A diesel generator set group parallel operation control method and system based on fuzzy PID control

Abstract

The present application relates to the technical field of power repair and emergency power supply, and particularly relates to a diesel generator set group parallel operation control method and system based on fuzzy PID control. Real-time operation parameters of the diesel generator set are acquired to construct a fuzzy PID control input vector; a mapping relationship table between the control input vector and a preset parallel operation fuzzy PID control rule is established; a weighted average algorithm is used for defuzzy calculation to obtain a real-time PID parameter correction amount and correct the current PID control parameter; after voltage leveling, frequency adjustment and phase calibration of each unit based on automatic quasi-synchronous parallel operation logic, the units are connected in parallel to construct a parallel control group; power distribution is performed on the speed regulator and excitation controller of each unit according to the real-time fuzzy PID control parameter, and cyclic adjustment is performed until the error converges. The problems of poor parallel operation stability and uneven power distribution of the diesel generator set group caused by fixed traditional PID control parameters are solved, intelligent control of multi-unit parallel operation is realized, and the system operation stability and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of power emergency repair and power supply technology, and in particular to a method and system for parallel operation control of diesel generator sets based on fuzzy PID control. Background Technology

[0002] In recent years, with the increasing frequency and diversification of socio-economic activities, and the continuous expansion in the number and scale of major events such as sports competitions, cultural performances, and international conferences, large venues, as the main venues for these events, bear significant social responsibility and economic value. Therefore, the safety and reliability of their power supply have become paramount. Power outages or accidents can not only disrupt the normal operation of events but also potentially cause personal injury and property damage. Thus, timely identification and resolution of problems and potential hazards in venue power supply systems, and the promotion of optimization and improvement of venue microgrid power supply systems, are of great importance.

[0003] Currently, emergency power supplies mainly include diesel generators, micro gas turbines, battery energy storage systems represented by UPS and EPS, and backup power lines. Diesel generator sets are widely used due to their strong continuous power supply capability, large capacity, and relatively simple control. However, in practical applications, diesel generator sets operate slowly, leading to a series of problems: the continuity and reliability of the entire power supply system are greatly reduced, power quality and economy deteriorate, and the traditional single-unit operation mode can no longer meet the high reliability power supply requirements, making parallel operation of multiple diesel generator sets a trend. However, most existing parallel control systems for diesel generator sets adopt traditional PID control methods, which have problems such as slow response, poor anti-disturbance capability, and difficulty in suppressing power oscillations. Especially when facing sudden large load changes, the system stability is poor, and the continuity of power supply cannot be guaranteed. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a control method for parallel operation of diesel generator sets based on fuzzy PID control, comprising the following steps: S1. By acquiring multi-source data, the real-time operating parameters of each diesel generator set are obtained. Based on the preset diesel generator set operating condition data, the error signal and error change rate are calculated. The error signal and error change rate are then fuzzified to construct the fuzzy PID control input vector. S2. Obtain the preset parallel operation fuzzy PID control rules, and based on the fuzzy PID control input vector, establish a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rules; S3. Based on the mapping table, the fuzzy PID control input vector is defuzzified using a weighted average algorithm to obtain the real-time PID parameter correction amount, and the current PID control parameters are corrected to obtain the real-time fuzzy PID control parameters. S4. Based on the automatic quasi-synchronous parallel operation logic, according to the preset diesel generator set operating condition data, the voltage leveling, frequency adjustment and phase calibration of each diesel generator set are performed respectively, and each diesel generator set is driven to perform parallel operation, thus constructing a diesel generator set parallel control group. S5. Based on the real-time fuzzy PID control parameters, power distribution is performed on the speed governor and excitation controller of each diesel generator set in the parallel control group. The real-time operating parameters of each diesel generator set are collected again to calculate the error signal and error rate of change until the error signal and error rate of change converge.

