A photovoltaic power quality monitoring method, system, equipment and storage medium

Abstract

This application provides a photovoltaic power quality monitoring method, system, device, and storage medium, relating to the field of power quality monitoring technology. The method includes: predicting the initial trend of power generation of the photovoltaic power generation system within a preset time period based on historical operating data of the photovoltaic power generation system; adjusting the initial trend based on environmental information of the photovoltaic power generation system to generate a first trend of power generation of the photovoltaic power generation system within the preset time period; acquiring real-time and historical power consumption data from the user end, and combining the real-time and historical power consumption data to predict a second trend of user end power load within the preset time period; determining the power deviation value of the power grid within the preset time period based on the first and second trends, and generating a power regulation plan for the power grid based on the power deviation value. The technical effect of this application is: timely response to power fluctuations of the photovoltaic power generation system and user load, reducing power oscillations in the power grid.

Description

Technical Field

[0001] This application relates to the field of power quality monitoring technology, specifically to a photovoltaic power quality monitoring method, system, equipment, and storage medium. Background Technology

[0002] With the escalating global energy crisis and the continued deterioration of environmental pollution, the development and utilization of renewable energy has become a global consensus. Among these, photovoltaic (PV) power generation, due to its cleanliness, safety, and convenience, has become one of the most promising renewable energy sources. However, due to the intermittent and fluctuating nature of PV power generation, the connection of numerous PV power plants to the grid may cause power quality problems such as voltage and frequency issues, and in severe cases, even lead to large-scale power outages. Therefore, how to achieve a friendly interconnection between PV power generation systems and the power grid, ensuring the safe and stable operation of the grid, has become an urgent technical problem to be solved.

[0003] Existing technologies typically employ real-time monitoring, adjusting only when power deviation exceeds a threshold. This passive monitoring and adjustment method suffers from lag and difficulty in responding to power fluctuations in a timely manner, easily causing frequent grid oscillations. Summary of the Invention

[0004] This application provides a photovoltaic power quality monitoring method, system, equipment, and storage medium for timely response to power fluctuations in photovoltaic power generation systems and user loads, thereby reducing power oscillations in the power grid.

[0005] In a first aspect, this application provides a photovoltaic power quality monitoring method, the method comprising: acquiring environmental information and historical operating data of a photovoltaic power generation system; predicting an initial trend of power generation of the photovoltaic power generation system within a preset time period based on the historical operating data; adjusting the initial trend of power generation based on the environmental information to generate a first trend of power generation of the photovoltaic power generation system within the preset time period; acquiring real-time electricity consumption data and historical electricity consumption data at the user end, and predicting a second trend of electricity load at the user end within the preset time period by combining the real-time electricity consumption data and the historical electricity consumption data; determining the power deviation value of the power grid within the preset time period based on the first trend of power generation and the second trend of power generation, and generating a power regulation plan for the power grid based on the power deviation value.

[0006] By adopting the above technical solution, the system obtains the first trend by adjusting the initial trend based on environmental information and historical operating data. Simultaneously, it predicts the second trend by combining real-time and historical electricity consumption data from the user end, thus achieving accurate prediction of photovoltaic power generation and user load. Based on these two trends, the system can determine the power deviation of the power grid within a preset time period in advance and formulate a timely power adjustment plan. This avoids the lag of traditional passive monitoring methods, enabling timely response to power fluctuations in the photovoltaic power generation system and user load, and reducing power oscillations in the power grid.

[0007] Optionally, predicting the initial trend of the power generation of the photovoltaic power generation system within a preset time period based on the historical operating data includes: determining the average power generation of the photovoltaic power generation system in each time period based on the historical operating data, and determining the target average power generation energy of the photovoltaic power generation system in the time period corresponding to the preset time period; obtaining the current initial power generation of the photovoltaic power generation system; and combining the target average power generation and the initial power generation to predict the initial trend of the power generation of the photovoltaic power generation system within the preset time period.

[0008] By adopting the above technical solution, the average power generation and target average power generation for each time period are determined through analysis of historical operating data. Combined with the initial power generation of the photovoltaic power generation system, a power generation prediction model based on historical data is established. This prediction method fully utilizes the power generation patterns inherent in historical data, providing a prediction benchmark through the target average power generation, while also considering the current actual power generation status. This makes the prediction of initial trends more consistent with the operating characteristics of the photovoltaic power generation system, providing reliable basic data for subsequent environmental information adjustments.

[0009] Optionally, the environmental information includes temperature data and irradiance data. Adjusting the initial trend based on the environmental information to generate a first trend in the power generation of the photovoltaic power generation system within the preset time period includes: predicting the predicted power generation of the photovoltaic power generation system within the preset time period based on the temperature data and the irradiance data; and adjusting the initial trend based on the predicted power generation to generate a first trend in the power generation of the photovoltaic power generation system within the preset time period.

[0010] By adopting the above technical solution and incorporating temperature and irradiance data to correct the initial trend, dynamic adjustment of photovoltaic power generation forecasts is achieved. Since the power generation efficiency of photovoltaic systems is significantly affected by temperature and light intensity, including these environmental factors in the forecast model can more accurately reflect the real-time changes in power generation. By adjusting the initial trend based on the predicted power generation, the generated first trend retains the statistical regularity of historical data while incorporating the real-time impact of environmental factors, thereby improving the accuracy of power generation forecasts.

