data and system models, the voltage change trend in the future period of time is predicted. For example, if the input voltage of the PV inverter module fluctuates greatly, MPC can take the predicted voltage regulation value as the target, generate a control input by accurately predicting the output voltage, so that the output voltage meets the voltage requirements. For example, make the output voltage meet the pre-set reasonable output range (220V±10%).

[0043] The present invention adopts an edge computing architecture, and completes control calculations through local devices. Its core is to construct a voltage regulation system based on a recurrent neural network model. The specific process is as follows.

[0044] Construct the voltage prediction model as follows:

[0045]

[0046] Among them, is the predicted voltage output of the voltage regulation system at time t+1 is the system state variable at time t, including historical voltage, reactive power, active power of the photovoltaic inverter module, and other environmental characteristics. In this application, the main source of other environmental characteristics is randomly generated environmental noise (range: [-0.5, 0.5]), which is used to simulate external interference or measurement error that the system may be subject to during operation. The introduction of environmental noise aims to enhance the robustness of the voltage prediction model so that it can better adapt to the uncertain factors in the actual operating environment.) is the control variable in the voltage regulation system, is the voltage prediction model based on the recurrent neural network, are the learnable parameters of the model.

[0047] In this application, the voltage prediction model based on the recurrent neural network uses the LSTM model, inputs the historical state data of the current system, and obtains the predicted values for the future time steps. Assuming s(t) is the state of the solar array at time t and u(t) is the control input variable at time t, they jointly constitute the input feature , which is input into the LSTM model.

[0048] The calculation formula of the forget gate is as follows.

[0049]

[0050] Among them, is the output of the forget gate, representing the proportion of information to be forgotten. σ is the sigmoid activation function, and the output range is [0,1]. W f is the weight matrix of the forget gate. b f is the bias term of the forget gate.

[0051] The input gate consists of two parts: the activation value of the input gate and the candidate value vector.

[0052] The activation value of the input gate is as follows.

[0053]

[0054] The candidate value vector is as follows.

[0055]

[0056] Among them, is the activation value of the input gate, representing the proportion of information to be updated. is the candidate value vector, representing the new information to be written into the cell state. W i and W c are the weight matrices of the input gate and the candidate value vector respectively. bi and b c are the bias terms of the input gate and the candidate value vector respectively.

[0057] The calculation formula of the output gate is as follows.

[0058]

[0059] Among them, is the activation value of the output gate, representing the proportion of the information to be output. W o is the weight matrix of the output gate. b o is the bias term of the output gate.

[0060] The update formula of the hidden state is as follows.

[0061]

[0062] Among them, is the updated hidden state. is the activation value of the cell state, used to control the output range of the hidden state, represents element-wise multiplication.

[0063] After passing through a fully connected layer, the hidden state of the LSTM is mapped to the output target variable as follows.

[0064]

[0065] Among them, are the weight parameters that can be learned by the LSTM model, randomly set by Gaussian initialization.

[0066] The constructed objective function is as follows.

[0067]

[0068] Among them, is the predicted value of the output voltage of the modulation module based on the LSTM model, is the target value of the preset output voltage of the modulation module, which is selected as 220V in this embodiment, is the tracking error, minimizing this error is that the system output approaches the set value; in order to reduce the dynamic change of the control input and avoid too drastic adjustment, , is the change amount of the control input, is the weight factor, used to balance the tracking error and the change of the control input.

[0069] The constraint conditions of the objective function are as follows.

[0070] ,

[0071] Among them, is the lower limit of the control input, is the upper limit of the control input. is the upper limit of the control input change rate, is the lower limit of the control input change rate.

[0072] Based on the gradient descent method to solve the above-mentioned predictive control objective function, the predicted value of the output voltage of the modulation module and the predicted value of the voltage regulation parameter are obtained, so as to achieve real-time control and update the prediction. The solution formula of the gradient descent method is as follows.

[0073]

[0074] Update the control variable according to the gradient as follows.

[0075]

[0076] Furthermore, the voltage regulation system communicates with the relay service in real time, and uploads the obtained information to the management end, which is convenient for information collection and control between the terminal and the master station. The relay service is the bridge between the voltage regulation system and the management end, responsible for data forwarding, buffering, and protocol conversion tasks, and is mainly responsible for uploading the voltage data output by the collected modulation module to the database (master station) and background of the State Grid.

