Supercapacitor-based hoisting machinery amplitude variation process energy management method and system

Abstract

The invention provides a hoisting machinery amplitude variation process energy management method and system based on a supercapacitor, and relates to the technical field of energy management, and the method comprises the steps: calculating the theoretical energy demand of an amplitude variation process through a neural network prediction model, correcting a dynamic grouping coefficient to obtain a power distribution coefficient, and calculating the power distribution coefficient; and taking the result as the upper limit of the charging and discharging power of each group of super capacitors, and adjusting the charging and discharging current through a self-adaptive PI controller. When the amplitude variation angle is reduced, regenerative braking energy exceeding the rechargeable power of the super capacitor is stored in a high-temperature superconducting energy storage inductor, and the energy is charged into the super capacitor according to a power distribution coefficient when the condition is allowed; when the amplitude variation angle is increased, the output power ratio of the main power supply to each group of super capacitors is optimized by adopting a dynamic programming algorithm, and power cooperative control is realized through a sliding mode and an active-disturbance-rejection controller, so that the stability of the system voltage is ensured. The energy utilization efficiency can be effectively improved, the service life of the supercapacitor is prolonged, and the system stability is ensured.

Description

Technical Field

[0001] The present invention relates to energy management technologies, and in particular to an energy management method and system for the luffing process of a crane based on supercapacitors. Background Art

[0002] During the luffing operation of a crane, its energy demand usually exhibits the characteristics of dynamic changes, including severe fluctuations in instantaneous power and instability in energy recovery efficiency. Traditional energy management methods are difficult to effectively cope with the power fluctuations during the luffing process, resulting in low system energy utilization efficiency, increasing the wear of mechanical components, and affecting the service life and operation reliability of the equipment. Existing technologies lack pertinence in energy distribution and power control and cannot achieve precise management of complex energy flows.

[0003] Due to its high power density, fast charge and discharge ability, and long cycle life, supercapacitors have become an ideal choice for the energy management system of a crane. However, supercapacitors have problems of capacity attenuation and unbalanced charge and discharge in practical applications, resulting in a decrease in their energy utilization efficiency. Especially during the luffing process, the dynamic energy demand does not match well with the charge and discharge characteristics of supercapacitors, further exacerbating the energy loss of the system. In addition, how to efficiently store the regenerative braking energy during the energy recovery stage is also a difficult point in the current technology.

[0004] Therefore, there is an urgent need for a comprehensive energy management method based on supercapacitors, which can achieve efficient management of energy flow through real-time power distribution, dynamic grouping, and intelligent control, and effectively extend the service life of the supercapacitor bank, thereby improving the energy utilization efficiency during the luffing process of a crane. Summary of the Invention

[0005] Embodiments of the present invention provide an energy management method and system for the luffing process of a crane based on supercapacitors, which can solve the problems in the prior art.

[0006] In the first aspect of the embodiments of the present invention,

[0007] An energy management method for the luffing process of a crane based on supercapacitors is provided, including:

[0008] Analyze the luffing angle rate and the suspended weight of the crane using a neural network prediction model, calculate the instantaneous power curve during the luffing process, take the integral value of the instantaneous power curve on the time axis as the theoretical energy demand during the luffing process, group the supercapacitors based on the theoretical energy demand and calculate the initial grouping coefficient, determine the capacity attenuation rate as the ratio of the initial grouping coefficient to the historical number of cycles, calculate the remaining life coefficient of each group of supercapacitors according to the capacity attenuation rate, and calculate the dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient;

[0009] A fuzzy controller based on the dynamic grouping coefficient is established, taking the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as the input quantities of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped into charge and discharge power correction values through the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power distribution coefficient. The power distribution coefficient is used as the upper limit of the charge and discharge power of each group of supercapacitors, and the charge and discharge current of each group of supercapacitors is adjusted by an adaptive PI controller, where the control parameters of the adaptive PI controller have an adaptive relationship with the power distribution coefficient and the terminal voltage deviation;

[0010] During the process of reducing the luffing angle, the regenerative braking power is compared with the rechargeable power of the supercapacitor bank calculated through the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the ratio of the power distribution coefficients of each group of supercapacitors; during the process of increasing the luffing angle, an intelligent power distributor is established based on the power distribution coefficients of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and uses an active disturbance rejection controller to suppress the load power fluctuation to ensure the stability of the system voltage.

[0011] In an optional embodiment,

[0012] A neural network prediction model is used to analyze the luffing angle rate and the lifting weight of the crane, calculate the instantaneous power curve during the luffing process, and take the integral value of the instantaneous power curve on the time axis as the theoretical energy demand during the luffing process. Grouping the supercapacitors based on the theoretical energy demand and calculating the initial grouping coefficient includes:

[0013] Construct a neural network prediction model, where the input layer receives the luffing angle rate signal and the lifting weight signal, and the output layer outputs the instantaneous power curve during the luffing process;

[0014] Sampling the luffing angle rate signal and the suspended load weight signal to obtain a discrete data sequence, performing time-domain noise reduction and feature extraction on the discrete data sequence to obtain time-domain features, normalizing the extracted time-domain features by the maximum-minimum value method to obtain training samples, constructing a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term, and using an adaptive optimization method based on momentum to iteratively train the neural network prediction model until the weighted loss function converges to a preset threshold to obtain a trained neural network prediction model;

[0015] Performing piecewise integral operation on the instantaneous power curve output by the trained neural network prediction model on the time axis, adaptively adjusting the time window length of the integral interval according to the change trend of the luffing angle rate signal, where the time window length is positively correlated with the change rate of the luffing angle rate signal, and designing a double-loop state observer to filter the integral result to obtain the theoretical energy demand during the luffing process;

[0016] Dividing the theoretical energy demand by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, calculating the basic grouping number according to the ratio, and setting a redundancy coefficient based on the system redundancy requirement, multiplying the basic grouping number by the redundancy coefficient and rounding up to obtain the actual grouping number;

[0017] Real-time collecting the terminal voltage signals of each group of supercapacitors, and at the same time using a temperature sensor array to collect the temperature signals on the surface of each group of supercapacitors, and establishing a Coulomb counting model according to the terminal voltage signals to calculate the state of charge of each group of supercapacitors;

[0018] Constructing a fuzzy inference engine based on a triangular membership function, using the inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the state of charge as the input variables of the fuzzy inference engine, establishing an inference mechanism including fuzzification, rule inference, and defuzzification, outputting a grouping coefficient correction value through the fuzzy inference engine, determining a reference grouping coefficient according to the initial operating state of the system, and adding the grouping coefficient correction value to the reference grouping coefficient to obtain an initial grouping coefficient.

[0019] In an alternative embodiment,

[0020] Determining the ratio of the initial grouping coefficient to the historical number of cycles as the capacity attenuation rate, calculating the remaining life coefficient of each group of supercapacitors according to the capacity attenuation rate, and calculating the dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient, including:

[0021] Collect the charge and discharge cycle count value of the supercapacitor as the historical cycle number, determine the ratio of the initial grouping coefficient to the historical cycle number as the reference capacity attenuation rate, and adaptively adjust the sliding time window length according to the numerical value of the reference capacity attenuation rate. The sliding time window length decreases as the reference capacity attenuation rate increases;

[0022] Continuously collect the real-time terminal voltage monitoring signal and real-time temperature monitoring signal of the supercapacitor within the sliding time window length. Calculate the real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculate the real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculate the real-time capacity attenuation rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determine the ratio of the real-time capacity attenuation rate to the reference capacity attenuation rate as the attenuation rate correction coefficient;

[0023] Establish a Weibull distribution life prediction model based on the attenuation rate correction coefficient, and calculate the characteristic life parameter. The characteristic life parameter is directly proportional to the reciprocal of the attenuation rate correction coefficient, and the proportionality coefficient is adaptively adjusted according to the fluctuation degree of the attenuation rate correction coefficient; at the same time, use the maximum likelihood estimation method to calculate the shape parameter of the Weibull distribution life prediction model;

[0024] Input the characteristic life parameter and the shape parameter into the preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculate the product of the initial grouping coefficient and the remaining life coefficient to obtain the intermediate dynamic grouping coefficient;

[0025] Calculate the difference between the real-time capacity attenuation rates at adjacent sampling moments to obtain the capacity attenuation change rate. Take the normalized value of the capacity attenuation change rate as the smoothing coefficient, and perform first-order low-pass filtering on the intermediate dynamic grouping coefficient using the smoothing coefficient. The time constant of the filtering process is directly proportional to the smoothing coefficient, and output the result after filtering as the final dynamic grouping coefficient.

