Performance analysis of two different SVM-based field-oriented control schemes for eight-switch three-phase inverter-fed induction motor drives

Tang, Qidi; Ge, Xinglai; Liu, Yong-Chao

doi:10.1109/ipemc.2016.7512836

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…The eight active switch states are divided into six short vectors and two medium vectors, hence two possible SVM approaches are available. However, utilizing the six active short vectors and avoiding the two active medium vectors gives better performance than does utilizing the eight active vectors [47].…”

Section: Eight Switch (B8) Invertermentioning

confidence: 99%

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A Comparative Review of Three Different Power Inverters for DC–AC Applications

Abdel-Aziz,

Elgenedy,

Williams

2023

Energies

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This paper presents a comparative review of three different widely used power inverters, namely the conventional six-switch inverter; the reduced switch count four-switch inverter; and the eight-switch inverter. The later inverter can be reconfigured as a neutral-point diode-clamped inverter at the failure of one inverter leg. The three power inverters are compared and discussed with respect to cost, complexity, losses, common mode voltage, and control techniques. The paper is intended to serve as a guide regarding selecting the appropriate inverter for each specific application. Simulation results are presented to demonstrate the performance of the three power inverters, followed by a comprehensive comparison between the three power inverters.

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Section: Eight Switch (B8) Invertermentioning

confidence: 99%

“…Increasing the number of switches in the B8 inverter results in nine different voltage vectors as opposed to four voltage vectors in the B4 inverter. Importantly, a zero-voltage vector is available in the B8 inverter (being particularly useful for common mode voltage control) which is absent in the B4 inverter [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Review of Three Different Power Inverters for DC–AC Applications

Abdel-Aziz,

Elgenedy,

Williams

2023

Energies

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“…Support Vector Machines (SVM) are a set of supervised learning methods developed for classification, regression and outlier detection which is known by his high effective in high dimension spaces and for its use for training points in the decision function, being also memory efficient [25].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Glória

Cardoso

Sebastião

2021

Sensors

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Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

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“…1. Support Vector Machine (SVM) -used mostly for classification, it classifies the data by building n dimensions between two classes and by finding an optimal hyperplane to categorize the data, using the distance between the neighboring points and differentiating between the classes with minimum error margin [30]. In a simpler explanation, given training data, the algorithm outputs the best hyperplane that classifies new examples [31].…”

Section: Machine Learning Studymentioning

confidence: 99%

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Coelho

Glória

Sebastião

2020

IoT

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Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy.

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Performance analysis of two different SVM-based field-oriented control schemes for eight-switch three-phase inverter-fed induction motor drives

Cited by 16 publications

References 11 publications

A Comparative Review of Three Different Power Inverters for DC–AC Applications

A Comparative Review of Three Different Power Inverters for DC–AC Applications

Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

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