Relevant bibliographies by topics / Power system disturbance identification

Academic literature on the topic 'Power system disturbance identification'

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Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Power system disturbance identification"

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K.VEERA SUKUMAR, K. VEERA SUKUMAR, Dr L. RAVI SRINIVAS, B. MAHESH BABU, and Dr S. S. TULASI RAM. "Differential Evolution Based Power Quality Disturbance Identification and Mitigation in Power Systems." International Journal of Scientific Research 3, no. 1 (June 1, 2012): 164–68. http://dx.doi.org/10.15373/22778179/jan2014/52.

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Zhang, Yong Gang, Ming Yang Sun, Xian Feng Xu, and Wei Jin Zhuang. "Identification of the Maximum Wind Penetration Level during Over-Frequency Disturbances." Advanced Materials Research 805-806 (September 2013): 364–69. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.364.

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Todays power system is integrating increasingly variable and uncertain generation resources, especially wind power. As much of wind generators in the market contribute little or none to system inertia, power system is operated much closer to its dynamic security margin. To identify the maximum wind penetration of a power system following a pre-defined disturbance, the impact of increased wind penetration on post-disturbance stability is studied. In this paper, the disturbance is simulated by a short circuit that leads to the sudden disconnection of a large amount of load demand. When wind power covers a small portion of system demand, the post-disturbance frequency is not much affected by grid-connected wind generators. But when wind penetration is increased to a comparative high level, power system loses stability in the form of undamped frequency oscillation. Simulation results show that, in the occurrence of system disturbances, 60% feed-in wind penetration will make the power system loses stability. Anyway, taking into consideration of simulation accuracy, severity of disturbances and diversity of power systems, 60% must not be a precise result, it could just be used as a reference when analyzing other grids.
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Chen, Gang, Gan Li, Yu Fei Teng, Hua Zhang, and Li Jie Ding. "Implementation of Power Network Disturbance Identification System." Advanced Materials Research 1070-1072 (December 2014): 693–99. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.693.

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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.
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Borrás-Talavera, María Dolores, Juan Carlos Bravo, and César Álvarez-Arroyo. "Instantaneous Disturbance Index for Power Distribution Networks." Sensors 21, no. 4 (February 14, 2021): 1348. http://dx.doi.org/10.3390/s21041348.

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The stability of power systems is very sensitive to voltage or current variations caused by the discontinuous supply of renewable power feeders. Moreover, the impact of these anomalies varies depending on the sensitivity/resilience of customer and transmission system equipment to those deviations. From any of these points of view, an instantaneous characterization of power quality (PQ) aspects becomes an important task. For this purpose, a wavelet-based power quality indices (PQIs) are introduced in this paper. An instantaneous disturbance index (ITD(t)) and a Global Disturbance Ratio index (GDR) are defined to integrally reflect the PQ level in Power Distribution Networks (PDN) under steady-state and/or transient conditions. With only these two indices it is possible to quantify the effects of non-stationary disturbances with high resolution and precision. These PQIs offer an advantage over other similar because of the suitable choice of mother wavelet function that permits to minimize leakage errors between wavelet levels. The wavelet-based algorithms which give rise to these PQIs can be implemented in smart sensors and used for monitoring purposes in PDN. The applicability of the proposed indices is validated by using a real-time experimental platform. In this emulated power system, signals are generated and real-time data are analyzed by a specifically designed software. The effectiveness of this method of detection and identification of disturbances has been proven by comparing the proposed PQIs with classical indices. The results confirm that the proposed method efficiently extracts the characteristics of each component from the multi-event test signals and thus clearly indicates the combined effect of these events through an accurate estimation of the PQIs.
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Bereziuk, Iryna, Olena Holyk, and Valentyn Soldatenko. "Dynamic Design of Optimal Stochastic Stabilization System of Cutting Power on a Band Saw Machine." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 3(34) (October 2020): 169–74. http://dx.doi.org/10.32515/2664-262x.2020.3(34).169-174.

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The article is devoted to the development of methodological foundations for constructing an optimal system of stochastic stabilization of cutting power based on the results of structural identification of models of the dynamics of the system '' woodworking machine-cutting process '' and uncontrolled disturbance. In order to solve the problem of structural identification of the '' woodworking machine-cutting process ' system and the disturbance acting in the process of wood-cutting, the article proposes a special technology, the use of which made it possible to determine the transfer function of the '' woodworking machine-cutting process '' and estimate the spectral density of the disturbance acting during the processing. It has been established that when the physical and mechanical properties of wood and the state of the cutting tool change, the structure of the transfer function and spectral density does not change, but only the parameters change.As a result of solving the synthesis problem, the structure and parameters of the optimal controller are determined, which ensures the specified quality of the processed surface with minimal energy consumption. To assess the quality of control, it is proposed to use a quadratic criterion, which is the sum of two weighted variances of the stator current deviation of the main motion motor (characterizes energy costs) and the variance of the feed drive speed control signal.Studies of the robust stability of the optimal system with the obtained controller under the influence of unstructured disturbances made it possible to determine the class and estimate the maximum norms of unstructured disturbances at which the system maintains stability and a given control quality. The use of the proposed approach to the construction of an optimal system of stochastic stabilization of cutting power makes it possible to achieve a reduction in energy costs by 12% for a given quality of the processed surface by increasing the stabilization accuracy by two orders of magnitude.
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Bykhovsky, Alexander, and Joe H. Chow. "Power system disturbance identification from recorded dynamic data at the Northfield substation." International Journal of Electrical Power & Energy Systems 25, no. 10 (December 2003): 787–95. http://dx.doi.org/10.1016/s0142-0615(03)00045-0.

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Li, Hong Yi, Yi Fu, and Di Zhao. "Identification of Power Quality Disturbances Based on FFT and Attribute Weighted Artificial Immune Evolutionary Classifier." Applied Mechanics and Materials 530-531 (February 2014): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.277.

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Nowadays, the issue of Electromagnetic Compatibility is of great importance and urgency. In this paper, we propose a novel hybrid automatic identification system for power quality disturbances, which lays foundations for further analyzing the electromagnetic compatibility. Specifically, we firstly extract features by using the FFT and envelope detection method. Then we utilize the attribute weighted artificial immune evolutionary Classifier (AWAIEC) for classification of power quality disturbance events. Experimental results have shown that the proposed method performs better than existing approaches.
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Fan, Shaosheng, Xuhong Wang, and Siyang Yang. "Voltage Disturbance Signals Identification Based on ILMD and Neural Network." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (October 14, 2019): 2058007. http://dx.doi.org/10.1142/s0218001420580070.

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In order to identify the disturbance signal in power system and reduce the influence on system security, a voltage disturbance signal classifier based on improved local mean decomposition (ILMD) and BP neural network is proposed. ILMD is used to decompose the disturbance signal in three layers, and the product function (PF) component with amplitude-frequency information of voltage signal is obtained. The signal energy value constructed by PF component is used as the input of BP neural network to identify and classify the voltage disturbance signal. Experiments on four typical voltage disturbance signals show that the signal classifiers based on ILMD and BP neural networks have high accuracy and good working efficiency for the recognition and classification of voltage disturbance signals.
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Lu, Shiue-Der, Hong-Wei Sian, Meng-Hui Wang, and Rui-Min Liao. "Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis." Applied Sciences 9, no. 11 (May 30, 2019): 2228. http://dx.doi.org/10.3390/app9112228.

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The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.
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Bentley, E. C., G. A. Putrus, S. McDonald, and P. Minns. "Power quality disturbance source identification using self-organising maps." IET Generation, Transmission & Distribution 4, no. 10 (2010): 1188. http://dx.doi.org/10.1049/iet-gtd.2009.0498.

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Dissertations / Theses on the topic "Power system disturbance identification"

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Manmek, Thip Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Real-time power system disturbance identification and its mitigation using an enhanced least squares algorithm." Awarded by:University of New South Wales. Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/26233.

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This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems
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Glickman, Mark. "Disturbance monitoring in distributed power systems." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16497/.

