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No room for traditions - Computer-based algorithms as an alternative to traditional methods

  Knowledge

An increasing number of design parameters and boundary conditions constitute a key challenge in the optimization of electronic circuits. Com-puter-based automated algorithms offer a strong alternative to traditional empirical methods or extensive mathematical efforts.

The ever-increasing requirements power electronic systems have to meet are leading to circuits and controllers becoming increasingly complex. As a result, engineers are faced with a challenging array of design parameters and criteria. Frequently used methods such as empirical parameter optimization based on experimentally or mathematically determined initial values are uneconomical when a certain degree of com-plexity has been reached, as in the case of cascaded controllers. The use of computer-based optimization al-gorithms within an automated optimization process on the basis of suitable strategies from systems theory rather than heuristic methods represents an excellent alternative. The "Optimization Workbench" (OWB) frame-work was developed to run such algorithms within a circuit simulation. It offers a rich and intuitive front end with a number of ge-neric functions as well as a simulator inter-face, project configuration tools, and exten-sive post-processing capabilities. Additional algorithms can easily be added as external plug-ins. Initially, optimization using a genetic algo-rithm (GA), Monte Carlo (MC) analysis, and parameter sweep/permutation (PSP) are available. While MC merely involves a ran-dom search (often referred to as "analysis of statistical experiments") and PSP a simple scan, GA is a highly sophisticated, multi-cri-teria optimization method. Based on the nat-ural evolutionary process, it applies genetics to a wide range of complex optimization problems where target functions may be ir-regular and multimodal and information about the extreme values is difficult to find by other means.There are, of course, a number of other ex-tremely powerful methods for multi-criteria/multi-target optimization. Yet genetic algo-rithms are so fascinating from an engineer's point of view because these methods com-bine power and efficiency with user-friend-liness, because they are effectively "natural."The GA implemented in this study is not in-tended to be a detailed model of biological evolution, but rather to improve the dynam-ics and power quality of, for example, power electronic devices by iteratively applying ba-sic evolutionary principles such as selection, recombination, and mutation to the param-eter vectors of a simulation model. Beginning with a group of random parameter vectors within a defined search space, re-combination and mutation are used to form parameter vectors derived from them. Through the selection process, the suitable candidates for the next iteration are select-ed on the basis of their individual fitness, which in turn was determined by the simu-lation.

Highly flexible with minimal programming effort

In addition to the algorithm itself, the pro-cess of performing computer-based optimi-zation requires a variety of genetic compo-nents. The structure of the OWB according to these requirements is shown in Figure 1.

The OWB's project editor (Fig. 2a) is used to create a configuration for planned optimiza-tion runs. Based on the properties of the se-lected optimization algorithm, various dialog boxes are presented on the front end in ad-dition to generics such as simulator configu-ration and report settings. During the configuration stage, user actions, algorithm-specific dialog boxes, and param-eter settings are hosted from the editor en-vironment and can be controlled by the se-lected plug-in, if required. This approach offers a high degree of flexibility with mini-mal programming effort.Once an optimization run is started, the Op-timization Executor prepares the simulator and postprocessor before passing control to the algorithm. The plug-in now begins gen-erating parameter sets and captures their re-sults via simulation with a single synchro-nous method call. The collected results can be transferred to the postprocessor (Figure 2b) by calling another method. The postpro-cessor uses an internal automated function to dynamically adjust and update its layout based on the reported results. After and even during an optimization run, the user can examine the archived results in the postprocessor module. It offers three main components: a template-based param-eter report, a results table containing the re-ported values from the algorithm, and a highly configurable 2D result diagram syn-chronized with the contents of the table for the purpose of graphical analysis. In addition, the user benefits from various options for exporting the archived data to other appli-cations for deeper analysis.At the present time, three important plug-ins (GA, MC, PSP) are implemented by de-fault. GA, which is based on the open JGAP library, offers a rich set of tuning parameters and is therefore suitable for a wide range of optimization tasks. The MC analysis can be used for exploratory optimization as well as to pre-optimize parameter constraints for more sophisticated algorithms like GA. The PSP plug-in can be used to sample sys-tem properties within an n-dimensional search space by simulating all permutations of the parameter-specific target value vec-tors.An important feature of the OWB design concept is the ability to easily define addi-tional plug-ins. Since the OWB performs the genetic tasks itself, a well-documented and simplified interface design allows the user to focus on implementing the optimization al-gorithms. Should the user have expanded re-quirements, the interface still makes it pos-sible to influence or interact with most of the OWB's automated functions.With support for all .NET languages such as C#, VB.net, and VC++, the well-structured GUI, and the ability to use the freely avail-able IDE from Microsoft Visual Studio, imple-menting and debugging new plug-ins is easy and efficient. In this way, the OWB can be adapted to any optimization challenge that can be solved with computer-based methods.On a fundamental level, OWB was designed to provide an intuitive and efficient user in-terface with extensive error reporting in case of incorrect user action. In addition, due to the sometimes long optimization runtimes, the application was written to be stable and highly fault-tolerant to avoid sudden crashes and data loss.

Conclusion

The Optimization Workbench (OWB) present-ed in this article has proven to be stable and reliable in all of our numerous optimization runs. Designed as a convenient add-on for existing simulation environments, it enables computer-based optimization with minimal integration effort. The system makes it pos-sible to accelerate complex design processes and optimally exploit technical system re-serves.Thanks to the optimization capabilities al-ready implemented in the OWB, but also be-cause of the equally available plug-ins for the design of controllers and power electron-ics, the software is extremely versatile. With its intuitive user interface, it represents a convenient tool to assist development engi-neers in this domain.Consequently, the tool is already structured to be extended for operation in simulation cluster environments in order to reduce the required optimization time by running simu-lations in parallel. To optimize grid models, an interface to simulators such as ATP and Cerberus is currently being evaluated in or-der to meet future requirements resulting from smart grid developments.

 

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Figure 1: The most important components of an optimization
Figure 1: The most important components of an optimization