[0005] Based on the above method, the present invention also provides a parallel operation control system for a group of diesel generator sets based on fuzzy PID control. The system is implemented based on any one of the above-mentioned parallel operation control methods for a group of diesel generator sets based on fuzzy PID control. It includes a multi-source data acquisition and fuzzification module, which is used to acquire real-time operating parameters of each diesel generator set through multi-source data acquisition, calculate error signals and error change rates based on preset diesel generator set operating condition data, and perform fuzzification calculation on error signals and error change rates to construct fuzzy PID control input vectors. The fuzzy PID control rule mapping generation module is used to obtain the preset parallel fuzzy PID control rules and establish a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rules based on the fuzzy PID control input vector. The fuzzy PID control parameter generation module is used to perform defuzzification calculation on the fuzzy PID control input vector based on the mapping relationship table and the weighted average algorithm to obtain the real-time PID parameter correction amount, and correct the current PID control parameters to obtain the real-time fuzzy PID control parameters. The automatic quasi-synchronous paralleling module is used to perform voltage leveling, frequency adjustment and phase calibration on each diesel generator set according to the preset diesel generator set operating condition data based on the automatic quasi-synchronous paralleling logic, and drive each diesel generator set to operate in parallel to build a diesel generator set parallel control group. The parameter-driven and power distribution module is used to distribute power to the speed governor and excitation controller of each diesel generator set in the parallel control group according to the real-time fuzzy PID control parameters, and to collect the real-time operating parameters of each diesel generator set again to calculate the error signal and error change rate until the error signal and error change rate converge.

[0006] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the parallel operation control method for a group of diesel generator sets based on fuzzy PID control described above.

[0007] A storage medium storing a computer program thereon, which, when executed by a processor, implements the parallel operation control method for a group of diesel generator sets based on fuzzy PID control as described above.

[0008] This invention significantly reduces power periodic oscillations, speed oscillations, and voltage and frequency instabilities caused by fuel supply interference during parallel operation of diesel generator sets by replacing traditional PID control with a fuzzy PID control algorithm, thereby improving system stability. Fuzzy PID control can dynamically adjust control parameters according to the real-time system status, enabling the diesel generator set to have faster response speed and smaller overshoot under load changes or disturbance conditions, shortening the settling time and improving dynamic response performance. Fuzzy PID control combines the adaptive capability of fuzzy logic with the precision of PID control, better addressing the nonlinear and time-varying characteristics of diesel generator sets, improving the system's anti-interference capability and robustness. By optimizing the control strategies of the excitation and speed control systems, fluctuations in output current and voltage are effectively reduced, improving the power quality of the generator set. Simultaneously, it suppresses interactive oscillations, reduces mechanical vibration of key components such as the diesel engine governor and fuel injection pump, thereby extending equipment lifespan, reducing maintenance costs, and ensuring the stable operation of equipment such as electric drilling rigs. Attached Figure Description

[0009] Figure 1 This is a flowchart of a parallel operation control method for a group of diesel generator sets based on fuzzy PID control, according to an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of the structure of a diesel generator set group parallel operation control system based on fuzzy PID control according to an embodiment of the present invention.

[0011] Figure 3 This is a membership function diagram of the input error signal of the speed control system according to an embodiment of the present invention.

[0012] Figure 4 This is a membership function graph of the input error change rate of the speed regulation system according to an embodiment of the present invention.

[0013] Figure 5 This is a membership function diagram of the input error signal of the excitation system in an embodiment of the present invention.

[0014] Figure 6 This is a membership function graph of the input error change rate of the excitation system in an embodiment of the present invention.

[0015] Figure 7 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present invention.

[0016] Figure 8 This is a schematic diagram of the storage medium according to an embodiment of the present invention.

[0017] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache memory, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or machine that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or machine. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or machine that includes said element.

