Power electronic distributed cooperative control method and system for intelligent micro-grid

Abstract

The invention discloses a power electronic distributed cooperative control method and system for an intelligent micro-grid. The method comprises the following steps: constructing a distributed communication architecture based on adaptive topology; based on a distributed communication architecture, executing a fusion cooperative control algorithm: dynamically coordinating a power distribution proportion based on a distributed average consistency algorithm, and introducing a dynamic weight mechanism; in combination with an LSTM prediction model, historical and real-time data are utilized to predict the output fluctuation and load change trend of the distributed power supply, and a control strategy is optimized in advance; setting an intelligent abnormal response mechanism; and based on the intelligent abnormal response mechanism, carrying out dynamic scheduling by adopting a flexible power regulation method. In the aspect of flexible power adjustment, fuzzy logic control is organically combined with maximum power point tracking and PWM pulse width modulation, and dynamic scheduling is performed according to the real-time operation state of the micro-grid, so that accurate and efficient control of the output power of the inverter is realized, and the overall performance and stability of the micro-grid are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of power grids and relates to control technology of smart microgrids, and specifically to a power electronic distributed collaborative control method and system for smart microgrids. Background Art

[0002] As a key component of the future energy system, smart microgrids integrate distributed generation, energy storage systems, power electronics technology and control strategies to achieve efficient production, distribution and consumption of electricity. By optimizing the scheduling of distributed power sources and loads, it effectively improves energy utilization efficiency and enhances power supply reliability and power quality.

[0003] In the existing smart microgrid control, the communication architecture mostly adopts a fixed topology. The communication connection relationship between power electronic equipment is pre-set, and lacks the ability to make autonomous adjustments according to the real-time operating conditions of the microgrid. In terms of collaborative control algorithms, a single centralized control or a simple distributed control algorithm is often used. Centralized control relies on the central controller to collect all device data and uniformly calculate the control strategy, which has a heavy communication burden and poor system flexibility; simple distributed control algorithms are difficult to achieve accurate power balance control when dealing with the intermittent nature of distributed power sources and load fluctuations. In terms of power regulation, existing methods have limited means of regulating inverters and converters. Inverters mostly use conventional PWM control and lack the ability to adaptively adjust to complex working conditions of microgrids; converters mainly perform power conversion through a fixed number of levels, and the output voltage waveform quality is poor and the harmonic content is high. Summary of the invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, a power electronics distributed collaborative control method and system for smart microgrids are provided.

[0005] Technical solution: To achieve the above-mentioned purpose, the present invention provides a power electronic distributed collaborative control method for a smart microgrid, comprising the following steps:

[0006] S1: Building a distributed communication architecture based on adaptive topology;

[0007] S2: Based on the distributed communication architecture, execute the fusion collaborative control algorithm:

[0008] Dynamically coordinate the power allocation ratio based on the distributed average consensus algorithm and introduce a dynamic weight mechanism;

[0009] Combined with the LSTM prediction model, historical and real-time data are used to predict the output fluctuation of distributed power sources and load change trends, and the control strategy is optimized in advance;

[0010] S3: Set up intelligent exception response mechanism:

[0011] Each power electronic device continuously monitors the operating status of itself and its surrounding devices. Once an abnormality such as power mutation or voltage exceeding the limit is detected, the adaptive adjustment process is immediately started. The device autonomously analyzes the cause of the abnormality, dynamically adjusts the parameters of the collaborative control algorithm, recalculates the control strategy, and quickly restores the stable operation of the microgrid without relying on centralized control instructions.

[0012] S4: Based on the intelligent abnormal response mechanism, a flexible power adjustment method is used for dynamic scheduling.

[0013] Furthermore, the distributed communication architecture based on the adaptive topology in step S1 includes an intelligent topology decision module and a time synchronization network:

[0014] Intelligent topology decision module: An intelligent topology decision module is built into each power electronic device, and the intelligent topology decision module includes a data acquisition unit and a link optimization algorithm unit; the data acquisition unit is used to collect multi-dimensional data in real time; the link optimization algorithm unit automatically disconnects low-quality links and establishes new link connections based on a preset communication optimization algorithm;

[0015] Time synchronization network: By setting up a master clock device and slave clock devices in the network, each slave clock device periodically exchanges time synchronization messages with the master clock device, continuously calibrates the local clock, and ensures that the time synchronization accuracy between devices meets the requirements.

[0016] Furthermore, the power allocation ratio is dynamically coordinated based on the distributed average consistency algorithm in step S2 as follows: each device exchanges power allocation intentions through the communication network, and uses the distributed average consistency algorithm to iteratively update the local power allocation ratio until the power allocation of the entire network is consistent, thereby maintaining the power balance of the microgrid;

[0017] The dynamic weight mechanism is: weights are assigned to devices according to power generation stability. For distributed power devices with high power generation stability and strong power supply reliability, higher weights are given in the power allocation consistency calculation, making them more influential in power allocation decisions; while for devices with relatively poor stability, the weights are reduced accordingly.

[0018] Furthermore, the operation of the LSTM prediction model in step S2 includes:

[0019] Assume the input data is , the hidden state at the previous moment is , the memory unit at the previous moment is ;

[0020] Input gate calculation formula:

[0021] Forget gate calculation formula:

[0022] Output gate calculation formula:

[0023] Memory unit update formula:

[0024] The hidden state calculation formula at the current moment is:

[0025] in, is the sigmoid activation function, ⊙ is the element-by-element multiplication, is the weight matrix, is the bias vector.

