Transformer area differentiated resource integrated communication method based on intelligent fusion terminal

Abstract

The invention discloses a transformer area differentiated resource integrated communication method based on an intelligent fusion terminal, and the method comprises the following steps: S1, building a three-dimensional resource description model which comprises an equipment type, a communication protocol and data features, and carrying out the unified modeling of a photovoltaic inverter, an energy storage device and an intelligent electric meter; s2, dynamically calculating a communication priority weight according to the real-time state of the equipment and the operation requirement of the power grid; S3, selecting an optimal communication channel through a hybrid decision model; and S4, realizing protocol non-sensitive interaction by adopting an AST driving architecture which comprises a grammar analysis layer and a semantic conversion layer. According to the method, deep coupling of a communication technology and power system control is achieved, and normal form transformation from passive adaptation to active optimization is achieved through breakthrough of the three technologies. Firstly, the multi-dimensional cooperative communication capability is improved, a dynamic priority mechanism is introduced, a three-dimensional weight model is constructed based on an equipment type, a power grid state and a service demand, and accurate distribution of communication resources is realized.

Description

Technical Field

[0001] The present invention relates to the field of power system communication technology, and in particular to an integrated communication method for differentiated resources in substations based on an intelligent fusion terminal. Background Art

[0002] Distribution substations are the terminal units of the power system, housing distribution transformers, distributed energy resources (such as photovoltaic and wind power), energy storage devices, smart meters, controllable loads, and other equipment. With the development of the Energy Internet, substations are transitioning from a traditional one-way power supply model to an intelligent model that integrates the coordinated interaction of source, grid, load, and storage.

[0003] Currently, substation communications mainly use the following technologies: 1. HPLC (high-speed power line carrier), which has the advantages of no need for additional wiring and low cost, but is susceptible to grid noise interference and has limited bandwidth (≤2Mbps); 2. Wireless communication (LoRa, ZigBee, 5G), which has the advantages of flexible deployment and support for mobile device access, but has limited coverage and is susceptible to environmental interference; 3. Fiber optic communication, which has the advantages of high bandwidth and low latency, but has high bandwidth and low latency.

[0004] In terms of communication in distribution substations, the main technical bottlenecks are: 1. Insufficient resource coordination capabilities. The communication requirements of photovoltaic, energy storage, load and other equipment vary greatly, and the existing system cannot be uniformly adapted. For example, photovoltaic inverters require high real-time performance (<100ms), while meter data allows minute-level delays. 2. Communication and energy efficiency are separated. Traditional communication systems only focus on data transmission and do not consider the operating status of the grid. For example, when the voltage exceeds the limit, the communication system fails to prioritize the transmission of control instructions. 3. Poor protocol interoperability. Equipment manufacturers use different communication protocols (such as Modbus, IEC 104, MQTT), which makes data exchange difficult. For example, photovoltaic inverters use Modbus-TCP, while energy storage systems use IEC 61850, requiring additional gateway conversion.

[0005] Traditional technical solutions suffer from inefficient resource coordination due to the heterogeneous communication requirements of different devices. For example, photovoltaic inverters (millisecond-level control), energy storage systems (second-level regulation), and smart meters (minute-level data collection) have significantly different latency and bandwidth requirements. Traditional technical solutions utilize the same QoS level and communication mode for all devices. This leads to critical instruction delays, as high-priority control instructions compete with low-priority data for channel resources, and bandwidth waste due to low-value data occupying high-bandwidth channels. Traditional technical solutions also suffer from low protocol conversion efficiency. This is due to device manufacturers using different protocols (Modbus / IEC 61850 / DL / T 645). Data must be parsed layer by layer from the physical layer to the data link layer to the application layer. These multiple protocol conversions increase end-to-end latency nonlinearly, leading to the accumulation of conversion delays. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one purpose of the present invention is to propose an integrated communication method for differentiated resources in substations based on intelligent fusion terminals. This method achieves deep coupling of communication technology and power system control, and realizes a paradigm shift from "passive adaptation" to "active optimization" through three major technological breakthroughs. First, the multi-dimensional collaborative communication capability is improved, a dynamic priority mechanism is introduced, and a three-dimensional weight model is constructed based on device type, grid status, and business needs to achieve precise allocation of communication resources.

[0007] To this end, the present invention proposes an integrated communication method for differentiated resources in a substation area based on an intelligent fusion terminal, comprising the following steps:

[0008] S1: Establish a three-dimensional resource description model that includes device type, communication protocol, and data characteristics, and uniformly model photovoltaic inverters, energy storage devices, and smart meters;

[0009] S2: Dynamically calculate the communication priority weight based on the real-time status of the device and the grid operation requirements:

[0010] Priority=0.6·Class+0.3·State+0.1·QoS;

[0011] Where: Class∈{1,0.7,0.3} corresponds to the following equipment types: control class, monitoring class and metering class;

[0012] The grid states corresponding to State∈{1,0.5,0.2} are: emergency / normal / idle;

[0013] QoS is the quality of service level, including a linear mapping of levels 1 to 5;

[0014] S3: Select the optimal communication channel through a hybrid decision model:

[0015]

[0016] S4: Adopts AST-driven architecture to achieve seamless protocol interaction, including syntax parsing layer and semantic conversion layer.

[0017] Preferably, the three-dimensional resource description model quantifies communication requirements through the following parameters:

[0018] Delay tolerance:

[0019]

[0020] Bandwidth requirements:

[0021]

[0022] Where: η is the compression ratio, ΔD is the data increment;

[0023] Dynamic Priority:

[0024]

[0025] ΔV represents the current voltage deviation, L critical represents the emergency load ratio, α = 0.6, β = 0.4.

[0026] Preferably, the hybrid decision model of S3 selects the optimal communication channel including the following steps:

[0027] S3.1: Channel quality assessment: Real-time monitoring of each channel status, including latency, packet loss rate, and signal-to-noise ratio;

[0028] S3.2: Dynamic decision making: Selecting the communication mode using the fuzzy logic controller FLC;

[0029] S3.3: Input variable: device priority P priority , channel quality, data volume B width ;

[0030] S3.4: Output variables: Communication mode weights, including 5G / HPLC / LoRa.

[0031] The advantages of the present invention compared with the prior art are:

[0032] 1. Break down information silos and model differentiated resources: Enable unified access and collaborative communication for differentiated resources such as photovoltaic inverters, energy storage devices, and smart meters, eliminate protocol barriers between devices, establish a three-dimensional resource description model that includes device type, communication protocol, and data characteristics, and achieve multi-source heterogeneous data fusion through normalization processing.

[0033] 2. Improve energy efficiency and synergy, and dynamically adapt communication protocols: Compatible with multi-mode communication protocols such as HPLC, LoRa, and Modbus, it eliminates data exchange barriers and establishes a joint optimization mechanism for communication resource allocation and power regulation, ensuring reliable transmission of control commands while reducing overall system energy consumption.

[0034] 3. Enhanced dynamic adaptability: Through intelligent spectrum sharing and protocol-free interaction technology, we can cope with complex operating scenarios such as changes in substation topology and fluctuations in new energy sources, build a dynamic substation resource assessment system, and set differentiated QoS levels based on device type (such as photovoltaic inverters > smart meters > environmental sensors).

[0035] The core of this invention lies in building an integrated communication system combining resource awareness, communication adaptation, and cross-domain optimization. The specific implementation architecture is as follows: First, resources within the distribution substation are integrated and modeled based on device characteristics, creating a three-dimensional communication characteristic model for each resource type. Next, an intelligent communication adaptation layer is designed to support dynamic multi-mode communication selection, compatible with multi-mode communication protocols such as HPLC, LoRa, and Modbus, and employs a hybrid decision-making model to select the optimal communication channel. Finally, a cross-domain collaborative optimization layer is designed to establish a dual-objective joint optimization model for communication and power. Edge computing enables hierarchical data processing, reducing communication latency for key devices to less than 100ms.

[0036] The present invention establishes a protocol-free interaction system and adopts an AST (abstract syntax tree) driven architecture to achieve a breakthrough in protocol conversion delay. Finally, it realizes the joint optimization of communication and energy efficiency and uses the improved NSGA-III algorithm to solve the Pareto frontier of communication energy consumption and grid loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 It is the Pareto front solution set of the present invention. DETAILED DESCRIPTION

[0039] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0040] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to internal communication between two components or the interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0041] The present invention will be described in further detail below with reference to the accompanying drawings.

[0042] Combine Figure 1 The present invention proposes a method for integrating differentiated resources in a substation area based on an intelligent fusion terminal, comprising the following steps:

[0043] S1: Establish a three-dimensional resource description model that includes device type, communication protocol, and data characteristics, and uniformly model photovoltaic inverters, energy storage devices, and smart meters;

[0044] S2: Dynamically calculate the communication priority weight based on the real-time status of the device and the grid operation requirements:

[0045] Priority=0.6·Class+0.3·State+0.1·QoS;

[0046] Where: Class∈{1,0.7,0.3} corresponds to the following equipment types: control class, monitoring class and metering class;

[0047] The grid states corresponding to State∈{1,0.5,0.2} are: emergency / normal / idle;

[0048] QoS is the quality of service level, including a linear mapping of levels 1 to 5;

[0049] S3: Select the optimal communication channel through a hybrid decision model:

[0050]

[0051] S4: Adopts AST-driven architecture to achieve seamless protocol interaction, including syntax parsing layer and semantic conversion layer.

[0052] Preferably, the three-dimensional resource description model quantifies communication requirements through the following parameters:

[0053] Delay tolerance:

[0054]

[0055] Bandwidth requirements:

[0056]

[0057] Where: η is the compression ratio, ΔD is the data increment;

[0058] Dynamic Priority:

[0059]

[0060] ΔV represents the current voltage deviation, L critical represents the emergency load ratio, α = 0.6, β = 0.4.

[0061] Preferably, the hybrid decision model of S3 selects the optimal communication channel including the following steps:

[0062] S3.1: Channel quality assessment: Real-time monitoring of each channel status, including latency, packet loss rate, and signal-to-noise ratio;

[0063] S3.2: Dynamic decision making: Selecting the communication mode using the fuzzy logic controller FLC;

[0064] S3.3: Input variable: device priority P priority , channel quality, data volume B width ;

[0065] S3.4: Output variables: Communication mode weights, including 5G / HPLC / LoRa.

[0066] In order to more clearly illustrate the specific embodiment of the present invention, an embodiment is provided below:

[0067] The advantages of this invention embody the deep coupling of communication technology and power system control, achieving a paradigm shift from "passive adaptation" to "active optimization" through three major technological breakthroughs. First, it enhances multi-dimensional collaborative communication capabilities, introduces a dynamic priority mechanism, and constructs a three-dimensional weighting model based on device type, grid status, and service requirements to achieve precise allocation of communication resources.

[0068]

[0069] Class: Device type (control class = 1, monitoring class = 0.7, measurement class = 0.3)

[0070] State: Grid status (Emergency = 1, Normal = 0.5, Idle = 0.2)

[0071] QoS: Quality of Service (1-5 linear mapping)

[0072] Then, a protocol-free interaction system was established, and an AST (abstract syntax tree) driven architecture was adopted to achieve a breakthrough in protocol conversion delay. Finally, communication-energy efficiency joint optimization was achieved, and the improved NSGA-III algorithm was used to solve the Pareto frontier of communication energy consumption and power grid loss. Figure 1 shown.

[0073] Resource Panoramic Perception Layer

[0074] First, we establish a resource-wide perception layer, modeling it based on device characteristics. We then build a three-dimensional communication feature model for each resource type to quantify its communication requirements.

[0075] DeviceProfile=(C latency ,B width ,Ppriority );

[0076] C latency Delay tolerance, which defines the maximum allowable delay based on the device type.

[0077]

[0078] B width Represents bandwidth demand, dynamically calculates the real-time data generation rate of the device, and combines compression algorithms to optimize bandwidth usage.

[0079]

[0080] Where: η is the compression ratio, ΔD is the data increment;

[0081] P priority Represents dynamic priority, which adjusts the priority weight based on the real-time status of the power grid.

[0082]

[0083] ΔV represents the current voltage deviation, L critical represents the emergency load ratio, α = 0.6, β = 0.4.

[0084] Communication intelligent adaptation layer

[0085] Then, an intelligent communication adaptation layer is established, and a hybrid decision model is used to select the optimal communication channel to achieve dynamic selection of multi-mode communication:

[0086]

[0087] The communication intelligent adaptation layer has a built-in multi-mode communication dynamic selection algorithm. This algorithm selects the optimal communication channel based on a hybrid decision model. The process is as follows:

[0088] 1) Channel quality assessment: Real-time monitoring of each channel status (delay, packet loss rate, signal-to-noise ratio):

[0089] 2). Dynamic decision-making: Use fuzzy logic controller (FLC) to select the communication mode.

[0090] 3). Input variable: device priority P priority , channel quality, data volume B width .

[0091] 4) Output variable: Communication mode weight (5G / HPLC / LoRa).

[0092] The communication intelligent adaptation layer designs protocol conversion middleware through the protocol non-sensing engine to achieve automatic translation of heterogeneous protocols.

[0093] First, the original protocol data frame is parsed using an AST (Abstract Syntax Tree) for syntax analysis. Then, based on semantic matching within a knowledge graph, a pre-trained model (such as BERT) is used to implement semantic-level conversion mapping of instructions. For example, for Modbus to IEC 61850, Modbus register 40001 is mapped to IEC 61850LN:PVCM1 / W.phaseA.current. Finally, the data packet is repackaged according to the target protocol specification, and a dynamic CRC checksum is added for data reconstruction.

[0094] CRC=CRC16_CCITT(payload||timestamp);

[0095] Cross-collaborative optimization layer

[0096] A communication-energy efficiency joint optimization model was established, with a built-in communication-power dual-objective optimization function. A multi-objective optimization problem was established with the dual goals of maximizing resource utilization and minimizing system energy consumption. The improved NSGA-III algorithm was used to solve the problem:

[0097] Objective function:

[0098]

[0099] Constraints:

[0100] τ total ≤τ max ,SINR≥15dB;

[0101] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for integrated communication of differentiated resources in a substation area based on an intelligent fusion terminal, characterized in that: The following steps are involved: S1: Establish a three-dimensional resource description model that includes device type, communication protocol, and data characteristics, and uniformly model photovoltaic inverters, energy storage devices, and smart meters; S2: Dynamically calculate the communication priority weight based on the real-time status of the device and the grid operation requirements: Priority=0.6·Class+0.3·State+0.1·QoS; Where: Class∈{1,0.7,0.3} corresponds to the following equipment types: control class, monitoring class and metering class; The grid states corresponding to State∈{1,0.5,0.2} are: emergency / normal / idle; QoS is the quality of service level, including a linear mapping of levels 1 to 5; S3: Select the optimal communication channel through a hybrid decision model: S4: Adopts AST-driven architecture to achieve seamless protocol interaction, including syntax parsing layer and semantic conversion layer.

2. The method for integrated communication of differentiated resources in a substation area based on an intelligent converged terminal according to claim 1, characterized in that: The three-dimensional resource description model quantifies communication requirements through the following parameters: Delay tolerance: Bandwidth requirements: Where: η is the compression ratio, ΔD is the data increment; Dynamic Priority: ΔV represents the current voltage deviation, L critical represents the emergency load ratio, α = 0.6, β = 0.

4.

3. The method for integrated communication of differentiated resources in substations based on intelligent converged terminals according to claim 1, characterized in that: The hybrid decision model of S3 selects the optimal communication channel including the following steps: S3.1: Channel quality assessment: Real-time monitoring of each channel status, including latency, packet loss rate, and signal-to-noise ratio; S3.2: Dynamic decision-making: Using the fuzzy logic controller (FLC) to select the communication mode; S3.3: Input variable: device priority P priority , channel quality, data volume B width ; S3.4: Output variables: Communication mode weights, including 5G / HPLC / LoRa.

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