Amount Of Power Research Articles

Copper nanofluids under minimum quantity lubrication during drilling of AISI 4140 steel

ABSTRACT A significant quantity of power consumed in drilling process is converted into heat energy due to friction between the surfaces of tool and work piece. This extreme heat generated affects the performance of cutting tool which, leads to poor surface finish on drilled surfaces. The cutting fluid plays a vital function in improving the tool life and surface finish since it acts as a coolant and lubrication. In this research, the experiments have been carried out with difference combination of three operating parameters such as cutting speed, machining environment and feed rate using minimal quantity lubrication technique in drilling of AISI 4140 steel. The conventional coolant (CC) in flood lubrication condition, coconut oil and copper nanofluid are used as the levels of machining environments. The surface roughness and flank wear are observed as the responses and analysed using an integrated technique Average S/N ratio-based response surface methodology (ASN-RSM). The experimental results of copper nanofluid are compared with CC and coconut oil. The surface roughness, tool wear and chip morphology are also evaluated under conventional coolant, coconut oil and nanofluid. The confirmation tests are executed with the near optimal setting obtained from ASN-RSM technique and results are compared with the initial setting. It was observed that copper nanofluid-based optimal setting predicted using ASN-RSM has reduced the surface roughness and flank wear by 71% and 53% respectively over the responses obtained at coconut oil based initial setting. It is confirmed that copper nanofluid has a significant contribution in reducing surface roughness and flank wear.

Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings

The combined action of different equipment connected to an electrical installation is capable of causing unexpected changes in the load kinds inside the installation; these load variations are responsible for some electrical failures. In this paper a methodology to classify and to identify the load kinds in industrial environments is presented. Energy power quantities (EPQ) and current values are used to establish indexes in order to use them as features for a C-means algorithm and perform the load classification. The experimentation is done in a healthcare facility gathering electrical data in different electric distribution boards. The results obtained from the classification method show variations in the load behavior along the day. Furthermore, some classes can be used to recognize equipment in the electrical installation for further inspection or failure detection.

Эффективность передачи информации двухсимвольными ансамблями в симплексных системах связи на базе ТСК

The theory of electrical communication provides transmission of two types of information (messages): continuous messages and discrete. Continuous messages are characterized by an infinite number of their values on a finite time interval. An example of such a message may be the value of a quantity of the direction power of the microphone output: the greater the resolving power of the measuring device, the more different voltage values. Discrete messages (as opposed to continuous ones) are characterized by a finite number of values over an infinite time interval. For example, several volumes of a work of art have a finite number of characters (letters) that must be transferred. Advantages of digital methods of display, processing, transmission, accumulation and storage of information provide them with the primary use. Actually, various methods of digital conversion are used to increase the speed of information transfer - particularly, information compression. In this article were researched the efficiency of information transmission by two-character ensembles in simplex systems based on timed signal constructions. Timer coding, as opposed to positional, in which the information of the transmitted symbol is determined by the type of signal on the nyquist interval, keeps stored information in the length of several separate time segments of the signal tci and their mutual position. In this case, the duration of each of the signal segments is not less than the nyquist interval: t ci = t 0 + zD . Comparison of information parameters of two-character ensembles at positional and time coding had been made. Determined the information capacity of the nayquist element and the influence of the number of redundant elements ( r = n - m ) on entropy of the message at positional block coding. Optimal parameters of timed signal constructions for the code set affecting power were determined. Timer signal constructions are proposed as a code summarization mechanism, which provides transmission of information about the state of more than two sources in one codeword.

