Journal of Applied Dynamic Systems and Control
Volume:4 Issue: 1, Winter and Spring 2021

Solar energy is a renewable source that can be used for a wide range of applications especially for power generation. In this paper, thermoeconomic analysis is performed for a steam Rankine cycle whereas solar energy is used for producing steam using parabolic trough solar collectors (PTSC). For this purpose, firstly the modeling of the solar collectors is performed. Then the mass and energy equations are solved to obtain the thermodynamic state of each point. The cost of each component is calculated using the purchase costs of the system components as a function of thermodynamic parameters. The effect of active parameters such as inlet temperature and pressure of steam turbine, solar irradiation intensity, pinch point, interest rate,  and lifetime are investigated on the number of solar collectors in rows, the total aperture area, the thermal efficiency and the average cost of electricity. The results shows that the average cost of electricity decreases with increasing the inlet temperature and pressure of steam turbine. The average cost of electricity is calculated to be less than 3 cent/kWh while it is normally more than 10 cent/kWh.

This paper studies structural properties of bandwidth limited Industrial Networked Control Systems in which the number of simultaneous network accesses is limited. Conventional control theory assumes that the measured output is available as a vector at each time instant and the input vector can be applied simultaneously to the actuator. However, when the system is bandwidth limited, it is not possible to instantly read all of the sensors and nor it is possible to apply the whole input (control) vector to the actuator. As a result, a system which is controllable (reconstructible) for its own, may not preserve this property when the communication medium is bandwidth limited. The communication medium therefore changes the overall system structural properties. This is of utmost importance since existing control and estimation algorithms are based on the assumptions that the whole output vector (input vector) is read from sensors (applied to actuators) instantly. We assume that communication medium is shared among both sensors and actuators. It is shown that controllability and reconstructibility analysis could not be performed independently (as inherently assumed or ignored by previous works in this field). For the special case of equidistant sequences with separate read/write cycles, we prove that controllability and reconstructibility analysis’s can be done independently if a specific procedure is followed. Examples are included to clarify the results.

Wind energy is one of the extra ordinary sources of renewable energy due to its clean character and free availability. We used a static synchronous compensator (STATCOM) to supply the reactive power and voltage support to a wind farm equipped with Induction Generators(IGs) and power system during grid faults. Also, the STATCOM is used in steady state to supply reactive power in power system.  Simulation results show that the wind turbine bus voltage is quickly reestablished shortly after the fault has been cleared, and therefore, the wind turbine remains in service during the presence of STATCOM. The results prove the effectiveness of the proposed STATCOM controller in terms of fast damping the power system oscillations and restoring the power system stability. The choice of this study is justified by simulation in MATLAB.

In this paper, a closed-loop supply chain is modeled to obtain the best allocation and location of retailers including production centers, retailers' centers, probabilistic customers, collection and disposal centers. In this study, by considering electric conversion of CO2 to O2 in vehicles, the amount of environmental pollution is minimized. Furthermore, two strategies are considered to find the best places for retailers by focusing on: 1- the type of expected movement (Rectangular, Euclidean, Euclidean Square, and Chebyshev); 2- expected coverage (distance and time). To this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares strategies 1 and 2 and selects the best competitor. Based on the selected strategy, the best allocation is made by employing a heuristic algorithm and the locations of the best retailers are determined. As the proposed model is NP-hard in its nature of the problem, a meta-heuristic, namely, a non-dominated sorting genetic algorithm is employed for the solution process. Eventually, to authenticate and confirm the effectiveness of the suggested model, a numerical example is given and solved utilizing optimization software, and the results are analyzed.

According to the crucial role of gas turbines in electricity production without significant harmful effects on the environment, this paper is aimed at the modeling and simulation of a particular type of these systems known as V94.2. Gas turbine is an instrument for power generation, which is capable of producing a vast amount of energy by considering its size and weight. Despite all the advantages and applications of gas turbines, the use of these systems is not free of difficulties because of their remote controls in such a way that it is estimated that about a quarter of turbine price is spent on its launching. To tackle this problem, a mathematical model has been proposed for V94.2 gas turbines as a result of the review of the pieces of research done on the modelling of gas turbines in the past few years and on the basis of Rowen model. In the following, the capability of fuzzy type-2 controllers has been sed to ensure the system stability. The results of the simulation in MATLAB software clearly shows that the output variables of V94.2 gas turbine reach a specific situation and place after applying the inputs at the right time.

The directional sensor networks (DSNs) are mainly focused to prolong the network lifetime and to optimize the energy consumption of sensors. The number of sensors deployed in an environment is much higher than those required for providing the coverage; therefore, the energy-aware methods are needed to select the sensors. Coverage is considered a major problem in DSNs and is a criterion for quality of service (QOS).In this regard, the sensor scheduling method has been discussed by researchers to prolong the sensor lifetime in a network. The present paper proposes an NSGAII-based algorithm to solve the sensors 'scheduling. This paper aimed at finding a practical solution in solving the multi-objective problems by using the multi-objective evolutionary algorithm method. There are two parameters presented for evaluating the solutions, including the number of sensors, the target coverage. To confirm the high performance of the proposed algorithm, it was compared with the recently presented algorithm. According to the simulation findings, the algorithm had better results in the comparison parameters.

