International Journal of Industrial Electronics, Control and Optimization
Volume:5 Issue: 1, Winter 2022

Emergency demand response (EDR) and under frequency load shedding (UFLS) are used as two separate methods for frequency restoration of power system after the usual methods of frequency control are not able to maintain the frequency stability of the system. This paper proposes the optimized emergency demand side management (OEDSM) method which improves the performance of previous methods by integrating UFLS and EDR methods along with introducing new critical status detection and optimization modules. The advantages of the proposed method are simultaneous operation of EDR and UFLS processes, the high speed of critical condition detection using the proposed emergency index, increasing the speed of the algorithm with parallel operation of modules, and optimal load shedding by providing a separate optimization module. In order to validate and evaluate the performance of the proposed method, the power system was tested under different scenarios using DIgSILENT software, which the extracted results indicate better performance of the proposed method in frequency restoration, as well as improvement of utilization and power quality of the system compared to previous methods.

This paper presents a novel event-triggered predictive control (ETPC) approach for the stabilization of discrete-time output-feedback networked control systems (NCSs). The studied NCS is considered to be subject to both random external input and output disturbances, and network imperfections including random communication delay, random packet dropout, packet disorder, limitation of network bandwidth, and network resources. In the proposed algorithm, an observer-based event detector is designed for reducing the number of sent packets through the communication network using the estimated system states by the Luenberger observer. In this way, the system’s energy resources are saved and network-induced effects are skipped. A switched predictive controller with multiple gains are used to compensate for network-induced effects. Controller gains are designed compatible with different possible values of delays and packet dropouts. A novel augmented representation of the state-space equations of the system is derived to design observer gain and controller gains. The asymptotic stability of the system is guaranteed by designing the observer and controller based on the Lyapunov function through solving linear matrix inequalities (LMIs). Putting all the aforementioned points together has made the whole framework presented in this paper a comprehensive one. The effectiveness of the proposed approach is demonstrated by comparative simulation results.

This paper presents a new algorithm for the identification of a specific class of hybrid systems. Hybrid System identification is a challenging problem since it involves the estimation of discrete and continuous states simultaneously. Using the method known as product of errors, this problem could be formulated such that, the identification of continuous state to be independent of discrete state estimation. We propose a new iterative weighted least squares algorithm (IWLS) for the identification of switched auto regressive exogenous systems (SARX). In this method, the parameters of only one subsystem are updated at each iteration while the parameters of the other subsystems are assumed known. In the method, all four main parameters of hybrid systems, namely Subsystem degrees, Number of subsystems, Unknown parameters vector and switching signal are estimated. Simulation results shows that our proposed method has a good performance in identifying the subsystems parameters and switching signal. Also, the superiority of our algorithm is shown by modeling of two SARX systems.

Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator models or methods can be improved by correct application of data filtering or feature weighting techniques. Numerous models have also been proposed based on machine learning methods for data modeling. This study proposes a new model consisted of data filtering and feature weighting techniques to improve the estimation accuracy in the final step of data modeling. The model proposed in this study consists of three layers. Tools and techniques in the first and second layers of the proposed model select the most effective features and weight features with the help of LSA (Lightning Search Algorithm). By combining LSA and an artificial neural network in the third layer of the model, an estimator model is developed from the first and second layers, significantly improving the final estimation accuracy. The upper layers of this model filter out and analyze data of lower layers. This arrangement significantly increased the accuracy of final estimation. Three datasets of real projects were used to evaluate the accuracy of proposed model, and the results were compared with those obtained from different methods. The results were compared based on performance criteria, indicating that the proposed model effectively improved the estimation accuracy.

Nowadays time series analysis is an important challenge in engineering problems. In this paper, we proposed the Comprehensive Learning Polynomial Autoregressive Model (CLPAR) predict linear and nonlinear time series. The presented model is based on the autoregressive (AR) model but developed in a polynomial aspect to make it more robust and accurate. This model predicts future values by learning the weights of the weighted sum of the polynomial combination of previous data. The learning process for the hyperparameters and properties of the model in the training phase is performed by the metaheuristic optimization method. Using this model, we can predict nonlinear time series as well as linear time series. The intended method was implemented on eight standard stationary and non-stationary large-scale real-world datasets. This method outperforms the state-of-the-art methods that use deep learning in seven time series and has better results compared to all other methods in six datasets. Experimental results show the advantage of the model accuracy over other compared methods on the various prediction tasks based on root mean square error (RMSE).