[0011] Optionally, the step of combining the real-time electricity consumption data and the historical electricity consumption data to predict the second trend of the user terminal's electricity load within a preset time period includes: determining the average electricity load of the user terminal in each time period based on the historical electricity consumption data, and determining the target average electricity load of the user terminal in the time period corresponding to the preset time period; and combining the target average electricity load and the real-time electricity consumption data to predict the second trend of the user terminal's electricity load within the preset time period.

[0012] By employing the aforementioned technical solution, an average and target average electricity load for each time period is obtained through analysis of historical electricity consumption data. This data is then combined with real-time electricity consumption data for prediction, thus constructing a dynamic electricity load prediction model. This prediction method considers both historical statistical patterns of electricity load and incorporates current actual electricity consumption conditions, enabling the second trend to more accurately reflect the electricity consumption behavior characteristics of users. This provides reliable electricity load prediction data for subsequent power deviation calculations and adjustment plan formulation.

[0013] Optionally, determining the power deviation value of the power grid within the preset time period based on the first trend and the second trend includes: determining the output power of the photovoltaic power generation system within the preset time period based on the first trend; determining the power consumption of the user within the preset time period based on the second trend; and performing a difference calculation between the output power and the power consumption to obtain the power deviation value of the power grid within the preset time period.

[0014] By adopting the above technical solution and analyzing the first and second trends of change, the output power of the photovoltaic power generation system and the power consumption at the user end are obtained respectively, and the power deviation value is obtained through difference calculation. This calculation method unifies the dynamic change characteristics of the generation side and the consumption side, realizing an accurate assessment of the power balance state of the power grid within a preset time period. By pre-calculating the power deviation value, the system can identify potential power imbalance problems in advance, providing an accurate basis for formulating reasonable power regulation plans, thereby effectively preventing power grid fluctuations.

[0015] Optionally, generating a power regulation plan for the power grid based on the power deviation value includes: when the power deviation value is greater than a preset power threshold, obtaining the direction and magnitude of the change in the power deviation value; determining a target time point for power regulation based on the direction and magnitude of the change; determining the regulation power value of the power grid at the target time point based on the predicted value of the power deviation value at the target time point; and generating a power regulation plan for the power grid that includes regulation timing and regulation amount based on the target time point and the regulation power value.

[0016] By adopting the above technical solution and setting a preset power threshold as a trigger condition, the direction and magnitude of power deviation changes when the value exceeds the limit, determining the optimal adjustment timing and amount, and thus generating a power regulation plan containing specific adjustment sequences and amounts. This proactive regulation mechanism based on power deviation prediction not only enables precise power regulation at appropriate times, avoiding the lag of regulation measures, but also, by pre-planning the adjustment sequence and amount, makes power regulation more stable, effectively preventing secondary power fluctuations caused by improper regulation and improving the operational stability of the power grid.

[0017] Optionally, after generating the power regulation plan for the power grid based on the power deviation value, the method further includes: sending the power regulation plan to the power grid dispatch control center and receiving the power grid dispatch control center's power grid dispatch instructions generated based on the power regulation plan; controlling the photovoltaic power generation system to adjust its power generation according to the power grid dispatch instructions, and simultaneously sending power consumption control signals to the user end to control the user end to adjust its power consumption; obtaining the first actual power generation of the photovoltaic power generation system and the second actual power consumption of the user end, comparing the first actual power generation and the second actual power consumption to obtain the power deviation value of the actual operation of the power grid; determining whether the power deviation value meets the preset power grid stable operation conditions; if it meets the preset power grid stable operation conditions, generating a power grid operation quality report; if it does not meet the preset power grid stable operation conditions, regenerating the power regulation plan for the power grid until the power grid operation is stable.

[0018] By adopting the above technical solution, the power regulation plan is transformed into specific dispatch instructions, and coordinated control is implemented on both the generation and consumption sides. The regulation effect is verified by real-time monitoring of the first actual power generation and the second actual power consumption. The system compares the power deviation value with preset grid stability operating conditions, enabling real-time evaluation and dynamic optimization of the regulation results. When the regulation effect is unsatisfactory, the strategy can be adjusted promptly and a new regulation plan can be formulated, thereby ensuring the continuous and stable operation of the grid. This intelligent regulation scheme with a feedback mechanism improves the reliability and adaptability of grid power regulation.

[0019] Secondly, this application provides a mobile-based smart light pole on-site maintenance system, the system comprising: a receiving module, a parsing module, a conversion module, an execution module, and an output module; wherein, the receiving module is used to receive encrypted instructions sent by the mobile terminal based on a customized communication protocol after the mobile terminal is authenticated; the parsing module is used to decrypt the encrypted instructions and parse the decrypted encrypted instructions according to the communication protocol to generate a parsing result; the conversion module is used to convert the parsing result into a target instruction in the communication protocol format of the corresponding target smart device and send the target instruction to the target smart device, the target smart device being any one of the plurality of smart devices; the execution module is used to receive the execution result sent by the target smart device, convert and encrypt the execution result according to the customized communication protocol to generate a target execution result; the output module is used to send the target execution result to the mobile terminal, so that the mobile terminal decrypts and displays the target execution result, and generates an on-site maintenance plan based on the decrypted target execution result.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program of any of the photovoltaic power quality monitoring methods described above.

