Supervisory Optimization of the MGT-CCHP System Using Model Predictive Control

Yi Zhang,Fan Zhang,Xiao Wu,Junli Zhang,Li Sun,Jiong Shen 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

This paper proposes a supervisory optimal control structure for the micro-gas-turbine based combined cooling heating and power (MGT-CCHP) system. In the upper layer of the structure, a dynamic optimal reference governor is developed to calculate the optimal operating points according to the given economic performance indexes, and in the lower layer of the structure, model predictive control is utilized to track the given operating points, so that a dynamic optimality can be achieved under the input-output constraints during the optimization. Moreover, a disturbance term is introduced in the model to lump the effect of unmeasured disturbances and plant behavior variations, thus, their influences on the supervisory optimization can be removed. The advantages and effectiveness of the proposed method are demonstrated through the simulations on an 80kW MGT-CCHP simulator.

금융감독권의 최적 형태에 관한 고찰

김재영(Jae-Young Kim),최동범(Dong Beom Choi) 서울대학교 경제연구소 2021 經濟論集 Vol.60 No.2

발전 도상 경제의 금융 감독 정책은 미시적인 영역에서의 시장 실패를 시정하여 투명하고 건전한 제도를 확립하고, 이를 통해 경제 주체의 원활한 자금의 조달 및 국가 경제의 양적 발전에 기여 하는 것을 목표로 한다. 한편 선진국의 금융 감독의 경우 거시건전성의 관점에서 과도한 위험의 추구와 금융 시스템 불안정에서 비롯되는 사회적 비용을 억제하는 방향으로 감독의 주안점이 옮겨가며, 이는 경제성장 및 금융 시장의 발달 상태에 따라 금융 감독의 우선 목표 및 최적 감독 체계 또한 변할 수 있음을 의미한다. 이에 금융 산업의 발전과 금융 안정성 유지가 동시에 책무로 주어진 우리의 현행 감독 구조는 발전 도상 상태에서는 적절한 시너지 효과가 있을 것이나, 선진 경제로의 이행을 이룬 상태에서는 오히려 두 목표의 상충에 의한 왜곡을 일으킬 가능성이 크다. 본 연구는 금융 감독의 경제학적 의의 및 각각의 감독 구조에 관한 경제학적 쟁점과 국제적 제도 개편의 현황에 대해 논하고 우리에게 바람직한 개편의 방향을 제시한다. Financial supervision in developing economies aims to address market failures at the micro-level and establish reliable institutions to contribute to the nations’ economic growth ultimately. In developed economies, on the other hand, supervisory authorities should adopt a more macropruendtial perspective to mitigate systemic instability and social costs. While the focal point of the supervision should shift as the financial markets mature, Korea still adopts the supervisory architecture that may no longer be optimal. This study examines various options and suggests the desirable structure for the country.

Research on an Improved Ant Colony Optimization Algorithm for Solving Traveling Salesmen Problem

Wenli Lei,Fubao Wang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.9

In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization(ACO) in solving the complex function, the traditional ant colony optimization algorithm is analyzed in detail, an improved ant colony optimization(IWSMACO) algorithm based on information weight factor and supervisory mechanism is proposed in this paper. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanisms in order to dynamically adjust path selection probability and randomly select the behavior rules for further intelligentializing the ant colony. The supervisory mechanism added the dynamic convergence criterion of supervisory distance and used the optimal pheromone update strategy to self-adaptively select the excellent ants for updating the pheromone trails, and improve the solution qualities of each iteration, better guide the later ants for learning. Finally, the proposed IWSMACO algorithm is carried out by 12 TSP instances. The simulation experiment results show that the proposed IWSMACO algorithm can not only avoid falling into the local optimum, but also enhance the convergence speed. And it takes on remarkable optimized ability and higher search accuracy.

Gaussian Process Approximate Dynamic Programming for Energy Management of Parallel Hybrid Electric Vehicles

Jin Woo Bae,Dohee Kim,Jeongsoo Eo,Kwang-Ki K. Kim 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

This paper presents two non-parametric Bayesian techniques–Gaussian Process Dynamic Programming (GPDP) and Gaussian Process Dynamic Programming-Receding Horizon Control (GPDP-RHC)–for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles (HEVs) are powered by engine and electric machine and assigning the required traction power to the two sources. It is known as the supervisory control which can be formulated as an optimal control problem. To solve the supervisory optimal control, we adopt the approximate dynamic programming (ADP) with Gaussian processes (GPs) which are used for value function approximation in optimal control problem. High-fidelity models of real-world vehicle data for battery, engine, and electric machine are used to obtain discrete dynamic programming (DP) solutions for a known driving cycle. To overcome limitations in real-time application of DP, we use non-parametric Bayesian function approximation techniques using GPs. The state-value tables obtained by dynamic programming are approximated by Gaussian process regression. Furthermore, the future value function is predicted by GPDP in one-step lookahead with RHC. For demonstration of optimality and efficiency, the proposed GPDP-RHC solution is compared with both the offline global DP solution and real-driving result.

DEVELOPMENT OF EQUIVALENT FUEL CONSUMPTION MINIMIZATION STRATEGY FOR HYBRID ELECTRIC VEHICLES

J. PARK,박장현 한국자동차공학회 2012 International journal of automotive technology Vol.13 No.5

Power distribution between an internal combustion engine and electric motors is one of main features of hybrid electric vehicles that improves their fuel economy. An equivalent fuel consumption minimization strategy can instantaneously identify the optimal power distribution by converting the battery power into the equivalent fuel power and minimizing the overall fuel consumption. To guarantee the effectiveness of the strategy, it is essential to find the proper value of the conversion factor used to obtain the equivalent fuel power. However, finding the proper value is not a straightforward process because it is necessary to consider the overall power conversion efficiencies and battery charge sustaining strategy for the target driving cycle in advance. In this study, a model-based parameter optimization method is introduced to find the optimal conversion factor. A hybrid electric vehicle simulation model capable of estimating fuel consumption was developed, and the optimal conversion factor was discovered using a genetic algorithm that evaluates its population members using the simulation model. A series of simulations and vehicle tests was conducted to verify the effectiveness of the optimized strategy, and the results show a distinct improvement in fuel economy.

Data Preprocessing Using Hybrid General Regression Neural Networks and Particle Swarm Optimization for Remote Terminal Units

Wen-Hui Chen,Jun-Horng Chen,Shih-Chun Shao 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.2

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interfer-ence or lack of calibration. In this study, an effective approach based on the fusion of the general re-gression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.

이동거리의 변화에 따른 플러그인 하이브리드 전기차의 최적 제어

금동석(Dongsuk Kum),Huei Peng,Norman Bucknor 한국자동차공학회 2013 한국자동차공학회 부문종합 학술대회 Vol.2013 No.5