A smart photovoltaic monitoring system and monitoring method
By dynamically adjusting the heating method and cycle time through an intelligent photovoltaic monitoring system, the problem of low power generation efficiency caused by the incompatibility of photovoltaic panels in snow treatment in existing technologies has been solved, achieving more efficient snow melting and energy saving.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANXI YINGTAILIDA TECH CO LTD
- Filing Date
- 2026-02-03
- Publication Date
- 2026-06-02
Smart Images
Figure CN122137336A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent photovoltaic monitoring, and in particular to an intelligent photovoltaic monitoring system and monitoring method. Background Technology
[0002] Photovoltaic power generation, due to its cleanliness, renewable nature, and wide applicability, is playing an increasingly important role in electricity supply. Photovoltaic power plants are typically located in remote, sparsely populated areas with harsh working environments, making them highly susceptible to natural conditions. Snow and ice, a common winter weather phenomenon, pose a significant threat to photovoltaic modules, and the remote locations of these power plants make manual snow removal inconvenient. Therefore, timely and automated snow removal for photovoltaic panels is a core technology for ensuring stable operation of photovoltaic power generation in winter. However, existing snow removal methods cannot effectively control the snow removal process according to the actual snow accumulation and melting rate, leading to poor photovoltaic panel power generation efficiency. Therefore, how to dynamically adjust snow removal methods to adaptively improve the power generation efficiency of photovoltaic panels is a technical problem that urgently needs to be solved by those skilled in the art.
[0003] Chinese Patent Publication No. CN118713585A discloses a method, system, medium, and program product for preventing snow accumulation on outdoor photovoltaic panels. The method includes: determining whether the covering material on the surface of the photovoltaic panel is snow based on real-time monitoring video when the real-time load-bearing weight of the photovoltaic panel is greater than a preset weight; if the covering material is snow, determining the load-bearing sub-weight of each area of the photovoltaic panel; determining the bottom area of the photovoltaic panel where the load-bearing sub-weight is greater than a preset threshold; controlling the heating wire in the bottom area of the photovoltaic panel to operate at a first power; controlling the heating wire in other areas of the photovoltaic panel to operate at a second power, where the first power is greater than the second power; and controlling the photovoltaic panel to vibrate according to a preset mode. This technical solution avoids the refreezing of snow to form ice by controlling the operating power of the heating wire in different areas of the photovoltaic panel, thus reducing the impact on photovoltaic power generation. However, this technical solution has the following problems: although it considers zoned heating of the photovoltaic panel, it does not consider the impact of environmental conditions and the operating mode of the photovoltaic panel on the snow melting effect, resulting in the heating power of the heating components not being able to effectively adapt to the actual scene requirements, ultimately leading to poor heating performance. Summary of the Invention
[0004] To address this, the present invention provides an intelligent photovoltaic monitoring system and method to overcome the shortcomings of existing technologies that fail to consider the impact of environmental conditions and the working mode of photovoltaic panels on snow melting, which leads to the heating power of the heating components being unable to effectively adapt to the needs of the actual scenario, ultimately resulting in poor heating performance.
[0005] To achieve the above objectives, the present invention provides an intelligent photovoltaic monitoring system, comprising: The pressure analysis unit is used to determine the snow pressure status based on the preset snow range in which the snow pressure characterization value corresponding to the target photovoltaic panel is located. An ablation analysis unit, which is connected to the pressure analysis unit, is used to determine the ablation state based on the current ambient temperature and the estimated influence coefficient. The heating control unit is connected to the pressure analysis unit and the melting analysis unit respectively. It is used to determine the heating power of the heating component under full power heating based on the snow pressure state and melting state, or to determine the power supply and demand conditions based on the energy storage ratio and the power demand coefficient. The cycle time is determined based on the power supply and demand conditions as the base cycle time or the optimized cycle time. The demand analysis unit is connected to the pressure analysis unit, the ablation analysis unit and the heating control unit respectively, and is used to determine the power demand coefficient based on the power stability of the scenario. The parameter setting unit is connected to the pressure analysis unit, the ablation analysis unit, the heating control unit, and the demand analysis unit, respectively. It is used to determine the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within a preset time period, and to determine the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical scheduling frequency and the continuous melting frequency.
[0006] Furthermore, the heating control unit responds to the first preset heating condition and determines the heating power of the heating component under full-power heating based on the snow pressure difference; The heating power of the heating component under full-power heating is positively correlated with the difference in snow pressure. The first preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to that monitoring cycle is the first snow pressure state or the melting state is the first melting state.
