Electric energy quality treatment control method and system

Abstract

The invention relates to the field of electric energy quality control, and discloses an electric energy quality treatment control method and system, and the method comprises the following steps: obtaining a voltage or current signal of a power grid node; performing multi-fractal spectrum analysis on the signal to generate a disturbance portrait for representing a disturbance type and a spectrum width for quantifying the disturbance severity; packaging a local disturbance portrait, spectral width and residual compensation margin into a collaborative information packet, and interacting with an adjacent controller through a local communication network; each controller adaptively allocates roles through a distributed decision algorithm based on the received information; and generating and executing a corresponding power quality management control instruction according to the role. According to the method, accurate identification is realized through the disturbance portraits, and dynamic optimal configuration of governance resources is realized by using distributed collaborative decision and adaptive role allocation, so that negative interaction between controllers is effectively avoided, and the overall efficiency and system stability of power quality governance are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality control, and in particular to a power quality management control method and system. Background Art

[0002] With the widespread application of power electronics in industrial production and daily life, a large number of nonlinear and impactful loads have been connected to the power grid, leading to increasingly prominent power quality issues such as harmonics and voltage fluctuations. To suppress these disturbances, power quality management devices such as active power filters (APFs) have been widely deployed.

[0003] In modern power distribution networks, it is usually necessary to install power quality management devices at multiple key nodes to achieve on-site treatment of pollution sources. However, the multiple management devices in the existing technology often adopt an independent operation mode. Each device only executes the preset control strategy based on its local measurement information, and there is a lack of effective information interaction and coordination mechanism between each other. This "each for themselves" approach has significant technical bottlenecks. When multiple controllers compensate for the same disturbance at the same time, the lack of coordination may lead to unstable compensation effects, and even exacerbate system resonance due to adverse interactions with the grid impedance and other controllers, endangering the stable operation of the grid.

[0004] Furthermore, traditional control strategies are often limited in functionality and lack adaptability. Controllers are typically set to a fixed compensation mode, making it difficult to accurately distinguish and respond to complex and changing disturbance types, such as background harmonics and transient oscillations. This rigid control approach cannot dynamically adjust its role in the governance task and the compensation resources invested based on the electrical distance from the disturbance source, the actual severity of the disturbance, and the status of other governance units in the system. This not only wastes governance resources but also limits the overall governance effectiveness of the system.

[0005] Therefore, how to achieve intelligent collaboration among multiple distributed power quality management devices so that they can accurately identify grid disturbance states and adaptively optimize control strategies is a technical problem that needs to be urgently solved in the current power quality control field. Summary of the Invention

[0006] Existing power quality management methods typically implement passive compensation only after power quality issues are detected. When multiple management devices exist in the power grid, each independently controlled based on local information can trigger negative interactions between devices and even lead to systemic resonance. Furthermore, existing methods have limited adaptability when faced with dynamic, wide-spectrum disturbances caused by large-scale nonlinear loads and distributed power generation, making it difficult to fundamentally address complex power quality issues.

[0007] To solve the above technical problems, the present invention aims to provide a power quality governance control method and system. By performing multifractal spectrum analysis on grid signals to identify disturbance types and establishing a distributed collaborative control mechanism, multiple controllers can perform adaptive role differentiation and task collaboration according to the disturbance types, thereby achieving targeted and systematic governance of power quality problems.

[0008] In the first aspect of the present invention, a power quality governance control method is provided, which is applied to a system composed of multiple power quality governance controllers. Each power quality governance controller in the system executes the method, and the method includes the following steps: Step S100: Collect the grid signal at its connection point to obtain a time series.

[0009] Step S200: Calculate the multifractal spectrum based on the time series, and identify the disturbance image of the current grid signal according to the characteristics of the multifractal spectrum.

[0010] In a specific embodiment, this step first normalizes the collected time series to obtain an analysis sequence. Subsequently, a probability measure is constructed based on the analysis sequence, and the partition function is constructed according to the following formula : ; In the formula, is the scale parameter, is the probability measure that the sequence point falls into the th box with a scale of , is the real-order moment.

[0011] Then, the mass exponent is solved by the following formula : ; Finally, by performing a Legendre transform on the mass exponent , a multifractal spectrum composed of the singularity exponent and the spectral function is obtained. The transformation relationship is as follows: ; ; The spectral characteristics of the multifractal spectrum, including the spectral width, peak position, and symmetry, constitute the disturbance image. By matching the spectral characteristics calculated in real time with the standard spectral characteristics preset in the disturbance image library, the disturbance image is determined.

