Control method of well drilling winch super-capacitor energy storage system for optimizing fuzzy controller based on chaos beetle antennae algorithm

Abstract

The invention relates to the technical field of petroleum drilling and energy storage, in particular to a control method of a drilling winch super-capacitor energy storage system for optimizing a fuzzy controller based on a chaos beetle antennae algorithm, which comprises the following steps of: acquiring voltage data of a direct current bus in a petroleum drilling winch micro-grid, and setting a target voltage value of the voltage data; introducing the chaotic mapping into a beetle antennae algorithm to obtain a chaotic beetle antennae algorithm; a quantization factor and a scaling factor of the fuzzy controller are optimized by using a chaos longicorn beetle algorithm, and then control parameters of the fuzzy controller are corrected; and the fuzzy controller with the corrected control parameters is applied to a voltage outer loop of voltage and current double closed loop control so as to realize charging and discharging of the super capacitor. According to the method, chaotic mapping is introduced into the beetle antennae algorithm, the quantization factor and the scaling factor of the fuzzy controller are optimized, dynamic adjustment of parameters of the fuzzy controller is achieved, and then the recycling rate of the super capacitor on drilling winch drilling feedback electric energy can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil drilling energy storage, and in particular to a control method for a drilling winch supercapacitor energy storage system based on a chaotic beetle whisker algorithm optimized fuzzy controller. Background Art

[0002] During the process of the drawworks motor lowering the drill bit, the drill bit drives the drawworks motor in a reverse state, and a large amount of feedback electric energy is dissipated by the braking unit in the form of heat energy. This part of the feedback electric energy cannot be effectively recycled. Therefore, we need to provide a control method for the drilling drawworks supercapacitor energy storage system based on a chaotic beetle whisker algorithm to optimize the fuzzy controller.

[0003] At present, a voltage closed-loop control method based on PI regulation is usually used to realize the charging and discharging of supercapacitors. However, in traditional PI control, the fixed PI parameters cannot adapt to the complex working conditions of the drilling winch system, it is difficult to achieve precise control of the bidirectional DC / DC converter, and the recovery efficiency of drilling energy is low. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in order to solve the technical problem of low recovery rate of electric energy fed back by drilling winch during drilling, the present invention provides a control method for a supercapacitor energy storage system of a drilling winch based on a chaotic beetle whisker algorithm to optimize a fuzzy controller, which can realize dynamic adjustment of the parameters of the fuzzy controller, thereby improving the recovery rate of the electric energy fed back by the supercapacitor during drilling winch during drilling.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a control method of a drilling winch supercapacitor energy storage system based on a chaotic longhorn beetle whisker algorithm to optimize a fuzzy controller, comprising the following steps:

[0006] S1. Obtain the voltage data of the DC bus in the oil drilling winch microgrid and set its target voltage value;

[0007] S2, introducing chaotic mapping into the beetle whisker algorithm, using chaotic mapping to perturb the search space, improving the global search capability of the beetle whisker algorithm, and obtaining a chaotic beetle whisker algorithm;

[0008] S3, using the chaotic beetle whisker algorithm to optimize the quantization factor and the proportional factor of the fuzzy controller, and then correct the control parameters of the fuzzy controller to obtain the fuzzy controller with the corrected control parameters;

[0009] S4, obtaining the fuzzy controller for correcting the control parameters in step S3, and applying the fuzzy controller for correcting the control parameters to the voltage outer loop of the voltage-current dual closed-loop control to realize the charging and discharging of the supercapacitor.

[0010] Therefore, introducing chaotic mapping into the beetle whisker algorithm can combine the ergodicity of chaotic mapping and the optimization ability of the beetle whisker algorithm to optimize the quantization factor and proportional factor of the fuzzy controller, realize the dynamic adjustment of the fuzzy controller parameters, and thus improve the recovery rate of the supercapacitor for the feedback electric energy of the drilling winch.

[0011] Furthermore, the step S2 comprises the following steps:

[0012] S2-1, initializing the position of the longicorn beetle, and using the generated random vector as the search direction of the longicorn beetle's whiskers;

[0013] S2-2, update the positions of the left and right antennae of the longicorn;

[0014] S2-3, using the ITAE function as the objective function, calculate the fitness values ​​of the left and right whiskers of the longhorn beetle and update the position of the longhorn beetle;

[0015] S2-4, introduce the perturbation mechanism of Bernoulli chaotic mapping, compare the fitness value obtained after the perturbation with the current optimal value, so as to obtain the current optimal position of the longicorn;

[0016] S2-5. Determine whether the end condition is met. If so, take the current optimal position as the global optimal position. If not, return to step S2-2 until the end condition is met.

