This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimiza...
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This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimization (bwo). The proposed reduced switch 13- level MLI scheme uses a single Photovoltaic (PV) source which can be suitable for grid integration. The proposed bwo algorithm minimizes the Total Harmonic Distortion (THD) of output voltage with low operational time compared to other existing nature based algorithms considering large searching area. The weighted THD (WTHD) of the output voltage is also minimized in order to reduce the effect of lower order harmonics from the output voltage in a greater extent. The convergence rate and level of accuracy of bwo algorithm is compared with two different bio inspired algorithms for justification. The MLI operation is carried out with fundamental frequency, minimizing the switching stress while lowering the power loss compared to PWM or Sinusoidal PWM switching schemes. A single PV panel with multi winding flyback converter is used for medium voltage application through reduced switch MLI, serving the purpose of both isolation and reduction of energy sources. Simulation and experimental analysis are carried out with online control technique using a three phase 13-level reduced MLI to validate the proposed concept on a practical system.
The Internet of Things highly depends on computing environments like cloud computing to process and store information. The use of cloud computing by smart devices leads to challenges such as delay and increased energy...
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The Internet of Things highly depends on computing environments like cloud computing to process and store information. The use of cloud computing by smart devices leads to challenges such as delay and increased energy consumption of sensors. A primary solution to the mentioned problems is fog computing. Task scheduling is the most critical issue that significantly affects improving the performance of cloud-fog systems. Task scheduling is an NP-hard problem, and applying data mining methods and metaheuristic algorithms to obtain optimal solutions in a reasonable computing time is a fundamental requirement. This paper proposes a new model based on metaheuristic algorithms using the combination of golden jackal optimization (GJO) and beluga whale optimization (bwo) algorithm called GJObwo to solve the task scheduling problem in a cloud-fog environment. In the hybrid model, the bwo algorithm is used to solve the issues of the GJO algorithm, such as getting stuck in the local optimum and imbalance between the exploration and exploitation stages. Performing the exploration and exploitation steps is essential because correct execution may lead to efficient solutions. Also, the k-means algorithm inspired by clustering is used to prioritize tasks. The evaluation of the hybrid model has been done using continuous optimization functions and task scheduling problems. First, the hybrid model has been implemented on 68 standard functions and compared with particle swarm optimization (PSO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), ant lion optimizer (ALO), and GJO and bwo algorithms. Then, the hybrid model has been tested on the task scheduling problem and compared with WOA, GJO, and bwo algorithms. The results show that the hybrid model has effectively minimized the makespan rate and the degree of imbalance. Also, the average improvement percentage of the combined model based on the PIR criterion compared to the four algorithms S
The present study aimed to predict the maximum seasonal wave height by new integrative data driven methods. For this purpose, two data-driven techniques, that are, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and...
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The present study aimed to predict the maximum seasonal wave height by new integrative data driven methods. For this purpose, two data-driven techniques, that are, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Regression (SVR), were applied, and a bwo algorithm was used as an integrated method (ANFIS-bwo and SVR-bwo). In addition, the Particle Swarm Optimization (PSO) algorithm was used as a method integrated with SVR and ANFIS (SVR-PSO and ANFIS-PSO) to compare the performance of the newly developed methods (ANFISbwo and SVR-bwo). The wave data were collected in different seasons by a buoy station deployed in the southern Baltic Sea by the Institute of Hydro-Engineering of the Polish Academy of Sciences. Seasonal simulations were performed to investigate the effect of seasons on the maximum wave height. The wave data constituted an unevenly spaced time series. The maximum wave height was modeled using the maximum wave height period (Tmax), the significant wave height (Hs), the significant wave period (Ts), and time steps (Delta t). The results showed that the application of bwo and PSO algorithms increased the accuracy of ANFIS and SVR by about 18.45%. Moreover, the results show that PSO increased the accuracy of ANFIS and SVR by about 17.98% and 21.59%, respectively. The results of different runs indicated that the bwo is more stable to reach the global solution than PSO. The results also show that show that SVR-bwo is the most accurate model.
In order to improve voltage quality and meet the safety and economic operation of the distribution network, reactive power optimization of the distribution network adopts voltage control by reducing system losses. Com...
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In order to improve voltage quality and meet the safety and economic operation of the distribution network, reactive power optimization of the distribution network adopts voltage control by reducing system losses. Compare and analyze the Beluga whale optimization algorithm(bwo) with particle swarm optimization algorithm and dragonfly algorithm, and compare the convergence effects and differences in solving the optimal values of each algorithm. Establish a mathematical model for reactive power optimization with the minimum active power loss in the system. Using the power flow balance equation and voltage upper and lower limits as constraints, determine the optimal reactive power compensation amount for the system and select a reactive power compensator to achieve precise compensation. Taking the IEEE33 node distribution network system as an example, simulation analysis was conducted on the Matlab platform. The results showed that the bwo algorithm has faster convergence speed, better global search ability, and optimization ability, proving that the algorithm can reduce network losses and improve voltage quality.
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