New energy equipment management method based on big data

Abstract

The invention discloses a new energy equipment management method based on big data, and relates to the technical field of new energy equipment, and the method comprises the following steps: data collection, power generation efficiency prediction, prediction of the power generation efficiency of photovoltaic equipment through employing a power generation efficiency prediction algorithm, and electric quantity storage optimization. The method comprises the following steps of: obtaining optimized storage electric quantity through an electricity consumption storage optimization algorithm, detecting a battery state, detecting the battery state by utilizing a battery health evaluation algorithm, obtaining a battery health value and performing data backup, and has the characteristics of quantifying a result by considering the influence of environmental factors such as real-time temperature and irradiance on the power generation efficiency, and improving the power generation efficiency. According to the battery health assessment algorithm, the battery state is assessed by combining the battery charge-discharge cycle index factor, the aging factor and the storage capacity factor, the battery problem is found in time, manual experience is not needed, regular detection is not needed, and the equipment maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy equipment, and in particular to a new energy equipment management method based on big data. Background Art

[0002] As the global demand for renewable energy continues to increase, the development of new energy has gradually gained people's attention. New energy is relative to traditional energy such as coal, oil, and natural gas. It is also called unconventional energy, which refers to various energy forms other than traditional energy. Among them, photovoltaic power generation is a technology that uses the photovoltaic effect of the semiconductor interface to directly convert light energy into electrical energy. It is mainly composed of three parts: solar panels, controllers, and inverters, and the main components are composed of electronic components. After the solar cells are connected in series and packaged for protection, they can form a large-area solar cell module, which is combined with power controllers and other components to form a photovoltaic power generation device.

[0003] At present, during the photovoltaic power generation process, the power generation efficiency will change with the environment, making it difficult to accurately predict the power generation efficiency and difficult to determine the maintenance timing. Usually, regular inspections are mainly carried out, which will affect the power generation quality. At the same time, the health status of the battery changes with the increase in usage time and charge and discharge times. Traditional methods are difficult to predict the battery's decline trend. Currently, we can only rely on experience or regular inspections, and battery problems cannot be discovered in time, resulting in premature battery damage and increased maintenance costs. Summary of the invention

[0004] The purpose of the present invention is to provide a new energy equipment management method based on big data, which solves the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a new energy equipment management method based on big data, comprising the following steps:

[0006] Data collection: a centralized communication base station is set up in the photovoltaic power station, and a sensor network is deployed to monitor the operating status data and external environment data of the photovoltaic equipment in real time, and the two types of data are uploaded to the cloud platform for storage;

[0007] Power generation efficiency prediction: Based on the operating status data and external environment data monitored by sensors, the power generation efficiency prediction algorithm is used to predict the power generation efficiency of photovoltaic equipment and obtain the predicted power generation efficiency E pred , and to predict the power generation efficiency E pred Set the minimum threshold Y1, when E pred When <Y1, the operation and maintenance personnel take maintenance measures;

[0008] Power storage optimization, by predicting the power generation efficiency E pred The predicted power generation Q pefAnd use the power storage optimization algorithm to get the optimized storage power Q opt , according to electricity demand, adjust the charging and discharging strategy to balance photovoltaic power generation, grid demand and battery life;

[0009] Battery status detection: During the operation of photovoltaic equipment, the battery health assessment algorithm is used to detect the battery status and obtain the battery health value H. predict , and set the battery health value H predict The maintenance threshold is Y2. When H predict When it is less than Y2, it means that the battery condition is too bad and needs to be maintained to ensure the quality of power storage;

[0010] Data backup: regularly back up all data during the operation of the photovoltaic equipment for subsequent reference.

