A New Energy Grid-Connected Power Quality Monitoring Method, Device, Terminal and Medium

Abstract

The present invention discloses a method, device, terminal and medium for monitoring the power quality of new energy grid connection. Among them, a method for monitoring the power quality of new energy grid connection includes: deploying dual-mode sensors at grid connection nodes to collect power quality data; adjusting the frequency of data collection according to the current operation mode of the power grid; analyzing the power quality data to identify power quality anomalies; adjusting the grid connection mode according to the analysis results of the power quality data, and managing load response. The present invention realizes the real-time and accurate acquisition of power quality data by designing dual-mode sensors. At the same time, combined with deep learning algorithms, it can more accurately identify power quality anomalies, optimize the grid connection mode and load response according to the analysis results, and enhance the adaptability of the power grid to new energy fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality monitoring, and particularly to a new energy grid-connected power quality monitoring method, device, terminal and medium. Background Art

[0002] With the acceleration of the global energy transformation, the power generation proportion of new energy, especially renewable energy such as wind energy and solar energy, has rapidly increased in the power system. However, due to the intermittency and volatility of new energy power generation, its grid connection process poses severe challenges to the stability of the power system and power quality. Traditional power quality monitoring systems mainly rely on fixed-mode sensor configurations and cannot effectively adapt to the dynamically changing new energy power generation characteristics in the power grid.

[0003] Although certain progress has been made in power quality monitoring and control, there are still some significant deficiencies. First, traditional power quality monitoring devices usually adopt single-mode sensors and cannot flexibly adjust monitoring parameters according to the dynamic changes of the power grid and the characteristics of new energy power generation. The limitations of such single-mode sensors result in the accuracy and timeliness of monitoring data being restricted when dealing with different power generation conditions and power grid operation modes, making it difficult to meet the requirements of modern power grids for real-time performance and accuracy. Second, in the face of high-frequency power quality abnormal events, traditional monitoring systems are prone to false alarms or missed alarms and cannot provide precise grid connection adjustment suggestions, thus affecting the overall efficiency of new energy grid connection and the stability of the power grid. Summary of the Invention

[0004] The purpose of the present invention is to provide a new energy grid-connected power quality monitoring method to solve the existing problems.

[0005] To solve the above technical problems, the present invention provides the following technical solutions, including: a new energy grid-connected power quality monitoring method, characterized by including: deploying dual-mode sensors at grid connection nodes to collect power quality data; adjusting the frequency of data collection according to the current operation mode of the power grid; analyzing the power quality data to identify power quality abnormalities; adjusting the grid connection mode according to the analysis results of the power quality data, and managing load response.

[0006] As a preferred solution of the new energy grid-connected power quality monitoring method described in the present invention, during grid connection operation, power quality data is collected once per second, and the power quality data includes the voltage, current, power factor and harmonics of the power grid, the stress, temperature and vibration data of grid equipment; when abnormal fluctuations are detected, the collection frequency is adjusted to 10 times per second, and the power quality data is transmitted to the central processor through an encrypted wireless network.

[0007] As a preferred solution of the new - energy grid - connected power quality monitoring method described in the present invention, the following steps are involved: Identifying power quality anomalies includes: extracting features from power quality data and converting the extracted features into a state vector, where the state vector includes voltage level, current intensity, power factor, harmonic components, and stress, temperature, and vibration values of the equipment; inputting the state vector into a DQN model to identify power quality anomalies; the DQN model includes an input layer, a hidden layer, and an output layer, the hidden layer includes two fully - connected layers, each layer has 64 neurons, and the ReLU activation function is used.

[0008] As a preferred solution of the new - energy grid - connected power quality monitoring method described in the present invention, the following steps are involved: The dual - mode sensor includes a first sensor, a second sensor, a power source, a bracket, and a shape - memory alloy housing. The first sensor includes an optical - fiber sensing element, a detector, and a light source. The second sensor includes a voltage sensor, a current sensor, and a signal processor. The first sensor and the second sensor are fixed by the bracket.

[0009] As a preferred solution of the new - energy grid - connected power quality monitoring method described in the present invention, the following steps are involved: Adjusting the grid - connection mode includes: evaluating the availability of the generator set according to the analysis result of power quality data; respectively setting decision thresholds and adjustment modes. The decision thresholds include the full - load threshold T1 of the basic generator set, the start - up threshold T2 of the standby generator set, and the extreme - load threshold T3. The adjustment modes include the basic mode M1, the enhanced mode M2, the standby mode M3, and the emergency mode M4; selecting the adjustment mode according to the decision thresholds, the real - time grid load, and the total power - generation capacity, and reconfiguring the grid topology and transferring the overloaded load to the standby line.

[0010] As a preferred solution of the new - energy grid - connected power quality monitoring method described in the present invention, the following steps are involved: Managing load response includes: charging the battery during low - load periods and releasing the stored electricity during high - load periods.

[0011] As a preferred solution of the new - energy grid - connected power quality monitoring method described in the present invention, the following steps are involved: The user interface displays the power quality data in real - time through a holographic display screen. When power quality anomalies occur, the user interface issues visual and auditory alarms, and at the same time, an analysis report pops up. The analysis report includes the cause of the anomaly, the historical trend, and provides operation suggestions.

[0012] The present invention also provides a new energy grid-connected power quality monitoring device, including: for implementing the new energy grid-connected power quality monitoring method described in any one of the above, the new energy grid-connected power quality monitoring device includes: a collection module, configured to deploy dual-mode sensors at grid connection nodes to collect power quality data; a data collection optimization module, configured to adjust the frequency of data collection according to the current operation mode of the power grid; an analysis module, configured to analyze the power quality data to identify power quality anomalies; a decision-making module, configured to adjust the grid connection mode according to the analysis result of the power quality data and manage load response; a monitoring module, configured to display the power quality data in real time through a holographic display screen. When a power quality anomaly occurs, the user interface issues visual and auditory alarms, and at the same time pops up an analysis report, which includes the cause of the anomaly, historical trends, and provides operation suggestions.

[0013] The present invention also provides a terminal device, including:

[0014] One or more processors;

[0015] A memory, coupled to the processor, for storing one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the new energy grid-connected power quality monitoring method described in any one of the above.

[0017] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the new energy grid-connected power quality monitoring method described in any one of the above.

[0018] Advantages of the present invention: The present invention realizes real-time and accurate collection of power quality data by designing dual-mode sensors. At the same time, combined with deep learning algorithms, it can more accurately identify power quality anomalies, and optimize the grid connection mode and load response according to the analysis results, enhancing the adaptability of the power grid to new energy fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0020] Figure 1 It is a schematic flowchart of the new energy grid-connected power quality monitoring method described in the first embodiment of the present invention;

[0021] Figure 2Schematic flowchart of power quality data analysis according to the first embodiment of the present invention;

[0022] Figure 3 Schematic flowchart of adjusting the grid connection mode according to the first embodiment of the present invention. Detailed implementation manners

[0023] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0025] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments.

[0026] The present invention is described in detail in conjunction with the schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience of explanation, the cross-sectional views showing the device structures will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width and depth should be included.

[0027] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner and outer" are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0028] Unless otherwise clearly specified and defined in this invention, the terms "installation, connection, and coupling" shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this invention can be understood according to specific circumstances.

[0029] Embodiment 1

[0030] Referring to Figures 1 to 3 , which is the first embodiment of the present invention. This embodiment provides a method for monitoring the power quality of new energy grid connection, including:

[0031] S1: Deploy dual-mode sensors at the grid connection nodes to collect power quality data.

[0032] The grid connection nodes can be photovoltaic inverters, wind turbines, energy storage systems, large-scale grid access points, etc., and are not limited in this embodiment.

[0033] The dual-mode sensor includes a first sensor, a second sensor, a power source, a bracket, and a shape memory alloy housing. The first sensor includes an optical fiber sensing element, a detector, and a light source. The second sensor includes a voltage sensor, a current sensor, and a signal processor. The first sensor and the second sensor are fixed by the bracket.

[0034] Specifically, the optical fiber sensing element utilizes the characteristics of light wave transmission in the optical fiber and can continuously sense the measured quantity along the length direction of the optical fiber. Among them, the cladding part of the optical fiber is etched by hydrofluoric acid solution, and the optical fiber sensing element is embedded with a microfluidic channel. The microfluidic channel introduces a colloidal solution containing magnetic nanoparticles. When there is an external magnetic field, the distribution pattern of the magnetic nanoparticles will change with the intensity and direction of the external magnetic field. The magnetic particles tend to aggregate into chains along the magnetic field direction, and the refractive index of the colloid will change accordingly.

[0035] The light source emits light of a specific wavelength, which is transmitted through the optical fiber and received by the detector. The reflected spectrum is analyzed to measure physical quantities. Preferably, the first sensor designed in this application can highly sensitively detect stress, temperature, and vibration in the power quality, thereby improving the overall measurement accuracy and response speed.

[0036] In the second sensor, the signal processor analyzes and calculates the voltage and current data collected by the voltage sensor and the current sensor to obtain harmonics and power factors.

[0037] Further, the first sensor and the second sensor are fixed by a bracket. The bracket includes a base, a column, and an adjustable fixing plate. The base is used to provide stability, and the adjustable fixing plate is installed on the column to ensure that different types of sensors can be fixed.

[0038] Preferably, the sensor housing is made of shape memory alloy, enabling the sensor to automatically adjust its sensitivity when detecting abnormal vibration or temperature, effectively reducing false alarms and optimizing measurement accuracy when the sensor environment changes. The power supply utilizes electromagnetic interference or micro-vibrations in the power grid environment to obtain electrical energy for powering the first sensor and the second sensor, reducing dependence on external power sources and making the sensors more stable and reliable during long-term operation.

[0039] S2: Adjust the frequency of data collection according to the current operating mode of the power grid.

[0040] During grid-connected operation, power quality data is collected once per second. The power quality data includes the voltage, current, power factor, and harmonics of the power grid, as well as the stress, temperature, and vibration data of grid equipment.

[0041] When abnormal fluctuations are detected, the collection frequency is adjusted to 10 times per second, and the power quality data is transmitted to the central processor via an encrypted wireless network to ensure a rapid response to power quality issues.

[0042] The data transmission process uses high-speed optical fibers or wireless networks to ensure that the collected power quality data can be quickly transmitted to the central processor for analysis.

[0043] S3: Analyze the power quality data to identify power quality anomalies.

[0044] Refer to Figure 1 , after receiving the power quality data, the central processor extracts the features of the power quality data and converts the extracted features into a state vector. The state vector includes voltage level, current intensity, power factor, harmonic components, as well as the stress, temperature, and vibration values of the equipment.

[0045] The state vector is input into the DQN (Deep Q-Network) model to identify power quality anomalies. The DQN model includes an input layer, a hidden layer, and an output layer. The hidden layer includes two fully connected layers for extracting and transforming the features of the state vector. Each layer has 64 neurons, and the ReLU activation function is used to increase the non-linear expression ability of the network. The output layer is used to output the Q value of each action, i.e., the expected return of each action under a given state.

[0046] Further, the DQN model is trained, and the specific steps are as follows:

[0047] (1) Randomly initialize the weights of the DQN model and the target DQN model;

[0048] (2) During the training process, with a certain probability ε, select a random action (exploration), otherwise select the action with the highest Q value (exploitation);

[0049] (3) Update the Q value using the Bellman equation. Calculate the target Q value as:

[0050]

[0051] where r is the reward and γ is the discount factor, represents the maximum value of the expected return that can be obtained by selecting the best action a′ in the new state s′.

[0052] (4) Update the weights of the DQN model through the loss function and backpropagation:

[0053]

[0054] where represents the Q value of taking action a in the current state s.

[0055] S4: Adjust the grid connection mode according to the power quality data analysis results and manage the load response.

[0056] Refer to Figure 3 and dynamically select the grid connection mode according to the load demand and power generation capacity. Specifically:

[0057] (1) Evaluate the availability of the generating units according to the power quality data analysis results, that is, preferentially dispatch the equipment with normal power quality data analysis results;

[0058] (2) Set the decision threshold and adjustment mode respectively. The decision thresholds include the full-load threshold T1 of the basic generating unit (set as the maximum load capacity of the basic generating unit), the start-up threshold T2 of the standby generating unit (set as 110% of the full load of the basic generating unit), and the extreme load threshold T3 (the absolute load capacity boundary). The adjustment modes include the basic mode M1, the enhanced mode M2, the standby mode M3, and the emergency mode M4;

[0059] (3) Select the adjustment mode according to the decision threshold, the real-time grid load, and the total power generation capacity, and reconfigure the grid topology and transfer the overloaded load to the standby line.

[0060] Basic mode M1:

[0061] Condition: Real-time load ≤ T1.

[0062] Action: The basic generating set is operating normally, and there is no need to start the standby generating set.

[0063] Monitoring: Monitor the load changes to ensure that the load is within the carrying capacity of the basic generating set.

[0064] Enhanced mode M2:

[0065] Condition: T1 < real-time load ≤ T2.

[0066] Action: Start one or more of the standby generating sets to meet the increased load demand.

[0067] Action details: The priority of starting the standby generating sets should be based on their start-up time and power generation efficiency.

[0068] Standby mode M3:

[0069] Condition: T2 < real-time load ≤ total power generation capacity.

[0070] Action: Start all available standby generating sets.

[0071] Action details: While ensuring the load demand, optimize the start-up sequence of the generating sets to reduce fuel consumption and operating costs.

[0072] Emergency mode M4:

[0073] Condition: Real-time load > total power generation capacity.

[0074] Action: Start the emergency standby generating set and consider external measures (such as load demand response, load shedding, etc.).

[0075] Action details: Communicate with the dispatcher, implement load dispatching and demand response strategies to ensure the stability of power supply.

[0076] Furthermore, reconfigure the grid topology and transfer the overloaded load to the standby line.

[0077] In this embodiment, the grid topology configuration is taken as an example of a certain power grid system. This power grid system includes multiple power generation stations, substations and distribution lines. The topology structure of the power grid is determined by multiple switching devices (such as circuit breakers and transfer switches). s

[0078] Specifically, the configuration of the power grid system is as follows:

[0079] Distribution line: Line A: The main power supply line, connecting Power Station 1 to Substation 1; Line B: The standby line, connecting Power Station 2 to Substation 2; Line C: The line connecting Substation 1 and Substation 2. When the load of Line A decreases, part of the load is taken over by the standby Line B and Line C to maintain the normal operation of the power grid.

[0080] Power stations: Power Station 1, Power Station 2.

[0081] Substations: Substation 1, Substation 2.

[0082] It is detected that the power supply line (Line A) is overloaded under high load conditions, and the loads of Line B and Line C are still within the safe range.

[0083] Treatment measures: Disconnect the connection between Substation 1 and Line A, connect Substation 1 to the standby Line B, and adjust the load distribution of Line C to reduce the load on Line A. That is, the power grid system sends the following execution instructions to control the switchgear to execute according to the instructions:

[0084] Switch-off instruction: Disconnect the connection between Substation 1 and Line A;

[0085] Switch-on instruction: Transfer the load to Substation 1 through Line B;

[0086] Switch-adjustment instruction: Adjust the load distribution of Line C to reduce the load on Line A.

[0087] The reconfigured power grid topology becomes: Power Station 1 is connected to Substation 1 through Line C; Substation 1 is connected to Power Station 2 through Line B; Substation 2 is connected to Substation 1 through Line C. The load of Line A decreases, and part of the load is taken over by the standby Line B and Line C to maintain the normal operation of the power grid.

[0088] Furthermore, load response management is carried out, including charging the battery during low load periods and releasing the stored electrical energy during high load periods.

[0089] S5: The user interface displays the power quality data in real time through a holographic display screen. When the power quality is abnormal, the user interface issues visual and audible alarms, and at the same time pops up an analysis report, including the cause of the abnormality, historical trends, and provides operation suggestions.

[0090] According to the problem type and analysis results, the user interface provides a variety of operation suggestions. For example, adjusting the grid connection mode, managing load response, performing equipment maintenance, etc., and each suggested operation is accompanied by a concise step guide.

[0091] Embodiment 2

[0092] Taking a certain power grid as an example in this embodiment, the power quality data is analyzed. The power quality data is input into the central processor of Embodiment 1, and the power quality anomalies are identified through the DQN model:

[0093] Install multiple bimodal sensors at a certain grid connection node, and the collected historical power quality data is as follows:

[0094] Voltage (V): 230V, 232V, 228V, 234V, 220V

[0095] Current (A): 10.1A, 10.2A, 9.9A, 10.3A, 10.5A

[0096] Power factor: 0.98, 0.97, 0.96, 0.95, 0.94

[0097] Harmonic content (%): 2.8%, 3.0%, 3.2%, 3.1%, 3.3%

[0098] Equipment stress: 80, 85, 90, 88, 92

[0099] Temperature (°C): 65, 66, 67, 68, 70

[0100] Vibration (mm / s): 0.1, 0.12, 0.13, 0.11, 0.14

[0101] Furthermore, preprocess the power quality data: remove outliers and noise; standardize the power quality data for input into the DQN model.

[0102] Furthermore, define the state space and action space;

[0103] (1) Define the state space; one of the state vectors is as follows:

[0104] State = [0.95, 0.98, 0.96, 3.1, 0.90, 0.65, 0.12]

[0105] Each value represents the standardized voltage, current, power factor, harmonic content, equipment stress, temperature, and vibration.

[0106] (2) Define the action space:

[0107] Action 1: Issue a warning and conduct equipment inspection;

[0108] Action 2: Adjust the grid load to stabilize the voltage;

[0109] Action 3: Enable standby equipment;

[0110] Action 4: Conduct a system check.

[0111] Furthermore, collect real-time data:

[0112] Voltage: 220V

[0113] Current: 10.5A

[0114] Power factor: 0.94

[0115] Harmonic content: 3.3%

[0116] Device stress: 92

[0117] Temperature: 72°C

[0118] Vibration: 0.15 mm / s

[0119] Convert the real-time grid data into a state vector: State = [0.90, 1.02, 0.94, 3.3, 0.92, 0.72, 0.15]

[0120] Input the state vector into the DQN model. The DQN model identifies abnormal features such as voltage drop, current increase, power factor reduction, harmonic content increase, device stress, and temperature increase by comparing the current state with the normal patterns in historical data. Its output Q-values for each action are [3.2, 2.5, 1.8, 2.0]. Select the action with the highest Q-value, which is Action 1, indicating that there is an abnormal power quality in the grid, and issue a warning to notify the staff for inspection.

[0121] 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.

[0122] Embodiment 3

[0123] This provides a new energy grid-connected power quality monitoring device for implementing the steps of the new energy grid-connected power quality monitoring method in any of the above embodiments. The new energy grid-connected power quality monitoring device includes:

[0124] A collection module for deploying bimodal sensors at grid connection nodes to collect power quality data;

[0125] A data collection optimization module for adjusting the data collection frequency according to the current operation mode of the power grid;

[0126] An analysis module for analyzing the power quality data to identify power quality abnormalities;

[0127] A decision-making module for adjusting the grid connection mode according to the analysis results of the power quality data and managing load response.