[0077] Furthermore, the voltage control system further includes a remote control terminal, and the control terminal can run an APP program to achieve 7x24 system monitoring and manual adjustment. The App is mainly responsible for displaying the collected information in real time on the UI interface, and at the same time can adjust the voltage regulation according to the threshold set in advance by the user. Instead of manual verification and search for each photovoltaic component one by one, the labor cost is greatly reduced.

[0078] As Figure 2 shown, the present invention also discloses a photovoltaic grid-connected voltage control method based on edge computing, which is implemented based on the above control system, and includes the following steps.

[0079] S1. Obtain the output voltage of the current photovoltaic inverter module through a voltage sensor, and send the collected output voltage information to the protocol conversion unit of the protocol conversion module through a communication module.

[0080] S2. The protocol conversion unit sends the output voltage information to the voltage regulation system on the one hand, and sends the voltage information to the power grid on the other hand.

[0081] S3. The voltage regulation system predicts the future output voltage based on historical data and the current state, sets a target voltage and an objective function for minimizing energy loss, and performs optimal control. In this embodiment, the optimal control adopts PID control.

[0082] S4. Feed back the output voltage predicted by the voltage regulation system to the modulation module of the photovoltaic inverter module, and the modulation module adjusts the output voltage of the photovoltaic inverter module according to the predicted output voltage.

[0083] As Figure 2 shown, an application example of the present invention is given.

[0084] In this simulation experiment, we simulated the voltage fluctuations in the power system through simulation data, predicted the voltage using the LSTM model, and then calculated the adjusted voltage value through the adjustment formula, so as to gradually approach the voltage to the target value.

[0085] First, simulate the system settings of the voltage regulation system. The system parameters are set as filter inductance L = 5mH, grid voltage V = 220V, DC bus voltage V = 400V, sampling period , prediction time domain , control time domain 3, weight parameter = 0.01.

[0086] Based on the above parameters, the experimental input data s(t) consists of the actual voltage V, reactive power Q, active power P, environmental noise, and target voltage. The time simulation sequences of key variables such as voltage, reactive power, and active power are as Figure 3 shown, where the voltage range fluctuates between 205V and 235V, the reactive power and active power are between 40 - 60 kW and 50 - 70 kW respectively, and the environmental noise is randomly generated between [-0.5, 0.5]. After these data are normalized, they are input into the LSTM model for learning. We constructed time series data, where each group of inputs contains the data of the past 10 time steps, and the output is the voltage value at the current time step. 80% of the data was used as the training set and 20% of the data was used as the test set during model training.

[0087] After the prediction is completed, we use the following formula to adjust the predicted voltage to simulate the working principle of the voltage regulation system: adjusted_voltage = y_pred + (y_tar - y_pred) * 0.5, where y_pred represents the predicted voltage output of the voltage regulation system predicted by the model The stored variable, y_tar is the target voltage (220V), and 0.5 is the adjustment coefficient, indicating that the voltage is pulled closer to the target value by half in each adjustment process. Through this formula, the voltage deviation can be gradually corrected, making the adjusted voltage value gradually approach the target voltage. In the simulation results, we plotted four curves: the actual voltage, the predicted voltage, the adjusted voltage, and the target voltage. It can be observed that the actual voltage curve shows the dynamic changes of the original voltage fluctuations. The predicted voltage curve is relatively close to the actual voltage curve, indicating that the model can accurately capture the voltage change law. The adjusted voltage curve gradually approaches the target voltage and finally stabilizes, indicating that the adjustment strategy is effective and can significantly improve the voltage deviation problem.

