Unbalance regulation and control method and system for power distribution area

Abstract

The invention discloses a method and a system for regulating and controlling imbalance of a power distribution area, which are used for solving the technical problem of poor operation stability of the power distribution area caused by a traditional method for regulating and controlling imbalance of the power distribution area. The method comprises the following steps: acquiring three-phase bus voltage and three-phase current of a power distribution area; generating a power distribution area three-phase component voltage and a power distribution area power optimization objective function according to the power distribution area three-phase bus voltage and the power distribution area three-phase current; solving the power optimization objective function of the power distribution area, and outputting predicted power of the power distribution area; calculating a three-phase reference current of the power distribution area based on the predicted power of the power distribution area and the three-phase component voltage of the power distribution area; and according to the three-phase reference current of the power distribution area, generating a power distribution area imbalance regulation and control signal.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, and in particular to a method and system for regulating imbalances in distribution substations. Background Technology

[0002] In the process of transforming into a new power system, distribution substations, as the "nerve endings" of the power network, are undergoing profound changes brought about by the large-scale integration of distributed resources. The influx of diverse resources, such as the intermittent output of photovoltaic power generation, the random spatiotemporal charging behavior of electric vehicles, and the dynamic charging and discharging scheduling of energy storage devices, has disrupted the stable distribution pattern of traditional substation loads, resulting in a "multi-source disturbance" characteristic with strong randomness and high volatility in the load of each phase. For example, residential photovoltaic systems are mostly connected to a single phase, and their concentrated power generation at noon may cause a sudden drop in the load of a certain phase; the cluster charging of electric vehicles in the evening often causes a sudden increase in the load of another phase; and the dynamic charging and discharging operation of energy storage devices to smooth out fluctuations further exacerbates the instantaneous imbalance of the load of each phase, ultimately leading to a significant imbalance in the three-phase voltage and current of the substation.

[0003] This imbalance has multi-dimensional effects on the operation of the distribution area: in terms of power quality, problems such as voltage deviation and harmonic distortion occur frequently, directly affecting the normal operation of precision household appliances and industrial equipment; in terms of equipment losses, unbalanced current will cause additional copper and iron losses in transformers and lines, accelerate insulation aging, shorten equipment lifespan, and even cause overheating failures; in terms of system stability, long-term unbalanced operation may lead to malfunctions of relay protection devices and inaccurate distribution network voltage regulation, significantly increasing the risk of power outages and threatening the safety of users' electricity use.

[0004] Traditional methods for controlling power distribution area imbalances primarily rely on static compensation, such as using fixed capacitor banks for reactive power compensation and mechanical tap changers to regulate voltage. These methods depend on preset compensation parameters and fixed control strategies. Their core drawback is their inability to adapt to dynamically changing load scenarios. When sudden disturbances to distributed resources (such as a sharp drop in photovoltaic output or concentrated grid connection of electric vehicles) cause drastic fluctuations in the load of each phase, the static compensation device responds with lag and cannot adjust the compensation amount in real time, resulting in poor operational stability of the distribution area. Summary of the Invention

[0005] This invention provides a distribution transformer area imbalance control method and system to solve the technical problem of poor operational stability of distribution transformer areas caused by traditional distribution transformer area imbalance control methods.

[0006] The first aspect of this invention provides a method for controlling distribution network imbalance, comprising:

[0007] Obtain the three-phase bus voltage and three-phase current of the distribution substation area;

[0008] Based on the three-phase bus voltage and the three-phase current of the distribution substation area, generate the three-phase component voltage and power optimization objective function of the distribution substation area;

[0009] Solve the objective function for power optimization of the distribution station area and output the predicted power of the distribution station area;

[0010] Based on the predicted power of the distribution substation and the three-phase component voltage of the distribution substation, calculate the three-phase reference current of the distribution substation.

[0011] Based on the three-phase reference current of the distribution station area, an unbalanced control signal for the distribution station area is generated.

[0012] Optionally, the step of generating the three-phase component voltage and power optimization objective function of the distribution substation based on the three-phase bus voltage and the three-phase current of the distribution substation includes:

[0013] A symmetrical component analysis is performed on the three-phase bus voltage of the distribution station area to generate the three-phase component voltage of the distribution station area.

[0014] A discrete state-space model is constructed based on the three-phase component voltages and three-phase currents of the distribution station area.

[0015] Based on the discrete state-space model, a multi-step imbalance degree is constructed;

[0016] Based on the aforementioned multi-step imbalance, a power optimization objective function for distribution substations is constructed.

[0017] Optionally, calculating the three-phase reference current of the distribution substation based on the predicted power of the distribution substation and the three-phase component voltage of the distribution substation includes:

[0018] The reference power of the distribution station area is calculated using the predicted power of the distribution station area.

[0019] Calculate the three-phase reference current of the distribution station area based on the reference power of the distribution station area and the three-phase component voltage of the distribution station area.

[0020] Optionally, generating the distribution station imbalance control signal based on the three-phase reference current of the distribution station includes:

[0021] Based on the three-phase reference current of the distribution station area, a sliding mode error variable is constructed;

[0022] Based on the sliding mode error variable, determine the nonlinear control law variable;

[0023] Based on the preset frequency mapping weights and the nonlinear control law variables, an unbalanced control signal for the distribution station area is generated.

