The load forecasting problem is a complex nonlinear linked with social considerations, economic factors, and weather variations. In particular, for holidays challenging task as only small number of historical data available compared what normal weekdays weekends. This paper presents fuzzy polynomial regression method selection based on Mahalanobis distance incorporating dominant feature holiday forecasting. Selection past weekday relevant to given critical improvement the accuracy paper,...

Abstract This paper presents a new method to determine the locations and sizes of Distributed Generations (DGs) for loss reduction voltage profile enhancement in distribution systems. The strategic placement DG can help reduce power losses improve feeder profile. Fuzzy Goal Programming (FGP) is adopted handle multiobjective problem incorporating characteristics each individual load component. original objective functions constraints are transformed into function with fuzzy sets by FGP....

This paper presents an effective optimization scheme for the measurement-based load modeling based on sensitivity analysis of composite model parameters. Each parameter has different effects its dynamic response. Moreover, some parameters are insensitive to change others. To estimate interactions between parameters, their is analyzed by using eigenvalues Hessian matrix used in algorithm. Also, linear dependence two then identified examining condition number Jacobian matrix. With this...

Short-term load forecasting (STLF) is very important for planning and operating power systems markets. Various algorithms have been developed STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based reinforcement learning proposed to substitute the dependence expert’s experience. The consists of days, algorithm, STLF, artificial neural network....

With the rapid expansion of renewable energy, penetration rate behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM PV generation not metered real-time, distorts electric load and increases errors forecasting. In order to overcome problems caused by impact generation, an extreme gradient boosting (XGBoost) forecasting algorithm proposed. capacity estimated based on investigation deviation using a grid search. influence external factors was...

This paper proposes a computationally efficient technique for estimating the composite load model parameters based on analytical similarity of parameter sensitivity. When are updated in optimization procedure to best fit actual dynamics, i.e., measurements, similar sensitivity representation given mathematical structure same manner at every iterative step. research allows practically reducing number be identified estimation process and overall computational cost while preserving desired...

Protection against transient instability and a consequent out-of-step condition is major concern for the utility industry. An unstable system may cause serious damage to elements such as generators transmission lines, therefore detection essential operate safely. The traditional relays detect conditions by using distance timers. However, relay monitors only apparent impedance which an indirect function of generator angle, cannot cope with situation more severe very fast power swings can also...

This paper describes a study to determine the optimal resistance of superconducting fault current limiter (SFCL) connected wind-turbine generation system (WTGS) in series considering its protective coordination. The connection WTGS electric power grid may have serious effects on stability and reliability overall during contingency due increase currents. Moreover, it causes malfunction devices such as overcurrent relays (OCRs). To deal with this problem, SFCL is applied reduce level increased...

This paper presents a novel algorithm for developing dynamic equivalents of large-scale power systems. Generators are identified to the coherent groups according relation factors, which represent relative coupling degree between generators. While identified, participation numbers-which measure generators in group-are obtained. All and controllers group weighted by numbers aggregated construct equivalent. The proposed is applied 272-machine Korea Electric Power Corporation's system. results...

Summary form only given. This paper proposes a computationally efficient technique for estimating the composite load model parameters based on analytical similarity of parameter sensitivity. When are updated in optimization procedure to best fit actual dynamics, i.e., measurements, similar sensitivity representation given mathematical structure same manner at every iterative step. research allows practically reducing number be identified estimation process and overall computational cost...

Abstract: To manufacture red algae (RA) film, we used various plasticizers such as glycerol, sorbitol, sucrose, fructose, and polypropylene glycol (PPG), then determined the mechanical properties of RA films. The tensile strength (TS), elongation at break (E), water vapor permeability (WVP) films containing ranged between 0.43 to 9.10 MPa, 10.93% 47.17%, 1.28 1.42 ng m/m 2 sPa, respectively. fructose a plasticizer had best all evaluated. Incorporation nanoclay (Cloisite Na + 30B) improved...

본 논문은 현 배전계통계획시스템(DISPLAN)의 지역전력수요예측 알고리즘을 개선하여 다중회귀분석을 이용한 제시하였다. 알고리즘은 예측의 정확도를 높이기 위해 지역경제와 지역인구와 과거의 판매전력량을 입력변수로 사용하였다. 사례연구로 경북의 경산시, 구미시, 김...

수출용 딸기 "플라멩고" 품종의 수확 후 미생물학적 안전성 확보와 저장성 증대를 위해, 50 ppm 이산화염소수 또는 0.5% 푸마르산 용액과 5 kJ/㎡ UV-C 조사 병합처리에 따른 저장 중 미생물 수 감소 및 품질 변화에 미치는 영향을 조사하였다. 비가열 처리 후, 딸기는 4±1℃에서 12일 동안 저장하면서 실험을 수행하였다. 딸기의 초기 수에 있어서 대조구와 비교하여, 푸마르산-UV-C 병합 처리구에서 총 호기성 세균과 효모 곰팡이를 각각 2.09, 2.02 log CFU/g 감소시켰다. 또한, 12일에 푸마르산과 처리구의 곰팡이 수는 1.72 CFU/g으로 대조구의 5.10 CFU/g과 비교하여 3.38 CFU/g의 유의적인 차이로 가장 큰 감균 효과를 나타냈다. 처리구는 Hunter 색도 값에 부정적인 끼치지 않았다. 관능검사에 있어서는 처리구가 다른 처리구보다 높은 점수를 얻어 관능적 품질유지에도 효과가 있는 것으로 나타났다. 특히, 종합적 기호도는 5일부터 두...

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series due its accuracy predictability. The various ARIMA models are examined order find the optimal having minimum error of forecasting. proposed applied forecast 104-week pattern historical data Korea. effectiveness evaluated by from 2011 2012 2002 2010.

This paper proposes a method of constructing charging infrastructures in highway rest areas for the propagation and efficient operation electric vehicles (EV). The supply EVs was predicted with LPG as model. To determine reasonable capacities, locations, sizes stations, technical trends commercialization level systems were analyzed. annual must be able to fulfill daily maximum capacity throughout year. demand calculated average running distance, traffic volume, EV fuel efficiency. fast each...

Load forecasting is one of important issue in the power system. Especially, accurate mid-term load plays an essential role for maintenance generators and transmission systems. Because has time-series characteristic closely related to weather economic conditions, it necessary develop method considering these non-linear characteristics loads. To a good that effectively reflects features loads, using deep learning algorithm proposed. Proposed forecasts weekly peak over next 104 weeks. In order...

Reliability evaluation of power distribution system is very important to both utilities and customers. It presents the probabilistic number duration interruption such as failure rate, SAIDI, SAIFI, CAIDI. However, it has a fatal weakness at reliability index because accuracy rate. In this paper, time-varying rate (TFR) equipment extracted from recorded data Korea Electric Power Corporation (KEPCO) in Korea. For TFR extraction, used that fault accumulated by KEPCO during 10 years. The...

To obtain a precise short-term load forecast for an education institute, the correlation between its electric demands and temperatures needs to be analyzed because demand is sensitive temperature changes. With aim of developing improved forecasting algorithm arbitrary fluctuations in daily, weekly, yearly patterns university campus Seoul were correlated with trends during respective periods. The data then compared past patterns. An optimal exponential smoothing coefficient according selected...

Short-term load forecasting (STLF) is essential for stable and efficient power system operation. Recently, many papers have been published on application of STLF using artificial neural networks (ANNs). Input data selection, normalization method the number hidden neurons are very important factors in modeling ANNs. In order to improve accuracy ANNs, several input selections various methods analyzed. The past load, temperature day used as STLF. case studies, ANNs with compared. Result studies...