[0021] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and executed by any of the above-mentioned photovoltaic power quality monitoring methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects:

[0023] By acquiring environmental information and historical operational data, the initial trend is adjusted to obtain the first trend. Simultaneously, a second trend is predicted by combining real-time and historical electricity consumption data from the user end. This enables accurate prediction of the photovoltaic power generation system's output and the user's electricity load. Based on these two trends, the system can determine the power deviation of the power grid within a preset time period and promptly formulate power regulation plans. This avoids the lag inherent in traditional passive monitoring methods, enabling timely responses to power fluctuations in the photovoltaic power generation system and user loads, and reducing power oscillations in the power grid. Attached Figure Description

[0024] Figure 1 This is a schematic flowchart of a photovoltaic power quality monitoring method provided in an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the structure of a photovoltaic power quality monitoring system provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0029] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0030] As the penetration rate of photovoltaic (PV) power generation in the power system continues to increase, its inherent intermittent and fluctuating characteristics pose a severe challenge to the safe and stable operation of the power grid. Traditional grid dispatching methods mainly rely on the controllability of fossil fuel power generation, making it difficult to cope with the power balance problem after large-scale PV integration. Current common solutions include passive response regulation that adjusts the grid by monitoring changes in PV output in real time, simple predictive regulation that relies solely on historical data to predict power generation, and energy storage solutions that use large-capacity energy storage systems to smooth out power fluctuations. These existing technologies have shortcomings such as insufficient prediction accuracy, difficulty in accurately grasping the changing trends of PV power generation, ineffective coordination of two-way regulation of power generation and consumption, lack of systematic monitoring and evaluation mechanisms, and the need to improve the adaptability and economic efficiency of the regulation schemes.

[0031] The photovoltaic power quality monitoring method of this invention is mainly applicable to scenarios such as grid-connected operation of large-scale photovoltaic power plants, commercial and industrial rooftop photovoltaic systems, regional distribution network operation, and demonstration projects of new power systems. These include ground-mounted power plants with a photovoltaic installed capacity greater than 50MW, industrial parks with intelligent power management systems, commercial complexes with large electricity loads, microgrid systems with bidirectional regulation capabilities, regional power grids with high photovoltaic penetration rates, intelligent distribution systems requiring coordinated balancing of power generation and consumption, integrated energy systems primarily based on new energy sources, and energy internet projects integrating photovoltaic and energy storage. This invention establishes a multi-level prediction-monitoring-regulation system, achieving accurate monitoring and proactive regulation of photovoltaic power quality, which can significantly improve the safety and economy of power grid operation.

[0032] Figure 1 This is a schematic flowchart of a photovoltaic power quality monitoring method provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105:

[0033] S101, acquire environmental information and historical operating data of the photovoltaic power generation system.

[0034] In a specific implementation, it is first necessary to obtain environmental information and historical operating data of the photovoltaic power generation system. Environmental information includes temperature data and irradiance data, which directly affect the power generation efficiency and output power of the photovoltaic power generation system.

[0035] Temperature data is collected by temperature sensors installed on the surface of photovoltaic modules, with the surface temperature of the modules recorded at a sampling interval of 10 minutes; irradiance data is monitored in real time by an irradiance meter set up in the photovoltaic power station, with the sampling interval also being 10 minutes.

[0036] Historical operating data includes operating parameters of the photovoltaic power generation system at different time periods, such as power generation, output, grid-connected current, and voltage. This data is collected and stored through a data acquisition system at the power plant site, with a sampling period of 5 minutes. The acquisition of environmental information and historical operating data forms the data foundation for subsequent power generation prediction. By analyzing the correspondence between environmental parameters and power generation, a more accurate power generation prediction model can be established.

[0037] Specifically, the data acquisition system preprocesses the collected data, including outlier removal and data normalization, and stores the processed data in the power plant's local database. When power generation forecasting is needed, the system uses historical data from the past 30 days as training samples, combined with real-time environmental information, to provide reliable data support for subsequent power generation forecasts. By establishing a comprehensive data acquisition system, not only can the accuracy of power generation forecasts be improved, but also system anomalies can be detected in a timely manner, ensuring the safe and stable operation of the power plant.

[0038] S102, based on historical operating data, predict the initial trend of power generation of the photovoltaic power generation system within a preset time period.

[0039] In order to make a preliminary prediction of the power generation trend of the photovoltaic power generation system, the system first needs to determine the average power generation of the photovoltaic power generation system in each time period based on historical operating data.

[0040] Specifically, the system sets the preset duration to the next 4 hours and divides these 4 hours into 24 10-minute time intervals. For each 10-minute time interval, the system uses historical power generation data from the same period within the last 30 days and employs a weighted average method to calculate the average power generation for that period. The historical data closer to the current date has a higher weight to reflect the timeliness of the data.

[0041] Based on this, the system further determines the target average power generation of the photovoltaic power generation system within a preset time period. The initial power generation of the photovoltaic power generation system is obtained through real-time data acquisition, reflecting the system's current actual operating status. Combining the target average power generation and the initial power generation, the system employs an improved exponential smoothing prediction algorithm to predict the initial trend of power generation within the preset time period. This algorithm fully considers the periodic variation characteristics and short-term continuous characteristics of power generation, and by exponentially weighting historical data, the prediction results not only reflect historical patterns but also respond promptly to changes in system status.

[0042] Based on the above embodiments, as an optional implementation, in S102, predicting the initial change trend of the photovoltaic power generation system's output within a preset time period based on historical operating data specifically includes S21-S23:

[0043] S21. Based on historical operating data, determine the average power generation of the photovoltaic power generation system in each time period, and determine the target average power generation energy of the photovoltaic power generation system in the time period corresponding to the preset duration.

[0044] S22, obtain the initial power generation of the current photovoltaic power generation system.

