Oil extraction equipment energy optimization storage method and system based on super capacitor

Abstract

The application provides a supercapacitor-based oil exploitation equipment energy optimization storage method and system, relates to the field of oil exploitation equipment energy management, and comprises the following steps: collecting power waveform data, identifying periodic fluctuation characteristics, constructing a multi-electrode partition structure, charging according to a target charging voltage, dividing a charge storage layer, determining an activation sequence, monitoring and identifying abnormal fluctuations, and adjusting an electrical connection topology structure to generate an energy transmission path. The application improves energy storage efficiency, prolongs equipment operation time, enhances system stability, and reduces energy costs.

Description

Technical Field

[0001] This invention relates to energy management technology for oil extraction equipment, and more particularly to a method and system for optimizing energy storage in oil extraction equipment based on supercapacitors. Background Technology

[0002] Oil extraction equipment experiences significant power fluctuations during operation, exhibiting cyclical energy demands. Traditional oil extraction equipment energy supply systems primarily rely on a combination of conventional power sources and energy storage devices to meet normal operating requirements. As oil exploration and development expands into deeper, more geologically complex areas, the demands on the stability and efficiency of energy supply for extraction equipment are increasing. Supercapacitors, due to their high power density, high charge / discharge efficiency, and long cycle life, have gradually become an important component of energy storage systems for oil extraction equipment.

[0003] Current energy storage methods for oil extraction equipment suffer from the following shortcomings: Existing supercapacitor energy storage systems typically employ a single charge-discharge control strategy, which cannot be optimized for the unique power fluctuation characteristics of oil extraction equipment. This makes it difficult to effectively capture and utilize energy from periodic fluctuations, resulting in low energy utilization efficiency. Traditional supercapacitor structural designs lack targeted zoning management, and the charge-discharge control of each electrode area lacks differentiated regulation mechanisms, making it impossible to perform refined energy configuration based on the energy demand characteristics of the equipment under different operating conditions. Existing supercapacitor energy storage systems lack real-time identification and response mechanisms for abnormal power fluctuations occurring during the operation of oil extraction equipment, resulting in poor system adaptability and difficulty in dynamically optimizing and adjusting the energy storage structure under changing operating conditions. Summary of the Invention

[0004] This invention provides a method and system for optimizing energy storage in oil extraction equipment based on supercapacitors, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a method for optimized energy storage in oil extraction equipment based on supercapacitors, comprising:

[0006] Collect power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics;

[0007] The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage of each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage, and the charge storage amount of each electrode partition after charging is measured.

[0008] Based on the charge storage capacity of each electrode zone, the supercapacitor is divided into multiple charge storage layers. The power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation order of the charge storage layers is determined according to the power demand sequence.

[0009] Monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from historical patterns from the time change trajectory, and extract the feature parameters of abnormal fluctuation segments to update the power waveform data;

[0010] Based on the updated power waveform data, the energy demand parameters of the oil extraction equipment are calculated. The electrical connection topology between multiple supercapacitors is adjusted according to the energy demand parameters, and the energy transmission path corresponding to the electrical connection topology is generated. Through the energy transmission path, power is supplied to each subsystem of the oil extraction equipment according to the activation order of the charge storage layer.

[0011] Collect power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics, including:

[0012] Power acquisition units are set at the input and output ends of the oil extraction equipment to collect instantaneous voltage and current, calculate the ratio of input power to output power as the power utilization rate, and record the change of power utilization rate over time to form power waveform data.

[0013] The power waveform data is adaptively segmented, and the waveform asymmetry is calculated based on the rise time and fall time of each segment. Abnormal fluctuation points are identified based on the waveform asymmetry, and smooth waveform data is obtained after removing abnormal fluctuation points.

[0014] The smooth waveform data is converted into a power change trend curve. The peak interval and trough interval of the trend curve are calculated. The periodic fluctuation range is determined based on the consistency of the interval. The waveform features within the periodic fluctuation range are extracted as periodic fluctuation features.

[0015] In the periodic fluctuation characteristics, the turning points of power utilization rate are marked. Turning points that are greater than the preset power utilization rate threshold are recorded as peak points, and turning points that are less than the preset power utilization rate threshold are recorded as valley points. The peak points and valley points are connected in time sequence to form a power extreme point sequence.

[0016] The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage for each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage. The charge storage capacity of each electrode partition after charging is measured, including:

[0017] The supercapacitor is divided into multiple electrode zones by an insulating isolation plate, and a charging interface and a voltage acquisition interface are respectively configured at the positive and negative ends of the electrode zones.

[0018] Calculate the power difference between adjacent peak points and valley points in the power extreme point sequence, allocate the power difference according to the number of electrode zones to obtain the zoned energy storage demand, and determine the target charging voltage of the electrode zone based on the zoned energy storage demand.

[0019] The electrode partitions are arranged in descending order of target charging voltage to determine the charging sequence. Charging voltage is applied to the electrode partitions through the charging interface, and the charging voltage value of the electrode partitions is monitored in real time through the voltage acquisition interface.

[0020] Charging stops when the voltage acquisition interface detects that the charging voltage of the electrode zone has reached the target charging voltage. The voltage and current values ​​of the electrode zone are measured through the voltage acquisition interface, and the charge storage capacity of the electrode zone is calculated based on the measurement results.

[0021] Based on the charge storage capacity of each electrode zone, the supercapacitor is divided into multiple charge storage layers. The power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation sequence of the charge storage layers is determined according to the power demand sequence, including:

[0022] The charge density distribution curve is calculated based on the charge storage capacity of each electrode partition. The gradient value of the charge density distribution curve is obtained. A charge isolation band is constructed where the gradient value exceeds the preset separation threshold. The supercapacitor is divided into multiple charge storage layers through the charge isolation band.

[0023] The system collects the voltage and current values ​​at the input and output terminals of each subsystem of the oil extraction equipment, calculates the instantaneous power of each subsystem, extracts the fluctuation frequency and fluctuation amplitude to form a power feature vector, and generates a power demand sequence based on the power feature vector.

[0024] The rate of change of the charge density distribution curve in the charge storage layer is calculated as the charge release rate parameter. The correspondence between the power demand sequence and the charge release rate parameter is analyzed to generate a charge storage layer activation time series table. The start and end times of charge release are recorded in the charge storage layer activation time series table.

[0025] The charge migration pathways between charge storage layers are calculated based on the charge storage layer activation timing table. The opening time of the charge migration pathway is set according to the start and end times of charge release. The charge flow priority of the charge migration pathway is determined according to the order of charge release rate parameters. The activation order of the charge storage layers is determined according to the opening time of the charge migration pathway and the charge flow priority.

[0026] Monitoring the time-varying trajectory of power extreme point sequences, identifying anomalous fluctuation segments deviating from historical patterns from the time-varying trajectory, and extracting characteristic parameters of anomalous fluctuation segments to update the power waveform data includes:

[0027] Record the sampling timestamps of the power extreme point sequence, construct the time change trajectory curve, divide the time change trajectory curve into multiple observation intervals according to the time window length, extract the fluctuation components in each observation interval, and statistically analyze the amplitude and rate of change of the fluctuation components to generate fluctuation distribution characteristics.

[0028] The fluctuation distribution characteristics are grouped according to the length of the time window, the historical pattern baseline of each group is extracted, the current fluctuation distribution characteristics are compared with the historical pattern baseline, and abnormal fluctuation segments are identified based on the joint deviation of fluctuation amplitude and rate of change, and the location of abnormal fluctuation segments is marked.

[0029] The abnormal fluctuation segment is segmented and linearized, and the changing trend and fluctuation duration interval of the linearized curve are extracted. The changing trend and fluctuation duration interval are constructed into a fluctuation feature trajectory in the amplitude-time coordinate system, and the feature parameters of the abnormal fluctuation segment are extracted based on the fluctuation feature trajectory.

[0030] Write the abnormal fluctuation segment feature parameters into the power waveform data, update the power waveform data fluctuation trend based on the abnormal fluctuation segment feature parameters, and extract new abnormal fluctuation segment feature parameters based on the updated power waveform data fluctuation trend to update the power waveform data.

[0031] The abnormal fluctuation segment is segmented and linearized. The changing trend and fluctuation duration of the linearized curve are extracted. The changing trend and fluctuation duration are then used to construct a fluctuation feature trajectory in the amplitude-time coordinate system. Based on the fluctuation feature trajectory, the characteristic parameters of the abnormal fluctuation segment are extracted, including:

[0032] The direction of power value change in the abnormal fluctuation segment is detected, and the turning point of the change direction is marked as the segmentation point. The abnormal fluctuation segment is divided into multiple fluctuation sub-segments according to the segmentation point. A piecewise linearized curve is constructed by the correspondence between power value and time series. The slope of the piecewise linearized curve is extracted as the trend of change, and the start and end times of the fluctuation sub-segments are extracted as the fluctuation duration interval.