[0026] Furthermore, the flexible power regulation method is used for dynamic scheduling in step S4, including:

[0027] When the microgrid is in steady-state operation, the flexible power regulation based on fuzzy logic control is combined with PWM pulse width modulation for dynamic scheduling;

[0028] When the microgrid is in dynamic change, the flexible power regulation based on fuzzy logic control is combined with the maximum power point tracking MPPT for dynamic scheduling;

[0029] When the microgrid is in an abnormal situation, the flexible power regulation based on fuzzy logic control is combined with PWM pulse width modulation and maximum power point tracking MPPT to coordinate dynamic scheduling.

[0030] Furthermore, the process of performing dynamic scheduling in step S4 by combining flexible power regulation based on fuzzy logic control with PWM pulse width modulation includes:

[0031] A1: Basic parameter settings:

[0032] After the inverter is started, the PWM module sets basic parameters according to the rated parameters and initial operating conditions of the inverter to maintain the basic power output regulation of the inverter; at the same time, the fuzzy logic control module starts to collect data;

[0033] A2: Fuzzy logic control analysis:

[0034] The fuzzy logic control module converts the collected precise quantity into fuzzy quantity and performs reasoning operation through the preset fuzzy rule library;

[0035] A3: PWM parameter adjustment decision:

[0036] The fuzzy logic control module generates adjustment instructions for PWM pulse width modulation parameters according to the calculated power regulation strategy;

[0037] A4: PWM control execution:

[0038] After receiving the adjustment command sent by the fuzzy logic control module, the PWM pulse width modulation module immediately adjusts the relevant parameters of the PWM pulse; by changing the on and off time of the power switch device, the inverter output voltage and power are precisely adjusted; in steady-state operation, the PWM pulse width modulation maintains stable power output according to the initial set parameters; and when the microgrid undergoes dynamic changes, the fuzzy logic control optimizes the PWM parameters in real time;

[0039] A5: Real-time monitoring and feedback:

[0040] During the entire operation process, the fuzzy logic control module and the PWM pulse width modulation module continuously monitor the output power and voltage of the inverter and the operating status of the microgrid; if it is found that the actual operating conditions deviate from the expected regulation effect, the fuzzy logic control module will re-analyze and calculate, and adjust the PWM parameters again to form a closed-loop control.

[0041] Furthermore, the process of performing dynamic scheduling in step S4 by combining flexible power regulation based on fuzzy logic control with maximum power point tracking (MPPT) includes:

[0042] B1: Initialization and data collection:

[0043] The power generation equipment is connected to the inverter. After the inverter is started, the MPPT module starts to work and collects the output voltage and current data of the photovoltaic array in real time. At the same time, the fuzzy logic control module is also initialized to collect voltage and current data.

[0044] B2: MPPT maximum power calculation:

[0045] The MPPT module calculates the maximum power value based on the collected voltage and current data;

[0046] B3: Fuzzy logic control:

[0047] The fuzzy logic control module converts the collected precise data into fuzzy quantities and performs reasoning operations based on the preset fuzzy rule base;

[0048] B4: Power regulation coordination:

[0049] When generating the power regulation strategy, the fuzzy logic control module will refer to the maximum power value calculated by the MPPT module to ensure that the adjusted inverter output power does not exceed the maximum power, and avoid excessive regulation that causes the power generation equipment to deviate from the maximum power point;

[0050] B5: Control signal output and execution:

[0051] The fuzzy logic control module converts the final power regulation strategy into a control signal and sends it to the power switch device drive circuit of the inverter; the drive circuit adjusts the on and off time of the power switch device according to the signal to adjust the output power of the inverter.

[0052] Furthermore, in step B2, the MPPT module uses a perturbation observation method to calculate the maximum power value, which specifically includes:

[0053] In the perturbation and observation method, the operating voltage at the access end of the power generation equipment is periodically disturbed. , observe the direction of power change to find the maximum power point;

[0054] The voltage after each disturbance is: ; The power change is: ;

[0055] like , then the next disturbance direction is the same as this one, that is, continue to increase ;like , then the next disturbance direction is opposite to this one, that is, it decreases ; By continuously adjusting the voltage, the maximum power value under the current working conditions is found.

[0056] Furthermore, the flexible power regulation based on fuzzy logic control in step S4 is coordinated with the combination of PWM pulse width modulation and maximum power point tracking MPPT to perform dynamic scheduling, including:

[0057] The ultimate goal of both is to optimize the performance of the inverter and improve the power quality and power generation efficiency.

[0058] State information coordination, fuzzy logic control needs to adjust the control strategy according to the real-time operating status of the microgrid and the status of each power electronic device. The power generation equipment status information obtained by the MPPT module is an important part of the overall operating status of the microgrid.

[0059] Control strategy coordination, in the control process of the inverter, the flexible power regulation of fuzzy logic control and the maximum power point tracking are coordinated with each other. When the microgrid load changes, the flexible power regulation module will adjust the output power of the inverter according to the load demand. During this process, the maximum power point tracking module will monitor the working status of the photovoltaic cell in real time to ensure that the photovoltaic cell can still work as close to the maximum power point as possible while the output power is adjusted.

[0060] Based on the above content, the present invention also provides a power electronic distributed collaborative control system for smart microgrids, including:

[0061] Communication architecture building module, used to build distributed communication architecture based on adaptive topology;

[0062] A fusion collaborative control module for performing collaborative control of power electronics in a smart microgrid;

[0063] An intelligent exception response mechanism module is used to execute an intelligent exception response mechanism;

[0064] The dynamic scheduling module is used to dynamically schedule the power electronic coordinated control of the smart microgrid.

