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Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and Kmeans for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature. [ABSTRACT FROM AUTHOR]

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non‐linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide‐area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K‐means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO‐E and the results compared to other alternative methods available in the literature. [ABSTRACT FROM AUTHOR]

The identification of coherent areas is an important step to fortify the power system against the potential spread of cascading failures. This processing task helps to establish the preliminary strategies to implement online control methodologies that increase the power system reliability. In this regards, a methodology to identify coherent areas based on sliding power spectral density algorithm is presented in this paper. A coherency index is estimated from self- and cross-power spectral densities information in order to identify the coherent areas for a specific frequency related to an inter-area oscillation mode. In order to facilitate and automatize the process, an identification criterion based in the coherency index is proposed. This enables the proposed approach to be applied offline or online. In addition, the computation of the self-power spectral density using a sliding window opens the possibility to detect the onset of transient processes. Synthetic signals and two test power systems with different dynamic characteristics are used to evaluate the potential application in large systems. [ABSTRACT FROM AUTHOR]

A method to solve a multi‐objective optimal power flow (MOOPF) problem with multiple and competing objective functions (OF) is presented. The modified flower pollination algorithm and the normal boundary intersection method are used in a complementary way to determine the Pareto front solution of the MOOPF problem. To help in the decision making process, an intuitive criterion based on the centroid concept is proposed to select the best compromise solution from the Pareto frontier. To demonstrate the capabilities of the proposed method, different OFs are combined to calculate the Pareto front solution on the IEEE 30 bus test system. Finally, a comparison of the proposed centroid based method against the well‐known fuzzy membership and entropy criterions is provided in the results section. [ABSTRACT FROM AUTHOR]

A global multiscale method based on a dynamic mode decomposition (DMD) algorithm to characterize the global behavior of transient processes recorded using wide-area sensors is proposed. The method interprets global dynamic behavior in terms of both, spatial patterns or shapes and temporal patterns associated with dynamic modes containing essentially single-frequency components, from which the mode shapes, frequencies and growth and decay rates of the modes can be extracted simultaneously. These modes are then used to detect the coherent and dominant structures within the data. The technique is well suited for fast wide-area monitoring and assessment of global instability in the context of modern data fusion-based estimation techniques. Results of the application of the proposed method to large, high-dimensional data sets are encouraging. [ABSTRACT FROM PUBLISHER]

• The padé method is extended to identifying the harmonic analysis of multiple signals. • Harmonic pollution propagation in modern electrical grids is determined by exploiting the spatio-temporal characteristics of the padé approach. • A visualization tool that provides to system operators the path of harmonic propagation. This paper discusses the application of a spatio-temporal analysis method based on the Padé framework to analyze harmonic distortion in electrical networks. Using the observation matrix of three-phase voltage profiles, a polynomial in the z -domain is obtained for each profile. and their Padé approximation produces a discrete transfer function, which captures the dominant harmonics embedded in the signals. The application for a wide area of harmonic monitoring is proposed by extending the Padé approximant to the multi-signal case. By assuming that all signals have a common set of frequencies, the Padé approximation is efficiently computed for all the measurements. The best set of poles is extracted by the minimum singular value decomposition of an extended Hankel matrix. Due to its multiscale nature, the method is well suited for the analysis and monitoring of harmonic distortion in electrical networks. The developed methodology is compared with various analytical approximation methods using simulated noisy signals, proving that it is robust against the most common type of harmonic perturbations. Moreover, the proposed technique is applied to simulated data from a three-phase IEEE 14-bus test system, where the harmonic penetration and propagation is also discussed. Finally, the Padé method is validated on real measured signals. [ABSTRACT FROM AUTHOR]

It is not uncommon for oscillatory electric power system modes to move close to a resonance in which eigenvalues coincide. In a weak resonance, the modes are decoupled and the eigenvalues do not interact. We analyze general perturbations of a weak resonance and find two distinct behaviors, including interactions near strong resonances in which the eigenvalues quickly change direction. The possible perturbations are illustrated with interactions between electromechanical modes in a 4-generator power system. Some of the interactions are similar to subsynchronous resonance and can lead to oscillatory instability. [ABSTRACT FROM AUTHOR]

