Harbor district power plant equipment health state assessment method and device

Abstract

The invention provides a harbor district power plant equipment health state assessment method and device, and the method comprises the steps: obtaining equipment voltage, photovoltaic panel temperature and inverter current data, calculating a voltage fluctuation rate, a temperature gradient and a harmonic distortion rate, and calculating an equipment influence coefficient to assess the health state. According to the method, multi-source data is collected in real time, the health state is evaluated, scheduling is dynamically optimized, the economical efficiency, the low-carbon performance and the reliability of the virtual power plant are improved, and the data processing and scheduling optimization capacity is enhanced.

Description

Technical Field

[0001] The present application relates to the field of port area equipment assessment, and in particular to a method and device for assessing the health status of power plant equipment in a port area. Background Art

[0002] With the rapid development of the energy industry and the continuous advancement of smart grid technology, port power plants, as important nodes of energy supply, have a direct impact on the stability and economic efficiency of the entire energy system due to their operating efficiency and equipment health. However, in traditional port power plant equipment management, the assessment of equipment health often relies on regular manual inspections and offline data analysis. This method is not only time-consuming and labor-intensive, but also difficult to reflect the actual operating status of the equipment in real time, resulting in potential equipment failures that cannot be discovered and handled in a timely manner, which may lead to greater safety accidents and economic losses. Specifically, traditional methods face the following challenges when assessing the health status of port power plant equipment:

[0003] First, the data collection is not real-time and comprehensive enough.

[0004] Traditional methods often rely on manual inspections or fixed data acquisition systems, which struggle to obtain real-time equipment operating data. In particular, these systems have limited real-time monitoring capabilities for key parameters such as voltage fluctuations, temperature variations, and harmonic distortion. This results in an inability to capture relevant signals when equipment anomalies occur, delaying fault diagnosis and resolution.

[0005] Second, there are limitations to the data analysis methods.

[0006] Traditional methods for analyzing equipment data often rely on simple statistical methods or empirical judgments. These methods fail to accurately reflect the complex operating conditions and potential failure risks of equipment. This is particularly true for analyzing complex electrical parameters such as high-frequency harmonic distortion, where traditional methods are insufficient to provide effective equipment health assessments.

[0007] Finally, there is a lack of comprehensive health status assessment indicators.

[0008] Traditional methods often rely on monitoring results from a single or a few parameters, lacking comprehensive evaluation metrics. This approach struggles to fully reflect the overall health of the equipment and potential failure risks, leading to biased and inaccurate assessment results. Summary of the Invention

[0009] The purpose of this application is to overcome the defects in the above-mentioned prior art and provide a method and device for evaluating the health status of power plant equipment in a port area.

[0010] This application provides a method for evaluating the health status of power plant equipment in a port area, including:

[0011] Obtaining voltage data of power plant equipment in the port area, surface temperature distribution data of photovoltaic panels, and output current data of inverters in the port area power plant;

[0012] Calculating the voltage fluctuation rate of the port area power plant equipment based on the voltage data of the port area power plant equipment;

[0013] Calculating the surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area;

[0014] Performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result;

[0015] The port area power plant equipment impact coefficient is calculated based on the voltage fluctuation rate of the port area power plant equipment, the surface temperature gradient of the port area photovoltaic panel and the high-frequency harmonic distortion rate of the port area power plant, and is used to evaluate the health status of the port area power plant equipment.

[0016] Optionally, obtaining the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant includes:

[0017] The voltage data of the port power plant equipment is collected through a customized agent embedded in the inverter.

[0018] Optionally, obtaining the port area power plant equipment voltage data, photovoltaic panel surface temperature distribution data, and port area power plant inverter output current data, and obtaining the port area photovoltaic panel surface temperature distribution data includes:

[0019] The surface temperature distribution data of the photovoltaic panels in the port area is collected by scanning the surface of the photovoltaic panels with an infrared thermal imager.