[0065] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0066] 1. In terms of flexible power regulation technology, compared with the traditional single power regulation method, the scheme of the present invention has obvious advantages. The traditional method is difficult to take into account the characteristics of the equipment itself and the overall needs of the microgrid, and the regulation effect is not good. The scheme of the present invention is aimed at the inverter equipment, and adopts a flexible power regulation method based on fuzzy logic control. The fuzzy logic controller uses the inverter input voltage, output power and real-time voltage deviation of the microgrid as input variables, and converts the precise quantity into fuzzy quantity through the preset fuzzy rule base for reasoning operation, so as to realize the dynamic adjustment of the driving pulse width of the inverter power switch device, quickly and smoothly adjust the inverter output power, and improve the voltage stability of the microgrid. For the converter equipment, multi-level conversion technology is used to realize flexible power regulation, increase the number of converter levels, make the output voltage waveform closer to the sine wave, reduce the harmonic content, and effectively improve the power quality when adjusting the power output size. In addition, the scheme of the present invention also combines fuzzy logic control with maximum power point tracking (MPPT) and PWM pulse width modulation, and dynamically dispatches according to different operating states of the microgrid. In steady-state operation, the combination of PWM pulse width modulation is preferred to maintain stable power output; when the power of distributed power sources fluctuates greatly, the combination of MPPT is preferentially triggered to ensure power generation efficiency; when the voltage of the microgrid fluctuates and the load changes suddenly, the combination of PWM pulse width modulation is emphasized to stabilize the voltage; in abnormal situations, the two combined methods work together to ensure the basic operation of the microgrid. The above combination and dispatching methods comprehensively improve the response speed and adjustment accuracy of the inverter to changes in the microgrid, and meet the microgrid's demand for high-quality power supply.

[0067] 2. In terms of collaborative control algorithms, compared with traditional single control algorithms, the scheme of the present invention has outstanding advantages. Traditional algorithms are often unable to effectively deal with the intermittency and load volatility of distributed power sources. The scheme of the present invention adopts a distributed average consistency algorithm in the consistency algorithm part, and introduces a dynamic weight mechanism. For distributed power supply equipment with high power generation stability and strong power supply reliability, a higher weight is given in the power distribution consistency calculation, and vice versa, so that the power distribution decision is more in line with the actual situation and the power balance of the microgrid is maintained. In terms of model predictive control, a prediction model based on a long short-term memory network (LSTM) is adopted, and the historical power, voltage, current and other data of the microgrid are used for training to effectively capture the long-term and short-term dependencies in the data. When predicting future trends, combined with the current and past equipment operating status, the output power fluctuations and load change trends of distributed power sources are more accurately predicted, providing a reliable basis for planning control strategies in advance. These unique technical means have greatly improved the accuracy and stability of microgrid power balance control, and significantly enhanced the ability of microgrids to cope with complex working conditions.

[0068] 3. Compared with the traditional fixed topology communication architecture, the solution of the present invention has significant advantages. The traditional architecture has fixed communication connections, which makes it difficult to cope with complex changes in the real-time operation of the microgrid, such as changes in equipment load, sudden failures of communication links, etc. The solution of the present invention collects multi-dimensional data such as microgrid network structure information, equipment load conditions, and communication link quality in real time by building an intelligent topology decision module into each power electronic device. When the equipment in a certain area is overloaded, causing the communication delay to increase, the intelligent topology decision module automatically disconnects the low-quality links between some non-critical devices based on the preset communication optimization algorithm, and establishes a new stable and efficient link connection to communicate with the equipment in this area with other lighter-loaded areas. This technical means realizes the autonomous dynamic adjustment of communication connections, ensures efficient information interaction, greatly improves the flexibility and reliability of the communication architecture, and effectively guarantees the timely and accurate transmission of key information between various devices in the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Dynamically adjust graphs for adaptive topology distributed communication architectures;

[0070] Figure 2 A collaborative control flow chart for fuzzy logic control of flexible power regulation;

[0071] Figure 3 It is a schematic diagram of the power regulation mode when the microgrid is in steady-state operation and dynamic changes;

[0072] Figure 4 It is a schematic diagram of the power regulation method when the microgrid is in an abnormal situation;

[0073] Figure 5Schematic diagram of power regulation collaboration when the microgrid is in an abnormal situation. DETAILED DESCRIPTION

[0074] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0075] Embodiment 1:

[0076] This embodiment provides a power electronic distributed collaborative control method for a smart microgrid, comprising the following steps:

[0077] S1: Building a distributed communication architecture based on adaptive topology;

[0078] like Figure 1 As shown in the figure, the distributed communication architecture based on adaptive topology includes an intelligent topology decision module and a time synchronization network:

[0079] Intelligent topology decision module: The intelligent topology decision module is built into each power electronic device. The intelligent topology decision module includes a data acquisition unit and a link optimization algorithm unit. The data acquisition unit is used to collect multi-dimensional data such as microgrid network structure information, equipment load conditions, and communication link quality in real time. The link optimization algorithm unit automatically disconnects low-quality links between some non-critical devices based on the preset communication optimization algorithm, and establishes new stable and efficient link connections at the same time, linking the devices in this area to other devices in areas with lighter loads.

[0080] Time synchronization network: By setting up master clock devices and slave clock devices in the network, each slave clock device (i.e., power electronic equipment) periodically exchanges time synchronization messages with the master clock device, continuously calibrates the local clock, and ensures that the time synchronization accuracy between devices meets the requirements (nanosecond level), providing a solid time benchmark guarantee for high-frequency critical information interaction, and further improving the accuracy and efficiency of information interaction.

[0081] In the distributed communication architecture based on adaptive topology, each power electronic device can autonomously adjust the communication connection relationship according to the real-time operation status of the microgrid to achieve efficient information exchange. Through this architecture, the devices can exchange key information including their own operation status, power allocation intention, etc. at high frequency based on precise time synchronization, ensuring that each device can grasp the overall situation of the microgrid in a timely manner.

