US20090218980A1

US20090218980A1 - AC Motor Controller

Info

Publication number: US20090218980A1
Application number: US12/435,817
Authority: US
Inventors: Mario Bilac; Chun Fan; Paul Terricciano
Original assignee: Siemens Energy and Automation Inc
Current assignee: Siemens Industry Inc
Priority date: 2005-10-17
Filing date: 2009-05-05
Publication date: 2009-09-03
Also published as: EP1938446A1; WO2007047143A1; CA2626044A1; US7791307B2; US20070096681A1

Abstract

A system, method, and device for monitoring an AC induction motor are disclosed. The exemplary system provides the architecture of an intelligent motor controller. On one hand, the controller has self-commission and adaptive tuning capability and can provide smooth start and stop for motor driven systems. On the other hand, the controller can provide fault detection, power metering, and history data logging, to ensure motor operates under optimal conditions. The exemplary system may have one or more sensors for monitoring characteristics of the AC induction motor and producing one or more inputs from the characteristics.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Provisional Patent Application No. 60/727,468 filed Oct. 17, 2005 entitled “INTELLIGENT SOFT STARTERS FOR AC INDUCTION MOTOR”, which is incorporated fully herein by reference.

TECHNICAL FIELD

The present invention relates generally to AC induction motors, and more particularly to regulation of the AC induction motor.

BACKGROUND INFORMATION

Direct on-line starting of Alternating Current (AC) induction motors causes harmful stress to upstream power supply systems and downstream mechanical systems. Solid-state soft starters providing voltage with reduced amplitude are used to ease that stress. AC induction motors are the workhorses in today's industries. AC induction motors have been widely used in different applications, for example, air compressors, centrifugal pumps, conveyors, cutting machinery, and so forth. These applications often may require soft starters to provide a smooth start for very diverse downstream mechanical systems. In current practice, systems have been providing operators with adjustable parameters such as starting voltage, starting current, deceleration time, and acceleration time via potentiometers, dip switches, or keypads. These systems also open the door to wrong settings that lead to unsatisfactory performance and the possibility of additional damage.
In current practice, voltage ramp algorithm and current limit algorithm are used to address variable load applications and constant load applications respectively. PID controllers are used in the algorithms for closed loop control. Because of nonlinearity of AC induction motors and uncertainty of their loads, conventional PID controllers alone are not capable of providing optimal control of AC induction motors. For instance, the standard voltage ramp algorithm currently used in industrial soft starters may not guarantee substantial reduction of torque and current peak values and may lose control at the end of soft start where the motor may become under-damped and power factor changes rapidly.
Unscheduled motor shutdowns are costly; hence it is of great interest that the motor controllers are also able to predict motor incipient faults. This invention presents an intelligent motor control algorithm that can provide not only soft start/soft stop but also prognosis on motor electrical, thermal, and mechanical faults.
Accordingly, an efficient and effective system and method is needed for regulating AC induction motors. In view of the foregoing, it is desirable to provide a system and method that minimizes operator involvement in adjusting soft starts. An intelligent soft start algorithm that would automatically adjust to different load characteristics is desired. A system and method may also prevent costly, unscheduled motor shutdowns.

SUMMARY

The present invention is a novel device, system, and method for monitoring one or more characteristics of the AC induction motor. The exemplary method may monitor for one or more characteristics of the AC induction motor and produces one or more inputs from the characteristics. The one or more inputs are supplied to a fuzzy logic controller. The fuzzy logic controller uses fuzzy logic to determine one or more outputs. The outputs are used to regulate the AC induction motor.
The invention may include the following embodiments. In one exemplary embodiment, the one or more outputs are firing angles of a control rectifier of the AC induction motor. In another embodiment, the one or more characteristics monitored are the line voltage on each of three phases. In another embodiment, the one or more outputs are power factors of the AC induction motor. In another embodiment, the one or more outputs regulate current drawn by the AC induction motor. In another embodiment, the one or more outputs set current limits drawn by the AC induction motor. In yet another embodiment, the one or more characteristics monitored are terminal voltages on each of three phases. In another embodiment, the one or more characteristics monitored are currents of each stator of the AC induction motor.
It is important to note that the present invention is not intended to be limited to a system or method which must satisfy one or more of any stated objects or features of the invention. It is also important to note that the present invention is not limited to the exemplary embodiments described herein. Modifications and substitutions by one of ordinary skill in the art are considered to be within the scope of the present invention, which is not to be limited except by the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the present invention will be better understood by reading the following detailed description, taken together with the drawings herein:

FIG. 1 is a block diagram of a system according to the exemplary fuzzy logic controller embodiment 100 of the present invention.

