다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

비만아 부모의 돌봄 경험: q 방법론

Overview of current development in electrical energy storage technologies and the application potential in power system operation

Hydrogen energy, economy and storage: Review and recommendation

Blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. Numerous sources coming from these backgrounds identify blockchains as having the potential to bring significant benefits and innovation. Blockchains promise transparent, tamper-proof and secure systems that can enable novel business solutions, especially when combined with smart contracts. This work provides a comprehensive overview of fundamental principles that underpin blockchain technologies, such as system architectures and distributed consensus algorithms. Next, we focus on blockchain solutions for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature and current business cases. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of blockchain activities and initiatives in the energy sector. Our study reviews 140 blockchain research projects and startups from which we construct a map of the potential and relevance of blockchains for energy applications. These initiatives were systematically classified into different groups according to the field of activity, implementation platform and consensus strategy used. 1 Opportunities, potential challenges and limitations for a number of use cases are discussed, ranging from emerging peer-to-peer (P2P) energy trading and Internet of Things (IoT) applications, to decentralised marketplaces, electric vehicle charging and e-mobility. For each of these use cases, our contribution is twofold: first, in identifying the technical challenges that blockchain technology can solve for that application as well as its potential drawbacks, and second in briefly presenting the research and industrial projects and startups that are currently applying blockchain technology to that area. The paper ends with a discussion of challenges and market barriers the technology needs to overcome to get past the hype phase, prove its commercial viability and finally be adopted in the mainstream.

/pdf/blockchain-technology-in-the-energy-sector-a-systematic-24t1ywmkp0.pdf

Blockchain technology in the energy sector: A systematic review of challenges and opportunities

The dynamics of large scale circulating fluidized bed (CFB) boiler power plant shows the behaves of large time delay, complex coupling and nonlinearities, which brings difficulties for controller design to improve the plant performance. This paper firstly introduces a well-established and validated nonlinear model of a 300MWe CFB power plant, and then the model is simplified through analysis and approximation which could show clearly the reason of difficulties associated with controller design. It is found that the coupling and nonlinearity mainly come from the opening of main steam governing valve under various load conditions, and it is hard to completely decouple the multivariable system. To avoid the difficulties of decoupling, a decentralized PID control system is finally proposed to control the unit power and main steam pressure after nonlinear model transformation. The simulation results show that the control system has improved dynamic performance in load reference tracking for a wide range of load changes, and also good disturbance rejection to the coal quality variation. The analysis and control in this paper provide the first step to move forward for future control quality improvement and advanced control strategy study.

Decentralized nonlinear control of 300MWe circulating fluidized boiler power unit

A new chaos cat swarm optimization algorithm based on saliency gradient for power equipment infrared and visible images registration

Electricity load forecasting provides the critical information required for power institutions and authorities to develop rational, effective, and economic dispatch plans. The load forecasting at the regional power system is important for optimal management and accommodating local renewable energy sources, which is a challenging task as the demand variations are more sensitive to local weather changes (such as temperature, humidity, precipitation, and wind speed) and consumers' activities and behaviours. The paper aims to develop a new prediction method using intelligent computational algorithms. Long Short-Term Memory (LSTM), a deep recurrent neural network, explores the long-term dependency of network memory sequence data to identify intrinsic variations in both horizontals (time series) and vertical (network depth) dimensions over a longer historical period. Support Vector Machine (SVM) is a typical learning method that has been successfully implemented to solve nonlinear regression and time series problems. This paper studies the two methods and adapts the two methods to become suitable algorithms for load prediction. The paper presents the algorithms, their applications and prediction results. The prediction performance is compared for using LSTM and SVM at ultra-short, short-term, medium-term, and long-term forecasting. The results show that LSTM has higher prediction accuracy than SVM in both ultra-short and short-term forecasts, but SVM is more capable of medium-term and long-term forecasting. Finally, the epoch time for LSTM and SVM is also calculated and compared.

Development and Comparison of Two Computational Intelligence Algorithms for Electrical Load Forecasts with Multiple Time Scales

The authors' previous simulation study indicated that different quantities of compressed air will be consumed by a servo-controlled pneumatic cylinder with different velocity profiles under the same control strategy. This motivated the authors to investigate if there is the most energy-efficient profile, which leads the system to use the least amount of compressed air. To avoid solving a set of complicated non-linear differential equations, the non-linear system is linearised through input/output state feedback, then energy efficient optimal control theory is applied to the linearised system. An optimal control strategy is derived with respect to the transformed states of the linear system model. The solution of the state trajectory results in an energy efficient profile. Simulation study has been conducted using both the linearised and original non-linear system models and the simulation results indicated that the profiles obtained in this paper are more energy efficient.

Jihong Wang

Papers

Decentralized nonlinear control of 300MWe circulating fluidized boiler power unit

A new chaos cat swarm optimization algorithm based on saliency gradient for power equipment infrared and visible images registration

Development and Comparison of Two Computational Intelligence Algorithms for Electrical Load Forecasts with Multiple Time Scales

Improvement of energy efficiency of servo pneumatic cylinders by optimising velocity profiles

Predictive functional control and its applications to a coordinated control system in power plants