Application of the arithmetic optimization algorithm to solve the optimal power flow problem in direct current networks

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation. © 2022 The Author

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time

Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size

Implementación de un panel fotovoltaico con rastreador solar y medición de su eficiencia para la extracción de energía, teniendo en cuenta el gasto energético de los motores

En el presente proyecto se midió la energía transformada por un panel fotovoltaico ajustado a un seguidor de sol en donde se hizo la comparación de la eficiencia cuando el panel se encuentra en estado estático y cuando el seguidor de sol se encuentra en estado de movimiento, teniendo en cuenta el gasto energético por parte motor del seguidor de sol. Al final se concluye si el panel (A-12V2.4W) mejora su eficiencia con el seguidor de sol propuesto. Para ello se hizo necesario el desarrollo del proyecto en dos etapas: En una primera fase se implementó un prototipo que fuese capaz de orientar los paneles solares en dirección perpendicular al sol, y para la subsiguiente etapa se procedió a realizar el desarrollo experimental y así dar las conclusiones pertinentes.Ingeniero Electrónicopregrad

Estimación de los parámetros del modelo de un solo diodo del módulo fotovoltaico aplicando el método de optimización basado en búsqueda de patrones mejorado

In this article, we propose the use of the optimization algorithm based on improved pattern search (IPSM), applying it to the estimation of the model parameters of a single diode of a photovoltaic cell. The parameters to be estimated are the photovoltaic current, the saturation current of the diode, the series resistance, the resistance in parallel, and the ideality factor of the diode. The estimation is made from the data obtained from a known curve, that is to say, that a photovoltaic cell could be characterized and from the data of the curve the parameters are extracted. The results are the identification of the parameters and the accuracy of the model concerning the reference at the point of maximum power (MPP). Additionally, a comparison is made with the model obtained with three estimations made with the particle swarm optimization algorithm (PSO), under the same conditions in the number of particles and iterations. The error found shows the similarity of the model with the reference obtained using the IPSM algorithm. En este artículo se propone el uso de un algoritmo de optimización basado en la búsqueda de patrones mejorada, aplicándolo a la estimación de los parámetros modelo de un diodo de una célula fotovoltaica. Los parámetros a estimar son corriente fotovoltaica, la saturación de corriente del diodo, la resistencia serie, la resistencia en paralelo y el factor de idealidad del diodo. La estimación es hecha a partir de los datos obtenidos de una curva conocida, es decir, que la celda fotovoltaica puede caracterizarse y los parámetros son extraídos de los datos de la curva. Los resultados son la identificación de los parámetros y la exactitud del modelo con respecto a la referencia en el punto de máxima potencia (MPP). Adicionalmente se realiza una comparación con el modelo obtenido con tres estimaciones realizadas con el algoritmo de optimización enjambre de partículas (PSO), bajo las mismas condiciones en cantidad de partículas y de iteraciones. El error encontrado muestra la similitud del modelo con la referencia obtenida de acuerdo con el algoritmo IPSM

Parameters estimation of the single diode model of a photovoltaic module based on the improved patterns search method

En este artículo se propone el uso del algoritmo de optimización búsqueda de patrones mejorado (IPSM), aplicándolo a la estimación de los parámetros del modelo de un solo diodo de una célula fotovoltaica. Los parámetros por estimar son: la corriente fotovoltaica, la corriente de saturación del diodo, la resistencia serie, la resistencia en paralelo y el factor de idealidad del diodo. La estimación se hace a partir de los datos obtenidos de una curva conocida, es decir que se podría caracterizar una celda fotovoltaica y a partir de los datos de la curva se extraen los parámetros. Los resultados son la identificación de los parámetros y la exactitud del modelo con respecto a la referencia en el punto de máxima potencia (MPP). Adicionalmente se realiza una comparación con el modelo obtenido con tres estimaciones realizadas con el algoritmo de optimización enjambre de partículas (PSO), bajo las mismas condiciones en cantidad de partículas y de iteraciones. El error encontrado, muestra la similitud del modelo con la referencia obtenido de acuerdo con el algoritmo IPSM. Palabra

MPPT of a Photovoltaic Panels Array with Partial Shading Using the IPSM with Implementation Both in Simulation as in Hardware

This article presents a method for the Maximum Power Point Tracking (MPPT) of a Photovoltaic (PV) panels array with partial shading, applying an Improved Pattern Search Method (IPSM). The method is simulated in PSIM @ and then implemented in hardware in the loop system, emulating the PV array on an industrial computer (Speedgoat) that allows real-time emulations and the IPSM is applied in an Arduino DUE. The experiments were carried out with TP245S-20/WD, KYOCERA KC200GT, YINGLY SOLAR JS65, and MSX60 photovoltaic panels. The results are the proper MPPT with changes in partial shading over time, inducing the increase and decrease of the maximum power point. The results obtained are the search for the global maximum power point in a matrix of panels in which, due to partial shading, it might have several local maximum power points, and thanks to the IPSM algorithm, it always manages to find the global maximum power point. Finally, the results are compared with other methods where it was found that IPSM had faster answers