[0022] Example 1: like Figure 1 As shown, this embodiment of the invention provides a control method for parallel operation of a diesel generator set group based on fuzzy PID control, including the following steps: S1. By acquiring multi-source data, the real-time operating parameters of each diesel generator set are obtained. Based on the preset diesel generator set operating condition data, the error signal and error change rate are calculated. The error signal and error change rate are then fuzzified to construct the fuzzy PID control input vector. S2. Obtain the preset parallel operation fuzzy PID control rules, and based on the fuzzy PID control input vector, establish a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rules; S3. Based on the mapping table, the fuzzy PID control input vector is defuzzified using a weighted average algorithm to obtain the real-time PID parameter correction amount, and the current PID control parameters are corrected to obtain the real-time fuzzy PID control parameters. S4. Based on the automatic quasi-synchronous parallel operation logic, according to the preset diesel generator set operating condition data, the voltage leveling, frequency adjustment and phase calibration of each diesel generator set are performed respectively, and each diesel generator set is driven to perform parallel operation, thus constructing a diesel generator set parallel control group. S5. Based on the real-time fuzzy PID control parameters, power distribution is performed on the speed governor and excitation controller of each diesel generator set in the parallel control group. The real-time operating parameters of each diesel generator set are collected again to calculate the error signal and error rate of change until the error signal and error rate of change converge.

[0023] Specifically, step S1 includes the following sub-steps: S101. By using a speed sensor and a voltage transformer, the actual speed and voltage values ​​of each diesel generator set are collected to obtain real-time operating parameters. These parameters are then compared with preset diesel generator set operating condition data to calculate the error signal and error change rate. S102. Construct an initial discrete universe of discourse and introduce a quantization factor to project the error signal and error rate of change into the discrete universe of discourse to obtain the fuzzy PID control universe of discourse; S103. Divide the fuzzy PID control domain into fuzzy PID control language variables to obtain a subset of fuzzy PID control variables, wherein the subset of fuzzy PID control variables includes negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; S104. Based on the membership function, calculate the membership values ​​of the error signal and the error rate of change in each control variable in the subset of fuzzy PID control variables, and construct the fuzzy PID control input vector based on the membership values.

[0024] Specifically, the implementation principle and flow of each sub-step in the above embodiments are as follows: First, real-time speed and voltage values ​​of each diesel generator set are collected using speed sensors and voltage transformers to obtain real-time operating parameters. These parameters are then compared with preset operating condition data for the diesel generator sets to calculate the error signal and error rate of change. The speed sensors monitor the real-time speed changes of the diesel generators, while the voltage transformers collect the voltage signal at the generator output. By comparing these parameters with preset operating condition parameters, the system deviation signal e and the deviation rate of change ec are obtained, providing the basic input for subsequent fuzzy control.

[0025] Then, an initial discrete universe of discourse is constructed, and a quantization factor is introduced to project the error signal and the rate of change of error into the discrete universe of discourse, thus obtaining the fuzzy PID control universe of discourse. For the speed control system, the universe of discourse for the diesel engine speed difference e is taken as [-3 3], and the rate of change of speed difference ec is taken as [-2 2]; for the excitation system, the universe of discourse for the input quantity e is [-1 1], and the universe of discourse for ec is set as [-8 8]. By using a quantization factor, continuous physical quantities are converted into discrete fuzzy quantities, which facilitates fuzzy inference calculations.

[0026] Next, the fuzzy PID control domain is partitioned into fuzzy PID control linguistic variables, resulting in a subset of fuzzy PID control variables. This subset includes negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB). The fuzzy sets for both the speed control system and the excitation system are defined as {NB, NM, NS, ZO, PS, PM, PB}, corresponding to the seven linguistic variables: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively, thus achieving a fuzzy description of the continuous control quantity.

[0027] Finally, based on the membership function, the membership values ​​of the error signal and the rate of change of error in each control variable of the fuzzy PID control variable subset are calculated, and the fuzzy PID control input vector is constructed based on the membership values. Triangular and Z-shaped membership functions are used to calculate the membership degrees of the input variables in each fuzzy subset, forming the input vector of the fuzzy controller and providing a quantitative basis for fuzzy inference. For the speed control system, the membership function graph of the input error signal is shown below. Figure 3 As shown, the membership function graph of the input error change rate is as follows. Figure 4 As shown; for the excitation system, the membership function graph of the input error signal is as follows. Figure 5 As shown, the membership function graph of the input error change rate is as follows. Figure 6 As shown.

[0028] Furthermore, in step S2, a preset parallel operation fuzzy PID control rule is obtained, and a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rule is established based on the fuzzy PID control input vector.