[0007] Furthermore, the heating control unit responds to the second preset heating conditions, determines the power supply and demand conditions based on the energy storage ratio and the power demand coefficient, and determines the cycle duration based on the power supply and demand conditions; If the electricity supply and demand conditions are that the proportion of energy storage is greater than the preset proportion of energy storage and the electricity demand coefficient is less than or equal to the preset electricity demand coefficient, then the cycle time is the benchmark cycle time. If the power supply and demand conditions are that the proportion of energy storage is less than or equal to the preset proportion of energy storage or the power demand coefficient is greater than the preset power demand coefficient, then the cycle time is the optimized cycle time. Among them, the optimized cycle duration is greater than the baseline cycle duration, and the second preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to the monitoring cycle is the second snow pressure state and the melting state is the second melting state.
[0008] Furthermore, the pressure analysis unit determines the snow pressure state based on the preset snow range in which the snow pressure characterization value corresponding to the target photovoltaic panel is located; The snow pressure state includes: a first snow pressure state in which the snow pressure characterization value is within a first preset snow range, and a second snow pressure state in which the snow pressure characterization value is within a second preset snow range.
[0009] Furthermore, the ablation state includes: a first ablation state where the current ambient temperature is less than the preset ambient temperature or the estimated influence coefficient is less than the preset estimated influence coefficient, and a second ablation state where the current ambient temperature is greater than or equal to the preset ambient temperature and the estimated influence coefficient is greater than or equal to the preset estimated influence coefficient.
[0010] Furthermore, the demand analysis unit determines the power demand coefficient based on the power stability of the scenario; The power demand coefficient is negatively correlated with the stability of power consumption in the scenario.
[0011] Furthermore, the parameter setting unit determines the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within the preset time period, and determines the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The photovoltaic panel transformation characteristic value is positively correlated with the effective angle ratio and the flat angle transformation frequency; The preset estimated influence coefficient is negatively correlated with the photovoltaic panel conversion characterization value.
[0012] Furthermore, the parameter setting unit determines the preset time period based on the historical scheduling frequency; The preset time period is positively correlated with the historical scheduling frequency.
[0013] Furthermore, the parameter setting unit performs a secondary adjustment on the preset time period based on the continuous melting frequency; The preset time period is positively correlated with the frequency of continuous melting.
[0014] The present invention also provides a monitoring method applied to the intelligent photovoltaic monitoring system, comprising: The snow pressure state is determined as either the first snow pressure state or the second snow pressure state based on the preset snow pressure range where the snow pressure characterization value corresponding to the target photovoltaic panel is located. The ablation state is determined as either the first ablation state or the second ablation state based on the current ambient temperature and the estimated impact coefficient. Determine the power demand coefficient based on the power stability of the scenario; The photovoltaic panel transformation characterization value is determined based on the effective angle ratio and the flat angle transformation frequency within the preset time period, and the preset estimated influence coefficient is determined based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical scheduling frequency; and the preset time period is further adjusted based on the frequency of continuous melting. The preset heating conditions are determined based on the melting state and the snow pressure state. Under the first preset heating condition, the heating method is determined to be full power heating, and under the second preset heating condition, the heating method is determined to be low power heating. During full-power heating, the heating power of the heating component is determined based on the snow pressure difference. In low-power heating, the power supply and demand conditions are determined based on the proportion of energy storage and the power demand coefficient, and the cycle time is determined based on the power supply and demand conditions as the benchmark cycle time or the optimized cycle time.
[0015] Compared with the prior art, the beneficial effects of the present invention are that the heating method is determined according to the snow pressure state and the melting state in the technical solution of the present invention. The snow pressure state reflects the degree of snow accumulation on the target photovoltaic panel, and the melting state reflects the influence of the current temperature and the predicted temperature change on the subsequent natural melting of the snow. Furthermore, different heating methods are selected according to different snow pressure states and melting states, so that the heating method can ensure the snow heating and melting effect while avoiding the problem of large energy consumption caused by a single heating method.
[0016] Furthermore, the technical solution of this invention reflects the power supply and demand situation in the actual scenario through the power storage ratio and power demand coefficient. The cycle time during heating is determined according to the power supply and demand situation, avoiding the problem that a single influencing factor may cause the cycle time to be difficult to meet the needs of the actual scenario. This makes the determination of the cycle time more dynamic and reasonable in response to complex climatic conditions, thereby reducing the ineffective operating time of the heating components and effectively saving power consumption.
[0017] Furthermore, the technical solution of the present invention reflects the influence of the photovoltaic panel's own angle on the heating component based on the effective angle ratio within a preset time period and the flat angle transformation frequency, thereby making the determination of the preset estimated influence coefficient more consistent with the actual working conditions.