[0033] Establish an amplitude-time coordinate system with time as the horizontal axis and power value as the vertical axis. Plot the slope value corresponding to the trend of change and the time period corresponding to the duration of fluctuation as a straight line segment. Use the connection point of adjacent straight line segments as feature points. Construct the fluctuation feature trajectory based on the connection of feature points.

[0034] The amplitude jump variables and time intervals between adjacent intersections in the fluctuation characteristic trajectory are extracted. The time intervals are weighted according to the magnitude of the amplitude jump variables. The weighted time interval sequence and the amplitude jump variable sequence constitute the characteristic parameters of the abnormal fluctuation segment.

[0035] Based on the updated power waveform data, the energy demand parameters of the oil extraction equipment are calculated. The electrical connection topology between multiple supercapacitors is adjusted according to these parameters, generating energy transmission paths corresponding to the electrical connection topology. Power is then supplied to each subsystem of the oil extraction equipment through these energy transmission paths, following the activation sequence of the charge storage layers:

[0036] Identify the location of fluctuation inflection points from the updated power waveform data, extract the power peak value and fluctuation period between adjacent inflection points, and obtain the single-cycle energy demand value based on the power peak value and fluctuation period.

[0037] The energy demand values ​​of multiple cycles are accumulated to form an energy demand time series, and the energy demand parameters of oil extraction equipment are generated based on the mapping relationship between the energy demand time series and the discharge curve of the supercapacitor.

[0038] Collect the voltage and current values ​​of supercapacitors to calculate the state of charge, construct an energy allocation matrix with the state of charge and energy demand parameters, extract the supercapacitor combination characteristics from the energy allocation matrix, determine the series and parallel relationships between supercapacitors based on the combination characteristics, and generate an electrical connection topology.

[0039] In the electrical connection topology, charge flow nodes and branches are marked, node voltage and branch current constraints are extracted, the charge storage layer is mapped as charge flow nodes, and charge flow paths are planned based on node voltage and branch current constraints to generate energy transfer paths.

[0040] An electrical connection is established between the charge storage layer and the subsystem load based on the energy transmission path. By controlling the on / off state of the electrical connection, power is supplied to each subsystem of the oil extraction equipment according to the activation sequence of the charge storage layer.

[0041] A second aspect of this invention provides an energy optimization storage system for oil extraction equipment based on supercapacitors, comprising:

[0042] The data acquisition unit is used to acquire power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics.

[0043] The partition calculation unit is used to construct the supercapacitor into a multi-electrode partition structure, calculate the target charging voltage of each electrode partition according to the power extreme point sequence, charge each electrode partition in sequence according to the target charging voltage, and measure the charge storage amount of each electrode partition after charging.

[0044] The demand calculation unit is used to divide the supercapacitor into multiple charge storage layers based on the charge storage capacity of each electrode partition, obtain the power demand sequence of each subsystem of the oil extraction equipment, and determine the activation order of the charge storage layers according to the power demand sequence.

[0045] The abnormal update unit is used to monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from the historical pattern from the time change trajectory, and extract the feature parameters of the abnormal fluctuation segments to update the power waveform data.

[0046] The energy transmission unit is used to calculate the energy demand parameters of the oil extraction equipment based on the updated power waveform data, adjust the electrical connection topology between multiple supercapacitors according to the energy demand parameters, generate the energy transmission path corresponding to the electrical connection topology, and supply power to each subsystem of the oil extraction equipment through the energy transmission path according to the activation order of the charge storage layer.

[0047] A third aspect of the present invention provides an electronic device, comprising:

[0048] processor;

[0049] Memory used to store processor-executable instructions;

[0050] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0051] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0052] In this embodiment, by identifying the periodic fluctuation characteristics in the power waveform data of oil extraction equipment and extracting the power extreme point sequence, accurate identification of energy demand patterns is achieved, providing a data foundation for subsequent optimization of energy storage strategies. A multi-electrode partitioned structure is adopted for the supercapacitor design, and the target charging voltage is calculated based on the power extreme point sequence, enabling targeted charging and discharging management for each electrode partition, significantly improving the energy storage efficiency of the supercapacitor. By dividing the supercapacitor into multiple charge storage layers and determining the activation sequence in conjunction with the power demand sequence of each subsystem, hierarchical management and precise allocation of energy resources are achieved, effectively reducing energy transmission losses. A time-varying trajectory monitoring mechanism for the power extreme point sequence is established, enabling timely identification of abnormal fluctuations and dynamic updates to the power waveform data, giving the system adaptive adjustment capabilities and improving the response speed to changes in operating conditions. By dynamically adjusting the electrical connection topology between multiple supercapacitors to form the optimal energy transmission path, directional and efficient energy transmission is achieved, significantly improving the energy utilization rate of oil extraction equipment and reducing energy consumption. Attached Figure Description

[0053] Figure 1 This is a schematic flowchart of an embodiment of the present invention for an energy optimization storage method for oil extraction equipment based on supercapacitors;

[0054] Figure 2 This is a flowchart of power waveform data feature extraction and anomaly identification in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0057] Figure 1 This is a schematic flowchart of an embodiment of the energy optimization storage method for oil extraction equipment based on supercapacitors according to the present invention. Figure 1 As shown, the method includes:

[0058] Collect power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics;

[0059] The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage of each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage, and the charge storage amount of each electrode partition after charging is measured.

[0060] Based on the charge storage capacity of each electrode zone, the supercapacitor is divided into multiple charge storage layers. The power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation order of the charge storage layers is determined according to the power demand sequence.

[0061] Monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from historical patterns from the time change trajectory, and extract the feature parameters of abnormal fluctuation segments to update the power waveform data;

[0062] Based on the updated power waveform data, the energy demand parameters of the oil extraction equipment are calculated. The electrical connection topology between multiple supercapacitors is adjusted according to the energy demand parameters, and the energy transmission path corresponding to the electrical connection topology is generated. Through the energy transmission path, power is supplied to each subsystem of the oil extraction equipment according to the activation order of the charge storage layer.

[0063] In one optional implementation, power waveform data of oil extraction equipment is collected, periodic fluctuation characteristics are identified from the power waveform data, and a sequence of power extreme points is extracted from the periodic fluctuation characteristics, including:

[0064] Power acquisition units are set at the input and output ends of the oil extraction equipment to collect instantaneous voltage and current, calculate the ratio of input power to output power as the power utilization rate, and record the change of power utilization rate over time to form power waveform data.

[0065] The power waveform data is adaptively segmented, and the waveform asymmetry is calculated based on the rise time and fall time of each segment. Abnormal fluctuation points are identified based on the waveform asymmetry, and smooth waveform data is obtained after removing abnormal fluctuation points.

[0066] The smooth waveform data is converted into a power change trend curve. The peak interval and trough interval of the trend curve are calculated. The periodic fluctuation range is determined based on the consistency of the interval. The waveform features within the periodic fluctuation range are extracted as periodic fluctuation features.

[0067] In the periodic fluctuation characteristics, the turning points of power utilization rate are marked. Turning points that are greater than the preset power utilization rate threshold are recorded as peak points, and turning points that are less than the preset power utilization rate threshold are recorded as valley points. The peak points and valley points are connected in time sequence to form a power extreme point sequence.

[0068] In practical applications, the power waveform data acquisition and processing system for oil extraction equipment can effectively monitor equipment operating status and predict equipment failures by identifying and analyzing power waveform characteristics. First, power acquisition units are installed at both the input and output ends of the oil extraction equipment to collect instantaneous voltage and current. The power acquisition units use high-precision current transformers and voltage sensors, with a sampling frequency of 1000Hz and a sampling accuracy of 16 bits. The instantaneous voltage sampling range is 0-1000V, and the instantaneous current sampling range is 0-500A. The acquired instantaneous voltage and current data are multiplied to obtain the input power Pin and output power Pout. The ratio of input power to output power, η=Pout / Pin, is calculated as the power utilization rate. The power utilization rate data is plotted with time on the horizontal axis and the utilization rate value on the vertical axis to form power waveform data. The recording interval is 0.1 seconds, and the continuous recording time is no less than 72 hours to ensure data integrity.

[0069] Adaptive segmentation processing is performed on the power waveform data. A sliding window method is used, with an initial window size of 60 seconds and an overlap rate of 50%. Within each window, the statistical characteristics of the power waveform are calculated, including mean, standard deviation, kurtosis, and skewness. When the difference in statistical characteristics between adjacent windows exceeds a preset threshold, a segmentation point is set at the boundary between the two windows. The preset thresholds are determined based on historical data statistics: a mean difference threshold of 5%, a standard deviation difference threshold of 10%, a kurtosis difference threshold of 1.5, and a skewness difference threshold of 0.8. For each waveform segment, the rise time Tr and fall time Tf are calculated, and the waveform asymmetry is defined as D = |Tr - Tf| / (Tr + Tf). When the waveform asymmetry D exceeds 0.3, the point is marked as an abnormal fluctuation point. Abnormal fluctuation points are replaced using cubic spline interpolation to obtain smooth waveform data.

[0070] The smoothed waveform data is converted into a power change trend curve. Wavelet transform is used, with a db4 wavelet basis and a decomposition level of 5. Wavelet coefficients from levels 3-5 are extracted to reconstruct the signal, yielding the trend curve. The peak interval Tp and trough interval Tv of the trend curve are calculated. Local maximum detection is used for peak identification, and local minimum detection is used for trough identification, with a detection window size of 30 seconds. The coefficient of variation (CVp) of the peak interval (CVp = σp / μp) and the coefficient of variation (CVv) of the trough interval (CVv = σv / μv) are calculated, where σp and σv are the standard deviations of the peak and trough intervals, respectively, and μp and μv are the means of the peak and trough intervals, respectively. When CVp < 0.15 and CVv < 0.15, it is determined to be a periodic fluctuation interval. For the identified periodic fluctuation intervals, waveform features are extracted, including period length, amplitude, waveform factor, and waveform steepness, which together constitute the periodic fluctuation characteristics.

[0071] Inflection points of power utilization rate are marked within the periodic fluctuation characteristics. An extreme point detection algorithm is used to calculate the first and second derivatives of the power utilization rate curve. A point is considered an inflection point when the first derivative is zero and the second derivative is not zero. When the second derivative is less than zero, the inflection point is a maximum; when the second derivative is greater than zero, the inflection point is a minimum. A preset power utilization rate threshold of 0.75 is set. When the power utilization rate value at an inflection point is greater than 0.75, it is recorded as a peak point; when the power utilization rate value at an inflection point is less than 0.75, it is recorded as a valley point. Peak points and valley points are connected in chronological order to form a power extreme point sequence P = {p1, p2, ..., pn}, where p1 represents the first extreme point, containing a timestamp t1 and a power utilization rate value η1.

[0072] Based on the extracted power extreme point sequence, energy storage optimization for oil extraction equipment is performed. A supercapacitor bank is connected in parallel to the power supply circuit of the oil extraction equipment. When the power utilization rate is detected to be at its peak, the supercapacitor enters a discharging state to provide auxiliary energy to the equipment and reduce the main power load; when the power utilization rate is detected to be at its trough, the supercapacitor enters a charging state to absorb excess energy. The charging and discharging control of the supercapacitor is based on a power prediction model. This model uses historical data from the power extreme point sequence, combined with time series analysis methods, to predict the power change trend within the next 5 minutes. The controller dynamically adjusts the charging and discharging power and duration of the supercapacitor according to the prediction results, so that the charging and discharging process matches the equipment's operating cycle.

[0073] In a practical application, a pumping unit in an oilfield was tested, and 120 hours of power waveform data were collected. After extracting the power extreme point sequence using the method described above, a typical working cycle of 120 seconds was identified, with an average peak power utilization rate of 0.85 and an average valley power utilization rate of 0.62. Based on the characteristics of the extreme point sequence, a supercapacitor bank with a total capacity of 500F and a rated voltage of 48V was configured, which can continuously provide 5kW auxiliary power for 30 seconds during the peak phase and charge at 3kW for 35 seconds during the valley phase.

[0074] In this embodiment, accurate acquisition and analysis of power waveform data of oil extraction equipment enables accurate identification of the equipment's working cycle, providing a data foundation for energy optimization. Adaptive segmentation and waveform asymmetry calculation effectively filter out abnormal fluctuations, improving the accuracy of feature extraction. Based on the periodic fluctuation feature extraction, a power extreme point sequence is extracted, providing precise timing basis for supercapacitor charging and discharging control. Combined with the high power density characteristics of supercapacitors, auxiliary energy is provided to the equipment during peak energy demand periods, and residual energy is recovered during off-peak periods, significantly improving the energy utilization efficiency of oil extraction equipment, reducing load fluctuations in the power supply system, extending equipment lifespan, and reducing energy consumption and operating costs.

[0075] In one optional implementation, the supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage for each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage. The charge storage capacity of each electrode partition after charging is measured, including:

[0076] The supercapacitor is divided into multiple electrode zones by an insulating isolation plate, and a charging interface and a voltage acquisition interface are respectively configured at the positive and negative ends of the electrode zones.

[0077] Calculate the power difference between adjacent peak points and valley points in the power extreme point sequence, allocate the power difference according to the number of electrode zones to obtain the zoned energy storage demand, and determine the target charging voltage of the electrode zone based on the zoned energy storage demand.

[0078] The electrode partitions are arranged in descending order of target charging voltage to determine the charging sequence. Charging voltage is applied to the electrode partitions through the charging interface, and the charging voltage value of the electrode partitions is monitored in real time through the voltage acquisition interface.

[0079] Charging stops when the voltage acquisition interface detects that the charging voltage of the electrode zone has reached the target charging voltage. The voltage and current values ​​of the electrode zone are measured through the voltage acquisition interface, and the charge storage capacity of the electrode zone is calculated based on the measurement results.

[0080] This embodiment provides an energy optimization storage method for oil extraction equipment based on supercapacitors. The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage of each electrode partition is calculated according to the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage, and the charge storage amount of each electrode partition after charging is measured.

[0081] The supercapacitor is divided into multiple electrode zones by insulating partitions. These partitions are made of polytetrafluoroethylene (PTFE) with a thickness of 0.5 mm and a withstand voltage rating of 2000V. The total volume of the supercapacitor is 400×300×200 mm, internally divided into six electrode zones by the insulating partitions. Each zone measures 130×150×200 mm. Each electrode zone contains 20 individual supercapacitors, each with a capacitance of 100F, a rated voltage of 2.7V, and an internal resistance of 0.01Ω. A charging interface and a voltage acquisition interface are respectively located at the positive and negative terminals of each electrode zone. The charging interface uses gold-plated copper alloy terminals with a maximum allowable charging current of 50A. The voltage acquisition interface connects to a high-precision voltage sensor with a sampling accuracy of 0.01V and a sampling frequency of 100Hz. Temperature sensors in all electrode zones monitor the operating temperature in real time, ensuring a safe operating range of -20 to 60℃.

[0082] Calculate the power difference between adjacent peak and valley points in the power extremum sequence. For the power extremum sequence, extract the set of all peak points Pp and valley points Pv. For each pair of adjacent peak and valley points, calculate the power difference ΔP = Pp - Pv. When the peak power is 85kW and the valley power is 55kW, the power difference ΔP is 30kW. Based on the power difference and the time difference Δt between adjacent extrema, calculate the required energy storage ΔE = ΔP × Δt. Assuming a peak-valley interval of 25s, the required energy storage ΔE = 30 × 25 = 750kJ. Allocate the calculated energy storage demand according to the number of electrode zones. An exponentially decreasing allocation strategy is adopted, so that the earlier electrode zones undertake more energy storage tasks, and the later electrode zones serve as supplements. For 6 electrode zones, the energy allocation ratio is 35%, 25%, 17%, 12%, 7%, and 4%. Based on the zoned energy storage demand and capacitor characteristics E = 0.5CV. 2 Calculate the target charging voltage for each electrode zone. The target charging voltage for the first zone is 40V, for the second zone it is 34V, for the third zone it is 28V, for the fourth zone it is 24V, for the fifth zone it is 18V, and for the sixth zone it is 14V.

[0083] The electrode partitions are arranged in descending order of target charging voltage to determine the charging sequence. A constant current-constant voltage charging mode is used during charging. In the constant current charging stage, the charging current is set to 30A. When the electrode partition voltage reaches 95% of the target charging voltage, it switches to the constant voltage charging stage. In the constant voltage charging stage, the charging voltage is maintained at the target charging voltage, and the charging current gradually decreases. Charging is considered complete when the charging current drops to 10% of the initial charging current (i.e., 3A). The charging voltage is applied to the electrode partitions through the charging interface. The maximum output power of the charging power supply is 5kW, with a voltage adjustment range of 0-50V and a current adjustment range of 0-100A. The charging controller uses a closed-loop control algorithm, adjusting the charging parameters based on the real-time collected electrode partition voltage and current, achieving a charging control accuracy better than ±0.5%. The electrode partition charging voltage value is monitored in real-time through the voltage acquisition interface, with a sampling period of 10ms.