[0024] Optionally, the calculation formula for the three-phase reference current of the distribution substation is as follows:

[0025] ;

[0026] in, Let k be the three-phase reference current of the distribution station area at time k; The reference power for the distribution area at time k; Let be the positive sequence component voltage of the three-phase component voltage of the distribution station area at time k; This is the unbalanced voltage compensation term at time k; Let k be the state vector at time k; , These are the proportional controller gain and the integral controller gain, respectively. The negative sequence component voltage is the three-phase component voltage of the distribution station area at time k. From the initial moment The cumulative sum of negative sequence voltages up to time k; k is the discrete time step.

[0027] Optionally, the unbalanced control signal of the distribution area specifically includes:

[0028] ;

[0029] in, The distribution area imbalance control signal at time t; To predict the dynamic weights of the controller for the model; Preset frequency mapping weights; The control input of the model predictor controller at time t; Let be the nonlinear control law variable at time t; The frequency is the disturbance frequency.

[0030] A second aspect of the present invention provides a distribution area imbalance control system, comprising:

[0031] The acquisition module is used to acquire the three-phase bus voltage and three-phase current of the distribution substation area;

[0032] The generation module is used to generate the three-phase component voltage and the power optimization objective function of the distribution substation area based on the three-phase bus voltage and the three-phase current of the distribution substation area.

[0033] The solution module is used to solve the objective function for power optimization of the distribution station area and output the predicted power of the distribution station area.

[0034] The calculation module is used to calculate the three-phase reference current of the distribution station area based on the predicted power of the distribution station area and the three-phase component voltage of the distribution station area.

[0035] The control module is used to generate an imbalance control signal for the distribution station area based on the three-phase reference current of the distribution station area.

[0036] A computer device provided in a third aspect of the present invention includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the distribution radio station imbalance control method as described in any of the preceding claims.

[0037] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the steps of the distribution radio station imbalance control method as described in any of the preceding claims.

[0038] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the distribution radio station imbalance control method as described in any of the preceding claims.

[0039] As can be seen from the above technical solutions, the present invention has the following advantages:

[0040] The above-mentioned technical solution of the present invention provides a method for unbalanced control of a distribution substation, which involves obtaining the three-phase bus voltage and three-phase current of the distribution substation; generating a three-phase component voltage and a power optimization objective function for the distribution substation based on the three-phase bus voltage and three-phase current; solving the power optimization objective function to output the predicted power of the distribution substation; calculating the three-phase reference current of the distribution substation based on the predicted power and the three-phase component voltage; and generating an unbalanced control signal for the distribution substation based on the three-phase reference current. Based on the above solution, the present invention generates a three-phase component voltage and a power optimization objective function for the distribution substation based on the obtained three-phase bus voltage and three-phase current of the distribution substation. By solving the power optimization objective function and calculating the reference current, it dynamically adapts to the randomness of asynchronous load access, enabling the generated control signal to achieve refined power allocation adjustment, thereby improving the operational stability of the distribution substation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0042] Figure 1 This is a flowchart illustrating the steps of a distribution radio station area imbalance control method provided in Embodiment 1 of the present invention.

[0043] Figure 2 This is a structural block diagram of a distribution radio station unbalance control system provided in Embodiment 2 of the present invention. Detailed Implementation

[0044] This invention provides a distribution transformer area imbalance control method and system to solve the technical problem of poor operational stability of distribution transformer areas caused by traditional distribution transformer area imbalance control methods.

[0045] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0046] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a distribution radio station imbalance control method provided in Embodiment 1 of the present invention.

[0047] This invention provides a method for controlling distribution network imbalance, comprising:

[0048] Step 101: Obtain the three-phase bus voltage and three-phase current of the distribution substation area.

[0049] The three-phase bus voltage of the distribution substation includes the three-phase (A-phase, B-phase, and C-phase) bus voltage of the substation.

[0050] The three-phase current of the distribution station area includes the three-phase (A-phase, B-phase, and C-phase) current of the station area.

[0051] It should be noted that the three-phase bus voltage and three-phase current of the distribution substation are collected in real time, and the positive-sequence, negative-sequence, and zero-sequence three-phase component voltages (i.e., the three-phase component voltages of the distribution substation) are generated by analysis. At the same time, the power optimization objective function with load fluctuation constraints (i.e., the power optimization objective function of the distribution substation) is constructed in combination with the current data, providing basic data support for subsequent precise control.

[0052] It is worth mentioning that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0053] Step 102: Based on the three-phase bus voltage and three-phase current of the distribution substation, generate the objective functions for optimizing the three-phase component voltage and power of the distribution substation.

[0054] The three-phase voltage components of a distribution station area include positive-sequence voltage, negative-sequence voltage, and zero-sequence voltage.

[0055] It should be noted that, based on the three-phase bus voltage and three-phase current of the distribution substation, the positive-sequence, negative-sequence, and zero-sequence voltage components are obtained by decomposing them using the symmetrical component method, which accurately characterizes the unbalanced state of the substation. At the same time, combined with current data, a power optimization objective function for the distribution substation is constructed with minimizing the three-phase power deviation and suppressing the zero-sequence current as its core, providing a quantitative control benchmark for subsequent optimization solutions.

[0056] Specifically, step 102 may include the following sub-steps S21-S24:

[0057] Step S21: Perform symmetrical component analysis on the three-phase bus voltage of the distribution substation area to generate the three-phase component voltage of the distribution substation area.

