Journal of Applied Dynamic Systems and Control
Volume:6 Issue: 1, Winter 2023

Software Reliability (SR) is a key non-operational feature measured when evaluating software quality. To enhance this feature, it is important to detect failures and mitigate them in the testing phase. SR can be increased by identifying and removing this failure from the defect data. The existing literature consists of many models/methods applicable to measuring SR, including the SR Growth Models (SRGMs). Generally, SRGMs are in two main types: parametric and non-parametric. As these models are diverse, when applying to a certain problem, the particular requirements and conditions of that problem should be taken into account. The current paper explains the fundamental concepts of reliability, then reviews the Parametric SR Growth Models (PSRGMs) and evaluates various approaches already proposed in this domain. In addition, this study investigates the SRGMs compatibility by means of a novel Parallel Real-valued Genetic Algorithm (PRGA)-based method. The results achieved under a variety of conditions for each model showed the extent of compatibility with GA.

Saving energy in various sections of the building and improving productivity in the face of energy crisis and environmental pollution can be one of the main challenges in the world and our country. In this paper, based on the necessity of controlling and minimizing energy consumption by considering the maximum optimal temperature conditions in different areas of the building, a control method based on the predictive controller is used, which takes into account the effects of the time of use for sources. The predictive controller predicts the optimal control input for future moments according to the definition of the cost function based on the ambient temperature error and the weighted expression of the control input. In this paper, the issue of optimization is thoroughly studied, evaluated, and simulated. The temperature change model in buildings and the proposed control scheme are implemented in MATLAB software and the system is simulated using the building environment. The simulation is performed under several different scenarios of time of use and conditions to show the performance of the proposed design. Based on the results of the proposed control method, the accuracy and performance of the model in different scenarios of building conditions are acceptable.

Distribution system reconfiguration occurs with switching operation. It is a simple and low-cost method that reduces system losses without adding additional equipment to the system. Radial structure, connection to the very high power transmission, microgrids, and distributed generation sources in the smart distribution network adds to the complexity of the problem. The Particle Swarm Optimization algorithm is suitable for a multi-objective and non-linear problem. This paper proposes a novel method for reconfiguration of distribution networks. The proposed method is based on Particle Swarm Optimization and Graph theory. The objective functions of the optimization problem are loss reduction, voltage profile, and Reliability improvement of the network. The proposed method has been conducted under consideration distributed generation and micro-grid on Esfarayen medium voltage distribution network. Three different scenarios without/with a distributed generation unit are investigated in this paper. The results also demonstrate that the proposed method is capable of finding the best solution.

Hashtags,i.e., terms that are prefixed by a # symbol, are vastly used in social media like Twitter, Instagram, etc. Hashtags present rich sentiment information about people's favorite topics and would make a text more accessible and popular. This paper proposed a model of the hashtag recommendation problem using an automatic summarizer using deep neural and Fuzzy logic system,as also some semantic text mining models. The final summarized text is based on Restricted Boltzmann Machine (RBM),and with the help of Extreme learning machines (ELM), improves the training data, then a fuzzy rule-based method on the sentences is done to build the final result.The experiments on two public data sets improved that the proposed model outperforms the related approaches and is more efficient improvement than previous methods.

Applying distributed energy resources (DERs) to power systems has been promoted as a promising option to meet the growing electricity demand. Despite significant economic, environmental, and resiliency benefits, integrating DER, including power electronic devices, into the existing power networks causes stability and power quality issues. Therefore, to meet stability and power quality standard limits, a sort of compensation using cost-effective and energy-efficient technologies and power electronics-based concepts is needed. This paper presents a novel configuration of compensating type custom power devices (CPDs) and fault current limiters (FCLs) for limiting balanced and unbalanced faults and improving the transient performance of distributed generation (DG) sources in a hybrid power system. Moreover, three power grid operational scenarios are addressed to reflect the impact of the type of fault and variable power generation capacity of DGs on transient stability. Four configurations are implemented using two FCLs (BFCL and SFCL) and two energy compensation devices (DVR and UPQC). The transient performance of involved DGs with and without applying proposed compensation methods is simulated. Simulation experiments were carried out using MATLAB/SIMULINK software. The simulation results indicate that UPQC- BFCL is the best solution in all scenarios that improve the transient stability of the proposed power system under both balanced and unbalanced faults.

Reducing the manufacturing costs of the products is considered of the main factors for lead-acid battery (LAB) manufacturers 'sustainability and survival in the automotive industry. Then in order to meaningfully decrease the due costs and enhance the competitive potential of the manufacturers in this field, it’s important to choose the suitable sites for recycling centers in this industry's supply chain. In the present study, data envelopment analysis (DEA) has been utilized for evaluating and choosing the recycling centers as befitting for the supply chain of LABs in the automotive industry. Pursuant to DEA and a set of criteria, as being critical for decision makers in this filed, various locations are evaluated. As this method prescribes, the locations are ranked using the maximum scores of efficiencies and after that, more appropriate centers are picked and the inappropriate ones get removed. A LAB supply chain related case study in the automotive industry has been employed for evaluating the influencing power and efficiency of proposed method and following that, a number of beneficial management results have been extracted.

