An adaptive voltage control method and system

Abstract

This invention discloses an adaptive voltage control method and system, primarily relating to the field of power system automation technology. It includes the following steps: real-time monitoring of voltage signals and load current signals at grid nodes; calculation of voltage deviation and its rate of change based on the monitored voltage signals; dynamic generation of control signals using an adaptive control algorithm based on the voltage deviation and rate of change; and adjustment of the power module output based on the control signals. The beneficial effects of this invention are: it achieves high-precision adaptive stabilization of grid voltage around the clock, effectively addressing power fluctuations from distributed energy sources such as photovoltaics and improving control accuracy, while simultaneously reducing line losses and enhancing power supply reliability.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, specifically an adaptive voltage control method and system. Background Technology

[0002] With the large-scale integration of intermittent distributed energy sources such as photovoltaics, the power flow of the distribution network has shifted from the traditional unidirectional radial pattern to a bidirectional, multi-source pattern, leading to frequent and rapid voltage fluctuations at grid nodes, posing a severe challenge to power quality. Against this backdrop, higher demands are placed on voltage stability control technology. Currently, voltage regulation devices widely used in the power grid, such as traditional mechanical on-load tap changers, capacitor banks with fixed compensation, and static reactive power compensation devices using conventional proportional-integral-derivative controllers, typically have their control parameters set according to typical operating conditions before commissioning and remain fixed during operation. However, these regulation methods cause the following problems: 1. Unable to adapt to dynamic changes, resulting in low voltage regulation accuracy: Fixed parameter control strategies cannot respond to the random and intermittent fluctuations in photovoltaic output and load, leading to lag or over-adjustment in control response, making it difficult to stabilize the voltage within the standard required range. 2. Reliance on manual experience for adjustment, resulting in unreliable response speed and effectiveness: When the system's operating conditions change significantly, technicians need to readjust the parameters on-site or remotely. This process is not only inefficient but also heavily reliant on personal experience, lacking scientific and real-time optimization methods, and thus cannot guarantee the best control effect. 3. Insufficient ability to cope with complex disturbances and the need to improve system reliability: Traditional control algorithms (such as conventional PID) have poor robustness in their control models for rapid and large-scale disturbances such as sudden increases / decreases in photovoltaic power, which can easily lead to voltage instability, increase line losses and the risk of malfunction of protection equipment, and affect power supply reliability.

[0003] Therefore, there is an urgent need for an adaptive voltage control method and system to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive voltage control method and system that achieves high-precision adaptive stabilization of grid voltage around the clock, effectively copes with power fluctuations of distributed energy sources such as photovoltaics and improves control accuracy, while reducing line losses and improving power supply reliability.

[0005] To achieve the above objectives, the present invention employs the following technical solution: On one hand, the present invention provides an adaptive voltage control method, comprising the following steps: Step S1: Monitor the voltage signal and load current signal of the power grid nodes in real time; Step S2: Calculate the voltage deviation and its rate of change based on the monitored voltage signal; Step S3: Using an adaptive control algorithm, dynamically generate control signals based on the voltage deviation and rate of change from step S2; Step S4: Adjust the output of the power module according to the control signal in step S3.

[0006] Preferably, in step S3, the adaptive control algorithm is a fuzzy proportional-integral-derivative control algorithm, and its control output is... Determined by the following formula: ; in, Indicates the first Control output quantity at each sampling time; Indicates the first The voltage deviation at each sampling time, that is, the difference between the target voltage and the actual voltage; , , These represent the proportional, integral, and differential coefficients, respectively. Indicates the sampling period; The proportional, integral, and derivative coefficients are based on the voltage deviation. and its rate of change Dynamic adjustment is achieved through fuzzy logic rules, where the rate of change... .

[0007] Preferably, the process of establishing the fuzzy proportional-integral-derivative control algorithm includes: voltage deviation and its rate of change The precise quantity is converted into the membership degree of the corresponding fuzzy set; Based on the preset fuzzy inference rules, the adjustment amounts for the proportional coefficient, integral coefficient, and differential coefficient are derived according to the membership degree. The adjustment quantity is fuzzified into a precise quantity, and the proportional, integral, and differential coefficients are updated based on the precise quantity.

[0008] Preferably, the adaptive control algorithm is a model predictive control algorithm, which generates the control signal by solving the following optimization problem in each sampling period: ; in, Indicates the length of the prediction time domain; Indicates the length of the control time domain; express Voltage reference value at any given time; express The predicted voltage value at that moment; express The amount of control at any given moment; This represents the weighting coefficient for changes in the control quantity.

