Photovoltaic inverter applied to photovoltaic microgrid of isolated island

Abstract

The invention relates to the technical field of energy management, in particular to a photovoltaic inverter applied to a photovoltaic micro-grid of an isolated island, and the photovoltaic inverter comprises an energy state classification module which extracts multiple parameters of temperature and illumination intensity based on the electric quantity and weather data of the photovoltaic grid, carries out the data classification, and generates a state classification index. According to the method, the energy state in the isolated island photovoltaic microgrid is predicted through the hidden Markov model, and the accuracy of energy management is improved. The model analyzes the energy output according to historical and real-time weather data, and can adjust the output of the energy storage system and the inverter in advance when the energy output fluctuates due to weather changes. The system is allowed to store energy actively in the peak period of energy supply, and the energy is saved when low output is predicted, so that the energy supply and demand are balanced, and the energy efficiency is maximized. In addition, the alternating-current and direct-current complementary dynamic adjusting system is driven by real-time monitoring data, and an energy conversion path is automatically selected.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a photovoltaic inverter applied to a photovoltaic microgrid on an isolated island. Background Art

[0002] The field of energy management technology focuses on optimizing energy efficiency and ensuring the reliability and economy of energy supply. In photovoltaic microgrid systems, energy management involves the integration and coordination of multiple energy sources (such as solar energy and wind energy) and the effective use of energy storage systems. Technical goals include reducing energy waste, improving system response speed, and achieving optimal control of energy flow through intelligent scheduling and real-time monitoring.

[0003] Among them, the photovoltaic inverter used in the photovoltaic microgrid on isolated islands is used in the photovoltaic microgrid system on isolated islands. It can convert the direct current generated by solar panels into alternating current suitable for power grids and household appliances. In an isolated island environment, it can also reduce dependence on external energy. The photovoltaic microgrid can not only meet the basic daily electricity needs of the island, but also serve as a backup power source in an emergency.

[0004] Although existing photovoltaic microgrid systems provide power conversion functions, they are prone to slow adjustment and insufficient response under highly variable environmental conditions. Especially in the closed environment of isolated islands, the independence of the power grid requires equipment to be able to respond to various external changes in real time, including weather changes and load fluctuations. However, traditional systems find it difficult to effectively foresee and prepare for sudden weather changes, resulting in the inability to meet energy needs at critical moments. In addition, existing technologies make it difficult for the system to provide sufficient power in real time when the load on the grid suddenly increases. For example, the grid cannot increase output during peak demand, or cannot reduce energy waste during low demand, which limits the efficiency of grid operation. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a photovoltaic inverter for a photovoltaic microgrid applied to an isolated island.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a photovoltaic inverter applied to a photovoltaic microgrid on an isolated island, the photovoltaic inverter comprising:

[0007] The energy status classification module extracts multiple parameters such as temperature and light intensity based on the photovoltaic power grid power and weather data, classifies the data, and generates a status classification index;

[0008] The energy conversion prediction module adopts the state classification index, analyzes the conversion frequency between each state, integrates future weather forecast data, predicts energy state changes through probability calculation, and obtains energy conversion prediction results;

[0009] The power regulation module adjusts the output parameters of the inverter item by item through the energy conversion prediction result, synchronously adjusts the power level and frequency matching prediction state, and generates adjustment parameter settings;

[0010] The optimal path analysis module adopts the adjustment parameter setting, compares the energy efficiency of AC and DC transmission, selects the transmission mode and path according to the current load of the network, and obtains the path selection result;

[0011] The output adjustment control module adjusts the output settings of the inverter based on the path selection result, updates the power output and frequency, optimizes the energy distribution, and generates an output control configuration;

[0012] The energy allocation module reconfigures the charging and discharging timing to balance the load demand, optimizes the energy utilization efficiency, and generates an energy allocation plan through the output control configuration.

[0013] The present invention has improvements in that the state classification index includes a standard threshold for data segmentation, an energy output range corresponding to each level of state, and a classification weight of environmental parameter influencing factors; the energy conversion prediction result includes a probability distribution of each state, an expected state conversion time point, and a state duration period; the adjustment parameter setting includes the maximum and minimum power output limits of the inverter, a frequency adjustment range, and a preset power adjustment step value; the output control configuration includes an adjusted power output curve, an adjustment of the set frequency, and an inverter response time setting; the energy allocation plan includes a priority ranking of charging and discharging, an adjusted schedule, and a target energy storage amount for each time period.

