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Electricity Load Pattern Analysis using Fuzzy C-Means Clustering Algorithm
SENFENG CEN,YONGJIANG ZHAO,YONG MIN KIM,IBRAHIM ALIYU,J. G. Hur,CHANG GYOON LIM 한국콘텐츠학회 2021 한국콘텐츠학회 ICCC 논문집 Vol.2021 No.12
Energy consumption has grown explosively in recent years, and energy shortages occurred occasionally. Demand Response (DR) programs could help energy management entities to balance power generation and consumption. The electricity consumption data were collected with the widely deployed advanced smart meters, which contain valuable information. Consumption data can be used to explore the consumption behavior and help Demand Side Management (DSM). This study proposed clustering algoritsshms to obtain the representative load patterns based on diurnal load profiles. First, we applied discrete wavelet transform (DWT) to extract features from 10-second interval daily electricity consumption data. Then using Principal Component Analysis (PCA) for dimensionality reduction. Lastly, implement Fuzzy C-Means (FCM) clustering algorithm to segment preserved features. Additionally, we discuss the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.
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고속 푸리에 변환 및 심층 신경망을 사용한 전력 품질 외란 감지 및 분류
첸센폰(Senfeng Cen ),임창균(Chang-Gyoon Lim) 한국전자통신학회 2023 한국전자통신학회 논문지 Vol.18 No.1
무작위 및 주기적인 변동하는 재생에너지 발전 전력 품질 교란으로 인해 발전 변환 송전 및 배전에서 더 자주 발생하게 된다. 전력 품질 교란은 장비 손상 또는 정전으로 이어질 수 있다. 따라서 서로 다른 전력 품질외란을 실시간으로 자동감지하고 분류하는 것이 필요하다. 전통적인 PQD 식별 방법은 특징 추출 특징 선택 및 분류의 세 단계로 구성된다. 그러나 수동으로 생성한 특징은 선택 단계에서 정확성을 보장하기 힘들어서 분류 정확도를 향상하는 데에는 한계가 있다. 본 논문에서는 16가지 종류의 전력 품질 신호를 인식하기 위해 CNN(Convolution Neural Networ)과 LSTM(Long Short Term Memory)을 기반으로 시간 영역과 주파수 영역의 특징을 결합한 심층 신경망 구조를 제안하였다. 주파수 영역 데이터는 주파수 영역 특징을 효율적으로 추출할 수 있는 FFT(Fast Fourier Transform)로 얻었다. 합성 데이터와 실제 6kV 전력 시스템 데이터의 성능은 본 연구에서 제안한 방법이 다른 딥러닝 방법보다 일반화되었음을 보여주었다. Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.
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Real-Time Demand Response Management for Controlling Load using Deep Reinforcement Learning
YONGJIANG ZHAO,SENFENG CEN,SEUNG JE SEONG,IBRAHIM ALIYU,CHANG GYOON LIM 한국콘텐츠학회 2021 한국콘텐츠학회 ICCC 논문집 Vol.2021 No.12
With the rapid economic growth and the improvement of living standards, electricity has become an indispensable energy source in our lives. Therefore, the stability of the grid power supply and the conservation of electricity are very important. Controlling load as an important part of incentive demand response (DR), it can achieve a rapid response and improve demand side resilience. Controlling load by manually formulating rules, some devices can be selectively turn off during peak power consumption. It reduces the cost of manual operation while reducing the peak value. However, it is difficult to optimize methods based on manual rules. In this paper, Soft Actor-Critic (SAC) is used as a control algorithm to optimize the control strategy. In order to focus on the optimization of the algorithm, the dataset in this paper is based on CityLearn. The results show that through the coordination of SAC to control load, realizes the goal of reducing both the peak load demand of power and the operation costs on the premise of regulating voltage to the safe limit.