[0084] Charging stops when the electrode zone charging voltage reaches the target charging voltage, detected by the voltage acquisition interface. After charging, the electrode zone is kept in voltage acquisition mode to monitor self-discharge. If the voltage drop rate exceeds 0.1V / h, a charging maintenance procedure is triggered to replenish the charge lost due to self-discharge. The voltage and current values ​​of the electrode zone are measured through the voltage acquisition interface. The voltage measurement range is 0-50V, and the current measurement range is 0-60A, with a measurement accuracy of 0.1%. Based on the measurement results, the charge storage capacity of the electrode zone is calculated as Q = C × V, where C is the equivalent capacitance of the electrode zone and V is the charging voltage. For the first electrode zone, the equivalent capacitance is 500F, the charging voltage is 40V, and the calculated charge storage capacity is 20000C. Simultaneously, the energy storage capacity of the electrode zone is calculated as E = 0.5 × C × V. 2 The energy storage capacity of the first electrode section is 400,000 J.

[0085] In a practical application, a pumping unit in an oilfield was tested. This pumping unit has a working cycle of 120 seconds and a power fluctuation range of 50-90 kW. A supercapacitor energy storage system with six electrode zones was constructed using the method described above, with a total equivalent capacitance of 2000 F and a maximum charging voltage of 40 V. The system response time is less than 100 ms, enabling it to rapidly adjust charging and discharging power according to load power changes. Under peak load conditions, the supercapacitor discharges to support equipment operation; under off-peak load conditions, the supercapacitor is charged in zones.

[0086] In this embodiment, a multi-electrode partition structure is constructed using insulating isolation plates, enabling refined management of supercapacitor energy storage. The target charging voltage is calculated according to the power extreme point sequence, ensuring precise matching of energy distribution across each electrode partition with the equipment load characteristics. A high-to-low charging sequence optimizes charging efficiency and energy distribution strategies. Real-time monitoring of the electrode partition charging status ensures charging accuracy and safety. The multi-electrode partition structure improves the utilization rate of the supercapacitor, and the exponentially decreasing energy distribution strategy enhances the energy storage system's adaptability to power fluctuations. Precise charging control and monitoring mechanisms extend the supercapacitor's lifespan, improve the energy efficiency of oil extraction equipment, reduce the impact of power fluctuations on the power supply system, decrease energy consumption, and lower operating costs.

[0087] In one optional implementation, the supercapacitor is divided into multiple charge storage layers based on the charge storage capacity of each electrode partition, the power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation order of the charge storage layers is determined according to the power demand sequence, including:

[0088] The charge density distribution curve is calculated based on the charge storage capacity of each electrode partition. The gradient value of the charge density distribution curve is obtained. A charge isolation band is constructed where the gradient value exceeds the preset separation threshold. The supercapacitor is divided into multiple charge storage layers through the charge isolation band.

[0089] The system collects the voltage and current values ​​at the input and output terminals of each subsystem of the oil extraction equipment, calculates the instantaneous power of each subsystem, extracts the fluctuation frequency and fluctuation amplitude to form a power feature vector, and generates a power demand sequence based on the power feature vector.

[0090] The rate of change of the charge density distribution curve in the charge storage layer is calculated as the charge release rate parameter. The correspondence between the power demand sequence and the charge release rate parameter is analyzed to generate a charge storage layer activation time series table. The start and end times of charge release are recorded in the charge storage layer activation time series table.

[0091] The charge migration pathways between charge storage layers are calculated based on the charge storage layer activation timing table. The opening time of the charge migration pathway is set according to the start and end times of charge release. The charge flow priority of the charge migration pathway is determined according to the order of charge release rate parameters. The activation order of the charge storage layers is determined according to the opening time of the charge migration pathway and the charge flow priority.

[0092] First, the supercapacitor is uniformly divided into 100 micro-regions along its longitudinal direction. The charge storage capacity of each region is measured using a high-precision charge sensor with a resolution of 0.01C and a measurement range of 0-5000C. The charge density distribution curve is represented by the function ρ(x) = Q(x) / V(x), where Q(x) is the charge quantity at position x and V(x) is the volume at position x. The measured charge storage data points are used to generate a continuous charge density distribution curve using a cubic spline interpolation algorithm. The gradient value of the charge density distribution curve is calculated using the central difference method: ∇ρ(x)≈[ρ(x+h)-ρ(xh)] / (2h), where h is the sampling interval, which is 5mm. When the gradient value exceeds the preset separation threshold of 50C / cm, the gradient is considered to be attenuated. 3 At a thickness of 2 mm, a charge isolation band is constructed at this location. The charge isolation band is made of a modified polymer material, with an insulation strength greater than 10 kV / mm. The established charge isolation band divides the supercapacitor into three charge storage layers, with the high-density layer having an average charge density of 120 C / cm³. 3 The medium-density layer has a temperature of 75°C / cm³, and the low-density layer has a temperature of 40°C / cm³. 3 .

[0093] High-precision data acquisition equipment is used to collect voltage and current values ​​at the input and output terminals of each subsystem of the oil extraction equipment. The voltage measurement range is 0-1000V with an accuracy of 0.1%, and the current measurement range is 0-500A with an accuracy of 0.1%. The sampling frequency is set to 1kHz. The instantaneous power calculation formula for each subsystem is P=U×I, where U is the voltage value and I is the current value. The power calculation for a three-phase system is P=3 1 / 2 ×U×I×cosφ, where φ is the phase angle. The fluctuation frequency is obtained by performing a Fast Fourier Transform on the power time series, focusing on frequency components in the range of 0.1-10Hz. The fluctuation amplitude is obtained by calculating the difference between the maximum and minimum power values. The power eigenvector is represented as V=[f1, f2, ..., f...]. n A1, A2, ..., A n Let f be the frequency component and A be the corresponding amplitude. The power characteristic vector of the oil pump drive system is [0.5Hz, 1.2Hz, 2.5Hz, 35kW, 18kW, 8kW], indicating the existence of three main frequency components with corresponding amplitudes of 35kW, 18kW, and 8kW, respectively. The power demand sequence is predicted using an autoregressive moving average model to predict power demand changes over the next 60 seconds. The autoregressive term in the model has an order of 3, the moving average term has an order of 2, and the prediction accuracy error is less than 5%.

[0094] The rate of change of the charge density distribution curve within the charge storage layer is calculated using the formula dρ / dt=(ρ2-ρ1) / Δt, where ρ2 and ρ1 are the charge densities at adjacent moments, and Δt is the time interval, taken as 0.1s. The charge release rate parameter is 6 for the high-density layer, 4 for the medium-density layer, and 2 for the low-density layer. The correspondence between the power demand sequence and the charge release rate parameter is established using the mapping function M(P)=k×P+b, where P is the power demand value, and k and b are fitting parameters. By fitting the actual data using the least squares method, k=0.08, b=1.5, and the fitting accuracy R0 is [value missing]. 2 =0.94. The charge storage layer activation timing table records the start and end times of charge release for each layer. The start time for charge release in the high-density layer is t0+0s, and the end time is t0+25s; for the medium-density layer, it is from t0+20s to t0+40s; and for the low-density layer, it is from t0+35s to t0+60s. t0 is the system startup time. A 5s overlap time window is set between each charge storage layer to ensure a smooth transition in energy supply and avoid sudden changes in power output.

[0095] The charge transfer pathway is made of copper alloy with a cross-sectional area of ​​50 mm². 2The maximum allowable current is 200A, and the length is 120mm. Three charge migration paths are established between the three charge storage layers: high-medium, medium-low, and high-low. Each path is equipped with a bidirectional controllable switch with a control accuracy of 1ms and a maximum switching frequency of 500Hz. The activation time for the high-medium migration path is from t0+20s to t0+25s, for the medium-low migration path from t0+35s to t0+40s, and for the high-low migration path from t0+35s to t0+60s. The charge flow rate is calculated using the formula F=ρ×v×A, where ρ is the charge density, v is the charge velocity, and A is the cross-sectional area of ​​the path. The charge velocity is proportional to the voltage difference across the path, with a proportionality coefficient of 2.5mm / s·V. The charge flow rate priority for the high-medium migration path is 1, for high-low it is 2, and for medium-low it is 3. The activation sequence of the charge storage layers is: first activate the high-density layer, then the medium-density layer, and finally the low-density layer, forming a stepped release mode. When switching between different layers, the controller uses a combined feedforward and feedback control algorithm to send out an activation signal 0.5 seconds in advance, achieving a switching smoothness of over 95%.

[0096] When implementing energy optimization control for the pumping unit, power data was collected with an average power of 85kW during the upstroke and 40kW during the downstroke, with a stroke cycle of 15s. Based on the power demand sequence, the high-density layer releases charge to meet the high power demand during the upstroke; when the power demand decreases, the system switches to the medium-density and low-density layers. The controller uses a 16-bit microprocessor with a processing speed of 150MIPS, a built-in 12-bit A / D converter, and a sampling rate of 100kHz. The controller monitors the charge density changes of each layer in real time, triggering a charging operation when the charge density drops to 20% of the maximum charge density. A gradient charging strategy is used, charging the low-density layer first, then the medium-density and high-density layers sequentially, with a charging current of 50A and a charging efficiency of 93%. The charging source uses a bidirectional DC / DC converter with an input voltage range of 380-420V, an adjustable output voltage range of 0-50V, a maximum output power of 10kW, and a conversion efficiency greater than 95%. During charging, the temperature of each electrode zone is monitored in real time. The temperature sensor has a measurement range of -40 to 120℃ and an accuracy of ±0.5℃. When the temperature exceeds 60℃, the forced air cooling system is activated, with a fan speed of 3000 rpm and a cooling efficiency of 2℃ / min.