A wireless sensor network (W SN) includes a series of nods , each of them containing some sensors which have a role in gathering data about the circuit in which they are distributed. Sensor networks enjoy variety of uses ; that is why they are attracted by many countries . In a wireless sensor network , few nods have already known their position which are called anchor node ; other nods must calculate their position accordingly . In this essay “ received signal strength indicator ” and “ Nero-Fuzzy ” are used in order to calculate unspecified - position nods ’ coordinate . Each unspecified - position node must know the position of some nearing anchor nodes . The more the nodes are , the more precise the coordinate is . In this essay , unspecified node ’ s coordinate is estimated according to two or more anchor nodes using logic fuzzy . Since the range covered by sensor networks is broad , finding all anchor nodes is a time - consuming task and needs a lot of memory . Therefore , distance limit between anchor nodes and unspecified nodes is considered . The area around anchor nodes and unspecified nodes is divided reticulated , and then , the unspecified node ’ s coordinate is estimated according to logic fuzzy rules . The estimated average error rate for 120 nodes using this method equals to 2 % of radio range , which is minimal in comparison to APS algorithm .

This article proposes the new VAD (Voice Activity Detection) method was made using Spectrogram Domain (Spectro-Temporal Response Field) space based on sparse representation. Spectrogram Domain components have two dimensions of time and frequency. On the other hand, using sparse representation in learning dictionaries of speech and noise and updating dictionaries, causes better separation of speech and noise segments. In this algorithm, using auditory spectrogram and sparse representation, an updating dictionaries with different atom sizes and K-SVD (k-means clustering method) and NMF (non-negative matrix factorization) learning methods were constructed and the results indicate that this method works well. For example, the proposed VAD performance was obtained in SNRs greater than 0dB is more than 92.71% and 91.21% in White noise and Car noise respectively, which shows the good performance of the proposed VAD compared to other methods. By comparing the NDS and MSC evaluation parameters with other methods, the results show better performance of the proposed method.

One of the most appropriate and efficient methods for evaluating the performance of homogenous decision-making units (DMU) is data envelopment analysis (DEA). Traditional DEA models are only able to evaluate DMUs with deterministic inputs and outputs, while in real-world problems, data are usually uncertain. So far, various approaches have been introduced to overcome the uncertainty of data. In this paper, two robust DEA models is presented to evaluate the performance of systems with continuous uncertain data under constant return to scale (CRS) and variable return to scale (VRS) conditions. The main advantage of the proposed robust DEA models over the previous robust DEA models is that they are able to formulate uncertainty in both input and output data. Moreover, these models are also developed directly on basic traditional DEA models (not alternative models). To demonstrate the applicability of two developed robust models, a numerical example is presented and the efficacy of models is exhibited.

In the study of a villa residential unit with an area of 100​​(m2), first, the energies required for hot water consumption and power consumption of public equipment used are calculated. The cooling and heating load was then calculated using carrier software, and the required electrical power is calculated with the COP coefficient. The maximum cooling load required on February 6 at 16 hours is equal to 6250 (W), and the electrical power required by Photovoltaic panels are calculated according to the amount of radiation and consumption during operation and according to the service period of one year, which is equal to 7545 (W). PEM fuel cells are used as a source of energy storage, and the maximum energy stored for 4 hours is equal to 52.9 (kWh), and the maximum of 1.59 (kg) of hydrogen gas is produced during one day. According to the electricity cost, the global average of 0.14 (kW/h) will be achieved after 9.5 years.

The Directional Sensor Networks (DSNs) have recently drawn considerable attention with respect to their extensive applications in various situations. In this regard, covering a set of targets in a specific region while maximizing network lifetime is considered as a major problem related to the DSN, which is resulted from limitation in sensing angle and battery power of directional sensors. The problem gets more challenging when the targets have different coverage quality requirements. In the present study, this problem is referred to as Priority-based Target Coverage (PTC) that has been proved to be an NP-complete problem. In this regard, a genetic-based algorithm along with a repair operator is developed, which is able to select a proper subset of directional sensors for providing the coverage quality requirements for all targets. In order to evaluate the performance of the proposed algorithm, several experiments were performed and the results were compared to those of another algorithms already introduced to literature.

This work seeks to investigate the possibilities of using hydrogen as a fuel in medium-large power generation plants. Possible strategies for converting fossil sources, particularly coal and natural gas, into electricity, using hydrogen-powered cycles, are examined, trying to quantify the energy efficiencies of thermodynamic cycles and the entire conversion process. It is observed how, by analyzing the entire conversion process from a thermodynamic point of view and taking as a reference possible thermodynamic cycles proposed in the literature (open H2-air and closed H2-O2 cycles), the overall efficiencies of the conversion process, from the source fossil to electricity passing through hydrogen as an energy vector, are overall lower than those obtainable through conventional systems used today, even assuming the use of advanced technologies (turbines with operating temperatures above 1500 ° C) not yet available on the market. In particular, the overall efficiencies of the conversion process are less than 40% in the case of coal and 50% in the case of natural gas. The road to using hydrogen, therefore, although interesting, is still long and difficult.