A mobile transformer area voltage quality treatment robot and method

Abstract

The application discloses a mobile transformer area voltage quality treatment robot and method, and belongs to the technical field of power system distribution network operation and maintenance. The mobile transformer area voltage quality treatment robot comprises a robot body, an energy storage module, a connection and execution module, a navigation control module and a communication module. The robot body adopts a low-speed new energy vehicle structure and has full-terrain moving capability in a transformer area. The energy storage module is integrated into the robot body and adopts a high-safety sodium ion battery pack. The sodium ion battery pack meets 6C continuous discharge and -40 DEG C to 60 DEG C wide temperature range working requirements and has a cycle life of not less than 6000 times. The navigation control module is provided with a laser radar, a visual camera and a multi-mode positioning unit. Through fusion of GPS / Beidou positioning and transformer area environment map data, an electrical plug and a state detection unit, the mechanical arm has multi-degree-of-freedom adjustment capability and is based on position information fed back by the visual camera.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network operation and maintenance technology, and more specifically, to a mobile transformer area voltage quality management robot and method. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power in distribution network areas continues to increase, the intermittent and fluctuating nature of PV power output leads to significant peak-valley differences in voltage in the distribution area: during the day when PV power output is high, the voltage in the distribution area is prone to exceed the rated range and cause overvoltage; at night when PV power output stops and the load increases, low voltage problems are prone to occur, which seriously affect the power supply quality and the efficiency of PV energy consumption.

[0003] Existing voltage mitigation solutions for photovoltaic (PV) distribution areas have significant limitations: fixed reactive power compensation devices cannot adapt to the spatial distribution differences of voltage fluctuations, limiting their mitigation scope; on-load tap-changing transformers are prone to negative voltage regulation effects when operating alone, leading to voltage instability; traditional mobile energy storage power stations rely on manual grid connection, resulting in low response efficiency, and the lithium-ion batteries used suffer from low-temperature performance degradation and high costs. Furthermore, voltage fluctuations in high-penetration PV distribution areas exhibit spatiotemporal randomness, making it difficult for existing fixed mitigation equipment to achieve precise coverage across all time periods and areas. Therefore, there is an urgent need for an integrated mitigation device with autonomous mobility, intelligent connection capabilities, and high-efficiency energy storage. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention aims to provide a mobile transformer area voltage quality management robot and method. This robot addresses the issues of prominent peak-valley voltage fluctuations in high-penetration photovoltaic transformer areas and the lack of flexibility in existing management solutions. Through autonomous movement, intelligent connection, wide-temperature-range high-efficiency energy storage, and collaborative scheduling capabilities, it enables precise voltage management in transformer areas, improves photovoltaic energy consumption efficiency, and reduces operation and maintenance costs.

[0005] To solve the above problems, the present invention adopts the following technical solution.

[0006] A mobile transformer substation voltage quality management robot includes a robot body, an energy storage module, a connection execution module, a navigation control module, and a communication module; The robot body adopts a low-speed new energy vehicle structure and has the ability to move in all terrains within the platform area. The energy storage module is integrated into the robot body and adopts a high-safety sodium-ion battery pack. The sodium-ion battery pack meets the requirements of 6C continuous discharge, wide temperature range operation of -40℃~60℃, and cycle life of not less than 6000 times. The navigation control module is equipped with a lidar, a visual camera and a multi-mode positioning unit. By integrating GPS / BeiDou positioning and the environmental map data of the transformer area, it can achieve autonomous path planning, dynamic obstacle avoidance and precise docking at designated points, with a positioning accuracy error of ≤5cm. The connection execution module includes a robotic arm, an electrical plug, and a status detection unit. The robotic arm has multi-degree-of-freedom adjustment capabilities. Based on the position information fed back by the vision camera, the robotic arm is driven to adjust its posture to complete the alignment and plugging / unplugging of the electrical plug with the dedicated socket on the power grid side. The electrical plug integrates with or is connected to the status detection unit, which can detect the voltage, current, and contact status at the plug, or receive feedback signals from the socket side to achieve self-judgment of the connection readiness status. The communication module supports 5G / edge computing communication protocols, enabling bidirectional data interaction with the intelligent fusion terminal of the distribution substation and the power supply command center backend, receiving dispatch instructions and uploading operating status, energy storage parameters and voltage management data, with a communication latency of ≤100ms. The robot's navigation control module and communication module are configured to control the robot body to move and dock at designated locations and times in high-penetration photovoltaic substations according to received scheduling instructions. The energy storage module is configured to store surplus energy by connecting to the power grid when a charging instruction is received, and to supplement active power by discharging when a discharging instruction is received, so as to achieve precise management of high and low voltage in the substation. It integrates all-terrain mobility, precise navigation, intelligent connection and efficient communication modules, and uses sodium-ion battery energy storage to achieve precise management of high and low voltage in high-penetration photovoltaic substations, adapting to complex substation environments and improving power supply stability.

