Energy supply system health status assessment method and system based on energy unit

Abstract

The present invention relates to the field of health assessment technology, and in particular to a method and system for assessing the health status of an energy supply system based on an energy unit. The method dynamically adjusts the output efficiency based on the preferred distribution deviation of the environmental parameters and output efficiency of the energy cabin in the time period; for the controllable energy cabin, the internal loss is analyzed by the accumulated energy output to determine its energy consumption demand; the uncontrollable energy cabin predicts the future energy supply demand based on the historical output and loss, and analyzes the energy consumption demand; the health assessment result of the energy unit is obtained by uniformly distributing the changes in the energy consumption demand of each energy cabin in time series. The present invention improves the accuracy of loss quantification analysis by correcting the environmental interference, dynamically allocates energy consumption in combination with the output prediction of the uncontrollable energy, performs health monitoring and assessment on the allocation situation, ensures the balance and stability of the allocation strategy of each energy cabin during efficient operation, and provides the energy unit with a more reliable and continuous stable output capability.

Description

Technical Field

[0001] The present invention relates to the technical field of health assessment, and in particular to a method and system for assessing the health status of an energy supply system based on an energy unit. Background Art

[0002] An energy unit is a comprehensive energy supply system composed of multiple energy modules, typically integrating them to provide complementary energy to meet dynamic user needs. Any failure in a single energy module can trigger a cascading failure, leading to power outages and even equipment damage. Therefore, health assessment is the core support for the intelligent operation and sustainable development of energy units. Through real-time monitoring, loss quantification, and dynamic optimization, it addresses the blindness, lag, and inefficiency of traditional management, providing a scientific basis for stable energy supply, cost reduction, efficiency improvement, and green transformation.

[0003] The advantage of combining multiple energy modules is that they balance the volatility of different energy sources. However, when different modules within an energy unit experience varying losses, this can have complex impacts on the unit's health. For example, if a module with low losses experiences a problem, it can temporarily maintain basic operation. However, a module with high losses can cause system imbalance and lead to unstable output power.

[0004] Typically, energy units meet users' energy needs by arranging energy output ratios in established energy compartments. This does not take into account that different energy compartments will experience varying degrees of loss under long-term energy output. If energy supply tasks are not evenly distributed over the long term, high-loss energy compartments may deteriorate rapidly due to overload. Relying solely on efficiency anomaly assessments to trigger maintenance will lead to maintenance delays and even trigger chain failures, resulting in reduced system power supply stability and affecting the overall life of the energy unit. Summary of the Invention

[0005] In order to solve the technical problem in the prior art that relying solely on efficiency anomaly assessment to trigger maintenance can lead to delayed maintenance and even cascading failures, resulting in reduced system power supply stability, the present invention aims to provide a method and system for assessing the health status of an energy supply system based on energy units. The technical solutions adopted are as follows:

[0006] The present invention provides a method for evaluating the health status of an energy supply system based on an energy unit, the method comprising:

[0007] Obtain the output efficiency and output energy of each energy cabin in the energy unit during different detection periods, as well as at least two environmental parameters; energy cabin types are divided into: controllable energy cabin and uncontrollable energy cabin;

[0008] According to the ideal distribution deviation between the environmental parameters and the output efficiency in all the test periods of each energy cabin, the output efficiency is adjusted to obtain the adjustment efficiency of each test period;

[0009] Based on the accumulated loss of output energy during operation of a single energy cabin and the deviation of the regulation efficiency during the detection period, the internal loss degree of the single energy cabin during the detection period is obtained; based on the internal loss degree of the controllable energy cabin, the energy consumption demand of the controllable energy cabin during the detection period is obtained; based on the historical output energy and internal loss degree of the uncontrollable energy cabin, the subsequent output energy is predicted and combined with the internal loss degree to obtain the energy consumption demand of the uncontrollable energy cabin during the detection period;

[0010] The health assessment results of the energy unit are obtained by uniformly analyzing the time-series changes in the energy consumption demand of each energy compartment.

[0011] Furthermore, the method for obtaining the regulation efficiency includes:

[0012] A multidimensional space is established with each environmental parameter as a dimension. For any energy chamber, the environmental parameters of the energy chamber in each detection period are mapped into the multidimensional space and clustered to obtain environmental performance clusters. Based on the preset ideal range of each environmental parameter for the energy chamber, the preferred environmental area is determined in the multidimensional space.

[0013] The adjustment degree of the energy cabin in each detection period is obtained based on the center point position deviation and the output efficiency deviation corresponding to the center point between the environmental performance cluster and the preferred environmental area in each detection period of the energy cabin;

[0014] The product of the adjustment degree of the energy cabin in each detection period and the corresponding output efficiency is used as the adjustment value; the sum of the adjustment value of the energy cabin in each detection period and the corresponding output efficiency is used as the regulation efficiency of the energy cabin in each detection period.

