Journal articles: '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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Ul Banna, Hasan, Sarika Khushalani Solanki, and Jignesh Solanki. "Data‐driven disturbance source identification for power system oscillations using credibility search ensemble learning." IET Smart Grid 2, no. 2 (April 23, 2019): 293–300. http://dx.doi.org/10.1049/iet-stg.2018.0092.

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Vighneshwari, B. Devi, and R. Neela. "Novel classifier design for optimising the accuracy for identification of disturbance in power system." International Journal of Power Electronics 12, no. 2 (2020): 213. http://dx.doi.org/10.1504/ijpelec.2020.10029995.

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Vighneshwari, B. Devi, and R. Neela. "Novel classifier design for optimising the accuracy for identification of disturbance in power system." International Journal of Power Electronics 12, no. 2 (2020): 213. http://dx.doi.org/10.1504/ijpelec.2020.108844.

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Gonzalez-Abreu, A. D., M. Delgado-Prieto, J. J. Saucedo-Dorantes, and R. A. Osornio-Rios. "Novelty Detection on Power Quality Disturbances Monitoring." Renewable Energy and Power Quality Journal 19 (September 2021): 211–16. http://dx.doi.org/10.24084/repqj19.259.

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Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.

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Yalcin, Turgay, and Muammer Ozdemir. "An Implementation of Exploratory Start for Power Quality Disturbance Pattern Recognition." Transactions on Environment and Electrical Engineering 1, no. 3 (October 12, 2016): 86. http://dx.doi.org/10.22149/teee.v1i3.50.

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Identification of system disturbances and detection of them guarantees smart grids power quality system reliability and long lasting life of the power system. The key goal of this study is to generate non - time consuming features for CPU, for recognizing different types of non-stationary and non-linear smart grid faults based on signal processing techniques. This paper proposes a new solution for real time power system monitoring against power quality faults focusing on voltage sag and noise. EEMD is used for noise reduction with first intrinsic mode function (imf1). Hilbert Huang Transform (HHT) is used for generating instantaneous amplitude (IA) and instantaneous frequency (IF) feature of real time voltage sag power signal. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize and remove EMI (Electromagnetic Interference)-Noise. In this study based on experimental studies, Hilbert Huang based pattern recognition technique was used to investigate power signal to diagnose voltage sag in power grid. SVM and Decision Tree (C4.5) were operated and their achievements were matched for calculation error and CPU time. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.

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Rashmi, S., and Shankaraiah. "A Novel Faulty Phase Identification Algorithm and Fast DQ Transform Technique for Voltage Sag Detection." Journal of Circuits, Systems and Computers 26, no. 12 (August 2017): 1750204. http://dx.doi.org/10.1142/s0218126617502048.

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The disturbances created in AC transmission line power flow due to various faults and unavoidable natural circumstances have to be monitored carefully for uninterrupted power supply to the consumer and to avoid unacceptable situations. Voltage sag is one such unwanted disturbance occurring in the power system due to varying causes like lightning, loose connections, accidental short circuits, tree branches touching the line, birds hitting the line, etc., owing to non-recoverable damage to sensitive equipment in industries which also leads to financial losses. This paper proposes a new technique using DQ transform and integrator for fast voltage sag detection which is possible within [Formula: see text]th of a cycle and a new algorithm to identify the faulty phase. The authors are able to detect the voltage sag within the lowest time of 0.3[Formula: see text]ms for single-phase fault and 0.2[Formula: see text]ms for two-phase and three-phase faults. The simulation results for various types of power system faults with different point-on-waveform (POW) are presented using MATLAB/Simulink tool.

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H. Jopri, M., A. R. Abdullah, M. Manap, T. Sutikno, and M. R. Ab Ghani. "An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 244–56. http://dx.doi.org/10.11591/eei.v7i2.1188.

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The identification of multiple harmonic sources (MHS) is vital to identify the root causes and the mitigation technique for a harmonic disturbance. This paper introduces an identification technique of MHS in a power distribution system by using a time-frequency distribution (TFD) analysis known as a spectrogram. The spectrogram has advantages in term of its accuracy, a less complex algorithm, and use of low memory size compared to previous methods such as probabilistic and harmonic power flow direction. The identification of MHS is based on the significant relationship of spectral impedances, which are the fundamental impedance (Z1) and harmonic impedance (Zh) that estimate the time-frequency representation (TFR). To verify the performance of the proposed method, an IEEE test feeder with several different harmonic producing loads is simulated. It is shown that the suggested method is excellent with 100% correct identification of MHS. The method is accurate, fast and cost-efficient in the identification of MHS in power distribution arrangement.

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Shi, Tao, Linan Qu, and Luming Ge. "Research on the Parameter Test and Identification Method of Electromechanical Transient Model for PV Power Generation." Electronics 9, no. 8 (July 22, 2020): 1184. http://dx.doi.org/10.3390/electronics9081184.

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Model and parameters are the indispensable conditions for the simulation calculation of power systems with a high proportion of photovoltaic power generation. Conventional models of power electronic devices are difficult to meet the requirement of power system electromechanical transient simulation, and the parameters are difficult to obtain. Aiming at this problem, this paper proposes a structure of an electromechanical transient simulation model of a photovoltaic power station and designs a set of photovoltaic power generation transient characteristic test systems based on a fault simulation device. Through a disturbance test and model parameter identification, the electromechanical transient simulation model and parameters of photovoltaic power generation are obtained. In this paper, based on the test system, the electromechanical transient characteristics of a certain type of photovoltaic inverter are modeled. The results show that the model can successfully describe the electromechanical transient characteristics of photovoltaic power generation, and the simulation results obtained based on the model parameters have a good fitting degree compared with the measured curve.

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Liu, Shu Jun, and Ping Liu. "Load Identification Modeling with Improved Model Structure." Applied Mechanics and Materials 740 (March 2015): 368–72. http://dx.doi.org/10.4028/www.scientific.net/amm.740.368.

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The running state of different induction motor in the load group will be moved toward two directions when it was occurred the large disturbance in the power system, one is to keep on running, another is out of step or stall. If the quantity of stall induction motor in the group is a large number, the dynamic of stall motors will effect on the stability of power system obviously. So it is necessary to develop the detailed load model to simulate the complicated dynamic characteristic of the load group. An improved synthesis load model which combines two kinds of induction motor is proposed, and the improved genetic algorithm is used to identify the parameter of load model. The case is studied to illustrate the effective and comprehensive adaptability of the proposed identification model.

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Hasan, Kazi Nazmul, Robin Preece, and Jovica V. Milanović. "Application of game theoretic approaches for identification of critical parameters affecting power system small-disturbance stability." International Journal of Electrical Power & Energy Systems 97 (April 2018): 344–52. http://dx.doi.org/10.1016/j.ijepes.2017.11.027.

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Chen, Cheng-I., Chien-Kai Lan, Yeong-Chin Chen, Chung-Hsien Chen, and Yung-Ruei Chang. "Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid." Energies 13, no. 4 (February 24, 2020): 1007. http://dx.doi.org/10.3390/en13041007.

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To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

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Yun, Luo. "An Islanding Detection Method for Photovoltaic Power Generation System Using Fluctuation Characteristic of PCC Harmonic Voltage." Advanced Materials Research 998-999 (July 2014): 574–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.574.

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In the photovoltaic power generation system, the harmonic voltage of the point of common coupling (PCC) is determined by system disturbance, local load and photovoltaic power generation system itself together. Based on the fluctuations of this harmonic voltage, a new islanding detection method for the PV system is proposed. By extracting the higher harmonic components from the wavelet transformation of point of common coupling (PCC) voltage, and then taking its fluctuations as the islanding identification and detection index, non-detection zone (NDZ) can be eliminated, without a plus disturbance. According to IEEE Std.1547, simulated verification of the proposed method is accomplished in the ideal photovoltaic grid system. When the PCC voltage and frequency are within the normal range, it can not only give a fast and accurate detection to the occurrence of islanding, but also identify the false islanding, such as voltage dips and switching loads, effectively avoiding false detection caused by the false islanding.

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Jin, Cuicui, Weidong Li, Liu Liu, Ping Li, and Xian Wu. "A Coherency Identification Method of Active Frequency Response Control Based on Support Vector Clustering for Bulk Power System." Energies 12, no. 16 (August 16, 2019): 3155. http://dx.doi.org/10.3390/en12163155.

