scholarly journals Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3123
Author(s):  
Jhon Montano ◽  
Andres Felipe Tobon Mejia ◽  
Andrés Alfonso Rosales Muñoz ◽  
Fabio Andrade ◽  
Oscar D. Garzon Rivera ◽  
...  
Keyword(s):  
Optimization Technique ◽  
Optimization Techniques ◽  
Current Profile ◽  
Unknown Parameters ◽  
Pv Panel ◽  
Lack Of Information ◽  
Best Response ◽  
Current Saturation ◽  
Power Profile ◽  
Highly Nonlinear

Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera KC200GT and Solarex MSX60, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.

Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method

2014 ◽  
Vol 5 (3) ◽  
pp. 14-41 ◽  
Author(s):  
Marwa Elhajj ◽  
Rafic Younes ◽  
Sebastien Charles ◽  
Eric Padiolleau
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Simulation Software ◽  
Optimization Techniques ◽  
Engineering System ◽  
Global Optimization Method ◽  
Unknown Parameters ◽  
Simulated Model

The calibration of the model is one of the most important steps in the development of models of engineering systems. A new approach is presented in this study to calibrate a complex multi-domain system. This approach respects the real characteristics of the circuit, the accuracy of the results, and minimizes the cost of the experimental phase. This paper proposes a complete method, the Global Optimization Method for Parameter Calibration (GOMPC). This method uses an optimization technique coupled with the simulated model on simulation software. In this paper, two optimization techniques, the Genetic Algorithm (GA) and the two-level Genetic Algorithm, are applied and then compared on two case studies: a theoretical and a real hydro-electromechanical circuit. In order to optimize the number of measured outputs, a sensitivity analysis is used to identify the objective function (OBJ) of the two studied optimization techniques. Finally, results concluded that applying GOMPC by combining the two-level GA with the simulated model was an efficient solution as it proves its accuracy and efficiency with less computation time. It is believed that this approach is able to converge to the expected results and to find the system's unknown parameters faster and with more accuracy than GA.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals Optimal control of the Sabatier process in microchannel reactors

2021 ◽  
Vol 128 (1) ◽  
Author(s):  
Sebastian Blauth ◽  
Christian Leithäuser ◽  
René Pinnau
Keyword(s):  
Flow Rate ◽  
Wall Temperature ◽  
Optimization Problems ◽  
Product Yield ◽  
Identification Problem ◽  
State Constraint ◽  
Optimization Techniques ◽  
Reacting Flow ◽  
Microchannel Reactor ◽  
Highly Nonlinear

AbstractWe consider the optimization of a chemical microchannel reactor by means of PDE-constrained optimization techniques, using the example of the Sabatier reaction. To model the chemically reacting flow in the microchannels, we introduce a three- and a one-dimensional model. As these are given by strongly coupled and highly nonlinear systems of partial differential equations (PDEs), we present our software package cashocs which implements the adjoint approach and facilitates the numerical solution of the subsequent optimization problems. We solve a parameter identification problem numerically to determine necessary kinetic parameters for the models from experimental data given in the literature. The obtained results show excellent agreement to the measurements. Finally, we present two optimization problems for optimizing the reactor’s product yield. First, we use a tracking-type cost functional to maximize the reactant conversion, keep the flow rate of the reactor fixed, and use its wall temperature as optimization variable. Second, we consider the wall temperature and the inlet gas velocity as optimization variables, use an objective functional for maximizing the flow rate in the reactor, and ensure the quality of the product by means of a state constraint. The results obtained from solving these problems numerically show great potential for improving the design of the microreactor.

Reliability/risk centered cost effective preventive maintenance planning of generating units

2018 ◽  
Vol 35 (9) ◽  
pp. 2052-2079 ◽  
Author(s):  
Umamaheswari E. ◽  
Ganesan S. ◽  
Abirami M. ◽  
Subramanian S.
Keyword(s):  
Preventive Maintenance ◽  
Optimization Technique ◽  
Solution Space ◽  
Fuzzy Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Statistical Measures ◽  
Highly Nonlinear ◽  
Optimal Maintenance ◽  
Maintenance Schedules

Purpose Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues. Design/methodology/approach The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS. Findings As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique. Originality/value As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

scholarly journals Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

2022 ◽  
Vol 2022 ◽  
pp. 1-18
Author(s):  
Dereje Tekilu Aseffa ◽  
Harish Kalla ◽  
Satyasis Mishra
Keyword(s):  
Optimization Technique ◽  
Research Work ◽  
Optimization Techniques ◽  
Magnetic Sensors ◽  
Batch Size ◽  
Raspberry Pi ◽  
Prototype Development ◽  
Embedded Platform ◽  
Artificial Intelligence Technology ◽  
Banknote Recognition

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

scholarly journals Adaptive Day-Ahead Prediction of Resilient Power Distribution Network Partitions

2021 ◽  
Author(s):  
Chinmay Shah ◽  
Richard Wies
Keyword(s):  
Power Distribution ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Power Distribution Networks ◽  
Power Distribution Network ◽  
High Penetration ◽  
Islanded Mode

The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

Optimization in the Design and Control of Robotic Manipulators: A Survey

1989 ◽  
Vol 42 (4) ◽  
pp. 117-128 ◽  
Author(s):  
S. S. Rao ◽  
P. K. Bhatti
Keyword(s):  
Optimal Control ◽  
Robot Manipulator ◽  
Research Effort ◽  
Industrial Robot ◽  
Load Capacity ◽  
Robotic Manipulators ◽  
Optimization Techniques ◽  
Highly Nonlinear ◽  
Manipulator Design ◽  
And Control

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.

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