[0015] Furthermore, the method for obtaining the adjustment degree includes:

[0016] Calculate the distance between the center point of each environmental performance cluster and the center point of the preferred environmental area as the environmental impact of each environmental performance cluster;

[0017] The difference between the output efficiency of the energy cabin corresponding to the center point of each environmental performance cluster and the center point of the preferred environmental area is used as the power influence of each environmental performance cluster;

[0018] The adjustment degree of each detection period of the energy cabin is obtained by combining the environmental impact degree and power impact degree of the environmental performance cluster of the energy cabin in each detection period.

[0019] Furthermore, the method for obtaining the internal loss degree includes:

[0020] For any test period of any energy cabin, the output energy amounts of all test periods before the test period of the energy cabin are sorted in chronological order to obtain the energy amount sequence of the energy cabin;

[0021] Perform residual analysis on the energy quantity sequence of the energy cabin to obtain a residual sequence; when the normalized residual value in the residual sequence is greater than a preset screening threshold, the corresponding residual value is regarded as a high residual value;

[0022] The difference between the maximum output efficiency of the energy module during all test periods and the output efficiency during the test period is used as the relative loss of the energy module during the test period.

[0023] The internal loss degree of the energy cabin in the detection period is obtained by combining the ratio of the number of all high residual values ​​of the energy cabin to the total number of time periods, the sum of all high residual values ​​and the relative loss degree of the detection period.

[0024] Furthermore, obtaining the energy consumption requirement of the controllable energy cabin during the detection period based on the internal loss of the controllable energy cabin includes:

[0025] The value of the negative correlation mapping of the internal loss degree of each controllable energy cabin during the detection period is used as the energy consumption demand degree of each controllable energy cabin during the corresponding detection period.

[0026] Furthermore, the method for obtaining the energy consumption demand of the uncontrollable energy cabin during the detection period includes:

[0027] Before each test period, a prediction is made based on the output energy distribution of each uncontrollable energy compartment in the test period time sequence, and the predicted energy amount for each test period is obtained by adjusting the internal loss degree;

[0028] In each detection period, the product of the negative correlation mapping value of the internal loss degree of each uncontrollable energy compartment and the predicted energy amount is used as the energy consumption demand of each uncontrollable energy compartment in each detection period.

[0029] Furthermore, the method for obtaining the health assessment results includes:

[0030] According to the distribution disorder degree of the energy consumption demand of each energy compartment in each detection period, the balance difference degree of each detection period is obtained;

[0031] According to the growth of the balance difference degree of the detection period in the local range before the current detection period, the balance difference growth degree of the current detection period is obtained;

[0032] The health assessment result of the energy unit in the current detection period is obtained by combining the difference between the equilibrium difference degree in the current detection period and the previous detection period, as well as the equilibrium difference growth degree.

[0033] Furthermore, the method for obtaining the balance difference includes:

[0034] For any detection period, calculate the average energy consumption demand of all energy compartments in the detection period as the demand average;

[0035] After calculating the difference between the energy consumption demand and the demand average of each energy compartment during the detection period, the sum of all differences is used as the equilibrium difference degree of the detection period.

[0036] The method for obtaining the equilibrium difference growth degree includes:

[0037] In the detection periods within the local range before the current detection period, the number of detection periods whose statistical balance difference is greater than that of the previous detection period is obtained, and the balance difference growth degree of the current detection period is obtained based on the proportion of the said number in the number of all detection periods within the local range.

[0038] The present invention also provides an energy supply system health status assessment system based on energy units, comprising:

[0039] A data acquisition module is used to obtain the output efficiency and output energy of each energy cabin in the energy unit during different detection periods, as well as at least two environmental parameters; energy cabin types are divided into: controllable energy cabin and uncontrollable energy cabin;

[0040] An output efficiency adjustment module is used to adjust the output efficiency according to the ideal distribution deviation between the environmental parameters and the output efficiency in all detection periods of each energy cabin to obtain the adjustment efficiency of each detection period;

[0041] The energy consumption demand analysis module is used to obtain the internal loss degree of a single energy cabin during the detection period based on the accumulated loss of the output energy of a single energy cabin during operation and the regulation efficiency deviation during the detection period; obtain the energy consumption demand of the controllable energy cabin during the detection period based on the internal loss degree of the controllable energy cabin; and obtain the energy consumption demand of the uncontrollable energy cabin during the detection period by predicting the subsequent output energy based on the historical output energy and internal loss degree of the uncontrollable energy cabin and combining it with the internal loss degree;

[0042] The health assessment module is used to obtain the health assessment results of the energy unit through the uniformity of the time series changes in the energy consumption demand of each energy compartment.