Power system normal form analysis has developed co-efficients and indices in modal coordinates to quantity nonlinear modal Interactions. We study the changes in the coefficients and indices when the power system equations are expressed in different coordinates or units and show that they can be normalized to be invariant to coordinate changes and thus intrinsic to the power system. The results are illustrated on a 4-generator System. An example shows that the coefficients and indices not only detect non-linear interactions but also can become very large near a strong resonance in the system linearization. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning approach for power quality monitoring in systems with distributed generation sources is presented. The proposed method focuses in the multi-scale analysis of multi-component signals for power quality disturbances classification. The proposed methodology combines a signal processing stage using variational mode decomposition (VMD) to obtain the times scales of multi-component signals, and a deep learning stage using a simple feedforward neural network (FFNN) to classify the disturbances. The simple proposed architecture allows minimum training time of the classification model. In addition, the proposed method is able to classify different disturbance combinations based on a reduced training-set. The proposed VMD-FFNN method is tested using synthetic and simulated signals, and it is compared with other well-known methods based on convolutional and recurrent deep neuronal networks. Finally, the proposed method is assessed using lab measurements in order to shown its performance in a real-world environment. • A multi-component signal analysis with a minimum training class number. • A fast-training process for the managing of different system configurations. • The detection of the disturbance's onset time, through an observation window analysis. [ABSTRACT FROM AUTHOR]

Until now, measurements from SM were expressed only as vectors (1D) or matrices (2D). In this work, a higher order representation, 3D in this case, is proposed. The new format allows to store multivariate data in a more natural form, and at the same time is possible to retrieve more information about a certain event. It should be stressed that, although in other disciplines such as data science the introduction of more dimensions is not required and it actually complicates the formulation of the problem, in power systems applications where data have multivariate nature, working with two dimensions leads to insufficient visibility of the problem and consequently to misinterpretation of the phenomena under investigation. Thus, although the proposed approach would certainly add complexity to find the solution when working with conventional algorithms such as PCA or LLE, the higher dimension does not represent an obstacle for the proposed approach and guarantees a more accurate interpretation of the solution. To validate the effectiveness of the proposed methodology, a three-way tensor built with data from the Electrical Reliability Council of Texas (ERCOT) is presented. The results demonstrate that is possible to extract more information than using conventional approaches based on 2-way arrangements (matrices). The innovation of using tensor decomposition for smart sensors data results on the following contributions: • Significant data compression. After the tensor decomposition has been performed, the original data sets are kept on new formats of the decomposition, which require significant less memory space. • Reduction and Visualization : The reduction is carried out for each dimension and thus, visualization and clustering of each variable is achieved. • Reconstruction of missing data. The iterative nature of the algorithm for solving the objective function during the tensor decomposition process, indirectly allows the estimation and reconstruction of missing data. Sophisticated geospatial metering devices used in today's networks such as the advanced metering infrastructure (AMI), wide area measurement system (WAMS) and supervisory control and data acquisition (SCADA) open new opportunities to monitor the security of the system in real time. Consequently, these metering infrastructures have received significant attention in recent years from data mining communities because of the new challenges involved on managing this information. One of the main challenges is the analysis of multivariable data, which represents datasets containing variables of different nature, which are linked. In this document a data mining technique that allows the analysis of multivariate data is presented. Moreover, an innovative application of an unsupervised data mining algorithm for smart meters data, particularly to Electrical Load Profile using tensor decomposition is presented. Since the proposed tensor representation allows to assign a given dimension to a particular variable involved; data reduction, data compression, data visualization and data clustering is archived separately for every variable. To validate the effectiveness of the proposed methodology, a three-way tensor built with data from the Electrical Reliability Council of Texas (ERCOT) is presented. The results demonstrate that is possible to extract more information than using conventional approaches based on 2-way arrangements (matrices). Additionally, the proposed algorithm is solved using an iterative approach, which indirectly enable to estimate missing data. [ABSTRACT FROM AUTHOR]

