A new energy power station passive multi-parameter sensing energy collaborative management system and method

Abstract

This invention provides a passive multi-parameter sensing energy collaborative management system and method for new energy power plants, belonging to the field of intelligent operation and maintenance technology for new energy power plants. The system includes: a passive multi-parameter sensing node module, a photovoltaic-thermal power integrated stacked generation module, a distributed energy storage module, an edge computing and node control module, a frame-integrated wireless relay module, and a network-level energy collaborative management module. The method includes: optimizing power generation efficiency under all operating conditions and dynamically adjusting the photovoltaic-thermal power MPPT; achieving local data processing and energy consumption matching through node-level wake-up and dynamic power consumption scheduling; collaboratively scheduling network-level energy and balancing node energy through wireless power replenishment; dynamically optimizing relay links and selecting optimal communication paths in real time; and dynamically adjusting system strategies according to inspection and fault requirements. This invention is adaptable to complex extreme operating conditions, eliminates the need for wiring and power swapping, reduces operation and maintenance costs, and improves node online rate, communication reliability, and the level of intelligent operation and maintenance of new energy power plants.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for new energy power plants, and in particular to a passive multi-parameter sensing energy collaborative management system and method for new energy power plants. Background Technology

[0002] With the rapid development of the new energy industry, large-scale projects such as centralized photovoltaic power plants, integrated wind-solar-storage power plants, and offshore wind farms have been implemented, making intelligent operation and maintenance of power plants a core development direction for the industry. As the core foundation of intelligent operation and maintenance, the measurement accuracy, environmental adaptability, power supply stability, and data transmission link reliability of the IoT monitoring system's sensor nodes directly determine the efficiency and reliability of power plant operation and maintenance. However, existing IoT monitoring systems for new energy power plants suffer from the following core technical defects: sensor nodes are mostly single-parameter designs, unable to simultaneously measure multiple state parameters, and lack integrated protection designs for complex extreme conditions such as strong electromagnetic interference, wide temperature range, high salt spray, strong winds and sandstorms, and low air pressure, resulting in large measurement accuracy drift and insufficient long-term operational reliability; photovoltaic-thermal power generation mostly adopts simple physical bonding methods, resulting in high interface thermal resistance and ineffective utilization of photovoltaic waste heat, leading to extremely low power generation efficiency in low thermal difference scenarios, making it difficult to meet the demand for stable all-weather power supply; node energy management, data acquisition, and edge... The independent computing systems cannot dynamically adjust their operating modes based on energy supply. The large amount of raw data backhaul leads to high power consumption, and there is an inherent technical contradiction between monitoring accuracy and low power consumption. Energy management is rudimentary, lacking a network-level collaborative scheduling mechanism. Energy cannot be shared between nodes, and some nodes are prone to power shortages and offline, resulting in monitoring network interruptions and coverage blind spots. Wireless repeaters are mostly stand-alone devices that require additional wiring and power supply. They cannot be deeply integrated with the existing framework of the power station and lack signal enhancement designs adapted to complex obstructed environments. Communication coverage and power supply coordination are insufficient, and network outages are prone to occur in extreme environments.

[0003] Currently, in existing technological research, in the field of passive sensing, some studies have attempted to use surface acoustic wave (SAW) technology to achieve single-parameter measurement, but multi-parameter integration and adaptation to extreme environments remain technical bottlenecks. In energy harvesting, research on photovoltaic-thermal power generation focuses primarily on material optimization and structural improvement, but the problems of high interfacial thermal resistance and low power generation efficiency under low thermal differential conditions have not been effectively solved. In energy management, dynamic power consumption scheduling strategies have been proposed, but these are mostly limited to the single-node level and lack network-level energy coordination mechanisms. In wireless communication, although self-organizing network technology has been applied to power plant monitoring, relay equipment is mostly installed independently, with little attention paid to conformal integration with the power plant's framework structure and energy coordination scheduling. The aforementioned research largely focuses on improving single technical points, failing to form system-level collaborative solutions, especially lacking a top-level design for deep coupling between the Internet of Things (IoT) and green energy, making it difficult to support the high-quality development of intelligent operation and maintenance of new energy power plants. Summary of the Invention

[0004] The purpose of this invention is to provide a passive multi-parameter sensing energy collaborative management system and method for new energy power plants, which solves the core technical problems in the prior art, such as the inability of sensing nodes to simultaneously take into account multi-parameter measurement and adaptation to complex extreme environments, low power generation efficiency of photovoltaic-thermal power modules with low thermal difference, contradiction between node power consumption and monitoring performance, and the inability of network-level energy to coordinate. This invention achieves deep coupling between the Internet of Things and green energy, and comprehensively improves the stability, efficiency and economy of intelligent operation and maintenance of new energy power plants.