[0007] As a further technical solution of the present invention, the sodium-ion battery pack is equipped with a dedicated BMS battery management system, which supports 0V lossless cycle discharge protection, and has real-time monitoring and thermal runaway early warning functions for battery temperature, SOC, and DOD parameters. The BMS battery management system is equipped with an active balancing circuit, which can control the temperature difference of individual battery cells within 2°C, and has an early thermal runaway prevention function based on changes in cell internal resistance. Through the dedicated BMS battery management system and the active balancing circuit, the battery temperature difference is precisely controlled, achieving early thermal runaway prevention, ensuring the safe and stable operation of the sodium-ion battery, and reducing the risk of battery failure.

[0008] As a further technical solution of the present invention, the end of the robotic arm is equipped with a force control sensor, which provides real-time feedback of the contact force during insertion and removal operations. The robotic arm controller has a built-in impedance control algorithm, which adjusts the compliance of the end of the robotic arm in real time according to the contact force fed back by the force control sensor, so as to compensate for alignment errors and avoid hard contact damage, thereby achieving efficient and stable insertion and removal operations. With the help of the force control sensor and the impedance control algorithm, the compliance of the robotic arm is adjusted in real time to achieve stable and accurate insertion and removal of electrical plugs, avoid interface damage, and improve the reliability and efficiency of connection operations.

[0009] As a further technical solution of the present invention, the navigation control module has a built-in machine learning algorithm, which can perform deep learning on historical inspection data collected by SLAM technology, dynamically update the semantic map of the transformer area environment, identify and mark the distribution of fixed obstacles such as utility poles and cable trenches, optimize path planning strategies, avoid inspection blind spots and dangerous areas, and dynamically update the semantic map of the transformer area by combining data collected by SLAM technology with machine learning algorithms, optimize path planning, effectively avoid obstacles and inspection blind spots, and improve the robot's navigation adaptability.

[0010] As a further technical solution of the present invention, the communication module supports local decision-making of edge computing nodes. When communication with the backend is interrupted, it can autonomously perform voltage management operations based on the preset strategy issued by the transformer area fusion terminal. After communication is restored, the data is automatically synchronized, which supports local decision-making of edge computing. When communication is interrupted, it can autonomously perform management operations. After communication is restored, the data is automatically synchronized, avoiding operation interruption and ensuring the continuity and reliability of transformer area voltage management.

[0011] A method for improving voltage quality in mobile distribution transformer areas includes the following steps: S1: The power supply command center backend collects real-time voltage data, load data, and photovoltaic output data of high-penetration photovoltaic distribution areas through the distribution area integration terminal to determine the voltage status of the distribution area: when the voltage is higher than the preset peak threshold, a charging scheduling instruction is generated; when the voltage is lower than the preset valley threshold, a discharging scheduling instruction is generated; at the same time, based on the specific location of the distribution area with abnormal voltage, the nearest and most effective electrical connection point is matched and synchronously sent to the robot. S2: The robot receives scheduling instructions through the communication module, and the navigation control module plans the optimal path to drive the robot body to the designated docking point and complete precise docking. S3: The connection execution module drives the robotic arm to connect the electrical plug to the socket on the power grid side. The built-in sensors detect the contact status and voltage matching degree. After confirming that the connection is ready, the robot sends the feedback to the backend. S4: After the connection is ready, the robot and the power grid side complete the electrical safety interlock confirmation through the preset handshake protocol, including insulation detection and voltage level matching verification. After confirming that there are no errors, the power circuit is unlocked, allowing the robot to enter the subsequent operation stage. S5: Perform charging or discharging operations according to instructions: When charging, draw excess electrical energy from the grid and store it in the energy storage battery pack; when discharging, output active power from the energy storage battery pack to compensate the load of the distribution area; monitor voltage changes and battery status in real time during the operation. S6: After the operation is completed, the robotic arm drives the electrical plug and socket to separate. The robot returns to the standby point or goes to the next treatment point according to the instructions from the background. The data of this operation is uploaded at the same time, realizing the full automation of voltage status monitoring, intelligent scheduling, safe connection and charging and discharging in the transformer area. It adapts to the needs of high photovoltaic penetration transformer areas and improves the intelligence and efficiency of voltage management.

