Power distribution network electric energy quality control method

Abstract

The invention discloses a power quality control method for a power distribution network. The method comprises the following steps: monitoring power quality parameters of a target node of the power distribution network in real time; analyzing and identifying the current power quality state based on the monitoring parameters; generating a compensation control instruction according to the state in combination with a preset control target or a dynamically adjusted control strategy; outputting the instruction to at least one electric energy quality adjusting device on the power distribution network; and triggering the device to execute the instruction, and performing dynamic compensation adjustment on the power quality parameters of the power distribution network. The method can realize effective management and control of the power quality of the power distribution network, and improves the power supply stability.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to power quality control technology for distribution networks, and particularly to a control method for real-time monitoring, state assessment, compensation strategy generation and dynamic adjustment of power quality parameters in distribution networks. It is applicable to power quality optimization control scenarios in complex distribution network environments containing distributed power sources and nonlinear loads. Background Technology

[0002] With the integration of new energy sources into the grid, the widespread adoption of electronic loads, and the deepening construction of smart grids, the power distribution network, as a crucial link connecting the power system and users, directly impacts the stability of industrial production, the safe operation of precision equipment, and the electricity experience of residents. Modern power distribution networks are characterized by high penetration of distributed power sources, diversified load types, and frequent dynamic fluctuations. Power quality problems such as voltage sags, harmonic pollution, and three-phase imbalance occur frequently, becoming one of the core challenges restricting the safe and economical operation of the power grid. Therefore, the demand for efficient and reliable power quality control technologies is increasingly urgent.

[0003] Currently, power quality control in distribution networks mostly employs passive compensation methods based on fixed thresholds, using devices such as Static Var Generators (SVG) and Active Power Filters (APFs) to adjust local parameters. Existing technologies rely heavily on single-point sampling data for monitoring, lacking the ability to perceive the overall state of the power grid. Control strategies are often pre-set and fixed, making it difficult to adapt to dynamic operating conditions caused by fluctuations in distributed power generation output and random load changes. Furthermore, the lack of coordination between devices during compensation execution easily leads to overcompensation or undercompensation, resulting in unsatisfactory power quality improvement.

[0004] The core problems with existing technologies include: first, the lag in state recognition, with traditional monitoring methods having long data processing cycles and difficulty in capturing transient changes in power quality in real time; second, poor adaptability of control strategies, with fixed compensation parameters unable to match the dynamic characteristics of the power grid, resulting in insufficient regulation accuracy; third, a lack of system coordination, with control conflicts easily arising when multiple devices operate independently, reducing overall compensation efficiency; and fourth, the lack of a closed-loop feedback mechanism, making it difficult to verify and correct the compensation effect in real time and form a continuously optimizing control loop. These problems limit the power quality management capabilities of existing technologies in complex distribution network environments, necessitating the development of new control methods that feature dynamic sensing, intelligent decision-making, and collaborative control. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a power quality control method for power distribution networks to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides a power quality control method for a power distribution network, comprising the following steps: Real-time monitoring of power quality parameters at target nodes in the distribution network; Based on the monitored power quality parameters, analyze and identify the current power quality status of the distribution network; Based on the current power quality status, and in conjunction with preset control objectives or dynamically adjusted control strategies, corresponding compensation control commands are generated. The compensation control command is output to at least one power quality conditioning device configured on the power distribution network; The power quality regulating device is triggered to execute the compensation control command to dynamically compensate and regulate the power quality parameters of the distribution network.

[0007] The above technical solution has the following beneficial effects: The power quality control method for distribution networks described in this invention monitors the power quality parameters of target nodes in real time and accurately identifies their current state. It then generates compensation control commands by combining preset control targets or dynamically adjusted control strategies. These commands drive the power quality regulating device to perform dynamic compensation and adjustment, enabling closed-loop control of the entire power quality process in the distribution network. This effectively improves the response speed and adjustment accuracy to problems such as voltage sags and harmonic pollution. Simultaneously, through dynamically adaptable control strategies, it can flexibly handle complex operating conditions such as distributed power source access and load fluctuations in the distribution network, reducing overcompensation or undercompensation, improving the stability and reliability of the distribution network operation, and ensuring the safe and efficient operation of various electrical equipment. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is an overall flowchart of a power quality control method for a power distribution network according to an embodiment of the present invention; Figure 2 This is a flowchart of step S10 in an embodiment of the present invention; Figure 3 This is a flowchart of step S20 in an embodiment of the present invention; Figure 4 This is a flowchart of step S30 in an embodiment of the present invention; Figure 5 This is a flowchart of step S40 in an embodiment of the present invention; Figure 6This is a flowchart of step S50 in an embodiment of the present invention; Figure 7 This is a functional block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Example 1 like Figure 1 As shown in the figure, this embodiment provides a power quality control method for a power distribution network, which includes the following steps: S10: Real-time monitoring of power quality parameters at target nodes in the distribution network; S20: Based on the monitored power quality parameters, analyze and identify the current power quality status of the distribution network; S30: Based on the current power quality status, and in conjunction with a preset control objective or a dynamically adjusted control strategy, generate a corresponding compensation control command; S40: Output the compensation control command to at least one power quality conditioning device configured on the power distribution network; S50: Trigger the power quality regulation device to execute the compensation control command to dynamically compensate and adjust the power quality parameters of the distribution network.

[0012] In this embodiment, the distribution network includes 10kV feeders and subordinate 380V user-side nodes, and is equipped with regulating devices such as active power filters (APF), static var generators (SVG), and smart circuit breakers. The control center uses an industrial-grade server to realize data processing and command issuance.

