Three-phase induction motors are becoming increasingly utilized in industrial field due to their better efficiency and simple manufacture. The speed control of an induction motor is essential in a variety of applications, but it is difficult to control. This research analyses the three-phase induction motor’s performance using field-oriented control (FOC) and direct torque control (DTC) techniques. The major aim of this work is to provide a critical evaluation of developing a simple speed controller for induction motors with improving the performance of Induction Motor (IM). For controlling a motor, different optimization approaches are accessible; in this research, a Fuzzy Logic Controller (FLC) with Fractional Order Darwinian Particle Swarm Optimization (FODPSO) algorithm is presented to control the induction motor. The FOC and DTC are controlled using FODPSO, and their performance is compared to the traditional FOC and DTC technique. Each scheme had its own simulation model, and the results were compared using hardware experimental and MATLAB-Simulink. In terms of time domain specifications and torque improvement, the proposed technique surpasses the existing method.

There are several methods for managing the torque of an induction motor control system, DTC and FOC methods have gained popularity owing to their ability to successfully follow speed and torque standards despite disturbances in load factors [

Due to the order reduction, easy implementation through power converters, great robustness and disturbance rejection, sliding mode control is regarded as the suitable technology for the robust nonlinear control of IM drives [

Several studies on FOC and DTC, including the use of further types of controllers to improve the quality of motor’s control system. Another problem of these articles and researches are primarily focused with evaluation of performance, which presupposes that everything is working smoothly that may always not be the case. As a result, these comparison studies include two power quality issues: short interruption and voltage sag [

Many studies have demonstrated that, FLC controllers are simple to design; nonetheless, FLC performance is dependent on the rule basis, membership functions (MFs) and number of rules. These parameters are established by a time-consuming error and trial approach. To circumvent these constraints, FLC design optimization approaches employ a differential optimization algorithm to construct FLC and regulate the speed of induction motors [

Furthermore, previous works investigates the similarities between DTC and FOC by re-evaluating the core ideas of both and looking for methods to integrate the two to build a control scheme that is both more accurate and faster. As a consequence, this study is compared with previous methodologies, with extra similar criteria included. The suggested work discusses the operation of FOC and DTC method in Segment 2, the function of FLC is explained in Segment 3, FODPSO approach is explained in Segment 4, the proposed Fuzzy-FODPSO based DTC and FOC methods are clearly explained in Section 5, the simulated outcome and comparison of different techniques are discussing in Segment 6, and conclusion in Segment 6.

Field Oriented Control (FOC) is the most often utilised control approach for high-performance induction motor applications. FOC method employs orthogonal transformation, where

The main disadvantage is that the control’s orientation is particularly sensitive to rotor resistance, which lowers the control’s resilience, high complexity and Coordinate transformation necessary. This limitation can be overcome by proposed Fuzzy-FODPSO based FOC approach.

A torque controller, an IGBT-based VSI, and a speed controller comprise the direct torque control drive. When the reference and rotor speeds are compared, an error signal is generated. The PI controller analyses the speed error and generates reference torque

Changing in error (ΔE) | Error (E) | ||||||
---|---|---|---|---|---|---|---|

NE3 | NE2 | NE1 | ZE | PE1 | PE2 | PE3 | |

NB | NB | NB | NB | NB | NM | Z | |

NB | NB | NB | NB | NM | Z | PS | |

NB | NB | NB | NM | Z | PS | PM | |

NB | NB | NM | Z | PS | PM | PB | |

NM | NM | Z | PS | PM | PB | PB | |

NS | Z | PS | PM | PB | PB | PB | |

Z | PS | PM | PB | PB | PB | PB |

The rotor flux and stator flux and complex form can be represented as

Based on the current failure, the inverter current is attempted to remain within the hysteresis controller’s specified band.

In this case, the flux controller has a hysteresis band width of

The FLC technique is highly beneficial for induction motor speed drives. FLC contains a predefined set of control rules, which are often developed from expert knowledge. The MFs of the linked input and output linguistic terms are commonly established on a shared discourse universe. Appropriate selection of output and input scaling factors (gains) or adjustment of other controller parameters are key jobs for the effective design of FLCs, which is frequently done through trial and error to get the finest control performance. The regulations are designed to meet the needs of the speed. FLC performance will improve when the number of rules is increased.