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Active frequency response (AFR) control is needed in current power systems. To solve the over-frequency problems of generators connected to non-disturbed buses during the AFR control period, the generators should be clustered into coherent groups. Thus, the control efficiency can be improved on the premise of ensuring control accuracy. Since the influencing factors (such as the model parameters, operation modes, and disturbance locations, etc.) of power system operation can be comprehensively reflected by the generator frequency, which is easily collected and calculated, the generator frequency can be used as the coherency identification input. In this paper, we propose a coherency identification method of AFR control based on support vector clustering for a bulk power system. By mapping data samples from the initial space to the high-dimensional feature space, the radius of the minimal enclosing sphere that can envelop all the data samples is obtained. Moreover, the coherency identification of generators is determined for AFR control according to the evaluating method of AFR clustering control effects and the evaluating index of cluster compactness and separation. The simulation results for the modified New England IEEE 10-generator 39-bus system and Henan power grid show that the proposed method is feasible and effective.

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Wu, Chao, Chao Lu, and Yingduo Han. "Closed-Loop Identification of Power System Based on Ambient Data." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/632897.

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Small fluctuations caused by random changes of loads exist continuously in power grids, which are called ambient signals. Using time-synchronized phasor measurements, the closed-loop identification of power system based on ambient data is discussed, which can reflect accurate operating conditions currently and provide critical information for system analyzing and controller designing. The closed-loop identification of a power system with multiple disturbances is theoretically studied, including the closed-loop identifiability, the consistency properties, and the convergence properties. The requirements for realizing the closed-loop identification are summarized, and the theoretical research results are validated by simulation examples.

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Shi, Hong-Jun, and Xu-Chen Nie. "Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy." Measurement and Control 54, no. 5-6 (April 29, 2021): 1015–25. http://dx.doi.org/10.1177/00202940211010829.

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In order to obtain the best power in the wind energy conversion system (WECS) of the direct-driven surface-mounted permanent magnet synchronous generator (SPMSG), active disturbance rejection control (ADRC) is introduced to track the motor speed in real time. The control algorithm provides a new design concept and an inherent robust controller component that requires very little system information. Aiming at the problem of system parameter mutation caused by internal factors and external environment changes, an adaptive controller with multi parameter identification is designed, and the disturbance caused by parameter changes is compensated in real time. The model predictive current control (MPC) technology for the sudden change of external environment is designed to accelerate the response speed of the current loop, so as to weaken the estimation of the current disturbance by the active disturbance rejection controller, and make the speed estimation more accurate. Simulation results show that the proposed control strategy is effective and satisfactory.

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Mustapha, Abdul Hadi Bin, R. Hamdan, F. H. Mohd Noh, N. A. Zambri, M. H. A. Jalil, Marlia Morsin, and M. F. Basar. "Fault location identification of double circuit transmission line using discrete wavelet transform." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1356. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1356-1365.

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The importance of supplying undisturbed electricity keep increasing due to modernization and lifestyle. Any disturbance in the power system may lead to discontinuation and degradation in the power quality. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to ensure reliable power delivery system. This paper will compare two prominent methods to estimate the fault location of double circuit transmission line. Those methods are Discrete Wavelet Transform algorithm and Fast Fourier Transform algorithm. Simulations has been carried out in MATLAB/Simulink and a variety of fault has been imposed in order to analyse the capability and accuracy of the fault location detection algorithm. Results obtained portrayed that both algorithms provide good performance in estimating the fault location. However, the maximum percentage error produced by the Discrete Wavelet Transform is only 0.25%, 0.6% lower than maximum error produces by Fast Fourier Transform algorithm. As a conclusion, Discrete Wavelet Transform possesses better capability to estimate fault location as compared to Fast Fourier Transform algorithm.

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Hussin, A. S., A. R. Abdullah, M. H. Jopri, T. Sutikno, N. M. Saad, and Weihown Tee. "Harmonic Load Diagnostic Techniques and Methodologies: A Review." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 690. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp690-695.

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This paper will review on the existing techniques and methodologies of harmonic load diagnostic system. The increasingly amount of harmonic producing load used in power system are the main contribution in quantifying each harmonic disturbance effects of the multiple harmonic producing loads and it became very important. Literature proposes two different techniques and methods on the harmonic source identification under the soft computing technique classification. The advantages and disadvantages of harmonic load identification techniques and methods are discussed in this paper. In the proposed method, the issue on the harmonic contribution is determine and transformed to a data correlation analysis. Several techniques to identify the sources of harmonic signals in electric power systems are described and reviewed based on previous paper. Comparative studies of the methods are also done to evaluate the performance of each techniques. However, without sufficient information in this inconsistent environment on the property of the power system, accurate harmonic producing load diagnosis methods are important and further investigations in this regard assumes great implication.