[0026] In an alternative embodiment,

[0027] Establish a fuzzy controller based on the dynamic grouping coefficient. Use the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors as the input quantities of the fuzzy controller. Map different combinations of the terminal voltage deviation and terminal voltage change rate to the charge and discharge power correction value through the fuzzy controller, and establish a recursive compensation model based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain the power distribution coefficient, including:

[0028] Collect the terminal voltage signals of each group of supercapacitors and perform digital filtering, calculate the average terminal voltage of the system, take the difference between the terminal voltage of each group of supercapacitors and the average terminal voltage of the system as the terminal voltage deviation, perform moving average filtering on the terminal voltage deviation to obtain the filtered terminal voltage deviation, perform differential operation on the filtered terminal voltage deviations at adjacent sampling times and combine with the sampling period to obtain the terminal voltage change rate, and perform amplitude limiting processing on the terminal voltage change rate to obtain the amplitude-limited terminal voltage change rate;

[0029] Divide the filtered terminal voltage deviation according to a preset interval and establish a first set of linguistic variables, set dynamically adjustable boundaries for the first fuzzy subset for each interval, and at the same time divide the amplitude-limited terminal voltage change rate according to a preset interval and establish a second set of linguistic variables, and adaptively adjust the boundary values according to the change trend of the terminal voltage deviation to obtain the second fuzzy subset boundary;

[0030] Perform fuzzy processing on the filtered terminal voltage deviation using an overlapping-degree optimized triangular membership function to obtain a first membership value, and perform fuzzy processing on the amplitude-limited terminal voltage change rate using an asymmetric trapezoidal membership function with configurable slope to obtain a second membership value;

[0031] Construct a fuzzy rule base according to the first membership value, the second membership value, the first set of linguistic variables, and the second set of linguistic variables, and use a variable-structure fuzzy control strategy to optimize the rules online to obtain an optimized set of fuzzy rules. Use the fuzzy sub-rule parallel inference method to perform fuzzy inference on the optimized set of fuzzy rules, and perform fuzzy comprehensive operation on the inference results of each sub-rule through adaptive weights to obtain a comprehensive fuzzy output;

[0032] Perform defuzzification processing on the comprehensive fuzzy output using the centroid method to obtain an initial power correction value, and perform dynamic amplitude limiting on the initial power correction value in combination with the system state parameters to obtain a charge-discharge power correction value;

[0033] Take the dynamic grouping coefficient as the initial value, construct a recursive compensation model with the charge-discharge power correction value as the target value. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient, and performs iterative operation using the recursive compensation model. Stop the iteration when the change amount of the power coefficient between two adjacent iterations is less than a preset change amount threshold to obtain a compensated grouping coefficient. Set the values less than the dead zone threshold in the compensated grouping coefficient to zero, and perform normalization processing on the values greater than the dead zone threshold to obtain a power distribution coefficient.

[0034] In an alternative embodiment,

[0035] The defuzzification processing of the synthesized fuzzy output is performed by an improved centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited according to the system state parameters to obtain the charge-discharge power correction value, including:

[0036] Perform defuzzification processing on the fuzzy control output. By calculating the membership degree of each rule, the discrete points of the output domain, and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value, an initial power correction value is obtained; where the area weight is calculated by piecewise linear approximation. For the trapezoidal region, the product of the mean of the membership degrees of adjacent discrete points and the discrete point spacing is used, and for the rectangular region, the product of the membership degree of the discrete point and the discrete point spacing is used;

[0037] Collect the system operating state parameters, including the state of charge, terminal voltage, and temperature. Calculate the deviation between the state of charge and the desired state of charge to obtain the state of charge deviation, calculate the deviation between the terminal voltage and the rated terminal voltage to obtain the terminal voltage deviation, and calculate the difference between the temperature and the temperature threshold to obtain the temperature deviation;

[0038] Based on the state of charge deviation, establish a first limiting function. Based on the terminal voltage deviation, establish a second limiting function. Based on the temperature deviation, establish a third limiting function. Take the product of the initial power correction value and the first limiting function, the second limiting function, and the third limiting function as the intermediate power correction value;

[0039] Perform piecewise linearization processing on the first limiting function, the second limiting function, and the third limiting function. Take the product of the limited function after the piecewise linearization processing and the initial power correction value as the final charge-discharge power correction value, which is used to adjust the charge-discharge power of the system.

[0040] In an optional embodiment,

[0041] During the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficients of each group of supercapacitors. The intelligent power distributor optimizes the output power ratio of the main power supply and each group of supercapacitors by using a dynamic programming algorithm, realizes the power coordination control of each group of supercapacitors and the main power supply through a sliding mode controller, and uses an active disturbance rejection controller to suppress the load power fluctuation to ensure the stability of the system voltage, including:

[0042] Obtain the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the power distribution coefficients corresponding to each group of supercapacitors;

[0043] An intelligent power distributor is established based on the power distribution coefficient. The real-time state of charge, terminal voltage, and bus voltage are used as system state variables, and the output power of the main power supply and the output power of each group of supercapacitors are used as control variables and input into the intelligent power distributor;

[0044] A target function is constructed in the intelligent power distributor, and the dynamic programming algorithm is used to solve the target function to obtain the output power ratio of the main power supply to each group of supercapacitors;

[0045] A sliding mode surface is constructed based on the deviation between the target value and the actual value of the output power ratio, and the control law of the sliding mode controller is designed based on the sliding mode surface. The control law includes an equivalent control term composed of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by the reaching law parameter;

[0046] Control commands for the main power supply and each group of supercapacitors are output through the sliding mode controller, and the load power fluctuation information after executing the control commands is collected. Based on the load power fluctuation information, the system output and tracking error are obtained through an extended state observer, and the state observation equation of the active disturbance rejection controller is constructed according to the system output and tracking error;

[0047] The output value of the state observation equation is compared with the reference input to obtain a compensation control amount, and the control commands for the main power supply and each group of supercapacitors are corrected according to the compensation control amount to suppress the load power fluctuation. The corrected control commands are applied to the main power supply and each group of supercapacitors to achieve power coordinated control and ensure system voltage stability. At the same time, the controlled system state is used as a new state variable and input into the intelligent power distributor for iterative optimization.

[0048] In an alternative embodiment,

[0049] The formula for constructing the target function in the intelligent power distributor is as follows:

[0050]

[0051] where, J represents the target function, T represents the total time, w 1 represents the weight of the bus voltage deviation term, V bus(t) represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w 2 represents the weight of the supercapacitor power distribution deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power distribution coefficient of the i-th group of supercapacitors, w 3 represents the weight of the main power supply power utilization rate deviation term, Pmain(t) represents the actual power value provided by the main power supply at time t, P main，max represents the maximum output power of the main power supply.

[0052] In the second aspect of the embodiments of the present invention,

[0053] a luffing process energy management system for a hoisting machine based on a supercapacitor is provided, including:

[0054] A first unit for analyzing the luffing angle rate and the suspended load weight of the hoisting machine by using a neural network prediction model, calculating an instantaneous power curve during the luffing process, taking the integral value of the instantaneous power curve on the time axis as the theoretical energy demand during the luffing process, grouping the supercapacitors based on the theoretical energy demand and calculating an initial grouping coefficient, determining the ratio of the initial grouping coefficient to the historical number of cycles as the capacity attenuation rate, calculating the remaining life coefficient of each group of supercapacitors according to the capacity attenuation rate, and calculating a dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient;

[0055] A second unit for establishing a fuzzy controller based on the dynamic grouping coefficient, using the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as the input quantities of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate to charge and discharge power correction values through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power distribution coefficient, taking the power distribution coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, where the control parameters of the adaptive PI controller have an adaptive relationship with the power distribution coefficient and the terminal voltage deviation;

[0056] A third unit for, during the process of decreasing the luffing angle, comparing the regenerative braking power with the rechargeable power of the supercapacitor bank calculated through the power distribution coefficient, when the regenerative braking power is greater than the rechargeable power, storing the excess energy in a high-temperature superconducting energy storage inductor, and real-time detecting the power distribution coefficient of each group of supercapacitors, when it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, charging the energy in the high-temperature superconducting energy storage inductor according to the ratio of the power distribution coefficients of each group of supercapacitors; during the process of increasing the luffing angle, establishing an intelligent power distributor based on the power distribution coefficients of each group of supercapacitors, the intelligent power distributor optimizing the output power ratio of the main power supply and each group of supercapacitors by using a dynamic programming algorithm, realizing the power coordination control of each group of supercapacitors and the main power supply through a sliding mode controller, and suppressing the load power fluctuation by using an active disturbance rejection controller to ensure the stability of the system voltage.