[0012] As a further technical solution of the present invention, S1 includes the following sub-steps: S11: Based on the transformer substation topology, calculate the sensitivity matrix of each node's voltage to changes in active power. S12: Combine the path distance from the robot's current position to each node with the sensitivity coefficient to establish a comprehensive objective function; S13: Solve the objective function, select the node with the highest comprehensive score as the designated connection point, achieve precise selection of connection point, balance governance effect and operation efficiency, avoid ineffective movement, improve the pertinence and timeliness of voltage governance in photovoltaic high penetration areas, and optimize the overall operation process.

[0013] As a further technical solution of the present invention, step S3 includes the following sub-steps: S31: Based on the visual sensor to identify the ArUco code or feature points on the socket, drive the robotic arm to quickly approach the preparation area; S32: Calculate 6D pose deviation using hand-eye calibration algorithm and perform micron-level pose correction; S33: Activate impedance control mode, monitor feedback from the end force sensor, stop advancement when the contact force exceeds the preset threshold, complete the physical connection, achieve precise and stable connection of the robotic arm, avoid damage from hard contact of electrical interfaces, improve connection reliability and efficiency, adapt to the accuracy requirements of high-voltage operations, and ensure the safety of the connection process.

[0014] As a further technical solution of the present invention, step S4 includes the following sub-steps: S41: Low-voltage wake-up: The robot sends a low-voltage wake-up signal to the grid-side controller via an auxiliary contact; S42: Insulation test: Apply DC high voltage to test the insulation resistance. If the insulation resistance value is greater than the preset safety value, the insulation is deemed qualified. S43: Circuit Closure: After both controllers confirm that there are no errors, they synchronously engage the main power contactor and lock the mechanical locking mechanism to build a full-process safety protection for high-voltage operations, avoid insulation hazards and misoperation, ensure the safety of equipment, power grid and operators, improve operational reliability, and meet high-voltage operation safety standards.

[0015] As a further technical solution of the present invention, step S5 includes the following sub-steps: S51: Mode Selection: Determine the constant current or constant voltage operating mode and target power value based on the voltage deviation of the transformer area; S52: Thermal Management: Monitors cell temperature in real time during operation. When the temperature rise rate exceeds the threshold, it automatically reduces power output and starts liquid cooling cycle. S53: Exit Mechanism: When a background stop command is received or the battery SOC reaches the protection limit, the power is reduced at a slope until it reaches zero, and then the circuit is disconnected. This achieves precise matching of charging and discharging to the needs of the distribution area, protects the energy storage battery, avoids overload and overheating, extends battery life, ensures stable and efficient voltage management, and improves the utilization rate of the energy storage module.

[0016] Compared with the prior art, the advantages of this invention are: This invention offers high flexibility in governance: its mobile design allows it to autonomously move to the required locations to perform governance operations based on the spatiotemporal distribution differences of voltage fluctuations in the distribution area, solving the problem of insufficient coverage by fixed equipment, and is particularly suitable for the random fluctuation characteristics of photovoltaic high-penetration distribution areas.

[0017] High level of intelligence: It integrates autonomous navigation, intelligent connection and collaborative scheduling capabilities, and can complete the entire voltage management operation without human intervention, reducing maintenance manpower costs, improving response efficiency, and the plug-in connection time is ≤30s, which is much faster than manual operation.

[0018] Excellent energy storage performance: It adopts sodium-ion battery packs, which are suitable for complex outdoor environments with wide temperature range, high rate and long life. Its low temperature performance and safety far exceed those of traditional lithium batteries. It has a significant cost advantage and can be widely used.