[0082] S2: Reference Figure 2 , based on the distributed communication architecture, execute the fusion cooperative control algorithm:

[0083] 1. Dynamically coordinate the power allocation ratio based on the distributed average consistency algorithm and introduce a dynamic weight mechanism;

[0084] The power allocation ratio is dynamically coordinated based on the distributed average consensus algorithm: each device exchanges power allocation intentions through the communication network, and uses the distributed average consensus algorithm to iteratively update the local power allocation ratio until the power allocation of the entire network is consistent, maintaining the power balance of the microgrid;

[0085] The dynamic weight mechanism is to assign weights to devices according to power generation stability. The weight assignment is a process that takes into account multiple factors and combines multiple methods to determine the weights. It needs to be flexibly adjusted and optimized according to the specific characteristics and operation requirements of the smart microgrid. For distributed power supply devices with high power generation stability and strong power supply reliability, a higher weight is given in the power allocation consistency calculation, so that it has a greater influence in the power allocation decision; while for devices with relatively poor stability, the weight is reduced accordingly.

[0086] 2. Combined with the LSTM prediction model, historical and real-time data are used to predict the output fluctuation of distributed power sources and load change trends, and the control strategy is optimized in advance:

[0087] The operation of the LSTM prediction model includes:

[0088] Assume the input data is , the hidden state at the previous moment is , the memory unit at the previous moment is ;

[0089] Input gate calculation formula:

[0090] Forget gate calculation formula:

[0091] Output gate calculation formula:

[0092] Memory unit update formula:

[0093] The hidden state calculation formula at the current moment is:

[0094] in, is the sigmoid activation function, ⊙ is the element-by-element multiplication, is the weight matrix, is the bias vector.

[0095] The LSTM prediction model uses the historical power, voltage, current and other data of the microgrid for training, and can effectively capture the long-term and short-term dependencies in the data. When predicting future trends, the model not only considers the current equipment operating status, but also combines the status changes over the past period of time, so as to more accurately predict the output power fluctuations of distributed power sources and the change trend of loads, providing a more reliable basis for planning control strategies in advance, and further improving the accuracy and stability of microgrid power balance control.

[0096] S3: Set up intelligent exception response mechanism:

[0097] Each power electronic device continuously monitors the operating status of itself and its surrounding devices. Once an abnormality such as power mutation or voltage exceeding the limit is detected, the adaptive adjustment process is immediately started. The device autonomously analyzes the cause of the abnormality, dynamically adjusts the parameters of the collaborative control algorithm, recalculates the control strategy, and quickly restores the stable operation of the microgrid without relying on centralized control instructions.

[0098] S4: Based on the intelligent abnormal response mechanism, a flexible power adjustment method is used for dynamic scheduling:

[0099] When each device is controlled based on real-time calculations and the output is adjusted through flexible power regulation technology, for inverter equipment, a flexible power regulation method based on fuzzy logic control is adopted. The fuzzy logic controller uses the input voltage, output power and real-time voltage deviation of the inverter as input variables, and converts these precise quantities into fuzzy quantities for reasoning operations through a preset fuzzy rule base. For example, when it is detected that the voltage of the microgrid is low and the output power of the inverter is large, the fuzzy logic controller outputs the corresponding control signal according to the rule base, dynamically adjusts the drive pulse width of the inverter power switch device, quickly and smoothly reduces the inverter output power, and increases the voltage of the microgrid. For converter equipment, multi-level conversion technology is used to achieve flexible power regulation. By increasing the number of levels of the converter, the output voltage waveform is closer to a sine wave, the harmonic content is reduced, and while adjusting the power output size, the power quality is effectively improved to meet the microgrid's demand for high-quality power supply, and further optimize the application effect of flexible power regulation technology on different power electronic equipment.

[0100] Flexible power regulation based on fuzzy logic control, using flexible power regulation method for dynamic scheduling includes:

[0101] like Figure 3As shown in the figure, when the microgrid is in steady-state operation, the flexible power regulation based on fuzzy logic control is combined with PWM pulse width modulation for dynamic scheduling; at this time, the power fluctuation of the microgrid is small, and the PWM pulse width modulation module sets a stable PWM pulse frequency and duty cycle according to the established rated parameters and initial operating conditions to maintain the basic power output regulation of the inverter. For example, the power load in residential areas is relatively stable at night. In this steady state, the inverter relies on PWM to stabilize the output power and ensure stable power supply. At the same time, the fuzzy logic control module continuously monitors data such as input voltage, output power, and real-time voltage deviation of the microgrid to prepare for possible dynamic changes.

[0102] like Figure 3 As shown, when the microgrid is in dynamic change, the flexible power regulation based on fuzzy logic control is combined with the maximum power point tracking MPPT for dynamic scheduling;

[0103] Dynamic changes include:

[0104] Large fluctuations in the power of distributed power sources: When the rapid movement of clouds causes a sudden change in the power of photovoltaic power generation, or when the power of wind power generation fluctuates greatly due to a sudden change in wind speed, the method of combining with maximum power point tracking (MPPT) is triggered first. The MPPT module responds quickly, and based on algorithms such as the disturbance observation method and the conductance increment method, combined with the real-time collected output voltage and current data of the power generation equipment, it quickly adjusts the operating point of the power generation equipment, recalculates and maintains the maximum power output. The fuzzy logic control module synchronously analyzes the voltage, power and other data of the microgrid, and flexibly adjusts the inverter output power to meet the dynamic power demand of the microgrid while ensuring that the maximum power calculated by MPPT is not exceeded.

[0105] Microgrid voltage fluctuations and load mutations: For example, in industrial areas, the microgrid voltage fluctuates and the power demand suddenly changes due to the startup or shutdown of large equipment. At this time, the focus is on the combination with PWM pulse width modulation. The fuzzy logic control module quickly converts the collected precise data into fuzzy quantities, and generates adjustment instructions for PWM pulse width modulation parameters based on the reasoning operation of the fuzzy rule base, such as adjusting the PWM pulse duty cycle and frequency, so that the inverter can quickly respond to microgrid changes, realize precise power regulation, and stabilize the microgrid voltage.

[0106] like Figure 4 and Figure 5As shown in the figure, when the microgrid is in an abnormal situation, the flexible power regulation based on fuzzy logic control is coordinated with PWM pulse width modulation and maximum power point tracking MPPT for dynamic scheduling. The combination with MPPT ensures that the power generation equipment can maintain safe and efficient operation as much as possible under fault conditions, and finds the maximum power point under limited conditions; the combination with PWM pulse width modulation adjusts the PWM parameters through fuzzy logic control, tries to stabilize the inverter output, avoids the expansion of faults, ensures the basic operation of the microgrid, and buys time for fault detection and repair.

[0107] It should be noted here that the synergy of the two combination methods is mainly reflected in the following three points:

[0108] The ultimate goal of both is to optimize the performance of the inverter and improve the power quality and power generation efficiency.

[0109] State information coordination, fuzzy logic control needs to adjust the control strategy according to the real-time operating status of the microgrid and the status of each power electronic device. The power generation equipment status information obtained by the MPPT module is an important part of the overall operating status of the microgrid.

[0110] Control strategy coordination, in the control process of the inverter, the flexible power regulation of fuzzy logic control and the maximum power point tracking are coordinated with each other. When the microgrid load changes, the flexible power regulation module will adjust the output power of the inverter according to the load demand. During this process, the maximum power point tracking module will monitor the working status of the photovoltaic cell in real time to ensure that the photovoltaic cell can still work as close to the maximum power point as possible while the output power is adjusted.

[0111] The process of dynamic scheduling based on flexible power regulation of fuzzy logic control combined with PWM pulse width modulation includes:

[0112] A1: Basic parameter settings:

[0113] After the inverter is started, the PWM pulse width modulation module sets basic PWM pulse frequency, duty cycle and other parameters according to the rated parameters and initial operating conditions of the inverter to maintain the basic power output regulation of the inverter; at the same time, the fuzzy logic control module starts to collect data such as the input voltage, output power of the inverter and the real-time voltage deviation of the microgrid;

[0114] A2: Fuzzy logic control analysis:

[0115] The fuzzy logic control module converts the collected precise quantities into fuzzy quantities and performs reasoning operations through the preset fuzzy rule base; for example, when the microgrid experiences dynamic conditions such as voltage fluctuations and power mutations, the fuzzy logic controller calculates the appropriate power regulation strategy based on the changes in these input variables;

[0116] A3: PWM parameter adjustment decision:

[0117] The fuzzy logic control module generates adjustment instructions for the PWM pulse width modulation parameters based on the calculated power regulation strategy; for example, if the fuzzy logic controller determines that the inverter output power needs to be quickly increased to cope with the increase in microgrid power demand, it will send instructions to the PWM pulse width modulation module to appropriately increase the duty cycle of the PWM pulse and may adjust the pulse frequency to achieve more efficient power output;

[0118] A4: PWM control execution:

[0119] After receiving the adjustment instruction sent by the fuzzy logic control module, the PWM pulse width modulation module immediately adjusts the relevant parameters of the PWM pulse; by changing the on and off time of the power switch device, the inverter output voltage and power are precisely adjusted; in steady-state operation, the PWM pulse width modulation mainly maintains the power output stability based on the initial setting parameters; and when the microgrid undergoes dynamic changes, the fuzzy logic control optimizes the PWM parameters in real time, so that the inverter can respond to the changes of the microgrid quickly and smoothly, realize the complementary advantages of hard regulation and flexible regulation, and improve the power quality and system stability;

[0120] A5: Real-time monitoring and feedback:

[0121] During the entire operation process, the fuzzy logic control module and the PWM pulse width modulation module continuously monitor the output power and voltage of the inverter and the operating status of the microgrid; if it is found that the actual operating conditions deviate from the expected regulation effect, the fuzzy logic control module will re-analyze and calculate, and adjust the PWM parameters again to form a closed-loop control to ensure that the inverter can always accurately adjust the power output according to the needs of the microgrid.

[0122] Through the above steps A1~A5, a series of closely connected steps, the flexible power regulation of the inverter based on fuzzy logic control and PWM pulse width modulation are organically combined. From the initial basic parameter setting, to the fuzzy logic control analysis to generate the regulation strategy, to the PWM parameter adjustment decision and execution, and the real-time monitoring and feedback of the whole process, the two complement each other. In steady state, PWM maintains stable power output; in the face of dynamic changes in the microgrid, fuzzy logic control timely optimizes PWM parameters, realizes accurate and efficient regulation of the inverter output voltage and power, and effectively guarantees the power quality and system stability of the microgrid.

[0123] The process of dynamic scheduling based on flexible power regulation of fuzzy logic control and maximum power point tracking (MPPT) includes:

[0124] B1: Initialization and data collection:

[0125] The power generation equipment is connected to the inverter. After the inverter is started, the MPPT module starts to work and collects the output voltage and current data of the photovoltaic array in real time. At the same time, the fuzzy logic control module is also initialized to collect voltage and current data, such as the input voltage and output power of the inverter and the real-time voltage deviation of the microgrid.

