Adaptive Pi Speed Controller for a Universal Motor

Elkady, Dina A; Azazy, Haitham Z.; Mansour, Arafa S.; Shokrallah, Shokry S

doi:10.21608/erjm.2015.66797

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…This is where PI controllers come into play. PI controllers are commonly employed in feedback control systems [31]. They adjust the system's output based on the discrepancy between the desired and actual outcome, known as the error signal [32].…”

Section: Incitement and Motivationmentioning

confidence: 99%

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

Ellithy,

Hasanien,

Alharbi

et al. 2024

Biomimetics

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Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable energy sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting the implementation of these technologies. This attribute is referred to as low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, a control system standard, is proving to be an efficient solution. This paper provides a unique algorithm-based approach of the Marine Predator Algorithm (MPA) for optimized tuning of the used PI controller, mainly focusing on inverter control, to improve the LVRT of the grid, leading to improvements in the overshoot, undershoot, settling time, and steady-state response of the system. The fitness function is optimized using the MPA to determine the settings of the PI controller. This process helps to optimally design the controllers optimally, thus improving the inverter control and performance and enhancing the system’s LVRT capability. The methodology is tested in case of a 3L-G fault. To test its validity, the proposed approach is compared with rival standard optimization-based PI controllers, namely Grey Wolf Optimization and Particle Swarm Optimization. The comparison shows that the used algorithm provides better results with a higher convergence rate with overshoot ranging from 14% to 40% less in the case of DC-Link Voltage and active power and also settling times in the case of MPA being less than PSO and GWO by 0.76 to 0.95 s.

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Section: Incitement and Motivationmentioning

confidence: 99%

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

Ellithy,

Hasanien,

Alharbi

et al. 2024

Biomimetics

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“…This experimental platform was realized by using an Arduino Uno board and Matlab. An experimental platform for controlling a DC motor using the dSPACE DS1104 system was also reported in [11]. In contrast, some recent works used numerical simulations to study speed control strategies, as in [12], where a PI controller was designed using the dominant pole compensation method.…”

Section: Introductionmentioning

confidence: 99%

Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries

et al. 2023

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This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

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Investigation on Sensorless PI-IFOC Speed Control Based Flux and Speed Estimator for High Power Induction Motor

Sepeeh,

Zulkifli,

et al. 2023

2023 IEEE Industrial Electronics and Applications Conference (IEACon)

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Adaptive Pi Speed Controller for a Universal Motor

Cited by 4 publications

References 15 publications

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries

Investigation on Sensorless PI-IFOC Speed Control Based Flux and Speed Estimator for High Power Induction Motor

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