In this paper, two new developed quasi Z-source inverters (QZSI) based on two new diode-cells are proposed. The proposed inverters are called continuous diode assisted quasi Z-source inverter (CDQAZSI) and discontinuous diode assisted quasi Z-source inverter (DDQAZSI). These inverters are thoroughly examined in three modes: two-cell QZSI, three-cell QZSI and n-cell QZSI which is called developed QZSI. Additionally, the equations of the diodes’ voltage and current, capacitors’ voltage stress, boost factor, inductor currents and dc-link current are computed. All equations are given for the proposed generalized topologies. The major advantage of these inverters is the potential of deriving desired boost factor value devoid of extra elements. Furthermore, increasing the quantity of utilized diode-cells in the proposed QZSI reduces the values of dc-link current, inductor current and diode current. Lastly, to verify the performance of the proposed topologies, practicality of the proposed QZSI is authenticated by simulation and experimental results through EMTDC/PSCAD software program and laboratory prototype.

In this paper, a two-level stacking technique with feature selection is used to detect power theft. The first level of this technique uses base classifiers such as support vector machine (SVM), naïve Bayes (NB), and AdaBoost selected by evaluating the F-score and diversity criteria. The appropriate features of the base classifiers are selected using a new feature selection algorithm based on the cheetah optimization algorithm (CHOA). This algorithm ensures diversification and intensification in each step of running by adjusting the Attention parameter of the cheetahs. In the second level, a single-layer perceptron (SLP) model is used to obtain the weight of the base classifiers and combine their predictions. The proposed framework is evaluated on the Irish Social Science Data Archive (ISSDA) dataset, and MATLAB R2020b is used for simulation and evaluation. The results of the accuracy, recall, precision, and F-score, specificity, and receiver operating characteristic (ROC) criteria indicated the high efficiency of the proposed framework in detecting power theft.

The paper introduces a novel adaptive fuzzy fractional-order (FO) fast terminal sliding mode control procedure for a class of nonlinear systems in the presence of uncertainties and external disturbances. For this purpose, firstly, using fractional calculus, a new FO nonlinear sliding surface is proposed and then, the corresponding FO fast terminal sliding mode controller (FOFTSMC) is designed to satisfy the sliding condition in finite time. Next, to eliminate the chattering phenomenon, a fuzzy system is constructed to design a continuous switching control law. The finite-time stability of the proposed adaptive fuzzy FOFTSMC (AFFOFTSMC) is proved using the concept of Lyapunov stability theorem. Finally, to illustrate the effectiveness of the proposed AFFOFTSMC, three examples are given as case studies. The numerical simulation results confirm the superiority of the proposed controller, which are the better robust performance, faster convergence, finite-time stability of the closed-loop control system, and a chattering free control effort compared to other mentioned control methods.

Electric spring (ES) is a new technology that can be used for fast demand-side management to balance the power between generation and consumption in smart grids. In this paper, the back-to-back structure of electric spring is controlled to operate simultaneously as electric spring and shunt active power filter (shunt-APF). That means the series part of the back-to-back electric spring regulates the critical load voltage and applies the demand-side management and the parallel part operates as a shunt active power filter capable of power factor correction and current harmonic compensation. In the proposed structure, due to harmonic compensation and power factor improvement by the parallel inverter, the output power capacity of the electric spring is increased compared with the first and second generation of electric springs (ES-1 and ES-2), and the performance is improved in critical conditions. Additionally, to improve the robustness of the control system against uncertainties in the grid system, two fuzzy logic controllers are designed to control the voltage of the electric spring and the DC link voltage. The theoretical analysis is validated by simulation results using MATLAB/SIMULINK software.

Quantum-dot Cellular Automata (QCA) is a new technology for eliminating some of the problems of existing technologies such as CMOS. Some of the key advantages of QCA are an intersection of wires in the same plane, high speed, small area, power consumption, complexity and low cost. Employing a three-input majority gate, a five-input majority gate and three logic gates, this study presents a full-adder circuit in a single layer which for higher efficiency and avoiding much complexity and based on the function of the intended full-adder circuit, the five-input gate is proposed. The proposed full-adder circuit and the proposed ripple adder circuit are compared with previous designs regarding complexity, number of cells, and area and the results are reported. Moreover, proposed circuits’ power consumption has been calculated by using QCApro. These results indicate that the proposed full adder design in comparison with previous similar design achieved 36%, 20% and 4.4% reduction in the number of cells, latency and power consumption, respectively.