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Online recorded responses can be used for aggregate dynamic load modelling, taking advantage of the advent of smart grids and the growing installation of phasor measurement units. Although several measurement-based dynamic load models have been proposed in the literature, still most network utilities and system operators take advantage of well-known formulations such as the polynomial and the exponential recovery models. However, these types of load models are only valid for a specific range of operating conditions, thus minimizing their applicability and efficiency. This is mainly due to the fact that the model parameter estimation procedure relies on iterative processes. To this extent, the specific scope of this paper is to present a comprehensive identification procedure for evaluating load models under different loading conditions and further to propose two generic modelling approaches that can be used to derive robust load models that are suitable for dynamic simulations over a wide range. Towards achieving the scope of this paper, Monte Carlo simulations are used to train and validate the data of the loading conditions. Finally, several simulations are performed within the DIgSILENT PowerFactory software to assess the accuracy of the proposed models. [ABSTRACT FROM AUTHOR]

The replacement of conventional synchronous generators with converter-interfaced renewable energy sources (RESs) reduces the overall inertia levels of modern power systems, leading to frequency stability issues. Moreover, the intermittent nature of RESs constitutes system inertia variable during the day, further complicating the frequency control procedure. Therefore, power system operators shall estimate close to real-time the overall inertia levels of their grids in order to ensure their secure and reliable operation. In this context, in this paper, a new methodology for the inertia estimation of multi-area power systems is formulated. The proposed method uses the modal sensitivity matrix to obtain a linear approximation of the relation between modal parameters and inertia constants. During the real-time operation, modal parameters are identified via system responses using the Matrix Pencil method. The identified modes and the derived sensitivity matrix are used to estimate the overall inertia of the examined power system. The effectiveness of the proposed method is validated by means of simulations performed in one-area, two-area, and three-area power system models. • A new methodology for inertia estimation of multi-area power systems is formulated. • A linear approximation between inertia constants and modal parameters is derived. • Modal parameters are identified by applying system identification techniques. [ABSTRACT FROM AUTHOR]

• The performance of three multi-channel mode estimation methods is evaluated. • A comparative analysis between single- and multi-channel approaches is performed. • Prony, matrix pencil and vector fitting mode identification methods are considered. • Modes from both the transmission and active distribution networks are obtained. • The impact of different parameters on the identification procedure is examined. Mode identification from post-disturbance ringdown responses is a valuable tool for the analysis of the dynamic performance and the stability margins of power systems. In this aspect, several techniques have been proposed, focusing mainly to single-signal analysis. However, considering large-scale power systems and especially future scenarios with high penetration of distributed energy resources, detailed network analysis at all voltage levels is required. As a result of these concerns, multi-channel mode identification algorithms have been developed. Scope of this paper is to evaluate the applicability and the performance of the most known multi-channel measurement-based identification approaches for the modal analysis of modern power systems incorporating active distribution networks. The algorithmic details and distinct characteristics of each method are briefly discussed. The examined methods are used to identify the dominant inter-area modes contained in ringdown responses at different levels of a combined transmission-distribution network. [ABSTRACT FROM AUTHOR]

• The performance of measurement-based inertia estimation methods is evaluated. • The sliding window method and the ARMAX modeling approach are considered. • Algorithmic details and distinct characteristics of each method are presented. • The impact of different parameters on the inertia estimation is examined. • Methods applicability is demonstrated via experimental results. The increasing penetration of converter-interfaced renewable energy sources (RESs) is expected to reduce the overall system inertia. Additionally, due to the intermittent nature of RESs, the overall inertia of modern power systems will vary significantly during the day, resulting in frequency stability issues. In this uncertain environment, it is important for power system operators to monitor in real-time the overall system inertia. Scope of this paper is to systematically evaluate the performance of the most known measurement-based inertia estimation techniques. These include ARMAX-based methods and methods based on the sliding window concept. Algorithmic details and distinct characteristics of each method are presented and discussed. The effect of several parameters on the accuracy of the examined methods is investigated by means of Monte Carlo simulations. For the analysis both ringdown and ambient data are considered. The performance of the examined methods is evaluated under different network conditions and configurations, using simulation results obtained from a system frequency response model and the IEEE 9-Bus system. The applicability of both approaches is also assessed using laboratory measurements. [ABSTRACT FROM AUTHOR]