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Hamzah, Noraliza, Wan Nor Ainin Wan Abdullah, and Pauziah Mohd Arsad. "Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique." Scientific Research Journal 2, no. 2 (December 31, 2005): 25. http://dx.doi.org/10.24191/srj.v2i2.9330.

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Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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Biswal, Birendra, and Sukumar Mishra. "Power signal disturbance identification and classification using a modified frequency slice wavelet transform." IET Generation, Transmission & Distribution 8, no. 2 (February 1, 2014): 353–62. http://dx.doi.org/10.1049/iet-gtd.2013.0171.

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Li, Jianwen, Gang Qin, Yonggang Li, and Xiaofei Ruan. "Research on power quality disturbance identification and classification technology in high noise background." IET Generation, Transmission & Distribution 13, no. 9 (May 7, 2019): 1661–71. http://dx.doi.org/10.1049/iet-gtd.2018.6262.

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Antipina, Ekaterina, Emir Tairov, and Evgeniia Markova. "Studying the complexity of identification of Volterra kernels for the case of a vector input signal of arbitrary dimension." E3S Web of Conferences 209 (2020): 03004. http://dx.doi.org/10.1051/e3sconf/202020903004.

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The work discusses the technique for constructing an integral model of a nonlinear dynamic system with a vector input based on Volterra polynomials as applied to a section of the steam-water path of the power unit of the Nazarovo power station. The complexity of the applying the technique presented in the work is analyzed, and the number of initial data required to build a mathematical model in the case of a vector input disturbance with an arbitrary dimension is calculated.

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Zaidi, Maryam Nabihah, Dalila Mat Said, Aida Fazliana Abdul Kadir, and Nasarudin Ahmad. "Harmonic Source Identification in Power Distribution System and Meter Placement using Network Impedance Approach." ELEKTRIKA- Journal of Electrical Engineering 19, no. 2 (August 29, 2020): 40–45. http://dx.doi.org/10.11113/elektrika.v19n2.228.

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The growing use of non-linear loads in the electrical systems has made harmonics a serious problem. Harmonic disturbance leads to degradation of power quality by deforming the current or voltage waveforms, thus, necessitating the effective techniques for harmonics detection. The purpose of this study is to propose a method for a single harmonic source identification in power distribution system by implementing a network impedance technique, and optimize the meters allocation by optimum meter placement algorithm (OMPA). The main advantage of this technique is that it results in enhanced accuracy with minimum vulnerability towards deviations in the measurements. Moreover, it minimizes the number of nodes for meter allocations, thereby resulting in economic advantages. To validate the results and effectiveness of the proposed methodology, a standard IEEE 13-Bus industrial network is designed using ETAP software and the algorithm is developed in MATLAB software. The validation of proposed algorithm OMPA is done by comparing its results with Monte Carlo Algorithm (MCA) technique. The results show that without any deviation in the network impedances, OMPA gives 89% accuracy as compared to 75% accuracy of MC. With the deviations in the harmonic impedances, the accuracy of both algorithms is decreased. For the deviation value ꝺ = 1-13 in the harmonic impedances, the overall accuracy of OMPA stays at 75%, while that of MCA drops down to 56%. The developed algorithm OMPA is not only better in performance in harmonics identification with minimum number of meters, but also shows more resistance to the variations in the harmonic impedances as compared to MCA.

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Panthi, Manikant. "Identification of Disturbances in Power System and DDoS Attacks using Machine Learning." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012096. http://dx.doi.org/10.1088/1757-899x/1022/1/012096.

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Baliuta, S. M., P. O. Chernenko, Iu V. Kuievda, and V. P. Kuevda. "IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY." Tekhnichna Elektrodynamika 2021, no. 1 (January 14, 2021): 32–39. http://dx.doi.org/10.15407/techned2021.01.032.

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An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case log-magnitude curve of uncertainties. Identification is performed in active way, determining datasets for each control channel from individual experiments. A linear frequency-modulated signal is selected as the input test disturbance. A simulation model of the controlled object is constructed and the numerical experiment is conducted using the identification procedure. References 11, figures 7.