Electricity purchasing strategy for the regional power grid considering wind power accommodation

In this paper, a monthly power purchase optimization problem for regional power grid is studied. Considering the uncertainties of wind power and the effect of day‐ahead market, an optimal model of power purchase for regional power grid is built. The proposed model considers wind power in the monthly electricity balance, and it also realizes double‐risk management of over peak regulation and power purchase loss. ‘SHARP ratio’ in economics is introduced as an index of purchasing benefit–risk coordination. It can reflect the comprehensive benefits of electricity purchase visually. The monthly purchase scheme is refined to peak, flat and valley periods of every day, and the optimal purchasing quantity of thermal power, wind power and interregion power are obtained. The simulation results show that the risk preference has a significant impact on the purchase decision‐making. Reducing wind power forecasting error and the difference between load peak and valley is conducive to achieving a better comprehensive benefit of power purchase. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Measuring the Internal Quantum Efficiency of Light-Emitting Diodes at an Arbitrary Temperature

We present a method of measuring the internal quantum efficiency (IQE) of light-emitting diodes (LEDs) as a function of driving current at an arbitrary temperature only with experimentally measurable quantities of optical power and current. The measurement procedure starts to find an exact value of the IQE at the reference point where the injection efficiency is considered as 100% and the radiative efficiency is calculated from the infinitesimal change of the relative external quantum efficiency (EQE). Once the IQE at the reference point is exactly known, the IQE at any other point is calculated by the relative ratio of the EQE values to the reference one. We show the theoretical formalism of the proposed method and demonstrate its validity using a commercial InGaN blue LED as an example. The comparison with the IQE obtained by the conventional temperature-dependent electroluminescence is also given for various temperatures.

A review of textile industry: Wet processing, environmental impacts, and effluent treatment methods

AbstractThe word “textile” means to weave and was taken from the Latin word “texere.” Nowadays, textiles not only fulfill humankind's basic necessity for clothing, they also allow individuals to make fashion statements. As one of the oldest industries, the textile industry occupies a unique place in India. It is responsible for 14% of the total industrial manufacture in India. However, the textile industry is also considered to be one of the biggest threats to the environment. Pretreatment, dyeing, printing, and finishing operations are among the various stages of the industrial textile manufacturing process. These fabrication operations not only utilize huge quantities of power and water, they also generate considerable amounts of waste. The textile industry utilizes a number of dyes, chemicals, and other materials to impart the required qualities to the fabrics. These operations produce a significant amount of effluents. The quality of effluents is such that they cannot be put to other uses, and they can create environmental problems if they are disposed of without appropriate treatment. This review discusses different textile processing stages, pollution problems associated with these stages, and their eco‐friendly alternatives. Textile wet processing is described in detail, as it is the key process in the industry and it also generates the greatest amount of pollutants in textile processing. The environmental impact of textile effluents is discussed, as textile effluents not only impose negative effects on the quality of water and soil, they also imperil plant and animal health. In this paper, various methods for treating textile effluents are described. Discussion of physical, chemical, biological, and advanced treatment technologies of effluent treatment are included in this paper.

Selection of organic acid leaching reagent for recovery of zinc and manganese from zinc-carbon and alkaline spent batteries

Zinc-carbon and alkaline batteries are often used in electronic equipment that requires small quantities of power. The waste from these batteries contains valuable metals, such as zinc and manganese, that are needed in many industries and can pollute the environment if not treated properly. This paper concerns the recovery of zinc and manganese metals from zinc-carbon and alkaline spent batteries with leaching method and using organic acid as the environmental friendly leaching reagent. Three different organic acids, namely citric acid, malic acid and aspartic acid, were used as leaching reagents and compared with sulfuric acid as non-organic acid reagents that often used for leaching. The presence of hydrogen peroxide as manganese reducers was investigated for both organic and non-organic leaching reagents. The result showed that citric acid can recover 64.37% Zinc and 51.32% Manganese, while malic acid and aspartic acid could recover less than these. Hydrogen peroxide gave the significant effect for leaching manganese with non-organic acid, but not with organic acid.

Evaluating and optimizing the operation of the hydropower system in the Upper Yellow River: A general LINGO-based integrated framework.