[0029] Specifically, using the fuzzy PID control input vector as two inputs, and based on the fuzzy PID control algorithm, PID adjustment control parameters are output. These PID adjustment control parameters include three outputs: the correction amount ΔKp, the integral coefficient ΔKi, and the derivative coefficient ΔKd. Fuzzy control rule tables are established for each of the three output parameters ΔKp, ΔKi, and ΔKd, with each table containing 49 control rules covering all possible input combinations. Through expert experience and system characteristic analysis, PID parameter correction strategies are determined under different combinations of errors and error change rates, forming a complete fuzzy inference knowledge base.

[0030] Specifically, the PID parameter correction strategy can be expressed as the following logical statement: Where A and B represent arbitrary state parameters, and C, D, and E represent arbitrary corresponding correction parameter values.

[0031] Specifically, the mapping relationship table for the correction amount ΔKp is shown in Table 1, the mapping relationship table for the integral coefficient Δki is shown in Table 2, and the mapping relationship table for the differential coefficient ΔKd is shown in Table 3.

[0032] Table 1 Mapping Relationship of Correction ΔKp

[0033] Table 2 Mapping Relationship of Integral Coefficients Δki

[0034] Table 3 Mapping Relationship of Differential Coefficients ΔKd

[0035] Furthermore, the real-time fuzzy PID control parameters described in step S3 are expressed as follows: ; ; ;in, , , This represents the parameters for real-time fuzzy PID control. , , This represents the preset PID control constant. , , This indicates the real-time PID parameter correction amount.

[0036] Specifically, for a speed control system, the output... , , The universe of discourse is taken as [-3 3]; for the excitation system, , , The output universes of discourse are set to [-1 1], [-0.5 0.5], and [-0.6 0.6], respectively. The fuzzy inference results are converted into precise numerical outputs using a weighted average method, enabling dynamic adjustment of the PID parameters.

[0037] Furthermore, step S4 includes real-time acquisition of voltage data, voltage frequency data, and phase data of each diesel generator set. When the voltage difference, frequency difference, and phase difference are all within the preset quasi-synchronization window, a closing command is executed to connect each diesel generator set in parallel, thereby obtaining a diesel generator set parallel control group.

[0038] Specifically, based on automatic quasi-synchronous parallel operation logic, and according to preset diesel generator set operating condition data, voltage leveling, frequency adjustment, and phase calibration are performed on each diesel generator set, and the sets are driven to operate in parallel, thus constructing a diesel generator set parallel control group. Voltage, frequency, and phase data of each diesel generator set are collected in real time. When the voltage, frequency, and phase differences are all within the preset quasi-synchronous window, a closing command is executed to parallelize the diesel generator sets, resulting in the diesel generator set parallel control group. Quasi-synchronous parallel operation ensures smooth parallel operation of each unit under the condition of electrical parameter matching, avoiding inrush current and mechanical stress, and ensuring the safe and stable operation of the system.

[0039] Furthermore, step S5 includes the following steps: S501. Based on the real-time fuzzy PID control parameters, power distribution is performed on the electronic speed governor and excitation controller of each diesel generator set in the parallel control group of diesel generator sets. S502. Monitors the amplitude of interactive oscillations during the power distribution process and continuously corrects the PID parameters through closed-loop feedback until the fluctuations in speed and voltage are lower than the preset threshold.

[0040] Furthermore, the power distribution in step S501 includes: distributing active power to each diesel generator set by adjusting the fuel injection quantity of each diesel generator set through an electronic speed governor; and distributing reactive power to each diesel generator set by adjusting the excitation current of each diesel generator set through an excitation controller.

[0041] Specifically, the implementation principle and flow of each sub-step in the above embodiments are as follows: First, based on real-time fuzzy PID control parameters, power distribution is performed on the electronic governors and excitation controllers of each diesel generator set within the parallel control group. Power distribution includes: distributing active power to each generator set by adjusting the fuel injection quantity through the electronic governor; and distributing reactive power by adjusting the excitation current of each generator set through the excitation controller. The electronic governor employs a special control mechanism, using electrical signals for signal acquisition, transmission, and control adjustment. It adjusts the fuel supply by changing the throttle rack displacement to ensure the stability of the AC power frequency generated by the generator. The excitation controller controls the generator terminal voltage and reactive power output by adjusting the excitation current to change the internal magnetic field strength of the generator, thereby achieving a reasonable distribution of reactive power among the parallel units.

