A method for reactive power network and electric energy evaluation based on ad hoc network topology awareness

Abstract

The application relates to the field of power distribution network automation and wireless communication technology, and discloses a self-organizing network topology sensing transformer area reactive power networking and electric energy evaluation method.The method comprises the following steps: each reactive power resource node initiates a joining request through a wireless self-organizing network protocol when accessing a transformer area, and the joining request is accompanied by a self-reactive power support potential value; an aggregation control center calculates a dynamic electric energy quality sensing factor according to the link quality indication and communication delay of all network nodes; based on the sensing factor and the potential value, a preferential scheduling path is established at a routing layer, and communication time slots are pre-assigned for high-potential nodes; during operation, the self-organizing network topology changes and message delay are monitored in real time, and the dynamic adjustment of the control strategy is triggered through a revised voltage qualification rate index.The application deeply couples the real-time topology quality of the wireless self-organizing network and the electric energy quality evaluation, realizes closed-loop feedback from the dynamic sensing of the communication network to the electric energy quality analysis, significantly improves the response speed and adaptive ability of the transformer area reactive power collaborative control under high-proportion new energy access, and has the advantages of plug-and-play and flexible deployment.

Description

Technical Field

[0001] This invention relates to the fields of distribution network automation and wireless communication technology, specifically to a method for reactive power networking and energy assessment in transformer substations with self-organizing network topology awareness. Background Technology

[0002] With the large-scale integration of distributed photovoltaic and other new energy sources into distribution substations, power quality problems such as voltage fluctuations, low power factor, and three-phase imbalance are becoming increasingly prominent. Traditional reactive power regulation methods (such as fixed capacitors and static compensation devices) mainly rely on setpoint and time-sequence control, which has a lagging response and cannot adapt to the intermittency and volatility of new energy sources. At the same time, although new equipment such as energy storage and charging piles have reactive power regulation potential, their resource utilization efficiency is low due to the lack of a unified networking and coordinated control mechanism.

[0003] For example, Chinese Patent Publication No. CN114567896A discloses a power quality compensation controller and control method capable of realizing a wireless self-organizing network. The controller includes a power supply module, a sampling module, a main control module, a wireless communication module, an output unit, and an upper-level control unit. The power supply module is connected to the sampling module, the main control module, and the wireless communication module. The main control module is connected to the sampling module, the wireless communication module, and the output unit. The wireless communication module is connected to the upper-level control unit. Each scenario is configured with a corresponding number of power quality compensation controllers according to specific capacity requirements. The power quality compensation controller performs master-slave coordination self-organizing network based on the equipment status within the scenario. The main control module in the controller performs data analysis and coordinates cooperation among peripheral devices to ensure that the compensation controller works normally according to the user's strategy.

[0004] Currently, wireless ad hoc networking technology is considered suitable for interconnecting equipment in low-voltage distribution substations due to its flexibility and low cost. However, most existing solutions only use the ad hoc network as a data acquisition channel, failing to fully consider the impact of the communication network's own quality, such as latency, packet loss, and topology changes, on power quality control. Power quality assessments are usually based on ideal communication conditions or offline simulations, unable to dynamically adjust the assessment model according to the real-time status of the communication network, leading to lagging or misjudgments in control strategies. Therefore, there is an urgent need for a method that can deeply couple dynamic networking with power quality analysis, utilizing real-time topology-aware information to improve the adaptability and robustness of reactive power control. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a self-organizing network topology-aware reactive power networking and power assessment method for transformer substations, thereby solving the aforementioned problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions.