[0128] A monitoring module, which is used to display power quality data in real time through a holographic display screen. When the power quality is abnormal, the user interface issues visual and auditory alarms, and at the same time pops up an analysis report. The analysis report includes the cause of the abnormality, historical trends, and provides operation suggestions.

[0129] Embodiment 4

[0130] This embodiment provides a terminal device, including:

[0131] One or more processors;

[0132] A memory, coupled to the processor, for storing one or more programs;

[0133] When the one or more programs are executed by the one or more processors, the one or more processors implement the new energy grid-connected power quality monitoring method as described above.

[0134] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the new energy grid-connected power quality monitoring method described above. The memory is used to store various types of data to support the operation of the terminal device. These data may include, for example, instructions for any application program or method operating on the terminal device, and application program-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Read-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0135] The terminal device can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the new energy grid-connected power quality monitoring method described in any one of the above embodiments, and achieve the same technical effects as those of the above method.

[0136] Embodiment 5

[0137] This embodiment provides a computer-readable storage medium. When the program instructions are executed by a processor, the steps of the new energy grid-connected power quality monitoring method described in any one of the above embodiments are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including program instructions, and the above program instructions can be executed by the processor of the terminal device to complete the new energy grid-connected power quality monitoring method described in any one of the above embodiments, and achieve the same technical effects as those of the above method.

[0138] It should be recognized that the embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method can be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes the computer to operate in a specific and predefined manner according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can run on a programmed application specific integrated circuit.

[0139] In addition, the operations of the processes described herein may be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or by a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.

[0140] Further, the method may be implemented in any type of computing platform operably connected to a suitable one, including but not limited to personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms, or communicating with charged particle tools or other imaging devices, etc. Aspects of the present invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into the computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer and, when read by the computer, can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, may be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the above-described steps in conjunction with a microprocessor or other data processor, the inventions described herein include these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques described in the present invention, the present invention also includes the computer itself. The computer program is capable of applying to input data to perform the functions described herein, thereby transforming the input data to generate output data stored in non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the transformed data represents physical and tangible objects, including a specific visual depiction of the physical and tangible objects generated on the display.

[0141] As used in this application, the terms "component", "module", "system", etc. are intended to refer to computer-related entities, which can be hardware, firmware, a combination of hardware and software, software, or software in operation. For example, a component can be, but is not limited to: a process running on a processor, a processor, an object, an executable file, a thread in execution, a program, and / or a computer. As an example, an application running on a computing device and the computing device can both be components. One or more components can exist in a process and / or thread in execution, and a component can be located in one computer and / or distributed between two or more computers. In addition, these components can execute from various computer-readable media having various data structures thereon. These components can communicate in a local and / or remote procedure manner through signals such as according to one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or communicates with other systems via a network such as the Internet in a signal manner).