[0042] Then, the amplitude of interactive oscillations during the power distribution process is monitored, and the PID parameters are continuously corrected through closed-loop feedback until the fluctuations in speed and voltage are below preset thresholds. The system continuously monitors the operating status of each unit. When interactive oscillations are detected, the fuzzy PID controller dynamically adjusts the control parameters based on the oscillation amplitude and frequency characteristics, and continuously optimizes the control effect through the closed-loop feedback mechanism until the system reaches a stable operating state.

[0043] This method employs a fuzzy logic controller with a dual-input, three-output structure. The inputs are the deviation value *e* and the rate of change of the deviation *ec*, while the outputs are the control parameter corrections ΔKp, ΔKi, and ΔKd of the PID controller. The Mamdani fuzzy logic controller primarily utilizes this type of controller. A fuzzy rule base established through expert experience can adaptively adjust the PID parameters based on the system's operating state, exhibiting better robustness and adaptability compared to traditional PID control. When multiple generator sets operate in parallel, precise adjustment of fuel quantity achieves a rational allocation of active power, while optimization of reactive power configuration is achieved through excitation system regulation. This effectively suppresses interactive oscillations during parallel operation, significantly improving the overall performance and stability of the diesel generator set parallel system.

[0044] Example 2

[0045] like Figure 2 As shown, as a preferred embodiment of the above embodiments, a parallel operation control system for a group of diesel generator sets based on fuzzy PID control is proposed. This system is implemented based on the parallel operation control method for a group of diesel generator sets based on fuzzy PID control described in any of the above embodiments, and includes: The multi-source data acquisition and fuzzification module is used to acquire real-time operating parameters of each diesel generator set through multi-source data acquisition. Based on preset diesel generator set operating condition data, it calculates error signals and error change rates, and performs fuzzification calculations on the error signals and error change rates to construct fuzzy PID control input vectors. The fuzzy PID control rule mapping generation module is used to acquire preset parallel operation fuzzy PID control rules and establish a mapping relationship table between the fuzzy PID control input vectors and fuzzy PID control rules based on the fuzzy PID control input vectors. The fuzzy PID control parameter generation module is used to defuzzify the fuzzy PID control input vectors based on the mapping relationship table using a weighted average algorithm to obtain real-time PID parameters. The system performs several steps: First, it calculates the correction amount and corrects the current PID control parameters to obtain real-time fuzzy PID control parameters. Second, it establishes a parallel control group for diesel generator sets. Third, it generates a parameter-driven and power-distribution module. This module allocates power to the governors and excitation controllers of each diesel generator set within the parallel control group based on the real-time fuzzy PID control parameters. It also collects the real-time operating parameters of each diesel generator set to calculate the error signal and error rate of change until the error signal and error rate of change converge.

[0046] Furthermore, the multi-source data acquisition and fuzzification module includes a speed sensor, a voltage transformer, and a fuzzy controller; the speed sensor is configured one-to-one with the diesel generator set to acquire the real-time speed value of each diesel generator set; the voltage transformer is configured one-to-one with the diesel generator set to acquire the actual voltage value of each diesel generator set; the fuzzy controller is used to receive error signals and error change rates, and obtain three output signals through fuzzification calculation.