[0005] To achieve the above objectives, this invention proposes a passive multi-parameter sensing energy collaborative management system for new energy power plants, comprising: a passive multi-parameter sensing node module, a photovoltaic-thermal power integrated cascaded power generation module, a distributed energy storage module, an edge computing and node control module, a frame-integrated wireless relay module, and a network-level energy collaborative management module, wherein: The passive multi-parameter sensing node module has a built-in integrated chip of surface acoustic wave (SAW) multi-resonator, and the node is equipped with a composite extreme environment protection structure. The photovoltaic-thermal power generation module adopts a three-layer eutectic bonding structure of photovoltaic-thermal power generation and heat dissipation: the top layer is a high-efficiency heterojunction photovoltaic cell, the middle layer is a bismuth telluride-based thermoelectric power generation chip, and the bottom layer is a honeycomb microchannel heat dissipation structure; it has a built-in full-condition efficiency optimization unit. The distributed energy storage module uses miniature all-solid-state energy storage capacitors, which are matched with each sensing node and each relay node. The power input end is connected to the power output end of the photovoltaic-thermal power generation module, and the power output end is connected to the power supply end of the passive multi-parameter sensing node module, the edge computing and node control module, and the frame-integrated wireless relay module, respectively. The edge computing and node control module has a built-in low-power MCU and a lightweight AI anomaly recognition model, and its signal input is connected to the signal output of the passive multi-parameter sensing node module. The frame-integrated wireless relay module adopts a conformal integrated design with the existing frame structure of the power station, including three types: relay module embedded in electrical cabinet rack, relay node integrated with photovoltaic bracket profile, and relay base station adapted to wind turbine tower bracket. It has built-in dual-mode communication module unit, signal amplification unit, link self-optimization module unit and relay end energy management unit. The relay end energy management unit is electrically connected to the photovoltaic-thermal power integrated stacked power generation module and the distributed energy storage module. The network-level energy collaborative management module is deployed in the power plant's intelligent operation and maintenance platform, and communicates bidirectionally with the edge computing and node control module through the framework-integrated wireless relay module.

[0006] Preferably, in the passive multi-parameter sensing node module, the piezoelectric substrate of the SAW multi-resonator integrated chip is a 128° YX-cut lithium niobate piezoelectric substrate. The chip integrates four resonant units—temperature, vibration, tilt angle, and partial discharge—on the same piezoelectric substrate. These four resonant units are arranged in a distributed manner, with acoustic isolation grooves between them, each with a width of 0.15~0.25mm. The temperature resonant unit measures from -55℃ to +150℃ with a measurement accuracy of ±0.2℃. The vibration resonant unit measures from 10Hz to 1kHz with a measurement error not exceeding 0. 0.1%; the tilt resonant unit has a measurement range of ±90°, with a measurement accuracy of ±0.05°~±0.1° under static conditions and ±0.1° under dynamic conditions; the partial discharge resonant unit has a measurement range of 10pC~1000pC; the protective structure adopts a fully sealed structure combining a metal shielding cavity, nano-ceramic encapsulation, and fluororubber, and the protective structure has a built-in anti-electromagnetic interference filter structure, with a protection level of IP69K, and can withstand the combined extreme working conditions of 100kV / m strong electromagnetic fields and a temperature range of -45℃~+125℃; the nodes achieve passive power supply and data transmission through wireless radio frequency signals.

[0007] Preferably, in the photovoltaic-thermal power generation module, the hot side of the thermoelectric generator is eutectic bonded to the photovoltaic cell backsheet via nano-solder. The nano-solder is a nano-tin-silver-copper solder, and the interfacial thermal resistance between the photovoltaic cell and the thermoelectric generator is no greater than 0.15 K·cm. 2 / W; The bottom layer is closely attached to the cold side of the thermoelectric generator. The honeycomb microchannel heat dissipation structure is made of 6061 aluminum alloy. The microchannel width is 0.3~0.8mm and the depth is 0.8~1.2mm. It can stably output electrical energy under low thermal difference conditions with a thermal difference of not less than 3K. The full-condition efficiency optimization unit dynamically adjusts the MPPT maximum power point tracking of the photovoltaic and thermoelectric units.

[0008] Preferably, the edge computing and node control module adopts a tiered wake-up and dynamic power consumption scheduling collaborative mechanism to collect energy supply status in real time and dynamically adjust the node's collection frequency, calculation accuracy, and transmission power. The tiered wake-up and dynamic power consumption scheduling collaborative mechanism is as follows: Under normal conditions, the node is in sleep mode, and only the core sensing unit collects data at a preset base frequency, completes preprocessing and feature value extraction locally, and only uploads the feature values; when the feature values ​​exceed a preset threshold, the edge computing unit is woken up, the collection frequency is increased, fault identification and classification are completed locally, and only the abnormal event results are uploaded; at the same time, the node's working mode is dynamically adjusted according to the remaining power of the distributed energy storage module. Among them, the lightweight AI anomaly recognition model is a lightweight CNN convolutional neural network model with no more than 500KB of model parameters, which supports local data denoising, feature extraction, fault type identification and severity classification.