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Hou, Run Min, Rong Zhong Liu, Yuan Long Hou, and Qiang Gao. "High-Power AC Servo System Identification Research Based on Wavelet Neural Network." Applied Mechanics and Materials 220-223 (November 2012): 997–1002. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.997.

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As a result of the non-linear characteristics and the uncertain disturbances in high-power AC servo system, it is difficult to construct an accurate mathematical model. In order to solve this problem, this article proposes a system identification method based on wavelet neural network. It makes full use of the advantages of the wavelet which combines neural network good time-frequency localization property and volatility of wavelet function and the nonlinear mapping capacity, self-learning and adaptive capacity of neural networks to solve the problem of non-unique RBF neural network approximation function expression. The simulation results show that the convergence rate, robustness and approximation accuracy of this method are better than the traditional neural network.

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Filipović, Vojislav. "ROBUST RECURSIVE IDENTIFICATION OF HAMMERSTEIN MODELS BASED ON WEISZFALD ALGORITHM." Facta Universitatis, Series: Automatic Control and Robotics 18, no. 2 (January 27, 2020): 127. http://dx.doi.org/10.22190/fuacr1902127f.

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The Hammerstein models can accurately describe a wide variety of nonlinear systems (chemical process, power electronics, electrical drives, sticky control valves). Algorithms of identification depend, among other, on the assumption about the nature of stochastic disturbance. Practical research shows that disturbances, owing the presence of outliers, have a non-Gaussian distribution. In such case it is a common practice to use the robust statistics. In the paper, by analysis of the least favourable probability density, it is shown that the robust (Huber`s) estimation criterion can be presented as a sum of non-overlapping - norm and - norm criteria. By using a Weiszfald algorithm - norm criterion is converted to - norm criterion. So, the weighted - norm criterion is obtained for the identification. The main contributions of the paper are: (i) Presentation of the Huber`s criterion as a sum of - norm and - norm criteria; (ii) Using the Weiszfald algorithm – norm criterion is converted to a weighted - norm criterion; (iii) Weighted extended least squares in which robustness is included through weighting coefficients are derived for NARMAX (nonlinear autoregressive moving average with exogenous variable) . The illustration of the behaviour of the proposed algorithm is presented through simulations.

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Song, Chunsheng, Yao Xiao, Chuanchao Yu, Wei Xu, and Jinguang Zhang. "H∞ active control of frequency-varying disturbances in a main engine on the floating raft vibration isolation system." Journal of Low Frequency Noise, Vibration and Active Control 37, no. 2 (August 21, 2017): 199–215. http://dx.doi.org/10.1177/1461348417725944.

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Reducing the vibration of marine power machinery can improve warships' capabilities of concealment and reconnaissance. Being one of the most effective means to reduce mechanical vibrations, the active vibration control technology can overcome the poor effect in low frequency of traditional passive vibration isolation. As the vibrations arising from operation of marine power machinery are actually the frequency-varying disturbances, the H∞ control method is adopted to suppress frequency-varying disturbances. The H∞ control method can solve the stability problems caused by the uncertainty of the model and reshape the frequency response function of the closed loop system. Two-input two-output continuous transfer function models were identified by using the system identification method and are validated in frequency domain of which all values of best fit exceeds 89%. The method of selecting the weighting functions on the mixed sensitivity problem is studied. Besides, the H∞ controller is designed for a multiple input multiple output (MIMO) system to suppress the single-frequency-varying disturbance. The numerical simulation results show that the magnitudes of the error signals are reduced by more than 50%, and the amplitudes of the dominant frequencies are attenuated by more than 10 dB. Finally, the single excitation source dual-channel control experiments are conducted on the floating raft isolation system. The experiment results reveal that the root mean square values of the error signals under control have fallen by more 74% than that without control, and the amplitudes of the error signals in the dominant frequencies are attenuated above 13 dB. The experiment results and the numerical simulation results are basically in line, indicating a good vibration isolation effect.

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You, Lu, Pan Zhiyuan, and Xu Wei. "Robustness Evaluation Strategy of Ubiquitous Power Internet of Things Based on Important Node Recognition." E3S Web of Conferences 136 (2019): 01011. http://dx.doi.org/10.1051/e3sconf/201913601011.