[0045] S23, combining the target average power generation and the initial power generation, predicts the initial trend of power generation change of the photovoltaic power generation system within a preset time period.

[0046] To improve the accuracy of photovoltaic power generation prediction, the prediction process needs to be refined.

[0047] For example, the system retrieves historical operating data from the past 30 days, divides a day into 144 time periods, each lasting 10 minutes, and calculates the average power generation within each time period. During the calculation, data from the most recent 7 days is assigned a weight of 0.5, data from days 8-14 are assigned a weight of 0.3, and data from days 15-30 are assigned a weight of 0.2. This weighted method determines the average power generation for each time period. For a preset 4-hour period, the system divides it into 24 time periods and calculates the target average power generation for each of these 24 time periods using the same weighted method.

[0048] Next, the system acquires the initial power generation of the photovoltaic power generation system in real time through on-site data acquisition devices. This power generation reflects the current actual operating status of the system. The system compares the current initial power generation with the target average power generation and calculates the rate of change of power generation. When the difference between the initial power generation and the target average power generation is less than 10%, the system predicts that the power generation will approach the target value at a relatively gradual rate; when the difference is greater than 10% but less than 20%, the system predicts that the power generation will change at a moderate rate; when the difference is greater than 20%, the system predicts that the power generation will adjust rapidly.

[0049] S103, based on environmental information, adjust the initial trend of change to generate the first trend of change of power generation of the photovoltaic power generation system within a preset time period.

[0050] During the operation of a photovoltaic power generation system, environmental information has a crucial impact on power generation. Therefore, it is necessary to correct the initial trend of change based on real-time collected temperature and irradiance data.

[0051] Specifically, the system collects the surface temperature of the photovoltaic modules in real time using temperature sensors deployed at different locations on the photovoltaic array, while continuously monitoring the solar irradiance of the power station area using a standard radiometer. The sampling interval is 10 minutes. In temperature data processing, the average value of multiple temperature sensors is taken as the module temperature at the current moment for each sampling, and the module temperature change within a preset time period is estimated based on the temperature change pattern.

[0052] For example, for every 1°C increase, the power generation decreases by 0.45%, and the system calculates the power generation correction value caused by the temperature change accordingly. In irradiance data processing, the system calculates the change in irradiance intensity within a preset time period based on the irradiance change trend, and directly maps the change in irradiance intensity to the change in power generation. The influence values ​​of temperature and irradiance on power generation are superimposed with the initial change trend. When the weather conditions are stable, the weight of historical data is 0.7, and the weight of environmental information is 0.3; when the weather changes drastically, the weight of historical data decreases to 0.3, and the weight of environmental information increases to 0.7, ultimately generating the first change trend of photovoltaic power generation within the preset time period.

[0053] Based on the above embodiments, as an optional implementation, in S103, the environmental information includes temperature data and irradiance data. Adjusting the initial trend based on the environmental information to generate a first trend of change in the power generation of the photovoltaic power generation system within a preset time period specifically includes S31-S32:

[0054] S31, based on temperature and irradiance data, predicts the power generation of the photovoltaic power generation system within a preset time period.

[0055] S32, adjust the initial trend of change based on the predicted power generation to generate the first trend of change of power generation of the photovoltaic power generation system within a preset time period.

[0056] To improve the accuracy of photovoltaic power generation prediction, it is necessary to fully utilize environmental information to correct initial trends. First, the system collects module temperature data in real time using temperature sensors deployed on the photovoltaic array surface, while simultaneously monitoring solar irradiance data of the power plant area using a standard radiometer. The sampling interval for both types of data is 10 minutes. Based on the currently collected temperature and irradiance data, the system estimates the temperature and irradiance changes within a preset time period.

[0057] Regarding the impact of temperature, the power generation of photovoltaic modules decreases by 0.45% for every 1°C increase in temperature, and the system calculates the change in power generation caused by temperature changes accordingly. Regarding the impact of irradiance, the power generation changes by 10% for every 100W / m² change in irradiance intensity, and the system calculates the change in power generation caused by changes in irradiance accordingly. By superimposing these two changes, the predicted power generation under the influence of environmental factors is obtained.

[0058] Next, the system weights and integrates the predicted power generation with the initial trend. When weather conditions are stable, the initial trend has a weight of 0.7, and the predicted power generation has a weight of 0.3. When weather conditions change drastically, the weight of the initial trend decreases to 0.3, and the weight of the predicted power generation increases to 0.7. Through this dynamic weight allocation method, the system generates the first trend of power generation of the photovoltaic power generation system within a preset time period.

[0059] The prediction of power generation trends is mainly carried out through the following steps. First, for temperature data, the system acquires the component temperatures at the current and previous time points. By calculating the rate of temperature change and its acceleration, it predicts the temperature value for each 10-minute period within the next 4 hours. For example, if the current temperature is 25℃, and the temperature rises at a rate of 0.5℃ / 10 minutes with a stable trend, it can be predicted that the temperature will reach 28℃ in 1 hour. Similarly, for irradiance data, the system analyzes the irradiance intensity change patterns at the current and previous time points to predict the irradiance intensity for each future time period. For example, if the current irradiance intensity is 800W / m², and it decreases at a rate of 20W / m² / 10 minutes, it is expected to drop to 680W / m² in 1 hour.

