On the Design of Variable Structure Load Frequency Controllers by Tabu Search Algorithm: Application to Nonlinear Interconnected Models

Al-Hamouz, Z.; Al-Musabi, Naji A.; Al-Duwaish, H.; Al-Baiyat, S.A.

doi:10.1080/15325000590951735

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…Recently, the design of SMC feedback gains has been formulated an optimization problem where search optimization algorithms are used [12][13][14]. In this way, the optimum settings of the SMC applied to power system control problems can be found even with the presence of nonlinearities in the model.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], genetic algorithms have been used to select the optimal feedback gain for load frequency control problem. Tabu search has been used in [13] for the selection of SMC feedback gains for multi-area nonlinear load frequency control. For a nonlinear single machine infinite system, particle swarm optimization has been used to select the feedback gains of the SMC controller [14].…”

Section: Introductionmentioning

confidence: 99%

“…For a nonlinear single machine infinite system, particle swarm optimization has been used to select the feedback gains of the SMC controller [14]. The feedback gain s selection of the SMC proposed in [12][13][14] is based on one operating point which results in fixed SMC gains for the entire operating points. Therefore, the performance of the controller away from the design operating point is, of necessity, a compromise.…”

Section: Introductionmentioning

confidence: 99%

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A neural network based adaptive sliding mode controller: Application to a power system stabilizer

Al-Duwaish

Al-Hamouz

2011

Energy Conversion and Management

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In this paper, a neural networks (NN) based adaptive sliding mode controller (SMC) is introduced. The selection of SMC feedback gains is normally based on one operating point and thus the performance of the controller away from the design operating point is, of necessity, a compromise. The Adaptive SMC is proposed to overcome the limitations imposed on the effectiveness of the SMC under different operating conditions. Neural networks are used for on-line prediction of the optimal SMC gains when the operating point changes. The proposed method has been applied to a power system stabilizer (PSS) of a single machine power system.Simulation results are included to demonstrate the performance of the proposed control scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neural network based adaptive sliding mode controller: Application to a power system stabilizer

Al-Duwaish

Al-Hamouz

2011

Energy Conversion and Management

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“…Several control strategies have also been proposed to ensure the normal working of the power system. Meta-heuristic optimization techniques such as particle swarm optimization (PSO) [14], genetic algorithm (GA) [15], biogeography-based optimization (BBO) [16], [17], krill herd algorithm (KHA) [18], teaching learning based optimization (TLBO) [19,20], bacteria foraging optimization (BFOA) [21], gravitational search algorithm (GSA) [22], hybrid PSO-pattern search (hPSO-PS) algorithm [23], hybrid FA-PS [24], Tabu search algorithm (TSA) [25], quasi-oppositional harmony search algorithm (QOHSA) [26], [27], BAT algorithm [28], backtracking search algorithm (BSA) [29] have been proposed. It is observed from the literature survey that the implementation of newly proposed optimization techniques for the frequency and tieline power control yielded good results than the older optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Comparative performance analysis of one rank cuckoo search technique based optimization for automatic generation control of interconnected power systems

Manoharan¹,

Dash²,

Sivalingam³

et al. 2017

IJET

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This paper presents a one rank cuckoo search optimization technique is proposed to design classical PID Controllers for Automatic Generation Control (AGC) of interconnected power systems. This method is proposed based on the original cuckoo search method. It was found in original cuckoo search the convergence speed is comparative lesser in reaching optimal solutions. To overcome the above mentioned problem one rank cuckoo search algorithm has been proposed which uses a bound by best solution technique to get the valid dimension so as to improve the system performance and rate of convergence. The proposed approach is applied to a four area hydro-thermal system in which area-1 and area-2 are steam reheat power plant and area-3 and area-4 are hydro power plant. The controller gains are derived using original cuckoo search and one rank cuckoo search methods. The superiority of the proposed approach is compared with the results obtained with original cuckoo search algorithm.

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“…Similarly, I controller improves the steady state but spoils the transient behavior. This problem is overcome by Variable Structure System (VSS) controller [23][24][25] which switches between P to PI during transient to steady state period. Integrating VSS with FGS forms VSFGS [26] controller.…”

Section: Introductionmentioning

confidence: 99%

Variable Structure Fuzzy Gain Schedule Based Load Frequency Control of Non-Linear Multi Source Multi Area Hydro Thermal System

Chandrakala

Balamurugan²,

Janarthanan³

et al. 2014

ijeei

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Abstract:The article focuses on the issues of Load Frequency Control (LFC) under non-linear strategies in multi source multi area hydro thermal system. On practical perspective dead band, boiler dynamics, reheat steam turbine along with hydro turbine operating under two different area capacities are considered in the system. When subjected to random load variations in both the areas, the system exhibits higher oscillations. The speed governor matches the generation with the demand. The offset in the area frequencies and tie-line power is removed by using secondary Proportional Integral (PI) controller. The PI controller is tuned using Ziegler Nichols' (ZN) and Fuzzy Gain Scheduling (FGS) method. The influence of high Proportional (P) controller gain during steady state and high Integral (I) controller gain during transient affects the system performance. Variable Structure System (VSS) helps to switch from P to PI controller during transient to steady state based on control error. The concept of VSS is applied to Fuzzy Gain Scheduling (FGS) PI controller. The performance of the optimal Variable Structure Fuzzy Gain Scheduled (VSFGS) controller under non-linear environment is judged and validated using performance indices.

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On the Design of Variable Structure Load Frequency Controllers by Tabu Search Algorithm: Application to Nonlinear Interconnected Models

Cited by 16 publications

References 14 publications

A neural network based adaptive sliding mode controller: Application to a power system stabilizer

A neural network based adaptive sliding mode controller: Application to a power system stabilizer

Comparative performance analysis of one rank cuckoo search technique based optimization for automatic generation control of interconnected power systems

Variable Structure Fuzzy Gain Schedule Based Load Frequency Control of Non-Linear Multi Source Multi Area Hydro Thermal System

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