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This paper analyses the structure and characteristics of ubiquitous power Internet of things (UP-IoT) from four levels: the perception layer, network layer, platform layer and application layer. The robustness of UP-IoT is defined from the perspective of system structure, and the internal and external disturbance factors of robustness are analysed. According to the scale-free characteristics of complex network, a robustness evaluation strategy for UP-IoT based on identification of important nodes is proposed. A set of robustness evaluation indexes, including degree centrality, betweenness centrality, closeness centrality, maximum connectivity and connectivity factors, are established to quantify the importance of nodes. The model in this paper is used to analyse the UP-IoT network model with 12 nodes and verify the feasibility of the evaluation strategy.

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Mollaei, Nader, and Seyyed Hadi Mousavi. "Application of a Hadoop-based Distributed System for Offline Processing of Power Quality Disturbances." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 695. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp695-704.

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Electric power quality is a critical issue for electric utilities and their customers and identification of the power quality disturbances is an important task in power system monitoring and protection. Offline processing of power quality disturbances provides an economic alternative for electric distribution companies, not capable of buying enough number of power quality analyzers for monitoring the disturbances online. Due to the wide frequency range of the disturbances which may happen in a power system, a high sampling rate is necessary for digital processing of the disturbances. Therefore, a large volume of data must be processed for this purpose for each node of an electric distribution network and such a processing has not yet been practical. However, thanks to the rapid developments of digital processors and computer networks, processing big databases is not so hard today. Apache Hadoop is an open-source software framework that allows for the distributed processing of large datasets using simple programming models. In this paper, application of Hadoop distributed computing software for offline processing of power quality disturbances is proposed and it is shown that this application makes such a processing possible and leads to a very cheaper system with widespread usage, compared to the power quality analyzers.

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Bai, Xiwei, Daowei Liu, Jie Tan, Hongying Yang, and Hengfeng Zheng. "Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory." Energies 12, no. 5 (February 26, 2019): 780. http://dx.doi.org/10.3390/en12050780.

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Accurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However, they ignore the variations of these critical nodes and regions over time and are not appropriate for online monitoring. To solve this problem, a novel data-driven dynamic identification scheme is proposed in this paper. Three temporal and three spatial attributes are extracted from their corresponding voltage phasor sequences and integrated via Gini-coefficient and Spearman correlation coefficient to form node importance and relevance assessment indices. Critical nodes and regions can be identified dynamically through importance ranking and clustering on the basis of these two indices. The validity and applicability of the proposed method pass the test on various situations of the IEEE-39 benchmark system, showing that this method can identify the critical nodes and regions, locate the potential disturbance source accurately, and depict the variation of node/region criticality dynamically.

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Benmiloud, Omar, and Salem Arif. "A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm." Journal of Electrical Engineering 70, no. 6 (December 1, 2019): 454–64. http://dx.doi.org/10.2478/jee-2019-0078.

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Abstract Dynamic equivalent (DE) is an important process of multi-area interconnected power systems. It allows to perform stability assessment of a specific area (area of interest) at minimum cost. This study is intended to investigate the dynamic equivalent of two relatively large power systems. The fourth-order model of synchronous generators with a simplified excitation system is used as equivalent to the group of generators in the external system. To improve the accuracy of the estimated model, the identification is carried in two stages. First, using the global search Sine Cosine Algorithm (SCA) to find a starting set values, then this set is used as starting point for the fine-tuning made through the Pattern Search (PS) algorithm. To increase the reliability of the model’s parameters, two disturbances are used to avoid the identification based on a specific event. The developed program is applied on two standard power systems, namely, the New England (NE) system and the Northeast Power Coordinating Council (NPCC) system. Simulation results confirm the ability of the optimized model to preserve the main dynamic properties of the original system with accuracy.

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Demers, Scott A., and Catilin W. Robinson-Nilsen. "Monitoring Western Snowy Plover Nests with Remote Surveillance Systems in San Francisco Bay, California." Journal of Fish and Wildlife Management 3, no. 1 (June 1, 2012): 123–32. http://dx.doi.org/10.3996/062011-jfwm-036.