[0043] The present invention has the following beneficial effects:

[0044] The present invention dynamically adjusts efficiency data based on the optimal distribution deviation of environmental parameters and output efficiency of the energy cabin in the time period, effectively eliminating the interference of external environmental conditions such as temperature and humidity on the efficiency evaluation, and ensuring that the internal performance of the energy cabin is truly reflected. For the controllable energy cabin, the internal loss is analyzed by the accumulated energy output to determine its energy consumption demand. Taking into account the instability of energy consumption caused by the uncontrollable energy cabin, the future energy supply demand is predicted in combination with the historical output and loss of the uncontrollable energy cabin, and the subsequent energy consumption demand is analyzed. By analyzing the uniformity of the changes in the energy consumption demand of each energy cabin in time series, the global health level of the energy unit is accurately evaluated, considering the excessive allocation of high-consumption energy cabins under dynamic task allocation, potential fault trends are identified, and timely warnings and maintenance strategies are optimized. The present invention improves the accuracy of loss quantification analysis by correcting environmental interference, dynamically allocates energy consumption in combination with the output prediction of uncontrollable energy, and performs health monitoring and evaluation through the continuous energy distribution situation to ensure the balance and stability of each energy cabin allocation strategy during efficient operation, providing the energy unit with a more reliable and continuous stable output capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flow chart of a method for evaluating the health status of an energy supply system based on an energy unit according to an embodiment of the present invention;

[0047] Figure 2 A flow chart of a method for obtaining regulation efficiency provided by one embodiment of the present invention;

[0048] Figure 3 A flow chart of a method for obtaining health assessment results provided by one embodiment of the present invention;

[0049] Figure 4 A structural diagram of an energy supply system health status assessment system based on energy units provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, features and effects of a method and system for evaluating the health status of an energy supply system based on an energy unit proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0052] The following describes in detail a method and system for evaluating the health status of an energy supply system based on energy units provided by the present invention with reference to the accompanying drawings.

[0053] An energy unit is a comprehensive energy supply system consisting of multiple energy modules, integrating different power generation types, such as solar, wind, or fuel generators, to meet user needs. Each energy module is an independent power generation unit within the energy unit. Each module has a specific power generation method, such as a solar module or a wind module, and its health is affected by its output efficiency and loss level.

[0054] Example 1:

[0055] See also Figure 1 , which shows a flow chart of a method for evaluating the health status of an energy supply system based on an energy unit according to an embodiment of the present invention. The method includes the following steps:

[0056] S1: Obtain the output efficiency and output energy of each energy cabin in the energy unit during different detection periods, as well as at least two environmental parameters; the energy cabins are divided into controllable energy cabins and uncontrollable energy cabins according to the output energy type.

[0057] During the operation of the energy cabin, the historical output efficiency and energy output of different energy cabins are analyzed by time period. In this embodiment of the present invention, a two-hour interval is used as a detection period. Voltage and current sensors installed in the energy cabin measure the voltage (V) and current (I) at the energy cabin's power output terminal at different times within a single detection period. The measurement frequency can be once every 1 second, and the average of the voltage and current values ​​at multiple times within a single detection period is calculated.

[0058] The engine of each energy cabin is a three-phase AC generator. The actual output power (unit: kW) of each detection period is obtained by combining the average current and voltage values ​​and the power factor. As an example, the expression of the actual output power is: Where, Expressed as actual output power, Expressed as the mean value of the voltage during the detection period, Expressed as the average current during the detection period, It is expressed as the power factor of the generator in the detection period. It should be noted that the calculation of the output power of the generator in the time period is a technical means well known to those skilled in the art and will not be described in detail here.

[0059] Because the energy collected by the energy cabin includes multiple types of energy, including uncontrollable energy cabins such as wind power generation and solar power generation, and controllable energy cabins such as fuel power generation, different energy sources correspond to different types of generators, so the input power calculation needs to be analyzed separately according to the generator type. For example, the input power of a wind turbine is calculated as: . is the air density, usually 1.225kg / m³, A is the blade swept area, in square meters (m²), V is the wind speed, in meters per second (m / s), is the wind energy utilization coefficient (dimensionless). The input power of the hydroelectric generator is calculated as: , is the density of water in kilograms per cubic meter ( ), g is the acceleration due to gravity, usually taken as 9.81 meters per second squared ( ), Q is the flow rate, the unit is cubic meters per second ( ), H is the water head or drop, in meters (m). The input power of a gas / fuel generator is the heat power released by fuel combustion. , m is the fuel consumption rate, unit is kg / h, q is the calorific value of fuel, unit is MJ / kg.

[0060] The output efficiency of different energy cabins at different times is obtained by the output power and input power. The expression of output efficiency is: , and calculate the product of duration and output power to get the output energy.

[0061] In order to facilitate the subsequent analysis of the interference of environmental factors on the efficiency of the energy cabin, different environmental parameters such as temperature, humidity or light intensity are obtained during the detection period. In an embodiment of the present invention, the average temperature, average humidity and average light intensity of the energy cabin in a single detection period are obtained through sensors such as temperature, humidity or light intensity as data for each environmental parameter.

[0062] S2: According to the ideal distribution deviation between the environmental parameters and the output efficiency in all detection periods of each energy cabin, the output efficiency is adjusted to obtain the adjustment efficiency of each detection period.

[0063] The same energy module may exhibit varying output efficiencies under different environmental factors. Interference from these environmental factors can lead to errors when assessing internal losses based on the current energy module efficiency. For example, high temperatures reduce air density and cooling efficiency, affecting combustion efficiency and the output efficiency of fuel-fired generators, resulting in lower output efficiency compared to conditions with better environmental conditions. To eliminate environmental interference when assessing internal losses, the current environmental interference can be eliminated by measuring the deviation of overall environmental factors from ideal conditions, resulting in an adjusted output efficiency.