[0012] Step S300: Broadcast an information packet containing the disturbance image to neighboring controllers.

[0013] In a specific embodiment, the information packet further includes: the identifier of the controller that issues the information packet, the current remaining compensation margin of the controller, and the severity of the disturbance characterized by the spectral width of the multifractal spectrum.

[0014] Step S400: Receive information packets from neighboring controllers, and in combination with its own status, determine its role in the group of controllers according to the disturbance image under a preset cooperation protocol.

[0015] In a specific embodiment, the cooperation protocol includes: If the disturbance image is a harmonic image, the controller determines, according to the status information of itself and neighboring controllers, that itself is the main compensation unit responsible for outputting the main compensation current, or determines that itself is the damping unit responsible for adjusting the output impedance to suppress potential resonance.

[0016] If the disturbance image is a voltage wave image, the controller determines that itself is a voltage support unit, and its control objective is to cooperate with other controllers for reactive power compensation.

[0017] Step S500: Generate and execute corresponding power quality governance control instructions according to the role.

[0018] In a specific embodiment, this step includes: dynamically correcting the control objective of the local controller according to the role determined in step S400, and generating the control instruction based on the corrected control objective. For example, when the role is the damping unit, its control instruction aims to make it present a specific damping impedance characteristic to the power grid.

[0019] The second aspect of the present invention provides a power quality governance control system, including a plurality of power quality governance controllers. The system is configured to execute the method described in any of the foregoing embodiments, and each power quality governance controller in the system includes: A data acquisition module, configured to acquire the power grid signal at its grid connection point to obtain a time series; A disturbance identification module, configured to calculate a multifractal spectrum based on the time series and identify the disturbance image of the current power grid signal according to the characteristics of the multifractal spectrum; An information interaction module, configured to broadcast an information packet containing the disturbance image to neighboring controllers and receive information packets from neighboring controllers; A cooperation decision module, configured to combine its own status and determine its role in the group of controllers according to the disturbance image under a preset cooperation protocol; An instruction generation module, configured to generate and execute corresponding power quality governance control instructions according to the role.

[0020] In summary, the present invention includes at least one of the following beneficial technical effects: 1. By introducing the multifractal spectrum to analyze power grid signals, the present invention can extract the inherent and deep - level dynamic characteristics of signals from time series, and generate a disturbance image representing the type of disturbance accordingly. This method not only quantifies the severity of the disturbance, but also realizes the qualitative identification of the fundamental type of the disturbance, thus providing a technical basis for subsequent targeted and differentiated governance strategies according to specific problems, and changing the limitation of traditional methods that only perform single compensation based on the disturbance amplitude.

[0021] 2. The present invention establishes a collaborative protocol and an adaptive role - assignment mechanism based on the disturbance image, enabling the distributed controller group to spontaneously form a collaborative governance structure with complementary functions according to the identified problem type. For example, in the harmonic governance task, the cooperation between the main compensation unit and the damping unit is formed. This collaborative method can actively suppress the power grid resonance that may be excited by high - power compensation behaviors, solve the technical problem that multiple governance devices operating independently in the prior art are prone to generate negative interaction effects, and improve the overall stability and governance effect of the system.

[0022] 3. The control method of the present invention is completely based on a distributed architecture without a central master controller. Each controller makes decisions based on unified rules and local information interaction. This decentralized structure enables the system to have good robustness and scalability. The failure of a single controller does not affect the operation of the rest of the system, and the addition of a new controller can be plug - and - play, being able to adapt to the dynamic changes of the power grid topology and operating conditions, and is particularly suitable for modern power systems with complex structures and variable disturbance sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic structural diagram of a power quality governance control system according to an embodiment of the present invention; Figure 2 It is a block diagram of the internal functional modules of a power quality governance controller according to an embodiment of the present invention; Figure 3 It is a schematic flowchart of a power quality governance control method according to an embodiment of the present invention; Figure 4 It is a logical flowchart of collaborative decision - making and role - assignment according to an embodiment of the present invention.