In  today's world,  distributed  generation  sources through  grid-connected  inverters  perform  other  roles in addition to generating  the  required  load’s  power. Among these roles, it is possible to perform the function of the inverter  as a virtual synchronous machine in the direction of  frequency stability in the presence of  a  fault, supporting  the network  in  the condition of  voltage  fault  caused  by the presence of  a  fault, and so on. It can be mentioned that doing each of these things, It requires changes in the control system, hardware or new topologies. Supporting the grid  in voltage sag conditions, that  is called  Low Voltage - Ride Through for an  inverter,  has  its  own standards and  requirements, so meeting them  requires new research and designs. In this paper, first, a review of the past work is done and then by presenting a new method, the inverter performance is improved in these conditions. In the presented method, the DC bus ripple will be very low and thus the high order harmonics will be reduced. Also, this  ripple is reduced  by using a two-way converter  and  suitable control of  the  inverter, unlike some of  the presented  methods that  transfer  it on the reactive power  produced or on the voltage of the solar cell side. Furthermore, in these cases, it is tried to reduce the DC bus ripple to the minimum value of the capacitive capacity of this bus. To evaluate the presented method, simulations have been performed in Simulink / MATLAB software and the results have been presented.

Closed-loop identification of multi-rate system with unknown parameters, that including prevalent non-uniform sampling data, is considered. The purpose is to identify a multi-rate closed loop model to approximate the parameters varying system. As far as the research has been done, the identification of multi-rate closed loop model with unknown parameters by using the expectation-maximization algorithm has not been done. To address this challenge, the two-stage method and expectation-maximization algorithm are applied in this paper to identify unknown parameters of system. In this case, by introducing the hidden variable, an EM is utilized to estimate the unknown model parameters. And also, it will be demonstrated that, to estimate of system parameters, Instead of the point estimate of the time variable, the full probability distribution of the time variable estimate is required. The performance of this procedure represents by simulation, and obtain consequences affirm that method has high precision and also has a high convergence speed. These simulations express that the performance of this algorithm is good, as the identified parameters accede the true parameters after several iterations. The Monte Carlo simulation with different noise realizations at SNR=26dB and SNR=46 dB are performed for showing the effectiveness of mentioned algorithm.

The rotor side converter (RSC) controls active and receptive powers at the stator side of DFIG. The turbine current in axis d (id) is modeled utilizing the parameters of a 660 kW wind turbine. The appraised wind speed within the specified wind turbine is 15 (m/s) and the wind cut-in and cut-out speed is 4 (m/s) and 25 (m/s) individually. In this paper, we utilized tuning the hybrid fuzzy PI controller based on the Mamdani inference system and selecting the triangular membership functions, and compare with three strategies. The proposed strategy is way better than a conventional PID controller, fuzzy logic, and hybrid fuzzy-PI controllers with Gaussian membership functions for active and reactive power control. Simulation and comparison think about are utilized to appear the effectiveness of the proposed hybrid fuzzy-PI controllers with triangular membership functions and fewer errors, ripple, and overshoot have been gotten.

Today, different markets and economic sectors are directly or indirectly affected by gold price; thus its prediction is a big challenge for both investors and researchers. On the other hand, the nonstationary and nonlinear patterns of gold price data cause the prediction process even more complex. To address this challenge, a hybrid model was developed in this paper to predict gold price, with a concentration on enhancing accuracy through considering the gold price data characteristics. To do this, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Gated recurrent units (GRU) were used to deal with the nonstationary and nonlinear nature of the gold price data. The former was first applied to the decomposition of time-series data of gold price into a number of components. Then, GRU was applied to the prediction of the components. To end with, all the components’ prediction results were summed up to attain the final prediction result. The efficiency of the developed model was evaluated using real-world gold data, which confirmed its superiority over the standard methods used for comparison.

A transmission line refers to an extended conductor that is specifically designed, often bundled together, to facilitate the transportation of a substantial quantity of electrical power at exceptionally high voltages from one station to another, accommodating the fluctuation in voltage levels. Insulation serves as a barrier between the conducting parts of a cable and the surrounding environment, preventing direct contact and minimizing the risk of electrical shocks to personnel. It helps maintain a safe operating environment for workers and the general public. In addition, Insulation helps maintain the desired voltage levels by preventing leakage or losses along the cable. It ensures that the power is efficiently transmitted without significant voltage drop, which is crucial for the proper functioning of electrical equipment and appliances connected to the power system. The necessity of insulation brings the paramount significance of insulation testing in transmission lines. This paper briefly presents the insulation testing techniques in transmission lines and summarizes the advantages and disadvantages of each method.

In this paper, the nonlinear modeling of ship roll angle movement in the presence of disturbances and its control through an adaptive method is investigated. One of the important issues in the control of vessels is to have a model of non-linear behavior, by using the dynamic equations of the ship's motion, the ship's roll motion equations is obtained in Third order dynamic equations. Another innovation presented in this paper is the design of a controller based on the input-output feedback linearization method and according to the proportional-integrator and derivative controller, which coefficients are adjusted by fuzzy logic to reject and reduce the sinusoidal disturbance effects. Therefore, another innovation of this paper is to minimize the effects of sinusoidal disturbance fluctuations in the roll angle of the ship in addition to achieving a zero degree roll angle. The results of the proposed method have been evaluated in MATLAB software and despite sinusoidal disturbances with a certain range, the proposed method has made the roll angle stable within an acceptable range and its effects on the roll angle of the ship have been eliminated.