Simplified Finite-State Predictive Torque Control Strategies for Induction Motor Drives

This thesis develops a simplified finite-state predictive torque control (FS-PTC) algorithm based on selected prediction vectors (SPVs). This reduces the number of voltage vectors required to be predicted and the objectives to be controlled. The sign of torque or stator flux deviation and the position of stator flux are used to select the prediction vectors. The proposed SPVs strategy also assists reducing the average switching frequency for a two-level voltage source inverter fed induction motor (IM) drive. As a result, the cost function is simplified, as the frequency term is not required. The proposed SPVs based FS-PTC is also applied to a three-level neutral-point clamped inverter driven IM drive. Using the SPVs strategy reduces the computational burden for the proposed three-level inverter fed drive without affecting the system performance. However, an appropriate weighting factor is required for torque and flux errors in the cost function. This leads to the development of a second simplified FS-PTC which does not require complex torque calculations in the prediction loop and hence tuning effort on the weighting factor. A new reference stator flux vector calculator (RSFVC) with an inner proportional-integral torque regulator is employed to convert the torque and flux amplitude references into an equivalent stator flux reference vector. This stator flux reference is used in the cost function for the flux error calculation. The required processing power for the RSFVC-based FS-PTC is further reduced by combining it with the SPVs strategy. Finally, a speed-sensorless simplified FS-PTC of IM supplied from a 3L-NPC inverter is proposed. The sensorless simplified FS-PTC yields improved torque, flux and speed responses, especially at low-speed. The proposed simplified FS-PTC strategies in terms of computational efficiency, cost function design, torque and flux responses, robustness and average switching frequency are validated through experimental results

Multi-objective Predictive Control of 3L-NPC Inverter Fed Sensorless PMSM Drives for Electrical Car Applications

This paper proposes a multi-objective FS-MPC approach based on three-step optimization for a surface-mounted PMSM fed by a 3L-NPC inverter. It helps to significantly reduce torque ripples, current harmonics while controlling the inverter's neutral point voltage. To overcome the drawbacks of using mechanical sensors, a sliding mode observer is used to estimate the machine speed and rotor angular position. Compared to existing works, the proposed control method is implemented using the proportionality between the electromagnetic torque and the current component on the q-axis to eliminate the computational redundancy related to the current and torque control. To further reduce torque ripples and current harmonics, a 3L-NPC inverter is used. Compared to other types of three-level inverters, it uses less power semiconductors and attenuates the problem of voltage fluctuation at the neutral point and current harmonics. Matlab/Simulink simulations of the proposed approach yield a current THD of 1.69 %

High-performance motor drives

This article reviews the present state and trends in the development of key parts of controlled induction motor drive systems: converter topologies, modulation methods, as well as control and estimation techniques. Two- and multilevel voltage-source converters, current-source converters, and direct converters are described. The main part of all the produced electric energy is used to feed electric motors, and the conversion of electrical power into mechanical power involves motors ranges from less than 1 W up to several dozen megawatts

A novel switching table for a modified three-level inverter-fed DTC drive with torque and flux ripple minimization

The use of a direct torque control (DTC) drive is a well-known control strategy that is applied frequently to induction motors. Although torque and stator flux ripples are major disadvantages of this approach, using a higher-level inverter helps to overcome these issues. In this paper, we propose a novel switching table with a modified control strategy for a three-level inverter to achieve ripple minimization, as well as smooth switching and neutral point balance; the latter features are generally ignored in many works. The proposed model is compared with a conventional DTC and an improved three-level inverter-fed voltage vector synthesis model in the Matlab/Simulink® environment with low, normal, and high-speed operation under load torque disturbances. The performance indexes and the comparative results confirm the effectiveness of the proposed model in reducing the torque and stator flux ripples by up to 70% and 78%, respectively, generating a lower total harmonic distortion (THD%) in all scenarios, in addition to maintaining the neutral point balance and preventing voltage jumps across the switches of the inverter

Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm

Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system\u27s grid-side difficulties

Prädiktive Regelung und Finite-Set-Beobachter für Windgeneratoren mit variabler Drehgeschwindigkeit

This dissertation presents several model predictive control (MPC) techniques and finite-position-set observers (FPSOs) for permanent-magnet synchronous generators and doubly-fed induction generators in variable-speed wind turbines. The proposed FPSOs are novel ones and based on the concept of finite-control-set MPC. Then, the problems of the MPC techniques like sensitivity to variations of the model parameters and others are investigated and solved in this work.Die vorliegende Dissertation stellt mehrere unterschiedliche Verfahren der modellprädiktiven Regelung (MPC) und so genannte Finite-Position-Set-Beobachter (FPSO) sowohl für Synchrongeneratoren mit Permanentmagneterregung als auch für doppelt gespeiste Asynchrongeneratoren in Windkraftanlagen mit variabler Drehzahl vor und untersucht diese. Für die Beobachter (FPSO) wird ein neuartiger Ansatz vorgestellt, der auf dem Konzept der Finite-Control-Set-MPC basiert. Außerdem werden typische Eigenschaften der MPC wie beispielsweise die Anfälligkeit gegenüber Parameterschwankungen untersucht und kompensiert

Direct torque control and dynamic performance of induction motor using fractional order fuzzy logic controller

Conventional direct torque control (DTC) is one of the best control systems for regulating the torque of an induction motor (IM). However, the DTC’s enormous waves in flux and torque cause acoustic noise that degrades control performance, especially at low speeds due to the DTC’s low switching frequency. Direct torque control systems, which focus just on torque and flux, have been proposed as a solution to these problems. In order to improve DTC control performance, this work introduces a fractional-order fuzzy logic controller method. The objective is to analyze this technique critically with regard to its efficacy in reducing ripple, its tracking speed, its switching loss, its algorithm complexity, and its sensitivity to its parameters. Simulation in MATLAB/Simulink verifies the anticipated control approach’s performance

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems

Optimal Control Technique of an Induction Motor

The squirrel cage induction motor (IM) has many advantages over other types of electric technique (FOC), classical direct torque control (DTC), and direct torque control with space vector modulation (DTC-SVM) is carried out. The objective of this paper is to decouple the mechanical quantities such as torque and flux in a way similar to the DC motor control. And also to minimize the torque and flux modulation of the IM. Torque oscillations can cause mechanical resonances and consequently acoustic noise, hence damaging the machine. Reducing the switching frequency significantly minimizes switching losses. The DTC-SVM control technique improves the performance of conventional DTC, which is characterized by low torque and flux modulation as well as a fixed switching frequency. Simulation results in MATLAB show that torque and current ripples are reduced with the improved DTC. DTC-SVM used for the traction control system is easy to implement in digital systems and also allows to move the photovoltaic panels according to the position of maximum sunshine to extract the maximum energy with high efficiency from the system

Adding inverter fault detection to model-based predictive control for flying-capacitor inverters

As inverters are often used in critical applications, reliability is an important issue. Especially the power electronic switches and gate drivers, the most essential components of the inverter, are vulnerable parts in real live operation. Therefore this paper focuses on open switch fault detection for multilevel inverters. When a single-switch open circuit fault occurs in one of the power electronic switches, the algorithm can detect the fault and the switch that is causing it. The detection is worked out for both a linear resistive inductive load and an induction motor. The proposed algorithm is an extension of an already available finite-set model based predictive control algorithm. Therefore no extra hardware or measurements are required. The paper also discusses a suggested method for reconfiguration after fault detection. Computer simulation and experimental verifications validate the proposed methods