Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparison Study

Sheta, Alaa; Faris, Hossam; Braik, Malik; Mirjalili, Seyedali

doi:10.1007/978-981-13-9263-4_9

Cited by 22 publications

(19 citation statements)

References 35 publications

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“…The current work focuses on the PID controller of a quadcopter whose parameters are to be tuned by meta-heuristic algorithms, including Genetic Algorithms (GA) [28], the Crow Search Algorithm (CSA) [29,30] and Particle Swarm Optimization (PSO) [31,32]. These meta-heuristics have long been accepted by the artificial intelligence community for their adequacy in solving a wide range of real applications by arriving at optimal or accurate solutions with respect to a compact set of parameters, such as time or cost [33][34][35].…”

Section: Objectives Of the Workmentioning

confidence: 99%

“…The PID controller was designed using closed-loop feedback control in order to stabilize the closed-loop system based on pre-defined equality characteristics. The PID controller can be described by a discrete transfer function as that given in Equation (30).…”

Section: Pid Controllermentioning

confidence: 99%

“…Notably, the fitness function proposed for GA, CSA and PSO to implement the problem under study is a multi-objective function that integrates several parameters, including K, o, r and s. This combination of these parameters is targeted to increase the gain of the PID controller in the optimization process as well as to contribute to measuring the impact of the closed system performance based on the step response. As a matter of fact, K in Equation ( 32) is utilized in Equation ( 31) to arrive at an optimal PID control system design as confirmed in [30]. This parameter is integrated into the multi-objective fitness function to produce a system with a much faster settling time.…”

Section: Proposed Multi-objective Fitness Functionmentioning

confidence: 99%

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Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

et al. 2021

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Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.

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Section: Objectives Of the Workmentioning

confidence: 99%

Section: Pid Controllermentioning

confidence: 99%

Section: Proposed Multi-objective Fitness Functionmentioning

confidence: 99%

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Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

et al. 2021

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“…Engineers of 21 st century are curious about the increasing complexity of the societal and technological challenges such as the parameter extraction problem in photovoltaic (PV) [1] and the problem of the Economic Load Dispatch (ELD) [2]. The ELD involves involves minimizing production costs by allocating power produced by each power system unit economically [3]. Although some solutions are proposed to reduce emissions and production costs [4] by considering both cost and emission optimally, leading to combined economic and emission dispatch (CEED), the ELD and CEED solutions require efficient optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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“…The complexity of power system operation and planning is increasing day by day. Economic Load Dispatch (ELD) is one such complex power system problems involving ELD involves reduction of the cost of production by economically allocating the power produced by each unit [1], [2]. In addition to reduction of production cost nowadays emphasis is laid on reduction of emission [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

et al. 2021

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The economic load dispatch (ELD) problems considering nonlinear characteristics where an optimal combination of power generating units is selected in order to minimize the total cost by economic allocation of power produced and the emission cost. As a consequence, optimal allocation is performed by considering both fuel cost and emission leading to Combined Economic and Emission Dispatch (CEED). This study presents a new Meta-heuristic algorithms (MHs) called the Turbulent Flow of Water Optimization (TFWO), which is based on the behaviour of whirlpools created in turbulent water flow, for solving different variants of ELD and CEED. To verify the robustness of the TFWO, various test network of CEED with effect of valve, and ELD with losses of transmission are incorporated. In comparison with seven well-known MHs such as Cuckoo Search Algorithm (CSA), Grey Wolf Algorithm (GW), Sine Cosine Algorithm (SCA), Earth Worm Optimization Algorithm (EWA), Tunicate Swarm Algorithm (TSA), Moth Search Algorithm (MSA) and Teaching Learning Based Optimization (TLBO), the TFWO provides the minimum fuel cost and significantly robust solutions of ELD problem over all tested networks. The results confirm the potential and effectiveness of the GWO to be a promising technique to solve various ELD problems.INDEX TERMS Turbulent Flow of Water Optimization (TFWO)); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristic Optimization Algorithms.

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Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparison Study

Cited by 22 publications

References 35 publications

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

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