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Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator's underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system. [ABSTRACT FROM AUTHOR]

This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data. [ABSTRACT FROM AUTHOR]

This paper introduces a novel machine learning (ML) model for the lightning performance analysis of overhead distribution lines (OHLs), which facilitates a data-centrist and statistical view of the problem. The ML model is a bagging ensemble of support vector machines (SVMs), which introduces two significant features. Firstly, support vectors from the SVMs serve as a scaffolding, and at the same time give rise to the so-called curve of limiting parameters for the line. Secondly, the model itself serves as a foundation for the introduction of the statistical safety factor to the lightning performance analysis of OHLs. Both these aspects bolster an end-to-end statistical approach to the OHL insulation coordination and lightning flashover analysis. Furthermore, the ML paradigm brings the added benefit of learning from a large corpus of data amassed by the lightning location networks and fostering, in the process, a "big data" approach to this important engineering problem. Finally, a relationship between safety factor and risk is elucidated. THe benefits of the proposed approach are demonstrated on a typical medium-voltage OHL. [ABSTRACT FROM AUTHOR]

Recent growing energy crisis in Europe, coupled with the rising energy prices worldwide, is a clear indication of the many difficulties awaiting the transition of modern societies away from fossil fuels. This Special Issue is dedicated to exploring novel approaches to the electrical power system dynamic security assessment, and related power disturbance issues, which are based on the applications of artificial intelligence (AI) techniques (i.e., by employing the machine learning, deep learning, and reinforcement learning). However, the importance of the electrical power systems to modern society mandates that further convincing results be provided in order to corroborate the robustness of AI based approaches. [Extracted from the article]

This paper presents a numerical computation method for determining the magnetic field of high-current busducts of circular cross-section geometry, based on the subdivision of the busduct phase conductors and screens into the conductor filaments and the subsequent application of the mesh-current method, with the aid of the geometric mean distance method. The mathematical model takes into account the skin effect and the proximity effects, as well as the complete electromagnetic coupling between phase conductors and metal enclosures (i.e., screens) of the single-phase isolated busduct system (of circular cross-section geometry). This model could be readily applied to the computation of the magnetic field of the Gas Insulated Transmission Lines (GIL) as well. [ABSTRACT FROM AUTHOR]

This paper elaborates on several important outstanding issues in the state-of-art of overvoltage protection selection for modern wind farms. The lack of experience with this still-new technology, together with the inherent complexity of wind farm electrical systems, entails several unresolved issues pertinent to the topic of overvoltage protection, particularly in relation to lightning-initiated surges. Firstly, several aspects of the wind turbine lightning incidence, along with the issues related to the selection of lightning current parameters (pertinent to the wind farm overvoltage protection), are addressed in this paper. Secondly, several issues in the state-of-art models of the wind farm electrical systems-for the lightning surge analysis-are addressed and discussed. Here, a well-known ElectroMagnetic Transients Program (EMTP) software package is often employed, with all of its benefits and some limitations. Thirdly, the metal-oxide surge arrester energy capability and the issues related to the selection of the surge arrester rated energy-in relation to the direct lightning strikes to wind turbines-is addressed. Finally, some general considerations concerning the overvoltage protection selection for wind farm projects, particularly regarding the installation of the metal-oxide surge arresters, are provided as well. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities. [ABSTRACT FROM AUTHOR]

Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the "big data" in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions. [ABSTRACT FROM AUTHOR]

This paper presents an electromagnetic transient analysis of lightning-initiated overvoltage stresses of the step-up transformers installed inside a nacelle of onshore, multi-megawatt, new-generation wind turbines. The increase in the wind turbine (WT) nominal power output, necessitated introducing the step-up transformer into the nacelle. A transformer installed inside a nacelle is subjected to completely different overvoltage stresses from those present if it were installed at the base of the WT tower. This has serious repercussions on its overvoltage protection (i.e., selection and installation of surge arresters) and insulation coordination. Furthermore, the overvoltage protection of medium-voltage cables (inside the tower) is also problematic when considering their length, proximity to the tower wall, and their screen grounding practices, and needs to be tackled in conjunction with that of the step-up transformer. This paper presents detailed models for the various components of the latest-generation WTs, intended for fast-front transient analysis and assembled within the EMTP software package. We further present the comprehensive results of the lightning-transient numerical simulations, covering both upward and downward (first and subsequent) strikes, their analysis, and recommendations for the optimal selection of medium-voltage surge arresters for the step-up transformers installed inside a nacelle. [ABSTRACT FROM AUTHOR]

The expected increase in the presence of electric vehicles raises numerous questions regarding their impact on the market relations. Depending on the agreement between the involved parties, the position of EVs changes from passive (traditional role) to active. Active EVs are beneficial for variability and uncertainty-intense modern power systems. To enable this transition, a suitable framework in the form of agreements is required in order to establish the terms and responsibilities. Following the presented agreements, we propose a novel method for evaluation of the benefits that the newly added EVs bring to the portfolio. The method comprises two steps, a Monte Carlo simulation of the EV driving/charging patterns and an optimization model for market related decision making. The method results in the estimates on economic savings resulting from adding EVs to portfolios. An illustrative example is used in order to give an idea of the range of the benefits. [ABSTRACT FROM AUTHOR]