[0014] The present invention is improved in that the energy status classification module includes:

[0015] The environmental monitoring submodule collects photovoltaic power grid electricity and weather data, including island temperature and light intensity, based on the climate conditions of the isolated island, and conducts real-time data monitoring to obtain an island environmental data set;

[0016] The output stratification analysis submodule uses the island environment data set to classify energy output into three levels of excellent, good, and poor according to light and temperature data, and generates stratified energy data;

[0017] The index building submodule integrates information from the hierarchical energy data, integrates multiple key factors such as light intensity and temperature fluctuation, encodes them according to the energy output level, and generates a state classification index.

[0018] The present invention is improved in that the energy conversion prediction module comprises:

[0019] The state trend analysis submodule analyzes the conversion trend of energy states in an isolated island environment through a hidden Markov model based on the state classification index, evaluates the impact of seasonality and climate change on state conversion, and obtains the dynamic analysis results of energy states;

[0020] The climate impact integration submodule combines the energy status dynamic analysis results with future climate condition data to generate a climate prediction model;

[0021] The conversion probability assessment submodule uses the climate prediction model and the hidden Markov model to analyze the conversion probability of energy states under given environmental conditions to obtain energy conversion prediction results.

[0022] The present invention is improved in that the hidden Markov model is according to the formula:

[0023]

[0024] Among them, π k represents the probability of state k in the initial observation period, n k is the number of occurrences of state k in the initial observation period, α′ is the weight coefficient of the influence of light intensity on the state, S k is the light intensity adjustment factor corresponding to state k, N is the total number of observations, ∑S is the sum of the light intensity adjustment factors of all states;

[0025] and

[0026]

[0027] Analyze the conversion trend of energy status in isolated island environments;

[0028] Among them, a jk is the state transition probability matrix, which represents the probability of transitioning from state j to state k, v is the time index, W is the end point of the total observation time period, and x vj is the indicator function for state j at time v, x (v+1)k is the indicator function of state k at time v+1, β′ is the weight coefficient of temperature influence, P vj is the temperature influence factor at time v and state j, γ′ is the weight coefficient of temperature influence, P (v+1)k is the temperature influence factor of state k at time v+1, and δ′ is used to adjust the weight of temperature influence in the state transition probability calculation.

[0029] The present invention is improved in that the power regulation module comprises:

[0030] The energy demand matching submodule analyzes the current energy demand of the isolated island based on the energy conversion prediction result, adjusts the power output of the inverter, and generates power matching parameters;

[0031] The frequency corresponding submodule uses the power matching parameters to adjust the inverter frequency to match the updated power setting to obtain the frequency adaptation setting;

[0032] The output adjustment submodule utilizes the frequency adaptation setting to optimize the overall output configuration of the inverter, including output power regulation and energy distribution, and generates adjustment parameter settings.

[0033] The present invention is improved in that the optimal path analysis module comprises:

[0034] The transmission efficiency analysis submodule compares the energy efficiency of AC and DC transmission modes in the photovoltaic power grid of the isolated island based on the adjustment parameter settings, analyzes their respective energy losses and transmission efficiencies, and generates photovoltaic power grid transmission efficiency data;

[0035] The network load evaluation submodule uses the photovoltaic power grid transmission efficiency data to analyze the current power grid load situation and load fluctuation characteristics, compares the performance of multiple transmission modes, and generates load status analysis results;

[0036] The energy transmission optimization submodule selects an energy transmission path that matches the needs of the isolated island power grid based on the load condition analysis results through a path selection algorithm, including comparing the energy efficiency and stability of each path to generate a path selection result.

[0037] The present invention is improved in that the path selection algorithm is according to the formula:

[0038] I path =ω1·η path +ω2·ΔP path +ω3·W f +ω4·F r +ω5·S f +ω6·R v

[0039] Calculate the optimal path selection index I path ;

[0040] Among them, ω1, ω2, ω3, ω4, ω5 and ω6 are weight coefficients, which are used to balance the impact of energy efficiency, power stability, weather impact, equipment failure rate, seasonal variation factor, and renewable energy output volatility, respectively. path is the energy efficiency of the path, ΔP path is the power stability of the path, W f is the weather impact factor, F r is the equipment failure rate, S f is the seasonal variation factor, R v The output volatility of renewable energy.

[0041] The present invention is improved in that the output adjustment control module comprises:

[0042] The power adjustment submodule reconfigures the power output parameters of the inverter based on the path selection result, including changing the power level to match the needs of the island power grid, and generates an updated power configuration.

[0043] The frequency calibration submodule utilizes the updated power configuration to iteratively adjust the frequency of the inverter, including calibrating the output frequency to match the updated power parameter, to obtain a frequency synchronization configuration.

[0044] The allocation optimization submodule adjusts and optimizes the energy allocation of the inverter through the frequency synchronization configuration and refers to the current energy output and demand conditions to generate an output control configuration.

[0045] The present invention is improved in that the energy allocation module comprises:

[0046] The timing optimization submodule rearranges the charging and discharging schedules based on the output control configuration to generate an optimized charging and discharging schedule.

[0047] The energy balance submodule uses the optimized charging and discharging schedule to analyze the load situation of the power grid, adjust the charging and discharging priority according to the energy supply situation, and generate a load management plan.

[0048] The equipment efficiency optimization submodule optimizes the operation and scheduling mode of the energy storage equipment through the load management plan and generates an energy distribution plan.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, the energy status in the isolated island photovoltaic microgrid is predicted by the hidden Markov model, thereby improving the accuracy of energy management. The model analyzes energy output based on historical and real-time weather data, and can adjust the energy storage system and inverter output in advance when weather changes cause energy output fluctuations. The system is allowed to actively store energy during peak energy supply periods and save energy when low output is predicted, thereby balancing energy supply and demand and maximizing energy efficiency. In addition, the AC / DC complementary dynamic regulation system is driven by real-time monitoring data and automatically selects the energy conversion path. It can also quickly adjust output parameters such as power and frequency to ensure the continuity of power supply and the stable operation of the system, thereby improving the system's adaptability to rapidly changing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention proposes a module diagram of a photovoltaic inverter for a photovoltaic microgrid applied to an isolated island;

[0052] Figure 2The present invention proposes a system framework diagram of a photovoltaic inverter for a photovoltaic microgrid applied to an isolated island;

[0053] Figure 3 is a schematic diagram of an energy status classification module of the present invention;

[0054] Figure 4 is a schematic diagram of an energy conversion prediction module of the present invention;

[0055] Figure 5 is a schematic diagram of a power regulation module of the present invention;

[0056] Figure 6 is a schematic diagram of an optimal path analysis module of the present invention;

[0057] Figure 7 A schematic diagram of an output adjustment control module of the present invention;

[0058] Figure 8 It is a schematic diagram of the energy allocation module of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying 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 limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 The present invention provides a technical solution: a photovoltaic inverter for a photovoltaic microgrid applied to an isolated island, the photovoltaic inverter comprising:

[0062] The energy status classification module extracts multiple parameters such as temperature and light intensity based on the photovoltaic power grid power and weather data, classifies the data, and generates a status classification index;

[0063] The energy conversion prediction module adopts state classification index, analyzes the conversion frequency between each state, integrates future weather forecast data, predicts energy state changes through probability calculation, and obtains energy conversion prediction results;

[0064] The power regulation module adjusts the output parameters of the inverter item by item based on the energy conversion prediction results, synchronously adjusts the power level and frequency to match the prediction status, and generates the adjustment parameter settings;

[0065] The optimal path analysis module uses adjustment parameter settings to compare the energy efficiency of AC and DC transmission, selects the transmission mode and path according to the current load of the network, and obtains the path selection result;

[0066] The output adjustment control module adjusts the output settings of the inverter based on the path selection results, updates the power output and frequency, optimizes energy distribution, and generates output control configurations;

[0067] The energy allocation module reconfigures the charging and discharging timing to balance the load demand, optimize the energy utilization efficiency, and generate an energy allocation plan through output control configuration.

[0068] The state classification index includes the standard threshold for data segmentation, the energy output range corresponding to each level of state, and the classification weights of environmental parameter influencing factors. The energy conversion prediction results include the probability distribution of each state, the expected state conversion time point and the state duration period. The adjustment parameter settings include the maximum and minimum power output limits of the inverter, the frequency adjustment range, and the preset power adjustment step value. The output control configuration includes the adjusted power output curve, the adjustment of the set frequency, and the inverter response time setting. The energy allocation plan includes the priority sorting of charging and discharging, the adjusted schedule, and the target energy storage for each time period.

[0069] See also Figure 2 and Figure 3 , the energy status classification module includes:

[0070] The environmental monitoring submodule collects photovoltaic power grid electricity and weather data, including island temperature and light intensity, according to the climate conditions of the isolated island, and conducts real-time data monitoring to obtain the island environmental data set.

[0071] Under the specific climatic conditions of isolated islands, the temperature and light intensity of the islands are systematically collected and analyzed. These data are collected in real time and transmitted to the central database. When collecting data, temperature sensors and light sensors are used, and the frequency of collecting data is set to once per minute. In addition, the module is equipped with a data cleaning function to automatically remove abnormal values. In this process, real-time data T i and S i The collected temperature and light data are recorded according to their average values ​​T avg and standard deviation S sd Standardization was performed for further analysis.