[0019] Significant governance results: By adopting the "peak-valley charging and discharging" mode, the problem of overvoltage caused by excessive photovoltaic output during the day is solved, and the low voltage gap caused by the rise in load at night is made up. At the same time, the efficiency of photovoltaic energy consumption is improved. Actual measurements show that the voltage fluctuation range of the distribution area can be controlled within ±5% of the rated value. Attached Figure Description

[0020] Figure 1 This is a topology diagram illustrating the application scenario of the mobile transformer area voltage quality management robot of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 This is a schematic diagram of the connection execution module topology of the present invention; Figure 4 This is a schematic diagram of the robot structure topology of the present invention. Detailed Implementation

[0021] 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.

[0022] This invention provides an embodiment 1 Please see Figure 1-4 A mobile transformer area voltage quality management robot includes a robot body, an energy storage module, a connection execution module, a navigation control module, and a communication module. The robot body adopts a low-speed new energy vehicle structure and has the ability to move in all terrains within the platform area. The energy storage module is integrated into the robot body and adopts a high-safety sodium-ion battery pack. The sodium-ion battery pack meets the requirements of 6C continuous discharge, wide temperature range operation of -40℃~60℃, and cycle life of not less than 6000 times. The navigation control module is equipped with a lidar, a visual camera and a multi-mode positioning unit. By integrating GPS / BeiDou positioning and the environmental map data of the transformer area, it can achieve autonomous path planning, dynamic obstacle avoidance and precise docking at designated points, with a positioning accuracy error of ≤5cm. The connection execution module includes a robotic arm, an electrical plug, and a status detection unit. The robotic arm has multi-degree-of-freedom adjustment capabilities. Based on the position information fed back by the vision camera, the robotic arm is driven to adjust its posture to complete the alignment and plugging / unplugging of the electrical plug with the dedicated socket on the power grid side. The electrical plug integrates with or is connected to the status detection unit, which can detect the voltage, current, and contact status at the plug, or receive feedback signals from the socket side to achieve self-judgment of the connection readiness status. The communication module supports 5G / edge computing communication protocols, enabling bidirectional data interaction with the intelligent fusion terminal of the distribution substation and the power supply command center backend, receiving dispatch instructions and uploading operating status, energy storage parameters and voltage management data, with a communication latency of ≤100ms. The robot's navigation control module and communication module are configured to control the robot body to move and dock at designated locations and times in high-penetration photovoltaic substations according to received scheduling instructions. The energy storage module is configured to store surplus energy by connecting to the power grid when a charging instruction is received, and to supplement active power by discharging when a discharging instruction is received, so as to achieve precise management of high and low voltage in the substation. It integrates all-terrain mobility, precise navigation, intelligent connection and efficient communication modules, and uses sodium-ion battery energy storage to achieve precise management of high and low voltage in high-penetration photovoltaic substations, adapting to complex substation environments and improving power supply stability.

[0023] The sodium-ion battery pack is equipped with a dedicated BMS battery management system, which supports 0V lossless cycle discharge protection and has real-time monitoring and thermal runaway early warning functions for battery temperature, SOC, and DOD parameters. The BMS battery management system is equipped with an active balancing circuit, which can control the temperature difference of individual battery cells within 2°C and has an early thermal runaway prevention function based on changes in cell internal resistance. Through the dedicated BMS battery management system and active balancing circuit, the battery temperature difference is precisely controlled to achieve early prevention of thermal runaway, ensuring the safe and stable operation of the sodium-ion battery and reducing the risk of battery failure.

[0024] The robotic arm is equipped with a force control sensor at its end, which provides real-time feedback on the contact force during insertion and removal operations. The robotic arm controller has a built-in impedance control algorithm that adjusts the compliance of the robotic arm end in real time based on the contact force feedback from the force control sensor. This compensates for alignment errors and avoids damage from hard contact, thereby achieving efficient and stable insertion and removal operations. By using the force control sensor and impedance control algorithm to adjust the compliance of the robotic arm in real time, the electrical plugs can be inserted and removed smoothly and accurately, avoiding interface damage and improving the reliability and efficiency of connection operations.

[0025] The navigation control module incorporates machine learning algorithms, which can perform deep learning on historical inspection data collected by SLAM technology to dynamically update the semantic map of the transformer area environment, identify and mark the distribution of fixed obstacles such as utility poles and cable trenches, optimize path planning strategies, and avoid inspection blind spots and dangerous areas. By combining data collected by SLAM technology with machine learning algorithms, the semantic map of the transformer area is dynamically updated, path planning is optimized, obstacles and inspection blind spots are effectively avoided, and the robot's navigation adaptability is improved.

