An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems

Eslami, Mahdiyeh; Shareef, Hussain; Mohamed, Azah; Khajehzadeh, Mohammad

doi:10.1016/j.ijepes.2012.07.028

Cited by 55 publications

(34 citation statements)

References 24 publications

(34 reference statements)

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“…Base on the participation factor in case study 1, for interarea with damping value less than 5% the contributed generators are coming from G2, G4, G5, G9, G10, G11, G12, G13, G14, and G15. Based on the [32,33] the minimum PSS that can be installed is half of the generator number. Hence, for this study, the PSS is installed to the G2, G4, G5, G9, G10, G11, G12, G13, G14, and G15.…”

Section: Results and Simulationsmentioning

confidence: 99%

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Djalal¹,

Setiadi²,

Lastomo³

et al. 2017

ijeei

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Small signal stability is one of the factor to assesst the reliability of a power system. Small signal stability is related to the ability of a power system to maintain synchronization after being exposed by small disturbance. In Indonesia, the reliability of power system is one of the important aspect that has to be achieved. However, very scant attention has been paid on studied small signal stability performance on Indonesia electricity. Hence, this paper studied the small signal stability analysis and its enhancement. 150 kV Sulselrabar interconnected power system is used as test system. To enhance the performance of the system designing coordinated controller of power system stabilizer (PSS) and redox flow batteries (RFB) using cuckoo search algorithm (CSA) is conducted. Eigenvalue, damping ratio, participation factor analysis, and time domain simulation is performed to assess the small signal stability performance and the successfulness of the CSA to optimize the PSS and RFB parameter. From the cases studies, it is found that 150 kV Sulselrabar has 12 local mode and 4 inter-area mode. Furthermore, the coordinated controller between PSS and RFB based on CSA could enhance the small signal stability performance of 150 kV Sulselrabar interconnected system indicated by higher damping, smallest overshoot and fastest settling time.

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Section: Results and Simulationsmentioning

confidence: 99%

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Djalal¹,

Setiadi²,

Lastomo³

et al. 2017

ijeei

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“…In this study of chaotic optimization, chaotic variables (cv) are generated by using the following logistic equation which is very sensitive to initial conditions [20].…”

Section: ) Chaotic Logistic Mapmentioning

confidence: 99%

Tuning of PID Controller for Realistic Load Frequency Control System using Chaotic Exponential PSO Algorithm

Pain¹,

Acharjee²

2016

IJET

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Abstract-For a realistic multi-area interconnected power system model with different generating units (reheat, non-reheat and hydro generating units) and real time different system parameters, design and analysis of load frequency control (LFC) are executed using new and unique chaotic exponential PSO (CHEPSO) algorithm. Considering both transient and steady-state specifications with proper weighting factors, four objective functions are formulated for practical LFC model with nonlinear security constraints such as generation rate constraint (GRC), governor dead band (GDB) and time delay. In the context of reliable convergence, multiple solutions, improvement of both transient and steady state performances, the performance of the proposed CHEPSO technique is compared with other techniques like real-coded genetic algorithm (RCGA), general PSO (GPSO) and exponential PSO (EPSO) techniques. The proposed technique gives the best performances for all cases.Keyword -Chaotic local search, Chaotic exponential particle swarm optimization, Security constraints, Realistic LFC system, Multiple solutions I. INTRODUCTIONThe dynamic of the power system changes due to load variation, disturbances and/or changing operating conditions. This leads to abrupt changes in frequency and inter area tie line power which make the power system most unreliable and unstable. Hence, the frequency and voltage profile of the power system will deviate from the normal operating condition. For successful operation under abnormal condition, the mismatch between generation and demand has to be corrected by applying suitable control strategy. Load frequency control (LFC) is an effective mechanism in power system operation and control for minimizing the mismatches and supplying good quality of electric power with sufficient reliability [1]. The primary objectives of the LFC are maintaining the frequency and tie line power at schedule values, improving the power system performance by making zero steady state error (SSE), minimum overshoot (M p ) and minimum settling time (t s ).Over the years, several control strategies had been successfully used to address the LFC design problems in order to improve the dynamic performance of the power system. Among the various controller used so far, the most widely used controller is the conventional proportional integral (PI) controller [1]. Though it has simple structure and easy implementation, it is failed to achieve the expected result for large scale interconnected power system. Therefore, a lot of control design approaches based on modern control theory like decentralized robust, adaptive and optimal control methods had been employed to LFC design [2]-[6]. All the above mentioned conventional control methodologies are model specific and designed for specific type of disturbances. Moreover, the order of the controllers becomes high as that of the plant. The dynamic of the power system is highly nonlinear. The nonlinearities present in the system are generation rate constraint (GRC), governor dead band (GDB) and...

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“…The factor β is a random number, which is drawn according to max 0.5 0.25 , / i i (6) where i denotes fitness evaluation number, and r n is a random number with uniform distribution in [0,1].…”

Section: A Optimization Problem Statementmentioning

confidence: 99%

“…Earlier reported approaches based on modified versions of genetic algorithm (GA) [5], particle swarm optimization (PSO) [6], and differential evolution (DE) [7] highlight the potential of metaheuristic optimization algorithms for solving the OPCDC. Due to the stochastic nature of the underlying evolutionary mechanism, further research is needed to ascertain the robustness of these algorithms, which also motivates the application and extension of emerging metaheuristic optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

Rueda

González-Longatt

2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Additional Information:• . The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

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An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems

Cited by 55 publications

References 24 publications

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Tuning of PID Controller for Realistic Load Frequency Control System using Chaotic Exponential PSO Algorithm

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

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