The FLC’s main processes are fuzzification process, fuzzy rule base, inference, and defuzzification process. Defuzzification generates output in the form of a crisp value based on a given set of membership functions and rules. FLC is employed in the developed method to calculate the torque command current from the actual and reference speeds. As stated in the equations below, the input information consists of rotor speed’s error (E) and change of error (∆E).

The fuzzy speed controller’s inputs (E &

The second phase (fuzzification) expresses the inputs with easy linguistic value by grouping each input. The third step (inference) describes how the fuzzy speed controller makes the IM decisions using control rules and linguistic concepts. Mamdani procedures are utilised in this work, due of its simplistic construction and design. The fuzzy rules for induction motor control table shows in

where, w

The proposed controller is utilised to alter the settings and govern the ideal values for the membership function variables using the FODPSO optimization technique.

This part presents and denotes FO-DPSO, a novel way for controlling the DPSO optimization technique on Pires et al. method to the classic PSO [

Where,

Where,

The FLC speed controller is extensively used due to its adaptability in nonlinear controller systems, low implementation cost, and simplicity; it is not based on a design mathematical. The typical FLC has a limitation in specifying the bounds of the MF’s input and output. This segment describes how to enhance FLC by using FODPSO optimization to determine the optimal bounds of MF input and output.

The schematic of the proposed Fuzzy-FODPSO based FOC method is depicts in

In this work, a FODPSO based fuzzy controller is suggested for use in the torque control loop of a DTC induction motor. The Fuzzy-FODPSO technique combines the Fractional Order Darwinian Particle Swarm Optimization algorithm and Fuzzy Logic Controller techniques.

From

From the proposed block diagram, the feedback element is the motor speed. The error signal (E) is derived by comparing the motor speed to the reference speed. The difference between the signal of error and the unit change in the error signal yields the change in the error signal (dE). The signals (E) and (dE) are fed into the FLC. In the fuzzy controller, the inputs are fuzzified and appropriate rules are defined. The output is subsequently acquired according to the rules, which is then defuzzified to obtain the control signal. The output variable is the pulse generator’s reference voltage vector. The gate signals for managing the inverter output frequency and voltage are created by the FODPSO generator based on the fuzzy logic control output. The FLC’s operation is determined by its membership function. The control signal is then utilised to change the inverter’s output frequency and voltage to achieve the required speed.

The stator flux module is composed of the following components:

The torque equation is as follows:

Motor torque and flux magnitudes are directly controlled in the DTC by controlling the stator flux vector. The DTC control technique is based on choosing the correct inverter switching pulses to directly regulate the speed and length of the stator flux vector. Here, the proposed fuzzy-FODPSO based speed control method is used well in the research to manage the speed of the induction motor with the help of the torque control method.

The suggested method is simulated in MATLAB. The associated outcomes are utilised to authorize the proposed Fuzzy-FODPSO based DTC and FOC approach, which governs the overall system performance.

A sensor-less field-oriented control of IM drive for a 200HP AC motor is developed using MATLAB-Simulink. As a result of using the Model Referencing Adaptive System technique to determine motor speed from terminal currents and voltages, the speed sensor is no longer working.

Different parameters | Value |
---|---|

Rated Voltage | 440 V |

Rotor inertia | 3 kgm^{2} |

Rated speed | 500 RPM |

Pole pairs | 2 |

Horse power | 200 HP |

Rotor resistance | 9 mΩ |

Rated power | 149.2 kW |

Rotor inductance | 0.3 mH |

Stator inductance | 0.3 mH |

Stator resistance | 15 mΩ |

As illustrated in

This FOC system has been discretized with a step time of 2 us. To imitate the control device, the speed controller uses a 140-s period of samples whereas the DTC controller employs a 20-s sampling interval. The inverter’s switching frequency is set to 5 kHz. At time t = 0.5 s, and time t = 2 s, the reference speed is fixed to 500 rpm and the speed is lowered to 0 rpm in the forward operating mode which is shown in

Likewise, at time t = 1 s, the 100 Nm torque is delivered to the motor and at 2.5 s, the torque is returned to 0 Nm, as illustrated in

When the motor operates, torque rises to a high value and then decreases to near zero since the motor is running with no load. As the load grows, so will the motor’s output torque to meet it. When the motor begins, the torque rises to a maximum and then decreases to near zero since the motor is running with no load. As the load rises, so will the motor’s output torque to cover it. The speed of the motor continues to increase to its end value when motor shaft receives the full load torque at t = 1 s, which is shown in

As the machine begins to ramp up at 0.5 s, the current’s magnitude climbs to 250 A and then decreases to 100 A at 0.85 s show in

The stator currents are altered in accordance with the speed command. In the steady state, the torque is zero since the induction motor works at no load. The simulation results reveal that the control system has high dynamic stability and performance in various operating modes.

A 3-

The motor begins to accelerate at 0.5 s, reaching a maximum overshoot of 507 rpm at 1.05 s and settling at 500 rpm at 1.25 s.

As shown in

Time domain specifications | FLC [ |
PSO-FLC [ |
Fuzzy-FODPSO based FOC | Fuzzy-FODPSO based DTC |
---|---|---|---|---|

Rise time (s) | 0.04 | 0.03 | 0.02 | 0.03 |

Overshoot (%) | 2.9 | 0.05 | 0.03 | 0.041 |

Settling time (%) | 0.08 | 0.005 | 0.0017 | 0.002 |

Steady State Error (%) | 0.7 | 0.4 | 0.001 | 0.002 |

Specifications | FOC | DTC | Proposed Fuzzy-FODPSO based FOC | Proposed Fuzzy-FODPSO based DTC |
---|---|---|---|---|

Torque during acceleration | 489 N.m | 294 N.m | 500 N.m | 300 N.m |

Negative peak starting current | −3050 A | −600 A | −3040 A | −550 A |

Peak overshoot speed | 501 rpm | 506 rpm | 503 rpm | 504 rpm |

Torque during deceleration | −402 N.m | −200 N.m | −400 N.m | −200 N.m |

Stator current at 500 rpm | 100 A | 100 A | 100 A | 100 A |

Torque | 118 N.m | 97 N.m | 120 N.m | 100 N.m |

Positive peak starting current | 1535 A | 1196 A | 1940 A | 1080 A |

Rising time | 0.42 s | 0.61 s | 0.02 s | 0.03 s |

Falling time | 0.42 s | 0.61 s | 0.033 s | 0.055 s |

The comparative study of various optimization approaches utilised in the control of induction motor is shown in

Different optization technique | Rise time | Fitness function | Switching loss | Algorithm complexity |
---|---|---|---|---|

DTC-Conventional | High | High | High | Simple |

FOC-Conventional | High | High | High | Simple |

DTC-GA | Medium | Medium | Medium | Complex |

FOC-GA | Medium | Medium | Medium | Complex |

DTC-BOA | Medium | Medium | High | Good |

FOC-BOA | Medium | Medium | High | Good |

FOC-Fuzzy | Low | Low | Low | More Complex |

DTC-Fuzzy | Low | Low | Low | More Complex |

FOC-Fuzzy-FODPSO | Low | Low | Low | Good |

DTC-Fuzzy-FODPSO | Very Low | Low | Low | Good |

The comparative control systems were put through their paces on an experimental test bench comprised of two 2.2 kW induction machines. The primary machine is powered by a 14 kVA inverter, which has complete control over the IGBT gates. The load machine serves as a load machine and is powered by a 3-kW inverter. A 1024-point incremental encoder measures the rotor position. And the electromagnetic torque is computed and measured immediately. The following

To determine the experimental characteristics, all techniques were torque and speed examined show in

According to the experimental results, conventional FOC, conventional DTC, GA-FOC, GA-DTC, and presented Fuzzy-FODPSO based FOC and Fuzzy-FODPSO based DTC all perform well. In general, FOC has a somewhat lower current THD and torque. The dynamics of DTC are rapid, but the torque ripples are greater. The proposed methodology exhibits good behaviour, with fewer torque ripples and quick dynamics. When the system is operated using Fuzzy-FODPSO, the results show a high responsiveness and highest ideal value compared to standard DTC and GA-DTC. The above

MATLAB-Simulink and hardware setup was used to construct a framework of direct torque control and field-oriented control methods for a 200 HP 3-phase induction motor. It has been observed that DTC method shows improved results when compared with FOC. The optimization technique of FODPSO based Fuzzy technique is proposed to control the FOC and DTC method. The performance analyses of both the methods are carried and the outcomes are compared. The research revealed that the proposed methodology had no overshoot and short settling time than the previous methods. The FODPSO based FOC perform better than the DTC method and other conventional method. Starting current is very less in case of DTC method and the torque requirement is also less for the same speed in DTC method. Based on the results, we can infer that the technique has an intriguing use in the field of machine control system design, which will be the focus of future research in addition to the implementation of this study on a real machine.

The authors with a deep sense of gratitude would thank the supervisor for his guidance and constant support rendered during this research.