[0072] The output stratification analysis submodule uses the island environment dataset to classify energy output into three levels: excellent, good, and poor according to the light and temperature data. The process of generating stratified energy data is as follows;

[0073] Using the standardized temperature and light data, energy output is divided into three levels: "excellent", "good", and "poor". The specific stratification is achieved by setting the threshold θ T and θ S Score the data points. To do this, define the hierarchical scoring formula: According to R score The value of is assigned a grade using a text description, such as a score over 100 is considered “excellent”, between 50 and 100 is “good”, and below 50 is “poor”. score is the scoring result of the data point. T is the threshold of temperature data. S is the threshold of illumination data.

[0074] The index construction submodule integrates information from hierarchical energy data, integrates multiple key factors such as light intensity and temperature fluctuation, encodes them according to the energy output level, and generates a state classification index in the following process;

[0075] Integrate information from hierarchical energy data and encode key factors such as temperature fluctuation and light intensity to generate a state classification index. Each state is classified according to its score result R score Assign a code C k , to facilitate quick retrieval and classification of states, the coding is calculated as follows: By dividing the score by 10 and taking the integer part, a code from 0 to 10 is generated to facilitate subsequent data processing and analysis. k According to the scoring result R score The computed status code.

[0076] See also Figure 2 and Figure 4 , the energy conversion prediction module includes:

[0077] The state trend analysis submodule analyzes the conversion trend of energy states in isolated island environments through hidden Markov models based on state classification indexes, evaluates the impact of seasonality and climate change on state conversion, and obtains the dynamic analysis results of energy states in the following process;

[0078] The transition probabilities between states are calculated based on environmental monitoring data. The state space is defined as the set of all possible energy states, and the initial state probability distribution and state transition probability matrix are calculated.

[0079] The hidden Markov model follows the formula:

[0080]

[0081] Among them, π k represents the probability of state k in the initial observation period, n k is the number of occurrences of state k in the initial observation period, α′ is the weight coefficient of the influence of light intensity on the state, which adjusts the contribution of light to the state probability, S k is the light intensity adjustment factor corresponding to state k, indicating the light conditions in this state, N is the total number of observations, ∑S is the sum of the light intensity adjustment factors of all states, which is used to normalize the influence of light intensity;

[0082] and

[0083]

[0084] Analyze the conversion trend of energy status in isolated island environments;

[0085] Among them, a jk is the state transition probability matrix, which indicates the probability of transitioning from state j to state k, v is the time index used to traverse the observation data, W is the end point of the total observation time period, and x vj is the indicator function of state j at time v, indicating that state j is observed at time point v, x (v+1)k is the indicator function of state k at time v+1, indicating that state k is observed at time point v+1, β′ is the weight coefficient of temperature influence, which adjusts the influence of the temperature factor at time point v on state j, P vj is the temperature influence factor of state j at time v, indicating the temperature condition of state j at that time point, γ′ is the weight coefficient of temperature influence, adjusting the influence of the temperature factor at time point v+1 on state k, P (v+1)k is the temperature influence factor of state k at time v+1, indicating the temperature condition of state k at that time point. δ′ is used to adjust the weight of temperature influence in the state transition probability calculation, affecting the temperature adjustment of the states at all time points.

[0086] The calculation process is as follows:

[0087] Set the environment and equipment characteristics parameters:

[0088] Assume that the light intensity adjustment factor S for each state k Obtained through the accumulation of long-term environmental monitoring data. The calculation formula is

[0089]

[0090] s kd is the light intensity in state k on day d, and D is the number of days.

[0091] Temperature influence factor P vj and P (v+1)kCalculated in a similar way:

[0092]

[0093] p vjh is the temperature at state j at hour h, where H is the number of hours.

[0094] State transition probability matrix calculation:

[0095] Define the original state transition probability matrix a jk .

[0096]

[0097] The weight coefficients β′, γ′, and δ′ were determined by regression analysis, analyzing the correlation between state transitions and environmental factors based on past data.

[0098] State transition probability matrix a jk It more accurately reflects the probability of transitioning from state j to state k, taking into account the influence of environmental factors such as temperature and light. By introducing actual environmental monitoring data, the model can better predict the probability of state transition under different environmental conditions, thereby improving the responsiveness and efficiency of the energy management system.

[0099] The climate impact integration submodule combines the results of the dynamic analysis of energy status and integrates the future climate condition data to generate a climate prediction model.

[0100] Analyze the energy state conversion probability under different climate conditions in historical data and infer the potential impact of future climate change on energy state. Climate prediction model calculation formula: b jk =β0+β1c k +β2c′ k , b jk Indicated by the climate factor c k and future predicted climate factor c′ k The predicted probability of transitioning from state j to state k under the influence of . β0, β1 and β2 are regression coefficients obtained by training with historical climate and state data. k and c′ k Represent current and predicted climate conditions, respectively.

