Abstract:
A method is provided to facilitate optimizing a winding and lamination configuration an electric machine. The method employs a computer including a microprocessor for executing computer functions, a database for storing optimization data, and a two-level optimization algorithm that has a first optimization module and a second optimization module. The method includes generating a plurality of data sets utilizing the first determining an optimum response surface based the data sets, utilizing the second module, determining an optimum data set based on the optimum response surface, utilizing the first module, and outputting an optimum winding and lamination configuration based on the optimum data set.

Description:
BACKGROUND OF INVENTION 
   This invention relates generally to electric machines and, more particularly, to an optimization strategy for designing induction motors and generators. 
   Known motors including synchronous machines, non-synchronous machines, and direct current (DC) machines, include a motor housing, a stator including one or more windings, or one or more permanent magnets, and a rotor assembly. The rotor assembly includes a rotor core and a rotor shaft that extends through the rotor core. The rotor is constructed of a plurality of laminations and includes one or more armature windings one or more permanent magnets. The motor housing includes at least one endshield and houses at least a portion of the rotor assembly. At least one bearing receives and the rotor shaft, and is positioned between the endshield and an inner bearing cap to enable the rotor shaft to rotate during operation. 
   At least some known motors are configured to satisfy pre-determined steady state operating requirements such as a rated voltage, a locked rotor voltage, and a breakdown voltage. Two key components for satisfying operating requirements are lamination geometry and winding variables. The lamination geometry and winding variables are configured to facilitate optimizing performance cost variables associated with the motor design. At least some known design methods attempt to optimize a winding after a lamination design is know. This design method may only provide acceptable results the bounds of the particular lamination, and as such, does not allow the assertion that a global optimum has been found. Other known methods attempt to simultaneously optimize all the winding variables and lamination geometry variables. This design is much more complex and computationally expensive. 
   SUMMARY OF INVENTION 
   In one aspect, a method is provided to facilitate optimizing a winding and lamination configuration of an electric machine. The method employs a computer including a microprocessor for executing computer functions, a database for storing optimization data, and a two-level optimization algorithm that has a first optimization module and a second optimization module. The method includes generating a plurality of data sets utilizing the first module, determining an optimum response surface based the data sets, utilizing the second module, determining an optimum data set based on the optimum response surface, utilizing the first module, and outputting an optimum winding and lamination configuration based on the optimum data set. 
   In another aspect, a system is provided for optimizing a winding and lamination configuration of an electric machine. The system includes a computer including a microprocessor for executing computer functions, a database coupled to the microprocessor for storing data, and a two-level optimization algorithm comprising a optimization module and a second optimization module. The two-level optimization algorithm uses data stored in the database and is executed via the microprocessor. 
   In yet another aspect, a two-level optimization algorithm is provided for optimizing a winding and lamination configuration of an electric machine. The two-level optimization algorithm includes a first optimization module and a second optimization module. The optimization module is configured to generate a first optimization solution based on output from the second optimization module, and the second optimization is configured generate a second optimization solution based on output from the first optimization module. Furthermore, the two-level optimization algorithm is also configured to generate a global optimization solution based on the first and second optimization solutions. 

   
     BRIEF DESCRIPTION OF DRAWINGS 
       FIG. 1  is schematic of a system to facilitate optimizing a winding-lamination configuration of an electric machine. 
       FIG. 2  is an exemplary embodiment of a detailed diagram of a two-level algorithm utilized by the system shown in FIG.  1 . 
       FIG. 3  is an alternate embodiment of a detailed diagram of a two-level algorithm utilized by the system shown in FIG.  1 . 
       FIG. 4  is a simplified block diagram of a server architecture used with the system shown in  FIG. 1  for facilitating optimizing a winding-lamination configuration. 
       FIG. 5  is an expanded version block diagram of an alternate embodiment of a architecture for facilitating optimizing a winding-lamination configuration. 
   

   DETAILED DESCRIPTION 
     FIG. 1  is schematic of a system  10  to facilitate optimizing a winding a lamination configuration of an electric machine in accordance with one embodiment of the present invention. System  10  includes a computer  14 , which includes a processor  18  suitable to execute all functions of computer  14 , and an electronic storage device, or database,  22  storing programs, information and data. Additionally, computer  14  is connected to a display  26  for displaying information, data, and graphical representations, and a user interface  30  that enables a user to input information, data, and queries to computer  14 , example a keyboard or a mouse. In the exemplary embodiment, computer  14  also a second electronic storage device  34 , which stores an optimization algorithm  38 . Optimization algorithm  38  implements a two-level optimization strategy that includes a winding optimization level and lamination optimization level. Accordingly, algorithm  38  includes a first module, such as winding optimization module  42  and a second module, such as lamination optimization module  46 . In an alternate embodiment algorithm  38  is included within database  22 . Two-level optimization algorithm  38  links a solution for an optimal lamination, computed at the lamination optimization level by lamination module  46 , to a solution for an optimal winding, computed at the winding level by winding  42 . 
