[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence-based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple-variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN-based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2014.0359

Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

A two-stage Energy Management System for smart buildings reducing the impact of demand uncertainty

Recent literature proposes some approaches that employ explicit equations for identifying the five parameters of the single-diode model describing a photovoltaic (PV) panel These methods avoid the iterative solution of a nonlinear system of equations, whose convergence is very sensitive to the guess solution Therefore, they are particularly suitable to perform parameter identification in real time and to be implemented on low-cost, low-performance processing platforms In this paper, the applicability of some explicit methods, previously validated under standard test conditions, is analyzed for a large class of panels under operating conditions that are different from the standard ones The study considers both a consolidated method for translating the PV model parameters as well as a novel approach The analysis allows assessing the most suitable parameter translation equations for each considered explicit identification method, highlighting the effectiveness of such explicit approaches under different operating conditions An in-depth validation based on experimental data concerning two commercial PV panels corroborates the analysis

Translation of the Single-Diode PV Model Parameters Identified by Using Explicit Formulas

An artificial neural network-based forecasting model of energy-related time series for electrical grid management

A novel common-mode (CM) EMI active filter for dc-fed motor drives is proposed. The active filter performs both the compensation of the CM voltage at the motor input and the mitigation of the leakage high-frequency CM currents, thus increasing the drive reliability and the vehicle electromagnetic compatibility (EMC). The filter scheme is based on a voltage feedback action and also includes a feed-forward action by exploiting a suitably estimated CM current. An optimized design of the CM voltage detection/injection systems is implemented. Moreover, the active filter is supplied by a smaller voltage than the dc link value; this permits a more performing amplifier to be used. The active filter behavior is analyzed theoretically and its performance is assessed by experiments. The realized prototype shows a good efficiency and compactness.

EMI Reduction in DC-Fed Electric Drives by Active Common-Mode Compensator

This paper proposes a theoretical study supporting the use of simplified single-diode photovoltaic (PV) models to accurately reproduce the behaviour of PV generators. In particular, a newly defined parameter is introduced, which allows to classify PV modules according to the possibility to neglect either the series- or the shunt resistance in the circuit model. On such a basis, equations allowing to identify the nonneglected resistance and the other unknown model parameters, are derived. The resolution of such equations is non-iterative, therefore, the proposed approach is suitable for on-line parameter identification, for example for supporting a maximum power point tracking circuit. The accuracy of the proposed method is assessed by practical cases of PV model parameter extraction.

Massimiliano Luna

Papers

A two-stage Energy Management System for smart buildings reducing the impact of demand uncertainty

Translation of the Single-Diode PV Model Parameters Identified by Using Explicit Formulas

An artificial neural network-based forecasting model of energy-related time series for electrical grid management

EMI Reduction in DC-Fed Electric Drives by Active Common-Mode Compensator

Generalized classification of PV modules by simplified single-diode models