[0058] It should be noted that, firstly, a mathematical model of the symmetrical components of the three-phase quantities in the distribution substation is established, that is, a symmetrical component analysis is performed on the three-phase bus voltage of the distribution substation to generate the three-phase component voltage of the distribution substation; among which, the positive sequence component voltage is specifically:

[0059] ;

[0060] Negative sequence component voltage:

[0061] ;

[0062] Zero-sequence component voltage:

[0063] ;

[0064] in, For rotation factor, That is, a phase rotation of 120°, used for separation of positive and negative sequence components; , , These are the three-phase (A-phase, B-phase, C-phase) bus voltages of the transformer substation. The zero-sequence component voltage reflects the phenomenon of three-phase common offset or ground potential drift. The negative sequence voltage represents the reverse rotational component that appears when the system is unbalanced. The positive-sequence voltage represents the rotating component of an ideal equilibrium system under normal conditions.

[0065] It is worth noting that the positive-sequence component represents the ideal equilibrium state, the negative-sequence component represents the system's unbalanced characteristics, and the zero-sequence component reflects the voltage deviation at the point of common. This model serves as the core foundation for subsequent unbalance calculations, control target setting, and control quantity generation.

[0066] Step S22: Construct a discrete state-space model based on the three-phase component voltages and three-phase currents of the distribution substation area.

[0067] It should be noted that the sequence component voltage is combined with the control target (i.e., the three-phase current of the distribution substation) to construct a discrete state-space model:

[0068] ;

[0069] The state vector is defined as follows:

[0070] ;

[0071] in, Let be the state vector at time k, which is the core carrier describing the imbalance state and regulation variables of the distribution transformer area. It contains the real part of the negative-sequence voltage component at time k. The imaginary part of the negative sequence voltage at time k Zero-sequence component voltage at time k and the three-phase current of the distribution station area at time k. , , , , , As a controller, it directly regulates variables; This is the state vector at time k+1, representing the predicted state of the system at the next time step, based on the current state. Control input External disturbances The calculated values ​​are the key to model prediction and rolling optimization. Includes active adjustment quantities output by the controller (such as energy storage charging and discharging commands, photovoltaic power adjustment signals, etc.); A is the state matrix; B is the control input matrix; E is the disturbance input matrix; Let k be the process noise vector at time k; Let be the output vector at time k, containing the measurable physical quantities of the system (such as the monitored values ​​of three-phase voltage and current); C is the output matrix. Let be the measurement noise vector at time k.

[0072] It is worth mentioning that this state-space structure is directly derived from the three-order component theory. Through discrete state-space expression, it successfully integrates the three-order component model with the dynamic control structure, laying a theoretical foundation for subsequent imbalance quantification, optimization target construction, and predictive control. The model serves as a unified bridge between the controller input and feedback state, while also connecting to future disturbance predictions to realize the prior control response.

[0073] In this embodiment, this step aims to establish a basic electrical model of the distribution substation. By decomposing the three-phase bus voltage into symmetrical components, positive-sequence, negative-sequence, and zero-sequence components are extracted. A rotation factor is used to project the three-phase signals into complex space, achieving mathematical modeling of the three-phase voltage in a symmetrical coordinate system. This model not only reflects the dominant component (positive sequence) of the system under ideal equilibrium conditions but also accurately extracts the causes of imbalance (negative sequence and zero sequence), providing a precise and quantifiable electrical basis for subsequent imbalance calculations and control target setting. It serves as the starting point for modeling the entire control strategy.

[0074] Step S23: Construct a multi-step imbalance degree based on the discrete state-space model.

[0075] It should be noted that, in order to achieve quantification and predictive control of the three-phase unbalanced state, a dynamic unbalance function UF(k) is defined, and its future evolution expression is constructed as the core of the objective function of the MPC controller (Model Predictive Control).

[0076] Furthermore, the unbalance factor (UF) is defined as the normalized quantity of the negative-sequence and zero-sequence voltage components:

[0077] ;

[0078] in, Let k be the imbalance at time k; , , The real part, imaginary part, and modulus are mapped to state vectors. The first 3 dimensions.

[0079] Furthermore, to perform future rolling optimization, an N-step imbalance evolution is constructed, that is, based on the discrete state-space model, a multi-step imbalance is constructed:

[0080] ;

[0081] Where i is the prediction step index; N is the prediction step number of the model predictive control (MPC); This is the imbalance evolution function, which describes the mapping relationship between the system state and the imbalance. The input is the state vector at time k+i. (Such as negative sequence voltage, zero sequence current, three-phase current, etc.), the output is the unbalance at the corresponding moment. This function can be constructed through physical modeling (such as analytical derivation based on the symmetric component method) or data-driven methods (such as machine learning fitting), and is a key model component for predicting future imbalances.

[0082] In this embodiment, based on the established voltage sequence component model, this step defines an index UF (Unbalance Factor) to measure the degree of three-phase imbalance, and sets UF approaching zero as the main control objective. Simultaneously, considering the system power flow relationship, a dual strategy of negative-sequence current reverse injection and active / reactive power balance is proposed to ensure the maintenance of system energy coordination during compensation. This strategy not only dynamically combats imbalance disturbances at the current level but also ensures the effectiveness of the inverter and the stability of system operation from a power perspective, providing physical constraints for constructing the objective function and subsequent optimized control.

[0083] Step S24: Based on the multi-step imbalance, construct the power optimization objective function for the distribution area.