[0026] The communication module supports local decision-making at the edge computing node. When communication with the backend is interrupted, it can autonomously execute voltage management operations based on preset strategies issued by the integrated terminal of the distribution area. After communication is restored, the data is automatically synchronized, thus avoiding operation interruption and ensuring the continuity and reliability of voltage management in the distribution area.

[0027] A mobile distribution transformer voltage quality management method includes the following steps: S1: The power supply command center collects real-time voltage data, load data, and photovoltaic output data of high-penetration photovoltaic distribution transformers through the distribution transformer fusion terminal, and judges the voltage status of the distribution transformers: when the voltage is higher than the preset peak threshold, a charging scheduling instruction is generated; when the voltage is lower than the preset valley threshold, a discharging scheduling instruction is generated; at the same time, according to the specific location of the distribution transformer with abnormal voltage, the nearest and most effective electrical connection point is matched and synchronously sent to the robot; S2: The robot receives scheduling instructions through the communication module, and the navigation control module plans the optimal path to drive the robot body to the designated docking point and complete precise docking. S3: The connection execution module drives the robotic arm to connect the electrical plug to the socket on the power grid side. The built-in sensors detect the contact status and voltage matching degree. After confirming that the connection is ready, the robot sends the feedback to the backend. S4: After the connection is ready, the robot and the power grid side complete the electrical safety interlock confirmation through the preset handshake protocol, including insulation detection and voltage level matching verification. After confirming that there are no errors, the power circuit is unlocked, allowing the robot to enter the subsequent operation stage. S5: Perform charging or discharging operations according to instructions: When charging, draw excess electrical energy from the grid and store it in the energy storage battery pack; when discharging, output active power from the energy storage battery pack to compensate the load of the distribution area; monitor voltage changes and battery status in real time during the operation. S6: After the operation is completed, the robotic arm drives the electrical plug and socket to separate. The robot returns to the standby point or goes to the next treatment point according to the instructions from the background. The data of this operation is uploaded at the same time, realizing the full automation of voltage status monitoring, intelligent scheduling, safe connection and charging and discharging in the transformer area. It adapts to the needs of high photovoltaic penetration transformer areas and improves the intelligence and efficiency of voltage management.

[0028] S1 includes the following sub-steps: S11: Based on the transformer substation topology, calculate the sensitivity matrix of each node's voltage to changes in active power. S12: Combine the path distance from the robot's current position to each node with the sensitivity coefficient to establish a comprehensive objective function; S13: Solve the objective function, select the node with the highest comprehensive score as the designated connection point, achieve precise selection of connection point, balance governance effect and operation efficiency, avoid ineffective movement, improve the pertinence and timeliness of voltage governance in photovoltaic high penetration areas, and optimize the overall operation process.

[0029] S3 includes the following sub-steps: S31: Based on the visual sensor to identify the ArUco code or feature points on the socket, drive the robotic arm to quickly approach the preparation area; S32: Calculate 6D pose deviation using hand-eye calibration algorithm and perform micron-level pose correction; S33: Activate impedance control mode, monitor feedback from the end force sensor, stop advancement when the contact force exceeds the preset threshold, complete the physical connection, achieve precise and stable connection of the robotic arm, avoid damage from hard contact of electrical interfaces, improve connection reliability and efficiency, adapt to the accuracy requirements of high-voltage operations, and ensure the safety of the connection process.

[0030] S4 includes the following sub-steps: S41: Low-voltage wake-up: The robot sends a low-voltage wake-up signal to the grid-side controller via an auxiliary contact; S42: Insulation test: Apply DC high voltage to test the insulation resistance. If the insulation resistance value is greater than the preset safety value, the insulation is deemed qualified. S43: Circuit Closure: After both controllers confirm that there are no errors, they synchronously engage the main power contactor and lock the mechanical locking mechanism to build a full-process safety protection for high-voltage operations, avoid insulation hazards and misoperation, ensure the safety of equipment, power grid and operators, improve operational reliability, and meet high-voltage operation safety standards.