[0047] Specifically, the implementation principle and process of the above system are as follows: First, the multi-source data acquisition and fuzzification module acquires the speed and voltage values ​​of each unit in real time through speed sensors and voltage transformers. It compares these values ​​with preset operating condition data to generate an error signal e and an error rate of change ec. Using a quantization factor, it projects these values ​​onto an initialized discrete universe of discourse and calculates their membership degrees to subsets of fuzzy variables based on membership functions, thus constructing a standard fuzzy control input vector. Next, the fuzzy PID control rule mapping generation module acquires preset parallel operation fuzzy rules and uses a fuzzy combination inference algorithm to match the input vector with the fuzzy PID control rules, establishing a logical mapping table between input states and output fuzzy quantities. Finally, the fuzzy PID control parameter generation module uses this mapping table and a weighted average algorithm to perform defuzzification calculations, deriving real-time PID parameter correction values. , , The system updates the current control parameters accordingly to obtain real-time fuzzy PID control parameters. Simultaneously, the automatic quasi-synchronous parallel operation module executes the detection logic in parallel. When the voltage, frequency, and phase between the unit to be paralleled and the bus enter the quasi-synchronous window, it drives the actuator to complete the parallel operation and establish a parallel control group. Finally, the parameter drive and power distribution module converts the calculated real-time fuzzy PID parameters into drive commands, which are applied to the governors and excitation controllers of each unit. By adjusting the fuel injection quantity and excitation current, it achieves dynamic distribution of active and reactive power and continuously collects operating parameters for closed-loop fine-tuning until the system error signal and error change rate reach a convergence state.

[0048] Example 3

[0049] Furthermore, as a preferred embodiment of the present invention, a terminal device for a control method of parallel operation of diesel generator sets based on fuzzy PID control is proposed, such as... Figure 7 As shown, the terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0050] The memory 210 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 211 and / or cache memory 212, and may further include read-only memory (ROM) 213.

[0051] The memory 210 also stores a computer program, which can be executed by the processor 220. This causes the processor 220 to execute any of the above-described methods for parallel operation control of diesel generator sets based on fuzzy PID control in this application embodiment. The specific implementation and technical effects are consistent with the implementation methods and achieved in the above-described embodiments, and some details will not be repeated. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0052] Accordingly, processor 220 can execute the aforementioned computer program, as well as program / utility 214. Bus 230 can be one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0053] Terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with it, and / or with any device that enables it to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interface 250. Furthermore, terminal device 200 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0054] Example 4

[0055] As a preferred embodiment of Example 1, a computer-readable storage medium is proposed for a control method for parallel operation of diesel generator sets based on fuzzy PID control. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned control methods for parallel operation of diesel generator sets based on fuzzy PID control. The specific implementation method and the achieved technical effects are consistent with those described in the above embodiments, and some details will not be repeated.

[0056] like Figure 8 As shown, the program product 300 provided in this embodiment for implementing the above-described fuzzy PID control-based parallel operation control method for diesel generator sets can be a portable compact disc read-only memory (CD-ROM) containing program code and can run on a terminal device, such as a personal computer. However, the program product 300 of this invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The program product 300 can employ any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0057] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0058] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for parallel operation of a group of diesel generator sets based on fuzzy PID control, characterized in that, Includes the following steps: S1. By acquiring multi-source data, the real-time operating parameters of each diesel generator set are obtained. Based on the preset diesel generator set operating condition data, the error signal and error change rate are calculated. The error signal and error change rate are then fuzzified to construct the fuzzy PID control input vector. S2. Obtain the preset parallel operation fuzzy PID control rules, and based on the fuzzy PID control input vector, establish a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rules; S3. Based on the mapping table, the fuzzy PID control input vector is defuzzified using a weighted average algorithm to obtain the real-time PID parameter correction amount, and the current PID control parameters are corrected to obtain the real-time fuzzy PID control parameters. S4. Based on the automatic quasi-synchronous parallel operation logic, according to the preset diesel generator set operating condition data, the voltage leveling, frequency adjustment and phase calibration of each diesel generator set are performed respectively, and each diesel generator set is driven to perform parallel operation, thus constructing a diesel generator set parallel control group. S5. Based on the real-time fuzzy PID control parameters, power distribution is performed on the speed governor and excitation controller of each diesel generator set in the parallel control group. The real-time operating parameters of each diesel generator set are collected again to calculate the error signal and error rate of change until the error signal and error rate of change converge.

2. The method for parallel operation control of diesel generator sets based on fuzzy PID control according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Real-time speed and voltage values ​​of each diesel generator set are collected by speed sensor and voltage transformer respectively to obtain real-time operating parameters, and compared with preset diesel generator set operating condition data to calculate error signal and error change rate. S102. Construct an initial discrete universe of discourse and introduce a quantization factor to project the error signal and error rate of change into the discrete universe of discourse to obtain the fuzzy PID control universe of discourse; S103. Divide the fuzzy PID control domain into fuzzy PID control language variables to obtain a subset of fuzzy PID control variables, wherein the subset of fuzzy PID control variables includes negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; S104. Based on the membership function, calculate the membership values ​​of the error signal and the error rate of change in each control variable in the subset of fuzzy PID control variables, and construct the fuzzy PID control input vector based on the membership values.

