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Journal articles on the topic 'Power system small-signal stability'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Shirvani, Mojtaba, Ahmad Memaripour, Meysam Eghtedari, and Hasan Fayazi. "Small signal stability analysis of power system following different outages." International Journal of Academic Research 6, no. 2 (March 30, 2014): 268–72. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.38.

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2

Khederzadeh, Mojtaba, and Mahdi Ehsan. "Power-system small-signal stability and maximum loadability." Canadian Journal of Electrical and Computer Engineering 21, no. 2 (April 1996): 81–85. http://dx.doi.org/10.1109/cjece.1996.7102130.

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3

Choo, Y. C., K. M. Muttaqi, and M. Negnevitsky. "Evaluation of small signal stability of a power system." Australian Journal of Electrical and Electronics Engineering 4, no. 3 (January 2008): 227–37. http://dx.doi.org/10.1080/1448837x.2008.11464189.

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4

Verma, Mayank Singh, Poonam Khatarkar, and Kumar Prabhakar. "Power System Small Signal Stability Analysis Using Facts Pod." International Journal of Computer Trends & Technology 67, no. 07 (July 25, 2019): 57–61. http://dx.doi.org/10.14445/22312803/ijctt-v67i7p109.

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5

Yousin Tang and A. P. S. Meliopoulos. "Power system small signal stability analysis with FACTS elements." IEEE Transactions on Power Delivery 12, no. 3 (July 1997): 1352–61. http://dx.doi.org/10.1109/61.637014.

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6

Jia, Hongjie, Xiaodan Yu, Yixin Yu, and Chengshan Wang. "Power system small signal stability region with time delay." International Journal of Electrical Power & Energy Systems 30, no. 1 (January 2008): 16–22. http://dx.doi.org/10.1016/j.ijepes.2007.06.020.

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7

Xu, Yan, Fushuan Wen, Hongwei Zhao, Minghui Chen, Zeng Yang, and Huiyu Shang. "Stochastic Small Signal Stability of a Power System with Uncertainties." Energies 11, no. 11 (November 1, 2018): 2980. http://dx.doi.org/10.3390/en11112980.

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The ever-increasing penetration of wind power generation and plug-in electric vehicles introduces stochastic continuous disturbances to the power system. This paper proposes an analytical approach to analyze the influence of stochastic continuous disturbances on power system small signal stability. The noise-to-state stability (NSS) and NSS Lyapunov function (NSS-LF) are adopted for stability analysis with respect to the magnitude of uncertainties in a power system. The power system is modeled as a set of stochastic differential equations (SDEs). The supremum of the norm of the covariance is employed to characterize the influence of magnitudes of uncertainties on the power system. Then the relationship between the magnitudes of stochastic variations and probabilistic stability is explicitly identified by NSS. The proposed method can assess the stochastic stability of the power system by checking some algebraic expressions. Hence, it has high computation efficiency compared with the well-established Monte Carlo based method. Besides, since the magnitudes of the stochastic variations are integrated into the definition of the stochastic stability, the proposed method provides theoretical explanations for the impacts of uncertainties.

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8

Dey, Prasenjit, Aniruddha Bhattacharya, and Priyanath Das. "Tuning of power system stabilizer for small signal stability improvement of interconnected power system." Applied Computing and Informatics 16, no. 1/2 (December 29, 2017): 3–28. http://dx.doi.org/10.1016/j.aci.2017.12.004.

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This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

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9

Lim Zhu Aun, Shalom, Marayati Bte Marsadek, and Agileswari K. Ramasamy. "Small Signal Stability Analysis of Grid Connected Photovoltaic." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 3 (June 1, 2017): 553. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp553-562.

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This paper primarily focuses on the small signal stability analysis of a power system integrated with solar photovoltaics (PV). The test system used in this study is the IEEE 39-bus. The small signal stability of the test system are investigated in terms of eigenvalue analysis, damped frequency, damping ratio and participation factor. In this study, various conditions are analyzed which include the increase in solar PV penetration into the system and load variation. The results obtained indicate that there is no significant impact of solar PV penetration on the small signal stability of large scaled power system.

