215 research outputs found
Less temperature-dependent high dielectric and energy-storage properties of eco-friendly BiFeO3–BaTiO3-based ceramics
No full textThe effects of eco-friendly (BiNa0.84K0.16)0.48Sr0.04TiO3 (BNKS)-content in 0.65Bi1.05FeO3–0.35BaTiO3 (BFBT) dielectrics were investigated by following simple solid state fabrication route. By the introduction of BNKS in BFBT matrix the average grain size was significantly reduced, with relatively high dense microstructure (relative density > 94%). The BNKS-modified BFBT dielectrics demonstrated thermally-stable εr (670–1005, from 30 °C to 500 °C), high Tmax (424 °C–465 °C), colossal εr-max (58880–69226), and εr-mid (2891–5652 ± 15%) across the broad range of temperature from 244 °C to 500 °C. At the optimum composition (x = 0.10) the temperature-dependent (30 °C–150 °C) substantially high energy-storage density (Wstore ∼ 0.81 J/cm3) and efficiency (η > 60%) in bulk ceramics were observed. The thermally-stable dielectric and energy storage properties suggest that present investigated dielectrics can be promising candidates for high temperature dielectric applications and power electronics. © 2019 Elsevier B.V.1
A study on scheduling of power generation sources based on operators risk propensity in preparation for Microgrid island operations
No full text학위논문(석사) -- 서울대학교대학원 : 공학전문대학원 응용공학과, 2022.2. 윤용태.As major countries officially committed to being carbon neutral by 2050, discussions on carbon neutrality have been growing all around the world. As the electricity supply from renewable energy is expanded due to the electrification of energy, distributed energy resources such as solar power generation and energy storage system in the power distribution system unit is expected to increase steeply. However, controlling many distributed energy resources increases the burden on grid operators to operate the power system stably and efficiently. Accordingly, microgrid was introduced to solve the emergence of many distributed energy resources in the distribution system and to create a smart power grid. By controlling distributed energy, it allows to reduce the burden of system operators on the control of power grid and to manage the grid more efficiently
Typically, microgrid operators determine the optimal operation by interacting with the main grid, including maintaining a connection with the main grid, and supplying electricity to or receiving electricity from the market. In addition, as the power market is formed and the area is expanded to the market for auxiliary services such as frequency adjustment, the role of microgrid operators in many microgrid environments is expected to increase. Accordingly, in deriving an operation plan for optimal market participation, microgrid operators must consider risk factors: i.e., uncertainty of power load due to abnormal weather, and intermittent output characteristics of variable renewable energy such as solar power. It is important for microgrid operators to establish strategies to reduce risk factors: especially, they should efficiently establish their own operation plans with power plants and adjust the load usage schedule. This is because, when microgrid island operation occurs, the microgrid must bear the demand only with its own power source and, if necessary, it must cut off the load. In addition, uncertainty analysis on the possibility of future risk differs depending on the operator's risk propensity, the resultant operating schedule, and expected return will vary accordingly. Therefore, it is necessary for the microgrid operator to receive information on the operation schedule and expected return that are tailored to their risk inclinations considering the given reserve conditions and environment
In this thesis report, I propose a modeling method to obtain a daily power generation schedule that matches the risk propensity of the operator considering the risk factors that may occur in the microgrid island operation with the main grid. To this end, I assumed a rule that power transactions with the main grid are suspended when the reserve band set as the power supply and demand deviation exceeds the allowed limit under the premise of the PXFC (Power Exchange for Frequency Control) market, where market participants are obliged to secure reserve capacity according to the principle of cost-causing costs. Accordingly, the risk standard of microgrid operators was defined as the condition of maintaining their reserve power generation capacity so as not to exceed the reserve band. To minimize the daily operation costs according to the risk propensity of the microgrid operator, the scheduling for each time period of the power source, such as start-up, stop, or ESS charging/discharging, was modeled.
The proposed model was evaluated through simulation using Python. In addition, the method was proposed to determine how to efficiently operate one's own power generation resources by comparing the microgrid operation schedule method and the expected cost in accordance with the risk propensity of the operator.주요국에서 2050년 탄소중립 목표를 공식 선언하며 전 세계적으로 탄소중립 논의가 확산되고 있다. 에너지의 전기화로 재생에너지 중심으로 전력공급이 확대됨에 따라 배전시스템 단위에서의 태양광 발전, 에너지저장장치 등 분산에너지가 더 가파르게 증가할 전망이다. 그러나 많은 수의 분산에너지를 제어하는 것은 계통운영자가 전력 시스템을 안정적이고 효율적으로 운영하는 데 있어 부담이 가중된다. 이에 마이크로그리드는 배전시스템에서 다수의 분산에너지 출현을 해결하고 스마트한 전력망을 만들기 위해 도입되었으며, 분산에너지를 조정하여 계통운영자의 제어를 위한 부담을 줄이고 보다 효율적인 운영을 가능하게 한다.