[0007] A self-organizing network topology-aware reactive power networking and power assessment method for transformer substations includes the following steps: S1: When each reactive power resource node accesses the distribution area, it initiates a join request via the wireless ad hoc network protocol, and attaches its own reactive power support potential value in the request message. ; S2: The aggregation control center collects link quality indicators from all online nodes. and end-to-end communication delay The dynamic power quality sensing factor at the current moment is calculated according to the following formula. : in This represents the average link quality index across the entire network. This represents the maximum link quality. To maintain a stable and unchanged number of nodes in the topology, This represents the total number of nodes. This represents the maximum end-to-end delay of packets within the current network. Preset weighting coefficients and satisfying , This is the delay decay factor; S3: The aggregation control center is based on each node. The result obtained in step S2 In the routing layer of the wireless ad hoc network, a priority scheduling path is established to pre-allocate TDMA communication time slots for nodes with high potential and good link quality. S4: During operation, the aggregation control center monitors the topology change events and message delays of the ad hoc network in real time, and corrects the voltage qualification rate index using the following formula: in, To determine the actual voltage pass rate, This is the maximum control command response cycle for a self-organizing network. This represents the threshold value required for the system's response speed. This is the penalty coefficient; when the correction is... When the threshold is lower than set, the aggregation control center automatically increases the control priority of the corresponding area and reallocates communication resources.

[0008] Preferably, the reactive power resource nodes in step S1 include photovoltaic inverters, static var generators, energy storage converters, and capacitor banks, and each node has a built-in wireless self-organizing network communication module.

[0009] Preferably, the reactive power support potential value The calculation formula is: in For the equipment's rated reactive power capacity, This is the state of charge coefficient for energy storage devices (taken as 1 for non-energy storage devices). The equipment availability coefficient is 1 for normal operation and 0 for fault or maintenance.

[0010] Preferably, the average link quality index in step S2 Calculated using an exponentially weighted average: in This is a smoothing factor.

[0011] Preferably, in step S2, the link quality indicators and communication delays of neighboring nodes are obtained through passive listening or active detection, and the aggregation control center periodically updates the neighbor matrix and routing table of the entire network.

[0012] Preferably, the method for establishing the priority scheduling path in step S3 includes: Based on each node Perform descending sorting, while combining with step S2. The candidate paths are comprehensively scored, and the path with the highest score is selected as the primary control path; the comprehensive scoring function is: in This is the normalized reactive power support potential value. The average communication delay of the path, These are the weighting coefficients.

[0013] Preferably, in step S4, when the aggregation control center detects that a node has not responded for three consecutive cycles or the link quality is lower than a preset threshold, topology reconstruction is triggered, and steps S2 and S3 are re-executed.

[0014] Preferably, the system has a required threshold for response speed. Adjustments are made in real time based on power variations of distributed photovoltaic systems within the transformer area: in They have made great contributions to the photovoltaic industry in the region. This refers to the rated capacity of the photovoltaic system.

[0015] Preferably, after step S4, the aggregation control center further includes: based on the corrected power quality index, using a multi-objective particle swarm optimization algorithm to generate the optimal reactive power output setting value for each reactive power resource node, and distributing it through a priority scheduling path.

[0016] An evaluation system for reactive power networking and energy assessment of distribution transformer areas based on self-organizing network topology awareness includes: Multiple reactive power resource nodes, each integrating a wireless ad hoc network communication module, are used to transmit reactive power support potential values ​​when accessing a transformer area. It requests to join and receives control commands; The aggregation control center includes: 1) Communication management unit, used to receive link quality information from each node and maintain the network topology; 2) Sensing factor calculation unit, used to calculate the dynamic power quality sensing factor according to step S2 in claim 1. ; 3) Path scheduling unit, used to... and Establish priority scheduling paths and allocate time slots; 4) Power quality adaptive assessment unit, used to correct the voltage compliance rate and trigger control strategy adjustment according to step S4 in claim 1; The aggregation control center communicates with each node through a wireless self-organizing network.

[0017] Preferably, the aggregation control center performs dynamic networking and adaptive power quality assessment of the reactive power resources of the distribution area based on the self-organizing network topology-aware reactive power networking and power assessment method.

[0018] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a self-organizing network topology-aware reactive power networking and power assessment method for transformer substations.