[0064] Preferably, in the embodiment of the present invention, the method for obtaining the adjustment efficiency can be found in Figure 2 , which shows a flow chart of a method for obtaining regulation efficiency provided by an embodiment of the present invention, the method comprising the following steps:

[0065] S201: Establish a multidimensional space with each environmental parameter as each dimension; for any energy cabin, map the environmental parameters of the energy cabin in each detection period to the multidimensional space and cluster them to obtain environmental performance clusters; determine the preferred environmental area in the multidimensional space based on the preset ideal range of the energy cabin under each environmental parameter.

[0066] In the multidimensional space, each environmental parameter represents a coordinate axis, with the data from smallest to largest representing the positive direction of the coordinate axis. Based on the data size of all environmental parameters in each detection period of the energy cabin, data points are mapped to the multidimensional space. After mapping all detection periods, density clustering is performed to obtain clusters of environmental performance. In this embodiment of the present invention, the clustering algorithm can adopt the DBSCSN density clustering algorithm. The clustering process is a well-known technical method to those skilled in the art and is not limited or detailed here.

[0067] The preferred space in the multi-dimensional space is divided according to the most suitable environmental conditions. Through the preset ideal range of the energy cabin under each environmental parameter, for example, the humidity is between 45% and 55%, the distribution of the preferred environmental area in the multi-dimensional space in the humidity dimension is 45% to 55%. The preferred environmental area is the most suitable environmental distribution area for the operation of the energy cabin.

[0068] S202: Obtaining the adjustment degree of the energy cabin in each detection period according to the center point position deviation and the center point corresponding output efficiency deviation between the environment performance cluster and the preferred environment area in each detection period of the energy cabin.

[0069] Each environmental performance cluster reflects a characteristic of the operating environment, such as high-temperature performance or high-temperature, high-humidity performance. Although some test periods may have varying environmental conditions, the efficiency deviations caused by the overall environmental characteristics are consistent. Furthermore, due to inherent loss deviations, directly analyzing data points against the preferred environmental region can obscure the true loss situation. Therefore, the degree of adjustment for each test period is determined by the deviation between the environmental performance cluster and the preferred environmental region.

[0070] In an embodiment of the present invention, the distance between the center point of each environmental performance cluster and the center point of the preferred environmental area is calculated as the environmental impact degree of each environmental performance cluster. The larger the distance, the greater the impact of the environment on the energy cabin efficiency during this detection period, and the more data adjustments should be made to reduce interference.

[0071] The difference between the output efficiency of the energy cabin corresponding to the center point of each environmental performance cluster and the center point of the preferred environmental area is further used as the power impact of each environmental performance cluster. The greater the difference between the output efficiencies, the greater the impact on the output power of the energy cabin under the corresponding environmental performance, and a greater degree of restoration is required.

[0072] Finally, the environmental impact and power impact of the environmental performance clusters in each detection period of the energy cabin are combined to obtain the adjustment degree of each detection period of the energy cabin. In an embodiment of the present invention, the ratio of the environmental impact of the environmental performance clusters in each detection period of the energy cabin to the maximum environmental impact of all environmental performance clusters is used as a relative adjustment ratio. The adjustment range of the environmental impact is constrained by the relative situation with the maximum impact. The product of the relative adjustment ratio and the power impact is further normalized as the adjustment degree of each detection period of the energy cabin. The greater the adjustment degree, the greater the effort required to eliminate the influence of environmental interference.

[0073] It should be noted that normalization is a technical means well known to those skilled in the art. The normalization options may be linear normalization or standard normalization, such as using a sigmoid function for normalization. The specific normalization method is not limited here.

[0074] S203: Adjust the output efficiency based on the adjustment degree of each detection period of the energy cabin to obtain the adjustment efficiency of each detection period of the energy cabin.

[0075] Adjustments are made based on output efficiency to minimize the impact of environmental interference on efficiency analysis. In this embodiment, the product of the energy module's adjustment degree and the corresponding output efficiency for each detection period serves as the adjustment value, and the sum of the adjustment value and the corresponding output efficiency for each detection period serves as the energy module's adjustment efficiency for each detection period. This initially eliminates environmental interference, confining consumption issues solely to internal use. This facilitates subsequent analysis of losses across different energy modules and adjustments to allocation strategies.

[0076] S3: Based on the accumulated loss of the output energy of a single energy cabin during operation and the deviation of the regulation efficiency during the detection period, the internal loss degree of the single energy cabin during the detection period is obtained; based on the internal loss degree of the controllable energy cabin, the energy consumption demand of the controllable energy cabin during the detection period is obtained; based on the historical output energy and internal loss degree of the uncontrollable energy cabin, the subsequent output energy is predicted and combined with the internal loss degree to obtain the energy consumption demand of the uncontrollable energy cabin during the detection period.