[0126] B2: MPPT maximum power calculation:

[0127] The MPPT module calculates the maximum power value based on the collected voltage and current data using the perturbation observation method, which includes:

[0128] In the perturbation and observation method, the operating voltage at the access end of the power generation equipment is periodically disturbed. , observe the direction of power change to find the maximum power point;

[0129] The voltage after each disturbance is: ; The power change is: ;

[0130] like , then the next disturbance direction is the same as this one, that is, continue to increase ;like , then the next disturbance direction is opposite to this one, that is, it decreases ; By continuously adjusting the voltage, the maximum power value under the current working conditions is found.

[0131] B3: Fuzzy logic control:

[0132] The fuzzy logic control module converts the collected precise data into fuzzy quantities and performs reasoning operations based on the preset fuzzy rule base; for example, when it is detected that the microgrid voltage is low and the inverter output power is large, the fuzzy logic controller outputs a corresponding power regulation signal, which is intended to adjust the inverter output power to increase the microgrid voltage;

[0133] B4: Power regulation coordination:

[0134] When generating the power regulation strategy, the fuzzy logic control module will refer to the maximum power value calculated by the MPPT module to ensure that the adjusted inverter output power does not exceed the maximum power, and avoid excessive regulation causing the power generation equipment to deviate from the maximum power point. If the fuzzy logic control module calculates that the inverter output power needs to be significantly reduced to stabilize the microgrid voltage, but the power value is lower than the maximum power determined by the MPPT, then the actual adjustment will be made with the maximum power as the upper limit.

[0135] B5: Control signal output and execution:

[0136] The fuzzy logic control module converts the final power regulation strategy into a control signal and sends it to the power switch device drive circuit of the inverter; the drive circuit adjusts the on and off time of the power switch device according to the signal to adjust the output power of the inverter, which not only meets the dynamic power demand of the microgrid, but also ensures that the photovoltaic array always outputs electrical energy at close to the maximum power, thereby improving the power generation efficiency and stability of the microgrid.

[0137] The inverter combines flexible power regulation based on fuzzy logic control with maximum power point tracking (MPPT). Through orderly and coordinated operations in each stage, in a complex microgrid environment, it not only takes advantage of fuzzy logic control to flexibly adjust power to adapt to dynamic needs, but also uses MPPT to accurately locate the maximum power point of the power generation equipment, avoiding the loss of power generation efficiency caused by excessive adjustment, achieving a dual improvement in power generation efficiency and microgrid stability, and providing a strong guarantee for the efficient and stable operation of the microgrid.

[0138] The flexible power regulation of a single inverter based on fuzzy logic control has certain defects. Since fuzzy logic control relies on a preset fuzzy rule base, when faced with complex and changeable microgrid operating conditions that are difficult to accurately model, the rule base may not be able to fully cover all situations, resulting in limited regulation accuracy. When rare power fluctuation patterns or abnormal conditions caused by equipment failures occur in the microgrid, fuzzy logic control may not be able to make the best decision quickly and accurately, affecting the stability and power quality of the microgrid. However, it has significant advantages when combined with other methods. Combined with maximum power point tracking MPPT, it can flexibly adjust the power to adapt to the dynamic needs of the microgrid while ensuring that the photovoltaic array always outputs electricity at close to the maximum power, avoiding excessive regulation that causes the power generation equipment to deviate from the optimal operating point, and improving power generation efficiency. Combined with PWM pulse width modulation, PWM maintains basic power regulation in steady state, and fuzzy logic optimizes PWM parameters in real time during dynamic changes, realizing the complementary advantages of hard regulation and flexible regulation, and enhancing the response speed and regulation accuracy of the inverter to changes in the microgrid.

[0139] The present invention comprehensively and deeply solves the key problems of communication, power balance, abnormal response and power quality faced by smart microgrids during operation by constructing a distributed communication architecture with adaptive topology, using a collaborative control algorithm that integrates distributed consistency and model prediction, setting an intelligent abnormal response mechanism and adopting flexible power regulation technology. In particular, in terms of flexible power regulation, fuzzy logic control is organically combined with maximum power point tracking and traditional PWM pulse width modulation, and dynamic scheduling is performed according to the real-time operating status of the microgrid, achieving accurate and efficient control of the inverter output power, greatly improving the overall performance and stability of the microgrid.

[0140] The distributed communication architecture based on adaptive topology constructed by the scheme of the present invention realizes the autonomous dynamic adjustment of the communication connection of the equipment and ensures the efficient interaction of information. In contrast, the traditional fixed topology communication architecture cannot adapt to the load changes of microgrid equipment, communication link failures and other situations in time, which easily leads to information transmission delays or even interruptions, seriously affecting the overall operation stability of the microgrid. This scheme uses a collaborative control algorithm that integrates distributed consistency and model prediction to significantly improve the accuracy and stability of microgrid power balance control. However, the traditional control algorithm cannot effectively cope with the dynamic changes of distributed power sources and loads, and often has problems such as unreasonable power distribution and large voltage fluctuations, which cannot meet the requirements of microgrids for high-quality power supply. The flexible power regulation technology of the scheme of the present invention realizes fast and smooth regulation of inverter and converter output according to the characteristics of the equipment and the needs of the microgrid, greatly improving the power quality. In the traditional power regulation method, the inverter regulation accuracy is insufficient and the converter output waveform is poor, which makes it difficult to ensure the power quality of the microgrid under complex working conditions, limiting the further development and application of the microgrid.

[0141] Embodiment 2:

[0142] Based on the control method provided in Example 1, this embodiment provides a power electronic distributed collaborative control system for a smart microgrid, including:

[0143] Communication architecture building module, used to build distributed communication architecture based on adaptive topology;

[0144] A fusion collaborative control module for performing collaborative control of power electronics in a smart microgrid;

[0145] An intelligent exception response mechanism module is used to execute an intelligent exception response mechanism;

[0146] The dynamic dispatch module is used to dynamically dispatch the power electronic coordinated control of the smart microgrid.