[0018] Furthermore, in the technical solution of this invention, the preset time period is determined based on historical scheduling frequency. The historical scheduling frequency reflects the demand of electrical equipment for the photovoltaic power station, thereby making the determination of the preset time period more in line with actual application needs. The preset time period is then adjusted a second time based on the continuous melting frequency. The continuous melting frequency reflects whether the natural temperature is conducive to snow melting and the degree of its favorableness, making the determination of the preset time period more in line with the actual environment, improving the accuracy of the preset time period, thereby improving the accuracy of the preset estimated influence coefficient, and further improving the accuracy of the melting state determination, thus improving the snow melting effect of this invention. Attached Figure Description
[0019] Figure 1 This is a unit connection diagram of the intelligent photovoltaic monitoring system of the present invention; Figure 2 This is a flowchart illustrating how the cycle duration is determined based on the energy storage ratio and the energy demand coefficient in this invention. Figure 3 This is a flowchart illustrating how the ablation state is determined based on the current ambient temperature and the estimated influence coefficient, according to the present invention. Figure 4 This is a schematic diagram of the monitoring method of the present invention applied to an intelligent photovoltaic monitoring system. Detailed Implementation
[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.
[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.
[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.
[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0024] Please see Figures 1 to 3 As shown, the present invention provides an intelligent photovoltaic monitoring system, comprising: The pressure analysis unit is used to determine the snow pressure status based on the preset snow range in which the snow pressure characterization value corresponding to the target photovoltaic panel is located. An ablation analysis unit, which is connected to the pressure analysis unit, is used to determine the ablation state based on the current ambient temperature and the estimated influence coefficient. The heating control unit is connected to the pressure analysis unit and the melting analysis unit respectively. It is used to determine the heating power of the heating component under full power heating based on the snow pressure state and melting state, or to determine the power supply and demand conditions based on the energy storage ratio and the power demand coefficient. The cycle time is determined based on the power supply and demand conditions as the base cycle time or the optimized cycle time. The demand analysis unit is connected to the pressure analysis unit, the ablation analysis unit and the heating control unit respectively, and is used to determine the power demand coefficient based on the power stability of the scenario. The parameter setting unit is connected to the pressure analysis unit, the ablation analysis unit, the heating control unit, and the demand analysis unit, respectively. It is used to determine the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within a preset time period, and to determine the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical scheduling frequency and the continuous melting frequency.
[0025] The application scenario of this invention is the automatic removal of snow from photovoltaic panels in winter. For example, in North my country and Northeast China, taking Northeast China as an example, including Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia, it is easily affected by the East Asian monsoon and the Siberian cold high pressure, and is characterized by long snowfall time, large snowfall and long snow accumulation period. Therefore, timely and automated snow removal for photovoltaic panels is the core technology to ensure the stable operation and maintenance of photovoltaic power generation in the region during winter.
[0026] This invention features a continuous monitoring cycle. The duration of a single monitoring cycle can be adaptively set by the user according to actual application needs. It is understood that the greater the impact of snow accumulation on photovoltaic power generation in actual application scenarios, the shorter the duration of a single monitoring cycle. This invention provides a value for the duration of a single monitoring cycle, which is 15 minutes.
[0027] In this invention, the target photovoltaic panel comprises several sub-panels, which are arranged in an array on a photovoltaic support. The photovoltaic support has an angle adjustment function, and its specific structure is not limited. Each target photovoltaic panel is equipped with a corresponding heating and snow removal device, which includes a resistance heating strip and a power supply device for supplying power to the resistance heating strip. The resistance heating strip is located in the gaps between the arrayed sub-panels. When powered, the resistance heating strip generates heat to remove snow accumulation on the target photovoltaic panel. The above is readily understood by those skilled in the art and will not be elaborated further. The electrical equipment in this invention utilizes the electrical energy generated by the target photovoltaic panel through solar energy.
[0028] This invention utilizes several historical records. Each historical record includes at least the heating power corresponding to each full-power heating cycle, the energy storage ratio, the energy demand coefficient, the maximum snow pressure, the minimum snow pressure, the ambient temperature, the weighting coefficient, the upper limit angle of the photovoltaic panel, the lower limit angle of the photovoltaic panel, the stability of power consumption in the scenario, the duration of the time period, and the historical dispatch frequency. Furthermore, each historical record is equipped with a qualification mark, which records whether the historical record meets the user's needs. Whether a historical record meets the user's needs can be determined, but is not limited to, based on whether the snow melting rate meets the user's power generation requirements. Determining whether a historical record meets the user's needs based on self-defined indicators is content already known to those skilled in the art and is not limited here.