[0009] Preferably, the frame-integrated wireless relay module constructs a low-power wireless sensor self-organizing network covering the entire power station, enabling bidirectional data transmission and wireless energy scheduling between sensor nodes and network-level energy collaborative management modules. The relay body is embedded in the profile cavity of the power station frame / rack / support, using the frame metal structure as the antenna reflector, and is equipped with a multi-antenna array signal enhancement structure. The multi-antenna array of the frame-integrated wireless relay module adopts a hybrid omnidirectional and directional array design. The directional antenna is adjusted along the angle of the frame / support profile, with a maximum gain of not less than 12dBi, a maximum single-hop transmission distance of not less than 3km, supports a maximum of 16 hops of multi-hop transmission, and dynamically adjusts the routing depth according to the network scale. The link self-optimization module unit has a built-in improved AODV self-organizing network routing algorithm, with a link switching response time of no more than 50ms and a network outage self-healing time of no more than 200ms. The frame-integrated wireless relay module incorporates a three-level management architecture to achieve coordinated energy scheduling, supporting multi-hop transmission, dynamic link optimization, and link breakage self-healing. In the three-level management architecture, the module-level MPPT adopts a synchronous rectified MPPT unit, with a built-in hybrid MPPT algorithm combining the perturbation observation method and the conductance increment method. The node-level energy storage uses a miniature all-solid-state energy storage capacitor with a rated capacity of 5~20F, a rated voltage of 3.3V, and a charge-discharge cycle count of no less than 100,000 times. The network-level energy scheduling supports multi-node wireless radio frequency energy transmission, with a near-field replenishment distance of no more than 1m and a far-field directional replenishment distance of no more than 5m, requiring the use of a high-gain antenna. The embedded repeater module of the rack-integrated wireless repeater unit adopts a modular snap-fit ​​structure, corresponding to the standard cabinet profile slots of the power station, with a protection level of IP67, an operating temperature range of -40℃ to +85℃, and a built-in grounding and EMC protection structure shared with the rack, which can withstand strong electromagnetic interference of 50kV / m. The relay-end energy management subunit of the frame-integrated wireless relay module supports wireless radio frequency energy forwarding function, which forwards the power of the energy surplus node to the energy shortage node through wireless radio frequency, with a maximum forwarding power of 10W and a forwarding efficiency of not less than 35%.

[0010] Preferably, the network-level energy collaborative management module constructs a three-level management architecture of module-level MPPT, node-level energy storage, and network-level energy scheduling. This architecture is used to collect the energy status and workload of all network nodes in real time, dynamically schedule surplus energy nodes to wirelessly replenish energy to energy-deficient nodes, and dynamically adjust the energy allocation and working mode of each node according to the power plant's operation and maintenance needs.

[0011] This invention also proposes a passive multi-parameter sensing energy collaborative management method for new energy power plants, comprising the following steps: Step S1: Real-time acquisition of light intensity, ambient temperature, photovoltaic cell temperature, and temperature difference between the hot and cold ends of the thermoelectric unit; full-condition efficiency optimization; dynamic adjustment of the MPPT maximum power point tracking parameters of the photovoltaic unit and the thermoelectric unit. Step S2: The control sensor node runs in sleep mode by default. The core sensing unit collects data at a preset base frequency, performs preprocessing and feature extraction locally, and uploads the feature values. When the feature values ​​exceed the preset threshold, the edge computing unit is woken up to increase the collection frequency. The fault type is identified and the severity is classified locally, and the abnormal event results are uploaded. At the same time, the remaining power of the distributed energy storage module and the output power of the power generation unit are collected in real time, and the collection frequency, calculation accuracy and transmission power of the node are dynamically adjusted. Step S3: The integrated wireless relay module collects real-time data on the power generation status, remaining energy storage capacity, and workload of all nodes in the network. The energy status of the nodes is then categorized. When a node with insufficient energy is detected, surplus energy nodes within a preset range are dispatched to replenish the energy supply to the node via wireless radio frequency energy transmission. Simultaneously, the energy forwarding function of the relay unit extends the replenishment range. The operating mode of the node with insufficient energy is also dynamically adjusted. Specifically, the dynamic optimization steps for the relay link are as follows: The frame-integrated wireless relay module collects the signal strength, link transmission delay, communication error rate, and relay node energy status of each node in the entire network in real time. Through the link self-optimization module unit, it dynamically adjusts the communication power, channel, and multi-hop transmission path of each relay node to ensure the stability of the core data transmission link. Step S4: Based on the inspection plan, equipment maintenance requirements, and fault warning information of the power plant operation and maintenance platform, dynamically adjust the energy allocation and working mode of nodes in key monitoring areas, and simultaneously adjust the communication power and link priority of relay nodes in the corresponding areas.

[0012] Preferably, in step S2, the specific rules for dynamic power consumption scheduling are as follows: When the remaining power of the distributed energy storage module is ≥80%, the node operates in full power mode, the sampling frequency is increased to the preset maximum frequency, and full-parameter synchronous measurement and edge computing functions are enabled. When the remaining power of the distributed energy storage module is 30%~80%, the node operates in balanced mode, maintains the basic acquisition frequency, and enables the anomaly detection function; When the remaining power of the distributed energy storage module is ≤30%, the node operates in low power mode, reduces the acquisition frequency to the preset minimum frequency, disables unnecessary parameter measurement and non-core edge computing functions, and retains only the abnormal threshold monitoring and core data transmission functions.

[0013] Preferably, in step S3, the node energy state classification rule is as follows: A remaining power level of ≥70% is considered an energy surplus node, 20%~70% is considered a normal energy node, and ≤20% is considered an energy shortage node. The energy dispatching rule is as follows: Priority is given to dispatching no more than 3 nodes that are closest to the node and have the largest energy surplus to replenish the energy of the node in need, and the energy replenishment stops when the remaining power of the node in need is ≥40%.

[0014] Preferably, in step S3, the specific rules for dynamic optimization of the relay link are as follows: When the signal quality of a certain link falls below a preset threshold, it automatically switches to the backup optimal link; When a relay node is low on energy, it automatically reduces unnecessary communication load, forwards non-core data to nearby surplus relay nodes, and includes them in the network-level energy scheduling scope.

[0015] Therefore, this invention proposes a passive multi-parameter sensing energy collaborative management system and method for new energy power plants, the beneficial effects of which are as follows: (1) This invention integrates four SAW resonant units on a single chip, enabling synchronous passive measurement of temperature, vibration, tilt angle and partial discharge at a single node. Combined with an integrated composite protection structure, it solves the technical contradiction between weather resistance and multi-parameter measurement in existing technologies. It can be adapted to all scenarios of composite extreme working conditions in new energy power plants, without the need for wiring or batteries, and achieves maintenance-free operation throughout the entire life cycle.