[0017] Further, in step S2-2, the position update of the left and right antennae of the longhorn beetle is expressed as:

[0018]

[0019] Where: x t is the coordinate of the centroid of the beetle at the tth iteration, x left is the position of the left antenna of the longicorn, x nght is the position of the right antenna of the longhorn beetle, D is the distance between the center of mass of the longhorn beetle individual and the antenna, is a random unit vector.

[0020] Furthermore, in step S2-3, the expression of the ITAE function is:

[0021]

[0022] Where: e(t) is the error of the system at time t.

[0023] Further, in step S2-4, the expression for calculating the fitness values ​​of the left and right whiskers of the longhorn beetle and updating the position of the longhorn beetle is:

[0024]

[0025] Where: f(xleft ) is the fitness value of the left antenna of the longicorn beetle, f(x right ) is the fitness value of the right antenna of the longicorn, S t is the step length of the longicorn at the tth iteration, and sign is the sign function.

[0026] Furthermore, in step S2-4, the expression of the Bernoulli chaotic map is introduced as follows:

[0027]

[0028] Where: X n+1 . is a chaotic sequence generated by Bernoulli mapping, with a value range of (0, 1), and ε is a chaos control parameter.

[0029] Further, the fuzzy controller includes: a fuzzy controller controls a voltage outer loop, applies fuzzy control to the voltage outer loop of the voltage and current double closed-loop control, and the fuzzy controller outputs a fuzzy controller parameter adjustment value after fuzzifying an input error e and an error rate ec;

[0030] The adjusted fuzzy control parameters are expressed as:

[0031]

[0032] Where: K p0 , K i0 are the initial values ​​of the fuzzy controller, ΔK p is the proportional coefficient adjustment value generated by fuzzy control, ΔK i The integral coefficient adjustment value generated by fuzzy control, K p , K i These are all adjusted fuzzy control parameters.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] Introducing the Bernoulli chaotic map into the beetle whisker algorithm can combine the ergodicity of the chaotic map with the optimization ability of the beetle whisker algorithm to optimize the quantization factor and proportional factor of the fuzzy controller, realize the dynamic adjustment of the fuzzy controller parameters, and thus improve the recovery rate of the supercapacitor for the feedback energy of the drilling drawworks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0036] Figure 1 It is a flow chart of a control method of a drilling winch supercapacitor energy storage system based on a chaotic longhorn beetle whisker algorithm optimized fuzzy controller of the present invention;

[0037] Figure 2 It is a structural schematic diagram of the chaotic longhorn beetle whisker algorithm fuzzy controller of the present invention;

[0038] Figure 3 It is a comparison diagram of the convergence curves of the objective function before and after the chaotic mapping of the present invention is combined with the beetle whisker algorithm;

[0039] Figure 4 It is a curve comparison diagram of the supercapacitor SOC change under the control of the traditional fuzzy controller of the present invention and the chaotic longhorn beetle algorithm optimized fuzzy controller. DETAILED DESCRIPTION

[0040] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0041] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0042] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] like Figures 1 to 4 As shown, it is the best embodiment of the present invention. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beetle algorithm optimization fuzzy controller (PI controller) of this embodiment includes the following steps:

[0044] S1. Obtain the voltage data of the DC bus in the oil drilling winch microgrid and set its target voltage value;

[0045] S2, introducing chaotic mapping into the beetle whisker algorithm, using chaotic mapping to perturb the search space, improving the global search capability of the beetle whisker algorithm, and obtaining a chaotic beetle whisker algorithm;

[0046] S3, using the chaotic beetle whisker algorithm to optimize the quantization factor and the proportional factor of the fuzzy controller, and then correct the control parameters of the fuzzy controller to obtain the fuzzy controller with the corrected control parameters;

[0047] S4, obtain the fuzzy controller of the correction control parameter in step S3, and apply the fuzzy controller of the correction control parameter to the voltage outer loop of the voltage and current double closed-loop control to realize the charging and discharging of the supercapacitor. Thus, the chaotic map is introduced into the beetle beard algorithm, and the ergodicity of the chaotic map and the optimization ability of the beetle beard algorithm can be combined to optimize the quantization factor and the proportional factor of the fuzzy controller, and realize the dynamic adjustment of the fuzzy controller parameters, so as to improve the recycling rate of the supercapacitor to the electric energy fed back from drilling by the drilling winch.