[0029] Specifically, the heating control unit responds to the first preset heating condition and determines the heating power of the heating component under full power heating based on the snow pressure difference; The heating power of the heating component under full-power heating is positively correlated with the difference in snow pressure. The first preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to that monitoring cycle is the first snow pressure state or the melting state is the first melting state.
[0030] Heating power = Base heating power + k1 × Snow pressure difference; where k1 is the adjustment coefficient corresponding to the snow pressure difference. The value of k1 can be adaptively set by the user according to the actual application requirements. It can be understood that the greater the contribution of the snow pressure difference to the heating power, the greater the value of k1. The contribution level can be learned through historical records combined with machine learning, and the user can determine it through their own experience. This is content that is easy for those skilled in the art to understand, and will not be elaborated here. This invention provides a value of k1, in which k1 = 0.4. In the calculation of heating power, the base heating power and the snow pressure difference are dimensionless calculations, and the unit of heating power is kW.
[0031] Snow pressure difference = snow pressure characterization value - preset snow pressure characterization value; The heating power is the operating power of the power supply device corresponding to the snow removal device. The user can adaptively set the reference heating power according to the actual application environment. It can be understood that the lower the temperature of the application environment, the higher the reference heating power. This invention provides a method for determining the reference heating power by extracting the minimum heating power under full power heating in the historical records that meet the user's needs, and recording the average value of the minimum heating power after removing outliers as the reference heating power. The outlier removal method can be, but is not limited to, the 3σ criterion method or the IQR method.
[0032] Specifically, the heating control unit responds to the second preset heating condition, determines the power supply and demand conditions based on the energy storage ratio and the power demand coefficient, and determines the cycle duration based on the power supply and demand conditions. If the electricity supply and demand conditions are that the proportion of energy storage is greater than the preset proportion of energy storage and the electricity demand coefficient is less than or equal to the preset electricity demand coefficient, then the cycle time is the benchmark cycle time. If the power supply and demand conditions are that the proportion of energy storage is less than or equal to the preset proportion of energy storage or the power demand coefficient is greater than the preset power demand coefficient, then the cycle time is the optimized cycle time. Among them, the optimized cycle duration is greater than the baseline cycle duration, and the second preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to the monitoring cycle is the second snow pressure state and the melting state is the second melting state.
[0033] Energy storage ratio = Electricity stored in the energy storage system / Total energy storage capacity of the energy storage system; The heating control unit responds to the second preset heating condition and adopts a low-power heating mode. Low-power heating means that when heating the target photovoltaic panel, it cycles through the first heating power and the second heating power. The first heating power is the same as the reference heating power, and the second heating power is less than the first heating power. It can be understood that the environmental conditions under the second preset heating condition are conducive to snow melting. Therefore, by cycling, energy loss during heating can be reduced. The user can set the value of the second heating power according to energy saving needs. The greater the user's energy saving needs, the smaller the value of the second heating power. One possible value for the second heating power is 60% of the reference heating power. During low-power heating, after running at the first heating power for a certain period of time, it runs at the second heating power for a fixed period of time, and the above steps are repeated.
[0034] When heating photovoltaic panels, a cyclic heating method is adopted, in which full-power heating and low-power heating alternate. The baseline cycle time is as follows: first, heating with the first power is used for 60% of the total heating time, followed by heating with the second power for 5 minutes. The total heating time can be adaptively set by the user according to their acceptance of energy consumption. It can be understood that the lower the user's acceptance of energy consumption, the smaller the total heating time. This invention provides a value for the total heating time, in which the total heating time = 70% of the monitoring cycle time.
[0035] This invention provides a baseline loop duration and an optimized loop duration. The optimized loop duration is calculated as: Baseline Loop Duration + Effective Difference × k2. Here, k2 is an adjustment coefficient corresponding to the effective difference. Users can adaptively set the value of k2 according to their actual application needs. It is understood that the greater the contribution of the effective difference to the optimized loop duration, the larger the value of k2. This invention provides a value for k2: k2 = 0.5. In the calculation of the optimized loop duration, both the baseline loop duration and the effective difference are dimensionless, and the unit of the optimized loop duration is minutes.
[0036] The effective difference is the larger of the difference between the energy storage ratio and the difference between the energy demand coefficient. Specifically, the difference between the energy storage ratio and the energy storage ratio is calculated as follows: Energy storage ratio difference = Preset energy storage ratio - Energy storage ratio; Energy demand coefficient difference = Preset energy demand coefficient - Energy demand coefficient.