[0016] (2) This invention solves the technical problems of high interface thermal resistance and low power generation efficiency under low thermal difference in the prior art by combining a three-layer stacked eutectic bonding structure with a honeycomb microchannel heat dissipation structure. The module integration significantly improves the power generation efficiency compared with the traditional bonding structure. It can output stably under low thermal difference and realize the all-weather self-powered power supply of green energy to sensing nodes and relay nodes.

[0017] (3) This invention achieves local data processing by combining a tiered wake-up and dynamic power consumption scheduling collaborative mechanism with lightweight AI edge computing, which greatly reduces the amount of data transmission and node power consumption, while improving the response speed of abnormal event identification. By dynamically matching the energy supply status with the node working mode, it solves the technical contradiction of the incompatibility between monitoring accuracy, real-time performance and low power consumption in the prior art, and provides accurate and real-time front-end data support for the intelligent operation and maintenance of power plants.

[0018] (4) The present invention constructs a three-level energy management architecture, realizes the deep coupling of distributed energy collection and Internet of Things sensor network, solves the technical problems of mismatch between energy supply and node working requirements and network-level energy incompatibility in the prior art, significantly improves node online rate and overall network energy utilization rate, solves the problem of monitoring data interruption caused by node power shortage and offline, and provides continuous and stable system support for intelligent operation and maintenance of power plants.

[0019] (5) Through the collaborative management of energy and data, the present invention can dynamically adjust the system operation strategy according to the power plant operation and maintenance needs, realize the full-process collaboration of equipment status monitoring, fault early warning and operation and maintenance scheduling, improve the power plant operation and maintenance efficiency, significantly reduce equipment failure downtime, and fully support the upgrade needs of unmanned and intelligent operation and maintenance of new energy power plants. Attached Figure Description

[0020] Figure 1 A schematic diagram of the overall architecture of a passive multi-parameter sensing energy collaborative management system for a new energy power station; Figure 2 This is a schematic diagram of the structure of a passive multi-parameter sensing node module; Figure 3 This is a schematic diagram of the layered structure of a photovoltaic-thermal power generation module. Figure 4 This is a flowchart of a passive multi-parameter sensing energy collaborative management method for new energy power plants. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0023] Example 1 like Figure 1 As shown, this invention provides a passive multi-parameter sensing energy collaborative management system for new energy power plants, including a passive multi-parameter sensing node module, a photovoltaic-thermal power integrated stacked generation module, a distributed energy storage module, an edge computing and node control module, a frame-integrated wireless relay module, and a network-level energy collaborative management module. These modules collaborate and link together to construct a passive, self-powered, and intelligent sensing and energy management system adapted to complex extreme operating conditions. The core functions of each module are as follows: The passive multi-parameter sensor node module serves as the core of the system's data acquisition, and its structure is as follows: Figure 2 As shown, the SAW multi-resonator integrated chip based on a 128° YX-cut lithium niobate piezoelectric substrate simultaneously achieves passive measurement of four parameters: temperature, vibration, tilt angle, and partial discharge. Furthermore, its IP69K-level composite protection structure adapts to extreme operating conditions with a wide temperature range of -45℃ to +125℃ and strong electromagnetic interference of 100kV / m. Requiring no wired power supply, it achieves passive power supply and data transmission via wireless radio frequency, providing accurate and weather-resistant raw data for power plant equipment condition monitoring.

[0024] The photovoltaic-thermal power integrated tandem power generation module is the core of the system's self-powered operation at all nodes, such as... Figure 3As shown, a three-layer eutectic bonding structure of photovoltaic-thermal-heat dissipation is adopted. The interfacial thermal resistance is reduced by nano-tin-silver-copper solder, and the honeycomb microchannel heat dissipation structure achieves stable power generation under a low thermal difference of no more than 3K. The built-in full-condition efficiency optimization unit dynamically adjusts the MPPT parameters of photovoltaic and thermoelectric units to maximize the utilization of light energy and photovoltaic waste heat, providing all nodes with all-weather green passive power supply.

[0025] Distributed energy storage modules are the foundation of system energy storage and allocation. They use miniature all-solid-state energy storage capacitors (5~20F / 3.3V, charge-discharge cycles ≥100,000 times) and are matched with sensing nodes and relay nodes. They receive and store the power output of photovoltaic-thermal power modules, providing a stable power supply for the operation of each module. They are the hardware foundation for realizing dynamic scheduling of node power consumption.

[0026] The edge computing and node control module is the core of the system's local intelligent control. It has a built-in low-power MCU and a lightweight CNN model (parameters ≤500KB). Through a coordinated mechanism of tiered wake-up and dynamic power scheduling, it dynamically adjusts the node acquisition frequency, calculation accuracy and transmission power according to the remaining energy storage power. At the same time, it completes local data preprocessing, feature extraction and fault identification and classification, which greatly reduces the power consumption of raw data transmission and achieves a balance between monitoring accuracy and low power consumption.

[0027] The frame-integrated wireless relay module serves as the system's data transmission and energy forwarding hub. It conformally integrates with the existing power station frame (cabinet-embedded, photovoltaic bracket integrated, wind turbine tower adapted). Utilizing the frame's metal structure as the antenna reflector, it constructs a low-power self-organizing network for the entire power station through an omnidirectional + directional hybrid antenna array (maximum gain ≥12dBi). The built-in improved AODV algorithm enables link self-optimization (switching response ≤50ms, network outage self-healing ≤200ms), while also possessing wireless radio frequency energy forwarding capabilities (maximum 10W, efficiency ≥35%), achieving bidirectional data transmission and extended energy replenishment range.

[0028] The network-level energy collaborative management module is the core of the system's top-level scheduling. It is deployed on the power plant's intelligent operation and maintenance platform and constructs a three-level management architecture of module-level MPPT, node-level energy storage, and network-level energy scheduling. It collects the energy status and workload of all network nodes in real time, dynamically schedules surplus nodes to wirelessly replenish energy to nodes with insufficient energy, and adjusts the node energy allocation and working mode according to the power plant's operation and maintenance needs to achieve global collaborative optimization of the entire network's energy.

[0029] Example 2 like Figure 4As shown, this invention provides a passive multi-parameter sensor energy collaborative management method for new energy power plants. Based on the aforementioned passive multi-parameter sensor energy collaborative management system for new energy power plants, it focuses on four core aspects: full-condition power generation optimization, dynamic scheduling of node power consumption, network-level energy collaboration, and dynamic adaptation to operation and maintenance requirements. The system achieves intelligent operation through a step-by-step, rule-based approach. The specific steps are as follows: Step S1: Real-time acquisition of light intensity, ambient temperature, photovoltaic cell temperature, temperature difference between the hot and cold ends of the thermoelectric unit, and equipment operating parameters. Through the module's built-in full-condition efficiency optimization unit, combined with the hybrid MPPT algorithm of perturbation observation method + conductivity increment method, the maximum power point tracking parameters of the photovoltaic unit and the thermoelectric unit are dynamically adjusted to maximize the utilization of light energy and photovoltaic waste heat, ensuring that the power generation efficiency of the photovoltaic-thermal power module is always in the optimal state under different operating conditions such as strong light, weak light, and low thermal difference.

[0030] Step S2: The edge computing and node control module controls the sensor nodes to run in sleep mode by default. The core sensing unit collects data at a preset base frequency, performs preprocessing and feature extraction locally, and uploads the feature values. When the feature values ​​exceed a preset threshold, the edge computing unit is woken up to increase the collection frequency. The fault type is identified and the severity is classified locally, and the abnormal event results are uploaded. At the same time, the remaining power of the distributed energy storage module and the output power of the power generation unit are collected in real time. The node's collection frequency, calculation accuracy, and transmission power are dynamically adjusted. Based on the remaining power of the distributed energy storage module, a three-level power consumption scheduling rule is executed. When the remaining power of the distributed energy storage module is ≥80%, the node operates in full power mode, the sampling frequency is increased to the preset maximum frequency, and full-parameter synchronous measurement and edge computing functions are enabled. When the remaining power of the distributed energy storage module is 30%~80%, the node operates in balanced mode, maintains the basic acquisition frequency, and enables the anomaly detection function; When the remaining power of the distributed energy storage module is ≤30%, the node operates in low power mode, reduces the acquisition frequency to the preset minimum frequency, disables unnecessary parameter measurement and non-core edge computing functions, and retains only the abnormal threshold monitoring and core data transmission functions.

[0031] Step S3: The network-level energy collaborative management module collects the power generation status, remaining energy storage capacity, and workload of all nodes in the entire network in real time through the frame-integrated wireless relay module, and classifies the node energy status according to the following rules: A remaining power level of ≥70% is considered an energy surplus node, 20%~70% is considered a normal energy node, and ≤20% is considered an energy shortage node. The energy dispatching rule is as follows: Priority is given to dispatching no more than 3 nodes that are closest to the node and have the largest energy surplus to replenish the energy of the node in need of energy, and the energy replenishment stops when the remaining power of the node in need of energy is ≥40%; When a power-deficient node is detected, surplus energy nodes within a preset range are dispatched to replenish the power to the power-deficient node via wireless radio frequency energy transmission. Simultaneously, the energy forwarding function of the relay unit extends the replenishment range, and the operating mode of the power-deficient node is dynamically adjusted. The specific steps of the relay link dynamic optimization are as follows: The integrated wireless relay module collects real-time data on signal strength, link transmission delay, communication error rate, and relay node energy status from all nodes in the network. Through a link self-optimization module, it dynamically adjusts the communication power, channel, and multi-hop transmission path of each relay node to ensure the stability of core data transmission links. The specific rules for dynamic relay link optimization are as follows: When the signal quality of a certain link falls below a preset threshold, it automatically switches to the backup optimal link; When a relay node is low on energy, it automatically reduces unnecessary communication load, forwards non-core data to nearby surplus relay nodes, and includes them in the network-level energy scheduling scope.