[0084] It should be noted that, combining the state equation and the unbalance objective function, the control input sequence optimization objective is defined, that is, based on the multi-step unbalance, the power optimization objective function of the distribution area is constructed:

[0085] ;

[0086] in, To predict the objective function value for rolling optimization, a weighted comprehensive evaluation is performed on three dimensions: "imbalance suppression effect," "control of input smoothness," and "control of input sparsity" over the next N steps. This is achieved by minimizing... The controller is guided to find the optimal balance among multiple objectives, ensuring that the system can quickly eliminate three-phase imbalance while operating stably and efficiently. The unbalance weighting factor; Weighting factor to control the rate of change of input; It is the L2 norm (Euclidean norm); for It continuously controls the changes in the input to smooth the control signal; To control the input sparsity weighting factor, , , This represents the weighting factor in the cost function; for The control inputs at any time (such as energy storage charging and discharging power commands, photovoltaic output adjustment signals, load switching commands, etc.) are the "execution variables" that the controller applies to the system. It is the L1 norm (Manhattan norm).

[0087] It is worth mentioning that this optimization objective function comprehensively considers several key requirements for system operation: the first term aims to minimize the system's imbalance and improve the symmetry of the three-phase voltage; the second term limits the rate of change of the control input to ensure a smooth and stable control process; and the third term improves energy efficiency and reduces redundant regulation through sparsity constraints on the control signal. The weighting coefficients for each term are as follows. , , It can be flexibly adjusted according to the system's sensitivity to imbalance, smoothness, and energy efficiency. Meanwhile, the objective function is based on future disturbance prediction. As a priori input, it guides the evolution trajectory of state variables, thereby endowing the controller with a certain degree of foresight and proactive adjustment capability.

[0088] Step 103: Solve the objective function for power optimization of the distribution station area and output the predicted power of the distribution station area.

[0089] The predicted power of a distribution substation includes the load power, photovoltaic output power, and energy storage power (unit: W). The load power is the total active power consumption of all electrical equipment in the distribution substation (such as air conditioners, lighting, and home appliances in residential households, electrical equipment in commercial places, and small industrial loads) at the predicted time. The photovoltaic output power is the active power output capacity of the distributed photovoltaic power generation system (such as residential photovoltaic panels and small photovoltaic power stations) in the substation at the predicted time. The energy storage power is the interaction value between the energy storage equipment (such as lithium battery energy storage and flywheel energy storage) in the substation and the system's active power at the predicted time.

[0090] It should be noted that this invention introduces a future power disturbance prediction mechanism. By using short-term prediction models (such as LSTM (Long Short-Term Memory)) and exponential smoothing, it estimates the changes in photovoltaic power generation, energy storage status, and load power (i.e., distribution area load power, photovoltaic output power, and distribution area energy storage power) over several future sampling periods. The prediction results are injected into the system control loop as boundary conditions for model predictive control (MPC), enabling the controller to adjust in advance based on potential future disturbance trends. Through the logical closed loop of prediction, decision-making, and regulation, the controller's ability to perceive sudden disturbances is enhanced, achieving a forward-looking response from the control system.

[0091] Specifically, changes in photovoltaic power generation, energy storage status, and load power are deeply correlated through a transmission chain of "external disturbance - system status - imbalance". The calculation logic is as follows: fluctuations in photovoltaic power generation, energy storage charging and discharging behavior, and changes in load power all belong to external disturbances of the distribution area (corresponding to the state equation in the equation). Its fluctuations directly alter the system power flow, causing three-phase current / voltage imbalances; these disturbances are addressed through the state equations. The system state vector acting on subsequent time steps (For example, a sudden increase in photovoltaic power can cause a sudden change in the current of a certain phase, changing the negative sequence voltage component and affecting the state vector) (Deviating from equilibrium); and the change in the state vector will be directly reflected in the degree of imbalance (e.g., an increase in negative sequence voltage leads to...). (rise), eventually at The first item This is reflected in the increase in control costs. To make To achieve "foresight," short-term forecasting models such as LSTM and exponential smoothing are needed to estimate changes in photovoltaic power generation, energy storage status, and load power over several future sampling periods, thereby obtaining predicted values ​​for future disturbances. Then, by substituting these values ​​into the state equations, the subsequent system state and imbalance can be predicted. The optimization process is highly dependent on the accuracy of these power predictions—the more accurate the power predictions, the better the subsequent system state. With imbalance The more reliable the prediction results, the better. The better the optimization results are adapted to the dynamic changes in the actual system, the better they can meet the needs of the actual system. In short, the power changes of photovoltaics, energy storage, and loads (distribution area load power, photovoltaic output power, distribution area energy storage power) indirectly affect the system through the transmission path of "disturbance → state → imbalance". The calculation, while the future power disturbance prediction mechanism is to allow It is the core support that enables early response to power fluctuations and ensures that the control strategy adapts to the dynamic changes of the system.

[0092] Step 104: Calculate the three-phase reference current of the distribution substation based on the predicted power and the three-phase component voltage of the distribution substation.

[0093] It should be noted that, in order to ensure the consistency of the control strategy objective (i.e., minimizing the three-phase imbalance UF), feedforward modeling is required based on future power disturbances of the system to generate three-phase reference currents. The three-phase reference currents of the distribution area include the reference currents of the three phases (A phase, B phase, and C phase) of the distribution area, i.e. , , This reference current serves as both the target value for the Sliding Mode Control (SMC) design and the setting of the control signal limit term in the MPC cost function.

[0094] Specifically, step 104 may include the following sub-steps S41-S42:

[0095] Step S41: Calculate the reference power of the distribution station area using the predicted power of the distribution station area;

[0096] Step S42: Calculate the three-phase reference current of the distribution station area based on the reference power and the three-phase component voltage of the distribution station area.