[0057] In a third aspect of the embodiments of the present invention,

[0058] a kind of electronic device is provided, including:

[0059] a processor;

[0060] a memory for storing instructions executable by the processor;

[0061] wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0062] In a fourth aspect of the embodiments of the present invention,

[0063] a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0064] In this embodiment, by analyzing the working conditions of the hoisting machinery and the aging characteristics of the supercapacitors, the charging and discharging power distribution of each group of supercapacitors is dynamically adjusted to avoid overcharging and over-discharging, and the losses of each group of supercapacitors are balanced, thereby prolonging the overall service life. During the process of the luffing angle decreasing, the regenerative braking energy is preferentially stored in the supercapacitors, and the part exceeding the rechargeable power of the supercapacitors is stored in the high-temperature superconducting energy storage inductor, and the energy in the high-temperature superconducting energy storage inductor is supplemented to the supercapacitors at an appropriate time to recover the regenerative braking energy to the maximum extent. During the process of the luffing angle increasing, the output power of the main power supply and the supercapacitors is intelligently distributed, and sliding mode control and auto-disturbance rejection control technologies are adopted to suppress the load power fluctuation, ensure the system voltage stability, and improve the reliability of the crane operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic flowchart of the energy management method for the luffing process of the hoisting machinery based on supercapacitors according to the embodiments of the present invention;

[0066] Figure 2 is a schematic structural diagram of the energy management system for the luffing process of the hoisting machinery based on supercapacitors according to the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0068] The technical solution of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0069] Figure 1 This is a schematic flowchart of the energy management method for the luffing process of a hoisting machine based on a supercapacitor according to an embodiment of the present invention. As Figure 1 shown, the method includes:

[0070] S101. Analyze the luffing angle rate and load weight of the hoisting machine using a neural network prediction model, calculate the instantaneous power curve during the luffing process, take the integral value of the instantaneous power curve on the time axis as the theoretical energy demand during the luffing process, group the supercapacitors based on the theoretical energy demand and calculate the initial grouping coefficient, determine the capacity attenuation rate as the ratio of the initial grouping coefficient to the historical cycle number, calculate the remaining life coefficient of each group of supercapacitors according to the capacity attenuation rate, and calculate the dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient;

[0071] S102. Establish a fuzzy controller based on the dynamic grouping coefficient, use the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors as the input quantities of the fuzzy controller, map different combinations of the terminal voltage deviation and terminal voltage change rate to the charge and discharge power correction value through the fuzzy controller, establish a recursive compensation model based on the charge and discharge power correction value to correct the dynamic grouping coefficient in real time to obtain the power distribution coefficient, use the power distribution coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjust the charge and discharge current of each group of supercapacitors through an adaptive PI controller, where the control parameters of the adaptive PI controller have an adaptive relationship with the power distribution coefficient and the terminal voltage deviation;

[0072] S103. During the process of decreasing the luffing angle, compare the regenerative braking power with the rechargeable power of the supercapacitor bank calculated through the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, store the excess energy in the high-temperature superconducting energy storage inductor, and real-time detect the power distribution coefficient of each group of supercapacitors. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, charge the energy in the high-temperature superconducting energy storage inductor according to the ratio of the power distribution coefficients of each group of supercapacitors; during the process of increasing the luffing angle, establish an intelligent power distributor based on the power distribution coefficients of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors, realizes the power coordination control between each group of supercapacitors and the main power supply through a sliding mode controller, and uses an active disturbance rejection controller to suppress the load power fluctuation to ensure the stability of the system voltage.

[0073] Among them, during the process of decreasing the luffing angle, the regenerative braking power is detected in real time and compared with the rechargeable power of the supercapacitor bank calculated according to the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor. The high-temperature superconducting energy storage inductor is an efficient energy storage device used to temporarily store the energy that cannot be immediately absorbed by the supercapacitor. At the same time, the power distribution coefficient of the supercapacitor bank is detected in real time. When it is detected that the rechargeable power of a certain group of supercapacitors is higher than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed to each group of supercapacitors according to the ratio of the power distribution coefficient.

[0074] In an optional implementation manner, a neural network prediction model is used to analyze the luffing angle rate and the suspended load weight of the hoisting machinery, calculate the instantaneous power curve during the luffing process, and take the integral value of the instantaneous power curve on the time axis as the theoretical energy demand during the luffing process. Based on the theoretical energy demand, the supercapacitors are grouped and the initial grouping coefficient is calculated, including:

[0075] Construct a neural network prediction model, where the input layer receives the luffing angle rate signal and the suspended load weight signal, and the output layer outputs the instantaneous power curve during the luffing process;

[0076] Sample the luffing angle rate signal and the suspended load weight signal to obtain a discrete data sequence, perform time-domain noise reduction and feature extraction on the discrete data sequence to obtain time-domain features, normalize the extracted time-domain features according to the maximum-minimum value method to obtain training samples, construct a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term, and use an adaptive optimization method based on momentum to iteratively train the neural network prediction model until the weighted loss function converges to a preset threshold to obtain a trained neural network prediction model;

[0077] Perform piecewise integral operation on the instantaneous power curve output by the trained neural network prediction model on the time axis, adaptively adjust the time window length of the integral interval according to the change trend of the luffing angle rate signal, the time window length is positively correlated with the change rate of the luffing angle rate signal, and design a double-loop state observer to filter the integral result to obtain the theoretical energy demand during the luffing process;

[0078] Divide the theoretical energy demand by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, calculate the basic grouping number according to the ratio, and at the same time set a redundancy coefficient based on the system redundancy requirement, multiply the basic grouping number by the redundancy coefficient and round up to obtain the actual grouping number;

[0079] Collect the terminal voltage signals of each group of supercapacitors in real time. At the same time, use a temperature sensor array to collect the temperature signals on the surface of each group of supercapacitors. Establish a Coulomb counting model based on the terminal voltage signals to calculate the state of charge of each group of supercapacitors.

[0080] Construct a fuzzy inference engine based on triangular membership functions. Use the inter-group deviation value of the terminal voltage signals, the maximum temperature difference value of the temperature signals, and the imbalance degree of the state of charge as the input variables of the fuzzy inference engine. Establish an inference mechanism including fuzzification, rule inference, and defuzzification. Output the correction value of the grouping coefficient through the fuzzy inference engine. Determine the reference grouping coefficient according to the initial operating state of the system. Add the grouping coefficient correction value to the reference grouping coefficient to obtain the initial grouping coefficient.

[0081] Exemplarily, first, construct a neural network model to predict the instantaneous power during the luffing process. The inputs of this model are the luffing angle rate and the load weight, and the output is the instantaneous power curve. To train this model, a large amount of luffing angle rate and load weight data need to be collected. For example, collect once every 0.1 seconds and continuously record for 10 minutes. These data form a discrete data sequence.

[0082] Then preprocess the collected discrete data sequence. This includes removing noise and extracting features. For example, a moving average filter can be used to remove high-frequency noise, and time-domain features such as mean, variance, and peak value can be extracted. To eliminate the influence of different feature dimensions, the extracted features are normalized. For example, the maximum-minimum method is used to scale the feature values to between 0 and 1. These processed data will be used as the training samples of the neural network model.

[0083] To optimize the performance of the neural network model, design a weighted loss function that includes the instantaneous power prediction error and the model complexity penalty term. For example, the mean squared error can be used as the prediction error term, and L2 regularization can be used as the model complexity penalty term. Use an adaptive optimization method based on momentum, such as the Adam optimizer, to iteratively train the neural network model until the weighted loss function converges to a preset threshold, such as 0.01.

[0084] The trained neural network model can be used to predict the instantaneous power curve during the luffing process. To calculate the theoretical energy requirement during the luffing process, the instantaneous power curve needs to be integrated on the time axis. The time window length of the integration interval needs to be adaptively adjusted according to the change trend of the luffing angle rate. For example, when the luffing angle rate changes rapidly, a shorter time window, such as 0.5 seconds, is used; when the luffing angle rate changes slowly, a longer time window, such as 2 seconds, is used. To improve the accuracy and stability of the integration result, design a double-loop state observer to filter the integration result.

[0085] Based on the calculated theoretical energy requirement and the rated energy storage capacity of a single supercapacitor, the basic number of supercapacitor groups can be calculated. If the theoretical energy requirement is 1000 joules and the rated energy storage capacity of a single supercapacitor is 200 joules, then the basic number of groups is 5. Considering the system redundancy requirement, a redundancy factor needs to be set, such as 1.2. Multiply the basic number of groups by the redundancy factor and round up, then the actual number of groups obtained is 6.

[0086] To monitor the state of the supercapacitor in real time, the terminal voltage signals and temperature signals of each group of supercapacitors need to be collected. For example, the terminal voltage signal is collected once every 1 second, and the temperature signal is collected once every 5 seconds. Based on the terminal voltage signal, a Coulomb counting model can be established to calculate the state of charge of each group of supercapacitors.

[0087] To further optimize the grouping of supercapacitors, a fuzzy inference engine based on a triangular membership function is constructed. The input variables of this fuzzy inference engine include the inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the state of charge. If the inter-group deviation value of the terminal voltage signal is 1 volt, the maximum temperature difference value of the temperature signal is 5 degrees Celsius, and the imbalance degree of the state of charge is 0.1, then the fuzzy inference engine will output a grouping coefficient correction value of 0.2. The reference grouping coefficient under the initial operating state of the system, such as 1, is added to the grouping coefficient correction value, then the initial grouping coefficient obtained is 1.2.