[0032] Step S4: The network-level energy collaborative management module dynamically adjusts the energy allocation and working mode of nodes in key monitoring areas based on the inspection plan, equipment maintenance requirements, and fault warning information of the power plant operation and maintenance platform. It also adjusts the communication power and link priority of relay nodes in the corresponding areas to ensure the real-time performance and reliability of data transmission in key areas, and achieves deep adaptation between system operation strategies and power plant operation and maintenance requirements. Detailed Implementation Method 1 Taking a 100MW centralized desert photovoltaic power station as the application scenario, this power station is located in the northwestern desert region and faces a complex extreme operating condition with a wide temperature range of -40℃ to +85℃, strong winds and sandstorms, strong ultraviolet radiation, and strong electromagnetic interference of 100kV / m around the inverter or inverter box. The operation and maintenance requirements are to achieve full-state monitoring and intelligent operation and maintenance of inverters, box transformers, and photovoltaic tracking brackets. The specific implementation methods and actual test results are as follows: 1. Hardware deployment and parameter configuration: Sensing nodes: Passive multi-parameter sensing nodes are deployed at key locations in inverters, transformer substations, and tracking brackets. The nodes are protected to adapt to desert conditions with strong winds and sandstorms. The SAW chip parameters are configured according to patented standards (temperature accuracy ±0.2℃, vibration error ≤0.1%). Power generation and energy storage module: Each sensing node and relay node is equipped with a photovoltaic-thermal power generation module, which uses nano-tin-silver-copper solder, and the microchannel heat dissipation structure is 0.5mm wide and 1.0mm deep. The micro all-solid-state energy storage capacitor has the following specifications: 10F / 3.3V. Relay Module: Three types of relay nodes are deployed according to the power station frame structure: electrical cabinet embedded relay: IP67, -40℃~+85℃, photovoltaic bracket integrated relay and box-type bracket adapted relay, to build a self-organizing network with a maximum routing depth of 16 hops, with an antenna gain configuration of 12dBi and a single hop transmission distance of 3km. Top-level scheduling: The power plant operation and maintenance platform deploys a network-level energy collaborative management module, builds a three-level energy management architecture, and configures core rules for energy classification, energy replenishment, and link optimization.

[0034] 2. System debugging and initialization: After completing the hardware deployment, MPPT parameters were calibrated for the photovoltaic-thermal power module, and the hybrid MPPT algorithm was optimized for the light characteristics of desert areas. The lightweight CNN model of the edge computing module was trained locally to adapt to the fault characteristics of the power station equipment. The ad hoc network was debugged, and signal quality thresholds and link switching rules were preset to complete the initialization of the energy status and communication status of all network nodes.

[0035] 3. Routine operation control: The system operates normally according to the steps of Implementation Method 2. The photovoltaic-thermal power module dynamically adjusts the MPPT parameters under the conditions of low thermal difference (3~5K) and alternating strong / weak light in the desert. The sensing nodes perform three-level power consumption scheduling according to the energy storage capacity. The network-level module monitors the energy status of all nodes in real time and performs wireless power replenishment and link optimization for energy-deficient nodes in remote desert areas. In accordance with the power station inspection plan, the monitoring priority of nodes in the maintenance area is increased.

[0036] The specific results are as follows: After six months of continuous operation at the 100MW desert photovoltaic power station, the system achieved the following technical results through actual measurement, thus overcoming the technical shortcomings of traditional monitoring systems: (1) All sensor nodes operate without faults under conditions of -40℃ to +85℃, strong electromagnetic interference, and strong wind and sand. The protective structure is undamaged. The measurement accuracy of the four parameters meets the patented standards. There is no drift. The nodes are maintenance-free throughout their entire life cycle. There is no need for wiring or power replacement, thus reducing maintenance costs.

[0037] (2) The photovoltaic-thermal power generation module outputs electricity stably under low thermal difference (3K) conditions, which improves the power generation efficiency compared with the traditional physical bonding photovoltaic-thermal power module. The distributed energy storage module has stable charging and discharging cycles, and all nodes can achieve all-weather self-powered energy supply.

[0038] (3) By using tiered wake-up and dynamic power consumption scheduling, the average power consumption of nodes is reduced, the lightweight CNN model completes fault identification and classification locally, the identification effect of abnormal event upload is accurate, the amount of original data transmission is greatly reduced, and the inherent technical contradiction between monitoring accuracy, real-time performance and low power consumption is successfully resolved.

[0039] (4) The frame-integrated self-organizing network covers the entire power station without blind spots, and the response performance of link switching and network outage self-healing is good, and the stability of communication transmission is greatly improved; the network-level energy collaborative scheduling effectively realizes energy sharing among nodes, and nodes lacking energy can be quickly identified and replenished. All nodes in the power station maintain a high online rate, and there is no problem of monitoring data interruption due to node lack of energy. The overall energy utilization efficiency of the network is significantly improved.

[0040] (5) The system can dynamically adjust the overall operation strategy according to the actual operation and maintenance needs such as power plant inspection plan and fault warning. It can effectively identify equipment faults in advance, greatly reduce the downtime of equipment faults, and provide stable and reliable technical support for the power plant to achieve unmanned and intelligent operation and maintenance.

[0041] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A passive multi-parameter sensing energy collaborative management system for new energy power plants, characterized in that, include: Passive multi-parameter sensing node module, photovoltaic-thermal power integrated stacked power generation module, distributed energy storage module, edge computing and node control module, frame-integrated wireless relay module, and network-level energy collaborative management module, among which: The passive multi-parameter sensing node module has a built-in integrated chip of surface acoustic wave (SAW) multi-resonator, and the node is equipped with a composite extreme environment protection structure. The photovoltaic-thermal power generation module adopts a three-layer eutectic bonding structure of photovoltaic-thermal power generation and heat dissipation: the top layer is a high-efficiency heterojunction photovoltaic cell, the middle layer is a bismuth telluride-based thermoelectric power generation chip, and the bottom layer is a honeycomb microchannel heat dissipation structure; it has a built-in full-condition efficiency optimization unit. The distributed energy storage module uses miniature all-solid-state energy storage capacitors, which are matched with each sensing node and each relay node. The power input end is connected to the power output end of the photovoltaic-thermal power generation module, and the power output end is connected to the power supply end of the passive multi-parameter sensing node module, the edge computing and node control module, and the frame-integrated wireless relay module, respectively. The edge computing and node control module has a built-in low-power MCU and a lightweight AI anomaly recognition model, and its signal input is connected to the signal output of the passive multi-parameter sensing node module. The frame-integrated wireless relay module adopts a conformal integrated design with the existing frame structure of the power station, including three types: relay module embedded in electrical cabinet rack, relay node integrated with photovoltaic bracket profile, and relay base station adapted to wind turbine tower bracket. It has built-in dual-mode communication module unit, signal amplification unit, link self-optimization module unit and relay end energy management unit. The relay end energy management unit is electrically connected to the photovoltaic-thermal power integrated stacked power generation module and the distributed energy storage module. The network-level energy collaborative management module is deployed in the power plant's intelligent operation and maintenance platform, and communicates bidirectionally with the edge computing and node control module through the framework-integrated wireless relay module.

