RECENT TRENDS IN POWER ELECTRONICS DRIVES

Abstract

The invention relates to an advanced power electronics drive system for controlling electric motors, designed to enhance efficiency, reliability, and fault detection. The system includes a power converter that converts DC power to AC power, coupled with a model predictive control (MPC) algorithm to optimize switching operations and motor performance. A fault detection module uses machine learning techniques to predict system failures by monitoring real-time operational parameters such as motor speed, torque, temperature, and voltage. The system incorporates wide-bandgap semiconductor devices, such as silicon carbide (SiC) or gallium nitride (GaN), to improve efficiency by reducing conduction and switching losses. An electromagnetic interference (EMI) reduction system is integrated to minimize high-frequency noise from the power converter. The invention offers improved energy efficiency, proactive maintenance, and enhanced operational reliability for applications in industrial machinery, electric vehicles, and renewable energy systems.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention provides an advanced power electronics drive system for electric motors, designed to improve system efficiency, reliability, and fault detection capabilities. The system integrates cutting-edge technologies such as wide-bandgap semiconductor materials, model predictive control (MPC) algorithms, machine learning-based fault detection, and advanced EMI reduction techniques. These components work together to address challenges like energy loss, electromagnetic interference (EMI), and system reliability in power electronics drives. Below are three embodiments of the invention, each demonstrating different aspects and applications of the technology.

In first embodiment, the invention is applied to industrial motor systems, such as those used in manufacturing equipment or automated machinery. The power electronics drive system includes a wide-bandgap semiconductor-based power converter that utilizes SiC MOSFETs to achieve high switching frequencies, thereby reducing conduction and switching losses. The power converter is responsible for converting DC power into AC power, which is supplied to the electric motor.

The system utilizes a model predictive control (MPC) algorithm that dynamically adjusts the switching operation of the power converter to maintain optimal motor performance. The MPC algorithm takes real-time feedback from sensors that measure motor speed, torque, and current, as well as environmental factors like temperature and supply voltage. The controller adjusts the duty cycle of the power converter based on this feedback, ensuring that the motor operates with the highest possible efficiency at all times.

To enhance system reliability, the fault detection module of this embodiment uses a machine learning (ML) model trained on historical data from the system. This model is capable of detecting early signs of motor winding failures, thermal overloads, or power converter malfunctions by continuously analyzing parameters such as current, voltage, and temperature. Once a potential fault is detected, the system alerts the operator and may automatically adjust the control strategy to mitigate further damage, providing an additional layer of protection for the motor and its components.

The system includes an active EMI filtering circuit, which uses passive components in conjunction with active control to reduce high-frequency electromagnetic emissions that may interfere with other equipment. This ensures compliance with EMC standards and maintains the reliability of the system in industrial settings.

In the second embodiment, the invention is applied to the powertrain system of an electric vehicle (EV). The drive system consists of a high-performance power converter based on SiC MOSFETs and a model predictive controller (MPC). The MPC algorithm is particularly advantageous in this embodiment due to the rapid changes in load that occur in electric vehicles, especially during acceleration or deceleration. The MPC continuously adjusts the switching of the power converter, optimizing the energy flow between the battery and the motor to maximize efficiency and extend battery life.

The electric vehicle's battery management system (BMS) is integrated with the power electronics drive system, providing additional data on battery health, charge status, and temperature. The controller uses this data to make informed decisions regarding the motor's operation, ensuring that energy is efficiently transferred from the battery to the motor, while also protecting the battery from overcharging or overheating.

To ensure fault detection, the machine learning-based fault detection module in this embodiment monitors critical system parameters such as motor temperature, current draw, and motor speed. By analyzing historical data, the system can predict and identify issues such as thermal degradation of the motor or potential failures in the inverter. This early detection allows the vehicle to enter a "limp mode" or perform necessary protective actions, preventing total system failure and avoiding costly repairs.

The active EMI reduction system is employed in this embodiment to reduce electromagnetic interference caused by the switching of high-frequency power electronics. Given the sensitive nature of EV components, especially in onboard communication and control systems, minimizing EMI ensures that the vehicle's electronic subsystems continue to operate without disruption, even during high-load conditions.

In third embodiment, the invention is applied to a renewable energy conversion system, such as a solar or wind power generation system, where the power electronics drive system manages the conversion of variable DC power (from the solar panels or wind turbine) to AC power suitable for grid feeding or local consumption. The SiC-based power converter in this system allows for efficient high-frequency operation, reducing losses and improving the overall efficiency of energy conversion.

The model predictive control (MPC) algorithm is used to optimize the power flow between the energy generation source (solar or wind) and the grid or local storage. The controller continuously adapts the power converter's operation based on real-time weather data, energy demand, and grid conditions. In the case of fluctuating renewable energy sources, the system ensures that the energy conversion process remains stable, preventing fluctuations in power output and ensuring that the energy provided to the grid or load is of consistent quality.

In this embodiment, the fault detection module focuses on monitoring both the renewable energy source (solar panel or wind turbine) and the power electronics system. For example, the system can detect issues such as panel or turbine malfunction, power converter faults, or grid synchronization issues. By applying machine learning to predict failures, the system can automatically switch to a safe mode or alert operators for preventative maintenance, reducing the risk of system downtime.

To meet the strict EMI standards required for grid-connected systems, the power electronics drive in this embodiment incorporates an active EMI filtering circuit. This circuit minimizes the electromagnetic noise produced by high-speed switching devices and ensures compliance with the grid's electromagnetic compatibility (EMC) requirements, ensuring that the renewable energy system operates seamlessly alongside other grid-connected devices.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A power electronics drive system for controlling an electric motor, comprising:
a power converter configured to convert direct current (DC) power to alternating current (AC) power for driving the electric motor;
a controller coupled to the power converter, the controller configured to implement a model predictive control (MPC) algorithm for optimizing switching operations of the power converter;
a fault detection module configured to monitor real-time operational parameters of the system, including motor speed, torque, temperature, and voltage, and predict potential failures using machine learning techniques;
a wide-bandgap semiconductor device integrated into the power converter to increase efficiency by reducing switching and conduction losses; and
an electromagnetic interference (EMI) reduction system to minimize high-frequency noise generated by the switching operations of the power converter.

2.The power electronics drive system of claim 1, wherein the wide-bandgap semiconductor device is selected from the group consisting of silicon carbide (SiC), gallium nitride (GaN), and diamond-based materials.

3.The power electronics drive system of claim 1, wherein the fault detection module utilizes a machine learning-based model selected from the group consisting of supervised learning algorithms, anomaly detection, and neural networks to predict system failures.

4.The power electronics drive system of claim 1, wherein the controller adjusts operational parameters based on feedback from temperature sensors, motor load conditions, and supply voltage variations to optimize motor efficiency.

5.The power electronics drive system of claim 1, wherein the EMI reduction system includes an active EMI filtering circuit that uses both passive components and active control strategies to minimize electromagnetic emissions.
6.The power electronics drive system of claim 1, wherein the machine learning-based fault detection module monitors parameters including motor winding health, inverter performance, and thermal overload conditions to detect potential system failures.

7.The power electronics drive system of claim 1, further comprising a thermal management system that uses real-time thermal feedback to dynamically adjust motor performance and prevent overheating.

8.The power electronics drive system of claim 1, wherein the controller is further configured to adjust the switching frequency of the power converter based on the load conditions to enhance system efficiency and reduce energy losses.

9.The power electronics drive system of claim 1, wherein the power converter includes a bi-directional converter for regenerative braking applications in electric vehicles.

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