The hydropower system in the Upper Yellow River (UYR), one of the largest hydropower bases in China, plays a vital role in the energy structure of the Qinghai Power Grid. Due to management difficulties, there is still considerable room for improvement in the joint operation of this system. This paper presents a general LINGO-based integrated framework to study the operation of the UYR hydropower system. The framework is easy to use for operators with little experience in mathematical modeling, takes full advantage of LINGO’s capabilities (such as its solving capacity and multi-threading ability), and packs its three layers (the user layer, the coordination layer, and the base layer) together into an integrated solution that is robust and efficient and represents an effective tool for data/scenario management and analysis. The framework is general and can be easily transferred to other hydropower systems with minimal effort, and it can be extended as the base layer is enriched. The multi-objective model that represents the trade-off between power quantity (i.e., maximum energy production) and power reliability (i.e., firm output) of hydropower operation has been formulated. With equivalent transformations, the optimization problem can be solved by the nonlinear programming (NLP) solvers embedded in the LINGO software, such as the General Solver, the Multi-start Solver, and the Global Solver. Both simulation and optimization are performed to verify the model’s accuracy and to evaluate the operation of the UYR hydropower system. A total of 13 hydropower plants currently in operation are involved, including two pivotal storage reservoirs on the Yellow River, which are the Longyangxia Reservoir and the Liujiaxia Reservoir. Historical hydrological data from multiple years (2000–2010) are provided as input to the model for analysis. The results are as follows. 1) Assuming that the reservoirs are all in operation (in fact, some reservoirs were not operational or did not collect all of the relevant data during the study period), the energy production is estimated as 267.7, 357.5, and 358.3×108 KWh for the Qinghai Power Grid during dry, normal, and wet years, respectively. 2) Assuming that the hydropower system is operated jointly, the firm output can reach 3110 MW (reliability of 100%) and 3510 MW (reliability of 90%). Moreover, a decrease in energy production from the Longyangxia Reservoir can bring about a very large increase in firm output from the hydropower system. 3) The maximum energy production can reach 297.7, 363.9, and 411.4×108 KWh during dry, normal, and wet years, respectively. The trade-off curve between maximum energy production and firm output is also provided for reference.

Power definitions for polyphase systems based on Fortescue's symmetrical components

Power definitions for polyphase power systems with non-sinusoidal and unbalanced voltages and currents are considered in this paper. Based on application of Fortescue's transformation to time-varying voltage and current space vectors, a new approach to power definitions is proposed. Definitions of the complex, apparent, active and non-active powers in terms of the symmetrical components have been proposed. The analysis related to the influence of zero-sequence voltages to power quantities has been performed. The symmetrical components provide calculation of powers by omitting erroneous contribution of the zero-sequence voltages. A significant property of the proposed power definitions in terms of the symmetrical components is that they are invariant to the choice of the voltage reference. Application of the proposed power definitions in terms of the symmetrical components has been demonstrated by the examples of three-phase three-wire and four-wire systems.

Design of remote control system for intelligent irrigation based on ZigBee and GPRS

The sensor nodes of wireless sensor networks (WSNs)-based agricultural monitoring system are required to collect the environmental data periodically, including temperature and humidity, and additionally anytime monitoring shall be required to be realised among the monitored area with minimised quantity of sensor nodes. To meet the mentioned goal, a solar power-moving schedule of mobile-based optimal coverage (SPMSC) is proposed. In accordance with the content of SPMSC, every sensor node is able to obtain energy through the solar panel. The moving schedule of sensor nodes is determined through predicting the quantity of solar power as to realise the optimal coverage project with the minimised quantity of sensor nodes and the energy consumption. As the simulation result indicates, the proposed SPMSC, compared with other similar projects, has the 4% decrease of sensor node quantity and the 10% increase of the network lifetime.

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period $p$ , the total cost of communication is reduced to a factor of $1/p$ relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period $p$ increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.

An Economic Rural Electrification Study Using Combined Hybrid Solar and Biomass-Biogas System

One of the prime requirements for socio-economic improvement in any nation is the condition of consistent electricity provide systems. A huge quantity of the world’s residents lives in isolated rural areas. This paper discusses the renewable hybrid power creation system which is proper for Khalardda village placed in Odisha. Every part of the load facts of the village is composed and as a result quantity of power to be created is designed. The technical, economic potential of solar PV-biomass-biogas hybrid system is considered.