[0097] It should be noted that the reference power for the distribution area is generated by the power prediction module in the distribution area system:

[0098] ;

[0099] in, The reference power for the distribution area at time k; Let k be the load power of the distribution station area at time k; Let be the photovoltaic output power at time k; Let be the energy storage power of the distribution radio station area at time k.

[0100] Three-phase reference current of the distribution area:

[0101] ;

[0102] in, Let k be the three-phase reference current of the distribution station area at time k; The reference power for the distribution area at time k; Let be the positive sequence component voltage of the three-phase component voltage of the distribution station area at time k; The unbalanced voltage compensation term at time k (i.e., the PI compensation term for the negative-sequence unbalanced voltage, based on state variables) (such as the construction of negative sequence voltage, negative sequence current, unbalance, etc.); Let k be the state vector at time k; , These are the proportional controller gain and the integral controller gain, respectively. The negative sequence component voltage is the three-phase component voltage of the distribution station area at time k. From the initial moment The cumulative sum of negative sequence voltages up to time k; k is the discrete time step.

[0103] It is worth mentioning that this construction ensures the logical consistency between the generated control quantity and the state variables, disturbance predictions, and the control objective function, so that the controller not only depends on the current state, but also has the ability to predictively adjust for future disturbance trends.

[0104] In this embodiment, the present invention analyzes the impact of disturbances at different frequency bands on the system based on the frequency domain characteristics of the disturbance source, and designs a dynamic controller weight mapping mechanism. Low-frequency disturbances, such as load cycle fluctuations, are mainly handled by MPC, mid-frequency disturbances are quickly suppressed by SMC (Sliding Mode Control), and high-frequency noise is suppressed by notch filters or low-pass filters. The outputs of each controller are weighted and superimposed with frequency-related weights to form the final control signal. This mechanism achieves optimal allocation of control resources in the disturbance frequency domain, improves the overall adaptability and efficiency of the control system, and provides strong support for three-phase symmetrical operation under multiple disturbance conditions. This model couples the predicted disturbance with the feedback voltage state to form a complete closed-loop control logic of power prediction → current target → control output.

[0105] Step 105: Generate the distribution area imbalance control signal based on the three-phase reference current of the distribution area.

[0106] It should be noted that, in order to improve the dynamic suppression capability against medium-frequency disturbances and modeling uncertainties, a nested adaptive sliding mode controller (SMC) is designed based on the optimal control results of the model predictive controller (MPC) to enhance the robust response of the system.

[0107] Specifically, step 105 may include the following sub-steps S51-S53:

[0108] Step S51: Construct sliding mode error variables based on the three-phase reference current of the distribution station area.

[0109] Step S52: Determine the nonlinear control law variables based on the sliding mode error variables.

[0110] It should be noted that this invention is based on the three-phase current components in the state variables. , , (corresponding to) , , Construct sliding mode error variables:

[0111] ;

[0112] in, For time k The sliding mode error variable of the phase; For time k Phase state variables; For time k Three-phase reference current of the distribution station area.

[0113] Furthermore, the sliding mode controller adjusts based on the error. Design nonlinear control law variables:

[0114] ;

[0115] in, Let be the nonlinear control law variable at time k; The equivalent control term in the sliding mode control at time k (derived based on the expected current change); The sliding mode gain at time k (dynamically changing to improve robustness); This is a boundary layer function (used to prevent chattering).

[0116] It is worth mentioning that, With MPC control input (Model Predictive Controller control input) By merging through a weighted mechanism, the system state is jointly driven. The evolution of this ensures that the system can still operate stably under uncertain disturbance conditions and maintain three-phase balance.

[0117] In this embodiment, the invention ensures that the gain increases when the error is large and automatically decreases when the error is small, forming a dynamic and robust closed-loop mechanism. Based on the MPC controller and combined with state-space equations and future disturbance prediction, an optimization problem with a multi-objective cost function is designed. The cost function comprehensively considers factors such as minimizing three-phase imbalance, smoothness of control input changes, and sparsity of control signals. By minimizing the cost function, the optimal control sequence is obtained within a rolling time window. The advantage of MPC is that it can link prediction and optimization, ensuring system stability while taking into account real-time performance. It is a core module for handling large-step disturbances and low-frequency dynamic problems.

[0118] Step S53: Generate the distribution area unbalance control signal based on the preset frequency mapping weights and nonlinear control law variables.

[0119] It should be noted that different disturbance sources have different spectral characteristics. This invention constructs a disturbance frequency response function. And establish a frequency mapping weight function, that is, preset frequency mapping weights:

[0120] ;

[0121] in, To predict the dynamic weights of the controller for the model; Preset frequency mapping weights.

[0122] The final controller output, i.e., the unbalanced control signal for the distribution radio area, is as follows:

[0123] ;

[0124] in, The distribution area imbalance control signal at time t; The controller dynamic weights for the model predictive controller (which vary with frequency; the calculation process can refer to the steps and principles of existing model predictive controller dynamic weights). Preset frequency mapping weights (which vary with frequency); The control input of the model predictor controller at time t; Let be the nonlinear control law variable at time t; The disturbance frequency is estimated by analyzing the frequency domain characteristics of the state variables, such as the rate of change of the amplitude of the negative sequence component, or the frequency peak of the three-phase current by Fast Fourier Transform (FFT).