2. The passive multi-parameter sensing energy collaborative management system for new energy power plants according to claim 1, characterized in that: In the passive multi-parameter sensing node module, the SAW multi-resonator integrated chip uses a 128° YX-cut lithium niobate piezoelectric substrate. The chip integrates four resonant units—temperature, vibration, tilt angle, and partial discharge—on a single piezoelectric substrate. These four resonant units are arranged in a distributed manner, with acoustic isolation grooves between them, each with a width of 0.15~0.25mm. The temperature resonant unit measures from -55℃ to +150℃ with a measurement accuracy of ±0.2℃. The vibration resonant unit measures from 10Hz to 1kHz with a measurement error of no more than 0.

1. The tilt resonant unit has a measurement range of ±90°, with a measurement accuracy of ±0.05°~±0.1° under static conditions and ±0.1° under dynamic conditions; the partial discharge resonant unit has a measurement range of 10pC~1000pC; the protective structure adopts a fully sealed structure combining a metal shielding cavity, nano-ceramic encapsulation, and fluororubber, and has a built-in anti-electromagnetic interference filter structure, with a protection level of IP69K, and can withstand the combined extreme conditions of 100kV / m strong electromagnetic fields and a temperature range of -45℃~+125℃; the nodes achieve passive power supply and data transmission through wireless radio frequency signals.

3. The passive multi-parameter sensing energy collaborative management system for new energy power plants according to claim 1, characterized in that: In the photovoltaic-thermoelectric integrated stacked power generation module, the hot surface of the thermoelectric power generation sheet is eutectically bonded with the back plate of the photovoltaic cell through nano solder, the nano solder adopts nano tin silver copper solder, and the interface thermal resistance between the photovoltaic cell and the thermoelectric power generation sheet is not greater than 0.15 K·cm 2 The bottom layer is closely attached to the cold surface of the thermoelectric power generation sheet, the honeycomb-shaped micro-channel heat dissipation structure adopts 6061 aluminum alloy material, the width of the micro-channel is 0.3-0.8 mm, the depth is 0.8-1.2 mm, and stable output power is obtained under the low thermal difference working condition with a thermal difference not less than 3K; the full working condition efficiency optimization unit dynamically adjusts the MPPT maximum power point tracking of the photovoltaic and thermoelectric units.

4. The passive multi-parameter sensing energy collaborative management system for new energy power plants according to claim 1, characterized in that: The edge computing and node control module adopts a phased wake-up and dynamic power consumption scheduling collaborative mechanism to collect energy supply status in real time and dynamically adjust the node's collection frequency, calculation accuracy and transmission power. The phased wake-up and dynamic power consumption scheduling collaborative mechanism is as follows: Under normal circumstances, the node is in sleep mode, and only the core sensing unit collects data at a preset basic frequency, completes preprocessing and feature value extraction locally, and only uploads the feature values. When the feature value exceeds the preset threshold, the edge computing unit is activated to increase the acquisition frequency, complete the fault identification and classification locally, and only upload the results of abnormal events; at the same time, the node working mode is dynamically adjusted according to the remaining power of the distributed energy storage module. Among them, the lightweight AI anomaly recognition model is a lightweight CNN convolutional neural network model with no more than 500KB of model parameters, which supports local data denoising, feature extraction, fault type identification and severity classification.

5. The passive multi-parameter sensing energy collaborative management system for new energy power plants according to claim 1, characterized in that: The frame-integrated wireless relay module constructs a low-power wireless sensor self-organizing network covering the entire power station, enabling bidirectional data transmission and wireless energy scheduling between sensor nodes and network-level energy collaborative management modules. The relay body is embedded in the profile cavity of the power station frame / rack / support, using the frame metal structure as the antenna reflector, and is equipped with a multi-antenna array signal enhancement structure. The multi-antenna array of the frame-integrated wireless relay module adopts a hybrid omnidirectional and directional array design. The directional antenna is adjusted along the angle of the frame / support profile, with a maximum gain of not less than 12dBi, a maximum single-hop transmission distance of not less than 3km, supports a maximum of 16 hops of multi-hop transmission, and dynamically adjusts the routing depth according to the network scale. The link self-optimization module unit has a built-in improved AODV self-organizing network routing algorithm, with a link switching response time of no more than 50ms and a network outage self-healing time of no more than 200ms. The frame-integrated wireless relay module incorporates a three-level management architecture to achieve coordinated energy scheduling, supporting multi-hop transmission, dynamic link optimization, and link breakage self-healing. In the three-level management architecture, the module-level MPPT adopts a synchronous rectified MPPT unit, with a built-in hybrid MPPT algorithm combining the perturbation observation method and the conductance increment method. The node-level energy storage uses a miniature all-solid-state energy storage capacitor with a rated capacity of 5~20F, a rated voltage of 3.3V, and a charge-discharge cycle count of no less than 100,000 times. The network-level energy scheduling supports multi-node wireless radio frequency energy transmission, with a near-field replenishment distance of no more than 1m and a far-field directional replenishment distance of no more than 5m, requiring the use of a high-gain antenna. The embedded repeater module of the rack-integrated wireless repeater unit adopts a modular snap-fit ​​structure, corresponding to the standard cabinet profile slots of the power station, with a protection level of IP67, an operating temperature range of -40℃ to +85℃, and a built-in grounding and EMC protection structure shared with the rack, which can withstand strong electromagnetic interference of 50kV / m. The relay-end energy management subunit of the frame-integrated wireless relay module supports wireless radio frequency energy forwarding function, which forwards the power of the energy surplus node to the energy shortage node through wireless radio frequency, with a maximum forwarding power of 10W and a forwarding efficiency of not less than 35%.