Review: Home energy management system in a Smart Grid scheme to improve reliability of power systems

Home energy management system (HEMS) concept rises from the development of smart homes that build interaction between users with their home appliances in order to operate automatically, multi-functionally, adaptably and efficiently. In line with technological developments and published regulations related to environmental issues, smart home applications evolve into HEMS applications which are not only to provide ease and convenience, but also to monitor and to make efficient energy use at home, thereby reducing peak power quantity and electricity bill. Smart grid is an intelligent power grid starting from its generation, transmission and distribution. It combines computing technology, artificial intelligence and communications technology which creates a smarter power system and is able to produce better power quality and lower generation cost. In the smart grid scheme, by means of HEMS applications, consumers can participate in improving the quality of power systems. This study will discuss about the development of HEMS in associated with smart grid technology particularly the role of HEMS application with its DSM (Demand Side Managment) and PEV (Plug-in Electric Vehicles) programs in the smart grid scheme to improve quality of power systems. Several studies have shown that the contribution of HEMS to the smart grid system can improve the power losses and voltage profile.

Phasor Alternatives to Friis’ Transmission Equation

Two alternatives to Friis’ transmission equation in terms of phasor voltage waves are presented. In one formulation, antennas are characterized by the complex effective length vectors. An additional form introducing field gain, which serves effectively as a phasor counterpart to the power gain, is proposed. Both forms show the same degree of symmetry and modularity as the original Friis’ equation, but thanks to using phasors instead of power quantities, they allow for superposition of fields or voltages. Although the new transmission equations are formulated in a frequency domain, they also constitute a simple and intuitive way to calculate time-domain responses of antennas, such as in ultrawideband systems. Application of the equations is demonstrated on a simple example of coupling between two nonaligned dipoles in the vicinity of a conductive reflector.

Core Abilities Evaluation Index System Exploration and Empirical Study on Distributed PV-Generation Projects

In line with the constraints of environmental problems and economic development, large-scale renewable-generation projects have been planned and constructed in recent years. In order to achieve sustainable power development and improve the power supply structure, China’s government has focused on distributed photovoltaic (PV) generation projects due to their advantages of clean emission and local consumption. However, their unstable output power still brings a series of problems concerning reliability, investment income, and available substitution proportion to traditional power, and so on. Therefore, it is imperative to understand the competitive development abilities of distributed PV generation projects and measure them effectively. First, through various investigation methods such as literature reviews, feasibility report analysis and expert interviews, the factors that influence the core abilities of distributed PV-generation projects were explored based on the micro-grid structure. Then, with the indexed exploration results, the factors were classified into 6 dimensions, i.e., investment and earning ability, production and operation ability, power-grid coordination ability, energy-conservation and emission-reduction ability, sustainable development ability, and society-serving ability. Meanwhile, an evaluation index system for core abilities of distributed PV-generation project was constructed using all quantitative indicators. Third, for examining the availability of the evaluation index system, combination weighting and techniques for order preference by similarity to an ideal solution (TOPSIS) methods were adopted to assess the practical distributed PV-generation projects. The case study results showed that installed capacity, local economy development, and grid-connected power quantity will influence the core abilities of distributed PV-generation project, obviously. The conclusions of the evaluation analysis on core abilities can provide useful references to operate and manage distributed PV-generation projects and promote their sustainable and health advantages. The proposed evaluation index system can also be used to assess power-generation projects in other types of energy, such as wind power and hydropower.

Improving Transient Stability in Power Systems by Using Fuzzy Logic Controlled SVC

<span>This paper presents the capability of a fuzzy logic based stabilizer used for generating the supplementary control signal to voltage regulator of static VAR compensator (SVC) for improving damping oscillations in power systems. Generator speed deviation and line active power were chosen as input signals for the fuzzy logic controller (FLC). The quantity of reactive power supplied/absorbed by SVC is determined based on the two input signal and deviation of terminal voltage at each sampling time. The effectiveness and feasibility of the proposed control is demonstrated with Single Machine Infinite Bus (SMIB) system and multi machine system which show improvement over the use of a fixed parameter controller. It has been observed that a robust controller is obtained with fuzzy logic controller.</span>

Assessing the contribution of automation to the electric distribution network reliability

Electrical distribution systems have changed significantly in the last years. Todays it is necessary to optimize the quality and quantity of power delivered to customers and to respond to current energy demand. In this sense, electric utilities are involved in network automation processes, supported in information and communication technologies, to improve network efficiency, reliability, security and quality of service. This paper aims to quantify the improvements achieved in the reliability indices with the automation of secondary substation (SS). As this automation process lies in the use of non-ideal communication channels, their latency and availability are considered. In order to complete the analysis from an experimental evaluation, this methodology has been applied to a real distribution network, included in the framework of several research projects developed in EU (European Union). Since the value of this reliability index has a remarkable influence on the revenues of the distribution system operator companies, these results provide a useful incoming for the strategic development of the distribution networks.