[0060] After determining the trends in temperature and irradiance, the system calculates the change in power generation for each time period. Taking a current power generation of 100kW as an example, if the predicted temperature rises by 3℃ after one hour, based on a temperature coefficient of -0.45% / ℃, this will lead to a 1.35% decrease in power generation, or a reduction of 1.35kW. Simultaneously, a 120W / m² decrease in irradiance, assuming a 10% impact per 100W / m², will result in a 12% decrease in power generation, or a reduction of 12kW. Considering both factors, the predicted power generation after one hour is 86.65kW.

[0061] S104: Obtain real-time and historical electricity consumption data from the user terminal, and combine the real-time and historical electricity consumption data to predict the second trend of electricity load change within a preset time period.

[0062] To achieve precise matching between photovoltaic power generation systems and user electricity loads, accurate prediction of user-end electricity consumption is necessary. The system first collects real-time electricity consumption data from user terminals via smart meters, including parameters such as active power, reactive power, voltage, and current, with a sampling interval of 5 minutes. Simultaneously, it retrieves historical electricity consumption data from the database for the past 30 days, reflecting the user's electricity consumption patterns and load characteristics.

[0063] During data processing, the system divides the preset time period into 24 10-minute time intervals. For each time interval, it calculates the average electricity load for the same period over the past 30 days and weights the data accordingly. Data from the most recent 7 days has a weight of 0.5, data from days 8-14 has a weight of 0.3, and data from days 15-30 has a weight of 0.2. Based on this, the system compares real-time electricity consumption data with weighted historical electricity consumption data. When the deviation between real-time and historical data is less than 10%, the system primarily relies on historical electricity consumption patterns for prediction. When the deviation is greater than 10%, the system increases the weight of real-time electricity consumption data to 0.6, while the weight of historical data decreases to 0.4. Through this dynamic weight allocation method, the system generates a second trend of user-end electricity load changes within the preset time period.

[0064] Based on the above embodiments, as an optional implementation, in S104, combining real-time electricity consumption data and historical electricity consumption data, predicting the second trend of user-end electricity load within a preset time period specifically includes S41-S42:

[0065] S41, based on historical electricity consumption data, determine the average electricity load of the user terminal in each time period, and determine the target average electricity load of the user terminal in the time period corresponding to the preset duration.

[0066] S42, combining the target average electricity load and real-time electricity data, predicts the second trend of user-end electricity load change within a preset time period.

[0067] To accurately predict the changing trends of electricity load at the user end, the system needs to make reasonable use of historical and real-time data. First, the system retrieves historical electricity consumption data for the past 30 days from the database, dividing each day into 144 time periods, each 10 minutes long. A weighted average is calculated for the electricity load in each time period, with the weight of the most recent 7 days' data at 0.5, data from days 8-14 at 0.3, and data from days 15-30 at 0.2. This weighting method considers the periodic characteristics and recent trends of electricity load, resulting in a more representative average electricity load value. For a preset 4-hour period, the system determines the target average electricity load for these 24 time periods.

[0068] For example, if the weighted average load for a certain period is 80kW, and the load fluctuation during that period is relatively small, then 80kW is set as the target average electricity load for that period. Next, the system collects real-time electricity consumption data from users via smart meters and calculates the deviation and rate of change between the real-time load and the target average load. When the deviation between the real-time load and the target average load is less than 10%, the system predicts that the load will approach the target value at a steady rate; when the deviation is between 10% and 20%, the system predicts that the load will change at a moderate rate; when the deviation is greater than 20%, the system predicts that the load will adjust rapidly. Based on this graded rate of change, the system generates a second trend curve for a preset time period.

[0069] S105, based on the first and second trends, determine the power deviation value of the power grid within a preset time period, and generate a power regulation plan for the power grid based on the power deviation value.

[0070] In order to achieve balanced regulation of grid power, the system needs to determine the power deviation and formulate corresponding regulation plans based on the changing trends of photovoltaic power generation and electricity load.

[0071] Specifically, the system first subtracts the predicted photovoltaic power generation from the predicted electricity load in the second trend according to time intervals to obtain the power deviation value for each 10-minute time period within a preset duration. When the power deviation value is positive, it indicates that the photovoltaic power generation is greater than the electricity load, and it is necessary to reduce the power generation or increase the electricity load; when the power deviation value is negative, it indicates that the photovoltaic power generation is less than the electricity load, and it is necessary to increase the power generation or decrease the electricity load.

[0072] The system sets adjustment thresholds based on the magnitude of the power deviation. When the absolute value of the power deviation is less than 5% of the system's rated capacity, no power adjustment is required. When the absolute value of the power deviation is between 5% and 15%, a primary adjustment scheme is activated, mainly by adjusting the operating periods of controllable loads to balance power. When the absolute value of the power deviation is greater than 15%, a secondary adjustment scheme is activated, which, in addition to adjusting controllable loads, also needs to control the output power of the photovoltaic inverter. When generating a power adjustment plan, the system prioritizes economical adjustment methods, such as interruptible load adjustment for industrial enterprises, and secondarily considers the power limitations of photovoltaic power generation.

[0073] Based on the first and second trends, the power deviation value of the power grid within the preset time period is determined, specifically including S51-S53:

[0074] S51, based on the first changing trend, determine the output power of the photovoltaic power generation system within a preset time period.

[0075] First, it is necessary to clarify the output power variation of the photovoltaic power generation system within a preset time period. Based on the first trend, the system divides the 4-hour preset time period into 24 time segments, each 10 minutes long, and calculates the power generation for each time segment.