6. The passive multi-parameter sensing energy collaborative management system for new energy power plants according to claim 1, characterized in that: The network-level energy collaborative management module constructs a three-tier management architecture: module-level MPPT, node-level energy storage, and network-level energy scheduling. It is used to collect the energy status and workload of all network nodes in real time, dynamically schedule surplus energy nodes to wirelessly replenish energy to energy-deficient nodes, and dynamically adjust the energy allocation and working mode of each node according to the power plant operation and maintenance needs.

7. A passive multi-parameter sensing energy collaborative management method for new energy power plants, implemented based on the passive multi-parameter sensing energy collaborative management system for new energy power plants as described in claims 1-6, characterized in that, Includes the following steps: Step S1: Real-time acquisition of light intensity, ambient temperature, photovoltaic cell temperature, and temperature difference between the hot and cold ends of the thermoelectric unit; full-condition efficiency optimization; dynamic adjustment of the MPPT maximum power point tracking parameters of the photovoltaic unit and the thermoelectric unit. Step S2: The control sensor node runs in sleep mode by default. The core sensing unit collects data at a preset base frequency, performs preprocessing and feature extraction locally, and uploads the feature values. When the feature values ​​exceed the preset threshold, the edge computing unit is woken up to increase the collection frequency. The fault type is identified and the severity is classified locally, and the abnormal event results are uploaded. At the same time, the remaining power of the distributed energy storage module and the output power of the power generation unit are collected in real time, and the collection frequency, calculation accuracy and transmission power of the node are dynamically adjusted. Step S3: The integrated wireless relay module collects real-time data on the power generation status, remaining energy storage capacity, and workload of all nodes in the network. The energy status of the nodes is then categorized. When a node with insufficient energy is detected, surplus energy nodes within a preset range are dispatched to replenish the energy supply to the node via wireless radio frequency energy transmission. Simultaneously, the energy forwarding function of the relay unit extends the replenishment range. The operating mode of the node with insufficient energy is also dynamically adjusted. Specifically, the dynamic optimization steps for the relay link are as follows: The frame-integrated wireless relay module collects the signal strength, link transmission delay, communication error rate, and relay node energy status of each node in the entire network in real time. Through the link self-optimization module unit, it dynamically adjusts the communication power, channel, and multi-hop transmission path of each relay node to ensure the stability of the core data transmission link. Step S4: Based on the inspection plan, equipment maintenance requirements, and fault warning information of the power plant operation and maintenance platform, dynamically adjust the energy allocation and working mode of nodes in key monitoring areas, and simultaneously adjust the communication power and link priority of relay nodes in the corresponding areas.

8. The passive multi-parameter sensing energy collaborative management method for new energy power plants according to claim 7, characterized in that: In step S2, the specific rules for dynamic power consumption scheduling are as follows: When the remaining power of the distributed energy storage module is ≥80%, the node operates in full power mode, the sampling frequency is increased to the preset maximum frequency, and full-parameter synchronous measurement and edge computing functions are enabled. When the remaining power of the distributed energy storage module is 30%~80%, the node operates in balanced mode, maintains the basic acquisition frequency, and enables the anomaly detection function; When the remaining power of the distributed energy storage module is ≤30%, the node operates in low power mode, reduces the acquisition frequency to the preset minimum frequency, disables unnecessary parameter measurement and non-core edge computing functions, and retains only the abnormal threshold monitoring and core data transmission functions.

9. The passive multi-parameter sensing energy collaborative management method for new energy power plants according to claim 7, characterized in that: In step S3, the node energy state classification rule is as follows: A remaining power level of ≥70% is considered an energy surplus node, 20%~70% is considered a normal energy node, and ≤20% is considered an energy shortage node. The energy dispatching rule is as follows: Priority is given to dispatching no more than 3 nodes that are closest to the node and have the largest energy surplus to replenish the energy of the node in need, and the energy replenishment stops when the remaining power of the node in need is ≥40%.

10. A passive multi-parameter sensing energy collaborative management method for new energy power plants according to claim 7, characterized in that: In step S3, the specific rules for dynamic optimization of relay links are as follows: When the signal quality of a certain link falls below a preset threshold, it automatically switches to the backup optimal link; When a relay node is low on energy, it automatically reduces unnecessary communication load, forwards non-core data to nearby surplus relay nodes, and includes them in the network-level energy scheduling scope.

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