[0097] In this embodiment, by calculating the charge density distribution curve and constructing the charge isolation band, hierarchical management of the charge inside the supercapacitor is achieved, improving the organization and retrieval efficiency of charge storage. Based on the power characteristic vector analysis of each subsystem, accurate prediction of the power demand of oil extraction equipment is achieved, enabling precise matching of energy supply with actual demand. By generating the activation timing table of the charge storage layer, fine control of the charge release process is achieved, avoiding redundancy and insufficiency in energy release. Based on the design of the charge migration path, flexible energy allocation between different charge storage layers is achieved, improving the dynamic response capability and adaptability of the system. By adopting a stepped release mode, the system can intelligently adjust the charge supply strategy according to the actual load, significantly improving energy utilization efficiency, reducing energy loss, extending equipment operating time, and ensuring the stability and continuity of the oil extraction process.

[0098] like Figure 2 The diagram shows the flowchart for power waveform data feature extraction and anomaly identification in this embodiment.

[0099] In one optional implementation, monitoring the time-varying trajectory of power extreme point sequences, identifying abnormal fluctuation segments that deviate from historical patterns from the time-varying trajectory, and extracting characteristic parameters of the abnormal fluctuation segments to update the power waveform data includes:

[0100] Record the sampling timestamps of the power extreme point sequence, construct the time change trajectory curve, divide the time change trajectory curve into multiple observation intervals according to the time window length, extract the fluctuation components in each observation interval, and statistically analyze the amplitude and rate of change of the fluctuation components to generate fluctuation distribution characteristics.

[0101] The fluctuation distribution characteristics are grouped according to the length of the time window, the historical pattern baseline of each group is extracted, the current fluctuation distribution characteristics are compared with the historical pattern baseline, and abnormal fluctuation segments are identified based on the joint deviation of fluctuation amplitude and rate of change, and the location of abnormal fluctuation segments is marked.

[0102] The abnormal fluctuation segment is segmented and linearized, and the changing trend and fluctuation duration interval of the linearized curve are extracted. The changing trend and fluctuation duration interval are constructed into a fluctuation feature trajectory in the amplitude-time coordinate system, and the feature parameters of the abnormal fluctuation segment are extracted based on the fluctuation feature trajectory.

[0103] Write the abnormal fluctuation segment feature parameters into the power waveform data, update the power waveform data fluctuation trend based on the abnormal fluctuation segment feature parameters, and extract new abnormal fluctuation segment feature parameters based on the updated power waveform data fluctuation trend to update the power waveform data.

[0104] During the process of optimizing the energy storage of supercapacitors in oil extraction equipment, the power extreme point sequence is collected in real time, and the collection frequency is set to 100Hz. Each data point contains power value and corresponding timestamp information. The power value is collected by a high-precision power analyzer with a measurement range of 0 - 500kW, a resolution of 0.1kW, and a timestamp accuracy of 1ms. The collected power extreme points include local maxima and local minima. The condition for determining an extreme point is: for any point P(i), if P(i) > P(i - 1) and P(i) > P(i + 1), then P(i) is a local maximum; if P(i) < P(i - 1) and P(i) < P(i + 1), then P(i) is a local minimum. The time-varying trajectory curve is constructed by connecting adjacent extreme points and is represented as the function F(t), where t is the time variable. The time-varying trajectory curve is divided into multiple observation intervals with a length of 30 seconds, and the observation intervals overlap by 10 seconds to ensure the continuity of analysis. Wavelet transform is applied to each observation interval to extract the fluctuation components. The db4 wavelet basis function is used for 5-layer decomposition to obtain the fluctuation components D1 to D5 with different frequency components. The amplitude of the fluctuation components is obtained by calculating the standard deviation of each component, and the change rate is obtained by calculating the mean of the first derivative. Taking the driving motor of the oil pump as an example, the amplitudes of D1 to D5 in a certain observation interval are 15.2kW, 8.7kW, 5.3kW, 2.1kW, 0.9kW respectively, and the change rates are 4.6kW / s, 2.1kW / s, 0.8kW / s, 0.3kW / s, 0.1kW / s respectively. The amplitudes and change rates of the fluctuation components in each observation interval are combined into a vector to form the fluctuation distribution characteristics.

[0105] The fluctuation distribution characteristics are grouped according to a time window length of 30 seconds, and each group contains historical data of the same time period within 24 hours. The kernel density estimation method is applied to each group of historical data to extract the distribution law and generate the historical law baseline. The kernel density estimation uses the Gaussian kernel function K(x)=(1 / 2π 1 / 2 )·exp(-x 2 / 2), and the bandwidth h is set to 0.5. The historical law baseline is represented as B(x)=(1 / nh)·∑K((x - x i ) / h), where n is the number of historical data points and xᵢ is the historical data point. The deviation degree calculation formula between the current fluctuation distribution characteristics and the historical law baseline is D = ∑w i ·|c i - b i |, where c i is the current eigenvalue, b i is the baseline value, and w iThe weighting coefficients are set as follows: the weighting coefficient for fluctuation amplitude is set to 0.6, and the weighting coefficient for the rate of change is set to 0.4. When the deviation D exceeds the threshold of 3.5, it is determined to be an abnormal fluctuation segment. The location of the abnormal fluctuation segment is marked by a timestamp interval, such as [t1, t2] indicating that the interval from time t1 to t2 is determined to be an abnormal fluctuation segment.

[0106] The identified abnormal fluctuation segments are segmented and linearized, and the optimal segmentation point is determined using a dynamic programming algorithm. The abnormal fluctuation segments are divided into several sub-segments, such that the power change within each sub-segment is approximately linear. The number of sub-segments is adaptively determined based on an error threshold, which is set to 5% of the standard deviation of the original data. After linearization, a linear equation P(t) = kt + b is fitted to each sub-segment, where k is the slope, representing the trend, and b is the intercept. The duration of the fluctuation is determined by calculating the slope change between adjacent sub-segments. When |kᵢ-kᵢ₋1| / |kᵢ₋1|>0.3, a trend change is considered to have occurred, and this is marked as the boundary of the fluctuation duration interval. Straight lines for each sub-segment are plotted in the amplitude-time coordinate system to form the fluctuation characteristic trajectory. Four key feature parameters are extracted from the fluctuation characteristic trajectory: maximum amplitude difference (A...). max ), average rate of change (R) av g), duration (T), and number of fluctuation cycles (N). For a certain abnormal fluctuation segment of the oil pump drive motor, the extracted feature parameters are: A max =45.6kW, R av g=5.2kW / s, T=28.3s, N=3.

[0107] The extracted abnormal fluctuation segment feature parameters are written into a power waveform data structure. The data structure includes fields: time interval, maximum amplitude difference, average rate of change, duration, and number of fluctuation cycles. The power waveform data is stored in key-value pairs, with the time interval as the key and the feature parameters as the values. The power waveform data fluctuation trend is updated based on the abnormal fluctuation segment feature parameters using an exponentially weighted moving average algorithm: T'(t) = α·T(t) + (1-α)·T'(t-1), where T'(t) is the updated trend value, T(t) is the current observation value, and α is a smoothing coefficient set to 0.3. The updated power waveform data fluctuation trend reflects the latest power demand changes of oil extraction equipment. Based on the updated power waveform data fluctuation trend, a sliding time window is applied to continuously monitor new abnormal fluctuation segments. The sliding window width is 30 seconds, and the step size is 5 seconds. When a new abnormal fluctuation segment is detected, the above feature extraction and update process is repeated. The cumulative update of the feature parameters enables the dynamic evolution of the power waveform data, allowing it to adapt to changes in the load of oil extraction equipment. The power waveform data is stored in a circular buffer containing information from the most recent 24 hours. The buffer size is 8640 data points, and one data point is recorded every 10 seconds.

[0108] In this embodiment, by monitoring the time-varying trajectory of power extreme point sequences, the energy demand pattern of oil extraction equipment is accurately characterized; multi-scale fluctuation components are extracted using wavelet transform, effectively separating load characteristics at different frequencies; a normal behavior model of power fluctuations is established based on the historical pattern baseline extraction method using kernel density estimation; abnormal fluctuation segments are accurately identified through deviation calculation, improving the sensitivity of abnormal event detection; piecewise linearization technology simplifies the expression of complex waveforms and reduces the computational complexity of feature extraction; an optimal piecewise algorithm based on dynamic programming achieves a fine characterization of fluctuation features; and a dynamic update mechanism for feature parameters enables power waveform data to evolve adaptively to adapt to changes in equipment operating status, providing accurate load prediction basis for supercapacitor energy optimization storage and improving the accuracy and adaptability of energy allocation.