[0031] S5 includes the following sub-steps: S51: Mode Selection: Determine the constant current or constant voltage operating mode and target power value based on the voltage deviation of the transformer area; S52: Thermal Management: Monitors cell temperature in real time during operation. When the temperature rise rate exceeds the threshold, it automatically reduces power output and starts liquid cooling cycle. S53: Exit Mechanism: When a background stop command is received or the battery SOC reaches the protection limit, the power is reduced at a slope until it is zero, and then the circuit is disconnected. This achieves precise matching of charging and discharging to the needs of the distribution area, protects the energy storage battery, avoids overload and overheating, extends battery life, ensures stable and efficient voltage management, and improves the utilization rate of the energy storage module. This invention provides an embodiment 2 This mobile voltage quality management robot measures 2.5m × 1.2m × 1.8m and weighs 1200kg. It is equipped with a 48V low-speed drive system, achieving a maximum speed of 5km / h and a range of ≥80km. The energy storage module uses a 50Ah sodium-ion battery pack with a total capacity of 24kWh, supporting 6C continuous discharge (peak power 144kW). It maintains 82% capacity at -40℃ and shows no significant cycle life degradation at 45℃.

[0032] The navigation control module is equipped with a 16-line LiDAR with a detection range of ≥50m, a 4K resolution visual camera with a frame rate of 30fps, and a multi-mode positioning unit that integrates GPS / BeiDou and IMU inertial measurement, achieving a positioning accuracy of ±3cm. It can identify obstacles with a diameter of ≥10cm and achieve dynamic obstacle avoidance. The docking execution module uses a 6-DOF robotic arm with a working radius of 1.5m and an end-effector repeatability of ±0.1mm. The electrical plug features a waterproof and dustproof design with an IP67 protection rating. It has a built-in voltage sensor with a range of 0-450V, a current sensor with a range of 0-500A, and a contact resistance detection accuracy of ±0.01Ω.

[0033] The communication module supports 5G SA mode, with downlink speed ≥1Gbps, uplink speed ≥200Mbps, and communication latency ≤80ms. It is also compatible with the LoRa IoT protocol and adapts to the data interaction needs of the edge computing nodes in the distribution area.

[0034] This invention provides an embodiment 3 In a rural area with high photovoltaic penetration, the installed photovoltaic capacity is 500kW, serving 120 households. There is a problem of excessively high voltage during the day (10:00-16:00, reaching a maximum of 253V, exceeding the rated 220V±10%) and excessively low voltage at night (18:00-22:00, reaching a minimum of 198V). The robot of this invention is used for voltage regulation. The specific application process is as follows: Preliminary deployment: Set up 3 power grid connection points in the transformer area, install dedicated waterproof sockets, build an environmental map and voltage monitoring network for the transformer area through the transformer area integration terminal, and set the robot standby point in the center of the transformer area.

[0035] Daytime Overvoltage Management: At 10:00 AM, the system detected a voltage rise to 245V in the transformer area and issued a charging command, designating connection point 1 with a charging power of 10kW. The robot autonomously planned its path, moving to the location in 2 minutes. The robotic arm completed the connection (22 seconds). After the sensors confirmed the connection was ready, charging began, storing excess photovoltaic energy into the sodium-ion battery pack. During charging, the system monitored voltage changes in real time. When the voltage dropped to 230V (rated upper limit), the charging power was adjusted to 5kW to maintain voltage stability. At 4:00 PM, photovoltaic output weakened, and the voltage dropped to 225V. The system issued a stop charging command, and the robot autonomously disconnected from the interface and returned to its standby location. This charging operation stored 85kWh of energy.

[0036] Low voltage mitigation at night: At 18:30, the voltage in the transformer area dropped to 202V. The control center issued a discharge command, designating connection point 2 with a discharge power of 8kW. The robot moved to the point, completed the connection, and started the discharge operation to compensate for the load shortfall. At 20:00, during the peak load period, the voltage dropped to 200V. The control center adjusted the discharge power to 12kW to maintain voltage stability. At 22:00, the load decreased, and the voltage rose to 215V. The discharge operation stopped, and the robot returned to its standby position. This discharge released 72kWh of energy.