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Power system generators are interconnected in a distributed network to allow sharing of power. If one of the generators cannot meet the power demand, spare power is diverted from neighbouring generators. However, this approach also allows for propagation of electric disturbances. An oscillation arising from a disturbance at a given generator site will affect the normal operation of neighbouring generators and might cause them to fail. Hours of production time will be lost in the time it takes to restart the power plant. If the disturbance is detected early, appropriate control measures can be applied to ensure system stability. The aim of this study is to improve existing algorithms that estimate the oscillation parameters from acquired generator data to detect potentially dangerous power system disturbances. When disturbances occur in power systems (due to load changes or faults), damped oscillations (or "modes") are created. Modes which are heavily damped die out quickly and pose no threat to system stability. Lightly damped modes, by contrast, die out slowly and are more problematic. Of more concern still are "negatively damped" modes which grow exponentially with time and can ultimately cause the power system to fail. Widespread blackouts are then possible. To avert power system failures it is necessary to monitor the damping of the oscillating modes. This thesis proposes a number of damping estimation algorithms for this task. If the damping is found to be very small or even negative, then additional damping needs to be introduced via appropriate control strategies. This thesis presents a number of new algorithms for estimating the damping of modal oscillations in power systems. The first of these algorithms uses multiple orthogonal sliding windows along with least-squares techniques to estimate the modal damping. This algorithm produces results which are superior to those of earlier sliding window algorithms (that use only one pair of sliding windows to estimate the damping). The second algorithm uses a different modification of the standard sliding window damping estimation algorithm - the algorithm exploits the fact that the Signal to Noise Ratio (SNR) within the Fourier transform of practical power system signals is typically constant across a wide frequency range. Accordingly, damping estimates are obtained at a range of frequencies and then averaged. The third algorithm applied to power system analysis is based on optimal estimation theory. It is computationally efficient and gives optimal accuracy, at least for modes which are well separated in frequency.
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Looja, Tuladhar R. "Control of Custom Power System using Active Disturbance Rejection Control." Cleveland State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=csu1438913443.

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Dong, Jingyuan. "Power System Disturbance Analysis and Detection Based on Wide-Area Measurements." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/30129.

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Wide-area measurement systems (WAMS) enable the monitoring of overall bulk power systems and provide critical information for understanding and responding to power system disturbances and cascading failures. The North American Frequency Monitoring Network (FNET) takes GPS-synchronized wide-area measurements in a low-cost, easily deployable manner at the 120 V distribution level, which presents more opportunities to study power system dynamics. This work explores the topics of power system disturbance analysis and detection by utilizing the wide-area measurements obtained in the distribution networks. In this work, statistical analysis is conducted based on the major disturbances in the North American Interconnections detected by the FNET situation awareness system between 2006 and 2008. Typical frequency patterns of the generation and load loss events are analyzed for the three North American power Interconnections: the Eastern Interconnection (EI), the Western Electricity Coordinating Council (WECC), and the Electric Reliability Council of Texas (ERCOT). The linear relationship between frequency deviation and frequency change rate during generation/loss mismatch events is verified by the measurements in the three Interconnections. The relationship between the generation/load mismatch and system frequency is also examined based on confirmed generation loss events in the EI system. And a power mismatch estimator is developed to improve the current disturbance detection program. Various types of power system disturbances are examined based on frequency, voltage and phase angle to obtain the event signatures in the measurements. To better understand the propagation of disturbances in the power system, an automated visualization tool is developed that can generate frequency and angle replays of disturbances, as well as image snapshots. This visualization tool correlates the wide-area measurements with geographical information by displaying the measurements over a geographical map. This work makes an attempt to investigate the visualization of the angle profile in the wide-area power system to improve situation awareness. This work explores the viability of relying primarily on distribution-level measurements to detect and identify line outages, a topic not yet addressed in previous works. Line outage sensitivity at different voltage levels in the Tennessee Valley Authority (TVA) system is examined to analyze the visibility of disturbances from the point of view of wide-area measurements. The sensor placement strategy is proposed for better observability of the line trip disturbances. The characteristics of line outages are studied extensively with simulations and real measurements. Line trip detection algorithms are proposed that employs the information in frequency and phase angle measurements. In spite of the limited FDR coverage and confirmed training cases, an identification algorithm is developed which uses the information in the real measurements as well as the simulation cases to determine the tripped line.
Ph. D.
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Chapman, Jeffrey W. (Jeffrey Wayne). "Power system control for large-disturbance stability : security, robustness and transient energy." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/39393.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (p. 223-228).
by Jeffrey Wayne Chapman.
Ph.D.
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Wall, Peter Richard. "Online prediction of the post-disturbance frequency behaviour of a power system." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/online-prediction-of-the-postdisturbance-frequency-behaviour-of-a-power-system(fbbe0a82-3c88-4633-8947-ef16a3616aea).html.