[0101] The conversion probability assessment submodule uses the climate prediction model and the hidden Markov model to analyze the conversion probability of energy states under given environmental conditions, and the process of obtaining the energy conversion prediction result is as follows;

[0102] Analyze the transition probability of energy states under given environmental conditions. Adjust the initial state transition probability matrix to reflect the expected impact of climate change on energy state transition. State transition probability formula: is the adjusted transition probability from state j to state k, γ k is a tuning parameter that reflects the sensitivity of climate prediction to the probability of state transition, b jk Forecast results from the climate impact integration submodule. This approach refines the original probabilistic model and provides more accurate predictions of energy state transitions.

[0103] See also Figure 2 and Figure 5 , the power regulation module includes:

[0104] The energy demand matching submodule analyzes the current energy demand of the isolated island based on the energy conversion prediction results, adjusts the power output of the inverter, and generates the power matching parameters in the following process:

[0105] Using the output of the forecasting model, this module calculates the average expected demand D by summarizing the energy demand for each state and the corresponding state probability: D = ∑ k E k ·p k , E k is the energy demand per unit time in state k, p k is the probability that the system is in state k. State probability p k It is derived from the energy state transition model, and its calculation method is as follows: According to the average expected demand D and the capacity limit of the equipment, the output power Q of the inverter is adjusted to: Q = min (Q max ,K1·D),Q max is the maximum output capacity of the inverter, and K1 is the regulation coefficient, which is used to adjust the output power according to current demand.

[0106] The frequency corresponding submodule uses the power matching parameters to adjust the inverter frequency to match the updated power setting, and the process of obtaining the frequency adaptation setting is as follows;

[0107] After adjusting the inverter's output power, the inverter's output frequency is adjusted to accommodate the updated power setting using the power matching parameter Q. The frequency is adjusted using the following formula: F base is the reference frequency of the inverter, Q nom is the nominal power. It ensures that the inverter output frequency is synchronized with the power demand, thus optimizing the overall energy efficiency and equipment performance.

[0108] The output adjustment submodule uses frequency adaptation settings to optimize the overall output configuration of the inverter, including output power regulation and energy distribution. The process of generating adjustment parameter settings is as follows;

[0109] Optimize the overall output configuration of the inverter, covering power regulation and energy distribution. The adjustment parameter setting G is calculated by the following formula: G = α2·F + β2·Q, where α2 and β2 are weight coefficients, respectively adjusting the impact of frequency F on the output configuration and the impact of power Q.

[0110] See also Figure 2 and Figure 6 , the optimal path analysis module includes:

[0111] The transmission efficiency analysis submodule compares the energy efficiency of AC and DC transmission modes in the photovoltaic power grid of the isolated island based on the adjustment parameter settings, analyzes their respective energy losses and transmission efficiencies, and generates the photovoltaic power grid transmission efficiency data in the following process;

[0112] The energy efficiency of AC and DC transmission in the photovoltaic power grid of isolated islands is compared. By analyzing the energy loss and transmission efficiency of each transmission method, the energy loss L of AC and DC transmission is defined. AC and L DC , and calculate the transmission efficiency η as follows: and η AC and η DC Represents the transmission efficiency of AC and DC respectively. in is the total energy input into the system. AC and L DC They represent the energy loss of AC and DC during transmission respectively.

[0113] The network load evaluation submodule uses the PV grid transmission efficiency data to analyze the current grid load situation and load fluctuation characteristics, compares the performance of various transmission methods, and generates the load status analysis results in the following process;

[0114] Using the above transmission efficiency data, analyze the current power grid load and load fluctuation characteristics. By comparing the performance of various transmission methods, calculate the current load C load and the predicted load P load The relationship between: and P load =∑ n σ n ·P n , C load Indicates the current actual load, which is composed of the power P of each node n With its maximum capacity P max,n The sum of the ratios of load represents the predicted load, which is determined by the power fluctuation σ of each node n and power P n The sum of the products of is calculated. n is the grid node index.

[0115] The energy transmission optimization submodule selects the energy transmission path that matches the needs of the isolated island power grid based on the load condition analysis results through the path selection algorithm, including comparing the energy efficiency and stability of each path. The process of generating the path selection result is as follows;

[0116] The energy transmission path that matches the needs of the isolated island power grid is selected through the path selection algorithm. The energy efficiency and stability of each path are compared to obtain the optimal path selection index.