FIG. 2 is a membership function for current I, a membership function for current ΔI, a membership function for current limit, and fuzzy rule matrix for the current limit setting according to an exemplary embodiments of the present invention.

FIG. 3 is a flow chart of a method according to a first exemplary embodiment of the present invention.

FIG. 4 is a flow chart of a method according to a second exemplary embodiment of the present invention.

DETAILED DESCRIPTION

Referring to FIG. 1, the system 100 may have a fuzzy logic controller 102. The fuzzy logic controller 102 receives input from sensors of the motor 104. To allow the invention to be applied both in-line and inside-delta, three phase control may be needed. The proposed algorithms hence may need motor sensors 104 for 3 line voltages, 3 motor terminal voltages, and/or 3 stator currents, which may be supplied as analog inputs. The fuzzy logic controller 102 applies If . . . then logic as will be discussed later herein to produce output. The output may be used to control the motor with motor regulating devices 106, for example, one of the outputs of the fuzzy logic controller 102 may be firing angles of Silicon Controlled Rectifiers (SCRs).
Architecturally, aspects of the fuzzy logic controller 102 can be located on a server, workstation, minicomputer, or microprocessor. Aspects of the invention can also be located on a stand-alone device, for example in AC motor monitoring system or combined within an AC motor system. Generally, the fuzzy logic controller 102, in terms of hardware architecture, includes a processor, memory 108, display 110 and one or more input and/or output (I/O) devices (or peripherals) 112 that are communicatively coupled via a local interface. The local interface can be, for example, one or more buses or other wired or wireless connections, as is known in the art. The local interface may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connection to enable appropriate communications among the components of a network. The systems and methods may be hardwired with the computer to allow them to perform various aspects of the invention, for example, additional devices may be used to convert analog inputs into discrete values that may be used by the fuzzy logic controller 102.
The systems and methods may also be incorporated in software used with a microprocessor. The software may be stored or loaded in the memory and may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing the methods and systems of the invention. The software may work in conjunction with an operating system. The operating system essentially controls the execution of the computer programs, such as the software stored within the memory, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.
The systems and methods may also include a Graphic User Interface (GUI) to provide a user-friendly interface which allows a user to select a current status of the motor application. The selection of a current status activates specific rules based on the current status selection.
Unlike the Objective of using AC drives, which requires accurate speed control, the objectives of the fuzzy logic controller 102 using exemplary soft start algorithms are to prevent voltage dip of power grid and shock vibration of the mechanical system. These objectives only pose qualitative requirements, which make fuzzy logic control a good candidate. Therefore, this system uses fuzzy control for acceleration and deceleration. The algorithm may adaptively set current limits and automatically compute SCR firing angle based on voltages across SCRs, motor currents, power factor, and change of these values.
Fuzzy logic controls may be described by a set of IF . . . THEN rules and tends to mimic human thinking. Fuzzy logic based control is based on mathematical theory, which makes it nonlinear and adaptive in nature. No motor parameters may be needed in fuzzy logic control. For fuzzy logic control, fuzzy inference rules that need to be developed may be based on insightful knowledge of the system. Although no model is needed for fuzzy logic control, insights on the system may always be indispensable. The inference rules selected have to be comprehensive enough to cover all possible scenarios.