[0020] Optionally, performing FFT analysis based on the output current data of the inverter of the port power plant includes:

[0021] FFT analysis is performed on the output current data of the inverter of the port power plant to extract the harmonic characteristics of the preset frequency band.

[0022] Optionally, the port power plant equipment impact coefficient is calculated based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel, and the high-frequency harmonic distortion rate of the port power plant, for evaluating the health status of the port power plant equipment, including:

[0023] When the influence coefficient of the port power plant equipment is less than a first threshold, determining that the health status of the port power plant equipment is good;

[0024] When the influence coefficient of the port power plant equipment is greater than or equal to a first threshold and less than a second threshold, determining that the health status of the port power plant equipment is an aging risk;

[0025] When the influence coefficient of the port power plant equipment is greater than or equal to a second threshold, it is determined that the health status of the port power plant equipment is that shutdown for maintenance is required.

[0026] The present application also provides a device for evaluating the health status of power plant equipment in a port area, comprising:

[0027] Acquisition module, which obtains the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant;

[0028] A voltage module, which calculates the voltage fluctuation rate of the port area power plant equipment based on the voltage data of the port area power plant equipment;

[0029] a temperature module for calculating a surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area;

[0030] An analysis module, performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result;

[0031] The calculation module calculates the impact coefficient of the port power plant equipment based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel and the high-frequency harmonic distortion rate of the port power plant, so as to evaluate the health status of the port power plant equipment.

[0032] Optionally, the acquisition module acquires the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant, and acquires the voltage data of the port power plant equipment, including:

[0033] The voltage data of the port power plant equipment is collected through a customized agent embedded in the inverter.

[0034] Optionally, the acquisition module acquires the port area power plant equipment voltage data, the photovoltaic panel surface temperature distribution data, and the port area power plant inverter output current data, and acquires the port area photovoltaic panel surface temperature distribution data, including:

[0035] The surface temperature distribution data of the photovoltaic panels in the port area is collected by scanning the surface of the photovoltaic panels with an infrared thermal imager.

[0036] Optionally, the analysis module performs FFT analysis based on the output current data of the inverter of the port power plant, including:

[0037] FFT analysis is performed on the output current data of the inverter of the port power plant to extract the harmonic characteristics of the preset frequency band.

[0038] Optionally, the calculation module calculates the port power plant equipment impact coefficient based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel, and the high-frequency harmonic distortion rate of the port power plant, for evaluating the health status of the port power plant equipment, including:

[0039] When the influence coefficient of the port power plant equipment is less than a first threshold, determining that the health status of the port power plant equipment is good;

[0040] When the influence coefficient of the port power plant equipment is greater than or equal to a first threshold and less than a second threshold, determining that the health status of the port power plant equipment is an aging risk;

[0041] When the influence coefficient of the port power plant equipment is greater than or equal to a second threshold, it is determined that the health status of the port power plant equipment is that shutdown for maintenance is required.

[0042] The beneficial effects of this application are:

[0043] The present application provides a method for evaluating the health status of port area power plant equipment, including: obtaining port area power plant equipment voltage data, photovoltaic panel surface temperature distribution data, and port area power plant inverter output current data; calculating the port area power plant equipment voltage fluctuation rate based on the port area power plant equipment voltage data; calculating the port area photovoltaic panel surface temperature gradient based on the port area photovoltaic panel surface temperature distribution data; performing FFT analysis based on the port area power plant inverter output current data, and calculating the port area power plant high-frequency harmonic distortion rate based on the FFT analysis result; calculating the port area power plant equipment influence coefficient based on the port area power plant equipment voltage fluctuation rate, the port area photovoltaic panel surface temperature gradient, and the port area power plant high-frequency harmonic distortion rate, for evaluating the health status of port area power plant equipment. The present application collects multi-source data in real time, calculates the equipment influence coefficient to evaluate the health status, and dynamically optimizes the scheduling strategy to achieve the coordinated improvement of the economy, low carbon and reliability of the port area virtual power plant, and improves data processing and scheduling optimization capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the health status assessment process of the power plant equipment in the port area of ​​this application. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementation of the present disclosure are not limited to the embodiments set forth herein. Rather, the embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0046] The present application provides a method and device for evaluating the health status of power plant equipment in a port area.

[0047] The implementation of this application requires the construction of a port area virtual power plant system that includes an energy equipment layer, a load management layer, an environmental perception layer, and a power grid interaction layer. By using full-stack point-of-sale technology, a data acquisition module is embedded in multi-protocol proxy devices, combined with GIS dynamic grid division and a layered deep reinforcement learning framework to achieve scheduling optimization. By simultaneously analyzing and processing four sets of core data, the system supports the real-time fusion and self-repair of multi-source heterogeneous data, improves data processing capabilities, and achieves the coordinated optimization of economy, low carbon, and reliability while ensuring equipment safety.

[0048] Please refer to Figure 1 As shown in the figure, a method for evaluating the health status of power plant equipment in a port area is implemented in the following specific steps:

[0049] S101: Acquire voltage data of the port power plant equipment, surface temperature distribution data of photovoltaic panels, and output current data of inverters of the port power plant;

[0050] In the port area virtual power plant system, core equipment data is obtained through the following methods:

[0051] Voltage data of power plant equipment in the port area: By embedding a customized agent in the inverter controller, covering the silicon-level to system-level data link, the device voltage sampling values ​​are collected in real time. ,in is the number of sampling points.

[0052] The customized agent ensures voltage acquisition accuracy and supports multi-protocol agent functions.

[0053] Photovoltaic panel surface temperature distribution data: Use an infrared thermal imager to scan the photovoltaic panel surface and obtain the maximum temperature and minimum temperature The infrared thermal imager captures the temperature distribution at high resolution, providing accurate input for subsequent temperature gradient calculations.

[0054] Inverter output current data: A customized agent is used to collect the original waveform data of the inverter output current. This data will be used for high-frequency harmonic characteristic analysis.

[0055] System complete data collection and area division:

[0056] In this embodiment, it is specifically necessary to explain that the multi-source data collection step implants a multi-protocol agent in the port area virtual power plant equipment through full-stack point embedding technology, collects the internal data of the target port area virtual power plant in real time, constructs a multi-source heterogeneous spatiotemporal feature matrix, and enhances data robustness by injecting noise and fault-tolerant interpolation, and finally forms a port area virtual power plant fault-tolerant scheduling training data set that supports deep learning-driven.

[0057] The port area virtual power plant fault-tolerant scheduling training data set includes energy equipment layer data, load management layer data, environmental perception layer data and power grid interaction layer data.

[0058] The energy equipment layer data includes power plant equipment data influencing parameters, energy storage equipment data influencing parameters and shore power equipment data influencing parameters.

[0059] The load management layer data includes the influencing parameters of container crane data and the influencing parameters of cold chain warehouse data.

[0060] The environmental perception layer data includes the influencing parameters of meteorological data and the influencing parameters of ship berthing behavior data.

[0061] The grid interaction layer data includes the influencing parameters of electricity price data and the influencing parameters of grid emergency data.

[0062] During the data acquisition process: Based on the CAN bus of the BMS system, the data frame is analyzed in real time, and the self-discharge current of the single battery is obtained through the self-discharge current of the single battery to obtain the leakage current intensity of the energy storage device. , based on the Coulomb counting method, the capacity attenuation index SOH of the energy storage device is dynamically calculated, and a three-axis acceleration sensor is installed in the battery cabinet to calculate the mechanical vibration entropy value of the energy storage device. .

[0063] Count contactor operation times and arc energy , calculate the wear coefficient of shore power pile ,Then the optical fiber temperature measurement system is used to collect the cable joint temperature time series data and calculate the shore power pile temperature rise rate dT / dt.

[0064] An eBPF probe is implanted in the PLC controller to capture the motor drive signal and mechanical status, record the power change gradient at the moment the motor starts, and obtain the power ramp rate dP / dt.

[0065] Temperature sensors are deployed in each cold storage partition to collect the cold chain warehouse load rate η, and the cold chain warehouse temperature deviation index TDI is calculated to dynamically quantify the cold chain stability.

[0066] Micro-meteorological stations were deployed and synchronized with the WRF model to generate 1 km × 1 km gridded forecast data. The standard deviation of irradiance variation in the next 15 minutes was calculated to obtain the irradiance fluctuation rate ΔI.

[0067] Identify cloud speed v and cloud cover using sky camera images.

[0068] The ship's MMSI code is intercepted and associated with the historical power consumption pattern library, and the berthing time deviation Db is calculated based on the difference between the actual berthing time and the planned time.

[0069] Connect to the power trading platform API, collect node marginal electricity prices and frequency modulation signals in real time, calculate the Shannon entropy of electricity price series based on sliding windows, quantify market uncertainty, and obtain electricity price fluctuation entropy. .

[0070] The protocol of demand response instructions is parsed to extract the load reduction amount ΔL, and the weight of the dispatch model reward function is adjusted according to the urgency of the power grid.

[0071] In this embodiment, the region division step, based on GIS port coordinates and equipment location data, divides the port area into A dynamic grid cells. Key nodes are anchored to the grid boundaries to establish a bidirectional mapping between grids and equipment. Each grid cell is associated with the internal data of the target port virtual power plant. A spatiotemporal encoder is introduced to generate B grid feature vectors, forming a spatiotemporal feature matrix that supports multi-source heterogeneous data processing.

[0072] S102: Calculating the voltage fluctuation rate of the port power plant equipment based on the voltage data of the port power plant equipment;

[0073] Based on the collected voltage data, the voltage fluctuation rate is calculated according to the following formula:

[0074]

[0075] This indicator reflects the voltage stability of the equipment, represents the voltage value of the i-th sampling, Represents the average voltage within the sampling window, Indicates the number of sampling points.

[0076] Calculation of complete system parameters:

[0077] In this embodiment, it should be specifically explained that the indicator calculation step establishes a mathematical model based on the collected data to calculate the indicators, obtains the energy equipment layer data influence coefficient, the load management layer data influence coefficient, the environmental perception layer data influence coefficient and the power grid interaction layer data influence coefficient, and constructs four types of core features.

[0078] Specific parameter calculations include:

[0079] Energy storage equipment capacity attenuation index calculation formula:

[0080]

[0081] in Indicates the actual available capacity of the battery. Indicates the nominal capacity of the battery.

[0082] The calculation formula of mechanical vibration entropy value of energy storage equipment is:

[0083]

[0084] in Indicates the energy ratio of the frequency band. Indicates the number of frequency bands.

[0085] Calculation formula for shore power pile wear coefficient:

[0086]

[0087] in Indicates the number of contactor operations. represents the kth arc energy, represents the kth arc duration.

[0088] Calculation formula for shore power pile temperature rise rate:

[0089]

[0090] Where T(t) represents the temperature at time t, and Δt represents the sampling interval.

[0091] Calculation formula for the temperature deviation index of cold chain warehouses:

[0092]

[0093] in Indicates the actual temperature, Indicates the target temperature, Indicates the allowable deviation.

[0094] Irradiance fluctuation rate calculation: calculate the standard deviation of irradiance changes in the next 15 minutes;

[0095] Calculation of electricity price fluctuation entropy: Calculate the Shannon entropy of electricity price series based on a sliding window to quantify market uncertainty;

[0096] Load reduction extraction: Protocol parsing for demand response instructions.

[0097] S103: Calculating the surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area;

[0098] Based on the surface temperature distribution data of the photovoltaic panel collected by the infrared thermal imager, the temperature gradient is calculated according to the following formula:

[0099]

[0100] in Indicates the maximum temperature of the photovoltaic panel surface. Represents the minimum surface temperature of a photovoltaic panel. This metric quantifies the thermal stress distribution of the panel and is a key input parameter for device aging assessment.

[0101] Energy equipment layer impact coefficient calculation: By combining the above formula with historical data, analyze equipment aging trends and accurately predict maintenance

[0102] In this embodiment, it should be specifically noted that the energy equipment layer data impact coefficient is a product of the power plant equipment impact coefficient, the energy storage equipment impact coefficient, and the shore power equipment impact coefficient to comprehensively reflect the overall health and risk status of the energy equipment layer. This is used to comprehensively assess the overall health and risk status of the energy equipment layer, determine scheduling priorities, provide quantitative indicators of the degree of impact of the energy equipment layer, and support dynamic adjustment of scheduling strategies:

[0103]

[0104] Wherein: The calculation formula of the influence coefficient of energy storage equipment is:

[0105]

[0106] This coefficient introduces the sine function and square root to enhance the modeling of the nonlinear relationship between leakage current and capacity attenuation, while smoothing the influence of vibration entropy.

[0107] Calculation formula for the impact coefficient of shore power equipment:

[0108]

[0109] in Represents the wear threshold of the shore power pile. This coefficient introduces the hyperbolic tangent function and the square term to enhance the modeling of the nonlinear relationship between wear and temperature rise rate.

[0110] S104: performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result;

[0111] Perform FFT analysis on the inverter output current data:

[0112] Perform FFT analysis on the inverter output current, extract the harmonic characteristics of the 2kHz-10kHz frequency band, and calculate the harmonic distortion rate (THD%) of this frequency band.

[0113] Mark the main resonant peak frequency:

[0114] This step accurately captures the aging characteristics of the equipment through frequency domain analysis, providing key indicators for equipment health status assessment.

[0115] Calculation of system complete impact coefficient:

[0116] In this embodiment, it is specifically necessary to explain that:

[0117] The calculation formula of the load management layer data impact coefficient is:

[0118]

[0119] in Indicates the maximum power climbing rate of the crane, It represents the minimum power climbing rate of the crane, and Pr represents the rated power of the crane.

[0120] This coefficient introduces the sine function and square root to enhance the modeling of the nonlinear relationship between power fluctuation and temperature control stability.

[0121] The calculation formula of the environmental perception layer data impact coefficient is:

[0122]

[0123] This coefficient introduces a hyperbolic tangent function and a square term to enhance the modeling of nonlinear relationships of irradiance fluctuations.

[0124] The calculation formula of the power grid interaction layer data impact coefficient is:

[0125]

[0126] Among them, ε represents a dynamic parameter that quantifies the real-time urgency of the power grid and is used to reflect the stability risk of the current operating state of the power grid.

[0127] This coefficient introduces logarithmic and exponential functions to enhance the modeling of the nonlinear relationship between electricity price fluctuations and demand response weights.

[0128] S105: Calculating the impact coefficient of the port power plant equipment based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel and the high-frequency harmonic distortion rate of the port power plant, for evaluating the health status of the port power plant equipment.

[0129] Calculate the influence coefficient of power plant equipment:

[0130]

[0131] This coefficient reflects the comprehensive health status of the equipment by calculating the product of voltage fluctuation rate, temperature gradient and harmonic distortion rate, and introduces logarithmic and exponential functions to enhance the modeling of the nonlinear relationship between voltage fluctuation and temperature gradient.

[0132] Health status classification:

[0133] When α<0.5: the equipment is in good health and operates according to the planned output.

[0134] When 0.5≤α<1.0: There is a risk of equipment aging, and it is necessary to limit output and enable energy storage to smooth fluctuations.

[0135] When α≥1.0: the equipment is in poor health and needs to be shut down for maintenance.