[0125] Furthermore, after obtaining the distribution substation imbalance control signal, the signal can be converted into energy storage charging and discharging power commands, photovoltaic output adjustment coefficients, and reactive power compensation device switching quantities through digital-to-analog conversion. By dynamically allocating the active / reactive power of each phase, the load difference between phases can be offset in real time, zero-sequence current can be suppressed, and the three-phase voltage / current can be quickly balanced. At the same time, frequency mapping weights are used to ensure priority protection of fundamental power quality during the control process, ultimately improving the stability of the distribution substation in the face of dynamic disturbances.

[0126] It is worth mentioning that the controller weighting mechanism enables the system to automatically and smoothly switch between predictive (MPC) and robust (SMC) modes under different disturbance conditions, improving the overall controller's adaptive capability under multi-source disturbance conditions and forming a closed-loop logic of "spectrum sensing - control strategy mapping - dynamic weighting".

[0127] In this embodiment, to enhance the system's rapid response to mid-frequency disturbances and modeling errors, the present invention introduces SMC as a robust compensation module for MPC. Sliding mode control defines a current deviation surface and constructs a control law to quickly drive the system state towards an ideal trajectory. To avoid chattering and control stiffness issues, the gain coefficient K(t) is designed to be adaptive, automatically adjusting according to the estimated network impedance and load changes, thereby achieving a balance between high dynamics and high robustness. This is a key support for the entire system to resist mid-frequency disturbances and nonlinear uncertainties.

[0128] For comparison of technical effects, existing technologies can be used as a reference. In recent years, with the large-scale integration of distributed photovoltaics, electric vehicles, and energy storage systems into distribution networks, traditional distribution substations are facing increasingly severe power quality problems such as three-phase imbalance, voltage fluctuations, and harmonic disturbances. To improve the stability and intelligent control capabilities of distribution networks, adaptive power quality control technology has become a research hotspot. This field is developing towards intelligence, multi-source collaboration, and high dynamic response, with core directions including dynamic compensation technologies based on power electronics (such as STATCOM (Static Synchronous Compensator) and APF (Active Power Filter)), coordinated control based on multi-port DC-DC topology, and fusion control algorithms (such as MPC, SMC, and deep reinforcement learning). Especially in the scenarios of "source-load-storage-charging" coordinated control and low-voltage distribution substations, the ability to predict and precisely control energy flow in real time has become an important indicator for evaluating the intelligence level of distribution substations. In the future, this field will continue to evolve towards higher integration, stronger adaptability, and lower latency control, helping to build an active distribution network in new power systems.

[0129] In modern power distribution areas, with the large-scale integration of distributed resources such as photovoltaic power generation, electric vehicles, and energy storage, the system faces significant load uncertainties and multi-source disturbances, easily leading to three-phase voltage and current imbalances. This results in degraded power quality, increased equipment losses, and worsened operational stability. Traditional static compensation methods suffer from response lag and are ill-suited to handling complex dynamic load fluctuations.

[0130] To address the aforementioned problems, this invention proposes a distribution transformer area imbalance control method. This method identifies typical imbalance characteristics such as uneven phase-to-phase load distribution and zero-sequence current by real-time acquisition of three-phase current and voltage data. Combined with a controller (such as MPC+SMC), it dynamically adjusts the energy storage / photovoltaic power allocation, achieving proactive power flow regulation and phase-to-phase reconfiguration, thus improving the system's adaptability to asynchronous load access. Specifically, the MPC constructs a distribution transformer area control model based on future load and energy storage status predictions, solving for the optimal control quantity within a finite time domain. This satisfies global optimization control under multiple constraints (such as power limitations and voltage ranges), making it particularly suitable for dynamic and complex multi-source distribution transformer area scenarios. The SMC possesses strong robustness to system nonlinearity and external disturbances, rapidly responding to voltage and current deviations, compensating for model uncertainties and response lags, and ensuring the dynamic stability and accuracy of the control system. The integration of LCL filters and digital control algorithms eliminates high-frequency harmonics and EMI interference, improving the output voltage waveform quality and equipment safety of the distribution transformer area. Furthermore, this invention comprehensively considers short-term load disturbances and medium- to long-term power trend predictions, constructs a unified optimized control framework, and introduces a spectrum partitioning mechanism to effectively distinguish low-frequency, medium-frequency, and high-frequency disturbance sources, and matches adaptive control strategies accordingly, thereby improving system symmetry and enhancing operational stability.

[0131] Specifically, this invention constructs a disturbance-driven state modeling system, realizing a complete mathematical modeling closed loop and dynamic feedback mechanism from external power disturbance modeling and future trend prediction input to three-phase imbalance tracking and multi-objective controller optimization, exhibiting strong systematicity and engineering adaptability. First, this method extends traditional static three-phase imbalance state modeling to a dynamic state evolution process driven by disturbances, considering the uncertain characteristics of the system under multi-source disturbances such as distributed photovoltaics, electric vehicles, and energy storage, making the model closer to the real operating environment. Second, a future disturbance prediction mechanism is introduced, using short-term load forecasting algorithms (such as LSTM and exponential smoothing) to obtain the load, photovoltaic, and energy storage states in future time periods, serving as prior inputs for model predictive control (MPC), improving the foresight and adaptability of the control strategy. Regarding the control strategy, a hybrid control architecture integrating MPC and adaptive sliding mode control (ASMC) is constructed. MPC is used for global rolling optimization, while sliding mode control provides robust real-time tracking response, balancing dynamics and stability. Furthermore, a frequency-mapped control weighting mechanism is designed to dynamically allocate control resources based on the disturbance spectrum characteristics: low-frequency disturbances are dominated by MPC, mid-frequency disturbances are responded to by sliding mode control, and high-frequency disturbances are suppressed by filters, achieving adaptive control in the disturbance frequency domain. The control target reference quantity, combined with prediction errors, forms a feedback correction loop during actual execution, achieving coordinated unity of dynamic adjustment and steady-state optimization. This method comprehensively improves the controller's ability to perceive, predict, and respond to imbalance states, and is applicable to the optimization and management of three-phase imbalances under complex operating conditions such as high penetration of new energy sources and medium- and low-voltage distribution substations, possessing good theoretical value and promising engineering application prospects.