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10

Sabati, Asghar, Ramazan Bayindir, Sanjeevikumar Padmanaban, Eklas Hossain, and Mehmet Rida Tur. "Small Signal Stability with the Householder Method in Power Systems." Energies 12, no. 18 (September 4, 2019): 3412. http://dx.doi.org/10.3390/en12183412.

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Voltage collapse in power systems is still considered the greatest threat, especially for the transmission system. This is directly related to the quality of the power, which is characterized by the loss of a stable operating point and the deterioration of voltage levels in the electrical center of the region exposed to voltage collapse. Numerous solution methods have been investigated for this undesirable degradation. This paper focuses on the steady state/dynamic stability subcategory and techniques that can be used to analyze and control the dynamic stability of a power system, especially following a minor disturbance. In particular, the failure of one generator among the network with a large number of synchronous generators will affect other synchronous generators. This will become a major problem and it will be difficult to find or resolve the fault in the network due to there being too many variables, consequently affecting the stability of the entire system. Since the solution of large matrices can be completed more easily in this complex system using the Householder method, which is a small signal stability analysis method that is suggested in the thesis, the detection of error and troubleshooting can be performed in a shorter period of time. In this paper, examples of different rotor angle deviations of synchronous generators were made by simulating rotor angle stability deviations up to five degrees, allowing the system to operate stably, and concluding that the system remains constant.

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11

Yu, Jiawei, Ziqian Yang, Jurgen Kurths, and Meng Zhan. "Small-Signal Stability of Multi-Converter Infeed Power Grids with Symmetry." Symmetry 13, no. 2 (January 20, 2021): 157. http://dx.doi.org/10.3390/sym13020157.

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Traditional power systems have been gradually shifting to power-electronic-based ones, with more power electronic devices (including converters) incorporated recently. Faced with much more complicated dynamics, it is a great challenge to uncover its physical mechanisms for system stability and/or instability (oscillation). In this paper, we first establish a nonlinear model of a multi-converter power system within the DC-link voltage timescale, from the first principle. Then, we obtain a linearized model with the associated characteristic matrix, whose eigenvalues determine the system stability, and finally get independent subsystems by using symmetry approximation conditions under the assumptions that all converters’ parameters and their susceptance to the infinite bus (Bg) are identical. Based on these mathematical analyses, we find that the whole system can be decomposed into several equivalent single-converter systems and its small-signal stability is solely determined by a simple converter system connected to an infinite bus under the same susceptance Bg. These results of large-scale multi-converter analysis help to understand the power-electronic-based power system dynamics, such as renewable energy integration. As well, they are expected to stimulate broad interests among researchers in the fields of network dynamics theory and applications.

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12

Fang, Wanliang, and H. W. Ngan. "Enhancing small signal power system stability by coordinating unified power flow controller with power system stabilizer." Electric Power Systems Research 65, no. 2 (May 2003): 91–99. http://dx.doi.org/10.1016/s0378-7796(02)00218-3.

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13

He, Tingyi, Shengnan Li, Shuijun Wu, and Ke Li. "Small-Signal Stability Analysis for Power System Frequency Regulation with Renewable Energy Participation." Mathematical Problems in Engineering 2021 (April 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/5556062.

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With the improvement of the permeability of wind and photovoltaic (PV) energy, it has become one of the key problems to maintain the small-signal stability of the power system. Therefore, this paper analyzes the small-signal stability in a power system integrated with wind and solar energy. First, a mathematical model for small-signal stability analysis of power systems including the wind farm and PV station is established. And the characteristic roots of the New England power system integrated with wind energy and PV energy are obtained to study their small-signal stability. In addition, the validity of the theory is verified by the voltage drop of different nodes, which proves that power system integrated with wind-solar renewable energy participating in the frequency regulation can restore the system to the rated frequency in the shortest time and, at the same time, can enhance the robustness of each unit.

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14

Gopi, Pasala. "Small Signal and First-Swing Stability Enhancement in InterArea Power System." IOSR Journal of Electrical and Electronics Engineering 2, no. 2 (2012): 12–18. http://dx.doi.org/10.9790/1676-0221218.

