In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the genera...
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ISBN:
(纸本)9781479953530
In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the generalization performance for classification problems. In this system, IGSA is designed by combining the search strategy of particle swarm optimization and GSA to effectively reduce the problem of slow convergence rate, moreover, the continuous-value IGSA and binary IGSA are integrated in one algorithm in order to optimize the KELM parameters and feature subset selection simultaneously. This proposed hybrid algorithm is evaluated on several well-known UCI machine learning datasets. The results indicate that the superiority of the proposed model in terms of classification accuracy. Our hybrid method not only can select the most relevant feature subset, but also achieves a high classification accuracy over other similar state-of-the-art classifier systems.
Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation plants while satisfying technical and economic constraints. In recent years, increasing con...
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ISBN:
(纸本)9781479933501
Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation plants while satisfying technical and economic constraints. In recent years, increasing concern for climate change has driven lots of countries all over the world to promote energy generation from renewable resources. Moreover, applying rigid environmental regulations by regulating authorities has been made generation companies (GENCOs) to consider emission as an important constraint in their generation expansion planning. In this circumstance, renewable energy sources (RES) can be pointed out as an appropriate alternative to fossil fuel-fired units which have a remarkable share on releasing different contaminants especially CO2, as the major contributor to greenhouse effect, into the atmosphere. In this paper, to investigate the impact of RES penetration on the greenhouse gases mitigation, an integrated renewable-conventional GEP model including a suitably modified objective function and additional constraints is presented. In order to encourage the GENCO in more investment on renewable resources, one of the most popular incentive-based support schemes, namely feed-in-tariff, is incorporated into the model. The resulting problem is solved by a novel heuristic technique namely, gravitational search algorithm. Obtained results reveal that the penetration of RES is a critical option for emission reduction.
This paper presents a new hybrid algorithm based on the particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the optimal power flow (OPF) in power systems. Performance of this app...
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ISBN:
(纸本)9781479974627
This paper presents a new hybrid algorithm based on the particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the optimal power flow (OPF) in power systems. Performance of this approach for the OPF problem is studied and evaluated on the standard IEEE 30-bus test system with different objective functions. Simulation results on the OPF problem show that the hybrid PSOGSA algorithm provides effective and robust high-quality solution.
This paper presents a medium term load forecasting methodology based on a mixed statistical computational intelligence model. The methodology can be used by any entity (such as transmission and distribution operators,...
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ISBN:
(纸本)9781479958887
This paper presents a medium term load forecasting methodology based on a mixed statistical computational intelligence model. The methodology can be used by any entity (such as transmission and distribution operators, electricity suppliers or energy managers) interested in planning different activities with electricity. The methodology produces daily load profiles forecasts for all the 365 days of the next year. The statistical model predicts annual energy consumption and monthly and daily consumptions based on traditional regression models, while typical load profiles for each day of the week and every week of the year are produced using computational intelligence techniques based on self-organizing models with Kohonen neural networks or a heuristic optimization technique, namely the gravitational search algorithm.
In this study, a modified method for the displacement of landslide prediction is presented. This method is based on Takagi-Sugeno fuzzy neural network (T-S FNN), with an efficient hybrid optimization algorithm based o...
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ISBN:
(纸本)9781479938407
In this study, a modified method for the displacement of landslide prediction is presented. This method is based on Takagi-Sugeno fuzzy neural network (T-S FNN), with an efficient hybrid optimization algorithm based on the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA) applied to optimize the parameters for T-S FNN. Moreover, correlation analysis is an important analysis to look for the potential input variables for a predict model. Pearson cross-correlation coefficients (PCC) and mutual information (MI) are adopted in this paper. The performance of the obtained model is verified through two case studies in Baishuihe (BSH) and Liangshuijing (LSJ) landslide in the Three Gorges reservoir in China.
Reactive power flow in a power system is to be optimized by minimizing the real power loss and it is essential for maintaining the voltage stability of the system. The control variables such as magnitude of generator ...
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ISBN:
(纸本)9781479960422
Reactive power flow in a power system is to be optimized by minimizing the real power loss and it is essential for maintaining the voltage stability of the system. The control variables such as magnitude of generator bus voltage, transformer tap settings and shunt capacitors can optimize the reactive power flow. This paper uses the nature inspired gravitational search algorithm (GSA) to solve a multi objective and multi constrained optimal reactive power flow problem in power systems. The performance of the standard IEEE 30 bus test system is tested and the results prove the effectiveness of this algorithm. The results show best among other algorithms and found to be suitable for power system operation optimization problems.
Due to intermittency and random nature of wind power generation over short time period, unit commitment problem becomes more complex. In this paper, wind power forecasting uncertainty is represented by wind scenarios ...
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ISBN:
(纸本)9781479964154
Due to intermittency and random nature of wind power generation over short time period, unit commitment problem becomes more complex. In this paper, wind power forecasting uncertainty is represented by wind scenarios generated using Monte Carlo simulation. The computational burden in stochastic models that require scenario representation is reduced by creating clusters of commitment of units associated with a probability of occurrence from an initial set of large wind scenarios. gravitational search algorithm (GSA) is applied for solving wind-hydro-thermal coordination problem and a pseudo code based algorithm is suggested to deal with the equality constraints of the problem for accelerating the optimization process. The effectiveness of the proposed algorithms is demonstrated on a test system.
Overcurrent(OC) relay is one of the most commonly used protective relays in the power system. Coordination of overcurrent relays is a challenging task in the field of offshore wind farm (OWF). A reliable protection sy...
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ISBN:
(纸本)9781479960422
Overcurrent(OC) relay is one of the most commonly used protective relays in the power system. Coordination of overcurrent relays is a challenging task in the field of offshore wind farm (OWF). A reliable protection system is needed for transmitting large amount of power over long distances. An optimal relay coordination using overcurrent relay is used at four different points in the OWF. The optimal coordination of OC relay is carried out by the exploration of different meta heuristic algorithm such as Genetic algorithm (GA), Particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithm. The formulation of objective function is accomplished by two variables such as time multiplier setting (TMS) and plug setting multiplier (PSM).Thus this multiobjective optimization is explored to minimize the operating time of the associated relay that exists in the ac side of OWF.
parameter tuning has critical influences on the performance of evolutionary algorithms. Deliberate parameter investigation and changing the value of them is very expensive and time consuming. This paper has applied De...
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ISBN:
(纸本)9781479954865
parameter tuning has critical influences on the performance of evolutionary algorithms. Deliberate parameter investigation and changing the value of them is very expensive and time consuming. This paper has applied Design of Experiment (DOE) method to tune the parameters of gravitational search algorithms (GSA) systematically. Also, to reduce its complexity and increase the performance, simple modification has been presented to determine the number of effective objects (Kbest). Best configurations of 17 standard functions are obtained by executing DOE. Analysis of the results confirms that parameter tuning and Kbest modification have improved the performance of the GSA. Meanwhile, these results have been obtained by least experiments in acceptable time.
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