[0117] The path selection algorithm follows the formula:

[0118] I path =ω1·η path +ω2·ΔP path +ω3·W f +ω4·F r +ω5·S f +ω6·R v

[0119] Calculate the optimal path selection index I path ;

[0120] Among them, I path It is used to evaluate the overall performance of different transmission paths. ω1, ω2, ω3, ω4, ω5 and ω6 are weight coefficients, which are used to balance the impact of energy efficiency, power stability, weather impact, equipment failure rate, seasonal variation factor, and renewable energy output volatility, respectively. η path is the energy efficiency of the path, measuring the efficiency of energy transmission, ΔP path To evaluate the continuity and reliability of energy supply for the power stability of the path, W f is the weather impact factor, which indicates the potential impact of different weather conditions on energy transmission efficiency and stability. r is the equipment failure rate, which measures the frequency of equipment problems that may affect the reliability of energy supply, S f is the seasonal variation factor, reflecting the impact of seasonal changes on energy demand and supply, R v The volatility of renewable energy output refers to the uncertainty and fluctuation of renewable energy output due to weather changes or other factors.

[0121] The calculation process is as follows:

[0122] Determine the value of the impact factor:

[0123] Weather influence factor W f : By analyzing the correlation between historical weather data and energy output, a linear regression model is used to estimate the impact of weather conditions on energy output.

[0124] Assume a wtrepresents the weather conditions at time t, b yt Representing the corresponding energy output, the linear model can be expressed as:

[0125] b yt =μ+θa wt

[0126] The least square method is used to calculate θ (the influence coefficient of weather factors), then W f =θ.

[0127] Equipment failure rate F r : Calculated by counting the ratio of equipment failure times to total operating time in the past year. t is the number of failures, h t The total running time is:

[0128]

[0129] Seasonal variation factor S f : Analyze the energy demand change data of each season and use a simple average method to calculate the energy demand ratio of different seasons. Assume that the energy demand in spring is e sp , the total demand is e tot ,but:

[0130]

[0131] Renewable energy output volatility R v : Calculate the standard deviation of the output of renewable energy (such as wind and solar) as a measure of volatility. Let r t is the energy output in a specific time, and the average output is but:

[0132]

[0133] Calculate evaluation index I path :

[0134] I path =ω1·η path +ω2·ΔP path +ω3·W f +ω4·F r +ω5·S f +ω6·R v

[0135] The weighting coefficients ω1 to ω6 are adjusted based on historical performance and expert experience to ensure that all factors are appropriately considered according to their importance.

[0136] Evaluation Index I pathIt provides a quantitative indicator to help decision makers consider energy efficiency, stability, weather impact, equipment reliability, seasonal changes, and volatility of renewable energy when selecting the most suitable energy transmission path. Through this comprehensive assessment, the efficient and stable operation of the power grid can be ensured.

[0137] See also Figure 2 and Figure 7 , the output adjustment control module includes:

[0138] The power adjustment submodule reconfigures the power output parameters of the inverter based on the path selection results, including changing the power level to match the needs of the island power grid. The process of generating the updated power configuration is as follows;

[0139] Reconfigure the inverter power output parameters to match the island grid’s needs. Based on the selected energy transmission path I path and the current energy demand D, the inverter power level Q is reconfigured as follows: Q new =Q base ×(1+r·(DD base )), Q new It is the updated inverter power configuration. base is the basic power level, i.e. the power output before adjustment. D is the current energy demand. base is the baseline energy demand, usually the long-term average demand. r is the regulation factor, which is used to adjust the power output to match the demand changes. Ensure that the power output of the inverter matches the current and expected grid demand and optimize the power output.

[0140] The frequency calibration submodule uses the updated power configuration to iteratively adjust the frequency of the inverter, including calibrating the output frequency to match the updated power parameters, and the process of obtaining the frequency synchronization configuration is as follows;

[0141] Using the updated power configuration Q new , iteratively adjust the inverter frequency. The new frequency setting F new Adjusted by the following formula: F new is the inverter frequency after adjustment. base is the reference frequency, i.e. the frequency before adjustment. φ is the frequency adjustment factor, which adjusts the frequency according to the power change. The inverter frequency is updated synchronously by considering the change in power configuration to ensure that the frequency matches the power output.

[0142] The allocation optimization submodule adjusts and optimizes the energy allocation of the inverter through frequency synchronization configuration, referring to the current energy output and demand situation, and generates the output control configuration process as follows;

[0143] Configure F through frequency synchronization new, refer to the current energy output and demand situation, adjust and optimize the energy distribution of the inverter. The output control configuration G is calculated by the following formula: G = α·F new +β·Q new , G is the output control configuration, which is used to adjust the output settings of the inverter. α and β are weight coefficients, which are used to adjust the impact of frequency and power on the final output. This ensures that the output configuration of the inverter accurately matches the current grid demand and operating conditions, optimizing the performance and efficiency of the entire system.