[0077] The internal components of an energy module will experience some wear and tear during use, impacting its output efficiency and other factors. In severe cases, they may even require repair or scrapping. The energy unit is an integrated system, and the failure of a single energy module can trigger a chain reaction. For example, if one energy module fails, causing a sudden drop in power or shutting down, other energy modules may overload to compensate, potentially causing their own failures and impacting the lifespan of the entire energy unit. The internal wear and tear of a single energy module can be assessed by its current output efficiency and the cumulative amount of its historical energy output.

[0078] Preferably, in an embodiment of the present invention, the method for obtaining the internal loss degree includes:

[0079] First, for any detection period of any energy cabin, the output energy amounts of all detection periods before the energy cabin is run in chronological order to obtain the energy amount sequence of the energy cabin. Through the historical operation log, all the output energy amounts of a single energy cabin from the beginning of use to the current detection period are sorted.

[0080] Next, residual analysis is performed on the energy sequence of the energy cabin to obtain a residual sequence. Large residuals indicate possible operational problems with the energy cabin, leading to greater energy cabin losses. Therefore, the greater the proportion of periods with large residuals and the larger the sum of the residuals, the greater the energy cabin losses.

[0081] Therefore, when the normalized residual value in the residual sequence is greater than the preset screening threshold, the corresponding residual value is taken as a high residual value. First, the threshold is used to screen out the cases with large residuals. In the embodiment of the present invention, the preset screening threshold is set to 0.7, and the normalized residual value greater than 0.7 is determined as a high residual value. The specific numerical value can be adjusted by the implementer according to the specific implementation situation and is not limited here.

[0082] Further analysis of the gap between the current efficiency of the test period and the optimal efficiency in the time sequence shows that the larger the gap, the higher the loss. Therefore, the difference between the maximum output efficiency of the energy module in all test periods and the output efficiency of the test period is used as the relative loss of the energy module in the test period.

[0083] Finally, the internal loss degree of the energy cabin in the detection period is obtained by combining the ratio of the number of all high residual values ​​to the total number of the time period, the sum of all high residual values ​​and the relative loss degree of the detection period. In an embodiment of the present invention, the ratio of the number of all high residual values ​​to the total number of the time period is used as the high residual ratio, and the sum of all high residual values ​​is used as the residual loss degree. The larger the high residual ratio and the larger the residual sum, the more loss may be generated. The product of the high residual ratio, the residual loss degree and the relative loss degree of the energy cabin is used as the internal loss degree of the energy cabin in the detection period. The higher the internal loss degree, the smaller the energy consumption ratio of subsequent operation is allocated to reduce the possibility of failure.

[0084] Energy units contain multiple types of energy modules, including uncontrollable output modules for wind and solar power generation. Storing the output of these modules and then using it later inevitably results in energy losses. To avoid wasted energy, energy should be consumed directly and promptly. Therefore, when arranging energy output, it's important not only to balance losses across multiple modules but also to consider the output of the uncontrollable modules at different times.

[0085] Since the energy storage capacity of the controllable energy cabin is not considered, the energy consumption demand can be directly derived based on the internal loss degree. The energy consumption demand represents the proportion of energy consumption in the next period. In an embodiment of the present invention, the internal loss degree of each controllable energy cabin during the detection period is negatively correlated with the value mapped as the energy consumption demand of each controllable energy cabin during the corresponding detection period. The energy cabin with a greater internal loss degree is assigned a smaller proportion of energy consumption demand. Conversely, the lower the internal loss of the energy cabin, the higher the proportion of energy consumption demand can be assigned to maintain the energy consumption task.

[0086] It should be noted that negative correlation mapping is a technical means well known to those skilled in the art, and may be in the form of inverse proportion, etc. The selection of specific negative correlation mapping is not limited and elaborated herein.

[0087] Furthermore, for the uncontrollable energy cabin, a forecast adjustment of the energy output in the next period is added. When the predicted output energy is low, although the internal loss is small, the subsequent energy loss demand cannot meet the higher supply demand. Therefore, its demand should also be low to ensure the accuracy of energy consumption task allocation.

[0088] Preferably, in an embodiment of the present invention, the method for obtaining the energy consumption demand of the uncontrollable energy cabin during the detection period includes:

[0089] When allocating the proportion of energy consumption output for each uncontrollable energy compartment, current analysis can only be performed based on the output energy that may be generated in the next period. Therefore, before each detection period, a prediction is made based on the output energy distribution of each uncontrollable energy compartment in the detection period time series, and adjusted by the internal loss degree to obtain the predicted energy amount for each detection period. Considering that direct prediction is only based on historical power generation factors, the output amount that may be obtained in the next period due to weather and other factors is obtained. If the equipment loss is serious, even if the weather conditions are good, the actual power generation will be lower than the prediction. Therefore, the loss factor analysis is added to make the prediction closer to the actual situation.

[0090] In this embodiment of the present invention, the main prediction process includes performing a stationarity test on the energy quantity series of a single uncontrollable energy compartment. For example, a unit root test is used to determine whether the time series is stationary. The order (p, d, q) of the ARIMA model is determined based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series. p represents the autoregressive order, that is, the number of past output power values ​​included in the model as regression terms; d is the differencing order, used to make the time series stationary; and q is the sliding average order, that is, the number of past error terms included in the model as sliding average terms.