[0013] In step S10, monitoring nodes are set up at the 10kV bus nodes, important industrial load access points, and distributed photovoltaic grid connection points of the distribution network. Power quality parameters are collected in real time by the installed multi-functional power sensors. The sensor sampling frequency is set to 2kHz to ensure that transient power quality events can be captured. The collected parameters are transmitted to the local data acquisition terminal via shielded cables. After the terminal performs A / D conversion on the parameters, it uses a sliding window filtering algorithm (the window size is set to 50 sampling points) to filter out high-frequency noise. The processed parameters are then encapsulated into data packets at a frequency of once per second, marked with a timestamp accurate to milliseconds, and transmitted to the control center via optical fiber.

[0014] In step S20, the control center calls the built-in power quality status evaluation index system (where the voltage deviation threshold is ±5% of the rated voltage and the frequency deviation threshold is ±0.5Hz), and compares the real-time received monitoring parameters with the corresponding thresholds. For example, when the voltage of a certain node is monitored to be 360V (rated 380V) and the deviation reaches -5.26%, it is determined to be an abnormal voltage parameter. The source of the abnormal parameter is traced through fault tree analysis, and combined with the distribution network topology diagram, it is determined to be a voltage sag caused by the output fluctuation of the photovoltaic inverter. The severity level is calculated as Level I based on the deviation amplitude. At the same time, the analysis shows that the affected nodes include the photovoltaic grid connection point and three downstream residential load nodes.

[0015] In step S30, the control center retrieves a voltage compensation scheme from the basic control strategy library based on the identified voltage sag problem. Combining the real-time monitored photovoltaic output (currently 200kW, down 30% from 5 minutes ago) and load data (total load 450kW), the SVG adjustment parameters in the basic scheme are corrected, adjusting the reactive power compensation from the preset 50kvar to 80kvar. Through simulation verification using the model predictive control algorithm, it is predicted that the voltage can recover to 375V after compensation (deviation -1.32%), meeting the control target. Subsequently, the corrected strategy is converted into a compensation control command that includes the SVG firing angle adjustment value (adjusted from 30° to 25°) and execution time (immediate execution).

[0016] In step S40, the control center selects an SVG (Static Varistor) from the list of regulating devices based on the impact range and severity of the voltage sag. This SVG is located 1.2km from the fault node, has a current load rate of 60%, and a historical response time of 30ms. An encrypted communication link is established via industrial Ethernet (using the AES-128 encryption algorithm), and the compensation control command is transmitted in frame format. After receiving the command, the SVG returns an acknowledgment message containing the command checksum. Once the control center verifies the message, the command delivery is completed.

[0017] In step S50, after receiving the instruction, the SVG starts the actuator initialization program. After the core components such as the power module and capacitor bank are in normal condition, it adjusts the conduction timing of the internal IGBT according to the trigger angle adjustment value in the instruction and outputs 80kvar of reactive power. During the compensation process, the Hall sensor built into the SVG collects the output voltage waveform in real time. The control center compares the waveform with the preset target voltage waveform (380V sine wave). When the voltage deviation is detected to be 3V, the trigger angle is finely adjusted to 24.5° through incremental PID algorithm and continuously dynamically adjusted until the voltage stabilizes at 378V (deviation -0.53%). After the threshold requirement is met, the compensation stops.

[0018] The beneficial technical effects of this invention are as follows: by real-time monitoring of the power quality parameters of target nodes in the distribution network, the current power quality status is accurately identified. Compensation control commands are generated and output to the power quality regulating device in conjunction with preset control targets or dynamically adjusted control strategies. This triggers the device to execute the commands to dynamically compensate and adjust the power quality parameters. Real-time capture of power quality changes improves the timeliness and accuracy of status identification, enhances the adaptability of the control strategy to dynamic grid conditions, ensures accurate and effective compensation regulation, enables collaborative work among various regulating devices, avoids control conflicts, forms a closed-loop feedback continuous optimization mechanism, improves the control level of power quality in the distribution network, and ensures the safe and stable operation of the grid and the reliable operation of various electrical equipment.

[0019] like Figure 2 As shown, step S10 includes the following sub-steps: S101: Acquire raw parameters including RMS voltage, RMS current, frequency deviation, total harmonic distortion, and three-phase imbalance by a sensor array configured at the target node. S102: The collected raw parameters are digitally converted to generate digital data; S103: The digitized data is subjected to noise suppression processing using a sliding window filtering algorithm to obtain filtered power quality monitoring parameters; S104: Encapsulate the filtered power quality monitoring parameters into a monitoring data packet, mark the acquisition timestamp, and upload it to the control center.

[0020] In step S101, an array of eight sensors is deployed at the target node of the 10kV feeder in the distribution network, including four Hall voltage sensors (range 0-15kV), three Rogowski coil current sensors (range 0-1000A), and one frequency sensor (accuracy ±0.01Hz). The sensor array synchronously acquires raw parameters: the effective voltage value (in kV) and the effective current value (in A) are recorded every 10ms, and the frequency deviation (in Hz), total harmonic distortion (in %), and three-phase imbalance (in %) are recorded every 50ms. The total harmonic distortion needs to cover the acquisition of the 3rd to 21st harmonic components.

[0021] In step S102, the analog output signal (voltage signal range 0-5V) of the sensor array is connected to the data acquisition card (model PCI-6251) and digitized by a 16-bit A / D converter. The conversion formula is: Digital quantity = (Analog quantity / 5V) × 65535. For example, 3.8kV corresponds to an analog signal of 3.8V, which is converted into a digital quantity of 50176 (decimal). A current of 100A corresponds to an analog signal of 1V, which is converted into a digital quantity of 13107. Finally, a digital data stream of 100 sets per second is formed.

[0022] In step S103, a sliding window filtering algorithm is used to process the digitized data. The window length is set to 50 sampling points (corresponding to 0.5 seconds of data). Random noise is suppressed by calculating the arithmetic mean of the data within the window. For example, if the original data of the effective voltage value for a certain period is 3.82kV, 3.79kV, 3.85kV..., after window filtering, a smoothed value of 3.81kV is obtained. If the rate of change of the effective current value for three consecutive windows exceeds 10% (for example, a sudden increase from 100A to 120A), the sampling frequency is automatically increased from 100Hz to 200Hz to enhance the ability to capture dynamic changes.

[0023] In step S104, the filtered monitoring parameters are encapsulated into an MMS message format according to the IEC 61850 standard. The message includes the parameter name, value, unit, and status identifier (normal / abnormal), and a data acquisition timestamp accurate to milliseconds is added (format YYYY-MM-DDHH:MM:SS.XXX). After encapsulation, the data is uploaded to the control center server via fiber optic Ethernet (transmission rate 100Mbps) using the TCP / IP protocol. The upload interval is set to 1 second. If the network is interrupted, the local SD card cache (capacity 64GB) is activated, and the data is re-uploaded after the network is restored.

[0024] By comprehensively collecting various raw parameters through a sensor array, and ensuring data accuracy through digital conversion, sliding window filtering effectively suppresses noise and improves data quality. Combined with timestamp encapsulation and uploading, it can provide the control center with comprehensive, accurate, and real-time power quality monitoring data, providing a reliable foundation for subsequent status identification and control strategy generation. At the same time, dynamically adjusting the sampling frequency can enhance the capture capability when parameters change drastically, and the local cache design ensures data continuity. Overall, it improves the comprehensiveness, accuracy, and reliability of power quality monitoring in the distribution network.

[0025] like Figure 3 As shown, step S20 includes the following sub-steps: S201: Call the preset power quality status evaluation index system, which includes voltage deviation threshold, frequency deviation threshold and harmonic content limit value; S202: Compare the real-time monitored power quality parameters with the corresponding thresholds to identify abnormal parameters that exceed the threshold range; S203: Trace the abnormal parameters to determine the type of power quality problem by fault tree analysis, which includes at least one of voltage sag, harmonic pollution, and three-phase imbalance; S204: Calculate the severity level based on the magnitude by which abnormal parameters exceed the threshold; S205: Combine the distribution network topology model to analyze the number of affected nodes and load types to determine the scope of impact; S206: Generate a status assessment report that includes the type of power quality problem, the severity level, and the scope of impact.

[0026] In step S201, after receiving the monitoring data packet, the control center automatically invokes the preset power quality status evaluation index system. This system is specifically set as follows: voltage deviation threshold is ±5% of the rated voltage (e.g., the threshold range for a 380V rated voltage is 361-399V); frequency deviation threshold is ±0.5Hz (for a 50Hz rated frequency, the threshold range is 49.5-50.5Hz); and harmonic content limits are: 3rd harmonic ≤5%, 5th harmonic ≤4%, 7th harmonic ≤3%, and total harmonic distortion ≤8%. These threshold parameters are stored in the control center's relational database and can be dynamically updated via remote terminals.

[0027] In step S202, the control center compares the real-time monitored power quality parameters with the corresponding thresholds mentioned above point by point. For example, when the monitoring data of a certain industrial node shows a voltage of 358V (below the lower limit of 361V) and a 5th harmonic content of 4.2% (exceeding the upper limit of 4%), the system automatically marks these two parameters as abnormal parameters; at the same time, it uses the difference percentage calculation method to calculate the voltage deviation percentage as -5.79% and the 5th harmonic offset as 0.2%, and records the calculation process and results to the log file in real time.

[0028] In step S203, fault tree analysis is initiated to trace the source of the marked abnormal parameters: taking voltage abnormality as an example, the voltage deviation is decomposed from the top-level event downwards, and intermediate events such as distributed power output fluctuation, excessive line impedance, and sudden load change are investigated in sequence. Combined with the real-time line current (20% increase compared to the previous 10 minutes) and transformer oil temperature (normal range), it is finally determined that the voltage drop is caused by a sudden increase in the load of the welding machine. For the 5th harmonic abnormality, the spectral analysis is used to locate the nearby rectifier device and determine it to be a harmonic pollution problem.

[0029] In step S204, the severity level is calculated based on the magnitude of the abnormal parameter exceeding the threshold: the voltage deviation of -5.79% exceeds the threshold (±5%) by 0.79%, corresponding to a mild level (Level I); the 5th harmonic content of 4.2% exceeds the threshold by 5% ((4.2-4) / 4×100%), also classified as Level I; if a parameter is subsequently monitored to exceed the threshold by 30%, it is automatically upgraded to Level II, and if it exceeds 50% or more, it is classified as Level III.

[0030] In step S205, the distribution network topology model is called (using a graphical display based on the CIM standard). The voltage sag impact range is determined by the topology traversal algorithm, which includes the industrial node and two downstream commercial nodes. The affected load types are one welding machine (100kVA) and two sets of air conditioning equipment (total capacity 50kVA). The harmonic pollution impact range is the node and one adjacent residential node, involving the electrical equipment of three households.

[0031] In step S206, the system automatically generates a status assessment report, which includes: the power quality problem type is voltage sag (caused by a sudden increase in load) and 5th harmonic pollution (caused by the rectifier); the severity level is Level I (with the basis for deviation amplitude calculation); the affected area is marked with specific node numbers (#D102, #D103, etc.) and load capacity; the report is packaged in XML format, including the report generation timestamp and verification code, stored in the encrypted database of the control center and synchronously backed up to the cloud.

[0032] The advantages of this embodiment are that it achieves accurate identification of abnormal power quality parameters through a clear indicator system and quantitative comparison method; it improves the scientificity and efficiency of problem tracing by using fault tree analysis; it provides a precise basis for the formulation of subsequent control strategies by combining detailed analysis of severity level and impact scope; and the standardized assessment report ensures the integrity and consistency of information transmission, thereby improving the accuracy and reliability of power quality status assessment of the distribution network as a whole.

[0033] like Figure 4 As shown, step S30 includes the following sub-steps: S301: Retrieve the basic control strategy library that matches the current power quality state, the basic control strategy library containing standardized compensation schemes for different problem types; S302: Based on real-time monitored load fluctuation data and power grid topology, the parameters of the basic control strategy are modified to obtain a modified power quality compensation control strategy adapted to the current power grid operating conditions. S303: The modified power quality compensation control strategy is simulated and verified using a model predictive control algorithm, and the predicted value of the compensation effect is calculated. S304: When the predicted value of the compensation effect meets the control target, the corrected power quality compensation control strategy is converted into a compensation control instruction containing adjustment amount and execution timing.

[0034] In step S301, after receiving the status assessment report (including voltage sag and 5th harmonic pollution issues), the control center retrieves the corresponding standardized compensation scheme from the basic control strategy library through keyword matching. The basic scheme for voltage sag (Level I) is: activate the SVG device for reactive power compensation, with the initial compensation amount set to 1.2 times the load fluctuation. The basic scheme for 5th harmonic pollution (Level I) is: activate the 5th harmonic filtering function of the APF device, with the filtering depth set to 80%. This basic control strategy library uses a relational database for storage, and each scheme includes index information such as problem type tags, applicable level range, and regulating device type, supporting rapid retrieval by problem type.

[0035] In step S302, the control center acquires real-time load fluctuation data (the current load of the welding machine is 100kVA, an increase of 80kVA compared to before startup) and the power grid topology (the electrical distance between this node and the SVG device is 0.8km, and the line impedance is 0.02Ω / km), and corrects the parameters of the basic control strategy. For voltage sag compensation, according to the formula "corrected compensation amount = initial compensation amount × (1 + line loss coefficient)", the corrected reactive power compensation amount of the SVG is calculated to be 80kVA × 1.2 × (1 + 0.8 × 0.02) = 97.92kVA, which is rounded to 98kVA; for harmonic pollution compensation, combined with the real-time monitored 5th harmonic current value (15A), the APF filtering depth is corrected from 80% to 90% (because the current harmonic content slightly exceeds the threshold) to ensure the compensation effect.

[0036] In step S303, the control center calls the built-in model predictive control algorithm (prediction step size set to 5 control cycles, each cycle 1 second), and inputs the corrected compensation strategy into the distribution network simulation model (built on MATLAB / Simulink, including precise parameters of lines, loads, and regulating devices). Simulation results show that after implementing the voltage sag compensation strategy, the node voltage is predicted to rise to 372V after 1 second (deviation -2.1%, within the ±5% threshold range); after implementing the harmonic compensation strategy, the predicted value of the 5th harmonic content is 3.8% (below the 4% threshold), both meeting the control objectives. Simultaneously, the algorithm also outputs the voltage and current dynamic response curves during the compensation process, verifying the stability of the strategy.

[0037] In step S304, since the predicted compensation effect values ​​all meet the control targets (voltage deviation ≤ ±5%, harmonic content ≤ threshold), the control center converts the corrected compensation strategy into specific compensation control instructions. Specifically, the instruction for the SVG includes: an adjustment of 98 kvar reactive power, immediate start, and a duration of 5 minutes (subsequently dynamically adjusted based on real-time monitoring data); the instruction for the APF includes: a 5th harmonic filtering depth of 90%, synchronous start with the SVG, and continued until the harmonic content drops below 3.5%. The instruction format adopts the IEC61850 standard GOOSE (Generic Object Oriented Substation Event) message, including fields such as device address code, adjustment parameter value, and checksum.

[0038] The advantages of this embodiment are that by combining a basic strategy library with real-time parameter correction, it not only ensures the standardization of the control strategy but also improves its adaptability to dynamic operating conditions. The introduction of model predictive control algorithm verifies the compensation effect in advance, avoiding the risks that may be caused by blind execution. The final generated instructions, which include adjustment amounts and execution timing, provide a precise basis for subsequent device execution, thus improving the scientificity, accuracy, and reliability of the power quality compensation strategy for the distribution network as a whole.

[0039] like Figure 5 As shown, step S40 includes the following sub-steps: S401: Based on the scope and severity of the power quality problem, select at least two candidate regulating devices from the preset list of power quality regulating devices; S402: Establish an encrypted communication link with the candidate regulating device via an industrial Ethernet or 5G communication module; S403: The compensation control command is transmitted in segments according to the response priority order of the candidate regulating devices; S404: Receive the instruction confirmation message returned by the candidate adjustment device and complete the instruction delivery status verification.

[0040] In step S401, it is known that the distribution network has a Class I voltage sag (affecting 3 nodes) and a Class I 5th harmonic pollution (affecting 2 nodes). Candidate devices are selected from a pre-set list of power quality conditioning devices, which includes parameters such as the location, load rate, and response time of each device. The selection criteria are: the device's current load rate is below 70%, the electrical distance to the problematic node is less than 5km, and the historical compensation response time is less than 50ms. After screening, two candidate conditioning devices meet the criteria: an SVG device located near node #D102 (load rate 62%, distance 1.5km, response time 28ms) and an APF device at node #D105 (load rate 58%, distance 2.3km, response time 32ms).