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15

Chennakesavan, C. "Multi-Machine Small Signal Stability Analysis For Large Scale Power System." Indian Journal of Science and Technology 7, is6 (August 22, 2014): 40–47. http://dx.doi.org/10.17485/ijst/2014/v7sp6.5.

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16

Rueda, J. L., and I. Erlich. "Probabilistic framework for risk analysis of power system small-signal stability." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 226, no. 1 (October 24, 2011): 118–33. http://dx.doi.org/10.1177/1748006x11424534.

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17

Sun, Bin, Zhengyou He, Yong Jia, and Kai Liao. "Small-Signal Stability Analysis of Wind Power System Based on DFIG." Energy and Power Engineering 05, no. 04 (2013): 418–22. http://dx.doi.org/10.4236/epe.2013.54b081.

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18

Ayasun, Saffet. "Computation of time delay margin for power system small-signal stability." European Transactions on Electrical Power 19, no. 7 (October 2009): 949–68. http://dx.doi.org/10.1002/etep.272.

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19

Du, Wenjuan, Qiang Fu, and H. F. Wang. "Power System Small-Signal Angular Stability Affected by Virtual Synchronous Generators." IEEE Transactions on Power Systems 34, no. 4 (July 2019): 3209–19. http://dx.doi.org/10.1109/tpwrs.2019.2896149.

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20

Arroyo, J., R. Betancourt, A. R. Messina, and E. Barocio. "Development of bilinear power system representations for small signal stability analysis." Electric Power Systems Research 77, no. 10 (August 2007): 1239–48. http://dx.doi.org/10.1016/j.epsr.2006.09.014.

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21

Tomin, Nikita, Daniil Panasetsky, and Alexey Iskakov. "Stability of Power Grids: State-of-the-art and Future Trends." EPJ Web of Conferences 217 (2019): 01017. http://dx.doi.org/10.1051/epjconf/201921701017.

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The state of the art of transient stability and steady-state (small signal) stability in power grids are reviewed. Transient stability concepts are illustrated with simple examples; in particular, we consider two machine learning-based methods for computing region of attraction: ROA produced by Neural Network Lyapunov Function; estimation of the ROA of IEEE 39-bus system using Gaussian process and Converse Lyapunov function. We discuss steady state stability in power systems, and using Prony’s modal analysis for evaluating small signal stability for the 7 Bus Test system and real French power system.

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22

Dastas, Mark Brian, and Hwachang Song. "Renewable Energy Generation Assessment in Terms of Small-Signal Stability." Sustainability 11, no. 24 (December 11, 2019): 7079. http://dx.doi.org/10.3390/su11247079.

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The popularity and role of renewable energy in the power grid are increasing nowadays as countries are shifting to cleaner forms of energy. This brings new challenges in maintaining a secure and stable power system, as renewable energy is known to be intermittent in nature and may introduce stability issues to the grid. In this paper, a screening framework of renewable energy generation scenarios is proposed to determine which power system conditions and scenarios will make the system unstable. The scenario screening framework is based on a sensitivity analysis of the system eigenvalues with respect to the renewable energy penetration to the system. The average scheduled renewable energy output, forecasting error standard deviation, average forecasting error, and bus location of the renewable energy source were used to define a renewable energy generation scenario. Depending on the amount and variability of renewable energy, there is a possibility for a critical eigenvalue to cross the imaginary axis. The estimated eigenvalue location resulting from the penetration of variable renewable energy is computed by adding the computed eigenvalue sensitivity to the initial operating point. If any of the estimated system eigenvalues cross the imaginary axis, the power system might be unstable in this scenario, so it requires more detailed simulations and countermeasures. Renewable energy forecasting was done using the long short-term memory model, and the proposed method was simulated using the IEEE 39-bus New England test system. The results of the proposed method were verified by comparing the simulation results to the eigenanalysis solution. The obtained results have shown that the proposed method can determine whether the renewable energy generation scenario is critical in power system operation.

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23

Li, Chao Chun, Ta Hsiu Tseng, and Pei Hwa Huang. "New Eigenvalue Method for Power System Stability Analysis." Advanced Materials Research 732-733 (August 2013): 888–91. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.888.