[0088] Figure 4 Shows the simulation results of the voltage regulation system, including four curves of actual voltage, predicted voltage, adjusted voltage, and target voltage. The blue dotted line represents the actual voltage value of the system, that is, the real operating voltage data without any adjustment. It can be seen that it fluctuates with time, reflecting the dynamic behavior of the system affected by load changes and environmental factors. The orange dashed line represents the voltage value predicted by the model. It performs time series prediction based on historical input features (such as voltage, reactive power, active power, etc.). The overall trend should be relatively close to the actual voltage, indicating the model's ability to capture voltage dynamic changes. The green solid line shows the result after adjusting the predicted voltage, simulating the function of the voltage regulation system. The goal is to gradually adjust the predicted voltage to the target voltage (220V). As can be seen from the figure, the green solid line gradually approaches the red dashed line, indicating that the adjustment strategy has played a role and successfully converges the voltage to the target value. The red dashed line represents the target voltage (220V), which is used as a reference benchmark to measure the deviation degrees of the actual voltage, predicted voltage, and adjusted voltage. Overall, the simulation results verify the prediction ability of the model proposed in this patent and the effectiveness of the adjustment strategy.

[0089] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic grid-connected voltage control system based on edge computing, characterized in that: include: Solar cell array, photovoltaic inverter module and protocol conversion module; wherein: The photovoltaic inverter module includes a modulation module, a voltage sensor and a communication module. The modulation module is used to modulate the direct current output by the solar cell array into alternating current; the voltage sensor is used to detect the alternating current voltage data output by the modulation module; the communication module is used to receive the voltage data collected by the voltage sensor on the one hand, and send the received voltage adjustment parameter to the modulation module on the other hand to control the alternating current voltage output by the modulation module; The protocol conversion module includes a protocol conversion unit and a voltage regulation system. The protocol conversion unit receives the AC voltage data sent by the communication module on the one hand, and obtains the voltage allowed by the grid when the photovoltaic grid is connected to the grid on the other hand; the voltage regulation system obtains the AC voltage data output by the modulation module and the target grid-connected voltage data of the grid from the protocol conversion unit on the one hand, generates voltage regulation parameters based on model predictive control on the one hand, and sends the voltage regulation parameters to the communication module on the other hand; Generate voltage regulation parameters based on model predictive control, including: The voltage prediction model is constructed as follows: in, The voltage output predicted by the voltage regulation system at time t+1, is the system state variable at time t, is the control variable in the voltage regulation system, For the voltage prediction model based on recurrent neural network, are the learnable parameters of the model; The predictive control objective function is constructed as follows: in, Predicting voltage output for voltage regulation systems, The preset target value of the modulation module output voltage, is the length of the prediction time domain, To control the length of the time domain, , is the control input change at time t, which satisfies the constraint , is the control input lower limit, For control input upper limit, , To control the upper limit of input change rate, To control the lower limit of input change rate, Weight parameters for balancing tracking error and changes in control input; Solve the predictive control objective function to obtain the predicted value of the modulation module output voltage and the predicted value of the voltage regulation parameter.

2. A photovoltaic grid-connected voltage control system based on edge computing according to claim 1, characterized in that: Solving the predictive control objective function to obtain the predicted value of the modulation module output voltage and the predicted value of the voltage regulation parameter includes: The predictive control objective function is derived, and the optimal solution is iteratively solved by the gradient descent method.

3. A photovoltaic grid-connected voltage control system based on edge computing according to claim 1, characterized in that: The voltage regulation system communicates with the relay service in real time and uploads the acquired information to the management end, which facilitates information collection and control of the terminal and the master station.

4. A photovoltaic grid-connected voltage control system based on edge computing according to claim 1, characterized in that: The voltage control system also includes a remote control terminal, which can run an APP program to achieve 7x24 system monitoring and manual adjustment.

5. A photovoltaic grid-connected voltage control method based on edge computing, implemented based on the control system according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1. Obtain the output voltage of the current photovoltaic inverter module through a voltage sensor, and send the collected output voltage information to the protocol conversion unit of the protocol conversion module through a communication module; S2, the protocol conversion unit sends the output voltage information to the voltage regulation system on the one hand, and sends the voltage information to the power grid on the other hand; S3, the voltage regulation system predicts the future output voltage based on historical data and current status, sets the target voltage and the objective function of minimizing energy loss for optimization control, and the optimization control adopts PID control; S4. The output voltage predicted by the voltage regulation system is transmitted back to the modulation module of the photovoltaic inverter module, and the modulation module regulates the output voltage of the photovoltaic inverter module according to the predicted output voltage.

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