Marko Angjelichinoski

Pietro Danzi, Marko Angjelichinoski, Čedomir Stefanović et al.

In standard implementations of distributed secondary control for DC MicroGrids (MGs), the exchange of local measurements among neighboring control agents is enabled via off-the-shelf wireless solutions, such as IEEE 802.11. However, Denial of Service (DoS) attacks on the wireless interface through jamming prevents the secondary control system from performing its main tasks, which might compromise the stability of the MG. In this paper, we propose novel, robust and secure secondary control reconfiguration strategy, tailored to counteract DoS attacks. Specifically, upon detecting the impairment of the wireless interface, the jammed secondary control agent notifies its peers via a secure, low-rate powerline channel based on Power Talk communication. This triggers reconfiguration of the wireless communication graph through primary control mode switching, where the jammed agents leave the secondary control by switching to current source mode, and are replaced by nonjammed current sources that switch to voltage source mode and join the secondary control. The strategy fits within the software-defined networking framework, where the network control is split from the data plane using reliable and secure side power talk communication channel, created via software modification of the MG primary control loops. The simulation results illustrate the feasibility of the solution and prove that the MG resilience and performance can be indeed improved via software-defined networking approaches.

Marko Angjelichinoski, Pietro Danzi, Čedomir Stefanović et al.

We propose a novel framework for secure and reliable authentication of Distributed Energy Resources to the centralized secondary/tertiary control system of a DC MicroGrid (MG), networked using the IEEE 802.11 wireless interface. The key idea is to perform the authentication using power talk, which is a powerline communication technique executed by the primary control loops of the power electronic converters, without the use of a dedicated hardware for its modem. In addition, the scheme also promotes direct and active participation of the control system in the authentication process, a feature not commonly encountered in current networked control systems for MicroGrids. The PLECS-based simulations verifies the viability of our scheme.

Marko Angjelichinoski, Anna Scaglione, Petar Popovski et al.

We treat the emerging power systems with direct current (DC) MicroGrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the DC Mirogrids; the goal is to eliminate the need to rely on an external communication system for such purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate (i) the generation capacities of power sources, (ii) the load demands, and (iii) the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the Optimal Economic Dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous MicroGrid operation.

Pietro Danzi, Marko Angjelichinoski, Čedomir Stefanović et al.

Microgrids (MGs) rely on networked control supported by off-the-shelf wireless communications. This makes them vulnerable to cyber-attacks, such as denial-of-service (DoS). In this paper, we mitigate those attacks by applying the concepts of (i) separation of data plane from network control plane, inspired by the software defined networking (SDN) paradigm, and (ii) agile reconfiguration of the data plane connections. In our architecture, all generators operate as either voltage regulators (active agents), or current sources (passive agents), with their operating mode being locally determined, according the global information on the MG state. The software-defined MG control utilizes the fact that, besides the data exchange on the wireless channel, the power-grid bus can be used to create side communication channels that carry control plane information about the state of the MG. For this purpose, we adopt power talk, a modem-less, low-rate, power-line communication designed for direct current (DC) MGs. The results show that the proposed software-defined MG offers superior performance compared to the static MG, as well as resilience against cyber attacks.

Cedomir Stefanovic, Marko Angjelichinoski, Pietro Danzi et al.

The future smart grid is envisioned as a network of interconnected microgrids (MGs) - small-scale local power networks comprising generators, storage capacities and loads. MGs bring unprecedented modularity, efficiency, sustainability, and resilience to the power grid as a whole. Due to the high share of renewable generation, MGs require innovative concepts for control and optimization, giving rise to a novel class of smart energy applications, in which communications represent an integral part. In this paper, we review power talk, a communication technique specifically developed for direct current MGs, which exploits the communication potential residing within the MG power equipment. Depending on the smart energy application, power talk can be used either as a primary communication enabler, or an auxiliary communication system that provides resilient and secure operation. The key advantage of power talk is that it derives its availability, reliability, and security from the very MG elements, outmatching standard, off-the shelf communication solutions.