[0147] Embodiment 3:

[0148] In order to verify the stability of the present invention under different operating conditions, a multi-operating condition simulation experiment was designed in this embodiment.

[0149] Equipment list and description: See Table 1.

[0150] Simulation tools: MATLAB / Simulink platform, used for microgrid model building and control algorithm verification.

[0151] Test instruments: power analyzer (accuracy ±0.1%), oscilloscope (bandwidth 1GHz), power quality monitor (compliant with IEEE1547 standard).

[0152] Actual microgrid system: includes photovoltaic array (50kW), energy storage device (20kWh), controllable load (0-100kW) and power electronic converter and other equipment.

[0153] Table 1

[0154]

[0155] The test in this embodiment covers variables such as photovoltaic output fluctuation (±15%~±35%), load type (resistive / non-linear / mixed) and communication delay (5ms~15ms). Specific data are shown in Table 2.

[0156] Table 2

[0157]

[0158] The data in Table 2 show that even when the photovoltaic output fluctuates greatly (±35%) and the communication delay increases to 15ms, the voltage fluctuation is still controlled within ±3.8%, the power balancing time is less than 160ms, and the harmonic distortion rate is less than 5.2%, which is significantly better than the traditional solution (voltage fluctuation ±10%, balancing time 500ms).

[0159] In order to evaluate the reliability of the system in extreme environments, high temperature (60°C), low temperature (-20°C) and high proportion of communication link interruption (30%-50%) tests were carried out. The specific data are shown in Table 3.

[0160] Table 3

[0161]

[0162] From the data in Table 3, it can be seen that under high temperature conditions with energy storage SOC as low as 20% and 30% link interruption, the system still maintains stable operation and the voltage recovery time is only 80ms. In the low temperature test, even if 50% of the link is interrupted, the system can still maintain power supply through intelligent topology reconstruction, which verifies the survivability of the present invention under extreme conditions.

[0163] The present invention is compared with traditional centralized control, distributed consistency algorithm and single model predictive control. The test indicators include power allocation error, communication load and computational complexity. The specific data are shown in Table 4.

[0164] Table 4

[0165]

[0166] From the data in Table 4, it can be seen that the present invention achieves the best balance between power allocation accuracy (±1.8%) and communication efficiency (120Mbps). Compared with the traditional centralized control, the communication load is reduced by 57%; compared with the distributed consensus algorithm, the error is reduced by 48%. Although the computational complexity is slightly higher than the traditional centralized control, it is much lower than the pure model predictive control.

[0167] The microgrid in the park was tested for 72 hours. The monitoring indicators included photovoltaic power, load power, bus voltage and power quality parameters. The specific data are shown in Table 5.

[0168] Table 5

[0169]

[0170] From the data in Table 5, it can be seen that during the monitoring period, the bus voltage was stable at 381.8-383.2V (±0.8%), the frequency deviation was ≤±0.03Hz, and the THD average was 3.6%, which fully met the requirements of IEEE 1547. Only one equipment abnormality was detected at 18:45, and the system responded automatically and recovered within 12ms.

[0171] According to the above data analysis, the following effects can be obtained by using the solution of the present invention compared with the prior art:

[0172] Communication architecture optimization: Compared with the existing fixed topology, the solution of the present invention reduces communication delay by 30%, link failure rate by 40%, and network throughput by 25%. It can effectively cope with changes in equipment load and communication failures, and ensure efficient and stable information interaction.

[0173] Power balance accuracy: Through distributed consistency and model predictive control, the power allocation error is controlled within ±2%, which is significantly improved compared to the ±5% of the existing algorithm. It can effectively cope with the intermittent nature of distributed power sources and load fluctuations and maintain system power balance.

[0174] Improved power quality: The harmonic content of the inverter output voltage is reduced from 10% to 5%, the sinusoidality of the converter output waveform is increased by 35%, and the voltage deviation is stabilized within ±3%, meeting the high-quality power supply needs of the microgrid.

[0175] Abnormal response speed: The equipment's autonomous response time is shortened to 10ms, 80% faster than centralized control, significantly improving the system's anti-interference ability and reducing the scope of fault impact.

Claims

1. A power electronic distributed collaborative control method for smart microgrids, characterized in that: The steps include: S1: Building a distributed communication architecture based on adaptive topology; S2: Based on the distributed communication architecture, execute the fusion collaborative control algorithm: Dynamically coordinate the power allocation ratio based on the distributed average consensus algorithm and introduce a dynamic weight mechanism; Combined with the LSTM prediction model, historical and real-time data are used to predict the output fluctuation of distributed power sources and load change trends, and the control strategy is optimized in advance; S3: Set up intelligent exception response mechanism: Each power electronic device continuously monitors the operating status of itself and surrounding equipment, and immediately initiates the adaptive adjustment process once an abnormality is detected; S4: Based on the intelligent abnormal response mechanism, a flexible power adjustment method is used for dynamic scheduling.

2. A power electronic distributed collaborative control method for smart microgrids according to claim 1, characterized in that: The distributed communication architecture based on the adaptive topology in step S1 includes an intelligent topology decision module and a time synchronization network: Intelligent topology decision module: An intelligent topology decision module is built into each power electronic device, and the intelligent topology decision module includes a data acquisition unit and a link optimization algorithm unit; the data acquisition unit is used to collect multi-dimensional data in real time; the link optimization algorithm unit automatically disconnects low-quality links and establishes new link connections based on a preset communication optimization algorithm; Time synchronization network: By setting up a master clock device and slave clock devices in the network, each slave clock device periodically exchanges time synchronization messages with the master clock device, continuously calibrates the local clock, and ensures that the time synchronization accuracy between devices meets the requirements.

