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- 주제어 : Photovoltaic(PV) Generation System (검색결과 3 건)
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태양광 발전시스템의 출력성능 증대를 위한 변동제한 퍼지추론 MPPT가 적용된 DPP 시스템 : 변동제한 퍼지추론
전효찬 가천대학교 글로벌캠퍼스 일반대학원 2024 국내박사
In this study, a DPP system using improved variance-constrained fuzzy-inference MPPT is proposed to improve the output performance of photovoltaic generation system. In order to increase the stability and power production efficiency of photovoltaic generation systems, we looked at the trends in solar power generation system power conversion structures and the characteristics of MPPT control methods that have been studied until recently, and compared the pros and cons of each to construct a solar power generation system. An appropriate power conversion structure and control technique were adopted. In terms of the structure of the solar power generation system, the DPP system, which can control solar panels using only the power difference between solar panels, was adopted to minimize power conversion loss. In terms of controlling the photovoltaic generation system, research was conducted by adopting a fuzzy method that can identify similarities between data and infer conclusions like a human. As a result, an improved variance-constrained fuzzy-inference method, the fuzzy MPPT method based on change limit, was presented. This refers to a method that can prevent the voltage change amount from changing rapidly by comparing the past and current input data of the power change amount within the range of power change amount and voltage change amount and limiting the range close to 0. As a result of performing various simulation experiments with changes in solar radiation, the stability and maximum stability of voltage changes were found when applying the fuzzy MPPT method based on change limit presented in this paper compared to when applying the P&O method and when performing general fuzzy-based MPPT. It showed high system efficiency. As a result of building a solar DPP system and conducting an experiment by applying the variance-constrained fuzzy-inference MPPT method, it was verified that stability could be improved and power loss could be reduced compared to applying the P&O method and general fuzzy-based MPPT. 본 논문에서는 태양광 발전시스템의 출력성능 증대를 위하여 제안하는 변동제한 퍼지추론 MPPT(Maximum Power Point Tracking : 최대전력점 추적)가 적용된 DPP(Differential Power Processing : 차동전력조절) 시스템을 제시한다. 태양광 발전시스템의 안정성 및 전력 생산 효율을 높이기 위하여 최근까지 연구되었던 태양광 발전시스템 전력변환구조의 발전과정과 MPPT 제어 기법의 특성을 살펴보았으며 각각의 장·단점을 비교해 태양광 발전시스템을 구성하는 데에 있어 적합한 전력변환구조와 MPPT 제어 기법을 채택하였다. 태양광 발전시스템의 구조 면에서는 태양광 패널 사이의 전력차 부분만을 이용해 태양광 패널을 제어할 수 있는 DPP 시스템을 채택하여 전력 변환 손실을 최소화 시켰다. 태양광 발전시스템의 제어 면에서는 데이터들 간의 유사성을 파악하여 사람과 같이 결론을 추론할 수 있는 퍼지 기법을 채택하여 연구를 진행하였다. 그 결과 제안하는 퍼지 기법인 변동제한 퍼지추론 MPPT 기법을 제시하였다. 이는 전력 변화량과 전압 변화량의 범위 중 전력 변화량의 과거 입력 데이터와 현재의 입력 데이터를 비교하여 0에 근접한 범위를 제한함으로써 전압 변화량이 급격하게 변화하지 않도록 예방할 수 있는 기법을 의미한다. 일사량이 변화하는 다양한 모의실험을 수행한 결과, P&O 기법을 적용하였을 때와 일반적인 퍼지 기반 MPPT를 수행하였을 때보다 본 논문에서 제안하는 변동제한 퍼지추론 MPPT 기법을 적용하였을 때 전압 변화의 안정성 증대와 가장 높은 시스템 효율을 보였다. 태양광 DPP 시스템을 구축하고 제안하는 변동제한 퍼지추론 MPPT 기법을 적용하여 실험을 진행한 결과, P&O 기법과 일반적인 퍼지 기반 MPPT를 적용하였을 때보다 안정성을 높일 수 있었으며 전력 손실을 줄일 수 있다는 것을 검증하였다.
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Short-Term Forecasting for Small-Scale Distributed Energy Resources (DERs) in Power System
시리크리스나 아차레 목포대학교 대학원 2022 국내박사
Short-term forecasting (STF) is a critical process for power system operation and planning. Due to importance of forecasting in power system, various forecasting methods have been developed at the transmission level, and its performance has been enhanced gradually. On the other hand, the forecasting for small-scale resources is still a challenging issue, because a relatively small amount of power makes the higher uncertainty and volatility. Considering the increasing number of the distributed energy resources (DERs) and usefulness in power system operation, it is required to enhance the forecasting performance for the small-scale electricity load and small-scale photo-voltaic (PV) generation. In case of small-scale electricity load, due to changes in household demographics, lifestyles, and the number of electric appliances, and varying weather conditions, the power consumption is becoming diverse on a daily basis. In order to overcome these uncertainty, sufficient historical data is required. On the other hand, most of small-scale PV plants are installed at remote locations from the weather data centers (WDCs), and they do not usually have their own measuring apparatus to record for weather conditions. In fact, small-scale load and PV forecasting are suffering from adequate data collection issues to enhance its forecasting performance. Thus, a novel STF method is required to be developed for reducing higher forecasting errors that should assist several areas of the power system. As deep learning enables higher forecasting accuracy either with immense data or with homogeneous data for time series analysis, new hybrid STFs for both single household load series and small-scale PV generations have been proposed in this dissertation. For single households, a convolution neural network (CNN) based hybrid forecasting model is proposed using a data augmentation strategy. The proposed data augmentation technique can artificially enlarge the training data by incorporating homogeneous residual load series for the CNN-based model and work out the historical data requirement problem effectively. For small-scale PV plants, a long-short term memory (LSTM) based hybrid forecasting model is proposed that combines both a weather data mixing model and a similar days detection (SDD) method. The presented weather data mixing models compute adequate weather data from the relatively nearby weather data centers (WDCs) using proposed inverse distance and inverse correlation techniques. The newly developed SDD method detects similar days considering the different impact from the weather variables on PV power generation. Both weather data mixing models and SDD are combined for small-scale PV forecasting to lower the impact from long-distance and weather uncertainty problems. The simulation results of both presenting hybrid algorithms are compared with corresponding conventional methods that indicate the effectiveness and application of the proposed algorithms in the power system.
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