Disturbances In Power Network Research Articles

Intelligent priority based generation control for multi area system

ABSTRACT Increased innovation and automation for higher comfort level of living beings have stressed power sector for more units of power to be generated and supplied. To meet this demand, several generating stations are needed to be connected to supply this enormous load to end consumers. While interconnecting generated power of multi-area system, several problems are being encountered. In the current proposed work, 2-area interconnected power system has been considered and automatic generation control problem for single area loading and multi-area multi-type loading has been solved with the help of intelligent control strategy using TLBO algorithm. The proposed work has been processed and simulated in MATLAB and SIMULINK environment. Three types of loadings are considered in the proposed work. First one is single area fixed loading, second one is both area loading with different fixed load, and third one is increasing type load for fixed duration of time in single area. Based on the nature of severity of disturbances in power network, a fitness function has been designed for these multi-type of loadings to improve its transient response to avoid failure of synchronism and improve resiliency of power network to supply uninterrupted power to end consumers.

The Influence of Power Network Disturbances on Short Delayed Estimation of Fundamental Frequency Based on IpDFT Method with GMSD Windows

This paper presents an application of the IpDFT spectrum interpolation method to estimate the fundamental frequency of a power waveform. Zero-crossing method (ZC) with signal prefiltering was used as a reference method. Test models of disturbances were applied, based on real disturbances recorded in power networks, including voltage harmonics and interharmonics, transient overvoltages, frequency spikes, dips and noise. It was determined that the IpDFT method is characterized by much better dynamic parameters with better estimation precision. In an example, in the presence of interharmonics, the frequency estimation error was three times larger for the reference method than that for the IpDFT method. Furthermore, during the occurrence of fast transient overvoltages, the IpDFT method reached its original accuracy about three times faster than the ZC method. Finally, using IpDFT, it was possible to identify the type of disturbances: impulsive, step changes of frequency or voltage dips.

Mobile Emergency Generator Planning in Resilient Distribution Systems: A Three-Stage Stochastic Model With Nonanticipativity Constraints

Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are required to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple scenarios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model.

An Efficient Parallel 3-D FDTD Method for Calculating Lightning-Induced Disturbances on Overhead Lines in the Presence of Surge Arresters

In this paper, we propose an efficient modeling technique for incorporating surge arresters in finite-difference time-domain (FDTD) simulations of lightning-induced disturbances in power networks. Instead of using piecewise linear approximations for the V–I curve of an arrester, as usually done in the literature, we derive new updating equations for calculation of electric field components required at each FDTD time step. This is done in a semi-implicit manner where we first obtain a time-stepping relation of electric field based on the V–I curves of the arresters. The time-stepping relation in each time step is solved by the Newton–Raphson method to update the electric field components. The proposed technique is implemented into a nonuniform parallel FDTD code, which is used to simulate a three-phase distribution line of about 800 m equipped with lightning arresters and a neutral wire, installed at the International Center for Lightning Research and Testing at Camp Blanding Florida. The results of FDTD numerical simulations are found to be in excellent agreement with experimental data associated with triggered lightning.

Implementation of Power Network Disturbance Identification System

A power network disturbance identification system (PNDIS) was developed as a new function module of wide-area security defense system (WASDS), which has been commissioned in the dispatching center of a real power system. This paper describes the implementation of its software platform which consists of three parts: data sharing service, calculation engine and visualization module. The paper focuses on the design of the calculation engine, which is the key part of the platform, and the visualization module. Wavelet transform (WT) is used to identify the time and location of disturbance in the power system. Tests on measured data recorded by WAMS are presented in order to illustrate the benefits of the software platform and show its excellent performance.

Torsional Vibration of a Shafting System under Electrical Disturbances

Torsional vibration responses of a nonlinear shafting system are studied by a modified Riccati torsional transfer matrix combining with the Newmark-βmethod. Firstly, the system is modeled as a chain consisting of an elastic spring with concentrated mass points, from which a multi-segment lumped mass model is established. Secondly, accumulated errors are eliminated from the eigenfrequencies and responses of the system's torsional vibration by this newly developed procedure. The incremental transfer matrix method, combining the modified Riccati torsional transfer matrix with Newmark-βmethod, is further applied to solve the dynamical equations for the torsional vibration of the nonlinear shafting system. Lastly, the shafting system of a turbine-generator is employed as an illustrating example, and simulation analysis has been performed on the transient responses of the shaft's torsional vibrations during typical power network disturbances, such as three-phase short circuit, two-phase short circuit and asynchronous juxtaposition. The results validate the present method and are instructive for the design of a turbo-generator shaft.