[0041] In step S402, the control center first attempts to establish a communication link with the candidate regulating device via industrial Ethernet. For the SVG device, fiber optic connection is used, and a communication channel is built using the TCP / IP protocol, with SSL / TLS encryption protocol enabled to encrypt data transmission. For the APF device, due to the weak industrial Ethernet signal, it automatically switches to the 5G communication module, using a dedicated APN access, and establishing an encrypted tunnel using the IPSec protocol to ensure the security and stability of the communication link. The encryption key is automatically updated every 30 minutes.

[0042] In step S403, commands are transmitted according to the response priority of the candidate regulating devices. The priority is determined by a combination of problem relevance and response speed. The SVG device is directly related to the voltage sag problem and has a shorter response time, so its priority is higher than that of the APF device. The compensation control command is divided into multiple data fragments (each fragment is 1024 bytes in size). First, it is transmitted to the SVG device. Each fragment is accompanied by a sequence number and checksum. The transmission interval is 10ms. After the SVG device confirms that it has received all fragments, the command fragments are then transmitted to the APF device in the same way.

[0043] In step S404, after receiving all instruction fragments, the SVG device assembles them into a complete instruction and verifies it, then returns an acknowledgment message containing the instruction ID, reception timestamp, and verification result. The APF device, after completing instruction reception and verification, also returns an acknowledgment message. Upon receiving the acknowledgment messages from both devices, the control center checks the verification results in the messages. If both are confirmed as "verification passed," the instruction delivery is deemed successful, and the delivery time and device feedback information are recorded in the log. If no acknowledgment message is received or verification fails, the instruction will be retransmitted within 3 seconds.

[0044] The beneficial effects of the above technical solution are as follows: by selecting suitable candidate regulating devices through clear screening criteria, the effectiveness of compensation execution is ensured; the use of encrypted communication links ensures the security of command transmission and prevents information leakage or tampering; command transmission is segmented according to priority, which improves the orderliness and efficiency of command delivery; and the command confirmation and verification mechanism ensures the accurate reception of commands and avoids compensation failure due to command transmission problems, thereby improving the reliability and security of the command transmission link in the power quality control of the distribution network as a whole.

[0045] like Figure 6 As shown, step S50 includes the following sub-steps: S501: Triggers the actuator initialization program built into the power quality regulator to complete the self-check of the core components' status; S502: Adjust the firing angle or conduction angle of the power quality regulating device according to the phase compensation parameters in the compensation control command; S503: During the compensation process, the power quality characteristic waveform of the device output terminal is collected in real time; S504: Based on the deviation between the feedback power quality characteristic waveform and the preset target power quality characteristic waveform, dynamically fine-tune the control parameters of the power quality regulating device until the deviation is less than the preset threshold.

[0046] In step S501, after receiving the compensation control command, the SVG device and APF device immediately trigger their built-in actuator initialization program. The initialization program of the SVG device will sequentially perform status self-checks on the power module, capacitor energy storage unit, and IGBT drive circuit, and generate a self-check report by collecting the voltage, current, and temperature parameters of each component (for example, the power module temperature must be within the range of -40℃ to 85℃). The APF device focuses on checking the harmonic detection unit, filter inductor, and DC side voltage to confirm that the core components are in normal working condition (for example, the DC side voltage must be stable at 540V±5%). If the self-check detects an abnormality (for example, IGBT drive failure), it will immediately send an alarm signal to the control center and suspend the execution of the compensation command.

[0047] In step S502, the SVG device adjusts the firing angle of its internal IGBT according to the phase compensation parameters in the compensation control command. The command requires it to output 98kvar of reactive power, corresponding to an initial firing angle of 25°. The trigger pulse signal is precisely sent to the IGBT gate through the drive circuit, so that the firing angle is smoothly adjusted from the initial value to the target value. The adjustment process takes 0.5 seconds to avoid grid impact caused by parameter abrupt changes. The APF device, according to the phase parameters in the command, sets the conduction angle to 15°. By adjusting the conduction angle, it controls the engagement depth of the filter circuit to specifically suppress the 5th harmonic.

[0048] In step S503, during the compensation process, the Hall sensor configured at the output of the SVG device collects voltage and current waveforms in real time at a sampling frequency of 10kHz, generating power quality characteristic waveform data containing fundamental and harmonic components. Every 10ms, the data is packaged into a waveform data packet and uploaded to the control center. The high-speed oscilloscope at the output of the APF device synchronously collects the filtered current waveform, focusing on monitoring the amplitude change of the 5th harmonic. It also uploads characteristic waveform data at a fixed period to ensure that the compensation process can be traced in real time.

[0049] In step S504, the control center compares the voltage waveform fed back by the SVG device with the preset target waveform (380V sine wave, total harmonic distortion ≤5%), calculating an initial deviation of 3.2%. Then, it activates the incremental PID control algorithm to dynamically fine-tune the SVG's firing angle (from 25° to 24.8°). Simultaneously, it compares the 5th harmonic current waveform fed back by the APF device with the target waveform (5th harmonic content ≤3.5%), finding a deviation of 0.3%. Further optimization of the filtering effect is achieved by fine-tuning the conduction angle (from 15° to 14.9°). After three fine-tuning operations, the SVG output voltage waveform deviation decreases to 1.8%, and the APF's 5th harmonic content deviation decreases to 0.1%, both less than the preset threshold of 2%, at which point parameter adjustment stops.

[0050] The advantages of this embodiment are that the actuator initialization program ensures the reliability of the device in executing compensation commands through strict self-testing, avoiding operation with faults; precise adjustment of the trigger angle or conduction angle ensures the accuracy of compensation and reduces the impact on the power grid; real-time acquisition of characteristic waveforms enables dynamic monitoring of the compensation process; and the dynamic fine-tuning mechanism can continuously optimize control parameters according to deviations to ensure that the compensation effect is stable and meets the standards, thereby improving the accuracy and reliability of power quality regulation in the distribution network as a whole.