[0037] Application results show that, through the robot's peak-valley charging and discharging management, the voltage fluctuation range of the distribution area is controlled within 210V-230V, which meets the power supply quality standards. The photovoltaic energy consumption rate is increased by 15%, and the operation and maintenance cost is reduced by 60% compared with the manual on-duty mode. In summary, this invention offers high flexibility in governance: its mobile design allows it to autonomously move to the required locations to perform governance operations based on the spatiotemporal distribution differences of voltage fluctuations in the distribution area, solving the problem of insufficient coverage by fixed equipment, and is particularly suitable for the random fluctuation characteristics of photovoltaic high-penetration distribution areas.

[0038] High level of intelligence: It integrates autonomous navigation, intelligent connection and collaborative scheduling capabilities, and can complete the entire voltage management operation without human intervention, reducing maintenance manpower costs, improving response efficiency, and the plug-in connection time is ≤30s, which is much faster than manual operation.

[0039] Excellent energy storage performance: It adopts sodium-ion battery packs, which are suitable for complex outdoor environments with wide temperature range, high rate and long life. Its low temperature performance and safety far exceed those of traditional lithium batteries. It has a significant cost advantage and can be widely used.

[0040] Significant governance results: By adopting the "peak-valley charging and discharging" mode, the problem of overvoltage caused by excessive photovoltaic output during the day is solved, and the low voltage gap caused by the rise in load at night is made up. At the same time, the efficiency of photovoltaic energy consumption is improved. Actual measurements show that the voltage fluctuation range of the distribution area can be controlled within ±5% of the rated value.

[0041] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A mobile transformer substation voltage quality management robot, characterized in that: It includes the robot body, energy storage module, docking and execution module, navigation and control module, and communication module; The robot body adopts a low-speed new energy vehicle structure and has the ability to move in all terrains within the platform area. The energy storage module is integrated into the robot body and adopts a high-safety sodium-ion battery pack. The sodium-ion battery pack meets the requirements of 6C continuous discharge, wide temperature range operation of -40℃~60℃, and cycle life of not less than 6000 times. The navigation control module is equipped with a lidar, a visual camera and a multi-mode positioning unit. By integrating GPS / BeiDou positioning and the environmental map data of the transformer area, it can achieve autonomous path planning, dynamic obstacle avoidance and precise docking at designated points, with a positioning accuracy error of ≤5cm. The connection execution module includes a robotic arm, an electrical plug, and a status detection unit. The robotic arm has multi-degree-of-freedom adjustment capabilities. Based on the position information fed back by the vision camera, the robotic arm is driven to adjust its posture to complete the alignment and plugging / unplugging of the electrical plug with the dedicated socket on the power grid side. The electrical plug integrates or connects to the status detection unit, which can detect the voltage, current and contact status at the plug, or receive feedback signals from the socket side, so as to realize the self-judgment of the connection ready status. The communication module supports 5G / edge computing communication protocols, enabling bidirectional data interaction with the intelligent fusion terminal of the distribution substation and the power supply command center backend, receiving dispatch instructions and uploading operating status, energy storage parameters and voltage management data, with a communication latency of ≤100ms. The robot's navigation control module and communication module are configured to control the robot body to move and dock at designated locations and times in the photovoltaic high-penetration area according to the received scheduling instructions; the energy storage module is configured to store surplus electrical energy by connecting to the power grid when a charging instruction is received, and to supplement active power by discharging when a discharging instruction is received, so as to achieve precise management of high and low voltage in the area.

2. The mobile transformer substation voltage quality management robot according to claim 1, characterized in that: The sodium-ion battery pack is equipped with a dedicated BMS battery management system, which supports 0V lossless cycle discharge protection, and has real-time monitoring and thermal runaway early warning functions for battery temperature, SOC, and DOD parameters. The BMS battery management system is equipped with an active balancing circuit, which can control the temperature difference of individual battery cells within 2°C, and has an early thermal runaway prevention function based on changes in cell internal resistance.

3. The mobile transformer substation voltage quality management robot according to claim 1, characterized in that: The robotic arm is equipped with a force control sensor at its end, which provides real-time feedback on the contact force during insertion and removal operations. The robotic arm controller has a built-in impedance control algorithm that adjusts the compliance of the robotic arm end in real time based on the contact force feedback from the force control sensor to compensate for alignment errors and avoid hard contact damage, thereby achieving efficient and stable insertion and removal operations.