[0132] In summary, this invention achieves its goals through a multi-level fusion control scheme combining model predictive control (MPC), sliding mode control (SMDC), and filtering compensation. Specifically, MPC is used for feedforward optimization scheduling of the power flow in the transformer substations, combined with sliding mode control for rapid compensation of local disturbances and modeling errors. Furthermore, a filtering module smooths high-frequency noise, ensuring both control accuracy and robustness. This scheme organically combines optimization calculation with rapid dynamic compensation, effectively solving the problems of regulation lag and stability in traditional single control strategies under high-uncertainty scenarios such as nonlinear loads and electric vehicle integration.

[0133] Meanwhile, this invention can support the precise operation of the four-quadrant power conversion module, ensuring the dynamic balance of three-phase power in the distribution area. It actively adjusts voltage fluctuations and phase imbalances under multi-source access such as distributed photovoltaic, energy storage, and charging piles, improving the intelligence and flexibility of distribution area operation and providing an algorithmic basis for building a new type of distribution-side energy autonomous system.

[0134] Furthermore, this invention integrates disturbance modeling, future power prediction, and a multi-dimensional control strategy synergy mechanism. First, it employs a disturbance-driven three-phase unbalanced state modeling approach, extending the traditional static voltage imbalance description into a dynamic state-space expression, enabling the system state to reflect the impact characteristics of disturbance sources such as distributed photovoltaics, electric loads, and energy storage in real time. Second, it introduces a future power disturbance prediction mechanism, utilizing short-term load, photovoltaic, and energy storage state prediction results as prior information, embedded in the model predictive control (MPC) structure, significantly improving the controller's foresight and regulation accuracy. In terms of controller design, it innovatively proposes a hybrid control framework integrating MPC and adaptive sliding mode control (ASMC), where MPC is responsible for global rolling optimization, and sliding mode control is responsible for fast tracking compensation, enhancing system robustness and dynamic response performance. In addition, it designs a controller weight allocation mechanism based on disturbance spectrum characteristics to achieve classified control and response matching for disturbances of different frequencies (fundamental frequency, intermediate frequency, and high frequency). The synergistic effect of the above key technologies enables the controller to maintain voltage and current symmetry and operational stability under multi-source disturbances, significantly improving the three-phase imbalance suppression effect. This is the core of the practical effect and the application for protection of this invention.

[0135] In this embodiment of the invention, a method for unbalanced control of a distribution substation is provided. The method involves acquiring the three-phase bus voltage and three-phase current of the distribution substation; generating a three-phase component voltage and a power optimization objective function based on the three-phase bus voltage and three-phase current; solving the power optimization objective function to output the predicted power of the distribution substation; calculating the three-phase reference current based on the predicted power and the three-phase component voltage; and generating an unbalanced control signal based on the three-phase reference current. Based on this scheme, the invention generates the three-phase component voltage and power optimization objective function of the distribution substation based on the acquired three-phase bus voltage and three-phase current. By utilizing the power optimization objective function and the reference current calculation, the method can dynamically adapt to the randomness of asynchronous load access, enabling the generated control signal to achieve refined power allocation adjustment, thereby improving the operational stability of the distribution substation.

[0136] Please see Figure 2 , Figure 2 This is a structural block diagram of a distribution radio station unbalance control system provided in Embodiment 2 of the present invention.

[0137] This invention provides a distribution area imbalance control system, comprising:

[0138] The acquisition module 201 is used to acquire the three-phase bus voltage and the three-phase current of the distribution substation area;

[0139] The generation module 202 is used to generate the three-phase component voltage and power optimization objective function of the distribution substation area based on the three-phase bus voltage and the three-phase current of the distribution substation area.

[0140] Solver module 203 is used to solve the objective function for power optimization of distribution substations and output the predicted power of distribution substations.

[0141] Calculation module 204 is used to calculate the three-phase reference current of the distribution substation based on the predicted power of the distribution substation and the three-phase component voltage of the distribution substation.

[0142] The control module 205 is used to generate an imbalance control signal for the distribution station area based on the three-phase reference current of the distribution station area.

[0143] Furthermore, module 201 is specifically used for:

[0144] Symmetrical component analysis is performed on the three-phase bus voltage of the distribution substation area to generate the three-phase component voltage of the distribution substation area.

[0145] A discrete state-space model is constructed based on the three-phase component voltages and three-phase currents of the distribution substation area.

[0146] Based on the discrete state-space model, construct the multi-step imbalance degree;

[0147] Based on the multi-step imbalance, an objective function for power optimization of distribution substations is constructed.

[0148] Furthermore, the calculation module 204 is specifically used for:

[0149] The reference power of the distribution station area is calculated using the predicted power of the distribution station area.