3. The method for parallel operation control of diesel generator sets based on fuzzy PID control according to claim 1, characterized in that, The real-time fuzzy PID control parameters mentioned in step S3 are expressed as follows: ； ； ； in, , , This represents the parameters for real-time fuzzy PID control. , , This represents the preset PID control constant. , , This indicates the real-time PID parameter correction amount.

4. The method for parallel operation control of diesel generator sets based on fuzzy PID control according to claim 1, characterized in that, Step S4 includes real-time acquisition of voltage data, voltage frequency data, and phase data of each diesel generator set. When the voltage difference, frequency difference, and phase difference are all within the preset quasi-synchronization window, the closing command is executed to connect each diesel generator set in parallel, thus obtaining a diesel generator set parallel control group.

5. The method for parallel operation control of diesel generator sets based on fuzzy PID control according to claim 1, characterized in that, Step S5 includes the following sub-steps: S501. Based on the real-time fuzzy PID control parameters, power distribution is performed on the electronic speed governor and excitation controller of each diesel generator set in the parallel control group of diesel generator sets. S502. Monitors the amplitude of interactive oscillations during the power distribution process and continuously corrects the PID parameters through closed-loop feedback until the error signal and error change rate converge.

6. The method for parallel operation control of diesel generator sets based on fuzzy PID control according to claim 5, characterized in that, The power distribution in step S501 includes: distributing active power to each diesel generator set by adjusting the fuel injection quantity of each diesel generator set through an electronic speed governor; and distributing reactive power to each diesel generator set by adjusting the excitation current of each diesel generator set through an excitation controller.

7. A parallel operation control system for a group of diesel generator sets based on fuzzy PID control, wherein the system is implemented based on the parallel operation control method for a group of diesel generator sets based on fuzzy PID control as described in any one of claims 1-6, characterized in that, include: The multi-source data acquisition and fuzzification module is used to acquire real-time operating parameters of each diesel generator set through multi-source data acquisition, calculate error signals and error change rates based on preset diesel generator set operating condition data, and perform fuzzification calculation on error signals and error change rates to construct fuzzy PID control input vectors. The fuzzy PID control rule mapping generation module is used to obtain the preset parallel fuzzy PID control rules and establish a mapping relationship table between the fuzzy PID control input vector and the fuzzy PID control rules based on the fuzzy PID control input vector. The fuzzy PID control parameter generation module is used to perform defuzzification calculation on the fuzzy PID control input vector based on the mapping relationship table and the weighted average algorithm to obtain the real-time PID parameter correction amount, and correct the current PID control parameters to obtain the real-time fuzzy PID control parameters. The automatic quasi-synchronous paralleling module is used to perform voltage leveling, frequency adjustment and phase calibration on each diesel generator set according to the preset diesel generator set operating condition data based on the automatic quasi-synchronous paralleling logic, and drive each diesel generator set to operate in parallel to build a diesel generator set parallel control group. The parameter-driven and power distribution module is used to distribute power to the speed governor and excitation controller of each diesel generator set in the parallel control group according to the real-time fuzzy PID control parameters, and to collect the real-time operating parameters of each diesel generator set again to calculate the error signal and error change rate until the error signal and error change rate converge.

8. A parallel operation control system for diesel generator sets based on fuzzy PID control according to claim 7, characterized in that, The multi-source data acquisition and fuzzification module includes a speed sensor, a voltage transformer, and a fuzzy controller; the speed sensor is set up one-to-one with the diesel generator set to collect the real-time speed value of each diesel generator set. The voltage transformers are set up one-to-one with the diesel generator sets to collect the actual voltage values ​​of each diesel generator set. The fuzzy controller is used to receive error signals and error change rates, and to obtain three output signals through fuzzification calculation.

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