[0019] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the self-organizing network topology-aware reactive power networking and power assessment method for transformer substations. Compared to existing technologies, this invention discloses a method for reactive power networking and power assessment in transformer substations with self-organizing network topology awareness. ① The real-time topology quality of the wireless ad hoc network, including link quality indication, end-to-end communication delay, and topology stability, is introduced into the power quality assessment system in a quantitative form. A dynamic power quality perception factor is constructed, and the voltage qualification rate index is corrected in a closed loop based on this factor. Compared with the open-loop architecture in the existing technology where the ad hoc network is only used as a data acquisition channel, a complete feedback link from topology perception to assessment correction and then to strategy adjustment is established. This enables the dynamic changes of the communication network to be mapped in real time as the basis for power quality assessment correction, solving the problem of control strategy lag or misadjustment caused by neglecting communication quality in traditional solutions. ② When each reactive power resource node accesses the distribution area, it automatically initiates a join request through the wireless self-organizing network protocol and attaches its own reactive power support potential value. The aggregation control center senses the node access in real time and automatically completes topology construction and scheduling path optimization. The entire process does not require manual configuration of communication parameters or intervention in the networking process, realizing plug-and-play reactive power resources. This significantly reduces the engineering threshold and operation and maintenance costs of intelligent transformation on the distribution side, and is especially suitable for modern distribution area scenarios with high distributed photovoltaic penetration and frequent equipment changes. ③ By monitoring topology change events and message delay changes in real time through factors, when some communication links deteriorate, nodes exit or the network becomes congested, the sensing factor automatically decreases and triggers a rapid response mechanism to correct the voltage qualification rate index. Based on this, the aggregation control center automatically increases the control priority of the corresponding area, reallocates communication time slots, and even enables redundant paths. In the face of extreme scenarios where communication delay suddenly increases to 300ms, the voltage qualification rate of the present invention remains at least 94.6% and recovers to normal within 10 control cycles. Compared with the traditional solution, which drops to 87.3% during the blocking period, this improves the survivability and recovery capability of the system under harsh communication conditions. ④ Simulation and actual distribution area deployment verification show that after applying the method of this invention, the voltage qualification rate is increased by an average of 2.3 percentage points, voltage over-limit events are reduced by 60%, the reactive power loss of the whole network is reduced by 8.2%, the power factor fluctuation amplitude is reduced by 45%, and the problem of frequent voltage over-limit caused by the output fluctuation of distributed photovoltaic is suppressed. The above effects are achieved without adding additional hardware equipment, but only through the coordinated optimization of communication resources and control strategies. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the evaluation system for the self-organizing network topology-aware reactive power networking and power assessment method of the present invention. Figure 2 This is a flowchart illustrating the self-organizing network topology-aware reactive power networking and power assessment method for transformer substations according to the present invention. Figure 3 The dynamic power quality sensing factor of this invention A graph showing how network latency changes. Figure 4 This is a comparison chart of the 24-hour voltage qualification rate of a certain transformer area before and after the application of the method of this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0022] A self-organizing network topology-aware reactive power networking and power assessment method for transformer substations includes the following steps: S1: When each reactive power resource node accesses the distribution area, it initiates a join request via the wireless ad hoc network protocol, and attaches its own reactive power support potential value in the request message. ; S2: The aggregation control center collects link quality indicators from all online nodes. and end-to-end communication delay The dynamic power quality sensing factor at the current moment is calculated according to the following formula. : in This represents the average link quality index across the entire network. This represents the maximum link quality. To maintain a stable and unchanged number of nodes in the topology, This represents the total number of nodes. This represents the maximum end-to-end delay of packets within the current network. Preset weighting coefficients and satisfying , This is the delay decay factor; S3: The aggregation control center is based on each node. The result obtained in step S2 In the routing layer of the wireless ad hoc network, a priority scheduling path is established to pre-allocate TDMA communication time slots for nodes with high potential and good link quality. S4: During operation, the aggregation control center monitors the topology change events and message delays of the ad hoc network in real time, and corrects the voltage qualification rate index using the following formula: in, To determine the actual voltage pass rate, This is the maximum control command response cycle for a self-organizing network. This represents the threshold value required for the system's response speed. This is the penalty coefficient; when the correction is... When the threshold is lower than set, the aggregation control center automatically increases the control priority of the corresponding area and reallocates communication resources.