[0024] Among them, 10 is the power quality governance controller; 100 is the power quality governance control system; 110 is the data acquisition module; 120 is the disturbance identification module; 130 is the information interaction module; 140 is the collaborative decision - making module; 150 is the instruction generation module; 200 is the distribution network; 300 is the local communication network. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] Referring to the attached Figure 1 , Figure 1 FIG. is a schematic structural diagram of a power quality management and control system 100 according to an embodiment of the present invention. The present invention provides a power quality management and control system 100, which is deployed in a distribution network 200 and includes a plurality of power quality management controllers 10.

[0027] The plurality of power quality management controllers 10 are respectively installed at preset nodes of the distribution network 200, such as the common connection point of large non-linear loads or the grid connection point of distributed renewable energy. Data exchange is realized between the plurality of power quality management controllers 10 through a local communication network 300. The local communication network 300 can be an industrial Ethernet, a controller area network (CAN) bus, or a power line carrier communication network.

[0028] Referring to the attached Figure 2 , Figure 2 FIG. is an internal functional module block diagram of a power quality management controller 10 according to an embodiment of the present invention. Each power quality management controller 10 can be a power electronic inverter connected in parallel with the distribution network 200 in terms of hardware. Functionally, each power quality management controller 10 includes: a data acquisition module 110, a disturbance identification module 120, an information interaction module 130, a collaborative decision-making module 140, and an instruction generation module 150. In one embodiment, except for the sensing part of the data acquisition module 110, the functions of the remaining modules can be implemented by the embedded processor in the controller executing corresponding programs.

[0029] The data acquisition module 110 is configured to collect grid signals of the distribution network 200 through voltage and current sensors at its grid connection point to obtain a high-resolution discrete time series. The data acquisition module 110 outputs the time series it collects to the disturbance identification module 120.

[0030] The disturbance identification module 120 is connected to the output end of the data acquisition module 110. The disturbance identification module 120 is configured to receive the time series and perform multifractal spectrum calculation based on the time series to identify the disturbance image of the grid signal. The disturbance identification module 120 outputs the disturbance image it identifies and other relevant status information to the information interaction module 130 and the collaborative decision-making module 140.

[0031] The information interaction module 130 is connected to the disturbance identification module 120 and the collaborative decision-making module 140, and communicates with other power quality governance controllers 10 through the local communication network 300. The information interaction module 130 is configured to encapsulate the local state information generated by the disturbance identification module 120 into information packets and broadcast them to neighboring controllers, and at the same time receive information packets from neighboring controllers and transmit them to the collaborative decision-making module 140.

[0032] The collaborative decision-making module 140 is connected to the outputs of the disturbance identification module 120 and the information interaction module 130. The collaborative decision-making module 140 is configured to integrate its own state information provided by the disturbance identification module 120 and the state information of neighboring controllers provided by the information interaction module 130, and determine its own role at the current moment according to a preset collaborative protocol. The collaborative decision-making module 140 outputs the determined role information to the instruction generation module 150.

[0033] The instruction generation module 150 is connected to the output of the collaborative decision-making module 140. The instruction generation module 150 is configured to generate specific power quality governance control instructions according to the role determined by the collaborative decision-making module 140, such as generating a pulse width modulation (PWM) signal for driving a power electronic inverter. The control instructions are used to perform the final compensation or governance actions.

[0034] Refer to Appendix Figure 3 , Figure 3 is a schematic flowchart of a power quality governance control method according to an embodiment of the present invention. The present invention provides a power quality governance control method, which is periodically executed by each power quality governance controller 10 in the above-mentioned system 100. The method may include the following steps: S100, collect the grid signals at its grid connection point to obtain a time series.

[0035] S200, calculate the multifractal spectrum based on the time series, and identify the disturbance image of the current grid signal according to the characteristics of the multifractal spectrum.

[0036] S300, broadcast information packets containing the disturbance image to neighboring controllers.

[0037] S400, receive information packets from neighboring controllers, and combine its own state to determine its own role in the controller group according to the disturbance image under a preset collaborative protocol.

[0038] S500, generate and execute corresponding power quality governance control instructions according to the role.

[0039] Next, Figure 3 the specific steps in the power quality governance control method shown are elaborated in detail.

[0040] Step S100: Grid signal acquisition and preprocessing This step is executed by the data acquisition module 110 inside each power quality control controller 10. The data acquisition module 110 continuously monitors the connection point where it is connected to the distribution network 200 and periodically performs signal acquisition operations.