[0088] In this embodiment, by accurately predicting the energy requirement and adaptively adjusting the grouping, energy waste can be minimized and the energy utilization efficiency can be improved. Redundancy design and state monitoring can effectively avoid single-point failures and improve the reliability and stability of the system. Balancing the loads of each group of supercapacitors can prevent individual supercapacitors from being overcharged or over-discharged, thereby extending their service life.

[0089] In an alternative embodiment, the ratio of the initial grouping coefficient to the historical cycle count is determined as the capacity attenuation rate, and the remaining life coefficient of each group of supercapacitors is calculated based on the capacity attenuation rate. The calculation of the dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient includes:

[0090] Collect the charge-discharge cycle count value of the supercapacitor as the historical cycle count, determine the reference capacity attenuation rate as the ratio of the initial grouping coefficient to the historical cycle count, and adaptively adjust the sliding time window length according to the numerical value of the reference capacity attenuation rate. The sliding time window length decreases as the reference capacity attenuation rate increases;

[0091] Continuously collect the real-time terminal voltage monitoring signal and real-time temperature monitoring signal of the supercapacitor within the sliding time window length, calculate the real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculate the real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculate the real-time capacity attenuation rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determine the ratio of the real-time capacity attenuation rate to the reference capacity attenuation rate as the attenuation rate correction coefficient;

[0092] Establish a Weibull distribution life prediction model based on the attenuation rate correction coefficient, and calculate the characteristic life parameter. The characteristic life parameter is directly proportional to the reciprocal of the attenuation rate correction coefficient, and the proportionality coefficient is adaptively adjusted according to the fluctuation degree of the attenuation rate correction coefficient; at the same time, use the maximum likelihood estimation method to calculate the shape parameter of the Weibull distribution life prediction model;

[0093] Input the characteristic life parameter and the shape parameter into a preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculate the product of the initial grouping coefficient and the remaining life coefficient to obtain the intermediate dynamic grouping coefficient;

[0094] Calculate the difference between the real-time capacity attenuation rates at adjacent sampling times to obtain the capacity attenuation change rate, use the normalized value of the capacity attenuation change rate as the smoothing coefficient, perform a first-order low-pass filtering process on the intermediate dynamic grouping coefficient using the smoothing coefficient, the time constant of the filtering process is directly proportional to the smoothing coefficient, and output the result after the filtering process as the final dynamic grouping coefficient.

[0095] Exemplarily, first, collect the charge and discharge cycle count value of the supercapacitor. For example, by reading the counting chip built in the supercapacitor or by using an external counter to record the number of charge and discharge times, obtain the historical cycle number of the supercapacitor. Assume that the historical cycle number of a certain supercapacitor is 1000 times.

[0096] Then, determine the ratio of the initial grouping coefficient to the historical cycle number as the reference capacity attenuation rate. The initial grouping coefficient is a coefficient for grouping according to the initial performance indicators of the supercapacitor and can be set according to the actual situation. Assume that the initial grouping coefficient is 1, then the reference capacity attenuation rate is 1 / 1000 = 0.001.

[0097] Adaptively adjust the sliding time window length according to the magnitude of the reference capacity attenuation rate. The larger the reference capacity attenuation rate, the higher the aging degree of the supercapacitor, and the more frequently its state needs to be monitored. Therefore, the sliding time window length should be shorter. For example, when the reference capacity attenuation rate is 0.001, the sliding time window length is set to 10 minutes; when the reference capacity attenuation rate is 0.01, the sliding time window length is set to 1 minute.

[0098] Within the sliding time window length, continuously collect the real-time terminal voltage monitoring signal and real-time temperature monitoring signal of the supercapacitor. For example, collect the terminal voltage and temperature data every 1 second for 10 minutes.

[0099] Calculate the real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal. For example, calculate the difference between the terminal voltage at each moment and the average terminal voltage to form a deviation matrix.

[0100] Calculate the real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal. For example, calculate the ratio of the difference between the highest temperature and the lowest temperature to the average temperature as the temperature distribution uniformity coefficient.

[0101] Calculate the real-time capacity decay rate based on the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient. For example, perform a weighted sum of the root mean square value of the terminal voltage deviation matrix and the temperature distribution uniformity coefficient to obtain the real-time capacity decay rate. Suppose the calculated real-time capacity decay rate is 0.0012.

[0102] Determine the ratio of the real-time capacity decay rate to the reference capacity decay rate as the decay rate correction coefficient. In this example, the decay rate correction coefficient is 0.0012 / 0.001 = 1.2.

[0103] Establish a Weibull distribution life prediction model based on the decay rate correction coefficient and calculate the characteristic life parameter. The characteristic life parameter is directly proportional to the reciprocal of the decay rate correction coefficient, and the proportionality coefficient is adaptively adjusted according to the fluctuation degree of the decay rate correction coefficient. For example, when the decay rate correction coefficient fluctuates greatly, the proportionality coefficient takes a small value; when the decay rate correction coefficient fluctuates little, the proportionality coefficient takes a large value. Suppose the calculated result of the characteristic life parameter is 833.

[0104] Simultaneously use the maximum likelihood estimation method to calculate the shape parameter of the Weibull distribution life prediction model. Suppose the calculated shape parameter is 2.

[0105] Input the characteristic life parameter and the shape parameter into the preset Weibull distribution prediction equation to calculate the remaining life coefficient. Suppose the calculated remaining life coefficient is 0.9.

[0106] Calculate the product of the initial grouping coefficient and the remaining life coefficient to obtain the intermediate dynamic grouping coefficient. In this example, the intermediate dynamic grouping coefficient is 1 × 0.9 = 0.9.

[0107] Calculate the difference between the real-time capacity decay rates at adjacent sampling moments to obtain the capacity decay change rate. For example, if the real-time capacity decay rate at the current moment is 0.0012 and the real-time capacity decay rate at the previous moment is 0.0011, then the capacity decay change rate is 0.0001.

[0108] The normalized value of the capacity attenuation change rate is used as the smoothing coefficient. For example, the capacity attenuation change rate is divided by the maximum capacity attenuation change rate to obtain the normalized value, which is used as the smoothing coefficient. Assume the smoothing coefficient is 0.1.

[0109] The intermediate dynamic grouping coefficient is processed by first-order low-pass filtering using the smoothing coefficient, and the time constant of the filtering process is proportional to the smoothing coefficient. For example, the time constant is set to 10 times the smoothing coefficient. The result after the filtering process is output as the final dynamic grouping coefficient. Assume the final dynamic grouping coefficient is 0.89.

[0110] In this embodiment, through dynamic grouping, it is possible to avoid using supercapacitors with different degrees of aging together, thereby preventing the premature failure of supercapacitors with a relatively light degree of aging and extending the overall service life of the supercapacitors. Dynamic grouping can promptly detect and isolate failed supercapacitors, preventing them from affecting the operation of the entire system, thereby improving the reliability of the system. Through dynamic grouping, supercapacitors with similar performance can be used together, thereby improving the overall performance of the system, such as improving the charge and discharge efficiency and power density of the system.

[0111] In an alternative embodiment, a fuzzy controller based on the dynamic grouping coefficient is established. The terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors are used as the input variables of the fuzzy controller. Through the fuzzy controller, different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to the charge and discharge power correction values. Based on the charge and discharge power correction values, a recursive compensation model is established to perform real-time correction on the dynamic grouping coefficient to obtain the power distribution coefficient, including:

[0112] Collect the terminal voltage signals of each group of supercapacitors and perform digital filtering, calculate the average terminal voltage of the system, use the difference between the terminal voltage of each group of supercapacitors and the average terminal voltage of the system as the terminal voltage deviation, perform moving average filtering on the terminal voltage deviation to obtain the filtered terminal voltage deviation, perform differential operation on the filtered terminal voltage deviations at adjacent sampling times and combine with the sampling period to obtain the terminal voltage change rate, and perform amplitude limiting processing on the terminal voltage change rate to obtain the amplitude-limited terminal voltage change rate;

[0113] Divide the filtered terminal voltage deviation according to a preset interval and establish a first set of linguistic variables, set dynamically adjustable boundaries for the first fuzzy subsets for each interval, and at the same time divide the amplitude-limited terminal voltage change rate according to a preset interval and establish a second set of linguistic variables, and adaptively adjust the boundary values according to the change trend of the terminal voltage deviation to obtain the second fuzzy subset boundaries;

[0114] The filtered terminal voltage deviation is fuzzified using a triangle membership function with optimized overlap to obtain the first membership value, and the limited terminal voltage change rate is fuzzified using an asymmetric trapezoidal membership function with configurable slope to obtain the second membership value;

[0115] A fuzzy rule base is constructed according to the first membership value, the second membership value, the first set of linguistic variables, and the second set of linguistic variables, and the rules are optimized online using a variable structure fuzzy control strategy to obtain an optimized fuzzy rule set. The optimized fuzzy rule set is subjected to fuzzy inference using a fuzzy sub-rule parallel inference method, and the inference results of each sub-rule are subjected to fuzzy comprehensive operation through adaptive weights to obtain a comprehensive fuzzy output;

[0116] The comprehensive fuzzy output is defuzzified using the centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited in combination with system state parameters to obtain a charge-discharge power correction value;

[0117] Taking the dynamic grouping coefficient as the initial value and the charge-discharge power correction value as the target value to construct a recursive compensation model. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient, and uses the recursive compensation model for iterative operation. When the change amount of the power coefficient between two adjacent iterations is less than the preset change amount threshold, the iteration stops, and the compensated grouping coefficient is obtained. Values less than the dead zone threshold in the compensated grouping coefficient are set to zero, and values greater than the dead zone threshold are normalized to obtain the power distribution coefficient.

