Yazar "Bokoro, Pitshou N." seçeneğine göre listele

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    Application of Monte Carlo simulation and stochastic fractional search algorithm for solar PV placement considering diverse solar radiations
    (Elsevier, 2025) Barutcu, Ibrahim Cagri; Sharma, Gulshan; Bokoro, Pitshou N.; Celik, Emre
    This study employs Monte Carlo Simulation (MCS) within the structure of the Stochastic Fractional Search Algorithm (SFSA) to address circumstances involving uncertainty. The goal is to improve the system's performance by creating probability distribution functions for bus voltages and branch currents. We will use the resultant distribution in chance-constrained stochastic scheduling. The objective of the present research is to analyze the impact of uncertainties in the operation of photovoltaic (PV) systems, specifically in relation to different solar radiation conditions, on the amount of power loss. The approach focuses on including stochastic constraints in distribution systems instead of depending solely on precise deterministic boundaries. The goal is to enhance efficiency and ensure optimal consumption of power. This research enhances the knowledge base on PV unit positioning in distribution systems by integrating meta-heuristic optimization and MCS into a comprehensive framework. The investigation centers on the implementation of a chance-constrained method. We evaluate the optimization results using MCS under various uncertainty scenarios to demonstrate the effectiveness of the recommended approach. Furthermore, we conduct an analysis to assess the likelihood of exceeding the system's boundaries. The strategy's effectiveness is assessed by comparing the results of the SFSA with the Firefly algorithm (FA) utilizing probabilistic evaluation and simulation.
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    Frequency Support Studies of a Diesel-Wind Generation System Using Snake Optimizer-Oriented PID with UC and RFB
    (Mdpi, 2023) Rameshar, Vikash; Sharma, Gulshan; Bokoro, Pitshou N.; Celik, Emre
    The present paper discusses the modeling and analysis of a diesel-wind generating system capable enough to cater to the electrical power requirements of a small consumer group or society. Due to high variations of the load demand or due to changes in the wind speed, the frequency of the diesel-wind system will be highly disturbed, and hence to regulate the frequency and power deviations of the wind turbine system, an effective controller design is a necessary requirement, and therefore this paper proposes a novel controller design based on PID scheme. The parameters of this controller is effectively optimized through a new snake optimizer (SO) in an offline manner to minimize frequency and power deviations of an isolated diesel-wind system. The performance of SO-PID for the diesel-wind system is evaluated by considering the integral of time multiplied absolute error (ITAE), integral absolute error (IAE), and integral of time multiplied square error (ITSE). The results were calculated for a step change in load, step change in wind speed, load change at different instants of time with diverse magnitude, and for random load patterns, and they were compared with some of the recently published results under similar working conditions. In addition, the effect of an ultracapacitor (UC) and redox flow battery (RFB) on SO-PID was investigated for the considered system, and the application results demonstrated the advantages of our proposal over other studied designs.
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    Frequency Support Studies of a Microgrid Having DG-WTG Using ANFIS and with the Application of HAE-FC and RFB
    (Taylor & Francis Inc, 2023) Rameshar, Vikash; Sharma, Gulshan; Bokoro, Pitshou N.; Celik, Emre; Öztürk, Nihat
    The micro-grid (mu-grid) has picked up momentum worldwide with the ability to supply cost-effective, clean, and reliable electrical power to the present-day demand. The practical mu-grids are comprised of non-conventional and conventional sources such as wind turbine generators (WTG) and diesel generators (DG). Due to the encouragement of wind power which is exceedingly sporadic in nature and thus the frequency of the mu-grid is exceedingly vulnerable due to the erratic nature of wind speed. Variations in the load demand have also added to the vulnerability of the mu-grid at distinctive moments of time. Consequently, this paper appears to be a novel plan that utilizes artificial neuro-fuzzy inference system (ANFIS) within the built frequency regulation of the mu-grid. The proposed research has been employed within the mu-grid, and the application outcomes have taken all possibilities, such as load variety at the distinctive moment of time, modification of the load demand on the mu-grid, and step alteration of the wind input. The achieved results are coordinated with a few of the most recent results, which presents the ANFIS ahead over other strategies. Although there is a probable scope for improvement which subsequently involves the fuel cell (FC) with a hydrogen aqua electrolyzer (HAE) unit, as well as a redox flow battery (RFB), that is introduced one at a time in the mu-grid and the results of mu-grid are calculated for various working conditions to show the impact that the ANFIS technology has upon storage devices with regards to the mu-grid architecture.
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    Power Loss Minimization through Reconfiguration Using BPSO-Based Technique
    (Taylor & Francis Inc, 2023) Kirithikaa, S.; Narayanan, K.; Sharma, Gulshan; Bokoro, Pitshou N.; Celik, Emre; Bekiroglu, Erdal
    Power loss is influenced by uncertain and ever increasing loads in the system but can be minimized by network reconfiguration where the topological structure of the system is changed by altering the switching operation. In this work, a multi-objective framework through reconfiguration is formulated with loss minimization as primary objective and reliability and voltage profile improvement as secondary objectives. A binary particle swarm optimization (BPSO) base method is developed to reconfigure the network by fulfilling all distribution network constraints. The influence of the BPSO parameters to obtain the optimal switches with minimized power loss, reliability indices, and voltage deviations are studied by a faster convergence. The results have been compared for different weights of the objective to give a comprehensive understanding of the nature of the objective attempted in this work. IEEE 33 bus and radial IEEE 69 bus systems were considered for testing the proposed method.
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    Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm
    (Wiley, 2024) Barutcu, Ibrahim Cagri; Sharma, Gulshan; Celik, Emre; Bokoro, Pitshou N.
    In this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance-constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance-constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.