[0009] Preferably, the voltage prediction value is obtained through the following state-space model: ; ; in, express The system state variables at time t. Indicates control input, Indicates a measurable disturbance. , , , The system matrix is ​​obtained through online identification or offline modeling.

[0010] Preferably, the power module is a full-bridge inverter circuit composed of insulated gate bipolar transistors, and the output voltage is adjusted by pulse width modulation technology, with the duty cycle of the modulation wave determined by the control signal.

[0011] Preferably, the method further includes establishing voltage control effect evaluation indicators and making online corrections to the adaptive control algorithm parameters based on changes in the indicators. The evaluation indicators include: voltage settling time, overshoot, and steady-state error.

[0012] On the other hand, the present invention also provides an adaptive voltage control system for implementing the adaptive voltage control method described above, comprising: The signal monitoring module is used to acquire grid voltage signals and load current signals in real time. The signal processing module is used to calculate the voltage deviation and its rate of change. The control calculation module is used to run adaptive control algorithms and generate control signals. The power regulation module is used to adjust the power output according to the control signal; The communication interface module is used for data interaction with the superior monitoring system.

[0013] Preferably, the control computing module is based on a digital signal processor and equipped with an analog-to-digital converter and a pulse width modulation generator.

[0014] Preferably, the power regulation module includes a DC-side capacitor, a full-bridge inverter circuit, and an output filter circuit, wherein the drive signal of the insulated-gate bipolar transistor in the full-bridge inverter circuit is controlled by a pulse width modulation wave output by the control calculation module.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Achieved high-precision all-weather voltage stabilization: Through adaptive algorithms (such as fuzzy PID and model predictive control) that dynamically adjust the control signal based on voltage deviation and its rate of change, the system can respond quickly and accurately to photovoltaic power fluctuations and load changes. The algorithm ensures control accuracy through optimized calculations, stabilizing the voltage within ±2% of the rated value, which is significantly better than traditional fixed-parameter control with slow response and low accuracy.

[0016] 2. The system possesses self-optimizing adaptive capabilities: This invention fundamentally changes the passive mode of relying on manual experience to adjust parameters. The system can automatically adjust control parameters online based on real-time operating status (through fuzzy logic reasoning) or historical control effects (through effect evaluation and closed-loop correction), thereby adapting to system changes brought about by different operating conditions and long-term operation, achieving a leap from "fixed control" to "adaptive control".

[0017] 3. Effectively improves the economy and reliability of power grid operation: Precise and rapid voltage control effectively smooths out voltage fluctuations, directly resulting in reduced line losses. At the same time, strong anti-disturbance capabilities (such as dealing with sudden changes in photovoltaic power) enhance the power supply quality to critical loads and the operational reliability of the entire distribution system, reducing the risk of equipment failure due to voltage instability. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0019] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0020] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0021] Example: like Figure 1 As shown, this embodiment provides an adaptive voltage control method, including the following steps: Step S1: Monitor the voltage signal and load current signal of the power grid nodes in real time; Step S2: Calculate the voltage deviation and its rate of change based on the monitored voltage signal; Step S3: Using an adaptive control algorithm, dynamically generate control signals based on the voltage deviation and rate of change from step S2; Step S4: Adjust the output of the power module according to the control signal in step S3.

[0022] like Figure 2 As shown, this embodiment also provides an adaptive voltage control system, including: The signal monitoring module is used to acquire grid voltage signals and load current signals in real time. The signal processing module is used to calculate the voltage deviation and its rate of change. The control calculation module is used to run adaptive control algorithms and generate control signals. The power regulation module is used to adjust the power output according to the control signal; The communication interface module is used for data interaction with the superior monitoring system; The control and computing module is based on a digital signal processor and is equipped with an analog-to-digital converter and a pulse width modulation generator. The power regulation module includes a DC-side capacitor, a full-bridge inverter circuit, and an output filter circuit. The drive signal of the insulated-gate bipolar transistor in the full-bridge inverter circuit is controlled by the pulse width modulation wave output by the control calculation module.

[0023] In this embodiment, the application of the above method and system in this scenario is described in detail using the following distribution network nodes: To illustrate the implementation of this invention, consider a 10kV distribution network node A, which is connected to a 500kW photovoltaic power station and a variable load. Node A has a voltage level of 10kV, which is stepped down to 400V by a transformer to supply the local load. The photovoltaic power station is directly connected to the 400V bus via an inverter.