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자오 용지앙(Yongjiang Zhao),첸센폰(Senfeng Cen),성승제(Seung-Je Seong),허정규(J. G. Hur),임창균(Chang-Gyoon Lim) 한국전자통신학회 2023 한국전자통신학회 논문지 Vol.18 No.2
심층 강화 학습(Deep Reinforcement Learning)을 사용한 경로 계획에서 장애물을 자동으로 회피하기 위해 로봇을 학습시키는 일은 쉬운 일이 아니다. 많은 연구자가 DRL을 사용하여 주어진 환경에서 로봇 학습을 통해 장애물 회피하여 경로 계획을 수립하려는 가능성을 시도하였다. 그러나 다양한 환경에서 로봇과 장착된 센서의 오는 다양한 요인 때문에 주어진 시나리오에서 로봇이 모든 장애물을 완전히 회피하여 이동하는 것을 실현하는 일은 흔치 않다. 이러한 문제 해결의 가능성과 장애물을 회피 경로 계획 실험을 위해 테스트베드를 만들었고 로봇에 카메라를 장착하였다. 이 로봇의 목표는 가능한 한 빨리 벽과 장애물을 피해 시작점에서 끝점까지 도달하는 것이다. 본 논문에서는 벽과 장애물을 회피하기 위한 DRL의 가능성을 검증하기 위해 이중 심층 Q 네트워크(DDQN)를 제안하였다. 실험에 사용된 로봇은 Jetbot이며 자동화된 경로 계획에서 장애물 회피가 필요한 일부 로봇 작업 시나리오에 적용할 수 있을 것이다. It remains a challenge for robots to learn avoiding obstacles automatically in path planning using deep reinforcement learning (DRL). More and more researchers use DRL to train a robot in a simulated environment and verify the possibility of DRL to achieve automatic obstacle avoidance. Due to the influence factors of different environments robots and sensors, it is rare to realize automatic obstacle avoidance of robots in real scenarios. In order to learn automatic path planning by avoiding obstacles in the actual scene we designed a simple Testbed with the wall and the obstacle and had a camera on the robot. The robot's goal is to get from the start point to the end point without hitting the wall as soon as possible. For the robot to learn to avoid the wall and obstacle we propose to use the double deep Q networks (DDQN) to verify the possibility of DRL in automatic obstacle avoidance. In the experiment the robot used is Jetbot, and it can be applied to some robot task scenarios that require obstacle avoidance in automated path planning.
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Development of blockchain-based duck history and distribution management system
Ibrahim Aliyu,Usmonov Kamoliddin,Seung Je Seong,Senfeng Cen,Yongjiang Zhao,Chang Gyoon Lim 한국콘텐츠학회 2021 한국콘텐츠학회 ICCC 논문집 Vol.2021 No.12
This paper aims to design a blockchain-based duck supply chain management system that captures assets and data flow from the poultry farm until the final customer. The system is proposed to improve efficiency and transparency of the duck supply chain and address the public health concern of the source of duck product through the use of blockchain technology to provide a verifiable and traceability history of the duck production process. Thus, the main functions of the system design include the recording of the information associated with poultry rearing (from a hatchery to a grow outhouse), status report of the farm activities monthly, tracking the movement of product through the supply chain until delivery to the final consumer through a retail outlet.
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CNN-LSTM for Smart Grid Energy Consumption Prediction
Ibrahim Aliyu,Usmonov Kamoliddin,Seung Je Seong,Senfeng Cen,Yongjiang Zhao,Chang Gyoon Lim 한국콘텐츠학회 2021 한국콘텐츠학회 ICCC 논문집 Vol.2021 No.12
This paper aims to forecast monthly electricity across different kinds of consumers such as Residential, Industrial, Official and Commercial sectors. To achieve this, a CNN-LSTM prediction framework for energy demand in smart grid is proposed. Efficient consumption prediction is essential for effective Demand Response (DR) which can enable consumers to minimize their energy usage through proper load curtailment, consumption shift over time, or energy generation and storage at certain times to offer flexibility in the grid.
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