[0048] In this embodiment, step S2 includes the following steps:

[0049] S2-1, initializing the position of the longicorn beetle, and using the generated random vector as the search direction of the longicorn beetle's whiskers;

[0050] S2-2, update the positions of the left and right antennae of the longicorn;

[0051] S2-3, using the ITAE function as the objective function, calculate the fitness values ​​of the left and right whiskers of the longhorn beetle and update the position of the longhorn beetle;

[0052] S2-4, introduce the perturbation mechanism of Bernoulli chaotic mapping, compare the fitness value obtained after the perturbation with the current optimal value, so as to obtain the current optimal position of the longicorn;

[0053] S2-5, judging whether the end condition is met, if so, taking the current optimal position as the global optimal position, if not, returning to step S2-2 until the end condition is met;

[0054] In step S2-2, the position update of the left and right antennae of the longhorn beetle is expressed as:

[0055]

[0056] Where: x t is the coordinate of the centroid of the beetle at the tth iteration, x left is the position of the left antenna of the longicorn, x nght is the position of the right antenna of the longhorn beetle, D is the distance between the center of mass of the longhorn beetle individual and the antenna, is a random unit vector;

[0057] In step S2-3, the expression of the ITAE function is:

[0058]

[0059] Where: e(t) is the error of the system at time t;

[0060] In step S2-4, the expression for calculating the fitness values ​​of the left and right whiskers of the longhorn beetle and updating the position of the longhorn beetle is:

[0061]

[0062] Where: f(x left ) is the fitness value of the left antenna of the longicorn beetle, f(x right ) is the fitness value of the right antenna of the longicorn, S t is the step length of the longhorn beetle at the tth iteration, sign is the sign function;

[0063] In step S2-4, the expression of the Bernoulli chaotic map is introduced as follows:

[0064]

[0065] Where: X n+1 . is a chaotic sequence generated by Bernoulli mapping, with a value range of (0, 1), and ε is a chaos control parameter.

[0066] In this embodiment, the fuzzy controller includes: a fuzzy controller controls a voltage outer loop, applies fuzzy control to the voltage outer loop of the voltage and current double closed-loop control, and outputs a fuzzy controller parameter adjustment value after fuzzifying the input error e and the error rate ec;

[0067] The adjusted fuzzy control parameters are expressed as:

[0068]

[0069] Where: K p0 , K i0 are the initial values ​​of the fuzzy controller, ΔK p is the proportional coefficient adjustment value generated by fuzzy control, ΔK i The integral coefficient adjustment value generated by fuzzy control, K p , K i These are all adjusted fuzzy control parameters.

[0070] Specifically, the module control domain and fuzzy rules are set as shown in Table 1; the input and output domain in the fuzzy reasoning process adopts the normalized domain and is set to [-6, 6]; the fuzzy language variables are set to {NB, NM, NS, ZO, PS, PM, PB}, and the corresponding meanings are: negative large, negative medium, negative small, zero, positive small, positive medium, positive large; Table 1 is the fuzzy rule table, which sets K according to the error e and error change rate ec between the linear bus voltage and the target voltage. p , K i Table of rules for adjusting trends.

[0071] Table 1 Fuzzy rules table

[0072]

[0073] Take the ZJ70DB variable frequency drilling rig under 5000m drilling trip as an example:

[0074] Build a Simulink simulation model of the supercapacitor energy storage system for drilling winches, simulate the tripping of oil drilling winches, and conduct simulation tests combined with the chaotic longhorn beetle algorithm control system to verify the feasibility of this method;

[0075] like Figure 3 As shown in the figure, in order to verify the effectiveness of this method, the convergence speed of the chaotic beetle whisker algorithm (i.e., improved BAS algorithm) and the traditional algorithm (BAS algorithm) under the ITAE test function is tested in the MATLAB environment. The traditional algorithm reaches the global optimum after 15 iterations, while the chaotic beetle whisker algorithm reaches the global optimum after 11 iterations, which accelerates the convergence speed and proves the effectiveness of this method.

[0076] like Figure 4 As shown in the figure, under the control mode of the traditional fuzzy controller (PI), the supercapacitor SOC increased by about 10.3% in the 0-40S drilling process, and decreased by about 13.6% in the 80S-120S drilling process. Under the fuzzy controller with corrected control parameters, the supercapacitor SOC increased by about 12.3% in the 0-40S drilling process, and decreased by about 15.4% in the 80S-120S drilling process. Compared with the control mode of the traditional fuzzy controller, under the control mode of the fuzzy controller with corrected control parameters, the energy recovery in the drilling stage increased by 19.4%, the energy compensation in the drilling stage increased by 13.2%, and the recovery rate of the drawworks motor drilling energy was effectively improved.

[0077] In summary, the present invention introduces the Bernoulli chaotic map into the beetle whisker algorithm, which can combine the ergodicity of the chaotic map and the optimization ability of the beetle whisker algorithm to optimize the quantization factor and proportional factor of the fuzzy controller, realize dynamic adjustment of the fuzzy controller parameters, and thus improve the recovery rate of the supercapacitor for the feedback electric energy of the drilling winch.

[0078] The above description is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A control method for a drilling winch supercapacitor energy storage system based on a chaotic beetle whisker algorithm to optimize a fuzzy controller, characterized in that: The following steps are involved: S1. Obtain the voltage data of the DC bus in the oil drilling winch microgrid and set its target voltage value; S2, introducing chaotic mapping into the beetle whisker algorithm, using chaotic mapping to perturb the search space, improving the global search capability of the beetle whisker algorithm, and obtaining a chaotic beetle whisker algorithm; S3, using the chaotic beetle whisker algorithm to optimize the quantization factor and the proportional factor of the fuzzy controller, and then correct the control parameters of the fuzzy controller to obtain the fuzzy controller with the corrected control parameters; S4, obtaining the fuzzy controller for correcting the control parameters in step S3, and applying the fuzzy controller for correcting the control parameters to the voltage outer loop of the voltage-current dual closed-loop control to realize the charging and discharging of the supercapacitor.

2. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beetle whisker algorithm optimized fuzzy controller according to claim 1 is characterized in that: The step S2 comprises the following steps: S2-1, initializing the position of the longicorn beetle, and using the generated random vector as the search direction of the longicorn beetle's whiskers; S2-2, update the positions of the left and right antennae of the longicorn; S2-3, using the ITAE function as the objective function, calculate the fitness values ​​of the left and right whiskers of the longhorn beetle and update the position of the longhorn beetle; S2-4, introduce the perturbation mechanism of Bernoulli chaotic mapping, compare the fitness value obtained after the perturbation with the current optimal value, so as to obtain the current optimal position of the longicorn; S2-5. Determine whether the end condition is met. If so, take the current optimal position as the global optimal position. If not, return to step S2-2 until the end condition is met.

3. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beetle whisker algorithm optimized fuzzy controller according to claim 2 is characterized in that: In step S2-2, the position update of the left and right antennae of the longhorn beetle is expressed as: Where: x t is the coordinate of the centroid of the beetle at the tth iteration, x left is the position of the left antenna of the longicorn, x nght is the position of the right antenna of the longhorn beetle, D is the distance between the center of mass of the longhorn beetle individual and the antenna, is a random unit vector.

4. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beetle whisker algorithm optimized fuzzy controller according to claim 2 is characterized in that: In step S2-3, the expression of the I TAE function is: Where: e(t) is the error of the system at time t.

5. The control method of the drilling winch supercapacitor energy storage system based on the chaotic beetle whisker algorithm optimized fuzzy controller according to claim 2 is characterized in that: In step S2-4, the expression for calculating the fitness values ​​of the left and right whiskers of the longhorn beetle and updating the position of the longhorn beetle is: Where: f(x left ) is the fitness value of the left antenna of the longicorn beetle, f(x right ) is the fitness value of the right antenna of the longicorn, S t is the step length of the longicorn at the tth iteration, and sign is the sign function.

6. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beard algorithm optimized fuzzy controller according to claim 2 is characterized in that: In step S2-4, the expression of the Bernoulli chaotic map is introduced as follows: Where: X n+1 . is a chaotic sequence generated by Bernoulli mapping, with a value range of (0, 1). ε is the chaos control parameter.

7. The control method of the drilling winch supercapacitor energy storage system based on the chaotic longhorn beetle whisker algorithm optimized fuzzy controller according to claim 1 is characterized in that: The fuzzy controller includes: The fuzzy controller controls the voltage outer loop and applies fuzzy control to the voltage outer loop of the voltage and current dual closed-loop control. After the fuzzy controller fuzzifies the input error e and the error rate ec, it outputs the fuzzy controller parameter adjustment value: The adjusted fuzzy control parameters are expressed as: Where: K p0 , K i0 are the initial values ​​of the fuzzy controller, ΔK p is the proportional coefficient adjustment value generated by fuzzy control, ΔK i The integral coefficient adjustment value generated by fuzzy control, K p , K i These are all adjusted fuzzy control parameters.

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