Robustness Analysis with Respect to Exogenous Perturbations for Flatness-Based Exact Feedforward Linearization

A methodology to analyze robustness with respect to exogenous perturbations for exact feedforward linearization based on differential flatness is presented. The analysis takes into consideration the tracking error equation and makes thereafter use of a stability result by Kelemen coupled with results issued from interval analysis. This turns exact feedforward linearization based on differential flatness into a general control methodology for flat systems

On Norm-Based Estimations for Domains of Attraction in Nonlinear Time-Delay Systems

For nonlinear time-delay systems, domains of attraction are rarely studied despite their importance for technological applications. The present paper provides methodological hints for the determination of an upper bound on the radius of attraction by numerical means. Thereby, the respective Banach space for initial functions has to be selected and primary initial functions have to be chosen. The latter are used in time-forward simulations to determine a first upper bound on the radius of attraction. Thereafter, this upper bound is refined by secondary initial functions, which result a posteriori from the preceding simulations. Additionally, a bifurcation analysis should be undertaken. This analysis results in a possible improvement of the previous estimation. An example of a time-delayed swing equation demonstrates the various aspects.Comment: 33 pages, 8 figures, "This is a pre-print of an article published in 'Nonlinear Dynamics'. The final authenticated version is available online at https://doi.org/10.1007/s11071-020-05620-8

Using Open Data for Modeling and Simulation of the All Electrical Society in eASiMOV

The present study examines a future energy systems scenario, the so-called All Electrical Society (AES), which is defined by a very high number of active prosumers in the distribution grid in view of future 100% renewables-based energy systems. In this paper, we present data modeling methods that describe the power consumption behavior and power generation patterns via time series for 78 prosumers, each fully equipped with rooftop PV, two battery electrical vehicles and a heat pump. Quasi-dynamic simulations of a low voltage grid under stress conditions are performed using open data and free software. The simulatively determined increase in network utilization and congestion is also compared with the currently available grid capacity gained through extensive measurements in the examined distribution grid. The result is that in the AES scenario the current deployed electrical infrastructure of the distribution grid will be more than heavily overloaded, both the transformers and the respective power lines

Distribution Grid Monitoring Based on Widely Available Smart Plugs

During the last few years, smart home devices have become increasingly popular. Smart plugs, smart lights, and smart switches are now found in as many as 37 percent of German households, and the popularity of these devices is rising. Smart devices sometimes also integrate sensors for measuring voltage and current. The increase in renewable generation, e-mobility and heat pumps lead to scenarios for which the distribution grid was not originally designed. Moreover, parts of the distribution grid are only sparsely instrumented, which leaves the distribution grid operator unaware of possible bottlenecks resulting from the introduction of such loads and renewable generation. To overcome this lack of information, we propose a grid monitoring that is based on measurements of widely available smart home devices, such as smart plugs. In the present paper, we illustrate the collection and utilization of smart plug measurements for distribution grid monitoring and examine the extent and effect of measurement inaccuracy. For this evaluation, we analyze the measurements of multiple commercially available smart plugs and test the effect of measurement errors on the monitoring when using a single smart plug.Comment: 8 pages

Early Attack Detection for Securing GOOSE Network Traffic

The requirements for the security of the network communication in critical infrastructures have been more focused on the availability of the data rather than the integrity and the confidentiality. The availability of communication in IEC 61850 substations can be hindered by Generic Object Oriented Substation Event (GOOSE) poisoning attacks that might result in threats such as Denial of Service (DoS) or flooding attacks. In order to accurately detect similar attacks, a novel method for the Early Detection of Attacks for GOOSE Network Traffic (EDA4GNeT) is developed in the present work. The EDA4GNeT method considers the dynamic behavior of network traffic in electrical substations. A mathematical modeling of GOOSE network traffic is adopted for the anomaly detection based on statistical hypothesis testing. The developed mathematical model of the communication traffic can also support the management of the network architecture in IEC 61850 substations based on appropriate performance studies. To test the novel anomaly detection method and compare the obtained results with related works found in the literature, a simulation of a DoS attack against a 66/11kV substation with several experiments is used as a case study

A Generalized Framework for Chance-constrained Optimal Power Flow

Deregulated energy markets, demand forecasting, and the continuously increasing share of renewable energy sources call---among others---for a structured consideration of uncertainties in optimal power flow problems. The main challenge is to guarantee power balance while maintaining economic and secure operation. In the presence of Gaussian uncertainties affine feedback policies are known to be viable options for this task. The present paper advocates a general framework for chance-constrained OPF problems in terms of continuous random variables. It is shown that, irrespective of the type of distribution, the random-variable minimizers lead to affine feedback policies. Introducing a three-step methodology that exploits polynomial chaos expansion, the present paper provides a constructive approach to chance-constrained optimal power flow problems that does not assume a specific distribution, e.g. Gaussian, for the uncertainties. We illustrate our findings by means of a tutorial example and a 300-bus test case