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Abstract The western snowy plover Charadrius nivosus nivosus is listed as threatened under the U.S. Endangered Species Act of 1973 due to long-term population declines related, in part, to nest predation and human disturbance. In San Francisco Bay, California, numbers of predators of western snowy plovers and the potential for recreation-based human disturbances have increased during the past few decades and will likely increase for the foreseeable future. In an attempt to increase the reproductive success of western snowy plovers, managers have dedicated considerable resources to management practices including predator removal and habitat enhancement projects in San Francisco Bay. The unequivocal identification of western snowy plover nest predators and information regarding the behavioral responses of nesting plovers to human disturbance would inform management practices for this species. Therefore, we examined the efficacy of using a digital video surveillance system to identify nest predators of western snowy plovers in former salt evaporation ponds in San Francisco Bay and to assess its potential for use in behavioral studies. This system was designed to minimize disturbance to nesting plovers and limit predator bias at breeding sites that had little or no cover to camouflage or protect the equipment. The system included a small camera with infrared lights placed approximately 20 m from nests and a continuously operating recording unit and power supply that was positioned up to 300 m from nests. The system could be deployed within 20 min, run continuously for up to 5 d, and data could be retrieved without disturbing nesting birds. During a 2-y study period, we recorded six species depredating plover eggs and chicks: red-tailed hawk Buteo jamaicensis, common raven Corvus corax, California gull Larus californicus, northern harrier Circus cyaneus, ruddy turnstone Arenaria interpres, and gray fox Urocyon cinereoargenteus. Our results suggest that this surveillance system was effective for identifying western snowy plover nest predators, and the presence of the camera did not influence nesting success of monitored nests. This system could be integrated into conservation programs intended to improve reproductive success of this species and could be useful for conducting behavioral studies of western snowy plovers and other species.

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Et. al., K. RamaMohana Reddy. "Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4892–903. http://dx.doi.org/10.17762/turcomat.v12i3.1995.

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With the development of the technologies, the demand for good quality of electric power is increasing day by day. In Distributed Generation Systems (DGs), the quality of power can cause serious problems such as sensitive equipment's malfunction, the temperature riseof machines. Therefore, detection of power quality events in the power system is more important to take further actions. The existing power quality events classification methods have high computational time with low accuracy. In order to overcome this problem, this paper presents Discrete Packet Wavelet Transform-Kalman filter based Adaptive Neuro-Fuzzy approach for identification and classification of PQ events. The proposed method classifies the events with better classification accuracy, less convergence time and low in error prediction. The results show that the proposed method has better performance compared with the existing classification methods. The proposed method is Implemented and tested using MATLAB and it provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks etc..

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Peligrad, A. A., E. Zhou, D. Morton, and L. Li. "System identification and predictive control of laser marking of ceramic materials using artificial neural networks." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 216, no. 2 (March 1, 2002): 181–90. http://dx.doi.org/10.1243/0959651021541543.

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Laser marking of ceramic materials is a multivariable non-linear process. Real-time control of the process requires the understanding of system dynamics and parameter interaction. In this work, direct inverse control (DIC) and non-linear predictive control (NPC) based on artificial neural networks were applied. The output variable considered for the laser clay tile-marking process was melt pool temperature. The input quantities investigated were laser power and traverse speed. The results show that the NPC accomplished a better reference tracking than the DIC. It was also found that the beam velocity and laser power could well be used to counteract disturbances.

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Shavranskyi, M. V. "Modeling and identification of the main units of the thermal power plant (TPP) acting as automation objects." Prospecting and Development of Oil and Gas Fields, no. 2(67) (May 10, 2018): 62–69. http://dx.doi.org/10.31471/1993-9973-2018-2(67)-62-69.

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On the basis of the analysis of the heat and power processes, the modeling and identification of the main units of the TPP has been carried out in order to increase the efficiency and reliability of the functioning of the automatic control systems of the inertial circuits of the TPP boiler units, which ensures the system’s reduction in sensitivity to uncertainties of the object itself, external perturbations, time delay variations along the control channels and disturbance, and thus guarantees the given quality of regulation in maneuverable modes of operation. The investigated transfer functions (model structure) are easily implemented by software and do not require additional control equipment, additional signal measurement channels or special sensors for their implementation.

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Harasymiv, V. M., and T. H. Harasymiv. "Improvement of the transfer function quality indicators of the power control system at the electric drill engine shaft." Prospecting and Development of Oil and Gas Fields, no. 3(76) (September 27, 2020): 46–52. http://dx.doi.org/10.31471/1993-9973-2020-3(76)-46-52.