   Two-level optimization algorithm  38  processes small sets of variables at both the winding optimization level and the lamination optimization level. Additionally, solutions computed by modules  42  and  46  are decomposed such that more autonomy is given to lamination and winding designers, while at the same time taking into account both and lamination preferences. 
     FIG. 2  is an exemplary embodiment of a detailed diagram of two-level optimization algorithm  38  (shown in  FIG. 1 ) utilized by system  10  (shown in FIG.  1 ). Components  FIG. 2  identical to components of system  10  of  FIG. 1  are identified in  FIG. 2  using the same reference numerals as used in FIG.  1 . Database  22  (shown in  FIG. 1 ) winding parameters, including but limited to, wire size, number of turns, capacitor size, frame size, number of windings, and number of coils. Winding optimization module  42  utilizes a mathematical programming model for winding level that includes motor level variables, including but not limited to, winding material, i.e. copper or aluminum wire, capacitor size, motor housing size, frame size, number of windings, number of coils and wire size. Lamination module  46  utilizes a mathematical programming model for lamination level optimization that has continuous and discrete lamination geometry variables. Continuous lamination geometry variables, such as slot thickness, inner diameter, outer diameter and slot spacing, are variables that have an open set of values. For example, the slot thickness variable can be chosen to be 0.325 inches, or 1.0 inches, or 1.8 inches, or any number there between. Discrete lamination variables, such as sheet thickness, are variables that have a closed set of values. For example, possible sheet thickness values may only be values selected from available manufactured thicknesses. 
   A designer utilizes interface  30  (shown in FIG.  1 ), to input two sets of performance constraints. The first set of performance constraints relate to lamination geometries such as a number of lamination layers, and lamination size. The second set of performance constraints are determined by desired performance requirements, such as, but not to, motor size, efficiency, power output, cost, torque, current, current density, and motor speed. After the performance constraints are input, winding optimization module  42  utilizes the winding parameters stored in database  22  to compute and output a plurality solutions, or data sets, for the winding level optimization mathematical programming model. The plurality of solutions output are solutions of the winding level optimization mathematical programming model using different possible combinations of winding parameters and motor level variables. Thus, winding optimization module  42  varies both the motor level variables and the winding parameters used by the winding level optimization mathematical programming model to compute a plurality of different winding configurations. 
   The possible winding configurations are output to lamination module  46  wherein the winding configurations are used to solve the lamination level optimization mathematical programming model. Lamination module  46  computes an optimum lamination geometry configuration for each winding configuration output from winding module  42 . Lamination optimization module  46  uses outputs from winding optimization module  42  and lamination geometry variable data stored in database  22 , including machinability data, to compute a lamination geometry that will combine with each respective winding module output to satisfy at least one constraint of the first set of performance constraints input a designer. Each lamination geometry is then output to database  22 . Lamination module  46  then utilizes the lamination geometries stored in database  22  and the second set of performance constraints to determine an optimum geometry response surface. The optimum geometry response surface includes the lamination geometries computed by lamination optimization module  46  that satisfy at least one of the constraints in the set of performance constraints. 
   The optimum geometry response surface is then output to winding optimization module  42  wherein at least one optimum winding solution, or data set, is computed. To compute the optimum winding solution, winding optimization module  42  varies the level variables and a corresponding optimum lamination for each variation is obtained the optimum geometry response surface to produce a candidate winding-lamination configuration. Algorithm  38  (shown in  FIG. 1 ) is then used to compute manufacturing objectives, such as cost and efficiency ratings for each candidate winding-lamination configuration. Each candidate winding-lamination configuration is then output, along the corresponding cost and performance values, and evaluated to determine a global, or optimum desirable, winding-lamination configuration. In one embodiment, the candidate winding-lamination configurations output by algorithm  38  are stored in a database such database  22 . 
     FIG. 3  is an alternate embodiment of a detailed diagram of two-level optimization algorithm  38  (shown in  FIG. 1 ) utilized by system  10  (shown in FIG.  1 ). Components  FIG. 3  identical to components of system  10  of  FIG. 1  are identified in  FIG. 3  using the same reference numerals as used in FIG.  1 . Database  22  (shown in  FIG. 1 ) winding parameters such as wire size, number of turns, capacitor size, frame size, of windings, and number of coils. Database  22  also includes a list of standard manufactured laminations geometries and specific data relating to the characteristics of each lamination geometry, including but not limited to, slot thickness, sheet thickness, inner diameter, outer diameter, and slot spacing. Winding optimization module  42  a mathematical programming model to facilitate winding level optimization. The mathematical programming model utilizes motor level variables such as winding i.e. copper or aluminum wire, capacitor size, motor housing size, frame size, number of windings, number of coils and wire size. Lamination module  46  utilizes a mathematical programming model for lamination level optimization having lamination geometry variables, such as slot thickness, inner diameter, outer diameter, slot spacing and sheet thickness. 