[0011] Optionally, the power generation efficiency prediction algorithm process is as follows:

[0012]

[0013] Where E pred It is the predicted power generation efficiency;

[0014] P act is the actual power generation efficiency;

[0015] P max is the maximum power generation efficiency;

[0016] G Current irradiance, G ref is the standard irradiance;

[0017] T is the component temperature;

[0018] T ref is the standard component temperature;

[0019] T opt is the optimum operating temperature, derived from historical data;

[0020] α is the irradiance influence coefficient, which takes a value of 1;

[0021] β is the temperature influence coefficient, ranging from 0 to 1;

[0022] Predicted power generation efficiency E pred The higher the value, the stronger the photoelectric conversion capacity of the current photovoltaic equipment is, and vice versa. The predicted power generation Q is obtained through the prediction of photoelectric conversion efficiency. pef , set the predicted power generation efficiency E pred The lowest threshold is Y1. pred When <Y1, the operation and maintenance personnel take maintenance measures to reduce the waste of resources caused by maintenance.

[0023] Optionally, the power storage optimization algorithm process is as follows:

[0024]

[0025] Where Q opt It is the optimized storage capacity;

[0026] Q req is the grid demand;

[0027] Q pef is the predicted power generation;

[0028] Q max is the maximum storage capacity of the battery;

[0029] It is the trend value of battery power, indicating the rate of change of battery power over time;

[0030] Where Q bat is the current storage capacity of the battery;

[0031] is the derivative with respect to time t;

[0032] Specific:

[0033]

[0034] Where Q pef is the predicted power generation;

[0035] E pred It is the predicted power generation efficiency;

[0036] G irr is the average irradiance;

[0037] Δt is the length of the forecast period;

[0038] G ref is the standard irradiance;

[0039] The power storage optimization algorithm adjusts the battery charging and discharging strategy according to the real-time photovoltaic power generation, grid demand and the current status of the energy storage battery. When the power consumption is at peak, the grid demand is greater than the predicted power generation. At this time, it is necessary to replenish the power from the battery to the grid. When the power consumption is at low point, the grid demand is less than the predicted power generation. At this time, the excess power is stored in the battery.

[0040] Optionally, the battery health assessment algorithm process is as follows:

[0041]

[0042] Among them, H predict is the battery health value;

[0043] H0 is the initial health value of the battery, which is 1;

[0044] Q opt It is the optimized storage capacity;

[0045] Q max is the maximum storage capacity of the battery;

[0046] W1 is the storage power impact coefficient, which indicates the impact of storage power on battery health;

[0047] C cycle is the number of battery charge and discharge cycles;

[0048] C max is the maximum number of charge and discharge cycles of the battery;

[0049] W2 is the charge and discharge influence coefficient, which indicates the effect of the number of cycles on battery health;

[0050] e is the base of natural logarithms;

[0051] Age is the battery life;

[0052] W3 is the battery age influence coefficient;

[0053] Battery health value H predict Indicates the current battery status, battery health value H predict The higher the value, the better the battery condition, and the lower the value, the worse the battery condition. This is the battery health value H. predict Set the maintenance threshold to Y2. When H predict When it is less than Y2, it means that the battery condition is too bad and needs maintenance.

[0054] Optionally, the battery health value H predict When it is less than Y2, it means that the charging and discharging efficiency of the battery is reduced. At this time, the irradiance influence coefficient α in the power generation prediction algorithm is reduced to reflect the impact of the poor battery state on the power generation efficiency. Specifically:

[0055]

[0056] By adjusting the irradiance influence coefficient α in the power generation prediction algorithm when the battery health declines, the rate of excessive battery degradation can be slowed down and excessive load can be avoided.

[0057] Optionally, in the data collection step, after obtaining the operating status data and external environment data, a real-time monitoring dashboard for equipment operation is built based on the big data platform to visualize the photovoltaic equipment operating status data and external environment data, thereby ensuring that staff can quickly understand the operating status of the photovoltaic equipment.

[0058] Optionally, in the data collection step, all photovoltaic devices and sensors are connected to a unified data collection platform through Internet of Things technology to achieve remote data transmission and ensure the real-time and accuracy of the data.

