Application of GA optimization for automatic generation control design in an interconnected power system

Golpîra, Hêmin; Bevrani, Hassan

doi:10.1016/j.enconman.2011.01.010

Cited by 217 publications

(107 citation statements)

References 20 publications

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“…On the basis of the constraint, Groups will move toward a better search space to find out the individual with the highest adaptability through the survival of the fittest mechanism. This individual is the optimal solution of the optimization problem [22][23]. It is faster to find the optimal solution of parameters after the optimization of the genetic algorithm.…”

Section: Short-term Pv Power Generation Forecasting Model Based On Gamentioning

confidence: 99%

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Wang¹,

Ran²,

Song³

et al. 2017

Journal of Electrical Engineering and Technology

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-Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

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Section: Short-term Pv Power Generation Forecasting Model Based On Gamentioning

confidence: 99%

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Wang¹,

Ran²,

Song³

et al. 2017

Journal of Electrical Engineering and Technology

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“…The constraints of the MPC include constraints on the magnitude and change of system variables including the system inputs, states and outputs. Prediction expression for the state space model is introduced by (8) and its simplified expression is presented by (9).…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…These features make load frequency control a time delayed, constrained, multivariable and multi-objective control problem. Many investigations in the area of power system LFC have been reported [2,4,[5][6][7][8]. Nearly all such previous studies neglected time delay and some important features of the LFC.…”

Section: Introductionmentioning

confidence: 99%

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Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

Daniar

Shiroei

Aazami

2016

Archives of Control Sciences

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In this paper, a multivariable model based predictive control (MPC) is proposed for the solution of load frequency control (LFC) in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

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“…The fuzzy logic control on its different form was successfully applied to design/cater LFC and other control engineering problems and is reported in [7]-[9]. The GA is powerful search optimization technique that was conveniently used for tuning LFC parameters [10][11][12][13]. Another popular technique, PSO was widely used to solve the complex power system problems [14]- [17].…”

Section: Introductionmentioning

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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Application of GA optimization for automatic generation control design in an interconnected power system

Cited by 217 publications

References 20 publications

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

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

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