A Novel Approach for Estimating and Analyzing the Environmental Parameters: A Case Study for Renewable Energy Prospective
Abstract
On the one end, humans are struggling to reduce carbon footprints, while on the other, energy demand is increasing every day. Humans are looking for renewable energy sources to handle both problems. Static as well as dynamic systems are being built on the principle of self-sufficiency. Moreover, a realistic estimation of environmental parameters is vital for both ground-based applications and space-based applications. A realistic estimate of environmental parameters is crucial in the design and development of aerospace systems and other ground structures and systems. The chapter describes a novel and robust approach for estimating power and energy estimation and other environmental parameters. It starts with describing the environment module in which modeling of operational parameters, viz., temperature, pressure, and ambient wind speed, is explained. The environment module consists of models for atmosphere, wind, and solar radiation. A robust numerical-based method to calculate total solar energy generation from any shape is described. Operating temperature is vital to solar array performance. Therefore, effect of operating temperature on solar efficiency is also discussed at the end.KeywordsSolar energyWind modelHWM14
... Standard temperature variation with altitude[85] Variation of wind with altitude[79] ...
In recent years, there has been an increasing interest in the research and development of high-altitude airships. These systems
provide a suspended platform using buoyancy at 17–25 km altitude. They have an enormous yet untapped potential for
telecommunication, broadcasting relays, regional navigation, scientific exploration, and many other potential applications
in an affordable fashion. Consequently, a large volume of literature has emerged covering the various aspects of design and
development. This paper presents a state-of-the-art review of research and development in high-altitude airships. It covers
the critical area, e.g., worldwide involvement, shape optimization, thermal analysis, design studies, etc. Realistic prediction
of thermal behavior is essential to determine its effect on energy generation and altitude control. The paper discusses the
various thermal investigations and numerical modeling approaches to study thermal performance. The envelope is the largest
entity and directly contributes to the drag, hence energy demand. The energy generated also depends on the layout of the solar
array. Therefore, aerodynamic shape optimization and solar array layout optimization are also studied. The efficiency of the
solar array degrades as the temperature rises. The thermal model must be coupled to the design technique to compensate for
the power loss. Therefore, various design optimization methods are critically analyzed with and without thermal consideration.
Thermal behavior has been captured well in the existing literature through analytic, numerical, and machine-learning
techniques. However, more experimental investigations are needed to validate these studies under generic conditions. The
developed design techniques can design and optimize the airships conceptually only. Therefore, a comprehensive method
must be developed based on a robust MDO framework. The paper highlights the challenges in designing and developing
these systems and research gaps for future investigations.
This paper describes closed loop control of Diesel Generator (DG) supplying power to a Base Transceiver (BTS) load of a telecommunication tower, which is DC in nature. Detail modeling of Diesel Generator set has been presented.. The stability analysis of governor and excitation system has been carried out in frequency domain. When DG source is connected to the BTS load bus through bridge rectifier, the output waveform distorted due to high odd harmonics content. To solve this problem a unity power factor rectifier has been designed. For the control operation of this UPFC rectifier, double loop mode control method has been used and results are presented. The complete system has been simulated in MATLAB-SIMULINK environment.
Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.
In this paper, an optimum sizing and estimation of a hybrid solar photovoltaic plant is done in which battery storage is designed for four hours and rest requirement is fulfilled by AC mains. The system was designed for a University library in Uttar Pradesh, India to cover basic electricity needs when grid power is not available. Lighting and Fans were considered as basic electricity needs for each place. The photovoltaic array consists of 120 photovoltaic modules of 250 Wp each, for a combined 30kWp total installed power. In the University the overall requirement from 9: 00am to 04: 00pm is fulfilled by AC mains. At any instant when power from the grid is not available (up to 4 hrs.) the basic requirement such as fans and tube lights in the reading halls of the library are switched to SPV system either manually or automatically. Using this sizing and estimation study and then implementation of it, the requirement of overall basic load ie 30kWp is fulfilled. Also during the day, the basic load may be satisfied directly from the photovoltaic generator through a multilevel inverter (30kVA), while any energy surplus is directed to the battery storage. There is a battery bank design of 60 batteries of 240Ah. The batteries cover basic load peaks in absence of grid power. The paper presents sizing, estimation and payback for the complete system.
Aged demographics and doctor shortages have helped to push the growing need for intelligent healthcare systems around the world. These systems have recently benefitted from the development of the Internet of Things (IoT), big data and machine learning. However, these progresses result in vast volumes of data being generated and the processing of health data is a major problem. These data possess a variety of dynamic properties such as high dimensionality, irregularity and sparsity, making it difficult to enforce efficient processing. Big data analysis meets these challenges. In this paper, we suggest a groundbreaking analysis system for large-scale healthcare data obtained either from wearable IoT devices or from archived medical pictures. The proposed procedure would effectively resolve the problem of data heterogeneity through middleware between heterogeneous data sources and Map Reduce Hadoop clusters. The proposed structure also permits the use of fog computing and cloud systems to deal with problems faced by online and offline data processing, data storage, and data classification. It also ensures a comprehensive and secure awareness of medical data for patients.