[0109] In one optional implementation, the abnormal fluctuation segment is segmented and linearized to extract the trend and duration of the linearized curve. The trend and duration of the fluctuation are then used to construct a fluctuation feature trajectory in an amplitude-time coordinate system. Based on this trajectory, characteristic parameters of the abnormal fluctuation segment are extracted, including:

[0110] The direction of power value change in the abnormal fluctuation segment is detected, and the turning point of the change direction is marked as the segmentation point. The abnormal fluctuation segment is divided into multiple fluctuation sub-segments according to the segmentation point. A piecewise linearized curve is constructed by the correspondence between power value and time series. The slope of the piecewise linearized curve is extracted as the trend of change, and the start and end times of the fluctuation sub-segments are extracted as the fluctuation duration interval.

[0111] Establish an amplitude-time coordinate system with time as the horizontal axis and power value as the vertical axis. Plot the slope value corresponding to the trend of change and the time period corresponding to the duration of fluctuation as a straight line segment. Use the connection point of adjacent straight line segments as feature points. Construct the fluctuation feature trajectory based on the connection of feature points.

[0112] The amplitude jump variables and time intervals between adjacent intersections in the fluctuation characteristic trajectory are extracted. The time intervals are weighted according to the magnitude of the amplitude jump variables. The weighted time interval sequence and the amplitude jump variable sequence constitute the characteristic parameters of the abnormal fluctuation segment.

[0113] For identified abnormal power fluctuations in oil extraction equipment, the differential method is used to detect the direction of power value changes. The first-order difference between adjacent sampling points is calculated as Δp(i) = p(i+1) - p(i), where p(i) represents the power value at the i-th sampling point. When the sign of the difference changes from positive to negative or vice versa (i.e., Δp(i-1) × Δp(i) < 0), the point is marked as a segmentation point. To avoid noise interference, a threshold filtering condition is set: |Δp(i)| > 0.5kW; the sign change is only considered when the absolute value of the difference is greater than the threshold. In the pumping unit power waveform, segmentation points were detected at timestamps t1 = 15.2s, t2 = 28.5s, t3 = 42.1s, and t4 = 55.8s, with power values ​​of p1 = 85.4kW, p2 = 40.3kW, p3 = 82.7kW, and p4 = 42.1kW, respectively. Based on these segmentation points, the abnormal fluctuation segment is divided into multiple fluctuation sub-segments: [0, t1], [t1, t2], [t2, t3], and [t3, t4]. A least squares linear fit is applied to each fluctuation sub-segment, with the fitting formula p(t) = k × t + b, where k is the slope and b is the intercept. For the fluctuation sub-segment [0, t1], the fitted values ​​are k1 = 3.2 kW / s and b1 = 37.5 kW; for the fluctuation sub-segment [t1, t2], the fitted values ​​are k2 = -3.4 kW / s and b2 = 137.1 kW; for the fluctuation sub-segment [t2, t3], the fitted values ​​are k3 = 3.1 kW / s and b3 = -48.8 kW; and for the fluctuation sub-segment [t3, t4], the fitted values ​​are k4 = -3.2 kW / s and b4 = 260.8 kW. The fitted linear equations form piecewise linearized curves, with slopes k1, k2, k3, and k4 representing the changing trends of each sub-segment. The duration of the fluctuations is the time range of each sub-segment: [0, t1], [t1, t2], [t2, t3], and [t3, t4].

[0114] Establish an amplitude-time coordinate system, with the horizontal axis representing time (in seconds) and the vertical axis representing power (in kilowatts). Set the coordinate system resolution to 0.1 s / division for the time axis and 1 kW / division for the power axis, with a display range of [0, 100] seconds and [0, 100] kilowatts. Within this coordinate system, draw straight line segments based on the linear equation p(t) = k × t + b for each wave segment. The first wave segment [0, t1] corresponds to a straight line segment drawn from point (0, 37.5) to point (15.2, 85.4); the second wave segment [t1, t2] corresponds to a straight line segment drawn from point (15.2, 85.4) to point (28.5, 40.3); the third wave segment [t2, t3] corresponds to a straight line segment drawn from point (28.5, 40.3) to point (42.1, 82.7); and the fourth wave segment [t3, t4] corresponds to a straight line segment drawn from point (42.1, 82.7) to point (55.8, 42.1). The connection points between adjacent straight line segments are located at the coordinates corresponding to the segment points. These connection points are used as feature points, with coordinates of (15.2, 85.4), (28.5, 40.3), (42.1, 82.7), and (55.8, 42.1). By connecting these feature points, a fluctuation feature trajectory is constructed. The trajectory exhibits obvious periodic oscillation characteristics. Each cycle contains an upward segment and a downward segment, corresponding to the upstroke and downstroke of the pumping unit.

[0115] The amplitude jump variables and time intervals between adjacent feature points are extracted from the wave characteristic trajectory. The amplitude jump variable between feature points p1 and p2 is Δp1 = |p2 - p1| = |40.3 - 85.4| = 45.1 kW, and the time interval is Δt1 = |t2 - t1| = |28.5 - 15.2| = 13.3 s; the amplitude jump variable between feature points p2 and p3 is Δp2 = |p3 - p2| = |82.7 - 40.3| = 42.4 kW, and the time interval is Δt2 = |t3 - t2| = |42.1 - 28.5| = 13.6 s; the amplitude jump variable between feature points p3 and p4 is Δp3 = |p4 - p3| = |42.1 - 82.7| = 40.6 kW, and the time interval is Δt3 = |t4 - t3| = |55.8 - 42.1| = 13.7 s.

[0116] The time intervals are weighted according to the magnitude of the amplitude jump variable, and the weighting formula is Δt'i=Δti×(Δpi / Pavg). α Where Pavg is the average amplitude jump variable, calculated as Pavg = (Δp1 + Δp2 + Δp3) / 3 = (45.1 + 42.4 + 40.6) / 3 = 42.7kW; α is the weighting exponent, with a value of 0.5. The weighted time interval is calculated as: Δt'1 = 13.3 × (45.1 / 42.7) 1 / 2=13.7s, Δt'2=13.6×(42.4 / 42.7) 1 / 2 =13.5s, Δt'3=13.7×(40.6 / 42.7) 1 / 2 =13.4s. The weighted time interval sequence [13.7, 13.5, 13.4] is combined with the amplitude jump variable sequence [45.1, 42.4, 40.6] to form the characteristic parameters of the abnormal fluctuation segment.

[0117] The extracted characteristic parameters of abnormal fluctuation segments are stored in the supercapacitor controller. The parameters are stored using a key-value pair structure, where the key is the timestamp sequence [t1, t2, t3, t4], and the values ​​are the corresponding power value sequence [p1, p2, p3, p4], amplitude jump variable sequence [Δp1, Δp2, Δp3], and weighted time interval sequence [Δt'1, Δt'2, Δt'3]. The controller uses a 32-bit microprocessor with a main frequency of 200MHz, 512KB of SRAM, and 2MB of Flash storage. The computational complexity of the characteristic parameter extraction algorithm is O(n), where n is the number of sampling points in the abnormal fluctuation segment, typically not exceeding 1000 points. The algorithm execution time is less than 10ms, meeting real-time processing requirements. Based on the extracted characteristic parameters, the controller can identify the working cycle characteristics of the pumping unit, including the duration of the upstroke and downstroke, and power change characteristics. Based on these characteristic parameters, the controller calculates the optimal supercapacitor discharge strategy: 0.5s before the start of the pumping unit's upstroke, high-density layer charge is pre-released, and the power release curve follows the pattern p(t) = 3.2 × t + 37.5, lasting for 13.7s; during the downstroke, medium-density layer charge is released, and the power change curve is p(t) = -3.4 × t + 137.1, lasting for 13.5s; this cycle repeats to achieve precise matching between capacitor discharge and load demand.

[0118] In this embodiment, by segmenting and linearizing the abnormal fluctuation segment, the complex power waveform is simplified into a trend characterized by slope and a fluctuation duration range characterized by time range, significantly reducing the amount of data for waveform description. Based on segmented point detection technology, the turning points of the power waveform are accurately captured, ensuring the preservation of key features during linearization. The least squares fitting algorithm ensures a high degree of fit between the segmented linearized curve and the original waveform, reducing approximation errors. The fluctuation feature trajectory constructed in the amplitude-time coordinate system intuitively presents the spatiotemporal characteristics of power changes, facilitating subsequent analysis. By extracting amplitude jump variables and time intervals between adjacent feature points, the dynamic characteristics of load changes are characterized. The time interval weighting method based on amplitude jump variables strengthens the time characteristics of the large amplitude change range, improving the accuracy of abnormal fluctuation features. The extracted feature parameters provide an accurate load model for the optimized energy storage of supercapacitors, achieving precise matching between energy release and load demand.