[0024] In power distribution networks, photovoltaic (PV) power output is significantly affected by weather conditions. For example, cloud cover can cause power to drop drastically from 500kW to 100kW within minutes, and vice versa. Simultaneously, local loads (such as industrial motors and commercial air conditioners) may randomly start and stop, leading to voltage fluctuations. Traditional fixed-parameter control devices cannot respond quickly to these changes, resulting in voltage deviations (e.g., exceeding ±10% of the rated voltage) and impacting power quality. This embodiment utilizes an adaptive voltage control method to adjust reactive power or voltage reference values ​​in real time, stabilizing the voltage within ±2% of the rated value.

[0025] Step S1: Real-time monitoring of voltage and load current signals at grid nodes: Install voltage sensors (such as Hall voltage sensors) and current sensors (such as Hall current sensors) on the 400V bus to acquire voltage signals at a high sampling rate (e.g., 10kHz). and load current signal The sensor outputs an analog signal, which is converted into a digital signal by an analog-to-digital converter (ADC) for subsequent processing. To ensure accuracy, the ADC resolution is at least 12 bits, and the sampling period is... Set to a sampling frequency of 0.1ms (i.e., 10kHz), the monitoring module also integrates filtering circuits (such as a low-pass filter) to eliminate high-frequency noise. In practical applications, the voltage signal may contain harmonic components, so preprocessing is required, for example, using Fourier transform to extract the fundamental voltage (50Hz) as the actual voltage value. Target voltage Set to 400V (rated value), voltage deviation Calculated as .

[0026] Step S2: Calculate the voltage deviation and its rate of change based on the monitored voltage signal: Voltage deviation At each sampling time The rate of change was calculated. Calculation by difference: ;in, This represents the deviation at the previous sampling time. The rate of change reflects the trend of voltage change; a positive value indicates that the voltage is rising, and a negative value indicates that it is falling. For example, if the photovoltaic power suddenly drops, the voltage may drop, leading to... For positive and A positive value indicates that the voltage is deviating from the target value; To smooth out noise, the rate of change can be processed using a moving average filter; the calculated... and As input to the adaptive control algorithm.

[0027] Step S3: Dynamically generate control signals using an adaptive control algorithm. This embodiment supports two adaptive control algorithms: fuzzy proportional-integral-derivative (fuzzy PID) control and model predictive control (MPC). One or a combination of these algorithms can be selected based on the actual system requirements. These are explained in detail below: Fuzzy PID control algorithm: Fuzzy PID control combines the adaptability of fuzzy logic with the stability of PID control, and its control output... Determined by the following formula: ; in, , , For proportional, integral, and differential coefficients, The sampling period is 0.1 ms; these coefficients are not fixed but depend on the voltage deviation. and rate of change Dynamically adjusted using fuzzy logic rules; The process of establishing a fuzzy PID controller involves three steps: 1. Blurring: Transforming precise quantities... and Transform into membership degrees of fuzzy sets and define input variables. and The fuzzy sets are: negative large (NB), negative medium (NM), negative small (NS), zero (ZE), positive small (PS), positive medium (PM), and positive large (PB). The output variable is... , , This refers to the adjustment amount of the coefficient. The membership function uses a trigonometric function, for example: for The universe of discourse is set to [-20V, 20V], that is, the deviation range is ±20V. The membership function of NB is between -20V and -10V, with a peak value at -15V. for The universe of discourse is set to [-10V / s, 10V / s], the rate of change ranges from ±10V / s, the membership function of ZE is between -2V / s and 2V / s, and the peak value is 0. Specifically, measured by sensors , Then calculate the membership degree: The membership degree of PS is 0.7, and the membership degree of PM is 0.3. The membership degree of PS is 0.6, and the membership degree of ZE is 0.4. 2. Fuzzy Reasoning: Based on preset fuzzy rules, the adjustment amount is derived according to the membership degree. The rule base contains 49 rules (7×7), in the form of "If..." It is A and If it is B, then It's C. It's D. It is "E"; for example: Rule 1: If It is PB and If it is ZE, then It's PB. It's NB. It's Photoshop; Rule 2: If It's photoshopped and If it's PS, then It's PM. It's NS. It is ZE; Using the Mamdani inference method, the input membership degree is mined with the rule antecedent to obtain the membership degree of the rule consequent. Then, the output of all rules is aggregated through a max operation. For example, for the above... and Rules 1 and 2 are activated, and the output is... , , fuzzy sets; 3. Defuzzification: Converting fuzzy output into precise quantities. The precise value of the adjustment is calculated using the centroid method. ; in, It is the degree of membership. It outputs points in the universe of discourse, thus obtaining... , , Then, update the PID coefficients: , , ; in, , , These are the initial coefficients, set according to the system model (e.g., tuned using the Ziegler-Nichols method). Ultimately, control signals Used to regulate reactive power or voltage reference values ​​in photovoltaic inverters. Can be converted to reactive current reference value The inverter outputs reactive power to support the voltage. Model Predictive Control (MPC) Algorithm: MPC is a model-based control strategy that generates control signals by optimizing future behavior. In each sampling period, it solves the following optimization problem: ; in, To predict the time domain length (e.g., 20 steps, corresponding to 2ms). To control the length of the time domain (e.g., 5 steps). This is a weighting coefficient for the change in the control quantity (e.g., 0.1), used to smooth the control action; It is a future voltage reference value (constantly 400V). This is the predicted voltage value; The voltage prediction value is obtained through a state-space model: ; ; in, These are system state variables (such as discrete values ​​of voltage and current). It is the control input (such as the inverter modulation signal). It is a measurable disturbance (such as photovoltaic output or load current); matrix , , , Obtained through system identification, for example, for an RLC network model, the state variables include capacitor voltage and inductor current, and the matrix is ​​updated through offline modeling or online identification (such as recursive least squares method); Specifically, in this embodiment, the system model is simplified as follows: , , , ; Disturbance For the change in load current, during prediction, it is calculated from the current state. Let's start by recursively calculating the future state: ; ; ; Until Step, in which future disturbances It can be estimated through predictive models (e.g., based on historical data) or assumed to be a constant; The optimization problem is solved online using a quadratic programming solver (such as the interior-point method) to obtain the control sequence. , , , Only the first control variable is implemented. The solution is recalculated in the next sampling period.