[0118] Exemplarily, first, the terminal voltage signals of each group of supercapacitors are collected, and a digital filter (finite impulse response filter or infinite impulse response filter) is used to remove noise and interference. Specifically, a second-order Butterworth low-pass filter with a cut-off frequency of 10 Hz is used to filter the collected voltage signals. The average value of the terminal voltages of all supercapacitor groups is calculated as the system average terminal voltage. The terminal voltage of each group of supercapacitors is compared with the calculated system average terminal voltage to obtain a difference, and this difference is the terminal voltage deviation. To further smooth the terminal voltage deviation, a moving average filter is used for filtering. For example, a moving average filter with a window width of 5 is used to filter the terminal voltage deviation. The difference between the filtered terminal voltage deviations at two adjacent sampling moments is calculated, and this difference is divided by the sampling period (0.1 second) to obtain the terminal voltage change rate. To avoid the influence of outliers, the terminal voltage change rate is limited. The limited range is set to -10 V / s to 10 V / s. If the calculated terminal voltage change rate exceeds this range, it is limited within this range.

[0119] The filtered terminal voltage deviation is divided into several intervals, such as [-3V, -1V], [-1V, 1V], [1V, 3V], and a first set of linguistic variables is established, such as {negative large, negative small, zero, positive small, positive large}. A dynamically adjustable boundary of the first fuzzy subset is set for each interval. For example, the boundary of the "negative large" fuzzy subset can be set as [-4, -2], and the boundary of the "negative small" fuzzy subset can be set as [-2, 0], and so on. These boundary values can be dynamically adjusted according to the system operation state. At the same time, the limited terminal voltage change rate is also divided into several intervals, such as [-10V / s, -5V / s], [-5V / s, 0V / s], [0V / s, 5V / s], [5V / s, 10V / s], and a second set of linguistic variables is established, such as {negative large, negative small, zero, positive small, positive large}. The boundary values of the second fuzzy subset are adaptively adjusted according to the change trend of the terminal voltage deviation (for example, increasing or decreasing). For example, if the terminal voltage deviation continues to increase, the boundary of the "positive large" fuzzy subset can be adjusted in a larger direction, such as [8V / s, 10V / s].

[0120] The filtered terminal voltage deviation is fuzzified using a triangular membership function with optimized overlap to obtain a first membership value. For example, if the filtered terminal voltage deviation is 2V and the boundary of the "positive small" fuzzy subset is [1, 3], then its membership value is (3 - 2) / (3 - 1) = 0.5. The limited terminal voltage change rate is fuzzified using an asymmetric trapezoidal membership function with configurable slope to obtain a second membership value. For example, if the limited terminal voltage change rate is 6V / s, the boundary of the "positive large" fuzzy subset is [5, 10], the left slope is 1, and the right slope is 0.5, then its membership value is (10 - 6) / (10 - 5)×0.5 = 0.4

[0121] A fuzzy rule base is constructed based on the first membership value, the second membership value, the first set of linguistic variables, and the second set of linguistic variables. For example, a rule can be: If the terminal voltage deviation is "positive large" and the terminal voltage change rate is "positive large", then the power correction value is "negative large". A variable structure fuzzy control strategy is used to optimize the rules online. For example, the weights of the rules are adjusted or new rules are added according to the system operation conditions. An optimized fuzzy rule set is obtained. A fuzzy inference is performed on the optimized fuzzy rule set using a fuzzy sub - rule parallel inference method. For example, the Mamdani inference method is used. The inference results of each sub - rule are subjected to a fuzzy comprehensive operation through adaptive weights to obtain a comprehensive fuzzy output. For example, the weights are dynamically adjusted according to the activation strength of each sub - rule.

[0122] The defuzzification processing is performed on the synthesized fuzzy output by using the centroid method to obtain the initial power correction value. For example, if the fuzzy output is a triangular fuzzy number (0, 0.5, 1), then its defuzzified value is (0 + 0.5 + 1) / 3 = 0.5. The initial power correction value is dynamically limited in combination with the system state parameters (for example, the state of charge of the supercapacitor) to obtain the charge and discharge power correction value. For example, if the initial power correction value is 0.6, but the state of charge of the supercapacitor is already very high, the power correction value can be limited to 0.4.

[0123] Taking the dynamic grouping coefficient as the initial value and the charge and discharge power correction value as the target value, a recursive compensation model is constructed. This model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient. For example, if the deviation is large, a larger iteration step size is adopted; if the deviation is small, a smaller iteration step size is adopted. Using this model for iterative calculation, when the change amount of the power coefficient between two adjacent iterations is less than the preset change amount threshold (0.01), the iteration is stopped to obtain the compensated grouping coefficient. The values in the compensated grouping coefficient that are less than the dead zone threshold (0.05) are set to zero, and the values greater than the dead zone threshold are normalized to obtain the power distribution coefficient. If the compensated grouping coefficient is [0.1, 0.2, 0.3], then the normalized power distribution coefficient is [0.1 / 0.6, 0.2 / 0.6, 0.3 / 0.6] = [0.167, 0.333, 0.5].

[0124] In this embodiment, by dynamically adjusting the power distribution coefficient, the terminal voltages of each group of supercapacitors can be balanced faster, and the equalization time can be shortened. By using fuzzy control and a recursive compensation model, the power distribution can be controlled more precisely, thereby improving the terminal voltage equalization accuracy. By equalizing the terminal voltages of each group of supercapacitors, overcharging or over-discharging of individual supercapacitors can be avoided, thereby prolonging the service life of the entire supercapacitor bank.

[0125] In an alternative embodiment, the defuzzification processing of the synthesized fuzzy output by using an improved centroid method to obtain the initial power correction value, and dynamically limiting the initial power correction value in combination with the system state parameters to obtain the charge and discharge power correction value includes:

[0126] Performing defuzzification processing on the fuzzy control output, by calculating the membership degree of each rule, the discrete points of the output domain, and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value to obtain the initial power correction value; wherein the area weight is calculated by using piecewise linear approximation, for the trapezoidal region, it is the product of the mean of the membership degrees of adjacent discrete points and the discrete point spacing, and for the rectangular region, it is the product of the membership degree of the discrete point and the discrete point spacing;

[0127] Collect the operating state parameters of the system, including the state of charge, terminal voltage, and temperature. Calculate the deviation between the state of charge and the desired state of charge to obtain the state-of-charge deviation, calculate the deviation between the terminal voltage and the rated terminal voltage to obtain the terminal voltage deviation, and calculate the difference between the temperature and the temperature threshold to obtain the temperature deviation;

[0128] Based on the state-of-charge deviation, establish a first limiting function. Based on the terminal voltage deviation, establish a second limiting function. Based on the temperature deviation, establish a third limiting function. Take the product of the initial power correction value and the first limiting function, the second limiting function, and the third limiting function as the intermediate power correction value;

[0129] Perform piecewise linearization on the first limiting function, the second limiting function, and the third limiting function. Take the product of the piecewise-linearized limiting function and the initial power correction value as the final charge-discharge power correction value, which is used to adjust the charge-discharge power of the system.

[0130] Exemplarily, first, construct a fuzzy controller. The inputs of this controller are the factors affecting the battery charge-discharge power, such as the state-of-charge deviation and temperature deviation of the battery. The output is the correction value of the charge-discharge power. The rule base of the fuzzy controller is formulated according to expert experience or experimental data. For example, when the state-of-charge deviation is large and the temperature is high, the charging power should be reduced.

[0131] Next, perform defuzzification on the output of the fuzzy controller to obtain the initial power correction value. Specifically, divide the output universe of discourse into several discrete points, calculate the membership degree of each rule under the current input, and the area weight of the output membership function at each discrete point. Use the piecewise linear approximation method to calculate the area weight: for the trapezoidal region, use the product of the mean of the membership degrees of adjacent discrete points and the discrete point spacing; for the rectangular region, use the product of the membership degree of the discrete point and the discrete point spacing. Then, perform weighted summation on the product of the membership degree and the area weight and the discrete point value to obtain the initial power correction value.