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The most widely adopted methodology for the analysis of power system small signal stability relies on the approach in the frequency domain, i.e. to linearize the system equation to obtain a linear model as well as the system matrix of which the eigenvalues can be calculated to determine the system stability. However, we often have high order of system matrix and thus it will be undesirable to calculate and analyze all the system eigenvalues. This paper is to explore the problem of small signal stability for power system and the main purpose is to find out the worst-damped mode of system eigenvalues and thus to alleviate the effort for computing and analyzing all the system eigenvalues. The developed algorithm is to be tested on a sample power system to validate the feasibility of the proposed new eigenvalue calculation method.

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24

Yuan, Bo, Ming Zhou, Gengyin Li, and Xiao-Ping Zhang. "Stochastic Small-Signal Stability of Power Systems With Wind Power Generation." IEEE Transactions on Power Systems 30, no. 4 (July 2015): 1680–89. http://dx.doi.org/10.1109/tpwrs.2014.2353014.

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25

Guo, Chunyi, Anran Zheng, Zihan Yin, and Chengyong Zhao. "Small-signal stability of hybrid multi-terminal HVDC system." International Journal of Electrical Power & Energy Systems 109 (July 2019): 434–43. http://dx.doi.org/10.1016/j.ijepes.2019.02.031.

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26

Wang, Sijia, Xiangyu Wu, Gang Chen, and Yin Xu. "Small-Signal Stability Analysis of Photovoltaic-Hydro Integrated Systems on Ultra-Low Frequency Oscillation." Energies 13, no. 4 (February 24, 2020): 1012. http://dx.doi.org/10.3390/en13041012.

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In recent years, ultralow-frequency oscillation has repeatedly occurred in asynchronously connected regional power systems and brought serious threats to the operation of power grids. This phenomenon is mainly caused by hydropower units because of the water hammer effect of turbines and the inappropriate Proportional-Integral-Derivative (PID) parameters of governors. In practice, hydropower and solar power are often combined to form an integrated photovoltaic (PV)-hydro system to realize complementary renewable power generation. This paper studies ultralow-frequency oscillations in integrated PV-hydro systems and analyzes the impacts of PV generation on ultralow-frequency oscillation modes. Firstly, the negative damping problem of hydro turbines and governors in the ultralow-frequency band was analyzed through the damping torque analysis. Subsequently, in order to analyze the impact of PV generation, a small-signal dynamic model of the integrated PV-hydro system was established, considering a detailed dynamic model of PV generation. Based on the small-signal dynamic model, a two-zone and four-machine system and an actual integrated PV-hydro system were selected to analyze the influence of PV generation on ultralow-frequency oscillation modes under different scenarios of PV output powers and locations. The analysis results showed that PV dynamics do not participate in ultralow-frequency oscillation modes and the changes of PV generation to power flows do not cause obvious changes in ultralow-frequency oscillation mode. Ultra-low frequency oscillations are mainly affected by sources participating in the frequency adjustment of systems.

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27

Yan, Lei, and Kewen Wang. "A summary of impacts of wind power integration on power system small-signal stability." IOP Conference Series: Earth and Environmental Science 64 (May 2017): 012085. http://dx.doi.org/10.1088/1755-1315/64/1/012085.

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28

Radwan, Amr. "Small-Signal Stability Analysis of Multi-Terminal DC Grids." Electronics 8, no. 2 (January 26, 2019): 130. http://dx.doi.org/10.3390/electronics8020130.

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This paper presents a detailed small-signal analysis and an improved dc power sharing scheme for a six terminal dc grid. The multi-terminal DC (MTDC) system is composed of (1) two voltage-source converters (VSCs) entities operating as rectification stations; (2) two VSCs operating as inverting stations; (3) two dc/dc conversion stations; and (4) an interconnected dc networking infrastructure. The small-signal state-space sub-models of the individual entities are developed and integrated to formulate the state-space model of the entire system. Using the modal analysis, it is shown that the most critical modes are associated with the power sharing droop coefficients of the rectification stations, which are constrained by the steady-state operational requirements. Therefore, a second degree-of-freedom compensation scheme is proposed to improve the dynamic response of the MTDC system without influencing the steady-state operation. Time domain simulation results are presented to validate the analysis and show the effectiveness of the proposed techniques.