[0051] In a further embodiment, after obtaining the filtered power quality monitoring parameters using the sliding window filtering algorithm in step S103, the system automatically starts the trend analysis module. This module continuously tracks and calculates the filtered parameters in units of sampling periods (the original sampling frequency is 1kHz, i.e., the sampling period is 1ms). For example, for the effective current value parameter of a target node, the value in the nth sampling period is 100A, the value in the (n+1)th sampling period is 108A, and the value in the (n+2)th sampling period is 120A.

[0052] The system calculates the rate of change between adjacent periods using the formula: "Parameter change rate = (parameter value of the next period - parameter value of the previous period) / parameter value of the previous period × 100%". The calculation shows that the rate of change of the (n+1)th period relative to the nth period is (108-100) / 100×100%=8%, and the rate of change of the (n+2)th period relative to the (n+1)th period is (120-108) / 108×100%≈11.11%. At this point, although one period's rate of change exceeds 10%, the condition of three consecutive periods is not met, and the sampling frequency remains unchanged.

[0053] Monitoring continued until the (n+3)th sampling period, where the effective current value was 135A. Its rate of change relative to the (n+2)th period was (135-120) / 120×100%=12.5%. At this point, over three consecutive sampling periods (n+1 to n+3), the parameter change rates were 8%, 11.11%, and 12.5%, respectively. The latter two periods exceeded 10%, but not all three exceeded it consecutively, so the system did not adjust the sampling frequency.

[0054] When the effective current value is detected as 150A in the (n+4)th sampling period, the rate of change relative to the (n+3)th period is (150-135) / 135×100%≈11.11%; and 168A in the (n+5)th sampling period, the rate of change relative to the (n+4)th period is (168-150) / 150×100%=12%. At this point, the parameter change rate of these three consecutive sampling periods from (n+3) to (n+5) exceeds 10%, and the system triggers the sampling frequency adjustment mechanism, automatically increasing the original sampling frequency of 1kHz to 2kHz (the sampling period becomes 0.5ms).

[0055] After the sampling frequency is increased, the system continuously monitors parameter changes. When the parameter change rate is less than 5% for five consecutive sampling cycles (based on the new sampling frequency), the sampling frequency is automatically restored to the original 1kHz. For example, when the effective value of the current is stable at around 170A, and the change is less than 8A for five consecutive 0.5ms cycles with a change rate of less than 5%, the original sampling frequency is restored.

[0056] The advantage of this embodiment is that by analyzing the trend of the filtered data, it can keenly capture rapid changes in parameters and promptly increase the sampling frequency, which can more accurately capture the transient changes in power quality parameters and provide richer and more accurate data support for subsequent state identification and control strategy generation. When the parameters tend to stabilize, restoring the original sampling frequency can avoid unnecessary resource consumption, ensuring monitoring accuracy while taking into account the system's operating efficiency.

[0057] In one embodiment, step S20 specifically includes the following sub-steps: S201: Call the preset power quality status evaluation index system, which includes a voltage deviation threshold of ±5% relative to the rated voltage, a frequency deviation threshold of ±0.5Hz relative to the rated frequency of 50Hz, and a harmonic content limit value, wherein the 3rd harmonic ≤5%, the 5th harmonic ≤4%, the 7th harmonic ≤3%, and the total harmonic distortion rate ≤8%; S202: The real-time monitored power quality parameters are compared point by point with the corresponding thresholds. The parameter offset is determined by the difference percentage calculation method. When the absolute value of the offset exceeds 10% of the threshold limit, it is marked as an abnormal parameter and a three-level alarm mechanism is activated. S203: The abnormal parameters are traced back to their source using fault tree analysis. The analysis is carried out from three dimensions: equipment failure, load change, and external interference. The power quality problem type is determined, which includes at least one of the following: voltage sag with amplitude ≤ 80% of rated voltage and duration 10ms-1min; harmonic pollution with harmonic content of specific frequency exceeding the limit; and three-phase imbalance with negative sequence current to positive sequence current ratio ≥ 10%. S204: Calculate the severity level based on the extent to which abnormal parameters exceed the threshold. Where exceeding the threshold by 0-20% is mild level (Level I), exceeding by 20%-50% is moderate level (Level II), and exceeding by more than 50% is severe level (Level III). S205: Combine the distribution network topology model to analyze the number of affected nodes and load types to determine the scope of impact. Divide the scope into local areas with 5 or fewer nodes, regional areas with 6-20 nodes, and global areas with more than 20 nodes, and distinguish the affected proportions of residential load, industrial load, and commercial load. S206: Generate a status assessment report containing the power quality problem type with specific fault characteristic parameters, the parameter offset on which the severity level is calculated, and the affected node number and load capacity data of the affected area. The report is encapsulated in XML format and stored in an encrypted manner.

[0058] In some embodiments, the screening criteria for candidate adjustment devices in step S401 include: the current load rate of the device is less than 70%, the electrical distance from the problem node is less than 5km, and the historical compensation response time is less than 50ms.

[0059] In step S504, when dynamically fine-tuning the control parameters of the power quality regulating device, an incremental PID control algorithm is adopted. Its core formula is: Δu(k)=Kp[e(k)-e(k-1)]+Kie(k)+Kd[e(k)-2e(k-1)+e(k-2)], where Δu(k) is the output increment of the k-th adjustment, e(k) is the absolute value of the current compensation deviation, Kp is the proportional coefficient, Ki is the integral coefficient (Ki=Kp / Ti, Ti is the integral time constant), and Kd is the derivative coefficient (Kd=KpTd, Td is the derivative time constant). Initially, Kp=5.0, Ti=0.8s, and Td=0.1s are preset. The adjustment increment is calculated independently for the voltage deviation of the SVG device and the harmonic deviation of the APF device.

[0060] When the absolute value of the compensation deviation is large (e.g., the voltage deviation of SVG e(k) = 3.5%, exceeding the preset threshold of 2%), the system automatically adjusts the PID parameters: increasing the proportional coefficient Kp from 5.0 to 7.0 to improve the adjustment response speed and accelerate deviation convergence; extending the integral time constant Ti from 0.8s to 1.2s to reduce the intensity of the integral action and avoid overshoot due to integral accumulation; and increasing the derivative time constant Td from 0.1s to 0.2s to enhance the ability to predict the trend of deviation changes and suppress fluctuations caused by rapid deviation changes. For example, in the first fine adjustment, e(k) = 3.5%, e(k-1) = 4.0%, and e(k-2) = 4.5%, which, when substituted into the formula, yields: Δu(1) = 7.0 × (3.5 - 4.0) + (7.0 / 1.2) × 3.5 + 7.0 × 0.2 × (3.5 - 2 × 4.0 + 4.5) = -3.5 + 20.42 + 0 = 16.92, which corresponds to a trigger angle adjustment of -0.3° (the trigger angle decreases, and the reactive power output increases).

[0061] When the absolute value of the compensation deviation is moderate (e.g., the voltage deviation of the SVG drops to e(k) = 1.8%, close to the preset threshold), the PID parameters are adjusted as follows: Kp = 5.0 (restoring the initial value), balancing adjustment speed and stability; Ti = 0.8s (restoring the initial value), maintaining normal integral action to eliminate steady-state deviation; Td = 0.1s (restoring the initial value), maintaining moderate derivative action. At this point, if e(k) = 1.8%, e(k-1) = 2.5%, and e(k-2) = 3.0%, the calculation yields: Δu(2) = 5.0 × (1.8 - 2.5) + (5.0 / 0.8) × 1.8 + 5.0 × 0.1 × (1.8 - 2 × 2.5 + 3.0) = -3.5 + 11.25 + 0.25 = 8.0, which corresponds to a trigger angle adjustment of -0.15°. The adjustment range should be reduced when the deviation is large to avoid over-adjustment.

[0062] When the absolute value of the compensation deviation is small (e.g., the voltage deviation of SVG e(k) = 0.5%, less than the preset threshold), the system further optimizes the PID parameters: the proportional coefficient Kp is reduced from 5.0 to 3.0, reducing the adjustment sensitivity and preventing frequent adjustments caused by small deviations; the integral time constant Ti is shortened from 0.8s to 0.5s, enhancing the integral action and promoting the complete elimination of steady-state deviations; the derivative time constant Td is reduced from 0.1s to 0.05s, weakening the derivative action and avoiding unnecessary adjustments triggered by small fluctuations. For example, during the third fine-tuning, e(k) = 0.5%, e(k-1) = 0.8%, and e(k-2) = 1.2%, the calculation yields: Δu(3)=3.0×(0.5-0.8)+(3.0 / 0.5)×0.5+3.0×0.05×(0.5-2×0.8+1.2)=-0.9+3.0+0.075=2.175, corresponding to a trigger angle adjustment of -0.05°. The fine-tuning amplitude is further reduced to ensure that the deviation remains stable within the threshold.

[0063] When the absolute value of the compensation deviation remains below 50% of the preset threshold (e.g., e(k) = 0.3%, far below the 2% threshold), the system stops incremental PID adjustment, maintains the current control parameters unchanged, and enters the steady-state monitoring phase. If the deviation subsequently exceeds the threshold again, the parameter adaptive adjustment process is restarted. For example, after the 5th harmonic deviation of the APF device stabilizes at 0.3% for 3 cycles, fine-tuning stops, and deviation data is collected only once every 100ms until the deviation rises back to 1.2%, at which point adjustment is restarted.

[0064] The advantages of this technical solution are that the incremental PID control algorithm can effectively avoid large abrupt changes in the control quantity and reduce the impact on the power grid by adjusting the incremental rather than absolute value of the output; while the adaptive parameter adjustment based on the absolute value of the compensation deviation can respond quickly and suppress fluctuations when the deviation is large, and finely adjust and eliminate steady-state deviations when the deviation is small, thus balancing the adjustment speed and stability, improving the accuracy and robustness of dynamic power quality compensation, and ensuring that the distribution network parameters remain stable within the target range.

[0065] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0066] Example 4 See Figure 7 This application also provides an electronic device 600, which includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform a power quality control method for a power distribution network as described in the foregoing method embodiments.

[0067] This application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute a power quality control method for a power distribution network as described in the foregoing method embodiments.

[0068] This application also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to perform a power quality control method for a power distribution network as described in the foregoing method embodiments.

[0069] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device 600 suitable for implementing embodiments of this application. The electronic device 600 in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6The electronic device 600 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0070] like Figure 6 As shown, electronic device 60 may include a processing unit (e.g., central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of electronic device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0071] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keys, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 600 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0072] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of the embodiments of this application.

[0073] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof. The aforementioned computer-readable medium can be included in the aforementioned electronic device; or it can exist independently and not assembled into the electronic device.

[0074] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A power quality control method for a power distribution network, characterized in that, Includes the following steps: S10: Real-time monitoring of power quality parameters at target nodes in the distribution network; S20: Based on the monitored power quality parameters, analyze and identify the current power quality status of the distribution network; S30: Based on the current power quality status, and in conjunction with a preset control objective or a dynamically adjusted control strategy, generate a corresponding compensation control command; S40: Output the compensation control command to at least one power quality conditioning device configured on the power distribution network; S50: Trigger the power quality regulation device to execute the compensation control command to dynamically compensate and adjust the power quality parameters of the distribution network.

2. The method according to claim 1, characterized in that, Step S10 includes the following sub-steps: S101: Acquire raw parameters including RMS voltage, RMS current, frequency deviation, total harmonic distortion, and three-phase imbalance by a sensor array configured at the target node. S102: The collected raw parameters are digitally converted to generate digital data; S103: The digitized data is subjected to noise suppression processing using a sliding window filtering algorithm to obtain filtered power quality monitoring parameters; S104: Encapsulate the filtered power quality monitoring parameters into a monitoring data packet, mark the acquisition timestamp, and upload it to the control center.

3. The method according to claim 1, characterized in that, Step S20 includes the following sub-steps: S201: Call the preset power quality status evaluation index system, which includes voltage deviation threshold, frequency deviation threshold and harmonic content limit value; S202: Compare the real-time monitored power quality parameters with the corresponding thresholds to identify abnormal parameters that exceed the threshold range; S203: Trace the abnormal parameters to determine the type of power quality problem by fault tree analysis, which includes at least one of voltage sag, harmonic pollution, and three-phase imbalance; S204: Calculate the severity level based on the magnitude by which abnormal parameters exceed the threshold; S205: Combine the distribution network topology model to analyze the number of affected nodes and load types to determine the scope of impact; S206: Generate a status assessment report that includes the type of power quality problem, the severity level, and the scope of impact.

4. The method according to claim 1, characterized in that, Step S30 includes the following sub-steps: S301: Retrieve the basic control strategy library that matches the current power quality state, the basic control strategy library containing standardized compensation schemes for different problem types; S302: Based on real-time monitored load fluctuation data and power grid topology, the parameters of the basic control strategy are modified to obtain a modified power quality compensation control strategy adapted to the current power grid operating conditions. S303: The modified power quality compensation control strategy is simulated and verified using a model predictive control algorithm, and the predicted value of the compensation effect is calculated. S304: When the predicted value of the compensation effect meets the control target, the corrected power quality compensation control strategy is converted into a compensation control instruction containing adjustment amount and execution timing.

5. The method according to claim 1, characterized in that, Step S40 includes the following sub-steps: S401: Based on the scope and severity of the power quality problem, select at least two candidate regulating devices from the preset list of power quality regulating devices; S402: Establish an encrypted communication link with the candidate regulating device via an industrial Ethernet or 5G communication module; S403: The compensation control command is transmitted in segments according to the response priority order of the candidate regulating devices; S404: Receive the instruction confirmation message returned by the candidate adjustment device and complete the instruction delivery status verification.

6. The method according to claim 1, characterized in that, Step S50 includes the following sub-steps: S501: Triggers the actuator initialization program built into the power quality regulator to complete the self-check of the core components' status; S502: Adjust the firing angle or conduction angle of the power quality regulating device according to the phase compensation parameters in the compensation control command; S503: During the compensation process, the power quality characteristic waveform of the device output terminal is collected in real time; S504: Based on the deviation between the feedback power quality characteristic waveform and the preset target power quality characteristic waveform, dynamically fine-tune the control parameters of the power quality regulating device until the deviation is less than the preset threshold.

7. The method according to claim 2, characterized in that, Step S103 further includes: Trend analysis is performed on the filtered monitoring data. When the parameter change rate exceeds 10% within three consecutive sampling periods, the sampling frequency is automatically increased to twice the original frequency.

8. The method according to claim 3, characterized in that, Step S20 specifically includes the following sub-steps: S201: Call the preset power quality status evaluation index system, which includes a voltage deviation threshold of ±5% relative to the rated voltage, a frequency deviation threshold of ±0.5Hz relative to the rated frequency of 50Hz, and a harmonic content limit value, wherein the 3rd harmonic ≤5%, the 5th harmonic ≤4%, the 7th harmonic ≤3%, and the total harmonic distortion rate ≤8%; S202: The real-time monitored power quality parameters are compared point by point with the corresponding thresholds. The parameter offset is determined by the difference percentage calculation method. When the absolute value of the offset exceeds 10% of the threshold limit, it is marked as an abnormal parameter and a three-level alarm mechanism is activated. S203: The abnormal parameters are traced back to their source using fault tree analysis. The analysis is carried out from three dimensions: equipment failure, load change, and external interference. The power quality problem type is determined, which includes at least one of the following: voltage sag with amplitude ≤ 80% of rated voltage and duration 10ms-1min; harmonic pollution with harmonic content of specific frequency exceeding the limit; and three-phase imbalance with negative sequence current to positive sequence current ratio ≥ 10%. S204: Calculate the severity level based on the extent to which abnormal parameters exceed the threshold. Where exceeding the threshold by 0-20% is mild level (Level I), exceeding by 20%-50% is moderate level (Level II), and exceeding by more than 50% is severe level (Level III). S205: Combine the distribution network topology model to analyze the number of affected nodes and load types to determine the scope of impact. Divide the scope into local areas with 5 or fewer nodes, regional areas with 6-20 nodes, and global areas with more than 20 nodes, and distinguish the affected proportions of residential load, industrial load, and commercial load. S206: Generate a status assessment report containing the power quality problem type with specific fault characteristic parameters, the parameter offset on which the severity level is calculated, and the affected node number and load capacity data of the affected area. The report is encapsulated in XML format and stored in an encrypted manner.

9. The method according to claim 5, characterized in that, In step S401, the screening criteria for the candidate adjustment device include: The device's current load rate is below 70%, the electrical distance to the problem node is less than 5km, and the historical compensation response time is less than 50ms.

10. The method according to claim 6, characterized in that, In step S504, the dynamic fine-tuning adopts an incremental PID control algorithm, in which the proportional coefficient, integral time constant and derivative time constant are adaptively adjusted according to the absolute value of the compensation deviation.

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