[0076] Specifically, the system first reads the predicted power generation value for each time period in the first trend. For example, if the predicted power generation for a certain time period is 15 kWh, and that time period is 10 minutes, it is converted into an instantaneous output power of 90 kW. The system performs similar power conversions for all time periods within the preset duration, forming an output power variation curve. During the conversion process, the system also needs to consider the power conversion efficiency of the photovoltaic power generation system. For example, if the inverter efficiency of the system is 98%, the actual output power should be 88.2 kW.

[0077] S52, based on the second trend of change, determine the power consumption of the user terminal within a preset time period.

[0078] Specifically, the system reads the predicted electricity load value for each time period in the second trend. For example, if the predicted electricity load for a certain time period is 12 kWh, considering that the time period is 10 minutes, it is converted into an instantaneous power consumption of 72 kW. The system performs the same power conversion process for all time periods within the preset duration, forming the power consumption change curve at the user end. During the conversion process, the system also needs to consider the influence of the power factor. For example, if the power factor at the user end is 0.95, the actual active power should be 68.4 kW.

[0079] S53 calculates the difference between the output power and the power consumption to obtain the power deviation value of the power grid within a preset time period.

[0080] Specifically, the system reads the output power of the photovoltaic power generation system and the power consumption of the user terminal for each time period, and calculates the power deviation value by subtracting the power consumption from the output power. For example, if the output power of the photovoltaic power generation system is 88.2kW and the power consumption of the user terminal is 68.4kW in a certain time period, the power deviation value for that time period is 19.8kW, indicating a power surplus of 19.8kW; if the output power is 65kW and the power consumption is 85kW in a certain time period, the power deviation value is -20kW, indicating a power deficit of 20kW. The system performs similar calculations for all time periods within a preset duration, forming a complete power deviation variation curve.

[0081] Based on the above embodiments, as an optional implementation method, in S105, generating the power regulation plan of the power grid according to the power deviation value specifically includes S61-S64:

[0082] S61, when the power deviation value is greater than the preset power threshold, obtain the direction and magnitude of the power deviation value.

[0083] S62, determine the target time point for power adjustment based on the direction and magnitude of the change.

[0084] S63, based on the predicted value of the power deviation at the target time point, determine the regulation power value of the power grid at the target time point.

[0085] S64 generates a power regulation plan for the power grid, including regulation timing and regulation amount, based on the target time point and the regulation power value.

[0086] To achieve proactive regulation of grid power, the system needs to formulate a reasonable regulation plan based on the power deviation value. First, the system sets a preset power threshold of 5% of the grid's rated capacity. When the detected power deviation value exceeds this threshold, the system begins to analyze the changing trend of the power deviation value.

[0087] Specifically, the system determines the direction of change by comparing power deviation values ​​in adjacent time periods. For example, if the power deviation value in the current time period is 25kW and it increases to 30kW in the next time period, it is determined to be a positive change; at the same time, the change magnitude is calculated as 5kW / 10 minutes. Based on this trend, the system predicts when the power deviation value will reach a critical threshold. For example, if the power deviation value continues to increase at a rate of 5kW / 10 minutes, it is expected to reach 40kW after 30 minutes, and this moment is determined as the target time point when power regulation is required.

[0088] After determining the target time point, the system predicts the specific deviation value at that time point based on the trend of the power deviation value and sets it as the power value that needs to be adjusted. For example, if the predicted power deviation value at the target time point is 40kW, then -40kW is set as the adjustment power value, meaning that the generating power needs to be reduced by 40kW or the electrical load needs to be increased by 40kW. Based on the target time point and the adjustment power value, the system generates a detailed adjustment plan, including the specific timing of the adjustment and the adjustment amount for each time period. For example, the system can formulate a step-by-step adjustment scheme, starting 20 minutes before the target time point, adjusting 13.3kW every 10 minutes, and completing a total power adjustment of 40kW in three stages.

[0089] After generating the power regulation plan for the power grid based on the power deviation value, it also includes:

[0090] The power regulation plan is sent to the power grid dispatch control center, and the center receives the power grid dispatch instructions generated based on the power regulation plan. According to the power grid dispatch instructions, the photovoltaic power generation system is controlled to adjust its power output, and simultaneously, power consumption control signals are sent to the user terminals to control their power consumption. The first actual power output of the photovoltaic power generation system and the second actual power consumption of the user terminals are obtained, and compared to obtain the actual power deviation value of the power grid. It is determined whether the power deviation value meets the preset power grid stable operation conditions. If it meets the preset power grid stable operation conditions, a power grid operation quality report is generated. If it does not meet the preset power grid stable operation conditions, the power regulation plan is regenerated until the power grid operation is stable.

[0091] To ensure the effective execution of the power regulation plan and the stable operation of the power grid, the system has established a complete dispatch execution and feedback regulation mechanism. First, the system sends the generated power regulation plan to the power grid dispatch control center via an encrypted communication network. The dispatch control center then generates specific dispatch instructions based on the regulation plan and the current power grid operating status. For example, when a 40kW power reduction is required, the dispatch control center might issue an instruction requiring the photovoltaic power generation system to reduce its output power by 25kW, while simultaneously requiring users to increase their electricity load by 15kW. Upon receiving the dispatch instructions, the system controls the inverter to reduce its output power through the photovoltaic power generation system's energy management unit, and sends control signals to the adjustable loads at the user end through the intelligent electricity management system, such as starting energy storage devices for charging or adjusting the power of the air conditioning system.

[0092] During the execution of dispatch instructions, the system collects the first actual power generation of the photovoltaic power generation system and the second actual power consumption at the user end in real time, with a sampling interval of 1 minute. By calculating the difference between these two power values, the actual power deviation value of the power grid is obtained. The system compares this deviation value with the preset stable operation conditions of the power grid. The stable operation conditions require that the power deviation value does not exceed ±3% of the rated capacity of the power grid, and the power fluctuation rate is less than 0.5% / minute. If the power deviation value is found to meet these conditions for 10 consecutive minutes, the system determines that the power grid is operating stably, and then generates a power grid operation quality report that includes power fluctuation information, regulation process analysis, and energy efficiency assessment.

[0093] Based on the above method, this application also discloses a photovoltaic power quality monitoring system, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a photovoltaic power quality monitoring system provided in an embodiment of this application. The system includes: an acquisition module, a first prediction module, a generation module, a second prediction module, and an output module; wherein,

[0094] The system comprises the following modules: an acquisition module for acquiring environmental information and historical operating data of the photovoltaic power generation system; a first prediction module for predicting the initial trend of power generation of the photovoltaic power generation system within a preset time period based on historical operating data; a generation module for adjusting the initial trend based on environmental information to generate a first trend of power generation of the photovoltaic power generation system within a preset time period; a second prediction module for acquiring real-time and historical electricity consumption data from the user end, and combining the real-time and historical electricity consumption data to predict a second trend of electricity load at the user end within a preset time period; and an output module for determining the power deviation value of the power grid within a preset time period based on the first and second trends, and generating a power regulation plan for the power grid based on the power deviation value.

[0095] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0096] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0097] The communication bus 1002 is used to realize the connection and communication between these components.

[0098] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0099] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0100] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0101] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 3 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a photovoltaic power quality monitoring method.

[0102] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program of a photovoltaic power quality monitoring method stored in the memory 1005. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0103] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0104] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0110] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for monitoring photovoltaic power quality, characterized in that, The method includes: Acquire environmental information and historical operating data of photovoltaic power generation systems; Based on the historical operating data, predicting the initial trend of power generation of the photovoltaic power generation system within a preset time period includes: determining the average power generation of the photovoltaic power generation system in each time period based on the historical operating data, and determining the target average power generation of the photovoltaic power generation system in the time period corresponding to the preset time period; obtaining the current initial power generation of the photovoltaic power generation system; and combining the target average power generation and the initial power generation to predict the initial trend of power generation of the photovoltaic power generation system within the preset time period, including: setting the preset time period to the next 4 hours, and dividing these 4 hours into 24 10-minute time periods; for each 10-minute time period, calculating the target average power generation for that time period by calling historical power generation data for the same time period within the last 30 days and using a weighted average method, wherein the historical data closer to the current date has a higher weight. The process is extensive, specifically including: retrieving historical operating data from the last 30 days, dividing a day into 144 time periods, each lasting 10 minutes, calculating the average power generation within each time period, assigning a weight of 0.5 to the data from the last 7 days, 0.3 to the data from the last 814 days, and 0.2 to the data from the last 1530 days; obtaining the initial power generation of the photovoltaic power generation system, which reflects the current actual operating status of the system; comparing the current initial power generation with the target average power generation, calculating the rate of change of power generation; when the difference between the initial power generation and the target average power generation is less than 10%, the system predicts that the power generation will approach the target value at a relatively gradual rate; when the difference is greater than 10% but less than 20%, the system predicts that the power generation will change at a moderate rate; when the difference is greater than 20%, the system predicts that the power generation will adjust rapidly. Based on the environmental information, the initial trend of change is adjusted to generate a first trend of change in the power generation of the photovoltaic power generation system within the preset time period; Acquire real-time and historical electricity consumption data from the user terminal, and combine the real-time and historical electricity consumption data to predict a second trend in the user terminal's electricity load within a preset time period. This includes: determining the average electricity load of the user terminal in each time period based on the historical electricity consumption data, and determining the target average electricity load of the user terminal within the time period corresponding to the preset time period; combining the target average electricity load and the real-time electricity consumption data to predict a second trend in the user terminal's electricity load within the preset time period, including: dividing the preset time period into 24 10-minute time periods, calculating the average electricity load of the same time period within the most recent 30 days for each time period, and performing weighted processing on the data, wherein... The weight of data from the most recent 7 days is 0.5, the weight of data from the past 814 days is 0.3, and the weight of data from the past 1530 days is 0.

2. Real-time electricity consumption data is compared with weighted historical electricity consumption data. When the deviation between the real-time electricity consumption data and the historical data for the same period is less than 10%, the system predicts the second trend of the user-end electricity load within a preset time period based on historical electricity consumption patterns, generating a second trend curve within the preset time period. When the deviation is greater than 10%, the system increases the weight of the real-time electricity consumption data, raising the weight to 0.6 and decreasing the weight of the historical data to 0.4, predicting the second trend of the user-end electricity load within the preset time period, and generating a second trend curve within the preset time period. Determining the power deviation value of the power grid within the preset time period based on the first trend and the second trend includes: determining the output power of the photovoltaic power generation system within the preset time period based on the first trend; determining the power consumption of the user end within the preset time period based on the second trend; performing a difference calculation between the output power and the power consumption to obtain the power deviation value of the power grid within the preset time period; and generating a power regulation plan for the power grid based on the power deviation value.