   A designer utilizes interface  30  (shown in FIG.  1 ), to input two sets of performance constraints. The first set of performance constraints relate to lamination geometries such as a number of lamination layers, and a lamination size. The second set of performance constraints are determined by desired performance requirements, such as, but not to, motor size, efficiency, power output, cost, torque, current, current density, and motor speed. After the performance constraints are input, lamination optimization module  46  generates and outputs a plurality of solutions, or data sets, for the lamination level optimization mathematical programming model. The plurality of outputs solve for lamination geometries that satisfy at least one of the constraints in the first set of performance constraints. The plurality of lamination geometries generated is selected the standard manufactured lamination geometries stored in database  22 . 
   The possible lamination geometries are output to winding optimization module  42  wherein the lamination geometries are used to solve the winding level optimization mathematical program model. Winding optimization module  42  utilizes the winding parameters motor level variables stored in database  22  to compute at least one optimum winding configuration for each lamination geometry that will combine with the lamination geometry to satisfy at least one constraint of the second set of performance constraints. Each winding configuration is then output to database  22 . Winding module  42  then the winding configurations stored in database  22  to determine an optimum winding variable response surface for each lamination geometry output by lamination module  46 . The optimum winding variable response surfaces include all the computed winding configurations for the related lamination geometry. 
   The optimum winding variable response surfaces are then output to lamination  46  wherein at least one optimum lamination solution, or data set, is computed. To compute the optimum lamination solution, lamination module  46  varies the lamination geometry variables and corresponding optimum winding variables are obtained from the optimum winding variable response surfaces to produce a candidate winding-lamination configuration for each variation. Subject to at least one constraint in the second set of performance constraints, algorithm  38  (shown in  FIG. 1 ) is then used to compute manufacturing objectives, such as cost, magnetomotive force (MMF), efficiency rating, or combination of these, for each candidate winding-lamination configuration. Each winding-lamination configuration is then output, along with the corresponding cost and performance values, and evaluated to determine a global, or optimum desirable, lamination configurations. In one embodiment, the candidate winding-lamination configurations output by algorithm  38  are stored in a database such as database  22 . 
     FIG. 4  is a simplified block diagram of a server system  100  for optimizing a lamination configuration. In an alternative embodiment, computer  14  (shown in  FIG. 1 ) part of a computer network that is accessible using the Internet. System  100  includes a server system  112  and a plurality of client systems  114  connected to server system  112 . one embodiment, client systems  114  are computers, such as computer  14  (shown in  1 ), including a web browser, such that server system  112  is accessible to client systems  114  via the Internet. Client systems  114  are interconnected to the Internet through many interfaces including a network, such as a local area network (LAN) or a wide area network (WAN), dial-in-connections, cable modems and special high-speed ISDN lines. Client systems  114  could be any device capable of interconnecting to the Internet including a web-based phone or other web-based connectable equipment. A database server  116  is connected to a centralized database  120  containing product related information on a variety of products, as described below in greater detail. In one embodiment, centralized database  120  is stored on server system  112  and can be accessed by potential users at of client systems  114  by logging on to server system  112  through one of client systems  114 . In an alternative embodiment centralized database  120  is stored remotely from system  112 . 
     FIG. 5  is an expanded version block diagram of an alternate embodiment of a architecture  200  for optimizing a winding-lamination configuration, used in conjunction with the system shown in FIG.  1 . Components in system  200 , identical to components system  100  (shown in FIG.  4 ), are identified in  FIG. 5  using the same reference numerals as used in FIG.  4 . System  200  includes server system  212  and client systems  214 . Server system  212  further includes database server  216 , an application server  224 , web server  226 , a directory server  230 , and a mail server  232 . A disk storage unit  234  is coupled to database server  216  and directory server  230 . Servers  216 ,  224 ,  226 ,  230 ,  232  are coupled in a local area network (LAN)  236 . In addition, a system administrator&#39;s workstation  238 , a user workstation  240 , and a supervisor&#39;s workstation  242  are coupled to LAN  236 . Alternatively, workstations  238 ,  240 , and  242  are coupled to LAN  236  via an Internet link or are connected through an Intranet. 
   Each workstation,  238 ,  240 , and  242  is a personal computer, such as computer  14  (shown in  FIG. 1 ) having a web browser. Although the functions performed at the workstations typically are illustrated as being performed at respective workstations  238 ,  240 , and  242 , such functions can be performed at one of many personal computers coupled to LAN  236 . Workstations  238 ,  240 , and  242  are illustrated as being associated with separate functions only to facilitate an understanding of the different types of functions that can be performed by individuals having access to LAN  236 . 
   In another embodiment, server system  212  is configured to be communicatively coupled to various individuals or employees  244  and to third parties, e.g., internal or external auditors,  246  via an ISP Internet connection  248 . The communication in the exemplary embodiment is illustrated as being performed via the Internet, however, any other wide area network (WAN) type communication can be utilized in other i.e., the systems and processes are not limited to being practiced via the Internet. In addition, and rather than a WAN  250 , local area network  36  could be used in place of  250 . 
   In the exemplary embodiment, any authorized individual or an employee of the business entity having a workstation  254  can access the locomotive management system. One of the client systems includes a workstation  256  located at a remote location. Workstations  254  and  256  are personal computers having a web browser. Also, workstations  254  and  256  are configured to communicate with server system  212 . 
   While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with within the spirit and scope of the claims.