Overview of Control Algorithm Verification Methods in Power Electronics Systems

The paper presents the existing verification methods for control algorithms in power electronics systems, including the application of model checking techniques. In the industry, the most frequently used verification methods are simulations and experiments; however, they have to be performed manually and do not give a 100% confidence that the system will operate correctly in all situations. Here we show the recent advancements in verification and performance assessment of power electronics systems with the usage of formal methods. Symbolic model checking can be used to achieve a guarantee that the system satisfies user-defined requirements, while statistical model checking combines simulation and statistical methods to gain statistically valid results that predict the behavior with high confidence. Both methods can be applied automatically before physical realization of the power electronics systems, so that any errors, incorrect assumptions or unforeseen situations are detected as early as possible. An additional functionality of verification with the use of formal methods is to check the converter operation in terms of reliability in various system operating conditions. It is possible to verify the distribution and uniformity of occurrence in time of the number of transistor switching, transistor conduction times for various current levels, etc. The information obtained in this way can be used to optimize control algorithms in terms of reliability in power electronics. The article provides an overview of various verification methods with an emphasis on statistical model checking. The basic functionalities of the methods, their construction, and their properties are indicated

The Performance of Statistical Inference after Model Checking

Most standard statistical inference procedures rely on model assumptions such as normality, independent and identically distributed and the like. Often in practice, such assumptions are formally tested before applying the inference. Such a procedure does not ensure that the model assumptions are really fulfilled because the standard theory for popular inference tests does not take into account that the data has been selected by a previous model check. Applying a misspecification test violates the very model assumption it was meant to enforce. (``misspecification paradox''). In practice it is useful to have an alternative test in the case that the misspecification test rejects the model assumption. However, this does not completely address the misspecification paradox because there is still a certain probability that the model assumption is rejected when it is in fact true, and vice versa. This thesis is about investigating, theoretically and by simulations, the performance of such a combined procedure. A novel simulation process is proposed where samples can be randomly chosen from a situation where the model assumption is fulfilled or violated. A few combinations of distributions and statistical tests are considered and both level and power are presented and discussed. Although the levels show no strong evidence of choosing the combined procedure over the tests run without model checking, the power plots show that in certain conditions, it can be more powerful. A theory is presented where it is shown that in a particular situation and with reasonable assumptions, the combined procedure does have a higher power compared to unconditional tests. The assumptions were relaxed a little and the same conclusions could be made. Finally, a three stage testing procedure in two different scenarios, distributional shape and linear regression significance, are presented and discussed. The same conclusions can be made from the levels and powers

Attacking the V:On the resiliency of adaptive-horizon MPC

Inspired by the emerging problem of CPS security, we introduce the concept of controller-attacker games. A controller-attacker game is a two-player stochastic game, where the two players, a controller and an attacker, have antagonistic objectives. A controller-attacker game is formulated in terms of a Markov Decision Process (MDP), with the controller and the attacker jointly determining the MDP’s transition probabilities. We also introduce the class of controller-attacker games we call V-formation games, where the goal of the controller is to maneuver the plant (a simple model of flocking dynamics) into a V-formation, and the goal of the attacker is to prevent the controller from doing so. Controllers in V-formation games utilize a new formulation of model-predictive control we have developed called Adaptive-Horizon MPC (AMPC), giving them extraordinary power: we prove that under certain controllability conditions, an AMPC controller can attain V-formation with probability 1. We evaluate AMPC’s performance on V-formation games using statistical model checking. Our experiments demonstrate that (a) as we increase the power of the attacker, the AMPC controller adapts by suitably increasing its horizon, and thus demonstrates resiliency to a variety of attacks; and (b) an intelligent attacker can significantly outperform its naive counterpart

Investigations into Visual Statistical Inference

Statistical graphics play an important role in exploratory data analysis, model checking and diagnostics, but they are not usually associated with statistical inference. Recent developments allows inference to be applied to statistical graphics. A new method, called the lineup protocol, enables the data plot to be compared with null plots, in order to obtain estimates of statistical significance of structure. With the lineup protocol observed patterns visible in the data can be formally tested. The research conducted and described in this thesis validates the lineup protocol, examines the effects of human factors in the application of the protocol, and explains how to implement the protocol. It bridges the long existing gulf between exploratory and inferential statistics. In the validation work, additional refinement of the lineup protocol was made: methods for obtaining the power of visual tests, and p-values for particular tests are provided. A head-to-head comparison of visual inference against the best available conventional test is run for regression slope inference, using simulation experiments with human subjects. Results indicate that the visual test power is higher than the conventional test when the effect size is large, and even for smaller effect sizes, there may be some super-visual individuals who yield better performance than a conventional test. The factors that may influence the individual abilities are examined, and results suggest that demographic and geographic factors have statistically significant but practically insignificant impact. This work provides instructions on how to design human subject experiments to use Amazon\u27s Mechanical Turk to implement the lineup protocol

Parallel statistical model checking for safety verification in smart grids

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network