[0119] In one optional implementation, energy demand parameters for the oil extraction equipment are calculated based on updated power waveform data. The electrical connection topology between multiple supercapacitors is adjusted according to these parameters to generate energy transmission paths corresponding to the electrical connection topology. Power is then supplied to each subsystem of the oil extraction equipment via these energy transmission paths, following the activation sequence of the charge storage layers.

[0120] Identify the location of fluctuation inflection points from the updated power waveform data, extract the power peak value and fluctuation period between adjacent inflection points, and obtain the single-cycle energy demand value based on the power peak value and fluctuation period.

[0121] The energy demand values ​​of multiple cycles are accumulated to form an energy demand time series, and the energy demand parameters of oil extraction equipment are generated based on the mapping relationship between the energy demand time series and the discharge curve of the supercapacitor.

[0122] Collect the voltage and current values ​​of supercapacitors to calculate the state of charge, construct an energy allocation matrix with the state of charge and energy demand parameters, extract the supercapacitor combination characteristics from the energy allocation matrix, determine the series and parallel relationships between supercapacitors based on the combination characteristics, and generate an electrical connection topology.

[0123] In the electrical connection topology, charge flow nodes and branches are marked, node voltage and branch current constraints are extracted, the charge storage layer is mapped as charge flow nodes, and charge flow paths are planned based on node voltage and branch current constraints to generate energy transfer paths.

[0124] An electrical connection is established between the charge storage layer and the subsystem load based on the energy transmission path. By controlling the on / off state of the electrical connection, power is supplied to each subsystem of the oil extraction equipment according to the activation sequence of the charge storage layer.

[0125] The energy demand parameters for oil extraction equipment are calculated based on updated power waveform data. First, the power waveform data is processed and analyzed. By identifying the inflection points in the waveform, peak power and fluctuation period information are extracted. Taking an oil pumping unit as an example, the power waveform typically exhibits periodic changes, displaying different power characteristics during the upstroke and downstroke phases. When the pumping unit transitions from the upstroke to the downstroke, the power value changes significantly; this point can be identified as the inflection point.

[0126] For waveform data processing, a slope change rate threshold method is used to identify inflection points. When the slope change of the power curve exceeds a preset threshold, the point is marked as a candidate inflection point. Then, a minimum distance constraint is used for filtering to avoid too many inflection points in dense regions. Assume that an inflection point t is identified within the time period [t1, t2]. a and t b Then the peak power P in this interval max It can be expressed as the maximum power value within this time period, and the fluctuation period T is t. b -t a .

[0127] Based on the obtained peak power and fluctuation period, the single-cycle energy demand value E is calculated. cycle For the time period [t] a , t b ], E cycle It equals the power integral over time within that interval. For discretely sampled power data, the trapezoidal integral method is used for calculation. The energy demand values ​​over multiple periods are accumulated to form the energy demand time series E(t).

[0128] Energy demand time series are mapped to supercapacitor discharge curves to generate energy demand parameters. Supercapacitor discharge curves are typically represented by the relationship between voltage and discharge time. When establishing the mapping relationship, energy demand under different load conditions is correlated with the discharge characteristics of the supercapacitor under the corresponding conditions, extracting parameters including peak power demand, total energy demand, and load change rate.

[0129] The voltage and current values ​​of each cell in the supercapacitor bank are collected, and the state of charge (SOC) is calculated in real time. The SOC of a supercapacitor can be estimated using the voltage relationship: SOC = (V / V) * ... min ) / (V max -V min ), where V is the current voltage, V min and V max These represent the minimum and maximum operating voltages, respectively. An energy allocation matrix M is constructed by combining the state of charge and energy demand parameters of all supercapacitors. Each element M[i,j] in the matrix represents the contribution of the i-th supercapacitor to the j-th energy demand parameter.

[0130] The supercapacitor combination characteristics are extracted from the energy distribution matrix to determine the series and parallel relationships between supercapacitors. When multiple supercapacitors contribute significantly to the same energy demand parameter, they are considered to be connected in parallel; when the sum of the contributions of certain supercapacitors needs to reach a specific threshold, they are considered to be connected in series. For example, in the start-up phase of oil extraction, when high voltage is required, multiple supercapacitors are connected in series to provide higher voltage; while in the continuous operation phase, when a stable power supply is required, parallel connection is used to increase capacity.

[0131] After generating the electrical connection topology, the charge flow nodes and branches in the structure are marked. Nodes include the positive and negative terminals of the supercapacitor, connection points, and load connection points. A unique identifier is assigned to each node, and its voltage constraints are recorded; the current flow direction and current constraints are marked for each branch. The charge storage layer of the supercapacitor is mapped to charge flow nodes, establishing a correspondence between nodes and actual capacitor units.

[0132] Based on node voltage and branch current constraints, charge flow paths are planned. A shortest path algorithm from graph theory is used to transfer energy from the source node (supercapacitor) to the target node (load), while satisfying both voltage and current constraints. For complex topologies, an improved Dijkstra's algorithm can be employed, using voltage drop and energy loss as edge weights to find the optimal energy transfer path.

[0133] Based on the planned energy transmission path, an electrical connection is established between the charge storage layer and the subsystem load. By controlling the on / off state of the electrical connection, power is supplied to each subsystem of the oil extraction equipment according to the predetermined activation sequence of the charge storage layer. For example, during the pumping unit startup phase, the high-power capacitor unit is activated first to power the drive system; during stable operation, it switches to the medium-power unit; and in emergencies, the backup unit is activated to provide temporary energy support.

[0134] This method of adjusting the electrical connection topology of supercapacitors based on energy demand parameters can dynamically optimize energy storage and supply strategies for the power fluctuation characteristics of oil extraction equipment, improve system energy efficiency, extend equipment lifespan, and ensure the reliability and stability of energy supply at each working stage.

[0135] A second aspect of the present invention provides an energy optimization storage system for oil extraction equipment based on supercapacitors, the system comprising:

[0136] The data acquisition unit is used to acquire power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics.

[0137] The partition calculation unit is used to construct the supercapacitor into a multi-electrode partition structure, calculate the target charging voltage of each electrode partition according to the power extreme point sequence, charge each electrode partition in sequence according to the target charging voltage, and measure the charge storage amount of each electrode partition after charging.

[0138] The demand calculation unit is used to divide the supercapacitor into multiple charge storage layers based on the charge storage capacity of each electrode partition, obtain the power demand sequence of each subsystem of the oil extraction equipment, and determine the activation order of the charge storage layers according to the power demand sequence.

[0139] The abnormal update unit is used to monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from the historical pattern from the time change trajectory, and extract the feature parameters of the abnormal fluctuation segments to update the power waveform data.

[0140] The energy transmission unit is used to calculate the energy demand parameters of the oil extraction equipment based on the updated power waveform data, adjust the electrical connection topology between multiple supercapacitors according to the energy demand parameters, generate the energy transmission path corresponding to the electrical connection topology, and supply power to each subsystem of the oil extraction equipment through the energy transmission path according to the activation order of the charge storage layer.

[0141] A third aspect of the present invention provides an electronic device, comprising:

[0142] processor;

[0143] Memory used to store processor-executable instructions;

[0144] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0145] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0146] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimized energy storage in oil extraction equipment based on supercapacitors, characterized in that, include: Collect power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics; The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage of each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage, and the charge storage amount of each electrode partition after charging is measured. Based on the charge storage capacity of each electrode zone, the supercapacitor is divided into multiple charge storage layers. The power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation order of the charge storage layers is determined according to the power demand sequence. Monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from historical patterns from the time change trajectory, and extract the feature parameters of abnormal fluctuation segments to update the power waveform data; Based on the updated power waveform data, the energy demand parameters of the oil extraction equipment are calculated. The electrical connection topology between multiple supercapacitors is adjusted according to the energy demand parameters, and the energy transmission path corresponding to the electrical connection topology is generated. Through the energy transmission path, power is supplied to each subsystem of the oil extraction equipment according to the activation order of the charge storage layer.

2. The method according to claim 1, characterized in that, Collect power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics, including: Power acquisition units are set at the input and output ends of the oil extraction equipment to collect instantaneous voltage and current, calculate the ratio of input power to output power as the power utilization rate, and record the change of power utilization rate over time to form power waveform data. The power waveform data is adaptively segmented, and the waveform asymmetry is calculated based on the rise time and fall time of each segment. Abnormal fluctuation points are identified based on the waveform asymmetry, and smooth waveform data is obtained after removing abnormal fluctuation points. The smooth waveform data is converted into a power change trend curve. The peak interval and trough interval of the trend curve are calculated. The periodic fluctuation range is determined based on the consistency of the interval. The waveform features within the periodic fluctuation range are extracted as periodic fluctuation features. In the periodic fluctuation characteristics, the turning points of power utilization rate are marked. Turning points that are greater than the preset power utilization rate threshold are marked as peak points, and turning points that are less than the preset power utilization rate threshold are marked as valley points. The peak points and valley points are connected in time sequence to form a power extreme point sequence.