[0150] Calculate the three-phase reference current of the distribution substation based on the reference power and the three-phase component voltage of the distribution substation.

[0151] Furthermore, the control module 205 is specifically used for:

[0152] Based on the three-phase reference current of the distribution substation, a sliding mode error variable is constructed;

[0153] Determine the nonlinear control law variables based on the sliding mode error variables;

[0154] Based on the preset frequency mapping weights and nonlinear control law variables, the unbalanced control signal of the distribution station area is generated.

[0155] Furthermore, the calculation formula for the three-phase reference current of the distribution substation is as follows:

[0156] ;

[0157] in, Let k be the three-phase reference current of the distribution station area at time k; The reference power for the distribution area at time k; Let be the positive sequence component voltage of the three-phase component voltage of the distribution station area at time k; This is the unbalanced voltage compensation term at time k; Let k be the state vector at time k; , These are the proportional controller gain and the integral controller gain, respectively. The negative sequence component voltage is the three-phase component voltage of the distribution station area at time k. From the initial moment The cumulative sum of negative sequence voltages up to time k; k is the discrete time step.

[0158] Furthermore, the unbalanced control signal of the distribution radio area is specifically as follows:

[0159] ;

[0160] in, The distribution area imbalance control signal at time t; To predict the dynamic weights of the controller for the model; Preset frequency mapping weights; The control input of the model predictor controller at time t; Let be the nonlinear control law variable at time t; The frequency is the disturbance frequency.

[0161] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0162] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the distribution station imbalance control method as described in any of the above embodiments.

[0163] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the distribution radio station imbalance control method as described in any of the above embodiments.

[0164] This invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the distribution radio station imbalance control method as described in any of the above embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the imbalance of distribution radio stations, characterized in that, include: Obtain the three-phase bus voltage and three-phase current of the distribution substation area; Based on the three-phase bus voltage and the three-phase current of the distribution substation area, generate the three-phase component voltage and power optimization objective function of the distribution substation area; Solve the objective function for power optimization of the distribution station area and output the predicted power of the distribution station area; Based on the predicted power of the distribution substation and the three-phase component voltage of the distribution substation, calculate the three-phase reference current of the distribution substation. Based on the three-phase reference current of the distribution station area, an unbalanced control signal for the distribution station area is generated.

2. The distribution area imbalance control method according to claim 1, characterized in that, The step of generating the three-phase component voltage and power optimization objective function of the distribution substation area based on the three-phase bus voltage and the three-phase current of the distribution substation area includes: A symmetrical component analysis is performed on the three-phase bus voltage of the distribution station area to generate the three-phase component voltage of the distribution station area. A discrete state-space model is constructed based on the three-phase component voltages and three-phase currents of the distribution station area. Based on the discrete state-space model, a multi-step imbalance degree is constructed; Based on the aforementioned multi-step imbalance, a power optimization objective function for distribution substations is constructed.

3. The distribution area imbalance control method according to claim 1, characterized in that, The calculation of the three-phase reference current of the distribution substation based on the predicted power of the distribution substation and the three-phase component voltage of the distribution substation includes: The reference power of the distribution station area is calculated using the predicted power of the distribution station area. Calculate the three-phase reference current of the distribution station area based on the reference power of the distribution station area and the three-phase component voltage of the distribution station area.

4. The distribution area imbalance control method according to claim 1, characterized in that, The step of generating the distribution station imbalance control signal based on the three-phase reference current of the distribution station includes: Based on the three-phase reference current of the distribution station area, a sliding mode error variable is constructed; Based on the sliding mode error variable, determine the nonlinear control law variable; Based on the preset frequency mapping weights and the nonlinear control law variables, an unbalanced control signal for the distribution station area is generated.

5. The distribution area imbalance control method according to claim 3, characterized in that, The calculation formula for the three-phase reference current of the distribution substation area is as follows: ； in, Let k be the three-phase reference current of the distribution station area at time k; The reference power for the distribution area at time k; Let be the positive sequence component voltage of the three-phase component voltage of the distribution station area at time k; This is the unbalanced voltage compensation term at time k; Let k be the state vector at time k; , These are the proportional controller gain and the integral controller gain, respectively. The negative sequence component voltage is the three-phase component voltage of the distribution station area at time k. From the initial moment The cumulative sum of negative sequence voltages up to time k; k is the discrete time step.

6. The distribution area imbalance control method according to claim 4, characterized in that, The unbalanced control signal of the distribution station area is specifically as follows: ； in, The distribution area imbalance control signal at time t; To predict the dynamic weights of the controller for the model; Preset frequency mapping weights; The control input of the model predictor controller at time t; Let be the nonlinear control law variable at time t; The frequency is the disturbance frequency.

7. A distribution radio area imbalance control system, characterized in that, include: The acquisition module is used to acquire the three-phase bus voltage and three-phase current of the distribution substation area; The generation module is used to generate the three-phase component voltage and the power optimization objective function of the distribution substation area based on the three-phase bus voltage and the three-phase current of the distribution substation area. The solution module is used to solve the objective function for power optimization of the distribution station area and output the predicted power of the distribution station area. The calculation module is used to calculate the three-phase reference current of the distribution station area based on the predicted power of the distribution station area and the three-phase component voltage of the distribution station area. The control module is used to generate an imbalance control signal for the distribution station area based on the three-phase reference current of the distribution station area.

8. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the distribution radio station imbalance control method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the distribution radio station imbalance control method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the distribution radio station imbalance control method as described in any one of claims 1-6.

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