4. The mobile transformer substation voltage quality management robot according to claim 1, characterized in that: The navigation control module incorporates machine learning algorithms, which can perform deep learning on historical inspection data collected by SLAM technology to dynamically update the semantic map of the transformer area environment, identify and mark the distribution of fixed obstacles such as utility poles and cable trenches, optimize path planning strategies, and avoid inspection blind spots and dangerous areas.

5. A mobile transformer substation voltage quality management robot according to claim 1, characterized in that: The communication module supports local decision-making at the edge computing node. When communication with the backend is interrupted, it can autonomously perform voltage management operations based on the preset strategies issued by the integrated terminal of the distribution area. Data is automatically synchronized after communication is restored.

6. A mobile transformer substation voltage quality management method, characterized in that: Includes the following steps: S1: The power supply command center backend collects real-time voltage data, load data, and photovoltaic output data of high-penetration photovoltaic distribution areas through the distribution area integration terminal to determine the voltage status of the distribution area: when the voltage is higher than the preset peak threshold, a charging scheduling instruction is generated; when the voltage is lower than the preset valley threshold, a discharging scheduling instruction is generated; at the same time, based on the specific location of the distribution area with abnormal voltage, the nearest and most effective electrical connection point is matched and synchronously sent to the robot. S2: The robot receives scheduling instructions through the communication module, and the navigation control module plans the optimal path to drive the robot body to the designated docking point and complete precise docking. S3: The connection execution module drives the robotic arm to connect the electrical plug to the socket on the power grid side. The built-in sensors detect the contact status and voltage matching degree. After confirming that the connection is ready, the robot sends the feedback to the backend. S4: After the connection is ready, the robot and the power grid side complete the electrical safety interlock confirmation through the preset handshake protocol, including insulation detection and voltage level matching verification. After confirming that there are no errors, the power circuit is unlocked, allowing the robot to enter the subsequent operation stage. S5: Perform charging or discharging operations according to instructions: When charging, draws excess electrical energy from the grid and stores it in the energy storage battery pack; when discharging, outputs active power from the energy storage battery pack to compensate for the load in the distribution area; and monitors voltage changes and battery status in real time during the operation. S6: After the task is completed, the robotic arm drives the electrical plug and socket to separate. The robot returns to the standby point or goes to the next treatment point according to the instructions from the background, and uploads the data of this task in a synchronous manner.

7. The mobile transformer substation voltage quality management method according to claim 6, characterized in that: S1 includes the following sub-steps: S11: Based on the transformer substation topology, calculate the sensitivity matrix of each node's voltage to changes in active power. S12: Combine the path distance from the robot's current position to each node with the sensitivity coefficient to establish a comprehensive objective function; S13: Solve the objective function and select the node with the highest comprehensive score as the designated connection point.

8. The mobile transformer substation voltage quality management method according to claim 6, characterized in that: S3 includes the following sub-steps: S31: Based on the visual sensor to identify the ArUco code or feature points on the socket, drive the robotic arm to quickly approach the preparation area; S32: Calculate 6D pose deviation using hand-eye calibration algorithm and perform micron-level pose correction; S33: Activate impedance control mode, monitor feedback from the end force sensor, and stop advancing when the contact force exceeds the preset threshold to complete the physical connection.

9. A mobile transformer substation voltage quality management method according to claim 6, characterized in that: S4 includes the following sub-steps: S41: Low-voltage wake-up: The robot sends a low-voltage wake-up signal to the grid-side controller via an auxiliary contact; S42: Insulation test: Apply DC high voltage to test the insulation resistance. If the insulation resistance value is greater than the preset safety value, the insulation is deemed qualified. S43: Circuit Closure: After both controllers confirm that there are no errors, they synchronously engage the main power contactor and lock the mechanical locking mechanism.

10. A mobile transformer substation voltage quality management method according to claim 6, characterized in that: S5 includes the following sub-steps: S51: Mode Selection: Determine the constant current or constant voltage operating mode and target power value based on the voltage deviation of the transformer area; S52: Thermal Management: Monitors cell temperature in real time during operation. When the temperature rise rate exceeds the threshold, it automatically reduces power output and starts liquid cooling cycle. S53: Exit Mechanism: When a background stop command is received or the battery SOC reaches the protection limit, the power is reduced at a certain rate until it reaches zero, and then the circuit is disconnected.

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