[0132] For example, assume that the output universe of discourse is [-1, 1], and the discrete points are -1, -0.5, 0, 0.5, 1. The output membership function of a certain rule is triangular, with the center point at 0.5 and the base width of 1. The membership degree of this rule under the current input is 0.8. Then the area weight between the discrete points 0 and 0.5 is (0 + 0.8) / 2 × 0.5 = 0.2, and the area weight between the discrete points 0.5 and 1 is (0.8 + 0) / 2 × 0.5 = 0.2.

[0133] Then, collect the operating state parameters of the system, including the state of charge, terminal voltage, and temperature. Calculate the deviation between the state of charge and the desired state of charge, the deviation between the terminal voltage and the rated terminal voltage, and the difference between the temperature and the temperature threshold.

[0134] Assume that the current state of charge is 80% and the desired state of charge is 90%, then the state of charge deviation is -10%. The current terminal voltage is 4.1V and the rated terminal voltage is 4.2V, so the terminal voltage deviation is -0.1V. The current temperature is 30°C and the temperature threshold is 40°C, then the temperature deviation is -10°C.

[0135] Next, establish limiting functions based on the state of charge deviation, terminal voltage deviation, and temperature deviation respectively. The limiting functions map the deviations to a limited range, such as [0, 1].

[0136] Assume that the limiting function of the state of charge deviation is: when the deviation is less than -20%, the output is 0; when the deviation is greater than 20%, the output is 1; between -20% and 20%, the output changes linearly.

[0137] Take the product of the initial power correction value and the three limiting functions as the intermediate power correction value. Perform piecewise linearization on the three limiting functions, and take the product of the piecewise-linearized limiting functions and the initial power correction value as the final charge-discharge power correction value, which is used to adjust the charge-discharge power of the system.

[0138] Assume that the initial power correction value is 0.5, the output of the state of charge deviation limiting function is 0.8, the output of the terminal voltage deviation limiting function is 0.9, and the output of the temperature deviation limiting function is 0.7. Then the intermediate power correction value is 0.5×0.8×0.9×0.7 = 0.252.

[0139] In this embodiment, through dynamic limiting, it is possible to avoid damage to the battery in cases such as overcharging, over-discharging, or overheating, and extend the service life of the battery. Through fuzzy control, precise power adjustment can be performed according to the real-time state of the battery, improving the charge-discharge efficiency of the battery. The rule base of the fuzzy controller is easy to understand and modify, without the need for complex mathematical models, reducing the development difficulty of the control system.

[0140] In an alternative embodiment, during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficients of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, and realizes the power coordination control of each group of supercapacitors and the main power supply through a sliding mode controller, and uses an active disturbance rejection controller to suppress the load power fluctuation to ensure the stability of the system voltage, including:

[0141] Obtain the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the corresponding power distribution coefficients of each group of supercapacitors;

[0142] An intelligent power distributor is established based on the power distribution coefficient. The real-time state of charge, terminal voltage, and bus voltage are used as system state variables, and the output power of the main power supply and the output powers of each group of supercapacitors are used as control variables and input into the intelligent power distributor;

[0143] A target function is constructed in the intelligent power distributor, and the dynamic programming algorithm is used to solve the target function to obtain the output power ratio of the main power supply to each group of supercapacitors;

[0144] A sliding mode surface is constructed according to the deviation between the target value and the actual value of the output power ratio, and the control law of the sliding mode controller is designed based on the sliding mode surface. The control law includes an equivalent control term composed of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by the reaching law parameter;

[0145] Control commands for the main power supply and each group of supercapacitors are output through the sliding mode controller, and the load power fluctuation information after executing the control commands is collected. Based on the load power fluctuation information, the system output and tracking error are obtained through an extended state observer, and a state observation equation of the active disturbance rejection controller is constructed according to the system output and tracking error;

[0146] The output value of the state observation equation is compared with the reference input to obtain a compensation control amount, and the control commands for the main power supply and each group of supercapacitors are corrected according to the compensation control amount to suppress the load power fluctuation. The corrected control commands are applied to the main power supply and each group of supercapacitors to achieve power collaborative control and ensure system voltage stability. At the same time, the controlled system state is used as a new state variable and input into the intelligent power distributor for iterative optimization.

[0147] Exemplarily, first, the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors, as well as the corresponding power distribution coefficients of each group of supercapacitors, are obtained during the process of increasing the amplitude angle. Suppose there are two groups of supercapacitors, with the state of charge being 80% and 70% respectively, the terminal voltages being 2.5V and 2.4V respectively, and the bus voltage being 2.6V. The power distribution coefficient of the first group of supercapacitors is 0.6, and that of the second group is 0.4. These data can be obtained by real-time measurement through sensors and transmitted to the intelligent power distributor.

[0148] Next, an intelligent power distributor is established based on the obtained power distribution coefficient. The real-time state of charge, terminal voltage, and bus voltage of the supercapacitors are used as system state variables, and the output power of the main power supply and the output powers of each group of supercapacitors are used as control variables and input into the intelligent power distributor.

[0149] Construct the objective function in the intelligent power distributor. The design objective of the objective function is to minimize the output power of the main power supply while meeting the requirements of system voltage stability and taking into account the state of charge balance of each group of supercapacitors.

[0150] Use the dynamic programming algorithm to solve the objective function. The dynamic programming algorithm decomposes the problem into multiple sub-problems and obtains the optimal solution of the entire problem by solving the optimal solutions of the sub-problems. For example, time can be discretized, and the optimal output power ratio of the main power supply and each group of supercapacitors can be solved within each time step. Suppose that through the dynamic programming algorithm, the output power of the main power supply is calculated to be 10 kW, the output power of the first group of supercapacitors is 6 kW, and the output power of the second group of supercapacitors is 4 kW.

[0151] Construct a sliding mode surface based on the deviation between the target value and the actual value of the output power ratio. The sliding mode surface is a function used to describe the system state deviation. The goal of the sliding mode controller is to make the system state slide along the sliding mode surface to the desired state. For example, if the actual output power of the first group of supercapacitors is 5 kW, with a deviation of 1 kW from the target value of 6 kW, the value on the sliding mode surface will reflect this deviation.

[0152] Design the control law of the sliding mode controller based on the sliding mode surface. The control law consists of two parts: an equivalent control term and a switching control term. The equivalent control term is used to compensate for the influence of system dynamics, and the state tracking deviation term is used to eliminate the state deviation. The switching control term is used to ensure that the system state slides on the sliding mode surface. The reaching law parameter determines the speed at which the system state approaches the sliding mode surface.

[0153] The sliding mode controller outputs control commands for the main power supply and each group of supercapacitors. For example, the control commands may be to adjust the output power of the main power supply to increase by 1 kW, the output power of the first group of supercapacitors to increase by 0.6 kW, and the output power of the second group of supercapacitors to increase by 0.4 kW.

[0154] Collect the load power fluctuation information after executing the control commands. Suppose the load power fluctuation is 2 kW.

[0155] Based on the load power fluctuation information, obtain the system output and tracking error through an extended state observer. The extended state observer can estimate the unmodeled dynamics and disturbances in the system.

[0156] Construct the state observation equation of the active disturbance rejection controller according to the system output and tracking error. The state observation equation describes the evolution law of the system state.

[0157] Compare the output value of the state observation equation with the reference input to obtain the compensation control quantity. The compensation control quantity is used to offset the influence of the load power fluctuation on the system.

[0158] Modify the control commands for the main power supply and each group of supercapacitors according to the compensation control amount. For example, if the compensation control amount is -1 kW, then modify the control command of the main power supply to decrease by 1 kW.

[0159] Apply the modified control commands to the main power supply and each group of supercapacitors to achieve power collaborative control and ensure the stability of the system voltage. Take the controlled system state as the new state variable and input it into the intelligent power distributor for iterative optimization, continuously adjusting the power distribution strategy to adapt to the changing load demand and amplitude variation angle.

[0160] In this embodiment, the load power fluctuation is suppressed by the active disturbance rejection controller to ensure the stability of the system voltage, effectively avoiding the unstable influence caused by voltage fluctuation on the system, and improving the reliability and safety of the system. The intelligent power distributor uses the dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, achieving efficient utilization of energy, reducing energy consumption, and extending the service life of the supercapacitors. The fast response characteristic of the sliding mode controller can quickly track the target power distribution, enabling the system to quickly adapt to the change of the amplitude variation angle and improving the dynamic performance and adaptability of the system.

[0161] In an alternative embodiment, the formula for constructing the objective function in the intelligent power distributor is as follows:

[0162]

[0163] where, J represents the objective function, T represents the total time, w 1 represents the weight of the bus voltage deviation term, V bus(t) represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w 2 represents the weight of the supercapacitor power distribution deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power distribution coefficient of the i-th group of supercapacitors, w 3 represents the weight of the main power supply power utilization rate deviation term, P main(t) represents the actual power value provided by the main power supply at time t, P main，max represents the maximum output power of the main power supply.