[0136] Complete scheduling strategy optimization:

[0137] In this embodiment, it should be specifically explained that the scheduling strategy optimization step calculates the comprehensive scheduling index based on the obtained energy equipment layer data influence coefficient, load management layer data influence coefficient, environmental perception layer data influence coefficient and power grid interaction layer data influence coefficient, determines the scheduling optimization strategy priority, and then performs hierarchical scheduling optimization according to the hierarchical scheduling optimization method.

[0138] Specifically include:

[0139] Comprehensive scheduling index calculation formula:

[0140]

[0141] Scheduling optimization priority decision:

[0142] When K < 0.5: the scheduling priority is high, and the following strategy is implemented:

[0143] Limiting photovoltaic / wind power output and enabling energy storage to smooth fluctuations;

[0144] Prioritize power supply for critical loads such as container cranes;

[0145] Suspend electricity market transactions to prioritize local demand;

[0146] Scheduling optimization is performed according to the priorities of energy equipment layer > load management layer > environmental perception layer > grid interaction layer.

[0147] When 0.5≤K<1.0: the scheduling priority is medium, and the following policy is implemented:

[0148] Balance economy and reliability, and deliver output as planned;

[0149] Dynamically adjust energy storage charging and discharging strategies;

[0150] Participate in electricity market transactions and give priority to responding to high-yield demands;

[0151] Monitor load fluctuations and activate energy storage to smooth them out when appropriate;

[0152] Scheduling optimization is performed according to the priority of load management layer > grid interaction layer > energy equipment layer > environmental perception layer.

[0153] When K ≥ 1.0: The scheduling priority is low and the following policy is implemented:

[0154] Maximize economic benefits and maximize photovoltaic / wind power output;

[0155] Actively participate in electricity market transactions and enable demand response;

[0156] Give priority to responding to high compensation instructions and dynamically adjust load distribution;

[0157] Perform scheduling optimization according to the grid interaction layer > energy equipment layer > environmental perception layer > load management layer.

[0158] Layered optimization strategy:

[0159] In this embodiment, it should be specifically explained that the hierarchical scheduling optimization method includes:

[0160] Energy equipment layer optimization strategy:

[0161] When Ce<0.5: the equipment is in good health and outputs as planned;

[0162] When 0.5≤Ce<1.0: There is a risk of equipment aging, output is limited, and energy storage smoothing is enabled;

[0163] When Ce≥1.0: The equipment is in poor health and needs to be shut down for maintenance.

[0164] Load management layer optimization strategy:

[0165] When Cl<0.5: load stability is high and power is supplied as planned;

[0166] When 0.5≤Cl<1.0: there is a risk of load fluctuation, and energy storage is used to smooth the load;

[0167] When Cl ≥ 1.0: the load fluctuates greatly, and priority is given to ensuring power supply to critical loads such as container cranes.

[0168] Environmental perception layer optimization strategy:

[0169] When Cen<0.5: environmental uncertainty is low, scheduling is carried out according to plan;

[0170] When 0.5≤Cen<1.0: There is a risk of environmental fluctuation, and the output plan is adjusted dynamically;

[0171] When Cen ≥ 1.0: The environmental uncertainty is high and the backup power supply is enabled.

[0172] Grid interaction layer optimization strategy:

[0173] When Cg<0.5: the grid is highly stable and has priority in participating in power market transactions;

[0174] When 0.5≤Cg<1.0: There is a risk of grid fluctuations, so be cautious when participating in demand response.

[0175] When Cg≥1.0: The power grid fluctuates greatly, market transactions are suspended and power supply is prioritized.

[0176] In this embodiment, it is important to note that the data exchange step transmits the dispatch optimization strategy, comprehensive dispatch index, and internal data of the target port virtual power plant to the user data terminal, providing reference data for users to make adjustments. This method, through layered deep reinforcement learning and multi-objective dynamic weight adjustment, achieves coordinated optimization of economy, low carbon efficiency, and reliability while ensuring equipment safety, thereby improving the power plant's dispatch strategy optimization capabilities.