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29

Deng, Jun, Nan Xia, Jungang Yin, Jiliang Jin, Shutao Peng, and Tong Wang. "Small-Signal Modeling and Parameter Optimization Design for Photovoltaic Virtual Synchronous Generator." Energies 13, no. 2 (January 13, 2020): 398. http://dx.doi.org/10.3390/en13020398.

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With the continuous proliferation of renewable energy generation, distributed photovoltaic inverters operating at a maximum power point reduce the inertia of power systems, degrading system frequency stability and potentially causing severe oscillations in systems after being disturbed. The virtual synchronous generator (VSG) control method, which causes photovoltaic inverters to possess inertia and damping, now plays an important role in the field of distributed generation. However, while introducing the advantages of synchronous machines, problems with oscillations are also introduced and the stochastic fluctuation characteristic of photovoltaics results in the stochastic drifting of the operating point. This paper presents an adaptive controller parameter design method for a photovoltaic-VSG (PV-VSG) integrated power system. Firstly, a small-signal model of the PV-VSG is built and a state space model is deduced. Then, the small-signal stability and low frequency oscillation characteristics of the photovoltaic power generation system are analyzed. Finally, considering the limitations of system oscillations and the stochastic drifting of the operating point, a global optimization design method for controller parameters used to improve system stability is proposed. The time domain simulation shows that an optimized PV-VSG could provide sufficient damping in the case of photovoltaic power output changes across a wider range.

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30

Li, Chao Chun, and Pei Hwa Huang. "Fast Analysis of Power System Stability by Artificial Neural Network." Advanced Materials Research 732-733 (August 2013): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.848.

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Power system small signal stability concerns the ability of the power system to maintain stability subject to small disturbances. The analysis of small signal stability often has to deal with high-order system matrix due to the large number of generating units so that it is not easy to calculate and analyze the original system matrix and the whole set of eigenvalues. In this paper a new approach is proposed to take advantage of the specific feature of the parallel structure of artificial neural network for calculating the most critical eigenvalue or all eigenvalues of the unstable oscillation mode. The developed algorithm is tested on a sample power system to validate the feasibility of the proposed method for the calculation of the critical eigenvalue.

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31

Xu, Zhen Yu, Zhen Qiao, Qian He, Xu Zhang, and Jing Qi Su. "Impact of Large-Scale Integrated Wind Farms on the Small Signal Stability of Power System." Advanced Materials Research 805-806 (September 2013): 393–96. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.393.

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With the penetration of wind energy is becoming higher and higher in power grid, it is very important to investigate the impact of wind generations on small signal stability. In this paper, a complete small signal model of wind turbine with direct-drive permanent magnet generator is built to study the impact of large-scale wind farms on the small signal stability of power system. By means of simulation and eigenvalue analysis, an actual power system is investigated, and the damping characteristic of power grid under different wind power penetration is discussed.

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32

Brahmachary, Rupali, and Dipu Sarkar. "Small Signal Stability Assessment in Presence of SSSC for a Power System Under Fault Disturbance." International Journal of Smart Grid and Sustainable Energy Technologies 4, no. 1 (May 1, 2021): 136–39. http://dx.doi.org/10.36040/ijsgset.v4i1.3900.

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The use of FACTS controllers in power grids has resulted in the improvement of stability issues related to the power systems. When the FACTS controllers are used to damp out the power systems oscillation, there series controller inverter like static synchronous series compensator (SSSC) device are the most suitable to resolve the issue. In this paper, importance is given on the optimal operation of the SSSC and to the maintenance of the small signal stability of the system. Here the main focus is to check the system response to stability after the use of SSSC device.

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33

DOMÍNGUEZ-GARCÍA, J. L., O. GOMIS-BELLMUNT, F. BIANCHI, and A. SUMPER. "PSS CONTROLLER FOR WIND POWER GENERATION SYSTEMS." International Journal of Modern Physics B 26, no. 25 (September 10, 2012): 1246012. http://dx.doi.org/10.1142/s0217979212460125.