[0037] Users can adaptively set the preset energy storage ratio and preset energy demand coefficient according to actual application scenarios. It is understood that the smaller the user's tolerance for the impact of power supply and demand conditions, the larger the preset energy storage ratio and the smaller the preset energy demand coefficient. This invention provides a method for determining the preset energy storage ratio and preset energy demand coefficient, which extracts the corresponding energy storage ratio and energy demand coefficient from the historical records that meet the user's needs, filters out outliers, and records the average values of the energy storage ratio and energy demand coefficient after removing outliers as the preset energy storage ratio and preset energy demand coefficient, respectively.
[0038] Specifically, the pressure analysis unit determines the snow pressure status based on the preset snow range in which the snow pressure characterization value corresponding to the target photovoltaic panel is located; The snow pressure state includes: a first snow pressure state in which the snow pressure characterization value is within a first preset snow range, and a second snow pressure state in which the snow pressure characterization value is within a second preset snow range.
[0039] The snow pressure state also includes a third snow pressure state where the snow pressure characterization value is within a third preset snow pressure range, which does not require the heating component to be activated.
[0040] The methods for confirming the snow pressure characterization value include, but are not limited to, ultrasonic methods, direct measurement methods using pressure sensors, and vibration frequency methods. These detection methods are readily understood by those skilled in the art and will not be elaborated upon here. This invention provides a method for detecting snow pressure characterization values. Five pressure detection components are evenly arranged at the four corners and center of the back of a target photovoltaic panel. In this invention, the pressure detection components are flexible thin-film pressure sensors, which are encapsulated with waterproof adhesive. The average value of the pressure values measured in real time by the five flexible thin-film pressure sensors is recorded as the snow pressure characterization value.
[0041] Among them, the values in the first preset snow accumulation range are all greater than the first preset snow pressure characterization value, the values in the second preset snow accumulation range are all less than or equal to the first preset snow pressure characterization value and greater than the second preset snow pressure characterization value, and the values in the third preset snow accumulation range are all less than or equal to the second preset snow pressure characterization value.
[0042] The user can determine the maximum acceptable snow pressure characterization value based on the requirements for snow power generation efficiency or the photovoltaic panel power generation efficiency under different snow pressure characterization values in historical records. 70% of the maximum snow pressure characterization value is recorded as the first preset snow pressure characterization value and 30% of the maximum snow pressure characterization value is recorded as the second preset snow pressure characterization value.
[0043] Specifically, the ablation states include: a first ablation state where the current ambient temperature is less than the preset ambient temperature or the estimated influence coefficient is less than the preset estimated influence coefficient, and a second ablation state where the current ambient temperature is greater than or equal to the preset ambient temperature and the estimated influence coefficient is greater than or equal to the preset estimated influence coefficient.
[0044] The current ambient temperature is determined by randomly selecting several temperature detection points within the area where the photovoltaic panel is located and setting up temperature detection devices. The average value of the temperature detected by the temperature detection devices at the corresponding temperature detection points is recorded as the current ambient temperature.
[0045] The method for confirming the estimated impact coefficient is to obtain the temperature forecast curve, which is presented in a two-dimensional coordinate system. The horizontal axis of the two-dimensional coordinate system represents time, and the vertical axis represents ambient temperature. This can be understood as being based on intelligent weather instruments (such as Netatmo and Davis). The instrument obtains the ambient temperature change over a preset period after the current time and generates a temperature prediction curve. This is easily understood by those skilled in the art and will not be elaborated here. A preset ambient temperature line is constructed on the two-dimensional coordinate system where the temperature prediction curve is located. The preset ambient temperature line is a straight line with the preset ambient temperature as its vertical axis and the maximum and minimum values of its horizontal axis being the same as those of the temperature prediction curve. An effective temperature segment is constructed. The prediction influence coefficient is the absolute value of the difference between the maximum and minimum horizontal axes corresponding to the effective temperature segment / the absolute value of the difference between the maximum and minimum horizontal axes of the temperature prediction curve. The segment with the longest length that meets the effective screening criteria on the temperature prediction curve is recorded as the effective temperature segment. A segment is a part of the continuous line segment on the temperature prediction curve. If 90% of the length of a segment is above the preset ambient temperature line, then the segment meets the effective screening criteria.
[0046] The preset ambient temperature can be set by the user according to actual needs. It is understood that the higher the ambient temperature, the greater the melting efficiency. Therefore, the greater the user's demand for melting efficiency, the higher the preset ambient temperature. This invention provides a method for setting the preset ambient temperature by extracting the corresponding ambient temperature from the historical records that meet the user's needs, filtering out outliers, and recording the average value of the ambient temperature after removing outliers as the preset ambient temperature.
[0047] Specifically, the demand analysis unit determines the power demand coefficient based on the power stability of the scenario; The power demand coefficient is negatively correlated with the stability of power consumption in the scenario.
