Effect of constriction factor on minimization of transmission power loss using Particle Swarm Optimization

Patwardhan, Rushiraj P.; Mhetre, Santosh L.

doi:10.1109/icesa.2015.7503330

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…This equality constraint is relaxed by using a factor D f . The total charging and discharging energy must be less than or equal to the maximum energy capacity of ESU, represented in (48) and (49). Equation (50) represents the sum of discharging power in a day must be less than the total energy demand of that day at the bus where ESU is placed.…”

Section: Methodsmentioning

confidence: 99%

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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In recent days, aiming for power generation with less carbon emission, led to the high penetration of renewable energy into the distribution grid. To improve the intermittency caused by renewables and to increase the grid flexibility, grid integrated Energy Storage Units (ESUs) are proposed as the promising solution in the literature. However, considering the investment cost, ESUs are optimally placed by satisfying the network electrical constraints. On the other hand, consideration of environmental impacts and other practical constraints are also equally important. Therefore, in this article, on top of grid performance parameters, realistic parameters that may affect the location and its size such as (1) environmental impact, (2) land requirement & its associated cost for ESU installation and (3) renewable purchase obligation are formulated in the objective function. Decision making on ESU installation is a planning activity, which follows uncertainty. Consequently, it is essential to model the uncertainty parameters into the objective function, for better planning. In this article, optimal planning of hybrid ESUs based on realistic parameters along with uncertainty is addressed. For this study, a practical 156-bus distribution system of Dehradun district, India is considered. From the results obtained, it is evident that, formulating hybrid ESU constrained with the environmental impact has significantly decreased the emission of CO2 with maximum grid stability. INDEX TERMS Batteries, distributed power generation, environmental economics, power system planning, sustainable development.

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Section: Methodsmentioning

confidence: 99%

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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“…The system can be expressed in matrix notation. The position of the particles depend upon initial conditions (Eberhart and Shi, 2000;Patwardhan and Mhetre, 2015). The main advantage of this methodology is that without changing the basic equation, a constriction factor (χ) based on the acceleration constants c 1 , c 2 is incorporated in the PSO-O algorithm to update the velocity.…”

Section: Pso Algorithm With Constriction Factor (Pso-c)mentioning

confidence: 99%

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

Merugumalla

Navuri

2019

IJCISTUDIES

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The constriction factor (C-factor)-based particle swarm optimisation algorithm is proposed for the solar photovoltaic array powered brushless direct current (BLDC) motor drive control. The rotor position sensors are completely eliminated. Instead, it is determined by measuring the changes in back-emf. The CPSO algorithm is used for the tuning of proportional-integral-derivative (PID) controller parameters for the speed control of the drive. The BLDC motor drive is modelled in MATLAB/Simulink and trapezoidal back-emf waveforms are modelled as a function of rotor position using MATLAB code. This paper deals with rise time, settling time, peak overshoot and steady-state error under varying conditions and examines the effectiveness of the BLDC motor drive with the proposed algorithm. The comparison of simulation results of proposed algorithm with other PSO algorithms demonstrates the prominence of the constriction factor for the drive controller.

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“…Figure 1 shows the complete flowchart of the presented CF-PSO algorithm. However, this paper sets v max and v min to 4 and −4, respectively, and starts checking of the stopping criteria after 30 generations, through a systematic trial and error approach, and based on the experience as described in References [41][42][43]. Likewise, this paper sets other parameters as…”

Section: Constriction-factor Partcile Swarm Optimizationmentioning

confidence: 99%

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

2018

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Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

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Effect of constriction factor on minimization of transmission power loss using Particle Swarm Optimization

Cited by 7 publications

References 5 publications

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

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