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Small signal stability analysis for power systems with wind farm interaction is presented. Power systems oscillation modes can be excited by disturbance or fault in the grid. Variable speed wind turbines can be regulated to reduce these oscillations, stabilising the power system. A power system stabiliser (PSS) control loop for wind power is designed in order to increase the damping of the oscillation modes. The proposed power system stabiliser controller is evaluated by small signal analysis.

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34

Du, Wenjuan, Haifeng Wang, and L.-Y. Xiao. "Power system small-signal stability as affected by grid-connected photovoltaic generation." European Transactions on Electrical Power 22, no. 5 (July 20, 2011): 688–703. http://dx.doi.org/10.1002/etep.598.

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35

Asvapoositkul, Surat, and Robin Preece. "Impact of HVDC dynamic modelling on power system small signal stability assessment." International Journal of Electrical Power & Energy Systems 123 (December 2020): 106327. http://dx.doi.org/10.1016/j.ijepes.2020.106327.

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36

Ugalde-Loo, Carlos E., Enrique Acha, and Eduardo Licéaga-Castro. "Multi-machine power system state-space modelling for small-signal stability assessments." Applied Mathematical Modelling 37, no. 24 (December 2013): 10141–61. http://dx.doi.org/10.1016/j.apm.2013.05.047.

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Li, Xiangqi, Yunfeng Li, Li Liu, Weiyu Wang, Yong Li, and Yijia Cao. "Latin Hypercube Sampling Method for Location Selection of Multi-Infeed HVDC System Terminal." Energies 13, no. 7 (April 2, 2020): 1646. http://dx.doi.org/10.3390/en13071646.

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Owing to the stochastic states of power systems with large-scale renewable generation, the impact of high-voltage direct current (HVDC) systems on the stability of the power system should be examined in a probabilistic manner. A probabilistic small signal stability assessment methodology to select the best locations for multi-infeed high-voltage direct current systems in alternating current (AC) grids is proposed in this paper. The Latin hypercube sampling-based Monte Carlo simulation approach is taken to generate the stochastic operation scenarios of power systems with the consideration of several stochastic factors, i.e., load demand and power generation. The damping ratio of the critical oscillation modes and the controllability of power injection to oscillation modes are analyzed by the probabilistic small signal stability. A probabilistic index is proposed to select the best locations of high-voltage direct current systems for improving the damping of the oscillation modes. The proposed methodology is applied to an IEEE 39 bus system considering the stochastic load demand and power generation. The results of probabilistic small signal stability assessment and a time-domain simulation show that the installation of a high-voltage direct current system on the selected locations can effectively improve the system damping.

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38

Setiadi, Herlambang, and Karl O Jones. "Power System Design using Firefly Algorithm for Dynamic Stability Enhancement." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 3 (March 1, 2016): 446. http://dx.doi.org/10.11591/ijeecs.v1.i3.pp446-455.

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<p>Utilising additional devices in power systems have been developed by industry. Devices such as a Power System Stabilizer (PSS) and a Superconducting Magnetic Energy Storage (SMES) are commonly employed in industry. This work investigated the coordination of a PSS and SMES applied to a power system to enhance dynamic stability. To obtain optimal coordination, the parameters of the PSS and SMES are tuned using the Firefly Algorithm (FA). The simulation of the power system, PSS, and SMES has been performed using MATLAB and Simulink, and the FA run in Matlab. For testing the small signal stability, the eigenvalue of the system will be investigated, while for dynamic stability the system will be given an external disturbance. The rotor angle and frequency deviation of the power system are compared without a controller, with a PSS and SMES included, and with the PSS and SMES tuned by FA. The simulation results show that the proposed system can improve not only small signal stability (steady state stability) but also dynamic stability.</p>

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39

Bian, Xiao Yan, Li Ning Yang, Xin Xin Huang, and Yang Fu. "Effect of Wind Farm on Transient and Small-Signal Stability Based on BPA." Advanced Materials Research 860-863 (December 2013): 309–13. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.309.

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Large scale wind farm output variation always deteriorates the system stability. To study this problem, this paper builds the model of power system with the integration of large-scale wind farm based on BPA. The simulation results show that large oscillations of voltage and rotor angle of system will happen, when three-phase short circuit fault occurs on the main line for transmitting wind power. With wind farm output decreasing, the transient stability and small-signal stability of power system will be improved.