[0048] The power demand coefficient = baseline power demand coefficient × k3; where k3 = preset scenario power stability / scenario power stability. This means the power demand coefficient = baseline power demand coefficient × (preset scenario power stability / scenario power stability). The preset scenario power stability is greater than the scenario power stability, meaning k3 is greater than 1, and the power demand coefficient is greater than the baseline power demand coefficient. The scenario power stability is determined by uniformly extracting several power consumption detection times within the monitoring period and detecting the power consumption of the equipment corresponding to each detection time. Scenario power stability = Where M represents the number of moments within a single monitoring period. Let be the power consumption of the electrical equipment at time i. This represents the average power consumption of electrical equipment within a single monitoring period.
[0049] The value of the baseline power demand coefficient can be adaptively set by the user according to the actual application needs. It can be understood that the greater the demand of the electrical equipment for the target photovoltaic panel, the greater the baseline power demand coefficient. This invention provides a method for determining the value of the baseline power demand coefficient, which extracts the power demand coefficients in the historical records that meet the needs of the electrical equipment, filters out the outliers, and records the average value of the power demand coefficients after removing the outliers as the baseline power demand coefficient, where the power demand coefficient = power demand / available power.
[0050] The preset scenario power stability value can be adaptively set by the user according to the actual application needs. It can be understood that the smaller the user's tolerance for the impact of the power demand coefficient, the larger the preset scenario power stability value. This invention provides a method for determining the preset scenario power stability value, which extracts the corresponding scenario power stability value in the historical records that meet the user's needs, filters out outliers, and records the average value of the scenario power stability value after removing outliers as the preset scenario power stability value.
[0051] The number of power detection moments within a single monitoring cycle can be adaptively set by the user according to actual application needs. It is understood that the higher the user's requirements for the accuracy of power stability in the scenario, the larger the number of power detection moments will be. This invention provides a value for the number of power detection moments, which is 20.
[0052] Specifically, the parameter setting unit determines the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within a preset time period, and determines the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The photovoltaic panel transformation characteristic value is positively correlated with the effective angle ratio and the flat angle transformation frequency; The preset estimated influence coefficient is negatively correlated with the photovoltaic panel conversion characterization value.
[0053] The photovoltaic panel transformation characteristic value = α1 × effective angle ratio + α2 × horizontal angle transformation frequency; where α1 is the first weight coefficient and α2 is the second weight coefficient, and α1 + α2 = 1. The values of α1 and α2 can be set directly by the user based on domain experience, or by using statistical methods such as regression analysis or principal component analysis to determine the contribution of the effective angle ratio and horizontal angle transformation frequency to the photovoltaic panel transformation characteristic value, thereby determining the corresponding weight coefficient value. The greater the contribution, the greater the weight coefficient value. The weights can also be adjusted through historical data training (such as machine learning).
[0054] The operating trajectory of the photovoltaic panel within a preset time period is obtained. The operating trajectory represents the angle of the photovoltaic panel at different times. The effective angle percentage = the duration of the effective angle / the duration of the preset time period. The angle between the photovoltaic panel and the ground is recorded as the photovoltaic panel angle, and the photovoltaic panel angle within the preset angle range is recorded as the effective angle.
[0055] The preset angle range can be adaptively set by the user according to actual application needs. It is understood that the higher the user's requirement for the accuracy of the effective angle, the smaller the preset angle range. This invention provides a method for determining the preset angle range, which extracts the upper limit angle and lower limit angle of the photovoltaic panel from the corresponding angle range in the historical records that meet the user's needs, filters out outliers, and records the average values of the upper limit angle and lower limit angle of the photovoltaic panel after removing outliers as the preset upper limit angle and the preset lower limit angle of the photovoltaic panel, respectively. The preset angle range is when the preset upper limit angle of the photovoltaic panel is greater than or equal to the photovoltaic panel angle and the photovoltaic panel angle is greater than or equal to the preset lower limit angle of the photovoltaic panel.
[0056] In this invention, the working angle of the photovoltaic panel changes dynamically during operation to adapt to solar radiation and thus improve power generation efficiency. Therefore, the preset working angle of the photovoltaic panel within a preset time period after the current moment can be extracted. The working angle is the angle between the photovoltaic panel and the horizontal ground. The duration corresponding to the working angle of the photovoltaic panel being 180° is recorded as the flat angle duration. The flat angle transformation frequency = flat angle duration / duration of the preset time period.
[0057] Specifically, the parameter setting unit determines the preset time period based on the number of historical scheduling attempts; The preset time period is positively correlated with the historical scheduling frequency.