[0059] Optionally, in the data backup step, a mechanical hard disk is used to regularly back up the photovoltaic device data.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. The present invention predicts the power generation efficiency of photovoltaic equipment through a power generation efficiency prediction algorithm, adds the influence of environmental factors such as real-time temperature and irradiance on the power generation efficiency, and compares it with the power generation efficiency under standard environment, quantifies the results, and makes the predicted power generation efficiency E pred More accurate, and set the minimum threshold Y1 for it, when E pred When it is less than Y1, the operation and maintenance personnel take maintenance measures, thereby determining whether to perform maintenance based on the predicted power generation efficiency results, without the need for regular inspections, ensuring the power generation quality, and predicting the power generation efficiency E pred The predicted power generation Q pef , and then use the power storage optimization algorithm to get the optimized storage power Q opt ,The power storage optimization algorithm adjusts the charging and discharging strategies according to the actual power demand, ,avoiding over-charging and discharging of the battery while ensuring its ,use ,and ensuring the quality of battery storage.

[0062] 2. The present invention detects the battery status through a battery health assessment algorithm to obtain a battery health value H predict The battery health assessment algorithm evaluates the battery status by combining the battery charge and discharge cycle number factors, aging factors and storage capacity factors. Starting from real-time data, it can accurately predict the battery degradation trend and detect battery problems in time without relying on manual experience. By setting the maintenance threshold of the battery health value to Y2, when the battery health value is less than Y2, it means that the battery status is too poor and needs to be maintained. There is no need for regular inspection to ensure the quality of power storage and reduce the maintenance cost of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] For examples, see Figure 1 This implementation provides a new energy equipment management method based on big data, including the following steps:

[0066] Data collection: a centralized communication base station is set up in the photovoltaic power station, and a sensor network is deployed to monitor the operating status data and external environment data of the photovoltaic equipment in real time, and the two types of data are uploaded to the cloud platform for storage;

[0067] Power generation efficiency prediction: Based on the operating status data and external environment data monitored by sensors, the power generation efficiency prediction algorithm is used to predict the power generation efficiency of photovoltaic equipment and obtain the predicted power generation efficiency E pred , and to predict the power generation efficiency E pred Set the minimum threshold Y1, when E pred When <Y1, the operation and maintenance personnel take maintenance measures;

[0068] Power storage optimization, by predicting the power generation efficiency E pred The predicted power generation Q pef And use the power storage optimization algorithm to get the optimized storage power Q opt , according to electricity demand, adjust the charging and discharging strategy to balance photovoltaic power generation, grid demand and battery life;

[0069] Battery status detection: During the operation of photovoltaic equipment, the battery health assessment algorithm is used to detect the battery status and obtain the battery health value H. predict , and set the battery health value H predict The maintenance threshold is Y2. When H predict When it is less than Y2, it means that the battery condition is too bad and needs to be maintained to ensure the quality of power storage;

[0070] Data backup: regularly back up all data during the operation of the photovoltaic equipment for subsequent reference.

[0071] Specifically, in data backup, a mechanical hard disk is used to regularly back up the photovoltaic device data. Mechanical hard disks are low in price, large in storage capacity, and highly stable, and are suitable for backing up photovoltaic device data in this method.

[0072] In this embodiment, after collecting the operating status data and external environment data of the photovoltaic equipment, the power generation efficiency of the photovoltaic equipment is predicted by the power generation efficiency prediction algorithm. In the power generation efficiency prediction algorithm, the influence of real-time environmental factors on the power generation efficiency is considered to make the predicted power generation efficiency E pred More accurate, and by predicting the power generation efficiency E pred Set the minimum threshold Y1, when E pred When <Y1, it indicates the predicted power generation efficiency Epred The power generation efficiency is too low compared to the standard power generation efficiency. At this time, the operation and maintenance personnel can take maintenance measures to achieve accurate prediction of the power generation efficiency and determine whether to perform maintenance based on the results. Regular inspection is not required to ensure the power generation quality. pred The predicted power generation Q pef And use the power storage optimization algorithm to get the optimized storage power Q opt The power storage optimization algorithm adjusts the charging and discharging strategy according to the actual power demand, avoiding over-charging and discharging of the battery while ensuring its use, thereby improving the battery life and overall system efficiency.