일반적으로 마이크로그리드 운영자는 메인 그리드와 연결을 유지하고 전기를 공급하거나 시장에서 전기를 받는 등 메인 그리드와의 상호 작용을 통해 최적의 운영을 결정한다. 또한, 전력시장이 형성되고 주파수 조정과 같은 보조서비스 시장으로 영역이 확대됨에 따라 다수의 마이크로그리드 환경에서 마이크로그리드 운영자의 역할은 더욱 커질 것으로 예상한다. 이에 마이크로그리드 운영자는 최적의 시장참여를 위한 운영 방안을 도출함에 있어, 이상기후 발생에 따른 전력 부하의 불확실성과 태양광 등 가변재생에너지의 간헐적인 출력 특성 등 위험요인을 고려해야 한다. 메인 그리드와 거래중단이 발생할 경우 마이크로그리드는 자체 발전원으로만 수요를 부담해야 하며, 필요시 부하차단을 시행해야 할 수도 있기에 마이크로그리드 운영자는 자신의 발전원 운영 계획을 효율적으로 수립하거나 부하 사용 일정을 조정하는 등 위험요인을 감소시키는 전략을 세우는 것이 중요하다. 또한 미래의 위험요인이 발생할 확률에 대한 불확실성 분석은 운영자의 리스크 성향에 따라 다르며 그에 따라 결정되는 운영 스케쥴과 기대수익률은 변화하게 된다. 따라서 마이크로그리드 운영자는 주어진 예비력 여건 및 환경을 고려하여 자신의 리스크 성향에 맞춘 운영 스케쥴 방법과 기대수익률 정보를 받는 것이 필요하다.
본 연구보고서에서는 마이크로그리드가 메인 그리드와 전력거래가 중단됨에 따라 발생되는 위험요인을 고려하여 운영자의 리스크 성향에 맞는 일간 발전원 운영 스케쥴을 구하는 모델링을 제안하였다. 이를 위해 비용 유발자 부담 원칙에 따라 시장 참여자에게 예비력 확보 의무가 부여되는 PXFC (Power Exchange for Frequency Control) 시장을 전제하에 전력수급 편차로 설정한 예비력 밴드 위반 시 메인 그리드와 전력거래가 중단되는 규칙을 가정하였다. 이에 따라 예비력 밴드를 위반하지 않기 위한 자신의 예비 발전용량 유지조건을 위험성향 기준으로 정의하고 마이크로그리드 운영자 성향별 일간 운영비용 최소화를 목적으로 발전원의 시간대별 기동, 정지 또는 ESS 충방전 등에 대한 스케쥴링을 모델링하였다.
제안된 모델링은 파이썬을 활용한 시뮬레이션을 통해 검증하였으며, 운영자 성향에 따른 마이크로그리드 운영 스케쥴 방법과 기대비용을 비교하여, 자신의 발전자원을 어떤 방식으로 효율적으로 운영할 것인지를 결정하는 방안을 제시하였다.제1장 서 론 1
제1절 연구 배경 및 목적 1
제2절 연구보고서의 개요 및 구성 6
제2장 마이크로그리드 전력시장 7
제1절 마이크로그리드 역할과 기능 7
제2절 PXFC 시장 9
제3절 마이크로그리드 스케쥴링 구성요소 11
제3장 운영자 리스크 성향에 따른 마이크로그리드 발전원 스케쥴링 모델링 13
제1절 목적함수 13
제2절 제약조건 15
제4장 사례연구 25
제1절 시뮬레이션 조건 25
제2절 운영자 리스크 성향별 발전원 스케쥴 비교 30
제5장 결 론 35
참고문헌 37
Abstract 40석
A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition
No full textSpeech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker’s emotional state from an individual’s speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications
CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network
No full textArtificial intelligence, deep learning, and machine learning are dominant sources to use in order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) system is a basic necessity and an emerging research area of digital audio signal processing. However, SER plays an important role with many applications that are related to human–computer interactions (HCI). The existing state-of-the-art SER system has a quite low prediction performance, which needs improvement in order to make it feasible for the real-time commercial applications. The key reason for the low accuracy and the poor prediction rate is the scarceness of the data and a model configuration, which is the most challenging task to build a robust machine learning technique. In this paper, we addressed the limitations of the existing SER systems and proposed a unique artificial intelligence (AI) based system structure for the SER that utilizes the hierarchical blocks of the convolutional long short-term memory (ConvLSTM) with sequence learning. We designed four blocks of ConvLSTM, which is called the local features learning block (LFLB), in order to extract the local emotional features in a hierarchical correlation. The ConvLSTM layers are adopted for input-to-state and state-to-state transition in order to extract the spatial cues by utilizing the convolution operations. We placed four LFLBs in order to extract the spatiotemporal cues in the hierarchical correlational form speech signals using the residual learning strategy. Furthermore, we utilized a novel sequence learning strategy in order to extract the global information and adaptively adjust the relevant global feature weights according to the correlation of the input features. Finally, we used the center loss function with the softmax loss in order to produce the probability of the classes. The center loss increases the final classification results and ensures an accurate prediction as well as shows a conspicuous role in the whole proposed SER scheme. We tested the proposed system over two standard, interactive emotional dyadic motion capture (IEMOCAP) and ryerson audio visual database of emotional speech and song (RAVDESS) speech corpora, and obtained a 75% and an 80% recognition rate, respectively
Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features
No full textArtificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition is a challenging task for a machine. In addition, making it smarter so that the emotions are efficiently recognized by AI is equally challenging. The speech signal is quite hard to examine using signal processing methods because it consists of different frequencies and features that vary according to emotions, such as anger, fear, sadness, happiness, boredom, disgust, and surprise. Even though different algorithms are being developed for the SER, the success rates are very low according to the languages, the emotions, and the databases. In this paper, we propose a new lightweight effective SER model that has a low computational complexity and a high recognition accuracy. The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a modified pooling strategy that have more discriminative power for the SER. The proposed CNN model was trained on the extracted frequency features from the speech data and was then tested to predict the emotions. The proposed SER model was evaluated over two benchmarks, which included the interactive emotional dyadic motion capture (IEMOCAP) and the berlin emotional speech database (EMO-DB) speech datasets, and it obtained 77.01% and 92.02% recognition results. The experimental results demonstrated that the proposed CNN-based SER system can achieve a better recognition performance than the state-of-the-art SER systems
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