Handbook of Electrical Power System Dynamics

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

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Configurations to introduce carrier signals into the electrical power system

18th International Conference and Exhibition on Electricity Distribution (CIRED 2005) ◽

10.1049/cp:20051142 ◽

2005 ◽

Cited By ~ 1

Author(s):

I. Zamora ◽

A.J. Mazon ◽

J.J. Zamora ◽

H. Pujana

Keyword(s):

Power System ◽

Electrical Power ◽

Electrical Power System

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Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters

Energies ◽

10.3390/en14154581 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4581

Author(s):

Yuko Hirase ◽

Yuki Ohara ◽

Naoya Matsuura ◽

Takeaki Yamazaki

Keyword(s):

Power System ◽

System Dynamics ◽

Dimensional Space ◽

Supply And Demand ◽

Transient State ◽

Synchronous Generator ◽

Response Analysis ◽

Power System Dynamics ◽

Mode Decomposition ◽

Virtual Synchronous Generator

In the field of microgrids (MGs), steady-state power imbalances and frequency/voltage fluctuations in the transient state have been gaining prominence owing to the advancing distributed energy resources (DERs) connected to MGs via grid-connected inverters. Because a stable, safe power supply and demand must be maintained, accurate analyses of power system dynamics are crucial. However, the natural frequency components present in the dynamics make analyses complex. The nonlinearity and confidentiality of grid-connected inverters also hinder controllability. The MG considered in this study consisted of a synchronous generator (the main power source) and multiple grid-connected inverters with storage batteries and virtual synchronous generator (VSG) control. Although smart inverter controls such as VSG contribute to system stabilization, they induce system nonlinearity. Therefore, Koopman mode decomposition (KMD) was utilized in this study for consideration as a future method of data-driven analysis of the measured frequencies and voltages, and a frequency response analysis of the power system dynamics was performed. The Koopman operator is a linear operator on an infinite dimensional space, whereas the original dynamics is a nonlinear map on a finite state space. In other words, the proposed method can precisely analyze all the dynamics of the power system, which involve the complex nonlinearities caused by VSGs.

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Optical Voltage Transformer Based on FBG-PZT for Power Quality Measurement

Sensors ◽

10.3390/s21082699 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2699

Author(s):

Marceli N. Gonçalves ◽

Marcelo M. Werneck

Keyword(s):

Power System ◽

Power Quality ◽

Distribution System ◽

Electrical Power ◽

Quality Measurement ◽

Pockels Effect ◽

Electrical Power System ◽

Voltage Transformer ◽

Technical Literature ◽

Distribution Lines

Optical Current Transformers (OCTs) and Optical Voltage Transformers (OVTs) are an alternative to the conventional transformers for protection and metering purposes with a much smaller footprint and weight. Their advantages were widely discussed in scientific and technical literature and commercial applications based on the well-known Faraday and Pockels effect. However, the literature is still scarce in studies evaluating the use of optical transformers for power quality purposes, an important issue of power system designed to analyze the various phenomena that cause power quality disturbances. In this paper, we constructed a temperature-independent prototype of an optical voltage transformer based on fiber Bragg grating (FBG) and piezoelectric ceramics (PZT), adequate to be used in field surveys at 13.8 kV distribution lines. The OVT was tested under several disturbances defined in IEEE standards that can occur in the electrical power system, especially short-duration voltage variations such as SAG, SWELL, and INTERRUPTION. The results demonstrated that the proposed OVT presents a dynamic response capable of satisfactorily measuring such disturbances and that it can be used as a power quality monitor for a 13.8 kV distribution system. Test on the proposed system concluded that it was capable to reproduce up to the 41st harmonic without significative distortion and impulsive surges up to 2.5 kHz. As an advantage, when compared with conventional systems to monitor power quality, the prototype can be remote-monitored, and therefore, be installed at strategic locations on distribution lines to be monitored kilometers away, without the need to be electrically powered.

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Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2019-0278 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Diego A. Monroy-Ortiz ◽

Sergio A. Dorado-Rojas ◽

Eduardo Mojica-Nava ◽

Sergio Rivera

Keyword(s):

Power Systems ◽

Power System ◽

Model Reduction ◽

Electrical Power ◽

Schur Complement ◽

Power Transfer ◽

Electrical Power System ◽

Admittance Matrix ◽

Test Beds ◽

Power Transfer Distribution Factors

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

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Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

Energies ◽

10.3390/en14092471 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2471

Author(s):

Tommaso Bradde ◽

Samuel Chevalier ◽

Marco De Stefano ◽

Stefano Grivet-Talocia ◽

Luca Daniel

Keyword(s):

Dynamical Systems ◽

Power System ◽

System Dynamics ◽

Real Time ◽

Initial Conditions ◽

Test System ◽

Initial State ◽

Power System Dynamics ◽

Vector Fitting ◽

Time Modeling

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system.

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