Optimal Coordinated Design of Multiple Damping Controllers Based on PSS and UPFC Device to Improve Dynamic Stability in the Power System

Hussain, Ali Nasser; Malek, F.; Rashid, M. A.; Mohamed, Latifah; Affendi, Nur Adyani Mohd

doi:10.1155/2013/965282

Cited by 11 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new weight parameter w new is defined by ( 9) We have observed that the proposed new weight decreases and oscillates simultaneously for total iteration, whereas the conventional weight decreases monotonously from w max to w min [7] (Fig. 5).…”

Section: Chaotic Particle Swarm Optimization (Cpso)mentioning

confidence: 98%

Optimal Stabilizer PID Parameters Tuned by Chaotic Particle Swarm Optimization for Damping Low Frequency Oscillations (LFO) for Single Machine Infinite Bus system (SMIB)

Mijbas

Hasan

Salah

2020

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

In this paper, the Power system stabilizer (PSS) and (PID) are enhanced with a Chaotic Particle Swarm Optimization (CPSO) Damping Controller in order to suppression the Low-Frequency Oscillations (LFO) in a Single Machine Infinite Bus (SMIB) power system. Chaotic particle swarm optimization (CPSO) is used to tune the parameters of the PSS-PID. The design damping controller is an optimized lead-lag controller, which extracts the speed deviation of the generator rotor and generates the output feedback signal, which aims to modulate the reference values of the PSS-PID controller to achieve the best damping of LFO. In order to search the better damping option, the damping controller is applied to a series of the PSS-PID and the results are compared in two cases (PSS without PID and PSS with PID). The effectiveness of the proposed controller is achieved by time-domain simulation results in MATLAB environment, using three different operational conditions (Nominal, Light, and heavy). In addition, the results obtained from the PSS-PID were robust and more efficient compared to the PID only in terms of oscillations damping, overshoot minimizing and settling time reducing.

show abstract

Section: Chaotic Particle Swarm Optimization (Cpso)mentioning

confidence: 98%

Optimal Stabilizer PID Parameters Tuned by Chaotic Particle Swarm Optimization for Damping Low Frequency Oscillations (LFO) for Single Machine Infinite Bus system (SMIB)

Mijbas

Hasan

Salah

2020

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

show abstract

“…The model of linear dynamic by linearization of non−linear model for the operating condition. Figure 1 illustrates the linearized model of the power system as given by [16]: The construction of the matrices A and B are:…”

Section: Linearized Form Of Smib With Upfcmentioning

confidence: 99%

Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Neda¹

2020

IJECE

View full text Add to dashboard Cite

Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB.

show abstract

“…(4) Power oscillation incidence detection: judge whether the power oscillation amplitude of the fifth cycle is larger than 50 MW or not for any 500 kV tie line and record the oscillation curve for the oscillation property classification. buses at the power grid using the trained SVM model in procedure (3). As for the recognized result of the oscillation curves, 1 represents the negative damping oscillation and −1 represents the forced oscillation.…”

Section: Procedures Of the Oscillation Property Classificationmentioning

confidence: 99%

“…With the interconnection of large-scale power grids via high voltage long distance transmission lines, coupling between synchronous generators becomes weaker which leads to poor or negative damping ratio of power systems [1,2]. Power oscillation has become a major problem threatening the security of large-scale interconnected power systems [3,4]. In order to safely operate the power system, monitoring classification and suppression of the electric power oscillations in power grids has captured exponentially increasing attention in power engineering community in the past decade.…”

Section: Introductionmentioning

confidence: 99%

Active Power Oscillation Property Classification of Electric Power Systems Based on SVM

Liu

Yao

Wen

et al. 2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

Nowadays, low frequency oscillation has become a major problem threatening the security of large-scale interconnected power systems. According to generation mechanism, active power oscillation of electric power systems can be classified into two categories: free oscillation and forced oscillation. The former results from poor or negative damping ratio of power system and external periodic disturbance may lead to the latter. Thus control strategies to suppress the oscillations are totally different. Distinction from each other of those two different kinds of power oscillations becomes a precondition for suppressing the oscillations with proper measures. This paper proposes a practical approach for power oscillation classification by identifying real-time power oscillation curves. Hilbert transform is employed to obtain envelope curves of the power oscillation curves. Twenty sampling points of the envelope curve are selected as the feature matrices to train and test the supporting vector machine (SVM). The tests on the 16-machine 68-bus benchmark power system and a real power system in China indicate that the proposed oscillation classification method is of high precision.

show abstract

Optimal Coordinated Design of Multiple Damping Controllers Based on PSS and UPFC Device to Improve Dynamic Stability in the Power System

Cited by 11 publications

References 25 publications

Optimal Stabilizer PID Parameters Tuned by Chaotic Particle Swarm Optimization for Damping Low Frequency Oscillations (LFO) for Single Machine Infinite Bus system (SMIB)

Optimal Stabilizer PID Parameters Tuned by Chaotic Particle Swarm Optimization for Damping Low Frequency Oscillations (LFO) for Single Machine Infinite Bus system (SMIB)

Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Active Power Oscillation Property Classification of Electric Power Systems Based on SVM

Contact Info

Product

Resources

About