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The radical changes that are currently occurring in the nature of power systems means that in the future it may no longer be possible to guarantee security of supply using offline security assessment and planning. The increased uncertainty, particularly the reduction and variation in system inertia that will be faced in the future must be overcome through the use of adaptive online solutions for ensuring system security. The introduction of synchronised measurement technology means that the wide area real time measurements that are necessary to implement these online actions are now available.The objective of the research presented in this thesis was to create methods for predicting the post-disturbance frequency behaviour of a power system with the intent of contributing to the development of real time adaptive corrective control for future power systems. Such a prediction method would generate an online prediction based on wide area measurements of frequency and active power that are recorded within the period of approximately one second after a disturbance to the active power balance of the system. Predictions would allow frequency control to respond more quickly and efficiently as it would no longer be necessary to wait for the system frequency behaviour to violate pre-determined thresholds.The research presented in this thesis includes the creation of an online method for the simultaneous detection of the time at which a disturbance occurred in a power system, or area of a power system, and the estimation of the inertia of that system, or area. An existing prediction method based on approximate models has been redesigned to eliminate its dependence on offline information. Furthermore, the thesis presents the novel application of pattern classification theory to frequency prediction and a five class example of pattern classification is implemented.
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Singhavilai, Thamvarit. "Identification of electric power system dynamic equivalent." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15647.

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Rioual, Pascal. "Modélisation et commande d'un redresseur à MLI alimenté par un réseau déséquilibré." Cachan, Ecole normale supérieure, 1993. http://www.theses.fr/1993DENS0008.

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L'augmentation considérable ces dernières années du nombre de convertisseur sur le réseau d'alimentation se traduit par la nécessite de s'intéresser a la compatibilité entre les convertisseurs et le réseau. Le redresseur à mli est un convertisseur dont les caractéristiques lui permettent d'absorber des courants réseaux sinusoïdaux, et d'échanger de la puissance active et réactive avec le réseau. Le présent travail concerne l'étude de la modélisation et la commande de ce convertisseur ainsi que l'extension au cas d'un réseau déséquilibre. Le redresseur est contrôlé par une régulation cascade: des boucles internes régulent les courants réseaux, une boucle externe régule la tension de capacité. Les conséquences de l'apparition d'une perturbation de type creux de tension (distorsion harmonique sur les courants réseaux, oscillation a l'harmonique 2 des composantes continues) sont analysées puis simulées et vérifiées expérimentalement. Deux solutions sont étudiées: - une compensation s'appuyant sur une étude théorique du système. - la construction d'un régulateur robuste appliquant le principe du modèle interne de Wohnam et la méthode de placement de pôles par -stabilité. Les avantages obtenus permettent d'envisager une meilleure continuité de fonctionnement face aux creux de tension, un dimensionnement réduit du convertisseur, et la conservation des performances. Les domaines d'application concernes sont varies. Dans le domaine industriel, il s'agit des alimentations à bon facteur de puissance ou des procédés nécessitant le réglage du réactif
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Banejad, Mahdi. "Identification of Damping Contribution from Power System Controllers." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15851/.

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With the growth of power system interconnections, the economic drivers encourage the electric companies to load the transmission lines near their limits, therefore it is critical to know those limits well. One important limiting issue is the damping of inter-area oscillation (IAO) between groups of synchronous machines. In this Ph.D. thesis, the contribution of power system components such as load and static var compensators (SVC) that affect the IAO of the power system, are analysed. The original contributions of this thesis are as follows: 1-Identification of eigenvalues and mode shapes of the IAO: In the first contribution of this thesis, the eigenvalues of the IAO are identified using a correlation based method. Then, the mode shape at each identified resonant frequency is determined to show how the synchronous generators swing against each other at the specific resonant frequencies. 2-Load modelling and load contribution to damping: The first part of this contribution lies in identification of the load model using cross-correlation and autocorrelation functions . The second aspect is the quantification of the load contribution to damping and sensitivity of system eigenvalues with respect to the load. 3- SVC contribution to damping: In this contribution the criteria for SVC controller redesign based on complete testing is developed. Then the effect of the SVC reactive power on the measured power is investigated. All of the contributions of this thesis are validated by simulation on test systems. In addition, there are some specific application of the developed methods to real data to find a.) the mode shape of the Australian electricity network, b.) the contribution of the Brisbane feeder load to damping and c.) the effect of the SVC reactive power of the Blackwall substations on the active power supplying Brisbane.
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Liang, Wuxing. "Moving window subspace identification for power system oscillations." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517098.