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limitations. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for monitoring the power quality of a new energy grid-connected system, characterized in that: include: Deploy dual-modal sensors in grid-connected nodes to collect power quality data. During grid-connected operation, power quality data is collected once per second. This data includes grid voltage, current, power factor and harmonics, as well as stress, temperature, and vibration data of grid equipment. Adjust the frequency of data collection based on the current grid operation mode; Analyze power quality data and identify power quality anomalies; Adjust the grid connection mode and manage load response based on the power quality data analysis results; The dual-mode sensor includes a first sensor, a second sensor, a power supply, a bracket, and a shape memory alloy shell. The first sensor includes an optical fiber sensing element, a detector, and a light source. The second sensor includes a voltage sensor, a current sensor, and a signal processor. The first sensor and the second sensor are fixed by a bracket. In the optical fiber sensing element, the cladding portion of the optical fiber is etched by a hydrofluoric acid solution, and the optical fiber sensing element is embedded with a microfluidic channel. The microfluidic channel introduces a colloidal solution containing magnetic nanoparticles. When an external magnetic field is applied, the distribution pattern of the magnetic nanoparticles changes with the strength and direction of the external magnetic field. The magnetic nanoparticles tend to aggregate into chains along the direction of the magnetic field, and the refractive index of the colloid changes accordingly. The light source emits light of a specific wavelength, which is transmitted through the optical fiber, and the reflected spectrum is received by the detector. In the second sensor, the signal processor analyzes and calculates the voltage and current data collected by the voltage sensor and the current sensor to obtain harmonics and power factor. Furthermore, the first sensor and the second sensor are fixed by a bracket. The bracket includes a base, a column, and an adjustable fixing plate. The base is used to provide stability, and the adjustable fixing plate is mounted on the column. Features are extracted from power quality data and converted into a state vector, which includes voltage level, current intensity, power factor, harmonic components, and stress, temperature, and vibration values of the equipment. The state vector is input into a DQN model to identify power quality anomalies. The DQN model includes an input layer, a hidden layer, and an output layer. The hidden layer includes two fully connected layers, each with 64 neurons, using a ReLU activation function. Among them, the availability of the generator set is evaluated based on the power quality data analysis results, and the equipment with normal power quality data analysis results is dispatched first; Decision thresholds and adjustment modes are set separately. The decision thresholds include the basic generator set full load threshold T1, the standby generator set activation threshold T2, and the extreme load threshold T3. The basic generator set full load threshold T1 is the maximum load capacity of the basic generator set, the standby generator set activation threshold T2 is 110% of the basic generator set full load, and the extreme load threshold T3 is the absolute load capacity boundary. The adjustment modes include basic mode M1, enhanced mode M2, standby mode M3, and emergency mode M4. Select adjustment mode based on decision thresholds, real-time grid load and total generation capacity, as well as reconfigure grid topology and transfer excess load to backup lines; Basic Mode M1: Condition: real-time load ≤ T1; Action: The basic generator set operates normally and there is no need to activate the standby generator set; Enhanced Mode M2: Condition: T1 < real-time load ≤ T2; Action: Start one or more of the standby generator sets to meet the increased load demand; Alternate Mode M3: Condition: T2 < real-time load ≤ total power generation capacity; Action: Enable all available standby generator sets; Emergency Mode M4: Condition: real-time load > total power generation capacity; Action: Start the emergency standby generator set and consider external measures, including load demand response and load reduction.

2. The method for monitoring the power quality of a new energy grid-connected system according to claim 1, wherein: include: When abnormal fluctuations are detected, the acquisition frequency is adjusted to 10 times per second, and the power quality data is transmitted to the central processor via an encrypted wireless network.

3. The method for monitoring the power quality of a new energy grid-connected system according to claim 2, wherein: Managing load response includes: The battery is charged during low-load periods and the stored energy is released during high-load periods.

4. The method for monitoring the quality of power of a new energy grid-connected system according to claim 3, wherein: include: The user interface displays power quality data in real time through a holographic display screen. When the power quality is abnormal, the user interface issues visual and auditory alarms, and an analysis report pops up. The analysis report contains the cause of the abnormality, historical trends, and provides operational suggestions.

5. A new energy grid-connected power quality monitoring device, used to implement the new energy grid-connected power quality monitoring method according to any one of claims 1 to 4, characterized in that: The new energy grid-connected power quality monitoring device includes: The acquisition module is used to deploy dual-modal sensors in grid-connected nodes to collect power quality data; Data collection optimization module, used to adjust the frequency of data collection according to the current operation mode of the power grid; Analysis module, used to analyze power quality data and identify power quality anomalies; A decision-making module, used to adjust the grid connection mode and manage load response based on the results of power quality data analysis; The monitoring module is used to display power quality data in real time through a holographic display screen. When the power quality is abnormal, the user interface will issue visual and auditory alarms, and an analysis report will pop up at the same time. The analysis report contains the cause of the abnormality, historical trends, and provides operational suggestions.

6. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the new energy grid-connected power quality monitoring method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the new energy grid-connected power quality monitoring method according to any one of claims 1 to 4.

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