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In order to get better rigs performance the authors improve the schematic diagram of the power control system at the electric drill engine shaft using control input transfer function, where the electric drill is considered to be the control object which operates in conditions of a priori and current uncertainty under the influence of external perturbations. To improve the quality of the transient process under the conditions of changes in the parameters of the control object (when these parameters cannot be checked on), the authors have developed the algorithm of tuning the adaptive fuzzy PID controller based on the hybrid adaptive system. This algorithm includes the advantages of artificial neural networks and fuzzy logic. The efficiency of the algorithm with the parametric disturbance has been shown. It is concluded that the implementation of adaptive fuzzy PID controllers makes it possible to improve the quality of the transfer function of the power control system at the electric drill engine shaft when this drill operates in the indefinite conditions. The advantage of the developed algorithm is that it does not require special methods of the object parameters identification. Its implementation using modern microcontrollers is quite simple and accessible.

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Aribowo, Widi. "AN ADAPTIVE POWER SYSTEM STABILIZER BASED ON FOCUSED TIME DELAY NEURAL NETWORK." Jurnal Teknosains 7, no. 1 (July 13, 2018): 67. http://dx.doi.org/10.22146/teknosains.35130.

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In this paper, Power System Stabilizer is designed in Single Machine Infinite Bus (SMIB) and speed control is implemented with a dynamic topology based on Focused Time Delay Neural Network (FTDNN). In case of prediction and control, two individual strategies are concerned for the current projects. The first is identification the dynamics of system. The other is an optimization unit expected for minimization disturbances. The performance of the system with FTDNN-PSS controller is compared with a Conventional PSS (C-PSS), RNN-PSS and DTDNN PSS. The results show the effectiveness of FTDNN-PSS design, and superior robust performance for enhancement power system stability compared to Conventional PSS with different cases.

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Yankson, Samuel, and Mahdi Ghamkhari. "Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems." Future Internet 12, no. 1 (December 24, 2019): 4. http://dx.doi.org/10.3390/fi12010004.

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The automatic generation control mechanism in power generators comes into operation whenever an over-supply or under-supply of energy occurs in the power grid. It has been shown that the automatic generation control mechanism is highly vulnerable to load altering attacks. In this type of attack, the power consumption of multiple electric loads in power distribution systems is remotely altered by cyber attackers in such a way that the automatic generation control mechanism is disrupted and is hindered from performing its pivotal role. The existing literature on load altering attacks has studied implementation, detection, and location identification of these attacks. However, no prior work has ever studied design of an attack-thwarting system that can counter load altering attacks, once they are detected in the power grid. This paper addresses the above shortcoming by proposing an attack-thwarting system for countering load altering attacks. The proposed system is based on provoking real-time adjustment in power consumption of the flexible loads in response to the frequency disturbances caused by the load altering attacks. To make the adjustments in-proportion to the frequency disturbances, the proposed attack-thwarting system uses a transactive energy framework to establish a coordination between the flexible loads and the power grid operator.

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Sekar, Kavaskar, and Nalin Kant Mohanty. "High impedance fault detection in distribution system." International Journal of Advances in Applied Sciences 8, no. 2 (June 1, 2019): 95. http://dx.doi.org/10.11591/ijaas.v8.i2.pp95-102.

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High impedance faults (HIFs) present a huge complexity of identification in an electric power distribution network (EPDN) due to their characteristics. Further, the growth of non-linear load adds complexity in HIF detection. One primary challenge of power system engineers is to reliably detect and discriminate HIFs from normal distribution system load and other switching transient disturbances. In this study, a novel HIF detection method is proposed based on the simulation of an accurate model of an actual EPDN study with real data. The proposed method uses current signal alone and does not require voltage signal. Wavelet transform (WT) is used for signal decomposition to extract statistical features and classification of HIF into Non-HIF (NHIF) by Neural Networks (NNs). The simulation study of the proposed method provides good, consistent and powerful protection for HIF.

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Garcia, Carlos Iturrino, Francesco Grasso, Antonio Luchetta, Maria Cristina Piccirilli, Libero Paolucci, and Giacomo Talluri. "A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM." Applied Sciences 10, no. 19 (September 27, 2020): 6755. http://dx.doi.org/10.3390/app10196755.

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The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

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Journal articles on the topic 'Power system disturbance identification'

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