[0041] In a specific embodiment, the data acquisition module 110 uses its integrated voltage sensor and current sensor to sample at a preset sampling frequency the instantaneous voltage signal at the connection point or the instantaneous current signal synchronously. To ensure that high-frequency components and transient details contained in power quality disturbances can be captured, the sampling frequency is usually set to be above dozens of kilohertz. By performing continuous sampling within a preset time window, the module obtains a discrete time series consisting of sampling points, denoted as .

[0042] After obtaining the original time series , in order to eliminate the influence of the signal amplitude itself on the subsequent multifractal spectrum analysis results and focus the analysis on the internal structure and irregularity of the signal, the sequence needs to be preprocessed by normalization. The normalization operation linearly maps the amplitude range of the original sequence to the interval [0, 1]. This operation is completed through the following formula: ; where is the point index in the sequence, . is the th sampling value in the original time series . and are respectively the minimum and maximum values of the time series within this time window. is the th value in the analyzed sequence obtained after normalization.

[0043] After completing the preprocessing, the obtained analyzed sequence is transmitted to the disturbance identification module 120 as the input data for executing step S200.

[0044] Step S200: Disturbance image recognition based on multifractal spectrum This step is executed by the disturbance identification module 120 inside each power quality control controller 10, which receives the normalized analysis sequence provided by the data acquisition module 110 , and performs calculations based on this sequence. The purpose of this step is to extract the scale-invariant features contained in the analysis sequence , and quantify them into a multifractal spectrum, and finally determine the disturbance image of the current grid signal according to the characteristics of this spectrum

[0045] In a specific embodiment, the disturbance identification module 120 first covers the value range of the analysis sequence , that is, the interval [0, 1], with a series of consecutive small boxes of size . Then, the module counts the frequency of the sequence points falling into the th box, and uses this as an estimate of the probability measure of this box. Subsequently, based on all non-zero probability measures, a partition function related to the order moment and the scale is constructed through the following formula ; where is the probability measure that the points of the analysis sequence fall into the th box; is a continuously varying real-order moment, and the range of its values determines the detection degree of different singularity regions of the signal; is the scale parameter

[0046] Since there is a power-law relationship between the partition function and the scale in the multifractal system, it can be characterized by the mass exponent . This relationship is expressed as . Therefore, the mass exponent can be determined by performing a linear regression analysis on and at different scales and obtaining its slope. Its mathematical definition is ; By traversing the values within a preset range, a complete curve can be calculated

[0047] After obtaining the mass exponent , the multifractal spectrum can be solved through the Legendre Transform. The multifractal spectrum consists of the singularity exponent and the spectral function Joint description. Singularity index Characterizes the local singularity strength of the sequence, and the spectral function Describes the dimension of the set of points with the same singularity index. Their calculation formulas are as follows: ; ; By traversing the order moments values, a series of point pairs can be calculated. These point pairs form a curve in the coordinate system with as the abscissa and as the ordinate, which is the multifractal spectrum of the power grid signal. The characteristics of this spectrum diagram, such as the width of the spectrum, the peak position of the spectral function, and the symmetry of the spectrum diagram, jointly constitute the disturbance portrait of the power grid signal.

[0048] The disturbance recognition module 120 pre-stores a disturbance portrait library internally. In a specific embodiment, the construction method of the disturbance portrait library includes the following steps: First, establish a standard distribution network model through a power system simulation software (such as PSCAD or MATLAB / Simulink); Then, inject typical power quality disturbance events of a single type with different parameter settings into this model, such as injecting harmonic currents with changing amplitudes and frequencies, or setting voltage sags with different depths and durations; Next, perform multifractal spectrum analysis on the voltage or current time series generated in each simulation event, and extract key characteristic parameters such as its spectrum width, spectral peak position, symmetry, etc.; Finally, statistically average or classify the set of characteristic parameters corresponding to each disturbance type to form a standard disturbance portrait and store it in the library.

[0049] In another embodiment, the disturbance portrait library can also be established by collecting and processing historical waveform data of various disturbance events that have occurred and been clearly identified in the actual power grid through offline analysis.

[0050] The disturbance recognition module 120 performs pattern matching between the spectrum diagram features calculated in real time (such as spectrum width and spectral peak position, etc.) and the standard spectrum diagram features stored in the library. The disturbance type corresponding to the standard portrait with the highest matching degree is determined as the disturbance portrait of the current power grid signal and is assigned a unique disturbance portrait ID. At the same time, the calculated spectrum width is used as a quantization index to characterize the complexity of the disturbance.