Referring to FIG. 2, the fuzzy logic controller 102 may automatically adjust current limit settings during starting. First the digital values of current I and change of current ΔI are fuzzified into positive small (PS), positive big (PB) alike linguistic terms based on membership functions. Then, inference rules listed in the table are used to locate the area of CURLIUM on the membership function. Finally, a center of gravity method is used to de-fuzzify the CURLIM value. Similar fuzzy controllers can be designed for change of firing angle. Several fuzzy controllers may work together to achieve optimal control of the motor. Each controller may regulate various aspects of the motor.
Referring for FIG. 3, a first exemplary embodiment 300 may be used to control the acceleration and/or deceleration of an AC induction motor. The one or more motor sensors 104 monitor one or more characteristic of the AC induction motor (block 302). The characteristics may be converted into one or more inputs that may be utilized by the fuzzy logic controller 102, for example, converting analog signals into discrete incremental values or other required filtering (block 304). The one or more inputs are supplied to the fuzzy logic controller 102 (block 306). The fuzzy logic controller 102 applies fuzzy logic to determine one or more outputs as disclosed in the example associated with FIG. 2 (block 308). The outputs may be used to regulate the AC induction motor, for example, the SCR firing angles may be adjusted (block 310).
When motors are up to speed, the SCRs may be full on or by-passed to avoid any harmonics problems. All the power of digital signal processing can then be dedicated to fault diagnosis/prognosis. For example, a fault diagnosis controller may be used for thermal fault prediction. The fault diagnosis controller may be accomplished with the same microprocessor as the fuzzy logic controller 102 or may be performed by another microprocessor. For small induction machines (<50 hp), the maximum temperature limit of the stator may be reached before that of the rotor in both transient and running overload conditions, whereas large induction machines (>100 hp) may be stator limited under running overload conditions, and rotor limited under transient overload conditions. In order to predict thermal faults, both stator temperature and rotor temperature may be estimated. There are two technologies to estimate motor temperatures, namely resistance-based temperature estimation (RTE and conventional thermal model-based temperature estimation (TMTE).
TMTE models the motor as a thermal network from frame, to stator, then to rotor. Thermal resistance and capacitance for each component are computed from motor dimensions and material heat transfer coefficients. Heat input is computed from motor terminal voltage, current, and motor equivalent circuit parameters. Some AC drives on the market have built in TMTE. The major disadvantage of TMTE is that it assumes constant thermal resistance and capacitance. If there is a blocked cooling fan, thermal characteristics of the motor will change significantly and TMTE may not be able to accurately estimate the temperatures. Because electrical resistances of stator and rotor windings are direct indicators of their temperatures, RTE uses dynamic modeling of induction motor to estimate stator and rotor winding resistances based on measured motor terminal voltage and current. Unlike TMTE, RTE can estimate temperature under abnormal cooling conditions. TMTE can estimate temperatures at specific points where RTE can only give average temperatures of rotor and stator.
For thermal fault prediction or temperature estimation, the fault diagnosis controller may use RTE for it can predict temperature even when abnormal cooling circumstances happen. That is also the reason that RTE will provide better thermal protection than over current relays using trip class curves. RTE model parameters can also be relatively easier to obtain.
The fault diagnosis controller may also be used for mechanical fault prediction of the motor. Mechanical faults such as broken rotor bars, worn out bearings, and misaligned shaft may introduce certain frequency components in stator currents as shown in the table below.


Diagnosis Category	Faults	Characteristic Frequency

Low part	Broken rotor bar	f_b= (1 ± 2ks)f
(0-5f_r)	Air gap	f_e= [(kR ± n_d)(1 − s)/p ± v]f
	eccentricity
	Bolt loosening	0.5f_r,, f_r, 2f_r, 3f_r
	Oil ship	0.4f_r-0.45f_r
High part	Bearing inner race	f_i= Zf_r/2(1 + (d/D)cosα)
(>1 KHz)	defect
	Bearing outer race	f_o= Zf_r/2(1 − (d/D)cosα)
	defect
	Ball defect	f_b= Zf_r/d(1 − (d²/D²)cos²α)

Where

f_rMotor rotation frequency;
k 1, 2, 3, . . . ;
s motor slip;
f power supply frequency;
R number of rotor slots;
n_d0 in case of static eccentricity; 1, 2, 3, . . . in case of
dynamic eccentricity;
p number of pole pairs;
v stator MMF harmonics that present in the supply;
D diameter of pitch circle;
Z number of balls in the bearing;
d diameter of the ball;
α contact angle in radians.

This invention may use Fast Fourier Transform and/or wavelet transformation technologies to extract features of motor stator currents in both time domain and frequency domain. The fault diagnosis controller may use these inputs to provide outputs for maintenance of the motor. The system may also be used to provide power metering and history logging. One prognosis technique may include recording the motor operational data in memory 108, including patterns of power or current consumption, number of and the intervals between starts and stops, and so forth. This data may be readily available to microprocessor of the fault diagnosis controller. Furthermore, the fault diagnosis controller can perform power metering by implementing algorithms to calculate frequency, power factor, etc. Preventive maintenance can then be scheduled based on those recorded operational data to effectively reduce unplanned down-time.
Referring to FIG. 4, a second exemplary embodiment 400 may be used to control the maintenance of an AC induction motor. The one or more motor sensors 104 monitor one or more characteristics of the AC induction motor (block 402). The characteristics may be converted into one or more inputs that may be utilized by the fault diagnosis controller, for example, converting analog signals into discrete incremental values or other required filtering (block 404). The one or more inputs may be stored in memory 108 to produce a history of operation of the AC induction motor (block 406). The one or more inputs are supplied to the fault diagnosis controller (block 408). The fault diagnosis controller determines one or more outputs, for example, when to shut down operation of the motor due to thermal limits (block 410). The outputs may be used to perform or regulate the AC induction motor (block 412).
The systems and methods may be implemented using hardwired modules or programmable hardware. The systems and methods may be implemented within software that utilizes various components to implement the embodiments described herein. Aspects disclosed in the exemplary embodiment may be utilized independently or in combination with other exemplary embodiments. Moreover, it will be understood that the foregoing is only illustrative of the principles of the invention, and that various modifications can be made by those skilled in the art without departing from the scope and spirit of the invention. Persons skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments, which are presented for purposes of illustration rather than of limitation, and the present invention is limited only by the claims that follow.

Claims

1-20. (canceled)

21. A method for controlling an AC induction motor by an intelligent soft-starter device, comprising:

adaptively controlling acceleration and deceleration of the AC induction motor to start and stop the AC induction motor;

monitoring current and voltage of the AC induction motor when the AC induction motor is at steady state;

detecting faults in the AC induction motor based on the monitored current and voltage of the AC induction motor;

calculating power parameters of the AC induction motor based on the monitored current and voltage of the AC induction motor; and

storing a history of motor performance statistics of the AC induction motor and detecting trends for performance of the AC induction motor based on the stored history of motor performance statistics.

22. The method of claim 1, wherein said step of adaptively controlling acceleration and deceleration of the AC induction motor comprises:

adaptively controlling a voltage of the AC induction motor based on a load of the AC induction motor using adaptive sensorless speed and flux observers.

23. The method of claim 1, wherein said step of adaptively controlling acceleration and deceleration of the AC induction motor comprises:

adaptively controlling a voltage of the AC induction motor based on a load of the AC induction motor using a neural network.

24. The method of claim 1, wherein said step of adaptively controlling acceleration and deceleration of the AC induction motor comprises:

adaptively controlling a voltage of the AC induction motor based on a load of the AC induction motor using a fuzzy logic controller.

25. The method of claim 1, wherein said step of detecting faults in the AC induction motor based on the monitored current and voltage of the AC induction motor comprises:

detecting mechanical faults of the AC induction motor.

26. The method of claim 5, wherein said step of detecting mechanical faults of the AC induction motor comprises:

detecting mechanical faults of the AC induction motor based on a Fast Fourier Transform (FFT) of the current of the AC induction motor.

27. The method of claim 1, wherein said step of detecting faults in the AC induction motor based on the monitored current and voltage of the AC induction motor comprises:

detecting electrical faults of the AC induction motor.

28. The method of claim 1, wherein said step of detecting faults in the AC induction motor based on the monitored current and voltage of the AC induction motor comprises:

detecting thermal faults of the AC induction motor.

29. The method of claim 8, wherein said step of detecting thermal faults of the AC induction motor comprises:

estimating a temperature of the AC motor using sensorless resistance-based temperature estimation based on a dq0 model.

30. The method of claim 1, wherein said power parameters of the AC induction motor comprise at least one of frequency, power factor, and reactive power.

31. The method of claim 1, wherein said motor performance statistics of the AC induction motor comprise at least one of maximum power consumption, minimum power consumption, average power consumption, total number of starts, and interval between starts.

32. The method of claim 1, further comprising:

scheduling maintenance on at least one of the AC induction motor and a load of the AC induction motor based on the stored history of motor performance statistics of the AC induction motor.