[0073] Finally, the battery health evaluation algorithm is used to detect the battery status and obtain the battery health value H. predict The battery health assessment algorithm evaluates the battery status by combining the battery charge and discharge cycle number, aging factor and storage capacity factor. Starting from real-time data, it can accurately predict the battery degradation trend without relying on manual experience, and by setting the battery health value H predict The maintenance threshold is Y2. When H predict When it is less than Y2, it means that the battery condition is too poor and needs to be maintained. There is no need for regular inspection, which ensures the quality of power storage and reduces the maintenance cost of the equipment.

[0074] Furthermore, the power generation efficiency prediction algorithm process is as follows:

[0075]

[0076] Where E pred It is the predicted power generation efficiency;

[0077] P act is the actual power generation efficiency;

[0078] P max is the maximum power generation efficiency;

[0079] G Current irradiance, G ref is the standard irradiance;

[0080] T is the component temperature;

[0081] T ref is the standard component temperature;

[0082] T opt is the optimum operating temperature, derived from historical data;

[0083] α is the irradiance influence coefficient, which takes a value of 1;

[0084] β is the temperature influence coefficient, ranging from 0 to 1;

[0085] Specifically, by considering the impact of environmental factors such as real-time temperature and irradiance on power generation efficiency and comparing it with the power generation efficiency under standard environment, the results are quantified to predict the power generation efficiency E pred More accurate prediction of power generation efficiency E pred The higher the value, the stronger the photoelectric conversion capacity of the current photovoltaic equipment is, and vice versa. The predicted power generation Q is obtained through the prediction of photoelectric conversion efficiency. pef , set the predicted power generation efficiency E pred The lowest threshold is Y1. pred When <Y1, the operation and maintenance personnel take maintenance measures, including cleaning components, adjusting installation angles and optimizing power generation strategies. There is no need for regular inspections to ensure the quality of power generation and reduce the waste of resources caused by excessive cleaning and maintenance.

[0086] Furthermore, the power storage optimization algorithm process is as follows:

[0087]

[0088] Where Q opt It is the optimized storage capacity;

[0089] Q req is the grid demand;

[0090] Q pef is the predicted power generation;

[0091] Q max is the maximum storage capacity of the battery;

[0092] It is the trend value of battery power, indicating the rate of change of battery power over time;

[0093] Where Q bat is the current storage capacity of the battery;

[0094] is the derivative with respect to time t;

[0095] Specific:

[0096]

[0097] Where Q pef is the predicted power generation;

[0098] E pred It is the predicted power generation efficiency;

[0099] G irr is the average irradiance;

[0100] Δt is the length of the forecast period;

[0101] G ref is the standard irradiance;

[0102] Specifically, the power storage optimization algorithm adjusts the battery charging and discharging strategy according to the real-time photovoltaic power generation, grid demand and the current state of the energy storage battery. When the power consumption is at peak, the grid demand Q is calculated. req and predicted power generation Q pef If the grid demand is greater than the predicted power generation, the grid needs to be supplemented with electricity from the battery. During the low power consumption period, if the grid demand is less than the predicted power generation, the excess electricity will be stored in the battery to ensure that it can be supplemented in time when the photovoltaic power generation is insufficient, and when the photovoltaic power generation is surplus, the excess electricity can be stored efficiently, thereby improving the flexibility of photovoltaic power generation equipment. According to the actual power demand, the charging and discharging strategy is adjusted, which avoids excessive charging and discharging of the battery while ensuring its use, thereby improving the battery life and the overall efficiency of the system.

[0103] Furthermore, the battery health assessment algorithm process is as follows:

[0104]

[0105] Among them, H predict is the battery health value;

[0106] H0 is the initial health value of the battery, which is 1;

[0107] Q opt It is the optimized storage capacity;

[0108] Q max is the maximum storage capacity of the battery;

[0109] W1 is the storage power impact coefficient, which indicates the impact of storage power on battery health;

[0110] C cycle is the number of battery charge and discharge cycles;

[0111] C max is the maximum number of charge and discharge cycles of the battery;

[0112] W2 is the charge and discharge influence coefficient, which indicates the effect of the number of cycles on battery health;