[0091] For the next test period in the future, the trained ARIMA model is used to make a forecast. Using a recursive forecasting method, the output power of the future period is gradually calculated based on the existing forecast values ​​and model parameters to obtain a preliminary forecast value. Residual analysis is then performed to obtain the corresponding residual sequence.

[0092] The internal loss series for each historical detection period is obtained and mapped to the time period of the residual series. The cross-correlation function (CCF) is used to determine the lagged impact of losses on the residuals. A loss-residual model is established using support vector regression (SVR). The prediction results of the loss-residual model are added to the initial ARIMA forecast as a correction term, and a Markov chain is introduced to enhance robustness to obtain the final predicted energy output. This consideration of the impact of internal losses on output power makes the forecast more accurate.

[0093] During each testing period, the product of the negatively correlated internal loss value of each uncontrollable energy compartment and the predicted energy output is used as the energy demand for each uncontrollable energy compartment during that testing period. A lower internal loss value and a higher predicted energy output indicate that the compartment will likely provide more energy in the future and have lower internal losses. Therefore, higher energy output tasks can be assigned to maintain stable energy output.

[0094] S4: Obtain the health assessment result of the energy unit by uniformly varying the energy consumption demand of each energy compartment over time.

[0095] In order to avoid the life of the entire energy unit being shortened due to the large difference in losses among different energy compartments in the energy unit, the internal losses are balanced by analyzing the energy consumption demand. However, when a major problem suddenly occurs in the energy compartment and the system adjustment causes continuous imbalance in distribution, maintenance reminders are still required to detect the problem in time and avoid greater losses caused by delayed maintenance.

[0096] The energy demand of different energy compartments over multiple consecutive time periods can be used as a reference. When the energy demand of each energy compartment gradually converges over time, it indicates that the current energy system is in good health and can adjust its distribution without human intervention. Conversely, when the energy demand of each energy compartment becomes increasingly inconsistent over time, a certain energy compartment may experience sustained extreme distribution, and the energy unit may face the threat of loss, requiring timely human intervention.

[0097] Preferably, in the embodiment of the present invention, the method for obtaining the health assessment result can be found in Figure 3 , which shows a flow chart of a method for obtaining health assessment results provided by an embodiment of the present invention, the method comprising the following steps:

[0098] S401: Obtaining a balance difference degree for each detection period according to the degree of distribution disorder of the energy consumption demand of each energy compartment in each detection period.

[0099] The greater the difference between the energy consumption demand and the average demand of different energy compartments, the more uneven the internal losses of each energy compartment are, and there may be a greater problem with sustained energy consumption. In an embodiment of the present invention, for any detection period, the average of the energy consumption demand of all energy compartments in the detection period is calculated as the demand average. After calculating the difference between the energy consumption demand and the demand average of each energy compartment in the detection period, the sum of all differences is used as the balanced difference of the detection period. As an example, the expression of the balanced difference is: Where, Expressed as The balance difference of each detection period, Expressed as the total number of energy cabins, Expressed as The energy module is in the The energy consumption demand during each detection period, Expressed as The average demand during each testing period.

[0100] S402: Obtain the balance difference growth degree of the current detection period according to the growth of the balance difference degree of the detection period in the local range before the current detection period.

[0101] During the local monitoring period before the current detection, when the equilibrium difference shows an increasing trend, it means that its equilibrium state is developing in a more extreme direction, the self-regulation ability of the system cannot maintain the equilibrium state, and the health state is worse.

[0102] In this embodiment of the present invention, the number of detection periods within a local range preceding the current detection period in which the statistical equilibrium difference is greater than the previous detection period is used. The equilibrium difference growth rate for the current detection period is calculated based on the proportion of the statistical number to the total number of detection periods within the local range. The local range is set to the range of the previous ten detection periods, and the specific range setting is not limited.

[0103] When the balance difference of a detection period is greater than that of the previous detection period, it means that the balance situation of each energy compartment in the detection period is getting worse. The more detection periods in this situation are distributed in all local detection numbers, that is, the more significant the growth of the balance difference is, the worse the health situation in the current detection period is.

[0104] S403: The health assessment result of the energy unit in the current detection period is obtained by combining the difference between the equilibrium difference degree in the current detection period and the previous detection period, and the equilibrium difference growth degree.

[0105] Finally, the health assessment of the current detection period is performed based on the degree of growth of the balance difference in the current period and the balance difference growth. In an embodiment of the present invention, the difference between the balance difference of the current detection period and the previous detection period is used as the evaluation value, and the product of the evaluation value and the balance difference growth is normalized to obtain the health assessment result of the energy unit in the current detection period.