3. The method according to claim 1, characterized in that, The supercapacitor is constructed as a multi-electrode partition structure. The target charging voltage for each electrode partition is calculated based on the power extreme point sequence. Each electrode partition is charged sequentially according to the target charging voltage. The charge storage capacity of each electrode partition after charging is measured, including: The supercapacitor is divided into multiple electrode zones by an insulating isolation plate, and a charging interface and a voltage acquisition interface are respectively configured at the positive and negative ends of the electrode zones. Calculate the power difference between adjacent peak points and valley points in the power extreme point sequence, allocate the power difference according to the number of electrode zones to obtain the zoned energy storage demand, and determine the target charging voltage of the electrode zone based on the zoned energy storage demand. The electrode partitions are arranged in descending order of target charging voltage to determine the charging sequence. Charging voltage is applied to the electrode partitions through the charging interface, and the charging voltage value of the electrode partitions is monitored in real time through the voltage acquisition interface. Charging stops when the voltage acquisition interface detects that the charging voltage of the electrode zone has reached the target charging voltage. The voltage and current values ​​of the electrode zone are measured through the voltage acquisition interface, and the charge storage capacity of the electrode zone is calculated based on the measurement results.

4. The method according to claim 1, characterized in that, Based on the charge storage capacity of each electrode zone, the supercapacitor is divided into multiple charge storage layers. The power demand sequence of each subsystem of the oil extraction equipment is obtained, and the activation sequence of the charge storage layers is determined according to the power demand sequence, including: The charge density distribution curve is calculated based on the charge storage capacity of each electrode partition. The gradient value of the charge density distribution curve is obtained. A charge isolation band is constructed where the gradient value exceeds the preset separation threshold. The supercapacitor is divided into multiple charge storage layers through the charge isolation band. The system collects the voltage and current values ​​at the input and output terminals of each subsystem of the oil extraction equipment, calculates the instantaneous power of each subsystem, extracts the fluctuation frequency and fluctuation amplitude to form a power feature vector, and generates a power demand sequence based on the power feature vector. The rate of change of the charge density distribution curve in the charge storage layer is calculated as the charge release rate parameter. The correspondence between the power demand sequence and the charge release rate parameter is analyzed to generate a charge storage layer activation time series table. The start and end times of charge release are recorded in the charge storage layer activation time series table. The charge migration pathways between charge storage layers are calculated based on the charge storage layer activation timing table. The opening time of the charge migration pathway is set according to the start and end times of charge release. The charge flow priority of the charge migration pathway is determined according to the order of charge release rate parameters. The activation order of the charge storage layers is determined according to the opening time of the charge migration pathway and the charge flow priority.

5. The method according to claim 1, characterized in that, Monitoring the time-varying trajectory of power extreme point sequences, identifying anomalous fluctuation segments deviating from historical patterns from the time-varying trajectory, and extracting characteristic parameters of anomalous fluctuation segments to update the power waveform data includes: Record the sampling timestamps of the power extreme point sequence, construct the time change trajectory curve, divide the time change trajectory curve into multiple observation intervals according to the time window length, extract the fluctuation components in each observation interval, and statistically analyze the amplitude and rate of change of the fluctuation components to generate fluctuation distribution characteristics. The fluctuation distribution characteristics are grouped according to the length of the time window, the historical pattern baseline of each group is extracted, the current fluctuation distribution characteristics are compared with the historical pattern baseline, and abnormal fluctuation segments are identified based on the joint deviation of fluctuation amplitude and rate of change, and the location of abnormal fluctuation segments is marked. The abnormal fluctuation segment is segmented and linearized, and the changing trend and fluctuation duration interval of the linearized curve are extracted. The changing trend and fluctuation duration interval are constructed into a fluctuation feature trajectory in the amplitude-time coordinate system, and the feature parameters of the abnormal fluctuation segment are extracted based on the fluctuation feature trajectory. Write the abnormal fluctuation segment feature parameters into the power waveform data, update the power waveform data fluctuation trend based on the abnormal fluctuation segment feature parameters, and extract new abnormal fluctuation segment feature parameters based on the updated power waveform data fluctuation trend to update the power waveform data.

6. The method according to claim 5, characterized in that, The abnormal fluctuation segment is segmented and linearized. The changing trend and fluctuation duration of the linearized curve are extracted. The changing trend and fluctuation duration are then used to construct a fluctuation feature trajectory in the amplitude-time coordinate system. Based on the fluctuation feature trajectory, the characteristic parameters of the abnormal fluctuation segment are extracted, including: The direction of power value change in the abnormal fluctuation segment is detected, and the turning point of the change direction is marked as the segmentation point. The abnormal fluctuation segment is divided into multiple fluctuation sub-segments according to the segmentation point. A piecewise linearized curve is constructed by the correspondence between power value and time series. The slope of the piecewise linearized curve is extracted as the trend of change, and the start and end times of the fluctuation sub-segments are extracted as the fluctuation duration interval. Establish an amplitude-time coordinate system with time as the horizontal axis and power value as the vertical axis. Plot the slope value corresponding to the trend of change and the time period corresponding to the duration of fluctuation as a straight line segment. Use the connection point of adjacent straight line segments as feature points. Construct the fluctuation feature trajectory based on the connection of feature points. The amplitude jump variables and time intervals between adjacent intersections in the fluctuation characteristic trajectory are extracted. The time intervals are weighted according to the magnitude of the amplitude jump variables. The weighted time interval sequence and the amplitude jump variable sequence constitute the characteristic parameters of the abnormal fluctuation segment.

7. The method according to claim 1, characterized in that, Based on the updated power waveform data, the energy demand parameters of the oil extraction equipment are calculated. The electrical connection topology between multiple supercapacitors is adjusted according to these parameters, generating energy transmission paths corresponding to the electrical connection topology. Power is then supplied to each subsystem of the oil extraction equipment through these energy transmission paths, following the activation sequence of the charge storage layers: Identify the location of fluctuation inflection points from the updated power waveform data, extract the power peak value and fluctuation period between adjacent inflection points, and obtain the single-cycle energy demand value based on the power peak value and fluctuation period. The energy demand values ​​of multiple cycles are accumulated to form an energy demand time series, and the energy demand parameters of oil extraction equipment are generated based on the mapping relationship between the energy demand time series and the discharge curve of the supercapacitor. Collect the voltage and current values ​​of supercapacitors to calculate the state of charge, construct an energy allocation matrix with the state of charge and energy demand parameters, extract the supercapacitor combination characteristics from the energy allocation matrix, determine the series and parallel relationships between supercapacitors based on the combination characteristics, and generate an electrical connection topology. In the electrical connection topology, charge flow nodes and branches are marked, node voltage and branch current constraints are extracted, the charge storage layer is mapped as charge flow nodes, and charge flow paths are planned based on node voltage and branch current constraints to generate energy transfer paths. An electrical connection is established between the charge storage layer and the subsystem load based on the energy transmission path. By controlling the on / off state of the electrical connection, power is supplied to each subsystem of the oil extraction equipment according to the activation sequence of the charge storage layer.

8. An energy optimization storage system for oil extraction equipment based on supercapacitors, used to implement the method of any one of claims 1-7, characterized in that, include: The data acquisition unit is used to acquire power waveform data of oil extraction equipment, identify periodic fluctuation characteristics from the power waveform data, and extract the power extreme point sequence from the periodic fluctuation characteristics. The partition calculation unit is used to construct the supercapacitor into a multi-electrode partition structure, calculate the target charging voltage of each electrode partition according to the power extreme point sequence, charge each electrode partition in sequence according to the target charging voltage, and measure the charge storage amount of each electrode partition after charging. The demand calculation unit is used to divide the supercapacitor into multiple charge storage layers based on the charge storage capacity of each electrode partition, obtain the power demand sequence of each subsystem of the oil extraction equipment, and determine the activation order of the charge storage layers according to the power demand sequence. The abnormal update unit is used to monitor the time change trajectory of the power extreme point sequence, identify abnormal fluctuation segments that deviate from the historical pattern from the time change trajectory, and extract the feature parameters of the abnormal fluctuation segments to update the power waveform data. The energy transmission unit is used to calculate the energy demand parameters of the oil extraction equipment based on the updated power waveform data, adjust the electrical connection topology between multiple supercapacitors according to the energy demand parameters, generate the energy transmission path corresponding to the electrical connection topology, and supply power to each subsystem of the oil extraction equipment through the energy transmission path according to the activation order of the charge storage layer.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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