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Accurate and fast classification of FDEs is crucial to power systems situation awareness and stability control. However, various database used in recent studies makes it hard to directly compare different classification methods. To relieve the lack of a standardized database that can be used as a benchmark, this work proposes an open-source package which generates frequency events based on PowerFactory named PF-FEDG. PF-FEDG can produce various labeled FDEs with random parameters such as generator trips, load disconnections, line outages, frequency ramp ups, frequency ramp downs, and frequency oscillations. Furthermore, the package includes three reference FDEs classifiers based on deep learning techniques. These models are tested on the IEEE 39-bus system and IEEE 118-bus system and achieve high performance. The package generating capability and the reference classifiers can be used by the community as benchmarks for comparison and development of new algorithms for FDEs detection. The code of PF-FEDG is available on GitLab.

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into "model-based" methods and "model-free" methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of an improved resonance-type FCL, designed to enhance the transient stability of a photovoltaic farm. This FCL overcomes the well-known drawbacks associated with the conventional resonance-based FCLs. This FCL limits the fault during the fault period, quickly achieves stability during the recovery period, independent of the reactor's charging state, and notably reduces DC-link voltage fluctuations during faults. A power system simulation setup comprising a PV farm, synchronous generator, transformers, circuit breakers, transmission system, and several branches of loads is used for testing the proposed FCL. The simulation results validate that the proposed FCL better improves the system's stability and leads to improved fault current, PCC voltage, active power, reactive power, and DC-link voltage compared to other common types of resonant FCLs. [ABSTRACT FROM AUTHOR]

Facing the current sustainability challenges requires reduction in building stock energy usage towards achieving the European Green Deal targets. This can be accomplished by adopting techniques such as fault detection and diagnosis and efficiency optimization. Taking an Estonian school as a case study, an occupancy-based algorithm for scheduling ventilation operations in buildings is here developed starting only from energy use data. The aim is optimizing the system's operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use the historical data of a school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time-series methods, then creates 12 occupancy schedules that are either based on classical detection with an on-off method or on occupancy estimation for demand-controlled ventilation. We find that the latter replaces the 60% capacity of current on-off schedules by 30% or even 0%, with energy savings ranging from 3.5% to 66.4%. The corresponding costs are reduced from 18.1% up to 62.6%, while still complying with current national regulations for indoor air quality. Remarkably, our method can immediately be extended to other countries, as it relies only on occupancy schedules that ignore weather and other location-specific factors. [ABSTRACT FROM AUTHOR]

In this article, the state estimation problem of linear fractional order singular (FOS) systems subject to matrix uncertainties is investigated where a recursive robust algorithm is derived. Considering an uncertain discrete‐time linear FOS system with added process and measurement noises, we aim to design a robust Kalman‐type state estimation algorithm based on an optimal data fitting approach with a given sequence of observations. As a substitute for the stochastic formulation, this general filter is obtained by minimizing a completely deterministic regularized residual norm in its worst‐possible form at each step over admissible uncertainties. Analysis of the algorithm shows that not only does the proposed robust filter cover the traditional robust Kalman filters (KFs), but it also represents an extension of the nominal fractional singular KF (FSKF) when the system is not subject to uncertainties. Furthermore, besides giving a sufficient condition for the existence of the robust filter, we derive conditions for the asymptotic properties of the filter, where we demonstrate that the filter and the Riccati equation are stable and converge when an equivalent system is detectable and stabilizable. A numerical example is included to demonstrate the performance of the introduced filter. [ABSTRACT FROM AUTHOR]