Wastewater treatment plants (WWTPs) are very important facilities for mankind. They enable the removal and neutralisation of man-made pollutants. Therefore, it is important for wastewater treatment plants to operate a...
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Wastewater treatment plants (WWTPs) are very important facilities for mankind. They enable the removal and neutralisation of man-made pollutants. Therefore, it is important for wastewater treatment plants to operate as efficiently as possible so that the level of pollutants in the treated wastewater meets specific requirements. This paper concerns the design of a hierarchical nonlinear adaptive control system for dissolved oxygen (DO) concentration in wastewater for a biological Sequential Batch Reactor (SBR). The parameters of the control system used are optimised to ensure the best possible control quality and low energy consumption at the same time. Based on data collected from a case study WWTP, an Activated Sludge Model 2d (ASM2d) of the biological processes and a model of the aeration system are applied. The coyote optimization algorithm (COA) is used to optimize the adaptive controller parameters. A Proportional-Integral-Derivative (PID) control system is also developed to compare the control results. The results obtained from simulation studies for both control systems are presented. As a result of optimised parameters, higher wastewater treatment efficiency and reduced electricity consumption are achieved.
Considering the polarization effect of lithium-ion battery,this paper establishes a second-order RC model,and proposes an adaptive global optimal guided coyote optimal identification algorithm(AGCOA).It effectively so...
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Considering the polarization effect of lithium-ion battery,this paper establishes a second-order RC model,and proposes an adaptive global optimal guided coyote optimal identification algorithm(AGCOA).It effectively solves the problems of traditional heuristic algorithms that are easy to fall into local optima and slow *** battery testing equipment NEWARE BTS-4008 is used to charge and discharge the lithium-ion *** parameters identified by the AGCOA are used to predict the terminal voltage,and the extended Kalman filter algorithm(EKF) is used to estimate the battery *** results show that the predicted voltage obtained by the identification is basically the same as the actual voltage,and the SOC estimation error is small,which verifies the efficiency and accuracy of the algorithm.
In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distr...
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In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote optimizationalgorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly algorithm (FA), Whale optimizationalgorithm (WOA), and Particle Swarm optimization (PSO) to show the proposed method's prominence efficiency.
this paper presents a methodology utilizing coyote optimization algorithm (COA) for Global Maximum Power Point Tracking (GMPPT) considering the impact of partial shading on a PV system. COA is utilized to clarify GMPP...
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ISBN:
(纸本)9781728152899
this paper presents a methodology utilizing coyote optimization algorithm (COA) for Global Maximum Power Point Tracking (GMPPT) considering the impact of partial shading on a PV system. COA is utilized to clarify GMPP by comparing all the existing peaks on the PV curve. COA is also employed to control the boost converter. Different shading patterns are applied to a photovoltaic system. Simulations of this process are established utilizing MATLAB software. The suggested algorithm is compared with enhanced Grey Wolf optimization (E-GWO), Dragonfly algorithm (DA), Ant Lion optimization (ALO), and Particle Swarm optimization (PSO). According to the results, the suggested COA improves the tracking speed and accuracy of MPPT during partial shading conditions. Also, the suggested methodology is providing high efficiency for solar PV systems in any irradiation conditions.
In the paper, a modified coyote optimization algorithm (MCOA) is proposed for finding highly effective solutions for the optimal power flow (OPF) problem. In the OPF problem, total active power losses in all transmiss...
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In the paper, a modified coyote optimization algorithm (MCOA) is proposed for finding highly effective solutions for the optimal power flow (OPF) problem. In the OPF problem, total active power losses in all transmission lines and total electric generation cost of all available thermal units are considered to be reduced as much as possible meanwhile all constraints of transmission power systems such as generation and voltage limits of generators, generation limits of capacitors, secondary voltage limits of transformers, and limit of transmission lines are required to be exactly satisfied. MCOA is an improved version of the original coyote optimization algorithm (OCOA) with two modifications in two new solution generation techniques and one modification in the solution exchange technique. As compared to OCOA, the proposed MCOA has high contributions as follows: (i) finding more promising optimal solutions with a faster manner, (ii) shortening computation steps, and (iii) reaching higher success rate. Three IEEE transmission power networks are used for comparing MCOA with OCOA and other existing conventional methods, improved versions of these conventional methods, and hybrid methods. About the constraint handling ability, the success rate of MCOA is, respectively, 100%, 96%, and 52% meanwhile those of OCOA is, respectively, 88%, 74%, and 16%. About the obtained solutions, the improvement level of MCOA over OCOA can be up to 30.21% whereas the improvement level over other existing methods is up to 43.88%. Furthermore, these two methods are also executed for determining the best location of a photovoltaic system (PVS) with rated power of 2.0 MW in an IEEE 30-bus system. As a result, MCOA can reduce fuel cost and power loss by 0.5% and 24.36%. Therefore, MCOA can be recommended to be a powerful method for optimal power flow study on transmission power networks with considering the presence of renewable energies.
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