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Books on the topic "Power system disturbance identification"

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Ibrahim, Mohamed A. Disturbance analysis for power systems. Hoboken, N.J: Wiley, 2011.

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I, Shim Theodore, ed. Spectrum estimation and system identification. New York: Springer-Verlag, 1993.

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Xi tong bian shi ji qi zai shui dian neng yuan zhong de ying yong: System identification and its application in hydroelectric energy. Wuhan shi: Hubei ke xue ji shu chu ban she, 2008.

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Deborah, Ross. Puget Sound fuel blind integrated resource planning project report #1: Description of Working Group Members : identification of system interaction. Olympia, WA (925 Plum St. S.E., Bldg 4, P.O. Box 43165, Olympia 98504-3165): Washington State Energy Office, 1993.

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KKS identification system for power stations. VGB Technical Committe, 1988.

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Technische Vereinigung der Grosskraftwerksbetreiber (Germany), ed. KKS identification system for power stations: KKS guidelines. Essen: VGB-Kraftwerkstechnik GmbH, 1989.

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T, Leondes Cornelius, ed. System identification and adaptive control. Orlando: Academic Press, 1987.

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Institute of Electrical and Electronics Engineers. and IEEE Power Engineering Society. Power Generation Committee., eds. IEEE recommended practice for system identification in fossil-fueled power plants and related facilities. New York (345 East 47th Street, New York 10017): Institute of Electrical and Electronics Engineeres, 1986.

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T, Leondes Cornelius, ed. Analysis and control system techniques for electric power systems. San Diego: Academic Press, 1991.

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IEEE Power Engineering Society. Power Generation Committee. Excitation Systems Subcommittee. and IEEE Standards Board, eds. IEEE guide for identification, testing, and evaluation of the dynamic performance of excitation control systems. New York, N.Y: The Institute of Electrical and Electronics Engineers, 1990.

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Book chapters on the topic "Power system disturbance identification"

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Li, Qi, Jun Fang, and Jia Sheng. "Data-Driven Power Quality Disturbance Sources Identification Method." In Web Information Systems and Applications, 569–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_57.

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Jlassi, Zahra, Khadija Ben Kilani, Mohamed Elleuch, and Lamine Mili. "Power System Generator Coherency Identification for Large Disturbances by Koopman Modes Analysis." In Lecture Notes in Electrical Engineering, 187–202. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6403-1_13.

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Ledwich, Gerard, and Arash Vahidnia. "Identification of Power System Dynamics." In Power Systems, 27–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67040-5_3.

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Marconato, Roberto, and Alberto Berizzi. "Small-Disturbance Angle Stability and Electromechanical Oscillation Damping." In Handbook of Electrical Power System Dynamics, 477–569. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118516072.ch9.

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Pillai, S. Unnikrishna, and Theodore I. Shim. "Power Spectra and Positive Functions." In Spectrum Estimation and System Identification, 7–60. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8318-5_2.

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Lv, Ganyun, Xiushan Cai, and Yuanyuan Jin. "Improvement of Grey Relation Analysis and Its Application on Power Quality Disturbances Identification." In Fuzzy Systems and Knowledge Discovery, 1159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_144.

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Hossack, J., S. D. J. McArthur, E. Davidson, J. R. McDonald, and T. Cumming. "A Multi-Agent Intelligent Interpretation System for Power System Disturbance Diagnosis." In Applications and Innovations in Intelligent Systems X, 91–104. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0649-4_7.

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McArthur, Stephen D. J., James R. McDonald, and John Hossack. "A Multi-Agent Approach to Power System Disturbance Diagnosis." In Autonomous Systems and Intelligent Agents in Power System Control and Operation, 75–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05955-5_5.

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Salas-Cabrera, Rubén, Oscar Martínez-Hernández, Julio C. Rosas-Caro, Jonathan C. Mayo-Maldonado, E. Nacú Salas-Cabrera, Aaron González-Rodríguez, Hermenegildo Cisneros-Villegas, Rafael Castillo-Gutierrez, Gregorio Hernández-Palmer, and Rodolfo Castillo-Ibarra. "Parametric Identification of a Power-System Emulator." In Intelligent Automation and Systems Engineering, 79–92. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0373-9_7.