[0113] e is the base of natural logarithms;

[0114] Age is the battery life;

[0115] W3 is the battery age influence coefficient;

[0116] Specifically, the battery health value H predict Indicates the current battery status, battery health value H predictThe higher the value, the better the battery condition; the lower the value, the worse the battery condition. The battery health assessment algorithm evaluates the battery condition by combining the battery charge and discharge cycle times, aging factors, and storage capacity factors. By setting W1, W2, and W3, each influencing factor can be adjusted according to the actual situation. The battery degradation trend can be accurately predicted without relying on manual experience. The results are quantified to facilitate staff to quickly understand the current battery status. Then, by setting the battery health value H predict The maintenance threshold is Y2. When H predict When it is less than Y2, it means that the battery condition is too poor and needs to be maintained. There is no need for regular inspection to ensure the quality of power storage. Through real-time monitoring, battery problems can be discovered in time, avoiding the high costs caused by sudden failures and reducing the maintenance cost of the equipment.

[0117] Furthermore, when the battery health value H predict When it is less than Y2, it means that the charging and discharging efficiency of the battery is reduced. At this time, the irradiance influence coefficient α in the power generation prediction algorithm is reduced to reflect the impact of the poor battery state on the power generation efficiency. Specifically:

[0118]

[0119] Specifically, by adjusting the irradiance influence coefficient α in the power generation prediction algorithm when the battery health declines, the rate of excessive battery degradation can be slowed down, excessive load can be avoided, the expected performance of the system can be adjusted, excessive battery load can be avoided and energy utilization can be optimized, the pressure of system load on the battery can be reduced, and the battery life can be extended.

[0120] Furthermore, in the data collection step, after obtaining the operating status data and external environment data, a real-time monitoring dashboard for equipment operation is built based on the big data platform to visualize the operating status data and external environment data of the photovoltaic equipment, ensuring that the staff can quickly understand the operating status of the photovoltaic equipment.

[0121] Specifically, by setting up a real-time monitoring dashboard, staff can quickly obtain the current operating status of photovoltaic equipment, reducing the time for manual inspection and data extraction, making the decision-making process faster and more efficient.

[0122] Furthermore, in the data collection step, all photovoltaic equipment and sensors are connected to a unified data collection platform through the Internet of Things technology to achieve remote data transmission and ensure the real-time and accuracy of the data.

[0123] Specifically, traditional photovoltaic equipment data collection methods usually rely on manual recording and manual data input, which is prone to data omissions or inconsistencies. Through the Internet of Things technology, data collection and transmission automation greatly reduces manual intervention, reduces human errors, and ensures data accuracy and consistency.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A new energy equipment management method based on big data, characterized in that: The following steps are involved: Step S1: Data collection: a centralized communication base station is set up in the photovoltaic power station, and a sensor network is deployed to monitor the operating status data and external environment data of the photovoltaic equipment in real time, and the two types of data are uploaded to the cloud platform for storage; Step S2: Power generation efficiency prediction: The power generation efficiency of the photovoltaic equipment is predicted using the power generation efficiency prediction algorithm through the operating status data and external environment data monitored by the sensor to obtain the predicted power generation efficiency E pred , and to predict the power generation efficiency E pred Set the minimum threshold Y1, when E pred When <Y1, the operation and maintenance personnel take maintenance measures; Step S3: Power storage optimization, by predicting the power generation efficiency E pred The predicted power generation Q pef And use the power storage optimization algorithm to get the optimized storage power Q opt , according to electricity demand, adjust the charging and discharging strategy to balance photovoltaic power generation, grid demand and battery life; Step S4: Battery status detection: During the operation of the photovoltaic equipment, the battery status is detected using the battery health assessment algorithm to obtain the battery health value H predict , and set the battery health value H predict The maintenance threshold is Y2. When H predict When it is less than Y2, it means that the battery condition is too bad and needs to be maintained to ensure the quality of power storage; Step S5: Data backup: regularly back up all data during the operation of the photovoltaic equipment for subsequent reference.