[0177] This application adopts full-stack tracking technology and dynamic grid management. By simultaneously analyzing and processing four sets of core data (energy equipment layer, load management layer, environmental perception layer, and power grid interaction layer), it supports real-time fusion and self-repair of multi-source heterogeneous data, significantly improving data processing capabilities.

[0178] The present application also provides a device for evaluating the health status of power plant equipment in a port area, comprising:

[0179] Acquisition module, which obtains the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant;

[0180] A voltage module, which calculates the voltage fluctuation rate of the port area power plant equipment based on the voltage data of the port area power plant equipment;

[0181] a temperature module for calculating a surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area;

[0182] An analysis module, performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result;

[0183] The calculation module calculates the impact coefficient of the port power plant equipment based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel and the high-frequency harmonic distortion rate of the port power plant, so as to evaluate the health status of the port power plant equipment.

[0184] Optionally, the acquisition module acquires the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant, and acquires the voltage data of the port power plant equipment, including:

[0185] The voltage data of the port power plant equipment is collected through a customized agent embedded in the inverter.

[0186] Optionally, the acquisition module acquires the port area power plant equipment voltage data, the photovoltaic panel surface temperature distribution data, and the port area power plant inverter output current data, and acquires the port area photovoltaic panel surface temperature distribution data, including:

[0187] The surface temperature distribution data of the photovoltaic panels in the port area is collected by scanning the surface of the photovoltaic panels with an infrared thermal imager.

[0188] Optionally, the analysis module performs FFT analysis based on the output current data of the inverter of the port power plant, including:

[0189] FFT analysis is performed on the output current data of the inverter of the port power plant to extract the harmonic characteristics of the preset frequency band.

[0190] Optionally, the calculation module calculates the port power plant equipment impact coefficient based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel, and the high-frequency harmonic distortion rate of the port power plant, for evaluating the health status of the port power plant equipment, including:

[0191] When the influence coefficient of the port power plant equipment is less than a first threshold, determining that the health status of the port power plant equipment is good;

[0192] When the influence coefficient of the port power plant equipment is greater than or equal to a first threshold and less than a second threshold, determining that the health status of the port power plant equipment is an aging risk;

[0193] When the influence coefficient of the port power plant equipment is greater than or equal to a second threshold, it is determined that the health status of the port power plant equipment is that shutdown for maintenance is required.

[0194] The above description of the embodiments is intended to facilitate understanding and application of this application by those skilled in the art. It will be readily apparent to those skilled in the art that various modifications to the above embodiments can be made, and the general principles described herein can be applied to other embodiments without requiring creative effort. Therefore, this application is not limited to the above embodiments. Any improvements or modifications made to this application by those skilled in the art based on the disclosure of this application should fall within the scope of protection of this application.

Claims

1. A method for evaluating the health status of power plant equipment in a port area, characterized in that: include: Obtaining voltage data of the port power plant equipment, surface temperature distribution data of photovoltaic panels, and output current data of inverters in the port power plant; Calculating the voltage fluctuation rate of the port area power plant equipment based on the voltage data of the port area power plant equipment; Calculating the surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area; Performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result; The port area power plant equipment impact coefficient is calculated based on the voltage fluctuation rate of the port area power plant equipment, the surface temperature gradient of the port area photovoltaic panel and the high-frequency harmonic distortion rate of the port area power plant, and is used to evaluate the health status of the port area power plant equipment.

2. A method for evaluating the health status of power plant equipment in a port area according to claim 1, characterized in that: Obtain the voltage data of the power plant equipment in the port area, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the power plant in the port area, including: The voltage data of the port power plant equipment is collected through a customized agent embedded in the inverter.