Low voltage ride-through capability improvement of photovoltaic systems using a novel hybrid control

This paper proposes an advanced hybrid control method to enhance low voltage ride-through (LVRT) capability of grid connected photovoltaic (PV) power plants. Using the proposed method, when a low voltage fault occurs at the point of common coupling, the grid connected inverter overcurrent and its DC-link overvoltage can be effectively suppressed by tuning the PV array output power and triggering the flux coupling fault current limiters (FCLs). The AC-side voltage sag of the inverter can be significantly mitigated by generating reactive power from the inverter and injecting this into the FCLs. Employing the proposed method can also reduce the regulating quantity of output power from the grid inverter and effectively improve PV system dynamic recovery. DC-link voltage fluctuations can also be significantly smoothed by the application of feed forward compensation and the FCLs. Related theoretical derivation and simulation analysis under various scenarios, including asymmetric faults, confirm that the proposed control method can not only enhance the LVRT capability but also strengthen the transient performance of PV systems.

Reactive Power Billing Under Nonsinusoidal Conditions for Low-Voltage Systems

This paper analyzes reactive power definitions for billing purposes in low-voltage installations with high harmonic distortion levels. First, the theoretical definitions of power under nonsinusoidal conditions and their outcomes are presented and two of them are chosen as the focus to determine which one is the most appropriate for billing: nonactive power and fundamental reactive power. The regulatory framework of different countries is then analyzed and compared with the theoretical definitions. Subsequently, different reactive power measurement methods are analyzed and their outputs are again compared with the definitions. Simulations performed with typical nonsinusoidal voltage and current signals show that several measurement techniques can easily be used for fundamental reactive power measurement, whereas the measurement of nonactive power requires more effort. Finally, a regulatory comparison of the pros and cons of each reactive power quantity is conducted. It is concluded that the surplus of reactive power in electricity grids should be controlled via fundamental reactive power (or displacement power factor), since it is independent of the harmonic distortion, the responsibility for it can be easily assigned, and it is easy to be measured with the use of several simple electronic measurement techniques.

Holistic energy and failure aware workload scheduling in Cloud datacenters

The global uptake of Cloud computing has attracted increased interest within both academia and industry resulting in the formation of large-scale and complex distributed systems. This has led to increased failure occurrence within computing systems that induce substantial negative impact upon system performance and task reliability perceived by users. Such systems also consume vast quantities of power, resulting in significant operational costs perceived by providers. Virtualization – a commonly deployed technology within Cloud datacenters – can enable flexible scheduling of virtual machines to maximize system reliability and energy-efficiency. However, existing work address these two objectives separately, providing limited understanding towards studying the explicit trade-offs towards dependable and energy-efficient compute infrastructure. In this paper, we propose two failure-aware energy-efficient scheduling algorithms that exploit the holistic operational characteristics of the Cloud datacenter comprising the cooling unit, computing infrastructure and server failures. By comprehensively modeling the power and failure profiles of a Cloud datacenter, we propose workload scheduling algorithms Ella-W and Ella-B, capable of reducing cooling and compute energy while minimizing the impact of system failures. A novel and overall metric is proposed that combines energy efficiency and reliability to specify the performance of various algorithms. We evaluate our algorithms against Random, MaxUtil, TASA, MTTE and OBFIT under various system conditions of failure prediction accuracy and workload intensity. Evaluation results demonstrate that Ella-W can reduce energy usage by 29.5% and improve task completion rate by 3.6%, while Ella-B reduces energy usage by 32.7% with no degradation to task completion rate.