In this article we design tilt integral derivative controller with filter (TIDNC) for D.C Motor speed control. TIDN parameters are tuned by using flower pollinating algorithm (FPA). By using tuning parameters of TIDNC, we obtain desired step response. Thereafter comparative analysis is carried out with FPA based proportional integral derivative controller (PIDC), fractional order PIDC (FOPIDC) and tilt integral derivative controller (TIDC) in terms of desired step response and performance indices (PIs) like integral of square error (ISE), integral of time weighted absolute error (ITAE), integral of time weighted square error (ITSE) and integral of absolute error (IAE). After that, response of a D.C Motor with FPA is compared with other metaheuristic algorithms like particle swarm optimization (PSO) and firefly algorithms (FA).
The fast globalization of renewable energy-based technologies has enabled its wide speared utilization as well. This has shaped a new prospect of operation in the modern electricity system. But, its dependency on environmental factors leads to an uncertain scenario in the day-ahead electricity market. During this period, compromises are made in the genuine process of expenditure and resources of the producers to offset the capacity that decreases the profits for the producers. In general, a significant variety of scenarios need to be taken into account when describing uncertainty, thereby necessitating the need for techniques of scenario reduction. Therefore, to manage the intractable effects of solar radiation and wind speed instability, the function of the Beta and Weibull distribution of probability is implemented, respectively, and scenarios are minimized using forward-reduction algorithms. Besides, an underestimation and overestimation of the cost function are used to calculate the deviation of renewable influence. Thus, this paper is suggesting a valuable bidding strategy to maximize the remuneration of electricity producers in the presence of rival competitors and the instability of solar and wind energy. This problem has been prepared by taking the benchmark IEEE 30-bus network with and without renewable energy sources, and this problem has been solved by using the Gravitational Search Algorithm. The observations of the outcome demonstrate the appropriateness of the projected bid strategy in the presence of volatility of renewable energy.
This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.
Global solar radiation is variable in nature, hence its forecasting is very important. This paper aims to predict the global solar radiations using real experimental data collected over time by developing time series based neural networks in MATLAB. In this research work three models are developed, one for the daily prediction of global solar radiations and two for the hourly (with and without night times) prediction of the global solar radiations. The results obtained are evaluated using statistical analysis which confirmed that nonlinear autoregressive models are a good choice for predicting the global solar radiations. The regression coefficient was found high in the case of hourly prediction (including night hours) followed by hourly prediction (excluding night hours) and daily prediction (after averaging) respectively. The value of the best validation performance for hourly prediction (including night hours) was found 0.060887 with a regression coefficient of 0.96481. To further validate the applicability of the proposed algorithm, the model was compared with four time-series models developed in Waikato Environment for Knowledge Analysis. The results confirmed the high efficiency of our proposed models among others, based on the performance parameters root mean square error and mean Absolute error. The root mean square error for our proposed model came 1.28 which is the lowest as compared to other time series models. Also, the accuracy of the hourly prediction model was found higher as compared to daily prediction models. Further, the critical analysis of our method with various available methods in the literature is also discussed.
The Maximum power point tracking (MPPT) command makes it possible to find the optimal operating point of the wind generator following variations in the wind. Its principle is based on the automatic variation of the power coefficient to continuously maximize the power of the wind generator. This work is an attempt to study and discuss four most popular types of MPPT techniques such as: Perturbation and Observation (P&O), optimal torque (OT), On-Off control, and tip speed ratio (TSR). The Matlab-Simulink environment is used to analyse and then interpret the simulation results of these algorithms, and therefore show the performance and limitations of each algorithm. The comparison shows the efficiency and the superiority of the OT technique compared to the other MPPTs studied.
Photovoltaic generation has arisen as a solution for the present energy challenge. However, power obtained through solar technologies has a strong correlation with certain meteorological variables such as solar irradiation, wind speed or ambient temperature. As a consequence, small changes in these variables can produce unexpected deviations in energy production. Although many research articles have been published in the last few years proposing different models for predicting these parameters, the vast majority of them do not consider spatiotemporal parameters. Hence, this paper presents a new solar irradiation forecaster which combines the advantages of machine learning and the optimisation of both spatial and temporal parameters in order to predict solar irradiation 10 minutes ahead. A validation step demonstrated that the deviation between the actual and forecasted solar irradiation was lower than 4% in 82.95% of the examined days. With regard to the error metrics, the root mean square error was 50.80 W/m², an improvement of 11.27% compared with the persistence model, which was used as a benchmark. The results indicate that the developed forecaster can be integrated into photovoltaic generators’ to predict their output power, thus promoting their inclusion in the main power network.