In modern power grids, an increasing number of renewable sources are integrated via inverters, affecting the inertia of the overall power system. Thus, a low-inertia system formed is potentially vulnerable to fast grid collapse and catastrophic failures in case of any disturbances. The study of low-inertia systems is an important aspect of the stability and control of the power grid. The stability studies require system model representation in the time or frequency domain. The traditional modeling method is the state-space approach which requires detailed system knowledge that may not be available always. The impedance-based approach is computationally efficient but fails to provide information about the internal states of the system. To overcome these drawbacks, we develop a transfer function-based approach. The purpose of this study is to present a computationally efficient modeling method for a converter system that incorporates internal knowledge of the system. It provides flexibility in understanding the stability of the overall system through Bode, Nyquist, and pole-zero plots. The proposed Transfer Function-based approach is applied to the generalized model of the converter consisting of two types of inverters Grid Forming (GFM) and Grid Following (GFL), and its stability assessment is carried out in MATLAB.

In this article, we discuss the relationship between subcycle and instantaneous power metrics in polyphase ac systems. While both approaches have very low computational cost, which makes them suitable for real-time control of industrial processes, the two-sample subcycle scheme offers several distinct advantages over the purely-instantaneous approach. In particular, we show that: a) the level of steady-state fluctuation of subcycle power metrics that use only two time-domain samples is significantly lower than that of purely-instantaneous metrics, and b) the subcycle scheme generates several additional power metrics, which can be used to detect onset of faults, and accurately determine their duration. We illustrate our points using both synthetic and (real-life) industrial examples. In addition, we examine the connection between the two-sample subcycle scheme and other near-instantaneous approaches, based on derivatives of currents and voltages. We again observe a significantly lower numerical sensitivity for the two-sample subcycle quantities. Furthermore, two-sample quantities have a unique feature that they can naturally be extended to more samples, with concomitant reduction in fluctuations, better accuracy and with only moderate increase in computational cost. We posit that the subcycle approach has promise in detection, control, and classification applications.

We present an augmented continuous-discrete extended Kalman filter (EKF), capable of mitigating the effects of bad data and (random) measurement delay. We use an innovations-based Fisher-type scheme to identify and remove occurrences of corrupted sensor observations. We rely on time-stamp technology to accurately determine the duration of delay experience d by each received measurement packet. The length of delay is then used to construct a “delay-aware” optimum MMSE state estimate. A Flywheel Energy Storage System (FESS) example is used to demonstrate the high quality of dynamic state estimation achieved with the proposed method in the presence of bad sensor samples and delayed measurement packets.

We describe a method for generating causal, stable, numerically robust, IIR, customizable (application specific), perfect reconstruction (PR) filter-banks with zero (or adjustable) reconstruction delay. The ideas presented in this paper extend the concept of Dynamic Phasors as well as the concept of the so-called Phasor Bank Transform, which constitute specific subclasses of filter-banks. We provide an explicit compact characterization of the optimally-robust reconstruction stage of this transform, which can be used with any (customized) choice of its analysis stage. Since the Phasor Bank transform allows causal (real-time) delay-free perfect reconstruction of any input waveform, it is perfectly suited for (closed-loop) control applications. Although the motivating application area of Phasor Banks is Power Systems, such as tracking of time-variant harmonic content under both steady-state (nearly-periodic) and transient operating conditions, the technique presented here can be used in a variety of closed loop applications.

Distributed estimation is where the state of a dynamical system is to be estimated via a collection of geographically dispersed measurements over a sensor network. In order to implement the estimator, the sensors, in addition to sensing, implement a simple data fusion protocol that relies on inter-sensor communication. In this paper, we study distributed estimation of cyber-physical systems when there is an adversarial attack on the sensed and communicated information. We propose a novel methodology to address the detection of such attacks, and further incorporate appropriate remedial actions in the estimator. Our methodology is based on the notions of local consistency and nodal consistency and is further reinforced by the exploiting the underlying physical-layer in the cyber-physical description.

The paper formulates and solves the problem of optimizing power flows in polyphase systems with significant source (line) impedance. The authors present two fundamental performance bounds, which give rise to explicit formulas for the apparent power and optimally compensated load current. Properties of the solution are illustrated on the example of an asymmetrical three-phase induction motor supplied with unbalanced non-sinusoidal voltages.

This article reviews analytical results and practical experience with randomized modulation in power electronic converters. After providing a brief overview of power electronics and of relevant switching patterns, the authors present performance specifications for converters (in terms of electromagnetic interference, vibrations and acoustic noise), and classify various randomized switching schemes. They review the most directly related references next, and introduce the mathematical objects of primary interest-averaged autocorrelation and power spectrum. They then list some results for the (simpler) case of stationary modulation and consider in detail the more general case of randomized switching based on Markov chains. Their aim is twofold-to present results important for practice and to establish connections with several areas of applied probability. Finally, they consider the synthesis task for the two cases of randomized modulation, followed by brief conclusions.

This paper reviews some of the major approaches to modeling and simulation in power electronics, and provides references that can serve as a starting point for the extensive literature on the subject. The major focus of the paper is on averaged models of various kinds, but sampled-data models are also introduced. The importance of hierarchical modeling and simulation is emphasized.