Marko Angjelichinoski, Bijan Pesaran, Vahid Tarokh

Objective. In this paper, we consider the problem of cross-subject decoding, where neural activity data collected from the prefrontal cortex of a given subject (destination) is used to decode motor intentions from the neural activity of a different subject (source). Approach. We cast the problem of neural activity mapping in a probabilistic framework where we adopt deep generative modelling. Our proposed algorithm uses deep conditional variational autoencoder to infer the representation of the neural activity of the source subject into an adequate feature space of the destination subject where neural decoding takes place. Results. We verify our approach on an experimental data set in which two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The results show a peak cross-subject decoding improvement of $8\%$ over subject-specific decoding. Conclusion. We demonstrate that a neural decoder trained on neural activity signals of one subject can be used to robustly decode the motor intentions of a different subject with high reliability. This is achieved in spite of the non-stationary nature of neural activity signals and the subject-specific variations of the recording conditions. Significance. The findings reported in this paper are an important step towards the development of cross-subject brain-computer that generalize well across a population.

Marko Angjelichinoski, John Choi, Taposh Banerjee et al.

Objective. We consider the cross-subject decoding problem from local field potential (LFP) signals, where training data collected from the prefrontal cortex (PFC) of a source subject is used to decode intended motor actions in a destination subject. Approach. We propose a novel supervised transfer learning technique, referred to as data centering, which is used to adapt the feature space of the source to the feature space of the destination. The key ingredients of data centering are the transfer functions used to model the deterministic component of the relationship between the source and destination feature spaces. We propose an efficient data-driven estimation approach for linear transfer functions that uses the first and second order moments of the class-conditional distributions. Main result. We apply our data centering technique with linear transfer functions for cross-subject decoding of eye movement intentions in an experiment where two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The results show peak cross-subject decoding performance of $80\%$, which marks a substantial improvement over random choice decoder. In addition to this, data centering also outperforms standard sampling-based methods in setups with imbalanced training data. Significance. The analyses presented herein demonstrate that the proposed data centering is a viable novel technique for reliable LFP-based cross-subject brain-computer interfacing and neural prostheses.

Marko Angjelichinoski, Taposh Banerjee, John Choi et al.

We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. Previous reports have mainly relied on the spectral amplitude of the LFPs as a feature in the decoding step to limited success, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker's theorem to generate minimax-optimal estimates of the LFP signals which are later used as features in the decoding step. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space. The proposed complex spectrum-based decoder achieves prediction accuracy of up to $94\%$ at superficial electrode depths near the surface of the prefrontal cortex, which marks a significant performance improvement over conventional power spectrum-based decoders.

Marko Angjelichinoski, Anna Scaglione, Petar Popovski et al.

We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are set in accordance with the local power generation status of the generators. Therefore, the steady state voltage is inherently dependent on the generation capacities and the load, through a non-linear parametric model, which can be estimated. To have a well conditioned estimation problem, our solution avoids the use of an external communication interface and utilizes controlled voltage disturbances to perform distributed training. Using this tool, we develop an efficient, decentralized Maximum Likelihood Estimator (MLE) and formulate the sufficient condition for the existence of the globally optimal solution. The numerical results illustrate the promising performance of our MLE algorithm.

Marko Angjelichinoski, Cedomir Stefanovic, Petar Popovski et al.

Power talk is a novel concept for communication among units in a Micro Grid (MG), where information is sent by using power electronics as modems and the common bus of the MG as a communication medium. The technique is implemented by modifying the droop control parameters from the primary control level. In this paper, we consider power talk in a DC MG and introduce a channel model based on Thevenin equivalent. The result is a channel whose state that can be estimated by both the transmitter and the receiver. Using this model, we present design of symbol constellations of arbitrary order and analyze the error probability performance. Finally, we also show how to design adaptive modulation in the proposed communication framework, which leads to significant performance benefits.