[0164] Among them, the goal of the intelligent power distributor is to optimize the output of each power source in the system to maintain a stable bus voltage and efficiently utilize each power source. To achieve this goal, an objective function needs to be constructed to evaluate the advantages and disadvantages of different power distribution strategies.

[0165] First, it is necessary to determine the types and characteristics of each power source in the system. For example, the system may include a main power source, a supercapacitor bank, etc. For the main power source, its maximum output power needs to be determined. For the supercapacitor bank, parameters such as the capacity of each supercapacitor bank and the charge and discharge rate need to be determined. Assume that the system includes a main power source and two groups of supercapacitors, the maximum output power of the main power source is 100 kW, and the capacities of the two groups of supercapacitors are 10 kWh and 20 kWh respectively.

[0166] Secondly, it is necessary to determine the reference value of the bus voltage. The bus voltage is the power supply voltage for each load in the system and needs to be maintained within a certain range to ensure the normal operation of the load. Assume that the bus reference voltage is 500 V.

[0167] Next, it is necessary to determine the weights of each term in the objective function. The objective function usually contains multiple terms, such as the bus voltage deviation term, the supercapacitor power distribution deviation term, the main power source power utilization rate deviation term, etc. The weight of each term reflects the importance of this term in the objective function. Assume that the weight of the bus voltage deviation term is 0.5, the weight of the supercapacitor power distribution deviation term is 0.3, and the weight of the main power source power utilization rate deviation term is 0.2.

[0168] Then, based on the determined power source characteristics, bus voltage reference value, and objective function weights, the value of the objective function can be calculated. For example, at a certain moment, the bus voltage of the system is 490 V, the output powers of the two groups of supercapacitors are 10 kW and 20 kW respectively, and the output power of the main power source is 70 kW. Then the bus voltage deviation is 10 V, the supercapacitor power distribution deviation is calculated according to the pre-set power distribution coefficient, assume they are 1 kW and 2 kW respectively, and the main power source power utilization rate deviation is 30 kW. According to the set weights, the value of the objective function can be calculated.

[0169] Finally, through optimization algorithms, such as the particle swarm algorithm, genetic algorithm, etc., search for a power distribution strategy that minimizes the objective function. For example, through the optimization algorithm, a power distribution strategy can be found to minimize the bus voltage deviation, the supercapacitor power distribution deviation, and the main power source power utilization rate deviation.

[0170] Through the above steps, an intelligent power distributor can be constructed to achieve optimal control of each power source in the system.

[0171] In this embodiment, by optimizing the control of the outputs of each power supply, the stability of the bus voltage can be effectively maintained, thus ensuring the normal operation of the load. For example, when the load changes, the intelligent power distributor can automatically adjust the outputs of each power supply to maintain the stability of the bus voltage. By optimizing the power distribution strategy, the utilization rate of the main power supply can be increased, and the service life of the supercapacitor can be extended. For example, when the load is low, the intelligent power distributor can reduce the output of the main power supply and use the supercapacitor for power supply, thereby increasing the utilization rate of the main power supply. Through intelligent power distribution, the reliability and stability of the system can be improved. For example, when a certain power supply fails, the intelligent power distributor can automatically switch to other power supplies to ensure the normal operation of the system.

[0172] Figure 2 FIG. is a schematic structural diagram of an energy management system for the luffing process of a hoisting machine based on a supercapacitor according to an embodiment of the present invention, as Figure 2 shown, the system includes:

[0173] A first unit for analyzing the luffing angle rate and the hoisting weight of the hoisting machine by using a neural network prediction model, calculating an instantaneous power curve of the luffing process, taking the integral value of the instantaneous power curve on the time axis as the theoretical energy demand of the luffing process, grouping the supercapacitors based on the theoretical energy demand and calculating an initial grouping coefficient, determining the ratio of the initial grouping coefficient to the historical number of cycles as the capacity attenuation rate, calculating the remaining life coefficient of each group of supercapacitors according to the capacity attenuation rate, and calculating a dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient;

[0174] A second unit for establishing a fuzzy controller based on the dynamic grouping coefficient, using the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as the input quantities of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate to charge and discharge power correction values through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power distribution coefficient, using the power distribution coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, wherein the control parameters of the adaptive PI controller have an adaptive relationship with the power distribution coefficient and the terminal voltage deviation;

[0175] The third unit is used to compare the regenerative braking power with the rechargeable power of the supercapacitor bank calculated by the power distribution coefficient during the process of reducing the luffing angle. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is charged according to the ratio of the power distribution coefficients of each group of supercapacitors; during the process of increasing the luffing angle, an intelligent power distributor is established based on the power distribution coefficients of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordination control of each group of supercapacitors and the main power supply through a sliding mode controller, and uses an active disturbance rejection controller to suppress the load power fluctuation to ensure the stability of the system voltage.

[0176] In the third aspect of the embodiments of the present invention,

[0177] a kind of electronic device is provided, including:

[0178] a processor;

[0179] a memory for storing instructions executable by the processor;

[0180] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0181] In the fourth aspect of the embodiments of the present invention,

[0182] a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is realized.

[0183] The present invention can be a method, a device, a system and / or a computer program product. The computer program product can include a computer-readable storage medium, on which computer-readable program instructions for executing various aspects of the present invention are uploaded.

[0184] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy management of the luffing process of a lifting machinery based on supercapacitors, characterized in that: include: A neural network prediction model is used to analyze the amplitude change angle rate and the weight of the lifting machinery, and the instantaneous power curve of the amplitude change process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the amplitude change process. The supercapacitors are grouped based on the theoretical energy demand and the initial grouping coefficient is calculated. The ratio of the initial grouping coefficient to the historical cycle number is determined as the capacity decay rate. The remaining life coefficient of each group of supercapacitors is calculated according to the capacity decay rate, and the dynamic grouping coefficient is obtained based on the calculation of the initial grouping coefficient and the remaining life coefficient. A fuzzy controller based on the dynamic grouping coefficient is established, and the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors are used as input quantities of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to charge and discharge power correction values ​​through the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient. The power allocation coefficient is used as the upper limit of the charge and discharge power of each group of supercapacitors, and the charge and discharge current of each group of supercapacitors is adjusted through an adaptive PI controller, wherein the control parameters of the adaptive PI controller are in an adaptive relationship with the power allocation coefficient and the terminal voltage deviation; In the process of decreasing the amplitude angle, the regenerative braking power is compared with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors. In the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an anti-disturbance control controller to suppress load power fluctuations to ensure the stability of the system voltage.

2. The method according to claim 1, characterized in that The neural network prediction model is used to analyze the luffing angle rate and the hoisting weight of the lifting machinery, and the instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. The supercapacitors are grouped based on the theoretical energy demand and the initial grouping coefficients are calculated, including: A neural network prediction model is constructed, in which the input layer receives the amplitude change angle rate signal and the hoisting weight signal, and the output layer outputs the instantaneous power curve of the amplitude change process; The amplitude variation angle rate signal and the hoisting weight signal are sampled to obtain a discrete data sequence, time domain noise reduction and feature extraction are performed on the discrete data sequence to obtain time domain features, the extracted time domain features are normalized according to the maximum and minimum method to obtain training samples, a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term is constructed, and the neural network prediction model is iteratively trained using a momentum-based adaptive optimization method until the weighted loss function converges to a preset threshold, thereby obtaining a trained neural network prediction model; The instantaneous power curve output by the trained neural network prediction model is segmentedly integrated on the time axis, and the time window length of the integration interval is adaptively adjusted according to the change trend of the amplitude variation angle rate signal. The time window length is positively correlated with the change rate of the amplitude variation angle rate signal, and a dual-loop state observer is designed to filter the integration result to obtain the theoretical energy demand of the amplitude variation process; The theoretical energy demand is divided by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, the basic grouping number is calculated according to the ratio, a redundancy coefficient is set based on the system redundancy requirement, the basic grouping number is multiplied by the redundancy coefficient and the result is rounded up to obtain the actual grouping number; The terminal voltage signals of each group of supercapacitors are collected in real time, and the temperature signals on the surface of each group of supercapacitors are collected by using a temperature sensor array. A coulomb counting model is established according to the terminal voltage signals to calculate the charge state of each group of supercapacitors. A fuzzy inference device based on a triangular membership function is constructed, and the inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the charge state are used as input variables of the fuzzy inference device. An inference mechanism including fuzzification, rule reasoning, and defuzzification is established. The fuzzy inference device outputs a grouping coefficient correction value, and a benchmark grouping coefficient is determined according to the initial operating state of the system. The grouping coefficient correction value is added to the benchmark grouping coefficient to obtain the initial grouping coefficient.

