Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

Guha, Dipayan; Roy, Provas Kumar; Banerjee, Subrata

doi:10.1016/j.jestch.2016.07.004

Cited by 107 publications

(57 citation statements)

References 42 publications

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“…However, this method also does not show good performance in solving high-dimensional function problems. In the application of the GWO algorithm, Guha et al [59] applied the grey wolf algorithm to the load control problem in large-scale power systems. In literature [60], a multiobjective discrete grey wolf algorithm is proposed for the uniqueness of welding job scheduling problem.…”

Section: Related Work Of Gwomentioning

confidence: 99%

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Jiang

2019

Complexity

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Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.

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Section: Related Work Of Gwomentioning

confidence: 99%

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Jiang

2019

Complexity

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“…Among these, the first three wolves guide the other wolf for hunting. The entire procedure is summed up as [30]:…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

Bagyaveereswaran

Arulmozhivarman

2019

Processes

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In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters of the RTD-A controller. Its performance is later compared with that of the conventional standard proportional integral derivative controller. The gain scheduled RTD-A controller is designed and implemented on a nonlinear conical tank process. Also, various performances attributes such as the integral square error, integral absolute error, integral time absolute error, rise time, and settling time are calculated for the first-order process and conical tank process. The servo responses with RTD-A are also compared against the responses recorded from the conventional control schemes.Processes 2019, 7, 415 2 of 20 nonlinear systems deteriorates. It is mentioned that though the PID controller is widely used because of its simplicity and it cannot meet the requirements needed for better performances unless the controller is tuned properly [13]. Since PID controllers are fixed gain controllers, they cannot compensate the parameter variation in the plant and cannot adapt to changes in the environment [14]. Processes with large dead time, inverse response, and nonlinear processes are difficult to control efficiently by the PID [15]. In PID, there is no direct relationship between the three tuning parameters and setpoint tracking, disturbance rejection ability, and robustness. Therefore, achieving each performance attributes by individual tuning of PID is not straightforward. To overcome this problem, the model predictive controller (MPC) is widely used in industries which employ nonlinear process in particular. It is often implemented in supervisory mode hierarchically above the base PID controller. Therefore, its performance again primarily depends on the PID in the lower level. The PID has to implement the commands received from MPC [15,16]. MPC is also implemented as a direct control algorithm, mainly where classical PID structures cannot deliver required control performance, due to difficult dynamics, strong interactions, and active constraints [17]. The MPC utilises a distinct model of the process to predict the future response of the process. The disturbance rejection offered by MPC is much better when compared to that of PID. However, the MPC suffers from serious drawbacks in that it is complicated and its parameters are tough to calculate [18].An alternative control strategy to overcome the shortcomings of the existing controllers was proposed by Kapil Mukati which intended on combining the positive attributes of the PID controller and MPC [12]. The controller which was proposed was named as RTD-A because a good controller should possess all of the following characteristics, namely: robustness (R), set-point tracking (T), disturbance rejection (D), and overall aggressiveness (A). All the four tuning parameters are normalize...

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“…A hybrid FA-PS Fuzzy PID controller [15] presented and performance compared to DE, CPSO. An ACS algorithm based controller for simultaneous control of multi area power system interconnected with AC Tie line parallel with DC link [16].A type-2 FLC presented with error feedback learning proposed [17].…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Sliding Mode Controller Fed Load-Frequency Control of Multi-Area Deregulated Power System Network

Kumar¹,

Scholar²,

Ganesh³

et al. 2020

IJEAT

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This paper presents a Fractional Order Sliding Mode Controller (FOSMC) for load frequency control of multi area power system in deregulated environment. In deregulated power system the design of controller is more complicated due to contracted and un-contracted load demands. This proposed controller shall take care of system nonlinearities and uncertainties under bilateral contract scenario for sudden load disturbances. The performance of proposed controller compared to PI controller and without any controller.

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Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

Cited by 107 publications

References 42 publications

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

Fractional Order Sliding Mode Controller Fed Load-Frequency Control of Multi-Area Deregulated Power System Network

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