A system study on Montano Foods Corporation

Montano Foods Corporation is a manufacturer of Bottled Spanish Sardines located in Turno, Dipolog City, Zamboanga Del Norte, Philippines. The processing plant mainly produces six variants of bottled sardines (Corn oil, Olive Oil, Tomato Sauce in Corn Oil, Tomato Sauce in Olive Oil, Tuyo, and Bangus) of Bottled Spanish Style Sardines and supplies to leading stores nationwide. The main problem that was determined, with the help of the WOT-SUG Analysis, in the current production system of the company was the presence of bottled sardine rejects. To be more specific, out of the 965,952 bottles of sardines produced from April 2020 to September 2021, 1170 bottles were considered as rejects which resulted in Php 131,040 in opportunity loss of revenue. Once the presence of bottled sardine rejects was determined to be the main problem of the company, a problem analysis was performed through the construction of a Why-Why diagram. After the initial Why-Why diagram was constructed, the initial causes were then validated. After validating comprehensively, the following were determined to be the actual causes of the presence of bottled sardine rejects: Approximating oil placement (Specific cause of bottled reject caused: Bottle reject due to Oil Shortage and Oil Spillage) Bottles are slid across the table (Specific cause of bottled reject caused: Bottle reject due to Oil Shortage) Workers not fully closing the bottles (Specific cause of bottled reject caused: Bottle reject due to Loose Cap) Deformed bottle cap thread (Specific cause of bottled reject caused: Bottle reject due to Loose Cap) The alternative solutions that were generated revolved around the Oil-Filling step of the production process specifically addressing the validated root causes mentioned above. In choosing the solution to be applied in the proposed system, the KTDA or Kepner-Tregoe Decision Analysis was performed. The criteria taken into consideration for the KTDA were consulted with the management and workers of the company assigned in the oil filling stage. The “Modify Workplace Setup and Equipment Used by the Primary and Secondary Oil Fillers” ended up having the highest rating in the KTDA performed and thus, was the focus of the proposed system. As previously mentioned, the proposed system focused on modifying the workplace setup and equipment used by the Primary and Secondary Oil Fillers. To be more specific, two workers from the preceding stages of the oil filling stage were transferred to the primary oil filling step of the oil filling stage due to the fact that queueing time is incurred before this stage. As a result, there’ll be four primary oil filling workers in the proposed system which is two more compared to the present system. In terms of the equipment, the kettles used by the primary oil fillers and the scoopers with a short handle used by the secondary oil fillers would be replaced by insulated barrels/dispensers with a PVC Transparent hose attached to it. In utilizing this alternative equipment, the proposed system aims to reduce the body pain experienced by workers since the kettles used for oil-filling in the present system contribute to the stress experienced by the workers in parts of their body such as the arms. To have an idea of what implementing the proposed system may look like, a simulation and a cost-benefit analysis were performed. In performing the simulation, it was determined that the proposed system can potentially contribute to the workers being able to fill more sets (1 set = 408 bottles/17 cases) of bottled sardines with oil while incurring lesser overtime compared to the present system. Meanwhile, with the help of the Cost-Benefit Analysis performed, it was estimated that the company can experience an NPV of Php 303,108

MPPT of a photovoltaic panels array with partial shading using the IPSM with implementation both in simulation as in hardware

Altres ajuts: Basque Government project IT1207-19This article presents a method for the Maximum Power Point Tracking (MPPT) of a Photovoltaic (PV) panels array with partial shading, applying an Improved Pattern Search Method (IPSM). The method is simulated in PSIM@ and then implemented in hardware in the loop system, emulating the PV array on an industrial computer (Speedgoat) that allows real-time emulations and the IPSM is applied in an Arduino DUE. The experiments were carried out with TP245S-20/WD, KYOCERA KC200GT, YINGLY SOLAR JS65, and MSX60 photovoltaic panels. The results are the proper MPPT with changes in partial shading over time, inducing the increase and decrease of the maximum power point. The results obtained are the search for the global maximum power point in a matrix of panels in which, due to partial shading, it might have several local maximum power points, and thanks to the IPSM algorithm, it always manages to find the global maximum power point. Finally, the results are compared with other methods where it was found that IPSM had faster answers

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (Ploss) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of Ploss in both radial and mesh systems. It provided the best reduction in minimum Ploss in short processing times and showed excellent repeatability in each test system and scenario under analysis