[0051] After the calculation and matching are completed, the determined disturbance portrait ID and spectrum width Information such as... is transmitted to the information interaction module 130 and the collaborative decision-making module 140.

[0052] Step S300: Collaborative information exchange This step is executed by the information interaction module 130 inside each power quality control controller 10. Its purpose is to encapsulate the key information obtained by the local controller through the analysis in step S200 into a standardized information packet, and broadcast it to other controllers in the system through the local communication network 300, providing a data basis for subsequent collaborative decision-making.

[0053] In a specific embodiment, the information packet generated by the information interaction module 130 is a data structure with a predetermined format. This information packet includes the following data fields: A controller identifier (Controller ID): A unique address or number used to clearly identify the source controller that sends this information packet.

[0054] A perturbed portrait identifier (Portrait ID): That is, the unique ID of the perturbed portrait determined by matching with the perturbed portrait library in step S200.

[0055] A perturbed severity metric value: This value is quantified by the multifractal spectrum width calculated in step S200. The larger the spectrum width, the higher the non-uniformity and complexity of the signal.

[0056] A remaining compensation margin value: This value is used to characterize the remaining capacity of the current controller available for power quality control. For a controller with current compensation as the main function, its remaining compensation margin can be calculated by the following formula: ; where is the effective value of the rated output current of the power electronic inverter inside this controller, is the effective value of the compensation current actually output by this controller currently. This margin value indicates how much additional compensation capacity the controller can still provide.

[0057] A timestamp (Timestamp): Records the exact time when this information packet is created, used for the receiving party to judge the timeliness of the information.

[0058] The information interaction module 130 assembles all the above data fields into a complete information packet, and sends it out through the local communication network 300 in a broadcast or multicast manner. This broadcast operation is executed periodically, and its period is synchronized with the execution period of the entire control method to ensure that each controller in the system can timely obtain the latest status information of its neighboring controllers.

[0059] Step S400: Adaptive Role Allocation and Collaborative Decision-Making Refer to Appendix Figure 4 , Figure 4 is a logic flowchart of collaborative decision-making and role allocation according to an embodiment of the present invention. This step is executed by the collaborative decision-making module 140 inside each power quality governance controller 10. This module receives and integrates data from two aspects: On the one hand, it is the local state information from the disturbance identification module 120 of this controller (including local disturbance image ID, spectral width and remaining compensation margin ); On the other hand, it is the set of information packets received from neighboring controllers from the information interaction module 130.

[0060] The core function of this step is to analyze the integrated data according to a preset and deterministic collaboration protocol (rule set), and determine a clear and unique role for this controller.

[0061] In a specific embodiment, the collaborative decision-making module 140 will first process the information packets received from neighboring controllers. It will filter out the latest information according to the time stamp and construct a local environmental state table containing its own and all neighboring controllers' states. Subsequently, the module will enter different decision branches based on this state table, with the local disturbance image ID as the primary judgment basis. The specific rules of the collaboration protocol are as follows: Decision Branch 1: When the local disturbance image is a harmonic image If the disturbance image ID received by the collaborative decision-making module 140 from the disturbance identification module 120 is identified as a harmonic type, the module will enter the collaborative decision-making logic for harmonic governance. Under this logic, the controller will determine itself as the main compensation unit or the damping unit according to its relative state in the harmonic event.

[0062] Conditions for determining the main compensation unit: The collaborative decision-making module 140 compares the local disturbance severity measurement value (spectral width ) and the remaining compensation margin MM with the corresponding values of all other controllers in the local environmental state table.

[0063] Rules for determining the main compensation unit are: First, compare the values of all controllers that detect the same harmonic image ID, the controller with the largest value is the candidate; If there are multiple controllers with equal largest values, then further compare their remaining compensation margins , The controller with the maximum value is the candidate.

[0064] To ensure the uniqueness of the decision result under any circumstances, if there are still multiple candidates after the above comparison (i.e., and are all the same), then compare the controller identifiers (Controller ID) of these candidates, and the controller with the smaller number or lower network address will be finally determined as the main compensation unit.

[0065] In addition, for the finally determined main compensation unit, its remaining compensation margin must also be greater than a preset compensation margin threshold. The compensation margin threshold is used to ensure that the controller selected as the main compensation unit still has sufficient backup capacity to handle emergencies. In one embodiment, this threshold can be set to 50% according to the system's reliability requirements.

[0066] Conditions for being determined as a damping unit: If this controller detects a harmonic profile, but after comparison, it is found that there is another controller in the network that better meets the conditions for becoming the main compensation unit, the collaborative decision-making module 140 determines that there is already a main compensation unit in the network. At this time, if the value of this controller is still higher than a preset disturbance response threshold, the module will determine the role of this controller as a damping unit. The disturbance response threshold is used to prevent the controller from overresponding to minor disturbances that do not affect the power grid stability, and its specific value can be determined with reference to the limit requirements for harmonic content in relevant national standards for power quality or grid operation guidelines.

[0067] Decision branch two: When the local disturbance profile is a voltage wave profile If the disturbance profile ID received by the collaborative decision-making module 140 is recognized as a voltage sag, swell, or fluctuation type, the module will enter the collaborative decision-making logic for voltage support.

[0068] Conditions for being determined as a voltage support unit: In this scenario, the collaborative goal is for multiple controllers to jointly perform reactive power absorption and injection to stabilize the voltage of the local power grid. Therefore, as long as the disturbance profile ID detected by this controller is of the voltage fluctuation type and its disturbance severity metric exceeds a preset response threshold, the collaborative decision-making module 140 determines the role of this controller as a voltage support unit. Similar to the above disturbance response threshold, the setting of the voltage response threshold is also to make the controller's response focus on voltage fluctuation events that are sufficient to affect power quality, and its value can be set according to the regulations on the allowable range of voltage deviation in the grid guidelines.

[0069] All controllers determined to have this role will jointly execute the reactive power compensation instruction in the next step.

[0070] If the current perturbed image does not belong to any preset collaborative governance type, or the severity of the perturbation does not reach the response threshold, the collaborative decision-making module 140 determines the role of this controller as the standby monitoring unit. In this role, the controller only continues to perform monitoring and data broadcasting, and does not perform active compensation actions.

[0071] After completing the above decision-making process, the collaborative decision-making module 140 outputs the finally determined role identifier to the instruction generation module 150, which is used to guide the specific control behavior of the next step. Since all controllers execute the same and deterministic collaborative protocol, at any moment, for the same power grid event, the result of role allocation is unique and conflict-free.

[0072] Step S500: Generate and execute corresponding power quality governance control instructions according to the role This step is executed by the instruction generation module 150 inside each power quality governance controller 10. This module receives the role identifier determined by the collaborative decision-making module 140 in step S400. Based on this role identifier, the instruction generation module 150 selects and executes a specific control algorithm to generate the final control instruction for driving the power electronic inverter inside the controller, such as a pulse width modulation (PWM) signal.

[0073] In a specific embodiment, the instruction generation module 150 performs the following corresponding operations according to different role identifiers: When the role is the main compensation unit: The control objective of the instruction generation module 150 is to generate a compensation current whose magnitude is equal to the harmonic current component detected in the distribution network and whose phase is opposite. For this purpose, the module first needs to obtain an accurate harmonic current reference signal . This reference signal can be obtained by performing harmonic extraction on the load current or grid current collected by the data acquisition module 110. For example, the fast Fourier transform (FFT) algorithm can be used to analyze the current spectrum, separate each harmonic component and superimpose them, or a group of parallel band-pass filters can be used to extract harmonics of a predetermined order.

[0074] After obtaining the harmonic current reference signal The module inputs it into a high-performance current tracking controller. In one embodiment, this controller can be one or more parallel proportional-resonant (PR) controllers. Each PR controller is tuned to a specific harmonic frequency and can achieve zero-static error tracking of a sine signal of that frequency. The outputs of all PR controllers are superimposed to form a modulation signal. This modulation signal is finally sent to a PWM generator to generate the switching signal for driving the power switch device in the power electronic inverter, so that the controller injects the desired compensation current into the grid.

[0075] When the role is the damping unit: The control objective of the instruction generation module 150 is no longer to compensate for harmonic currents, but to change the output impedance characteristic of the controller itself so that it exhibits a high damping (i.e., resistive) characteristic at specific resonant frequencies. To this end, the module will adopt a virtual impedance control strategy.

[0076] This strategy is achieved by introducing a positive feedback path into the original control loop. Specifically, the instruction generation module 150 will monitor in real time the current output by this controller to the power grid . Then, this current signal passes through a virtual impedance link with a specific transfer function to obtain a voltage correction amount. This transfer function is designed to exhibit a large resistance value within the resonant frequency range that needs to be suppressed. Finally, this voltage correction amount is superimposed on the original voltage or current control instruction. This makes the output impedance of the controller equivalent to the parallel connection of its inherent impedance and the virtual impedance when viewed from the outside. By this method, the controller provides an absorption path for potential resonant currents without outputting large fundamental or harmonic currents, thereby suppressing power grid resonance.

[0077] When the role is the voltage support unit: The control objective of the instruction generation module 150 is to perform dynamic reactive power compensation according to the deviation of the power grid voltage to maintain the stability of the grid-connected point voltage. To this end, the module will execute a voltage closed-loop control strategy.

[0078] The module first compares the effective value of the grid-connected point voltage measured by the data acquisition module 110 with a standard voltage reference value (such as the rated voltage value of the power grid) to obtain the voltage deviation . This voltage deviation is then input into a proportional-integral (PI) controller. The output of the PI controller is the reference value of the reactive current that the controller needs to emit or absorb . This reference value is then sent to a low-level current decoupling controller based on the dq synchronous rotating coordinate system. This low-level controller calculates the corresponding d-axis and q-axis voltage commands according to and finally generates a PWM signal to drive the inverter to emit the required reactive power.

[0079] When the role is the standby monitoring unit: The instruction generation module 150 will not generate any active compensation or damping instructions. It will control the inverter to operate in the standby mode, maintaining the synchronous connection with the grid, but its output current instruction is set to zero. The controller only consumes a small amount of power necessary to maintain its own operation in this role and continuously executes the monitoring and decision-making processes of steps S100 to S400 until the network state changes and its role is transformed.

[0080] To further clarify the collaborative working process of the technical solution described in the present invention, the following will be illustrated by a specific working scenario example.

[0081] In the distribution network 200, two adjacent power quality management controllers 10 with the same functions are deployed, denoted as controller A and controller B respectively. In the initial state, the power grid operates smoothly, and both controllers are in the standby monitoring unit role. At this time, a large non-linear load (rectification workshop) is put into operation at the grid connection point near controller A and injects a large amount of 5th and 7th harmonic currents into the grid.

[0082] 1. Signal acquisition and disturbance identification (steps S100 - S200) Controller A: The current time series collected by its data acquisition module 110 shows serious non-sinusoidal distortion. Its disturbance identification module 120 performs multifractal spectrum analysis on this sequence and calculates a relatively wide spectrum width , and the shape of the spectrum diagram highly matches the standard portrait of "higher harmonics" in the disturbance portrait library. Therefore, controller A determines that the local disturbance portrait ID is "higher harmonics", and the severity is . At this time, since it has not output compensation current, its remaining compensation margin is close to 100%.

[0083] Controller B: Due to its certain electrical distance from the disturbance source, the current time series collected by its data acquisition module 110 also shows distortion, but the distortion degree is lower than that of controller A. After its disturbance identification module 120 analyzes, it also determines the disturbance portrait ID as "higher harmonics", but the calculated spectrum width is less than . Its remaining compensation margin is also close to 100%.

[0084] 2. Information exchange (step S300) The information interaction module 130 of controller A encapsulates and broadcasts an information packet, the content of which is {ID: A, PortraitID: higher harmonics, Severity: , Margin: }.

[0085] The information interaction module 130 of Controller B encapsulates and broadcasts an information packet with the content {ID: B, PortraitID: High-order Harmonic, Severity: , Margin: }.

[0086] 3. Cooperative Decision-making and Role Allocation (Step S400) Controller A: Its cooperative decision-making module 140 receives the information packet from Controller B. The local environmental status table it constructs shows that both controllers have detected "high-order harmonics". The module compares the severities and finds that , and its own compensation margin is sufficient. According to the rules of the harmonic governance branch in the cooperation protocol, Controller A determines its own role as the main compensation unit.

[0087] Controller B: Its cooperative decision-making module 140 receives the information packet from Controller A. It also constructs a status table and makes a comparison, finding that the disturbance severity of Controller A is higher and it meets the conditions to become the main compensation unit. According to the cooperation protocol, Controller B determines that there is already a more suitable main compensation unit in the network, so it will determine its own role as a damping unit to cooperate with the work of the main compensation unit.

[0088] 4. Instruction Generation and Execution (Step S500) Controller A (main compensation unit): Its instruction generation module 150 immediately starts the harmonic compensation algorithm. It extracts the 5th and 7th harmonic components in the load current through FFT analysis and generates a compensation current reference signal with the same magnitude and opposite phase to these harmonics . This signal is sent to the PR current controller, and finally a PWM drive signal is generated, enabling Controller A to inject a compensation current into the power grid to cancel out the harmonics generated by the disturbance source.

