A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic

Nethravathi, S.; Murali, Venkatakirthiga

doi:10.13052/dgaej2156-3306.3739

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…They formalize the problem of power control as an optimization problem of planning operation periods of electrical equipment over a certain time period. The majority of the research is based on algorithms that determine the optimal solution by imitating the principles by which wildlife systems function, like genetic algorithms [10,11], the particle swarm method [12,13], the ant colony algorithm [14], the ant lion algorithm [15], the cuckoo search algorithm [16], the gray wolf hunting optimization algorithm [17], the symbiotic organisms search algorithm [18], the hybrid genetic-air optimization algorithm [19], and the whale search algorithm [20].…”

Section: Electrical Complexes and Systemsmentioning

confidence: 99%

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Sinchuk,

Rogoza,

Mykhailenko

et al. 2024

Nauk. visn. nat. hirn. univ.

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Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

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Section: Electrical Complexes and Systemsmentioning

confidence: 99%

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Sinchuk,

Rogoza,

Mykhailenko

et al. 2024

Nauk. visn. nat. hirn. univ.

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“…The capacity to find ideal gains for power converters is very strong in metaheuristic bio-inspired algorithms. Several swarm intelligence optimization algorithms have been proposed for optimizing fuzzy parameters, including the Cat Swarm Algorithm (CSA) [79], Gray Wolf Optimization (GWO) [80], Bat Algorithm (BA) [81], Firefly Algorithm (FA) [82], Artificial Bee Colony (ABC) [83], Antlion Optimization (ALO) [84] and Whale Optimization Algorithm (WOA) [85]. The ALO is a highly influential metaheuristic algorithm that has garnered significant interest.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Azarinfar,

Khosravi,

Soroush

et al. 2024

IET Power Electronics

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A type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.

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Energy routing based optimal solution for residential peak power reduction with photovoltaic and electric vehicle penetration

Shivanaganna

Murali

2022

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic

Cited by 3 publications

References 44 publications

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Energy routing based optimal solution for residential peak power reduction with photovoltaic and electric vehicle penetration

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