[0023] The reactive power resource nodes in step S1 include photovoltaic inverters, static var generators, energy storage converters, and capacitor banks, and each node has a built-in wireless self-organizing network communication module.

[0024] The reactive power support potential value The calculation formula is: in For the equipment's rated reactive power capacity, This is the state of charge coefficient for energy storage devices (taken as 1 for non-energy storage devices). The equipment availability coefficient is 1 for normal operation and 0 for fault or maintenance.

[0025] The average link quality index in step S2 Calculated using an exponentially weighted average: in This is a smoothing factor.

[0026] In step S2, the link quality indicators and communication delays of neighboring nodes are obtained through passive listening or active detection, and the aggregation control center periodically updates the neighbor matrix and routing table of the entire network.

[0027] The method for establishing the priority scheduling path in step S3 includes: Based on each node Perform descending sorting, while combining with step S2. The candidate paths are comprehensively scored, and the path with the highest score is selected as the primary control path; the comprehensive scoring function is: in This is the normalized reactive power support potential value. The average communication delay of the path, These are the weighting coefficients.

[0028] In step S4, when the aggregation control center detects that a node has not responded for three consecutive cycles or the link quality is lower than a preset threshold, it triggers topology reconstruction and re-executes steps S2 and S3.

[0029] The system's required threshold for response speed Adjustments are made in real time based on power variations of distributed photovoltaic systems within the transformer area: in They have made great contributions to the photovoltaic industry in the region. This refers to the rated capacity of the photovoltaic system.

[0030] The step S4 is followed by the following: the aggregation control center generates the optimal reactive power output setting value for each reactive power resource node based on the corrected power quality index using a multi-objective particle swarm algorithm, and distributes it through a priority scheduling path.

[0031] An evaluation system for reactive power networking and energy assessment of distribution transformer areas based on self-organizing network topology awareness includes: Multiple reactive power resource nodes, each integrating a wireless ad hoc network communication module, are used to transmit reactive power support potential values ​​when accessing a transformer area. It requests to join and receives control commands; The aggregation control center includes: 1) Communication management unit, used to receive link quality information from each node and maintain the network topology; 2) Sensing factor calculation unit, used to calculate the dynamic power quality sensing factor according to step S2 in claim 1. ; 3) Path scheduling unit, used to... and Establish priority scheduling paths and allocate time slots; 4) Power quality adaptive assessment unit, used to correct the voltage compliance rate and trigger control strategy adjustment according to step S4 in claim 1; The aggregation control center communicates with each node through a wireless self-organizing network.

[0032] The aggregation control center performs dynamic networking and adaptive power quality assessment of reactive power resources in the distribution area based on the self-organizing network topology-aware reactive power networking and power assessment method.

[0033] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a self-organizing network topology-aware reactive power networking and power assessment method for transformer substations.

[0034] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the self-organizing network topology-aware reactive power networking and power assessment method for transformer substations.

[0035] System deployment and networking: such as Figure 1 As shown, the system of this invention was deployed in an actual distribution area (with high distributed photovoltaic penetration, configured with multiple SVG, photovoltaic inverters, and energy storage PCS). Each device is equipped with a wireless self-organizing network node module (using an existing commercial wireless self-organizing network communication solution). The aggregation control center uses an industrial-grade edge computing gateway (equipped with a general-purpose microprocessor and an embedded real-time operating system).

[0036] Reference Figure 2 In step S1, after the node powers on, it initiates a DODAG discovery request and carries the request in the DIO message. For example, taking a certain SVG as an example, if its rated reactive power capacity is large and its status is normal, then... Calculated based on rated capacity; for a given photovoltaic inverter, calculated based on its rated reactive power capacity. For a given energy storage PCS, calculate the available reactive power capacity based on its current state of charge. .