[0089] Controller B (damping unit): Its instruction generation module 150 starts the virtual impedance control algorithm. Instead of generating a compensation current reference signal, it equivalentizes its output impedance to a preset resistance value at the 5th and 7th harmonic frequency points according to the known power grid parameters and the harmonic frequencies to be suppressed (5th and 7th). This operation provides stable damping for the high-frequency compensation current of Controller A, suppresses the power grid resonance that may be caused by the compensation behavior, and ensures the stability of the entire governance process.

[0090] Through the above cooperation process, Controller A undertakes the main harmonic elimination task, while Controller B actively provides damping support for the system. The two achieve complementary functions through role differentiation. Compared with the scheme where both controllers independently perform compensation, the cooperation method of the present invention not only effectively addresses the power quality problem but also systematically avoids potential stability risks.

[0091] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power quality management and control method is applied to a system composed of multiple power quality management controllers, characterized in that, The method is executed by each power quality management controller in the system, and includes the following steps: Collect the grid signals at its grid connection point to obtain a time series; Calculate the multifractal spectrum based on the time series, and identify the disturbance image of the current grid signal according to the characteristics of the multifractal spectrum; Broadcast an information packet containing the disturbance image to neighboring controllers; Receive information packets from neighboring controllers, and combine with its own status to determine its role in the controller group according to the disturbance image under a preset cooperation protocol; Generate and execute corresponding power quality management control instructions according to the role; 2. The power quality governance control method according to claim 1, characterized in that The step of calculating the multifractal spectrum based on the time series includes: Construct a probability measure based on the time series and construct a partition function; Solve the quality index according to the partition function; Obtain the multifractal spectrum by performing a Legendre transform on the quality index; 3. The power quality management and control method according to claim 1, characterized in that The step of identifying the disturbance image of the current grid signal according to the characteristics of the multifractal spectrum includes: Match the spectral characteristics of the multifractal spectrum calculated in real time with the standard spectral characteristics in a preset disturbance image library to determine the disturbance image; 4. The power quality governance control method according to claim 1, wherein The information packet further includes: Controller identification, remaining compensation margin, and multifractal spectrum width characterizing the severity of the disturbance; 5. The power quality governance control method according to claim 1, wherein The step of determining its own role in the controller group according to the disturbance image under a preset cooperation protocol includes: If the disturbance image is a harmonic image, determine itself as the main compensation unit responsible for outputting the main compensation current or determine itself as the damping unit responsible for suppressing resonance according to the status of itself and neighboring controllers; 6. The power quality management and control method according to claim 1, characterized in that, The step of determining its own role in the controller group according to the disturbance image under a preset cooperation protocol includes: If the disturbance image is a voltage wave animation image, determine itself as a voltage support unit that cooperates with other controllers for reactive power compensation; 7. The power quality governance control method according to claim 5, characterized in that When the role is the main compensation unit, in the step of generating and executing corresponding power quality management control instructions according to the role, output the main harmonic compensation current to manage harmonics; 8. The power quality governance control method according to claim 5, wherein When the role is the damping unit, in the step of generating and executing corresponding power quality management control instructions according to the role, adjust its own output impedance to suppress the parallel resonance generated when the main compensation unit works; 9. The power quality management and control method according to claim 1, characterized in that The step of generating and executing corresponding power quality management control instructions according to the role includes: Dynamically correct the local control target according to the determined role, and generate the control instruction based on the corrected local control target; 10. A power quality management and control system, comprising a plurality of power quality management controllers, characterized in that, The system is configured to execute the method according to any one of claims 1-9, and each power quality management controller in the system includes: A data acquisition module for collecting the grid signals at the grid connection points of the respective controllers to obtain a time series; A disturbance identification module for calculating the multifractal spectrum based on the time series and identifying the disturbance image of the current grid signal according to the characteristics of the multifractal spectrum; An information interaction module for broadcasting an information packet containing the disturbance image to neighboring controllers and receiving information packets from neighboring controllers; A collaborative decision-making module, configured to determine its own role in the controller group according to the perturbed image under a preset collaborative protocol by combining its own state; An instruction generation module, configured to generate and execute corresponding power quality governance control instructions according to the role.

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