Optimal DG allocation in a smart distribution grid using Cuckoo Search algorithm

Buaklee, Wirote; Hongesombut, Komsan

doi:10.1109/ecticon.2013.6559624

Cited by 22 publications

(16 citation statements)

References 6 publications

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“…Penelitian ini akan diusulkan metode algoritma cuckoo search sebagai optimasi untuk membership function fuzzy logic, sehingga hasil yang diharapkan akan semakin optimal. Penggunaan metode cuckoo search juga semakin banyak digunakan dalam bidang ketenagalistrikan, diantaranya [11,12], dimana metode cuckoo search digunakan untuk mengoptimasi controller PID pada Motor DC, Load Frequency Control (LFC) dan optimasi penempatan Power System Stabilizer (PSS) pada sistem Sulselrabar.…”

Section: Pendahuluanunclassified

Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System

Djalal

Faisal

2017

LKJITI

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Forecasting the electrical load becomes important, because it can estimate electricity consumption over a certain time range. Accuracy in electric load forecasting can improve safety and reliability in the operation of power systems such as load flow, maintenance of generating units and scheduling of generating units. In this study used case study system Sulselrabar, which is currently growing, but still not much to discuss about the condition of the current system and which will come. Several methods for predicting electrical loads have been widely used, ranging from conventional to smart-based methods. In this research will be proposed method of artificial intelligence for forecasting Short Term load on Sulselrabar system. The method used is based Fuzzy Logic and Cuckoo Search Algorithm. The combination of Fuzzy logic and Cuckoo Search methods is chosen because the combination of both optimizes optimum fuzzy logic membership, so the forecasting results have a very small error. From the results of the research can be concluded that the result of load forecasting using Fuzzy Logic method optimized using Cuckoo Search Algorithm (FL-CSA) is better than Fuzzy Logic that is not optimized. The analysis results using input data 3 months before day H, to predict the load for one week on January 1 to 7 january 2014, and as a comparison used the predicted day H data. From the simulation results, the mean absolute percentage error (MAPE) is smaller using FLCSA, for the smallest MAPE on 1 January 2014 of 0.06785208%. While the highest MAPE on January 4, 2014 amounted to -0.44973%.

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Section: Pendahuluanunclassified

Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System

Djalal

Faisal

2017

LKJITI

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“…Cuckoo has aggressive reproduction strategy. These kinds of birds lay their eggs in the nests of other host birds with amazing abilities such as selecting the recently spawned nests and removing existing eggs that increase hatching probability of their eggs [26]. The host bird presumes those eggs as their own and takes care of them.…”

Section: Wind Turbine Modelmentioning

confidence: 99%

Optimum Fuzzy Logic Control System Design using Cuckoo Search Algorithm for Pitch Control of a Wind Turbine

Ganguly¹,

Kalam²,

Zayegh³

2017

AMA_C

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Renewable Energy Systems (RES) are being widely accepted as an alternative to standard conventional energy sources due to depletion of natural resources and their consequential environmental impact. With improving techniques, reducing costs and low environmental impact, wind energy has the potential to become the major part in the world's energy future. The efficiency and control of wind generator is of outmost importance as wind is an intermittent resource. Pitch angle control is the most common means for adjusting the aerodynamic torque of the wind turbine when the wind speed is above rated speed. This paper presents a methodology for designing an optimised Fuzzy Logic Controller (FLC) system using Cuckoo Search Algorithm (CSA) for enhancing the performance of wind turbine by maximizing the captured energy. The simulation results clearly show that the controller demonstrated high performance than conventional PID controller.

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“…A nest could have several cuckoo eggs, representing a set of new solutions. The bulk of the work is performed by the Lévy flight, which determines the distance to a nest, according to (15).…”

Section: Cuckoo Searchmentioning

confidence: 99%

“…The HS algorithm has the capacity to generate new solutions considering previous selected solutions in a vector, offering a good number of combinations to increase the flexibility and capacity to obtain better solutions [12], an advantage for locating DG [13], [14]. The CS has been applied to optimize power losses [15] and is also useful for improving energy efficiency and operation costs. The BA has also been applied to solve the problem of locating and sizing DG, obtaining good results [16].…”

Section: Introductionmentioning

confidence: 99%

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Candelo-Becerra

Riaño

Santander-Mercado

2015

TecnoL.

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Electric power losses have a significant impact on the total costs of distribution networks. The use of renewable energy sources is a major alternative to improve power losses and costs, although other important issues are also enhanced such as voltage magnitudes and network congestion. However, determining the best location and size of renewable energy generators can be sometimes a challenging task due to a large number of possible combinations in the search space. Furthermore, the multiobjective functions increase the complexity of the problem and metaheuristics are preferred to find solutions in a relatively short time. This paper evaluates the performance of the cuckoo search (CS), harmony search (HS), and bat-inspired (BA) algorithms for the location and size of renewable distributed generation (RDG) in radial distribution networks using a multiobjective function defined as minimizing the energy losses and the RDG costs. The metaheuristic algorithms were programmed in Matlab and tested using the 33-node radial distribution network. The three algorithms obtained similar results for the two objectives evaluated, finding points close to the best solutions in the Pareto front. Comparisons showed that the CS obtained the minimum results for most points evaluated, but the BA and the HS were close to the best solution. KeywordsCuckoo search, bat-inspired algorithm, renewable distributed generation, harmony search, photovoltaic energy systems, wind energy systems. ResumenLas pérdidas eléctricas tienen un impacto significativo en los costos totales de las redes de distribución. El uso de las energías renovables es una gran alternativa para mejorar las pérdidas y los costos, aunque también otros problemas en las magnitudes de las tensiones y la congestión de la red pueden ser mejorados. Sin embargo, determinar la mejor localización y dimensionamiento de generadores eléctricos renovables puede ser a veces una tarea difícil debido al gran número de combinaciones posibles existentes en el espacio de búsqueda. Además, el uso de funciones multiobjetivo incrementa la complejidad del problema y se prefiere usar las metaheurísticas para encontrar soluciones en un tiempo relativamente corto. En este trabajo se evalúa el desempeño de los algoritmos inspirados en búsqueda cuco, búsqueda armónica y murciélagos para la localización y dimensionamiento de la generación distribuida renovable en redes de distribución radiales, usando funciones como la minimización de las pérdidas de energía y los costos de la generación distribuida renovable. Las metaheurísticas fueron programadas en Matlab y evaluadas usando la instancia denominada red de distribución radial de 33 nodos. Los tres algoritmos evaluados obtuvieron resultados similares para los dos objetivos evaluados, encontrando Frentes de Pareto cercanos a las mejores soluciones. La comparación realizada mostró que la búsqueda cuco obtiene los mejores resultados, pero los algoritmos inspirados en murciélagos y búsqueda armónica obtuvieron resultados cercanos a la mejor soluci...

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Optimal DG allocation in a smart distribution grid using Cuckoo Search algorithm

Cited by 22 publications

References 6 publications

Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System

Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System

Optimum Fuzzy Logic Control System Design using Cuckoo Search Algorithm for Pitch Control of a Wind Turbine

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

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