[0037] Reference Figure 2 In step S2, the aggregation control center collects LQI and latency data from all nodes. Assume the current network has a certain number of nodes, the LQI is within the normal communication range, the ratio of stable nodes to total nodes is high, and the maximum latency is in the acceptable millisecond range. The results are calculated using the formula... The value of . Further assumption Below Actual measurement High, after correction A significant decrease triggers the adjustment mechanism.

[0038] The aggregation control center allocates shorter time slots to nodes with high potential values ​​and longer time slots to nodes with lower potential values. After adjustment, the maximum latency is reduced to 30ms, and the calculation is repeated. , The rebound to 92.5% indicates that the adjustment was effective.

[0039] Topology change scenario: Suppose a photovoltaic node leaves the network due to abnormal operating conditions. The aggregation control center detects that the node's LQI is 0 for three consecutive periods, removes it from the routing table, and triggers a network reconfiguration. The remaining 7 nodes rerun the DODAG process, and after stabilization... (Because the new link is not yet fully stable after the topology change). Recalculate. It dropped to 0.41, while Increased to 110ms, resulting in The voltage was further reduced to 75%. The aggregation control center immediately activated emergency control mode: increasing radio transmission power, enabling redundant paths, and briefly enabling fast single-hop communication (relayed by a nearby node). After three cycles, the network stabilized. Once the value is restored to above 0.55, exit emergency mode.

[0040] Simulation Comparison: A simulation model of a transformer substation with 12 nodes was built in the simulation platform, such as... Figure 4 As shown, the voltage qualification rate curves of the method of this invention are compared with those of the traditional fixed networking scheme (offline setting of QoS parameters, no dynamic sensing). In a 30-minute simulation, two communication blocking events (delay suddenly increasing to 300ms) were set. The results show: The voltage qualification rate of the traditional solution drops to 87.3% during the blockage period, and recovery is slow; The voltage qualification rate of the present invention is as low as 94.6%, and it returns to normal within 10 cycles; The solution of this invention reduces reactive power loss across the entire network by 8.2% and power factor fluctuation by 45%.

[0041] The above embodiments demonstrate that the present invention can effectively utilize self-organizing network topology sensing to improve the adaptability and stability of power quality control in distribution areas, and has significant engineering application value.

[0042] Figure 1 The system structure includes an aggregation control center (100) (including a communication management unit (101), a perception factor calculation unit (102), a path scheduling unit (103), and a power quality adaptive evaluation unit (104)) and multiple wireless ad hoc network nodes (photovoltaic inverters (200), SVG (201), energy storage PCS (202), and capacitor banks (203)). The nodes form a Mesh topology through the ad hoc network.

[0043] Figure 2 The complete process is demonstrated, including reactive resource nodes (200) connecting to the network and reporting potential values, aggregation control center (100) calculating sensing factors and establishing paths, and power quality adaptive assessment unit (104) correcting indicators and adjusting strategies.

[0044] Figure 3 Curve 300 shows that when the delay increases from 20ms (marker 302) to 200ms (marker 303), The value dropped from 0.95 to 0.55, effectively reflecting the deterioration of communication quality.

[0045] Figure 4 Curve 400 represents the voltage pass rate before adopting the method of this invention, and curve 401 represents the voltage pass rate after adopting the method of this invention. After applying this method, the voltage pass rate increased by an average of 2.3 percentage points, and the number of out-of-limit events decreased by 60%. It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for reactive power networking and energy assessment of transformer substations based on self-organizing network topology awareness, characterized in that, Includes the following steps: S1: When each reactive power resource node accesses the distribution area, it initiates a join request via the wireless ad hoc network protocol, and attaches its own reactive power support potential value in the request message. ; S2: The aggregation control center collects link quality indicators from all online nodes. and end-to-end communication delay The dynamic power quality sensing factor at the current moment is calculated according to the following formula. : in This represents the average link quality index across the entire network. This represents the maximum link quality. To maintain a stable and unchanged number of nodes in the topology, This represents the total number of nodes. This represents the maximum end-to-end delay of packets within the current network. Preset weighting coefficients and satisfying , This is the delay decay factor; S3: The aggregation control center is based on each node. The result obtained in step S2 In the routing layer of the wireless ad hoc network, a priority scheduling path is established to pre-allocate TDMA communication time slots for nodes with high potential and good link quality. S4: During operation, the aggregation control center monitors the topology change events and message delays of the ad hoc network in real time, and corrects the voltage qualification rate index using the following formula: in, To determine the actual voltage pass rate, This is the maximum control command response cycle for a self-organizing network. This represents the threshold value required for the system's response speed. This is the penalty coefficient; when the correction is... When the threshold is lower than set, the aggregation control center automatically increases the control priority of the corresponding area and reallocates communication resources.

2. The self-organizing network topology-aware reactive power networking and power assessment method for transformer substations as described in claim 1, characterized in that, The reactive power resource nodes in step S1 include photovoltaic inverters, static var generators, energy storage converters, and capacitor banks, and each node has a built-in wireless self-organizing network communication module.

3. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, The reactive power support potential value The calculation formula is: in For the equipment's rated reactive power capacity, This is the state of charge coefficient for energy storage devices (taken as 1 for non-energy storage devices). The equipment availability coefficient is 1 for normal operation and 0 for fault or maintenance.

4. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, The average link quality index in step S2 Calculated using an exponentially weighted average: in This is a smoothing factor.

5. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, In step S2, the link quality indicators and communication delays of neighboring nodes are obtained through passive listening or active detection, and the aggregation control center periodically updates the neighbor matrix and routing table of the entire network.

6. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, The method for establishing the priority scheduling path in step S3 includes: Based on each node Perform descending sorting, while combining with step S2. The candidate paths are comprehensively scored, and the path with the highest score is selected as the primary control path; the comprehensive scoring function is: in This is the normalized reactive power support potential value. The average communication delay of the path, These are the weighting coefficients.

7. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, In step S4, when the aggregation control center detects that a node has not responded for three consecutive cycles or the link quality is lower than a preset threshold, it triggers topology reconstruction and re-executes steps S2 and S3.

8. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, The system's required threshold for response speed Adjustments are made in real time based on power variations of distributed photovoltaic systems within the transformer area: in They have made great contributions to the photovoltaic industry in the region. This refers to the rated capacity of the photovoltaic system.

9. The method for reactive power networking and energy assessment of transformer substations with self-organizing network topology awareness as described in claim 1, characterized in that, The step S4 is followed by the following: the aggregation control center generates the optimal reactive power output setting value for each reactive power resource node based on the corrected power quality index using a multi-objective particle swarm algorithm, and distributes it through a priority scheduling path.

10. An evaluation system for reactive power networking and power assessment methods in transformer substations based on self-organizing network topology awareness, characterized in that, include: Multiple reactive power resource nodes, each integrating a wireless ad hoc network communication module, are used to transmit reactive power support potential values ​​when accessing a transformer area. It requests to join and receives control commands; The aggregation control center includes: 1) Communication management unit, used to receive link quality information from each node and maintain the network topology; 2) Sensing factor calculation unit, used to calculate the dynamic power quality sensing factor according to step S2 in claim 1. ; 3) Path scheduling unit, used to... and Establish priority scheduling paths and allocate time slots; 4) Power quality adaptive assessment unit, used to correct the voltage compliance rate and trigger control strategy adjustment according to step S4 in claim 1; The aggregation control center communicates with each node through a wireless self-organizing network.

11. The self-organizing network topology-aware reactive power networking and power assessment system for transformer substations as described in claim 10, characterized in that, The aggregation control center performs dynamic networking and adaptive power quality assessment of reactive power resources in the distribution area based on the method described in any one of claims 1-9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-9.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-9.

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