Effects of various power system stabilizers on improving power system dynamic performance

He, Peijun; Wen, Fushuan; Ledwich, Gerard; Xue, Yusheng; Wang, Kewen

doi:10.1016/j.ijepes.2012.10.026

Cited by 28 publications

(14 citation statements)

References 14 publications

(43 reference statements)

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“…To ensure the stability of

λ_{k}

, the calculated values of

(}{, {\overset{false¯}{α}}_{k} - 4 σ_{α k}, {\overset{false¯}{α}}_{k} + 4 σ_{α k})

should be on the left side of the complex plane. This can also be explained by the upper limit

α_{k}^{'}

and the extended value

ξ_{k}^{'}

as shown in (5) [26, 27]

({, \begin{matrix} {α^{'}}_{k} = {\overset{false¯}{α}}_{k} + 4 σ_{α_{k}} \leq α_{c}, \\ {ξ^{'}}_{k} = {\overset{false¯}{ξ}}_{k} - 4 σ_{ξ_{k}} \geq ξ_{c}, \end{matrix})

where α c and ξ c are acceptable threshold for damping constant and damping ratio, respectively. In (5), ξ c and α c are considered to be zero.…”

Section: Probabilistic Eigenvalue Sensitivity Indicesmentioning

confidence: 99%

Small signal stability analysis of doubly‐fed induction generator‐integrated power systems based on probabilistic eigenvalue sensitivity indices

Arefifar²,

et al. 2019

IET Generation, Transmission & Distribution

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“…To ensure the stability of

λ_{k}

, the calculated values of

(}{, {\overset{false¯}{α}}_{k} - 4 σ_{α k}, {\overset{false¯}{α}}_{k} + 4 σ_{α k})

should be on the left side of the complex plane. This can also be explained by the upper limit

α_{k}^{'}

and the extended value

ξ_{k}^{'}

as shown in (5) [26, 27]

({, \begin{matrix} {α^{'}}_{k} = {\overset{false¯}{α}}_{k} + 4 σ_{α_{k}} \leq α_{c}, \\ {ξ^{'}}_{k} = {\overset{false¯}{ξ}}_{k} - 4 σ_{ξ_{k}} \geq ξ_{c}, \end{matrix})

where α c and ξ c are acceptable threshold for damping constant and damping ratio, respectively. In (5), ξ c and α c are considered to be zero.…”

Section: Probabilistic Eigenvalue Sensitivity Indicesmentioning

confidence: 99%

Small signal stability analysis of doubly‐fed induction generator‐integrated power systems based on probabilistic eigenvalue sensitivity indices

Arefifar²,

et al. 2019

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

“…Power system security and stability greatly depends on the effectiveness of AGCs in the thermal power plants to counteract the power imbalance [3]. There are several traditional methods that address the load frequency control such as integral, proportional-integral (PI) and proportional-integral-differential (PID) controllers.…”

Section: Introductionmentioning

confidence: 99%

Observer based sliding mode frequency control for multi-machine power systems with high renewable energy

Tummala¹,

Inapakurthi²,

Ramanarao³

2018

J. Mod. Power Syst. Clean Energy

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With the global consciousness of climate change, renewable energy systems are prioritized over the conventional energy systems. The deep injection of renewables into the power systems is creating several challenges to the grid due to wide variations in their output power depending on the time of the day, weather etc. Of these challenges, frequency change plays a vital role in maintaining the power quality. This paper presents a novel sliding mode controller with non-linear disturbance observer to effectively mitigate the wide changes in the frequency. A sliding mode surface based on estimated disturbance along with states is designed. A sliding mode control law is proposed to compensate disturbances including variations in renewables, load and parameters under mismatched uncertainties. The proposed observer based controller is tested for three area multi-machine power system in MATLAB/Simulink. The simulated results proved to alleviate the frequency variations effectively compared to the conventional controllers.

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“…Robust design of PSS for SMIB power system was reported in Shayeghi et al Also, power system stabilizers were designed for MMPS based on local measurement in Kumar . Authors in He et al and Khodabakhshian et al show the superiority of multi‐band PSSs over the CPSS in SMIB and MMPS. Note that in the real‐world applications, tuning of the multi‐band PSS is a very challenging task, and thus the CPSS has gained a major role in the existing power system oscillation damping controllers.…”

Section: Introductionmentioning

confidence: 99%

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Younesi

Shayeghi

Moradzadeh

2017

Int Trans Electr Energ Syst

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Summary In this paper, an adaptive design of interline power flow controller (IPFC) using a reinforcement learning (RL) approach is utilized for damping of the low‐frequency oscillations (LFOs) in power system. This Q‐learning‐based adaptive damping controller is applied to a single‐machine and multi‐machine power system. The main advantages of the Q‐learning based controller are its robustness and adaptive behavior to change in the operation condition; also, it does not need any knowledge about the control system, making the control strategy suitable for the realistic power systems with high nonlinearities. In order to demonstrate the performance of the proposed RL‐based damping controller in both single‐machine and multi‐machine power system, nonlinear simulations are carried out using MATLAB/Simulink. Three cases of simulations are performed; the system equipped with (1) only optimal classical power system stabilizer (PSS), (2) coordinated designed of PSS and IPFC, and (3) RL‐based optimized IPFC. Krill herd optimization algorithm is used for coordination design of PSS and IPFC in both single machine and multi‐machine power systems. Suitable time‐domain performance indices in multiple operating conditions and different fault types are calculated for all 3 cases of simulations and are compared with one another. Simulation results demonstrate the effectiveness of the proposed control strategy in damping of the LFOs in the power systems.

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Effects of various power system stabilizers on improving power system dynamic performance

Cited by 28 publications

References 14 publications

Small signal stability analysis of doubly‐fed induction generator‐integrated power systems based on probabilistic eigenvalue sensitivity indices

Small signal stability analysis of doubly‐fed induction generator‐integrated power systems based on probabilistic eigenvalue sensitivity indices

Observer based sliding mode frequency control for multi-machine power systems with high renewable energy

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

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