[0144] See also Figure 2 and Figure 8 , the energy allocation module includes:

[0145] The timing optimization submodule rearranges the charging and discharging schedule based on the output control configuration, and the process of generating an optimized charging and discharging schedule is as follows;

[0146] Based on the output control configuration G, the charging and discharging schedule is rearranged to adapt to the dynamically changing energy demand and output. The schedule is adjusted by adjusting the coefficient ξ: T new =T old +ξ·G,T new is the optimized charging and discharging schedule, indicating the new arrangement time of charging and discharging activities. old is the original charging and discharging schedule, indicating the previously scheduled charging and discharging time. ξ is the time adjustment coefficient, which is used to adjust the schedule according to the output control configuration G, indicating the sensitivity or amplitude of the time adjustment. G is the output control configuration, indicating the degree of match between the current output setting and demand. Ensure that the charging and discharging schedule is synchronized with the output control configuration of the inverter, and optimize the charging and discharging process to adapt to the current energy output and demand.

[0147] The energy balance submodule uses the optimized charging and discharging schedule to analyze the grid load and adjust the charging and discharging priority according to the energy supply. The process of generating the load management plan is as follows;

[0148] Using the optimized charging and discharging schedule T new , analyze the actual load of the power grid and adjust the charging and discharging priority to optimize the overall energy balance of the power grid. The load management index Λ is calculated as follows: Λ=∑ i ω i ·(P i -P avg ), Λ is used to evaluate and adjust the charging and discharging priority. i is the grid load in time period i, indicating the actual grid usage in a specific time period. avg is the average grid load, which indicates the average load level of the grid in the past period of time. iis a weight coefficient, which is assigned according to the importance or priority of the time period, indicating the importance of different time periods. Through these calculations, a load management plan is generated to optimize the charging and discharging priority to balance the energy supply and demand of the entire system, reduce peak load pressure, and improve system stability.

[0149] The equipment efficiency optimization submodule optimizes the operation and scheduling of energy storage equipment through load management planning, and the process of generating energy distribution plan is as follows;

[0150] Based on the load management plan Λ, the operation and dispatching mode of the energy storage equipment is optimized. The energy distribution plan is calculated by the equipment efficiency optimization index ∈ to ensure the maximum energy efficiency of the system. The equipment efficiency optimization index ∈ is calculated as follows: ∈=∑ m λ m ΔE m ,∈ is used to evaluate the energy efficiency improvement under different configurations. ΔE m is the energy saving or additional consumption under configuration m, indicating the energy saved or consumed more under this configuration compared to the standard configuration. m The efficiency weight reflects the contribution of different configurations to the total efficiency and indicates the relative importance or effect of each configuration. Ensure that the operation mode of the energy storage equipment is optimized to achieve the most efficient use of energy, reduce unnecessary energy waste, and improve the overall energy efficiency of the system. In this way, it can ensure that the equipment operates at the best efficiency and adjust the energy distribution plan to adapt to changing needs and conditions.

[0151] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A photovoltaic inverter for a photovoltaic microgrid applied to an isolated island, characterized in that: The photovoltaic inverter comprises: The energy status classification module extracts multiple parameters such as temperature and light intensity based on the photovoltaic power grid power and weather data, classifies the data, and generates a status classification index; The energy conversion prediction module adopts the state classification index, analyzes the conversion frequency between each state, integrates future weather forecast data, predicts energy state changes through probability calculation, and obtains energy conversion prediction results; The power regulation module adjusts the output parameters of the inverter item by item through the energy conversion prediction result, synchronously adjusts the power level and frequency matching prediction state, and generates adjustment parameter settings; The optimal path analysis module adopts the adjustment parameter setting, compares the energy efficiency of AC and DC transmission, selects the transmission mode and path according to the current load of the network, and obtains the path selection result; The output adjustment control module adjusts the output settings of the inverter based on the path selection result, updates the power output and frequency, optimizes the energy distribution, and generates an output control configuration; The energy allocation module reconfigures the charging and discharging timing to balance the load demand, optimizes the energy utilization efficiency, and generates an energy allocation plan through the output control configuration.

2. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The state classification index includes a standard threshold for data segmentation, an energy output range corresponding to each level of state, and a classification weight of environmental parameter influencing factors; the energy conversion prediction result includes a probability distribution of each state, an expected state conversion time point, and a state duration period; the adjustment parameter setting includes the maximum and minimum power output limits of the inverter, a frequency adjustment range, and a preset power adjustment step value; the output control configuration includes an adjusted power output curve, an adjustment of the set frequency, and an inverter response time setting; the energy allocation plan includes a priority ranking of charging and discharging, an adjusted schedule, and a target energy storage amount for each time period.

3. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The energy status classification module includes: The environmental monitoring submodule collects photovoltaic power grid electricity and weather data, including island temperature and light intensity, based on the climate conditions of the isolated island, and conducts real-time data monitoring to obtain an island environmental data set; The output stratification analysis submodule uses the island environment data set to classify energy output into three levels of excellent, good, and poor according to light and temperature data, and generates stratified energy data; The index building submodule integrates information from the hierarchical energy data, integrates multiple key factors such as light intensity and temperature fluctuation, encodes them according to the energy output level, and generates a state classification index.

4. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1 is characterized in that: The energy conversion prediction module includes: The state trend analysis submodule analyzes the conversion trend of energy states in an isolated island environment through a hidden Markov model based on the state classification index, evaluates the impact of seasonality and climate change on state conversion, and obtains the dynamic analysis results of energy states; The climate impact integration submodule combines the energy status dynamic analysis results with future climate condition data to generate a climate prediction model; The conversion probability assessment submodule uses the climate prediction model and the hidden Markov model to analyze the conversion probability of energy states under given environmental conditions to obtain energy conversion prediction results.

5. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 4 is characterized in that: The hidden Markov model follows the formula: Among them, π k represents the probability of state k in the initial observation period, n k is the number of occurrences of state k in the initial observation period, α′ is the weight coefficient of the influence of light intensity on the state, S k is the light intensity adjustment factor corresponding to state k, N is the total number of observations, ∑S is the sum of the light intensity adjustment factors of all states; and Analyze the conversion trend of energy status in isolated island environments; Among them, a jk is the state transition probability matrix, which represents the probability of transitioning from state j to state k, v is the time index, W is the end point of the total observation time period, and x vj is the indicator function for state j at time v, x (v+1)k is the indicator function of state k at time v+1, β′ is the weight coefficient of temperature influence, P vj is the temperature influence factor at time v and state j, γ′ is the weight coefficient of temperature influence, P (v+1)k is the temperature influence factor of state k at time v+1, and δ′ is used to adjust the weight of temperature influence in the state transition probability calculation.

6. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The power regulation module comprises: The energy demand matching submodule analyzes the current energy demand of the isolated island based on the energy conversion prediction result, adjusts the power output of the inverter, and generates power matching parameters; The frequency corresponding submodule uses the power matching parameters to adjust the inverter frequency to match the updated power setting to obtain the frequency adaptation setting; The output adjustment submodule utilizes the frequency adaptation setting to optimize the overall output configuration of the inverter, including output power regulation and energy distribution, and generates adjustment parameter settings.

7. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The optimal path analysis module includes: The transmission efficiency analysis submodule compares the energy efficiency of AC and DC transmission modes in the photovoltaic power grid of the isolated island based on the adjustment parameter settings, analyzes their respective energy losses and transmission efficiencies, and generates photovoltaic power grid transmission efficiency data; The network load evaluation submodule uses the photovoltaic power grid transmission efficiency data to analyze the current power grid load situation and load fluctuation characteristics, compares the performance of multiple transmission modes, and generates load status analysis results; The energy transmission optimization submodule selects an energy transmission path that matches the needs of the isolated island power grid based on the load condition analysis results through a path selection algorithm, including comparing the energy efficiency and stability of each path to generate a path selection result.

8. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 7, characterized in that: The path selection algorithm is according to the formula: I path =ω1·η path +ω2·ΔP path +ω3·W f +ω4·F r +ω5·S f +ω6·R v Calculate the optimal path selection index I path ; Among them, ω1, ω2, ω3, ω4, ω5 and ω6 are weight coefficients, which are used to balance the impact of energy efficiency, power stability, weather impact, equipment failure rate, seasonal variation factor, and renewable energy output volatility, respectively. path is the energy efficiency of the path, ΔP path is the power stability of the path, W f is the weather impact factor, F r is the equipment failure rate, S f is the seasonal variation factor, R v The output volatility of renewable energy.

9. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The output adjustment control module comprises: The power adjustment submodule reconfigures the power output parameters of the inverter based on the path selection result, including changing the power level to match the needs of the island power grid and generating an updated power configuration; The frequency calibration submodule uses the updated power configuration to iteratively adjust the frequency of the inverter, including calibrating the output frequency to match the updated power parameter to obtain a frequency synchronization configuration; The allocation optimization submodule adjusts and optimizes the energy allocation of the inverter through the frequency synchronization configuration and refers to the current energy output and demand conditions to generate an output control configuration.

10. The photovoltaic inverter for photovoltaic microgrid applied to isolated islands according to claim 1, characterized in that: The energy allocation module comprises: The timing optimization submodule rearranges the charging and discharging schedule based on the output control configuration to generate an optimized charging and discharging schedule; The energy balance submodule uses the optimized charging and discharging schedule to analyze the load of the power grid, adjust the charging and discharging priority according to the energy supply situation, and generate a load management plan; The equipment efficiency optimization submodule optimizes the operation and scheduling mode of the energy storage equipment through the load management plan and generates an energy distribution plan.

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