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Arroyo-Figueroa, G., and L. Enrique Sucar. "EDAS – Event-Disturbance Analysis System for Fossil Power Plants Operation." In Lecture Notes in Computer Science, 706–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720076_63.

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Conference papers on the topic "Power system disturbance identification"

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Zang, Hongzhi, and Yishu Zhao. "Intelligent Identification System of Power Quality Disturbance." In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.314.

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Xiong, Wen, Li Wang, Renbo Wu, Zikang Li, Hao Liu, and Tianshu Bi. "Power System Disturbance Identification With Missing PMU Data." In 2020 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2020. http://dx.doi.org/10.1109/ispec50848.2020.9351130.

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Xiong, Xiaofu, and Wei Zheng. "Status and prospect of power disturbance identification research." In 2011 IEEE International Conference on Advanced Power System Automation and Protection (APAP). IEEE, 2011. http://dx.doi.org/10.1109/apap.2011.6180610.

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Feng, Dandan, Zhanfeng Deng, Tongxun Wang, Yingying Liu, and Lingling Xu. "Identification of disturbance sources based on random forest model." In 2018 International Conference on Power System Technology (POWERCON). IEEE, 2018. http://dx.doi.org/10.1109/powercon.2018.8602245.

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Bi, T. S., X. N. Song, J. T. Wu, and Q. X. Yang. "Novel method for disturbance identification in power systems." In 2006 IEEE Power Engineering Society General Meeting. IEEE, 2006. http://dx.doi.org/10.1109/pes.2006.1709431.

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Lee, Joohyun, and Seung-Ki Sul. "Identification of the System Inertia under Periodic Load Torque Disturbance." In 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia). IEEE, 2020. http://dx.doi.org/10.1109/ipemc-ecceasia48364.2020.9367637.

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Xiaohui Qin, Baiqing Li, Qiang Guo, Shen Hong, Qinyong Zhou, and Tianshu Bi. "Study on power system disturbance identification and location based on WAMS." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345756.

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Tingfang, Yang, Liu Pei, Zeng Xiangjun, and K. k. Li. "Application of Adaptive Generalized Morphological Filter in Disturbance Identification for Power System Signatures." In 2006 International Conference on Power System Technology. IEEE, 2006. http://dx.doi.org/10.1109/icpst.2006.321445.

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Wang, Chao, Huimin Gao, and Taoxi Zhu. "A New Method for Detection and Identification of Power Quality Disturbance." In 2006 IEEE PES Power Systems Conference and Exposition. IEEE, 2006. http://dx.doi.org/10.1109/psce.2006.296532.

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Moussa, Abd-Elmoniem, Mahmoud El-Gammal, Emtethal Abdallah, and Ahmed Abou El-Seoud. "Hardware-Software Structure for On-Line Power Quality Assessment: Part I." In ASME/IEEE 2004 Joint Rail Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/rtd2004-66022.

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Abstract:
The main objective of the proposed work is to introduce a new concept of advanced power quality assessment. The introduced system is implemented using applications of a set of powerful software algorithms and a digital signal processor based hardware data acquisition system. The suggested scheme is mainly to construct a system for real time detection and identification of different types of power quality disturbances that produce a sudden change in the power quality levels. Moreover, a new mitigation technique through generating feedback correction signals for disturbance compensation is addressed. The performance of the suggested system is tested and verified through real test examples. The obtained results reveal that, the introduced system detects fast and accurately most of the power quality disturbance events and introduces new indicative factors estimating the performance of any supply system subjected to a set number of disturbance events.
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Reports on the topic "Power system disturbance identification"

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Trudnowski, D. J. Power system identification toolbox: Phase two progress. Office of Scientific and Technical Information (OSTI), August 1994. http://dx.doi.org/10.2172/10184602.

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Pierre, John W., Richard Wies, and Daniel Trudnowski. Advanced Techniques for Power System Identification from Measured Data. Office of Scientific and Technical Information (OSTI), November 2008. http://dx.doi.org/10.2172/943294.

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Final report on the development of a 2 MW/10 second battery energy storage system for power disturbance protection. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/503449.

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Annual progress report on the development of a 2 MW/10 second battery energy storage system for power disturbance protection. Office of Scientific and Technical Information (OSTI), January 1996. http://dx.doi.org/10.2172/188603.

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