[0106] Later in the embodiment of the present invention, an early warning can be given through threshold judgment. When the health assessment result of the energy unit is greater than the preset health threshold, it means that the energy unit is in poor condition and requires maintenance and inspection. The preset health threshold can be set to 0.7, and the specific value can be adjusted by the implementer. The output energy requirement values ​​of different energy compartments in different time periods and the health status of the energy units obtained from the analysis are transmitted and stored in the database. The output energy of different energy compartments in the data table and the health status of the current energy unit are obtained in the controller through SQL query statements. The expected output energy of different energy compartments when analyzing a single time period is transmitted and displayed in a table.

[0107] In summary, the present invention dynamically adjusts the efficiency data based on the preferred distribution deviation of the environmental parameters and output efficiency of the energy cabin in the time period, effectively eliminates the interference of external environmental conditions such as temperature and humidity on the efficiency evaluation, and ensures that the internal performance of the energy cabin is truly reflected. For the controllable energy cabin, the internal loss is analyzed by the accumulated energy output to determine its energy consumption demand. Taking into account the instability of energy consumption caused by the uncontrollable energy cabin, the future energy supply demand is predicted in combination with the historical output and loss of the uncontrollable energy cabin, and the subsequent energy consumption demand is analyzed. Through the analysis of the uniformity of the changes in the energy consumption demand of each energy cabin in time series, the global health level of the energy unit is accurately evaluated, and the over-allocation of high-consumption energy cabins under dynamic task allocation is considered. Potential fault trends are identified, and timely warnings and maintenance strategies are optimized. The present invention improves the accuracy of loss quantification analysis through environmental interference correction, dynamically allocates energy consumption in combination with the output prediction of uncontrollable energy, and performs health monitoring and evaluation through the continuous energy distribution. It ensures the balance and stability of each energy cabin allocation strategy during efficient operation, and provides a more reliable and continuous stable output capability for the energy unit.

[0108] Example 2:

[0109] The embodiment of the present invention provides a system for evaluating the health status of an energy supply system based on an energy unit. Figure 4 , which shows a structural diagram of an energy supply system health status assessment system based on an energy unit provided by an embodiment of the present invention. The system includes: a data acquisition module 501, an output efficiency adjustment module 502, an energy consumption demand analysis module 503 and a health assessment module 504.

[0110] The data acquisition module 501 is used to obtain the output efficiency and output energy of each energy compartment in the energy unit during different detection periods, as well as at least two environmental parameters. Energy compartment types are divided into: controllable energy compartments and uncontrollable energy compartments;

[0111] The output efficiency adjustment module 502 is used to adjust the output efficiency according to the ideal distribution deviation between the environmental parameters and the output efficiency in all detection periods of each energy cabin to obtain the adjustment efficiency of each detection period;

[0112] Energy consumption demand analysis module 503 is used to obtain the internal loss degree of a single energy compartment during the detection period based on the accumulated loss of the output energy of the single energy compartment during operation and the regulation efficiency deviation during the detection period; obtain the energy consumption demand of the controllable energy compartment during the detection period based on the internal loss degree of the controllable energy compartment; and obtain the energy consumption demand of the uncontrollable energy compartment during the detection period by predicting the subsequent output energy based on the historical output energy and internal loss degree of the uncontrollable energy compartment and combining it with the internal loss degree;

[0113] The health assessment module 504 is used to obtain the health assessment result of the energy unit by analyzing the uniformity of the time series changes in the energy consumption demand of each energy compartment.

[0114] It should be noted that the system provided in the above embodiment is merely illustrated by the division of the above functional modules. In actual applications, the above functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. Since the specific implementation process of the energy unit-based energy supply system health status assessment system in this embodiment is the same as the specific implementation process of the energy unit-based energy supply system health status assessment method described above, it will not be further elaborated here.

[0115] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for evaluating the health status of an energy supply system based on an energy unit, characterized in that: The method comprises: Obtain the output efficiency and output energy of each energy cabin in the energy unit during different detection periods, as well as at least two environmental parameters; energy cabin types are divided into: controllable energy cabin and uncontrollable energy cabin; According to the ideal distribution deviation between the environmental parameters and the output efficiency in all the test periods of each energy cabin, the output efficiency is adjusted to obtain the adjustment efficiency of each test period; Based on the accumulated loss of output energy during operation of a single energy cabin and the deviation of the regulation efficiency during the detection period, the internal loss degree of the single energy cabin during the detection period is obtained; based on the internal loss degree of the controllable energy cabin, the energy consumption demand of the controllable energy cabin during the detection period is obtained; based on the historical output energy and internal loss degree of the uncontrollable energy cabin, the subsequent output energy is predicted and combined with the internal loss degree to obtain the energy consumption demand of the uncontrollable energy cabin during the detection period; The health assessment results of the energy unit are obtained by analyzing the uniformity of the energy consumption demand of each energy compartment in time series. The method for obtaining the internal loss degree includes: For any test period of any energy cabin, the output energy amounts of all test periods before the test period of the energy cabin are sorted in chronological order to obtain the energy amount sequence of the energy cabin; Perform residual analysis on the energy quantity sequence of the energy cabin to obtain a residual sequence; when the normalized residual value in the residual sequence is greater than a preset screening threshold, the corresponding residual value is regarded as a high residual value; The difference between the maximum output efficiency of the energy module during all test periods and the output efficiency during the test period is used as the relative loss of the energy module during the test period. The internal loss degree of the energy cabin in the detection period is obtained by combining the ratio of the number of all high residual values ​​of the energy cabin to the total number of the period, the sum of all high residual values ​​and the relative loss degree of the detection period; The energy consumption requirement of the controllable energy cabin during the detection period is obtained based on the internal loss of the controllable energy cabin, including: The value of negative correlation mapping of the internal loss degree of each controllable energy cabin during the detection period is used as the energy consumption demand of each controllable energy cabin during the corresponding detection period; The method for obtaining the energy consumption demand of the uncontrollable energy cabin during the detection period includes: Before each test period, a prediction is made based on the output energy distribution of each uncontrollable energy compartment in the test period time sequence, and the predicted energy amount for each test period is obtained by adjusting the internal loss degree; In each detection period, the product of the negative correlation mapping value of the internal loss degree of each uncontrollable energy compartment and the predicted energy amount is used as the energy consumption demand of each uncontrollable energy compartment in each detection period; The method for obtaining the health assessment result includes: According to the distribution disorder degree of the energy consumption demand of each energy compartment in each detection period, the balance difference degree of each detection period is obtained; According to the growth of the balance difference degree of the detection period in the local range before the current detection period, the balance difference growth degree of the current detection period is obtained; The health assessment result of the energy unit in the current detection period is obtained by combining the difference between the equilibrium difference degree in the current detection period and the previous detection period, as well as the equilibrium difference growth degree.

2. The method for evaluating the health status of an energy supply system based on an energy unit according to claim 1, characterized in that: The method for obtaining the regulation efficiency includes: A multidimensional space is established with each environmental parameter as a dimension. For any energy chamber, the environmental parameters of the energy chamber in each detection period are mapped into the multidimensional space and clustered to obtain environmental performance clusters. Based on the preset ideal range of each environmental parameter for the energy chamber, the preferred environmental area is determined in the multidimensional space. The adjustment degree of the energy cabin in each detection period is obtained based on the center point position deviation and the output efficiency deviation corresponding to the center point between the environmental performance cluster and the preferred environmental area in each detection period of the energy cabin; The product of the adjustment degree of the energy cabin in each detection period and the corresponding output efficiency is used as the adjustment value; the sum of the adjustment value of the energy cabin in each detection period and the corresponding output efficiency is used as the regulation efficiency of the energy cabin in each detection period.

3. The method for evaluating the health status of an energy supply system based on an energy unit according to claim 2, characterized in that: The method for obtaining the adjustment degree includes: Calculate the distance between the center point of each environmental performance cluster and the center point of the preferred environmental area as the environmental impact of each environmental performance cluster; The difference between the output efficiency of the energy cabin corresponding to the center point of each environmental performance cluster and the center point of the preferred environmental area is used as the power influence of each environmental performance cluster; The adjustment degree of each detection period of the energy cabin is obtained by combining the environmental impact degree and power impact degree of the environmental performance cluster of the energy cabin in each detection period.

4. The method for evaluating the health status of an energy supply system based on an energy unit according to claim 1, characterized in that: The method for obtaining the balance difference includes: For any detection period, calculate the average energy consumption demand of all energy compartments in the detection period as the demand average; After calculating the difference between the energy consumption demand and the demand average of each energy compartment during the detection period, the sum of all differences is used as the equilibrium difference degree of the detection period.

5. The method for evaluating the health status of an energy supply system based on an energy unit according to claim 1, characterized in that: The method for obtaining the equilibrium difference growth degree includes: In the detection periods within the local range before the current detection period, the number of detection periods whose statistical balance difference is greater than that of the previous detection period is obtained, and the balance difference growth degree of the current detection period is obtained based on the proportion of the said number in the number of all detection periods within the local range.

6. A system for evaluating the health status of an energy supply system based on an energy unit, for implementing the method for evaluating the health status of an energy supply system based on an energy unit as claimed in claim 1, characterized in that: include: A data acquisition module is used to obtain the output efficiency and output energy of each energy cabin in the energy unit during different detection periods, as well as at least two environmental parameters; energy cabin types are divided into: controllable energy cabin and uncontrollable energy cabin; An output efficiency adjustment module is used to adjust the output efficiency according to the ideal distribution deviation between the environmental parameters and the output efficiency in all detection periods of each energy cabin to obtain the adjustment efficiency of each detection period; The energy consumption demand analysis module is used to obtain the internal loss degree of a single energy cabin during the detection period based on the accumulated loss of the output energy of a single energy cabin during operation and the regulation efficiency deviation during the detection period; obtain the energy consumption demand of the controllable energy cabin during the detection period based on the internal loss degree of the controllable energy cabin; and obtain the energy consumption demand of the uncontrollable energy cabin during the detection period by predicting the subsequent output energy based on the historical output energy and internal loss degree of the uncontrollable energy cabin and combining it with the internal loss degree; The health assessment module is used to obtain the health assessment results of the energy unit through the uniformity of the time series changes in the energy consumption demand of each energy compartment.

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