Power quality (PQ) in microgrids is a relevant topic, particularly with the complex dynamic behaviour of disturbances produced by the electronic components intrinsic in these technologies. In this paper, a qualitative-quantitative hybrid approach named multiscale recurrence quantification decomposition (MSRQD) combined with support vector machines (SVM) is proposed for PQ monitoring in microgrid systems. MSRQD combines variational mode decomposition (VMD) with recurrence quantification analysis (RQA). To validate the effectiveness of the proposed method, it is compared against other methods. In addition, three study cases are conducted in a microgrid test system using simulations, and real measured data of a main distribution feeder to prove the classification capability of the proposed method in a real environment. This novel approach, provides qualitative graphics and quantitative statistical indexes for disturbance localisation, disturbance time evolution and disturbance characterisation, as well as 3D visualisation feature space of perturbations that makes easy the interpretation to the system operator. [ABSTRACT FROM AUTHOR]

• Results of survey to 10 transmission system operators from 8 countries. • Outline of the most important current challenges handling data in control rooms. • Description of situation with respect to artificial intelligence and machine learning applications. • Differences among utilities based on geographical, political and economic situation. • List of real needs that TSOs would like to have in operation. Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field. [ABSTRACT FROM AUTHOR]

This paper proposes a light, flexible, and yet reliable multiparametric estimation algorithm for grid-connected voltage-source-converter-based systems. This approach combines the nonlinear least squares (NLSQ) fitting algorithm and the steady-state extended harmonic domain (EHD) model to take into account the harmonic effects. The EHD model is used to take advantage of the harmonic content of the electrical signals to provide robustness and improved performance to the NLSQ estimation methodology. In addition, this harmonic formulation reduces the necessity of synchronized measurements. Details of the implementation are provided by an experimental case study, in which the grid equivalent (Thevenin voltage, inductance, and resistance), the ac-side filter (inductance and resistance), and the converter switching and conduction loss resistance are accurately estimated for three different operating conditions. [ABSTRACT FROM AUTHOR]

Highlights • Power flow modeling of hierarchically-controlled DG units for islanded AC microgrids. • The control includes a primary conventional droop control and a secondary control. • Comparisons against the solutions obtained with PSCAD and Simulink are presented. • The differences between the proposed model against the droop model are presented. • The solutions computed with the proposed models match with the time-domain models. Abstract This paper presents the power flow modeling of droop-controlled distributed generation units with secondary frequency and voltage restoration control for hierarchically controlled islanded microgrids. These models are incorporated in the conventional Newton-Raphson power flow method as a new bus, without the necessity of a slack bus, and include the gains of the control systems that influence the steady-state solution. Two case studies are addressed. In the first case study, comparisons of the proposed models against the steady-state solutions obtained with PSCAD and Simscape Power System of Simulink, where the closed-loop controls are explicitly modeled, are presented. In the second case study, the proposed method is contrasted against the droop-based approach. The results obtained exhibit low computing effort, reliability, and effectiveness of the proposed models since quadratic convergence behavior is maintained independently of the size and topology of the microgrid. Besides, it is demonstrated that hierarchical and droop controllers lead to different solutions, which confirms the necessity of including the hierarchical control in the power flow model. [ABSTRACT FROM AUTHOR]

This paper presents a control scheme of a wide-area controlled static VAr compensator (WAC-SVC) and its real-time implementation in a hardware-in-the-loop (HIL) simulation scheme with three control objectives: (1) to increase the critical clearing time, (2) to damp the power oscillations, and (3) to minimize the maximum line current. The proposed control scheme considers a correction strategy to compensate the delays up to 200 ms. In addition to this, a generator tripping scheme based on synchrophasor measurements to determine the proximity to the loss of synchronism is proposed. A delay compensation algorithm based on polynomial approximations is also developed. The proposed WAC-SVC is experimentally validated using a Real-Time Digital Simulator platform (RTDS), industrial communication protocols, a commercial device for PMU-based control implementations, and digital relays with PMU capability. The real-time simulation results confirm its effectiveness and feasibility in real industrial applications. Furthermore, practical guidelines to implement this kind of control schemes are provided. [ABSTRACT FROM AUTHOR]