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40

Rahman, Atta, Irtaza Syed, and Mukhtar Ullah. "Small-Signal Stability Criteria in AC Distribution Systems—A Review." Electronics 8, no. 2 (February 15, 2019): 216. http://dx.doi.org/10.3390/electronics8020216.

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AC distribution grid is prone to instability due to negative impedance and constant power nature of the load if it is dominant with power electronics-based components. There are various time-domain and frequency-domain modelling methods which use various methodologies and analytical tools. Also, there are many small-signal stability analysis (SSSA) methods and their different variants for different specific conditions and situation. This paper presents a review of SSSA methods in AC distribution grid using impedance-based models in a synchronous reference frame (SRF). By simplifying and converting the system into load and source subsystem, the impedances of both subsystems are determined by perturbation method. For a single-phase system, Hilbert transform can be used to derive the equivalent SRF model. Afterwards, the Nyquist stability criterion can be used for stability analysis.

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Alias, Feba, and Manohar Singh. "Coordinated control among PSS, WTG and BESS for improving Small-Signal Stability." International Journal of Emerging Electric Power Systems 22, no. 4 (June 2, 2021): 505–23. http://dx.doi.org/10.1515/ijeeps-2021-0102.

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Abstract The goal towards attaining a sustainable future has led to the rapid increase in the integration of converter control based generators (CCBGs). The low inertia response characteristics of CCBGs and the weak tie lines in interconnected systems pose a huge threat to Small-Signal Stability (SSS). Adequate damping of low-frequency oscillations (LFO) is pivotal in ensuring the maximum power transfer through the critical transmission corridors. These operational issues become more serious with the significant reduction in system inertia as a result of the high penetration of CCBGs. Therefore, appropriate control techniques are an absolute requirement for preventing LFOs from limiting the penetration of CCBGs in interconnected networks. This may also eventually lead to revisions in grid codes mandating CCBGs to provide auxiliary damping control. But, the progressive addition of multiple damping controllers for specific target modes can lead to the drifting of eigenvalues (EVs) associated with other electromechanical modes (EMs) in the system. This is due to the adverse interactions between multiple damping controllers in the uncoordinated control approach and may result in deteriorating SSS. Therefore, this paper proposes a simultaneous coordinated control among Battery Energy Storage System (BESS), Wind Turbine Generators (WTG) and Power System Stabilizer (PSS) for enhancing SSS in networks with high wind penetration by considering both inter-area (IA) and local modes. The performance of the proposed coordinated control is corroborated using IEEE 68 bus system for multiple operating scenarios for which the critical modes in the system have the lowest damping index (DI). The effectiveness of modulating the active power, reactive power and simultaneous modulation of both active and reactive power injected by BESS along with a dual-channel Optimized WTG Damping Controller (DOWDC) and PSS is evaluated. The impact of the different coordinated control strategies on voltage dynamics is also investigated. The simulation results validate the better performance of the proposed coordinated control over uncoordinated control approaches.

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Verdejo, Humberto, Pablo Moreira, Wolfgang Kliemann, Cristhian Becker, and José Delpiano. "An Analytical Model for Small Signal Stability Analysis in Unbalanced Electrical Power Systems." Applied Sciences 10, no. 24 (December 10, 2020): 8855. http://dx.doi.org/10.3390/app10248855.

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This paper presents a general model to carry out a small signal stability analysis in electrical power systems (EPSs) that operate in unbalanced conditions. The classic traditional approach is based on a phase representation where it is supposed that the triphasic electric grid does not suffer any variations during its operation. With the presence of unbalances in transmission lines and loads, it is necessary to develop a general model that allows answering the needs and challenges with which modern electric systems must deal. The present work firstly intends to address the three-phase representation of an EPS, including the controllers. The proposed model is applied to a classical test system, a three machine-nine bus system, considering all the dynamic and algebraic variations associated with angular stability analysis. The proposed approach to small signal stability analysis shows results that differ from the classical analysis. The results are backed up with time domain simulations, and therefore, these results could be used in the calculation of the controllers that operate in unbalanced multimachine systems.