3. A method for evaluating the health status of power plant equipment in a port area according to claim 1, characterized in that: Obtain the voltage data of the power plant equipment in the port area, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the power plant in the port area. Obtain the surface temperature distribution data of the photovoltaic panels in the port area, including: The surface temperature distribution data of the photovoltaic panels in the port area is collected by scanning the surface of the photovoltaic panels with an infrared thermal imager.

4. A method for evaluating the health status of power plant equipment in a port area according to claim 1, characterized in that: An FFT analysis is performed based on the inverter output current data of the port power plant, including: FFT analysis is performed on the output current data of the inverter of the port power plant to extract the harmonic characteristics of the preset frequency band.

5. A method for evaluating the health status of power plant equipment in a port area according to claim 1, characterized in that: The influence coefficient of the port power plant equipment is calculated based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel, and the high-frequency harmonic distortion rate of the port power plant, and is used to evaluate the health status of the port power plant equipment, including: When the influence coefficient of the port power plant equipment is less than a first threshold, determining that the health status of the port power plant equipment is good; When the influence coefficient of the port power plant equipment is greater than or equal to a first threshold and less than a second threshold, determining that the health status of the port power plant equipment is an aging risk; When the influence coefficient of the port power plant equipment is greater than or equal to a second threshold, it is determined that the health status of the port power plant equipment is that shutdown for maintenance is required.

6. A device for evaluating the health status of power plant equipment in a port area, characterized in that: include: Acquisition module, which obtains the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant; A voltage module, which calculates the voltage fluctuation rate of the port area power plant equipment based on the voltage data of the port area power plant equipment; a temperature module for calculating a surface temperature gradient of the photovoltaic panels in the port area based on the surface temperature distribution data of the photovoltaic panels in the port area; An analysis module, performing FFT analysis based on the output current data of the inverter of the port power plant, and calculating the high-frequency harmonic distortion rate of the port power plant based on the FFT analysis result; The calculation module calculates the impact coefficient of the port power plant equipment based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel and the high-frequency harmonic distortion rate of the port power plant, so as to evaluate the health status of the port power plant equipment.

7. A method for evaluating the health status of power plant equipment in a port area according to claim 6, characterized in that: The acquisition module acquires the voltage data of the port power plant equipment, the surface temperature distribution data of the photovoltaic panels, and the output current data of the inverter of the port power plant, and acquires the voltage data of the port power plant equipment, including: The voltage data of the port power plant equipment is collected through a customized agent embedded in the inverter.

8. A method for evaluating the health status of power plant equipment in a port area according to claim 6, characterized in that: The acquisition module acquires the port area power plant equipment voltage data, the photovoltaic panel surface temperature distribution data, and the port area power plant inverter output current data, and acquires the port area photovoltaic panel surface temperature distribution data, including: The surface temperature distribution data of the photovoltaic panels in the port area is collected by scanning the surface of the photovoltaic panels with an infrared thermal imager.

9. A method for evaluating the health status of power plant equipment in a port area according to claim 6, characterized in that: The analysis module performs FFT analysis based on the output current data of the inverter of the port power plant, including: FFT analysis is performed on the output current data of the inverter of the port power plant to extract the harmonic characteristics of the preset frequency band.

10. A method for evaluating the health status of power plant equipment in a port area according to claim 6, characterized in that: The calculation module calculates the impact coefficient of the port power plant equipment based on the voltage fluctuation rate of the port power plant equipment, the surface temperature gradient of the port photovoltaic panel, and the high-frequency harmonic distortion rate of the port power plant, for evaluating the health status of the port power plant equipment, including: When the influence coefficient of the port power plant equipment is less than a first threshold, determining that the health status of the port power plant equipment is good; When the influence coefficient of the port power plant equipment is greater than or equal to a first threshold and less than a second threshold, determining that the health status of the port power plant equipment is an aging risk; When the influence coefficient of the port power plant equipment is greater than or equal to a second threshold, it is determined that the health status of the port power plant equipment is that shutdown for maintenance is required.

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