Differential evolution for optimal tuning of power system stabilizers to improve power systems small signal stability

Author(s):  
Y. Chabane ◽  
A. Ladjici
2012 ◽  
Vol 26 (25) ◽  
pp. 1246012 ◽  
Author(s):  
J. L. DOMÍNGUEZ-GARCÍA ◽  
O. GOMIS-BELLMUNT ◽  
F. BIANCHI ◽  
A. SUMPER

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.


2017 ◽  
Vol 16 (1/2) ◽  
pp. 3-28 ◽  
Author(s):  
Prasenjit Dey ◽  
Aniruddha Bhattacharya ◽  
Priyanath Das

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.


Author(s):  
Herlambang Setiadi ◽  
Karl O Jones

<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>


Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 157
Author(s):  
Jiawei Yu ◽  
Ziqian Yang ◽  
Jurgen Kurths ◽  
Meng Zhan

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.


2019 ◽  
Vol 9 (6) ◽  
pp. 1109 ◽  
Author(s):  
Samundra Gurung ◽  
Sumate Naetiladdanon ◽  
Anawach Sangswang

This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed tuning method is based on a combination of an analytical method that assesses the small-signal-stability margin, and an optimization technique that utilizes this statistical information to optimally tune power-system controllers. The optimization problem is solved using a metaheuristic technique known as the firefly algorithm. Power-system stabilizers, as well as sodium–sulfur (NaS)-based BESS controllers with power-oscillation dampers (termed as BESS controllers) are modeled in detail for this purpose in DIGSILENT. The results show that the sole use of PSSs and BESS controllers is insufficient to improve dynamic stability under fluctuating input power due to the integration of renewable-energy resources. However, the proposed strategy of using BESS and PSS controllers in a coordinated manner is highly successful in enhancing PSSS under renewable-energy-resource integration and under different critical conditions.


2009 ◽  
Vol 129 (11) ◽  
pp. 1290-1298
Author(s):  
Hiroyuki Ishikawa ◽  
Yasuyuki Shirai ◽  
Tanzo Nitta ◽  
Katsuhiko Shibata

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