[0028] Step S4: Adjust the output of the power module according to the control signal: The power module is a full-bridge inverter circuit composed of insulated-gate bipolar transistors (IGBTs), which regulates the output voltage and control signal using pulse width modulation (PWM) technology. Duty cycle converted to PWM wave The relationship is: ; in, This is the gain factor (determined by the inverter's DC-side voltage). For example, if the DC-side voltage is 800V and the output AC voltage is 400V, then... ; The full-bridge inverter circuit consists of four IGBTs. The drive signal is generated by a PWM generator with a PWM frequency set to 10kHz. It is synchronized with the sampling and the duty cycle adjusts the width of the output pulse, thereby controlling the amplitude of the fundamental voltage output by the inverter. For example, when the duty cycle increases, the output voltage increases to compensate for voltage deviation. The output filter circuit (LC filter) smooths the PWM waveform and reduces harmonics. The filter inductor is 1mH and the filter capacitor is 50μF. Finally, the inverter outputs reactive power or active power (such as active power reduction) to stabilize the voltage.

[0029] The adaptive voltage control system includes the following modules: Signal monitoring module: It consists of voltage and current sensors, signal conditioning circuit and ADC. The sensors are LEM's LV-25P voltage sensor and LA-55P current sensor with an accuracy of ±0.5%. The ADC has a 16-bit resolution (such as ADS8568) and a sampling rate of 10kHz. Signal processing module: Based on a microcontroller (such as ARM Cortex-M4), it calculates voltage deviation and rate of change, and integrates digital filters (such as FIR filters) to remove noise; Control and calculation module: Based on a digital signal processor (DSP), such as TI's TMS320F28335, it runs an adaptive control algorithm. The DSP is equipped with an ADC interface and a PWM generator (ePWM module). The algorithm code is implemented in C language to optimize calculation efficiency. Power regulation module: includes DC side capacitor (1000μF), full-bridge inverter circuit (IGBT module such as FF100R12RT4) and output filter circuit (LC filter). The IGBT drive signal is controlled by the PWM output of DSP and driven through optocoupler isolation. Communication interface module: It adopts Ethernet or RS485 interface to communicate with the upper-level monitoring system (such as SCADA) to transmit voltage data, control status and alarm information. The protocol supports Modbus TCP or IEC 61850.

[0030] The system is deployed in a distribution box, with an ambient temperature range of -40°C to 85°C and a protection rating of IP54.

[0031] Establish evaluation indicators for voltage control effectiveness, including: Voltage settling time: The time from the occurrence of a disturbance to the voltage recovering to within ±2% of the rated value; Overshoot: The maximum percentage of voltage exceeding the rated value during the voltage response process; Steady-state error: The average deviation between the voltage after stabilization and the rated value; These metrics are calculated in each control cycle (e.g., 1 second). Based on changes in these metrics, the parameters of the adaptive control algorithm are adjusted online. For example: If the settling time exceeds 100ms, adjust the rule base of the fuzzy PID and increase the weight of the rate of change. If the overshoot is greater than 5%, then reduce the weighting factor of MPC. Smooth control of actions; If the steady-state error persists, increase the integral coefficient. The adjustment amount; The correction mechanism is implemented through a DSP, using gradient descent or an expert system, for example, by defining performance metrics. ,in , , As weights, by minimizing Adjust algorithm parameters online.

[0032] In a simulated case, the photovoltaic output dropped from 500kW to 100kW within 10 seconds, while the load increased by 50kW. Under traditional PID control, the voltage dropped from 400V to 375V (deviation -6.25%), the settling time was 500ms, and the overshoot was 8%. However, using the adaptive control of this invention: Fuzzy PID: Voltage as low as 392V (deviation -2%), settling time 100ms, overshoot 3%; MPC: Voltage down to 394V (deviation -1.5%), settling time 80ms, overshoot 2%; Performance evaluation indicators show that adaptive control is significantly better than traditional methods, and the online correction mechanism further optimizes parameters, adapting to equipment aging during long-term operation.

[0033] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An adaptive voltage control method, characterized in that, Includes the following steps: Step S1: Monitor the voltage signal and load current signal of the power grid nodes in real time; Step S2: Calculate the voltage deviation and its rate of change based on the monitored voltage signal; Step S3: Using an adaptive control algorithm, dynamically generate control signals based on the voltage deviation and rate of change from step S2; Step S4: Adjust the output of the power module according to the control signal in step S3.

2. The adaptive voltage control method according to claim 1, characterized in that, In step S3, the adaptive control algorithm is a fuzzy proportional-integral-derivative control algorithm, and its control output is... Determined by the following formula: ； in, Indicates the first Control output quantity at each sampling time; Indicates the first The voltage deviation at each sampling time, that is, the difference between the target voltage and the actual voltage; , , These represent the proportional, integral, and differential coefficients, respectively. Indicates the sampling period; The proportional, integral, and derivative coefficients are based on the voltage deviation. and its rate of change Dynamic adjustment is achieved through fuzzy logic rules, where the rate of change is defined as... .

3. The adaptive voltage control method according to claim 2, characterized in that, The process of establishing the fuzzy proportional-integral-derivative control algorithm includes: voltage deviation and its rate of change The precise quantity is converted into the membership degree of the corresponding fuzzy set; Based on the preset fuzzy inference rules, the adjustment amounts for the proportional coefficient, integral coefficient, and differential coefficient are derived according to the membership degree. The adjustment quantity is fuzzified into a precise quantity, and the proportional, integral, and differential coefficients are updated based on the precise quantity.

4. The adaptive voltage control method according to claim 1, characterized in that, The adaptive control algorithm is a model predictive control algorithm, which generates control signals by solving the following optimization problem in each sampling period: ； in, Indicates the length of the prediction time domain; Indicates the length of the control time domain; express Voltage reference value at any given time; express The predicted voltage value at that moment; express The amount of control at any given moment; This represents the weighting coefficient for changes in the control quantity.

5. The adaptive voltage control method according to claim 4, characterized in that, The predicted voltage value is obtained through the following state-space model: ； ； in, express The system state variables at time t. Indicates control input, Indicates a measurable disturbance. , , , The system matrix is ​​obtained through online identification or offline modeling.

6. The adaptive voltage control method according to claim 1, characterized in that, The power module is a full-bridge inverter circuit composed of insulated-gate bipolar transistors. The output voltage is adjusted by pulse width modulation technology, and the duty cycle of the modulation wave is determined by the control signal.

7. The adaptive voltage control method according to claim 1, characterized in that, It also includes establishing voltage control effect evaluation indicators and making online corrections to the parameters of the adaptive control algorithm based on changes in the indicators. The evaluation indicators include: voltage settling time, overshoot, and steady-state error.

8. An adaptive voltage control system for implementing an adaptive voltage control method as described in any one of claims 1-7, characterized in that, include: The signal monitoring module is used to acquire grid voltage signals and load current signals in real time. The signal processing module is used to calculate the voltage deviation and its rate of change. The control calculation module is used to run adaptive control algorithms and generate control signals. The power regulation module is used to adjust the power output according to the control signal; The communication interface module is used for data interaction with the superior monitoring system.

9. An adaptive voltage control system according to claim 8, characterized in that, The control and computing module is based on a digital signal processor and is equipped with an analog-to-digital converter and a pulse width modulation generator.

10. An adaptive voltage control system according to claim 8, characterized in that, The power regulation module includes a DC-side capacitor, a full-bridge inverter circuit, and an output filter circuit. The drive signal of the insulated-gate bipolar transistor in the full-bridge inverter circuit is controlled by the pulse width modulation wave output by the control calculation module.

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