[0058] Preset time period = Base period duration × k4; where k4 is the adjustment coefficient corresponding to the base period duration, k4 = historical scheduling frequency / preset historical scheduling frequency; the historical scheduling frequency is greater than the preset historical scheduling frequency, which means that k4 is greater than 1, that is, the preset time period duration is greater than the base period duration.
[0059] The user can adaptively set the value of the benchmark time period according to the actual application needs. It is understood that the higher the user's requirement for the accuracy of the preset time period, the longer the benchmark time period will be. This invention provides a method for determining the value of the benchmark time period, which extracts the corresponding time period length from the historical records that meet the user's needs, filters out outliers, and records the average value of the time period length after removing outliers as the benchmark time period length.
[0060] The historical scheduling frequency is determined as follows: several monitoring periods are extracted. For each monitoring period, the period is divided into several time segments with no overlap. The number of scheduling operations within each time segment is recorded as the time segment scheduling frequency. The average time segment scheduling frequency for the same time segment across all monitoring periods is recorded as the scheduling frequency for that time segment. Each time segment has its own scheduling frequency. The time segment with the highest scheduling frequency is recorded as the first preset time segment, and its corresponding scheduling frequency is recorded as the historical scheduling frequency.
[0061] The user can adaptively set the preset historical scheduling frequency according to the actual application needs. It is understood that the smaller the user's tolerance for the impact of the preset time period, the larger the preset historical scheduling frequency will be. This invention provides a method for setting the preset historical scheduling frequency, which extracts the corresponding historical scheduling frequency from the historical records that meet the user's needs, filters out the outliers, and records the average value of the historical scheduling frequency after removing the outliers as the preset historical scheduling frequency.
[0062] Specifically, the parameter setting unit makes secondary adjustments to the preset time period based on the continuous melting frequency; The preset time period is positively correlated with the frequency of continuous melting.
[0063] The preset time period after secondary adjustment = the duration of the preset time period without secondary adjustment + the frequency of continuous melting × k5; where k5 is the adjustment coefficient corresponding to the frequency of continuous melting. The value of k5 can be adaptively set by the user according to the actual application requirements. It can be understood that the greater the influence of the continuous melting frequency on the preset time period after secondary adjustment, the larger the value of k5. This invention provides a value of k5, k5=0.6; Continuous melting frequency = full power heating time within the preset time period / duration of the preset time period without secondary adjustment. In the calculation of the preset time period after secondary adjustment, the duration of the preset time period without secondary adjustment and the continuous melting frequency are dimensionless calculations, and the unit of the preset time period after secondary adjustment is min.
[0064] Please see Figure 4 As shown, this is a schematic diagram of the monitoring method of the present invention applied to an intelligent photovoltaic monitoring system. The present invention also provides a monitoring method applied to an intelligent photovoltaic monitoring system, comprising: The snow pressure state is determined as either the first snow pressure state or the second snow pressure state based on the preset snow pressure range where the snow pressure characterization value corresponding to the target photovoltaic panel is located. The ablation state is determined as either the first ablation state or the second ablation state based on the current ambient temperature and the estimated impact coefficient. Determine the power demand coefficient based on the power stability of the scenario; The photovoltaic panel transformation characterization value is determined based on the effective angle ratio and the flat angle transformation frequency within the preset time period, and the preset estimated influence coefficient is determined based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical scheduling frequency; and the preset time period is further adjusted based on the frequency of continuous melting. The preset heating conditions are determined based on the melting state and the snow pressure state. Under the first preset heating condition, the heating method is determined to be full power heating, and under the second preset heating condition, the heating method is determined to be low power heating. During full-power heating, the heating power of the heating component is determined based on the snow pressure difference. In low-power heating, the power supply and demand conditions are determined based on the proportion of energy storage and the power demand coefficient, and the cycle time is determined based on the power supply and demand conditions as the benchmark cycle time or the optimized cycle time.
[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.
Claims
1. An intelligent photovoltaic monitoring system, characterized in that, include: The pressure analysis unit is used to determine the snow pressure status based on the preset snow range in which the snow pressure characterization value corresponding to the target photovoltaic panel is located. An ablation analysis unit, which is connected to the pressure analysis unit, is used to determine the ablation state based on the current ambient temperature and the estimated influence coefficient. The heating control unit is connected to the pressure analysis unit and the melting analysis unit respectively, and is used to determine the heating power of the heating component under full power heating based on the snow pressure state and melting state, or to determine the power supply and demand conditions based on the energy storage ratio and the power demand coefficient. The demand analysis unit is connected to the pressure analysis unit, the ablation analysis unit and the heating control unit respectively, and is used to determine the power demand coefficient based on the power stability of the scenario. The parameter setting unit is connected to the pressure analysis unit, the ablation analysis unit, the heating control unit, and the demand analysis unit, respectively. It is used to determine the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within a preset time period, and to determine the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical scheduling frequency and the continuous melting frequency.