3. The method according to claim 1, characterized in that: The ratio of the initial grouping coefficient to the number of historical cycles is determined as the capacity decay rate, and the remaining life coefficient of each group of supercapacitors is calculated according to the capacity decay rate. The dynamic grouping coefficient obtained based on the calculation of the initial grouping coefficient and the remaining life coefficient includes: Collecting the charge and discharge cycle count value of the supercapacitor as the number of historical cycles, determining the ratio of the initial grouping coefficient to the number of historical cycles as the benchmark capacity decay rate, and adaptively adjusting the length of the sliding time window according to the numerical value of the benchmark capacity decay rate, wherein the length of the sliding time window decreases as the benchmark capacity decay rate increases; Continuously collecting the real-time terminal voltage monitoring signal and the real-time temperature monitoring signal of the supercapacitor within the length of the sliding time window, calculating the real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculating the real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculating the real-time capacity decay rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determining the ratio of the real-time capacity decay rate to the reference capacity decay rate as the decay rate correction coefficient; A Weibull distribution life prediction model is established based on the decay rate correction coefficient, and a characteristic life parameter is calculated, wherein the characteristic life parameter is proportional to the inverse of the decay rate correction coefficient, and the proportionality coefficient is adaptively adjusted with the degree of fluctuation of the decay rate correction coefficient; and at the same time, the shape parameter of the Weibull distribution life prediction model is calculated by using the maximum likelihood estimation method; Inputting the characteristic life parameter and shape parameter into a preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculating the product of the initial grouping coefficient and the remaining life coefficient to obtain an intermediate dynamic grouping coefficient; The difference of the real-time capacity decay rates at adjacent sampling moments is calculated to obtain the capacity decay change rate, and the normalized value of the capacity decay change rate is used as the smoothing coefficient. The smoothing coefficient is used to perform a first-order low-pass filtering process on the intermediate dynamic grouping coefficient. The time constant of the filtering process is proportional to the smoothing coefficient, and the result after the filtering process is output as the final dynamic grouping coefficient.

4. The method according to claim 1, characterized in that: A fuzzy controller based on the dynamic grouping coefficient is established, and the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors are used as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to charge and discharge power correction values ​​through the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient including: The terminal voltage signal of each group of supercapacitors is collected and digitally filtered, the average terminal voltage of the system is calculated, the difference between the terminal voltage of each group of supercapacitors and the average terminal voltage of the system is taken as the terminal voltage deviation, the terminal voltage deviation is subjected to sliding average filtering to obtain the filtered terminal voltage deviation, the voltage deviation of the filter rear end at adjacent sampling moments is differentially calculated and combined with the sampling period to obtain the terminal voltage change rate, and the terminal voltage change rate is subjected to amplitude limiting processing to obtain the terminal voltage change rate after amplitude limiting; The terminal voltage deviation after filtering is divided into a preset interval and a first language variable set is established, and a dynamically adjustable first fuzzy subset boundary is set for each interval. At the same time, the terminal voltage change rate after limiting is divided into a preset interval and a second language variable set is established, and the boundary value is adaptively adjusted according to the change trend of the terminal voltage deviation to obtain a second fuzzy subset boundary; A triangular membership function with optimized overlap is used to perform fuzzy processing on the terminal voltage deviation after filtering to obtain a first membership value, and an asymmetric trapezoidal membership function with configurable slope is used to perform fuzzy processing on the terminal voltage change rate after limiting to obtain a second membership value; A fuzzy rule base is constructed according to the first membership value, the second membership value, the first language variable set, and the second language variable set, and a variable structure fuzzy control strategy is used to perform online optimization on the rules to obtain an optimized fuzzy rule set, and a fuzzy sub-rule parallel reasoning method is used to perform fuzzy reasoning on the optimized fuzzy rule set, and the reasoning results of each sub-rule are fuzzily comprehensive calculated through adaptive weights to obtain a comprehensive fuzzy output; The integrated fuzzy output is defuzzified by using the centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited in combination with the system state parameter to obtain a charge and discharge power correction value; The dynamic grouping coefficient is taken as the initial value, and the charge and discharge power correction value is taken as the target value to construct a recursive compensation model. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient. The recursive compensation model is used to perform iterative calculations. When the power coefficient change between two adjacent iterations is less than a preset change threshold, the iteration is stopped to obtain the compensated grouping coefficient. The values ​​of the compensated grouping coefficient that are less than the dead zone threshold are set to zero, and the values ​​that are greater than the dead zone threshold are normalized to obtain the power allocation coefficient.

5. The method according to claim 4, characterized in that The integrated fuzzy output is defuzzified by using an improved centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited in combination with the system state parameter to obtain a charge and discharge power correction value, including: The fuzzy control output is defuzzified, and the initial power correction value is obtained by calculating the membership degree of each rule, the output domain discrete points and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value; wherein the area weight is calculated using piecewise linear approximation, and for a trapezoidal area, the product of the mean of the membership degree of adjacent discrete points and the discrete point spacing is used, and for a rectangular area, the product of the discrete point membership degree and the discrete point spacing is used; Collect system operation status parameters, including state of charge, terminal voltage and temperature, calculate the deviation between the state of charge and the expected state of charge to obtain the state of charge deviation, calculate the deviation between the terminal voltage and the rated terminal voltage to obtain the terminal voltage deviation, and calculate the difference between the temperature and the temperature threshold to obtain the temperature deviation; Establishing a first limiting function based on the state of charge deviation, establishing a second limiting function based on the terminal voltage deviation, establishing a third limiting function based on the temperature deviation, and taking the product of the initial power correction value and the first limiting function, the second limiting function and the third limiting function as an intermediate power correction value; The first limiting function, the second limiting function and the third limiting function are subjected to piecewise linearization processing, and the product of the limiting function subjected to the piecewise linearization processing and the initial power correction value is used as the final charge and discharge power correction value for adjusting the charge and discharge power of the system.

6. The method according to claim 1, characterized in that In the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, and realizes the power coordination control of each group of supercapacitors and the main power supply through a sliding mode controller. The self-disturbance rejection controller is used to suppress load power fluctuations to ensure the stability of the system voltage, including: Obtain the real-time state of charge, terminal voltage and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the power distribution coefficient corresponding to each group of supercapacitors; Establish an intelligent power distributor based on the power distribution coefficient, use the real-time state of charge, terminal voltage and bus voltage as system state variables, and input the main power supply output power and the output power of each group of supercapacitors as control variables into the intelligent power distributor; Constructing an objective function in the intelligent power distributor, solving the objective function using a dynamic programming algorithm, and obtaining an output power ratio between the main power supply and each group of supercapacitors; A sliding surface is constructed according to the deviation between the target value and the actual value of the output power ratio, and a control law of a sliding mode controller is designed based on the sliding surface, wherein the control law includes an equivalent control term consisting of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by a reaching law parameter; Outputting control instructions of the main power supply and each group of supercapacitors through the sliding mode controller, and collecting load power fluctuation information after executing the control instructions, obtaining system output and tracking error through an extended state observer based on the load power fluctuation information, and constructing a state observation equation of an auto-disturbance rejection controller according to the system output and tracking error; The output value of the state observation equation is compared with the reference input to obtain the compensation control quantity, and the control instructions of the main power supply and each group of supercapacitors are corrected according to the compensation control quantity to suppress load power fluctuations. The corrected control instructions are applied to the main power supply and each group of supercapacitors to achieve power coordinated control and ensure system voltage stability. At the same time, the controlled system state is input into the intelligent power distributor as a new state variable for iterative optimization.

7. The method according to claim 6, characterized in that The formula for constructing the objective function in the intelligent power distributor is as follows: Where J represents the objective function, T represents the total time, w1 represents the weight of the bus voltage deviation term, V bus(t) Represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w2 represents the weight of the supercapacitor power allocation deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power allocation coefficient of the i-th group of supercapacitors, w3 represents the weight of the main power supply power utilization deviation term, P main(t) Indicates the actual power value provided by the main power supply at time t, P main，max Indicates the maximum output power of the main power supply.

8. A supercapacitor-based energy management system for a lifting machinery luffing process, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to analyze the amplitude change angle rate and the lifting weight of the lifting machinery by using a neural network prediction model, calculate the instantaneous power curve of the amplitude change process, take the integral value of the instantaneous power curve on the time axis as the theoretical energy demand of the amplitude change process, group the supercapacitors based on the theoretical energy demand and calculate the initial grouping coefficient, determine the ratio of the initial grouping coefficient to the historical cycle number as the capacity decay rate, calculate the remaining life coefficient of each group of supercapacitors according to the capacity decay rate, and obtain the dynamic grouping coefficient based on the calculation of the initial grouping coefficient and the remaining life coefficient; The second unit is used to establish a fuzzy controller based on the dynamic grouping coefficient, taking the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as the input of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate into charge and discharge power correction values ​​through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, taking the power allocation coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, wherein the control parameters of the adaptive PI controller are in an adaptive relationship with the power allocation coefficient and the terminal voltage deviation; The third unit is used to compare the regenerative braking power with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient during the process of decreasing the amplitude angle. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged in proportion to the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an anti-disturbance control controller to suppress load power fluctuations to ensure the stability of the system voltage.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in 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 a processor, the method according to any one of claims 1 to 7 is implemented.

Images