2. The new energy equipment management method based on big data according to claim 1 is characterized by: The power generation efficiency prediction algorithm process is as follows: Where E pred It is the predicted power generation efficiency; P act is the actual power generation efficiency; P max is the maximum power generation efficiency; G Current irradiance, G ref is the standard irradiance; T is the component temperature; T ref is the standard component temperature; T opt is the optimum operating temperature, derived from historical data; α is the irradiance influence coefficient, which takes a value of 1; β is the temperature influence coefficient, ranging from 0 to 1; Predicted power generation efficiency E pred The higher the value, the stronger the photoelectric conversion capacity of the current photovoltaic equipment is, and vice versa. The predicted power generation Q is obtained through the prediction of photoelectric conversion efficiency. pef , set the predicted power generation efficiency E pred The lowest threshold is Y1. pred When <Y1, the operation and maintenance personnel take maintenance measures to reduce the waste of resources caused by maintenance.

3. The new energy equipment management method based on big data according to claim 2 is characterized in that: The power storage optimization algorithm process is as follows: Where Q opt It is the optimized storage capacity; Q req is the grid demand; Q pef is the predicted power generation; Q max is the maximum storage capacity of the battery; It is the trend value of battery power, indicating the rate of change of battery power over time; Where Q bat is the current storage capacity of the battery; is the derivative with respect to time t; Specific: Where Q pef is the predicted power generation; E pred It is the predicted power generation efficiency; G irr is the average irradiance; Δt is the length of the forecast period; G ref is the standard irradiance; The power storage optimization algorithm adjusts the battery charging and discharging strategy according to the real-time photovoltaic power generation, grid demand and the current status of the energy storage battery. When the power consumption is at peak, the grid demand is greater than the predicted power generation. At this time, it is necessary to replenish the power from the battery to the grid. When the power consumption is at low point, the grid demand is less than the predicted power generation. At this time, the excess power is stored in the battery.

4. The new energy equipment management method based on big data according to claim 3 is characterized in that: The battery health assessment algorithm process is as follows: Among them, H predict is the battery health value; H0 is the initial health value of the battery, which is 1; Q opt It is the optimized storage capacity; Q max is the maximum storage capacity of the battery; W1 is the storage power impact coefficient, which indicates the impact of storage power on battery health; C cycle is the number of battery charge and discharge cycles; C max is the maximum number of charge and discharge cycles of the battery; W2 is the charge and discharge influence coefficient, which indicates the effect of the number of cycles on battery health; e is the base of natural logarithms; Age is the battery life; W3 is the battery age influence coefficient; Battery health value H predict Indicates the current battery status, battery health value H predict The higher the value, the better the battery condition, and the lower the value, the worse the battery condition. This is the battery health value H. predict Set the maintenance threshold to Y2. When H predict When it is less than Y2, it means that the battery condition is too bad and needs maintenance.

5. The new energy equipment management method based on big data according to claim 4 is characterized in that: When the battery health value H predict When it is less than Y2, it means that the charging and discharging efficiency of the battery is reduced. At this time, the irradiance influence coefficient α in the power generation prediction algorithm is reduced to reflect the impact of the poor battery state on the power generation efficiency. Specifically: By adjusting the irradiance influence coefficient α in the power generation prediction algorithm when the battery health declines, the rate of excessive battery degradation can be slowed down and excessive load can be avoided.

6. The new energy equipment management method based on big data according to claim 1 is characterized in that: In the data collection step, after obtaining the operating status data and external environment data, a real-time monitoring dashboard for equipment operation is built based on the big data platform to visualize the operating status data and external environment data of the photovoltaic equipment, so as to ensure that the staff can quickly understand the operating status of the photovoltaic equipment.

7. The new energy equipment management method based on big data according to claim 1 is characterized by: In the data collection step, all photovoltaic devices and sensors are connected to a unified data collection platform through the Internet of Things technology to achieve remote data transmission and ensure the real-time and accuracy of the data.

8. The new energy equipment management method based on big data according to claim 1 is characterized by: In the data backup step, a mechanical hard disk is used to regularly back up the photovoltaic device data.

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