2. The intelligent photovoltaic monitoring system according to claim 1, characterized in that, The heating control unit responds to the first preset heating condition and determines the heating power of the heating component under full power heating based on the snow pressure difference. The heating power of the heating component under full-power heating is positively correlated with the difference in snow pressure. The first preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to that monitoring cycle is the first snow pressure state or the melting state is the first melting state.
3. The intelligent photovoltaic monitoring system according to claim 1, characterized in that, The heating control unit responds to the second preset heating condition, determines the power supply and demand conditions based on the energy storage ratio and the power demand coefficient, and determines the cycle duration based on the power supply and demand conditions. If the electricity supply and demand conditions are that the proportion of energy storage is greater than the preset proportion of energy storage and the electricity demand coefficient is less than or equal to the preset electricity demand coefficient, then the cycle time is the benchmark cycle time. If the power supply and demand conditions are that the proportion of energy storage is less than or equal to the preset proportion of energy storage or the power demand coefficient is greater than the preset power demand coefficient, then the cycle time is the optimized cycle time. Among them, the optimized cycle duration is greater than the baseline cycle duration, and the second preset heating condition is that a single monitoring cycle ends and the snow pressure state corresponding to the monitoring cycle is the second snow pressure state and the melting state is the second melting state.
4. The intelligent photovoltaic monitoring system according to claim 1, characterized in that, The pressure analysis unit determines the snow pressure status based on the preset snow range where the snow pressure characterization value corresponding to the target photovoltaic panel is located. The snow pressure state includes: a first snow pressure state in which the snow pressure characterization value is within a first preset snow range, and a second snow pressure state in which the snow pressure characterization value is within a second preset snow range.
5. The intelligent photovoltaic monitoring system according to claim 1, characterized in that, The ablation states include: the first ablation state where the current ambient temperature is less than the preset ambient temperature or the estimated influence coefficient is less than the preset estimated influence coefficient; and the second ablation state where the current ambient temperature is greater than or equal to the preset ambient temperature and the estimated influence coefficient is greater than or equal to the preset estimated influence coefficient.
6. The intelligent photovoltaic monitoring system according to claim 5, characterized in that, The demand analysis unit determines the power demand coefficient based on the power stability of the scenario; The power demand coefficient is negatively correlated with the stability of power consumption in the scenario.
7. The intelligent photovoltaic monitoring system according to claim 6, characterized in that, The parameter setting unit determines the photovoltaic panel transformation characterization value based on the effective angle ratio and the flat angle transformation frequency within the preset time period, and determines the preset estimated influence coefficient based on the photovoltaic panel transformation characterization value. The photovoltaic panel transformation characteristic value is positively correlated with the effective angle ratio and the flat angle transformation frequency; The preset estimated influence coefficient is negatively correlated with the photovoltaic panel conversion characterization value.
8. The intelligent photovoltaic monitoring system according to claim 7, characterized in that, The parameter setting unit determines the preset time period based on the historical scheduling frequency; The preset time period is positively correlated with the historical scheduling frequency.
9. The intelligent photovoltaic monitoring system according to claim 8, characterized in that, The parameter setting unit makes secondary adjustments to the preset time period based on the continuous melting frequency; The preset time period is positively correlated with the frequency of continuous melting.
10. A monitoring method applied to the intelligent photovoltaic monitoring system according to any one of claims 1 to 9, characterized in that, include: The snow pressure state is determined as either the first snow pressure state or the second snow pressure state based on the preset snow pressure range where the snow pressure characterization value corresponding to the target photovoltaic panel is located. The ablation state is determined as either the first ablation state or the second ablation state based on the current ambient temperature and the estimated impact coefficient. Determine the power demand coefficient based on the power stability of the scenario; The photovoltaic panel transformation characterization value is determined based on the effective angle ratio and the flat angle transformation frequency within the preset time period, and the preset estimated influence coefficient is determined based on the photovoltaic panel transformation characterization value. The preset time period is determined based on the historical number of scheduling operations; The preset time period is then adjusted again based on the frequency of continuous melting. The preset heating conditions are determined based on the melting state and the snow pressure state. Under the first preset heating condition, the heating method is determined to be full power heating, and under the second preset heating condition, the heating method is determined to be low power heating. During full-power heating, the heating power of the heating component is determined based on the snow pressure difference. In low-power heating, the power supply and demand conditions are determined based on the proportion of energy storage and the power demand coefficient, and the cycle time is determined as the baseline cycle time or the optimized cycle time based on the power supply and demand conditions.