3. The power electronic distributed collaborative control method for smart microgrid according to claim 1 is characterized in that: In step S2, the power allocation ratio is dynamically coordinated based on the distributed average consistency algorithm as follows: each device exchanges power allocation intentions through the communication network, and uses the distributed average consistency algorithm to iteratively update the local power allocation ratio until the power allocation of the entire network is consistent; The dynamic weight mechanism is to assign weights to devices based on power generation stability.

4. The power electronic distributed collaborative control method for smart microgrid according to claim 1, characterized in that: The operation of the LSTM prediction model in step S2 includes: Assume the input data is , the hidden state at the previous moment is , the memory unit at the previous moment is ; Input gate calculation formula: ; Forget gate calculation formula: ; Output gate calculation formula: ; Memory unit update formula: ; The hidden state calculation formula at the current moment is: ; in, is the sigmoid activation function, ⊙ is the element-by-element multiplication, is the weight matrix, is the bias vector.

5. The power electronic distributed collaborative control method for smart microgrid according to claim 1, characterized in that: The flexible power regulation method used in step S4 for dynamic scheduling includes: When the microgrid is in steady-state operation, the flexible power regulation based on fuzzy logic control is combined with PWM pulse width modulation for dynamic scheduling; When the microgrid is in dynamic change, the flexible power regulation based on fuzzy logic control is combined with the maximum power point tracking MPPT for dynamic scheduling; When the microgrid is in an abnormal situation, the flexible power regulation based on fuzzy logic control is combined with PWM pulse width modulation and maximum power point tracking MPPT to coordinate dynamic scheduling.

6. A power electronic distributed collaborative control method for smart microgrids according to claim 5, characterized in that: The process of performing dynamic scheduling in step S4 by combining flexible power regulation based on fuzzy logic control with PWM pulse width modulation includes: A1: Basic parameter settings: After the inverter is started, the PWM module sets basic parameters according to the rated parameters and initial operating conditions of the inverter to maintain the basic power output regulation of the inverter; at the same time, the fuzzy logic control module starts to collect data; A2: Fuzzy logic control analysis: The fuzzy logic control module converts the collected precise quantity into fuzzy quantity and performs reasoning operation through the preset fuzzy rule library; A3: PWM parameter adjustment decision: The fuzzy logic control module generates adjustment instructions for PWM pulse width modulation parameters according to the calculated power regulation strategy; A4: PWM control execution: After receiving the adjustment command sent by the fuzzy logic control module, the PWM pulse width modulation module immediately adjusts the relevant parameters of the PWM pulse; by changing the on and off time of the power switch device, the inverter output voltage and power are precisely adjusted; in steady-state operation, the PWM pulse width modulation maintains stable power output according to the initial set parameters; and when the microgrid undergoes dynamic changes, the fuzzy logic control optimizes the PWM parameters in real time; A5: Real-time monitoring and feedback: During the entire operation process, the fuzzy logic control module and the PWM pulse width modulation module continuously monitor the output power and voltage of the inverter and the operating status of the microgrid; if it is found that the actual operating conditions deviate from the expected regulation effect, the fuzzy logic control module will re-analyze and calculate, and adjust the PWM parameters again to form a closed-loop control.

7. The power electronic distributed collaborative control method for smart microgrid according to claim 5, characterized in that: The process of performing dynamic scheduling in step S4 by combining flexible power regulation based on fuzzy logic control with maximum power point tracking (MPPT) includes: B1: Initialization and data collection: The power generation equipment is connected to the inverter. After the inverter is started, the MPPT module starts to work and collects the output voltage and current data of the photovoltaic array in real time. At the same time, the fuzzy logic control module is also initialized to collect voltage and current data. B2: MPPT maximum power calculation: The MPPT module calculates the maximum power value based on the collected voltage and current data; B3: Fuzzy logic control: The fuzzy logic control module converts the collected precise data into fuzzy quantities and performs reasoning operations based on the preset fuzzy rule base; B4: Power regulation coordination: When generating the power regulation strategy, the fuzzy logic control module will refer to the maximum power value calculated by the MPPT module to ensure that the adjusted inverter output power does not exceed the maximum power, and avoid excessive regulation that causes the power generation equipment to deviate from the maximum power point; B5: Control signal output and execution: The fuzzy logic control module converts the final power regulation strategy into a control signal and sends it to the power switch device drive circuit of the inverter; the drive circuit adjusts the on and off time of the power switch device according to the signal to adjust the output power of the inverter.

8. The power electronic distributed collaborative control method for smart microgrid according to claim 7, characterized in that: In step B2, the MPPT module uses the perturbation observation method to calculate the maximum power value, which specifically includes: In the perturbation and observation method, the operating voltage at the access end of the power generation equipment is periodically disturbed. , observe the direction of power change to find the maximum power point; The voltage after each disturbance is: ; The power change is: ; like , then the next disturbance direction is the same as this one, that is, continue to increase ;like , then the next disturbance direction is opposite to this one, that is, it decreases ; By continuously adjusting the voltage, the maximum power value under the current working conditions is found.

9. A power electronic distributed collaborative control system for smart microgrids, characterized in that: include: Communication architecture building module, used to build distributed communication architecture based on adaptive topology; A fusion collaborative control module for performing collaborative control of power electronics in a smart microgrid; An intelligent exception response mechanism module is used to execute an intelligent exception response mechanism; The dynamic dispatch module is used to dynamically dispatch the power electronic coordinated control of the smart microgrid.

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