2. The photovoltaic power quality monitoring method according to claim 1, characterized in that, The environmental information includes temperature data and irradiance data. Adjusting the initial trend based on the environmental information to generate a first trend in the power generation of the photovoltaic power generation system within the preset time period includes: predicting the predicted power generation of the photovoltaic power generation system within the preset time period based on the temperature data and the irradiance data; and adjusting the initial trend based on the predicted power generation to generate a first trend in the power generation of the photovoltaic power generation system within the preset time period.

3. The photovoltaic power quality monitoring method according to claim 1, characterized in that, The step of generating a power regulation plan for the power grid based on the power deviation value includes: when the power deviation value is greater than a preset power threshold, obtaining the direction and magnitude of the change in the power deviation value; determining a target time point for power regulation based on the direction and magnitude of the change; determining the regulation power value of the power grid at the target time point based on the predicted value of the power deviation value at the target time point; and generating a power regulation plan for the power grid that includes regulation timing and regulation amount based on the target time point and the regulation power value.

4. The photovoltaic power quality monitoring method according to claim 1, characterized in that, After generating the power regulation plan for the power grid based on the power deviation value, the method further includes: sending the power regulation plan to the power grid dispatch control center and receiving the power grid dispatch control center's power grid dispatch instructions generated based on the power regulation plan; controlling the photovoltaic power generation system to adjust its power generation according to the power grid dispatch instructions, and simultaneously sending power consumption control signals to the user end to control the user end to adjust its power consumption; obtaining the first actual power generation of the photovoltaic power generation system and the second actual power consumption of the user end, comparing the first actual power generation and the second actual power consumption to obtain the power deviation value of the actual operation of the power grid; determining whether the power deviation value meets the preset power grid stable operation conditions; if it meets the preset power grid stable operation conditions, generating a power grid operation quality report; if it does not meet the preset power grid stable operation conditions, regenerating the power regulation plan for the power grid until the power grid operation is stable.

5. A photovoltaic power quality monitoring system, characterized in that, The system includes: an acquisition module, a first prediction module, a generation module, a second prediction module, and an output module; wherein, the acquisition module is used to acquire environmental information and historical operating data of the photovoltaic power generation system; the first prediction module is used to predict the initial change trend of the power generation of the photovoltaic power generation system within a preset time period based on the historical operating data, including: determining the average power generation of the photovoltaic power generation system in each time period based on the historical operating data, and determining the target average power generation of the photovoltaic power generation system in the time period corresponding to the preset time period; acquiring the current initial power generation of the photovoltaic power generation system; and combining the target average power generation and the initial power generation to predict the initial change trend of the power generation of the photovoltaic power generation system within the preset time period. The process includes: setting the preset duration to the next 4 hours, dividing these 4 hours into 24 10-minute time slots; for each 10-minute time slot, calculating the target average power generation for that time slot using a weighted average method by retrieving historical power generation data for the same time slot within the last 30 days, with historical data closer to the current date having a higher weight. Specifically, this involves retrieving historical operating data from the last 30 days, dividing the day into 144 time slots, each 10 minutes long, calculating the average power generation within each time slot, assigning a weight of 0.5 to data from the last 7 days, 0.3 to data from the last 814 days, and 0.2 to data from the last 1530 days; and obtaining the initial power generation of the photovoltaic power generation system. The power generation reflects the current actual operating status of the system; the initial power generation is compared with the target average power generation, and the rate of change of power generation is calculated. When the difference between the initial power generation and the target average power generation is less than 10%, the system predicts that the power generation will approach the target value at a relatively gradual rate; when the difference is greater than 10% but less than 20%, the system predicts that the power generation will change at a moderate rate; when the difference is greater than 20%, the system predicts that the power generation will adjust rapidly; the generation module is used to adjust the initial change trend according to the environmental information to generate a first change trend of the power generation of the photovoltaic power generation system within the preset time period; the second prediction module is used to acquire real-time electricity consumption data and historical electricity consumption data from the user end, and combine them with the... Using real-time electricity consumption data and historical electricity consumption data, predicting the second trend of the user-end electricity load within a preset time period includes: determining the average electricity load of the user-end in each time period based on the historical electricity consumption data, and determining the target average electricity load of the user-end in the time period corresponding to the preset time period; combining the target average electricity load and the real-time electricity consumption data, predicting the second trend of the user-end electricity load within the preset time period includes: dividing the preset time period into 24 10-minute time periods, calculating the average electricity load of the same time period in the most recent 30 days for each time period, and weighting the data, where the weight of the data in the most recent 7 days is 0.5, the weight of the data in the most recent 814 days is 0.3, and the weight of the data in the most recent 1530 days is 0.

2. The system compares real-time electricity consumption data with weighted historical electricity consumption data. When the deviation between the real-time electricity consumption data and the historical data for the same period is less than 10%, it predicts the second trend of the user-end electricity load within a preset time period based on historical electricity consumption patterns, generating a second trend curve within the preset time period. When the deviation is greater than 10%, the system increases the weight of the real-time electricity consumption data, raising the weight to 0.6 and decreasing the weight of the historical data to 0.4, predicting the second trend of the user-end electricity load within the preset time period, and generating a second trend curve within the preset time period. The output module is used to determine the power deviation value of the power grid within the preset time period based on the first trend and the second trend, including: determining the output power of the photovoltaic power generation system within the preset time period based on the first trend; determining the power consumption of the user-end within the preset time period based on the second trend; performing a difference calculation between the output power and the power consumption to obtain the power deviation value of the power grid within the preset time period, and generating a power adjustment plan for the power grid based on the power deviation value.

6. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-4.

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