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Gomes, S., N. Martins, and C. Portela. "Computing Small-Signal Stability Boundaries for Large-Scale Power Systems." IEEE Power Engineering Review 22, no. 12 (December 2002): 61. http://dx.doi.org/10.1109/mper.2002.4311915.

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Gomes, S., N. Martins, and C. Portela. "Computing small-signal stability boundaries for large-scale power systems." IEEE Transactions on Power Systems 18, no. 2 (May 2003): 747–52. http://dx.doi.org/10.1109/tpwrs.2003.811205.

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Feilat, E. A. "Performance Comparison of Adaptive Estimation Techniques for Power System Small-Signal Stability Assessment." Journal of Engineering Research [TJER] 7, no. 2 (December 1, 2010): 10. http://dx.doi.org/10.24200/tjer.vol7iss2pp10-23.

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This paper demonstrates the assessment of the small-signal stability of a single-machine infinite- bus power system under widely varying loading conditions using the concept of synchronizing and damping torques coefficients. The coefficients are calculated from the time responses of the rotor angle, speed, and torque of the synchronous generator. Three adaptive computation algorithms including Kalman filtering, Adaline, and recursive least squares have been compared to estimate the synchronizing and damping torque coefficients. The steady-state performance of the three adaptive techniques is compared with the conventional static least squares technique by conducting computer simulations at different loading conditions. The algorithms are compared to each other in terms of speed of convergence and accuracy. The recursive least squares estimation offers several advantages including significant reduction in computing time and computational complexity. The tendency of an unsupplemented static exciter to degrade the system damping for medium and heavy loading is verified. Consequently, a power system stabilizer whose parameters are adjusted to compensate for variations in the system loading is designed using phase compensation method. The effectiveness of the stabilizer in enhancing the dynamic stability over wide range of operating conditions is verified through the calculation of the synchronizing and damping torque coefficients using recursive least square technique.

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Shi, Li-Bao, Li Kang, Liang-Zhong Yao, Shi-Yao Qin, Rui-Ming Wang, and Jin-Ping Zhang. "Effects of Wind Generation Uncertainty and Volatility on Power System Small Signal Stability." Journal of Electrical Engineering and Technology 9, no. 1 (January 1, 2014): 60–70. http://dx.doi.org/10.5370/jeet.2014.9.1.060.

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SARVI, Mohammad, and Mohammad Reza SALIMIAN. "Optimal power flow by considering system security cost and small signal stability constraints." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 1709–28. http://dx.doi.org/10.3906/elk-1307-250.

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Đurić, Milenko B., Zoran M. Radojević, and Emilija D. Turković. "A reduced order multimachine power system model suitable for small signal stability analysis." International Journal of Electrical Power & Energy Systems 20, no. 5 (June 1998): 369–74. http://dx.doi.org/10.1016/s0142-0615(97)00027-6.

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Feng, Jun Qi, Da Xie, Yu Jian Jia, and Rui Lin Wang. "Small Signal Stability Analysis of Wind Turbine for Shaft Torsional Vibration Studies." Advanced Materials Research 805-806 (September 2013): 347–63. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.347.

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One critical task in wind turbine shaft torsional vibration study involves the modelling of wind turbine and power grid. Focus on the mechanical rotational system of wind turbine, this paper provides three-mass shaft model upon which one wind turbine to infinite bus model can be developed. The model based on small signal stability analysis is used to study the wind turbine shaft torsional vibration. For this reason, this paper concentrates on the union model of stall wind turbine and power grid. The small-signal stability model includes the mechanical system and electrical system. Each of the component-blocks of the wind turbine and power grid is modelled separately so that one can easily expand and modify the model to suit their needs. Then, this is followed by one case study to explain how the small-signal stability model can be used to study wind turbine shaft torsional vibration issues.

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Soleymani, Hossein, and Amin Hasanvand. "Estimation of Power System Stabilizer Parameters Using Swarm Intelligence Techniques to Improve Small Signal Stability of Power System." Advances in Science, Technology and Engineering Systems Journal 2, no. 4 (August 2017): 139–44. http://dx.doi.org/10.25046/aj020419.

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