Torsional Vibration of a Shafting System under Electrical Disturbances

Torsional vibration responses of a nonlinear shafting system are studied by a modified Riccati torsional transfer matrix combining with the Newmark-β method. Firstly, the system is modeled as a chain consisting of an elastic spring with concentrated mass points, from which a multi-segment lumped mass model is established. Secondly, accumulated errors are eliminated from the eigenfrequencies and responses of the system's torsional vibration by this newly developed procedure. The incremental transfer matrix method, combining the modified Riccati torsional transfer matrix with Newmark-β method, is further applied to solve the dynamical equations for the torsional vibration of the nonlinear shafting system. Lastly, the shafting system of a turbine-generator is employed as an illustrating example, and simulation analysis has been performed on the transient responses of the shaft's torsional vibrations during typical power network disturbances, such as three-phase short circuit, two-phase short circuit and asynchronous juxtaposition. The results validate the present method and are instructive for the design of a turbo-generator shaft.

Advanced Signal Processing Techniques for Feature Extraction in Data Mining

This paper gives a description of various signal processing techniques that are in use for processing time series databases for extracting relevant features for pattern recognition. In addition to describe the normally used signal processing methods, we also present a novel signal processing technique, which is a modification of the well-known Short-Time Fourier Transform (STFT) and Wavelet Transform for extracting suitable features for both visual and automatic classification of non-stationary time-series databases [3][5]. Although the new signal processing technique is useful for speech, medical, financial, and other types of time varying databases, only the power network disturbance time series is used for computing features for visual and automatic recognition. General Terms Signal processing, Time series database, pattern recognition, data mining.

Renewed Support Dawns in Europe: An Action to Develop Space Weather Products and Services

The effects of space weather span a range of sectors. They can cause radio communications problems; can disrupt synthetic aperture radar systems, the Global Positioning System (GPS), and the future European Galileo systems; and can increase radiation risks for aircraft crew and passengers. Electric power network disturbances and enhanced corrosion effects observed in long-distance fuel supply pipelines are other well-known effects of unfavorable space weather. In severe cases, large-scale power outages have also been traced to space weather phenomena (Figure 1). Research efforts in various countries—including the U.S. multiagency National Space Weather Program (http:// www.nswp.gov), the International Space Environment Service (ISES; http://www.ises-spaceweather.org/), and several European initiatives sponsored by the European Space Agency (ESA) and the European Commission (EC)—have demonstrated that adverse space weather poses a tangible threat to humans and modern technological systems and assets on the ground, in the air, and in space. Although methods to model some aspects of space weather have been developed by these agencies, all agree that their performance needs to be improved—in many cases, prediction accuracy is inadequate to allow the transition from models to reliable operational services. Further targeted research and development is needed. Funding for coordinated space weather science and applications effects in Europe has so far been on a project-by-project basis. Systematic national contributions to a pan-European space weather program are not yet in place, leading to fragmentation of European space weather initiatives. A certain level of

Time sequence data mining using time–frequency analysis and soft computing techniques

This paper presents a new approach for time series data mining and knowledge discovery. The relevant features of non-stationary time series data from power network disturbances are extracted using a multiresolution S-transform which can be treated either as a phase corrected wavelet transform or a variable window short-time Fourier transform. After extracting the relevant features from the time series data, an integrated LVQ neural network and various feed-forward neural network architectures are used for pattern recognition of disturbance waveform data. The fuzzy MLP outperforms all the other different connectionist models and is used in the final stage for encoding knowledge in the connection weights that are used to generate rules for fuzzy inferencing of the disturbance patterns. Overall pattern classification accuracy of 99% is achieved for power signal time series data. The knowledge discovery from the data has then been presented for selected patterns using the new quantification procedures. The approach presented in this paper is a general one and can be applied to any time series data sequence for mining for similarities in the data.

Hybrid S-Transform and Kalman Filtering Approach for Detection and Measurement of Short Duration Disturbances in Power Networks

This paper presents a new approach in the detection, localization, and classification of short duration disturbances in the power networks using a phase-corrected wavelet transform known as S-transform (ST) and an extended Kalman filter (EKF). The ST has excellent time-frequency resolution characteristics and provides detection, localization, and visual patterns suitable for automatic recognition of power quality events. The EKF, on the other hand, provides automatic classification and measurements of the frequently occurring power frequency short duration disturbances on the power networks. Thus, by combining both the ST and EKF, it is possible to completely classify and measure the short duration power quality disturbances. The proposed technique is applied to both simulated and experimentally obtained short duration power network disturbances in the presence of additive noise, and the results reveal significant accuracy in completely characterizing the power quality events.

S-transform-based intelligent system for classification of power quality disturbance signals

In this paper, a new approach is presented for the detection and classification of nonstationary signals in power networks by combining the S-transform and neural networks. The S-transform provides frequency-dependent resolution that simultaneously localizes the real and imaginary spectra. The S-transform is similar to the wavelet transform but with a phase correction. This property is used to obtain useful features of the nonstationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks.