A Modified Grey Wolf Optimizer For Improving Wind Plant Energy Production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

A modified grey wolf optimizer for improving wind plant energy production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

A modified sine cosine algorithm for improving wind plant energy production

This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The two modifications employed to the original SCA are in terms of the updated step size gain and the updated design variable equation. Those modifications are expected to enhance the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-SCA is applied to maximize energy production of a row of ten turbines. The statistical performance analysis shows that the M-SCA provides the highest total energy production as compared to other existing methods

H∞ Controller with Graphical LMI Region Profile for Gantry Crane System

This paper presents investigations into the development of H∞ controller with pole clustering based on LMI techniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional pole placement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system with respect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The results of the response with the controllers are presented in time domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control techniques is discussed and presented

Simple Pole Placement Controller for Elastic Joint Manipulator

This paper presents investigations into the development of simple pole placement controller for tip angular position tracking and deflection reduction of an elastic joint manipulator system. A Quanser elastic joint manipulator is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. The pole placement controller is designed based on integral state feedback structure and the feedback gain is computed based on the desired time response specifications of tip angular position. The proposed control scheme is also compared with a hybrid Linear Quadratic Regulator (LQR) with input shaper control scheme. The performances of the control schemes are assessed in terms of tip angular tracking capability, level of deflection angle reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed

GGrey Wolf Optimizer For Identification Of Liquid Slosh Behavior Using Continuous-Time Hammerstein Model

This paper presents the identification of liquid slosh plant using the Hammerstein model based on Grey Wolf Optimizer (GWO) method. A remote car that carrying a container of liquid is considered as the liquid slosh experimental rig. In contrast to other research works, this paper consider a piece-wise affine function in the nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The GWO method is used to tune both coefficients in the nonlinear function and transfer function of the Hammerstein model such that the error between the identified output and the real experimental output is minimized. The effectiveness of the proposed framework is assessed in terms of the convergence curve response, output response, and the stability of the identified model through the bode plot and pole zero map. The results show that the GWO based method is able to produce a Hammerstein model that yields identified output response close to the real experimental slosh output

A DC/DC buck-boost converter-inverter-dc motor control based on model-free PID controller tuning by adaptive safe experimentation dynamics algorithm

Model-free PID control is currently utilized for the examination of a DC/DC Buck-Boost Converter-Inverter-DC motor structure in this study through optimization of the adaptive safe experimentation dynamics (ASED) method. PID controller has been popularized on its uncomplicated construct, convenient employment with limited tuneable parameters, and broad applicability to diverse mechanistic circumstances. Demonstrated nonlinearity, complexity, and high dimensional parameters within MIMO structure of the DC/DC Buck-Boost Converter-Inverter-DC motor then demand controller with immense precision. The ASED method is hereby adopted as the optimization approach with considerable precision as needed towards fine-tuning the PID controller for its ability to minimize both output of control tracking and energy consumption at reduced processing interval by the exclusion of mathematical modeling in assessing input and output of the system. Traced outcomes regarding voltage of the converter and bidirectional angular velocity are further accounted for performance appraisal of the recommended motor system equipping model-free PID controller following optimization of the ASED approach. A comparison was further operationalized between the proposed ASED approach and its conventional SED-based counterpart. Convergence stability was successively reached by the proposed approach via undertaken simulation with minimization of the specified objective function. Acquired results hereby confirmed smaller values of the objective function and total norm error by the ASED approach towards the precision of operation tracing against the performance of its conventional counterpart

Model order reduction method based on improved sine cosine algorithm

This paper presents an improved sine cosine algorithm (iSCA) for the reduction of high-order single-input single-output (SISO) systems. The proposed iSCA is adopted to solve the imbalance portion of the exploration and exploitation stages in the standard sine cosine algorithm (SCA). Specifically, a nonlinear decreasing updated gain is adopted to provide a proper balance of exploration and exploitation stages. The proposed iSCA is expected to yield a most accurate reduced-order model for a particular original high-order system by minimizing the integral square error (ISE) between their system output responses. The effectiveness of the proposed technique is evaluated by reducing a 6 th order double pendulum overhead crane model. The obtained simulation results revealed that the proposed iSCA is highly effective and remarkably consistent in obtaining an ideal reduced-order model compared to its original version

Implementation of safe experimentation spiral dynamics algorithm for self-tuning of PID controller in elastic joint manipulator

This paper exclusively endorses the optimization of self-tuned PID using Safe Experimentation Spiral Dynamic Algorithm (SESDA) for elastic joint handling. SESDA is hereby devised by adoption of spiral function to a standard Safe Experimentation Dynamics Algorithm (SEDA). Such modification is implemented to exploit the ability of spiral function in enhancing both the algorithm's exploration competency and convergence accuracy. Rotating angle tracking and vibration were then commanded by employing a pair of self-tuned PID controllers to the elastic joint system in appraising the optimization efficacy of SESDA. Performance of the updated self-tuned PID controller was further assessed in accordance to the recorded outputs on angular motion trajectory tracking, vibration suppression and statistical evaluations centering its pre-established control fitness function. The proposed SESDA produced 6.51 %, 5.54 % and 8.51 % improvement of fitness function, tracking error and control input energy, respectively, as compared with the standard SEDA. Acquired results ultimately confirmed the excellence of SESDA towards self-tuned PID's superior regulatory precision against the standard SEDA as well as its variants

An improved marine predators algorithm-tuned fractional-order PID controller for automatic voltage regulator system

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers