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This paper deals with the application of Thyristor Controlled Series Reactor (TCSR) FACTS controller for mitigation of congestion in one or more lines of a power system. When generation is close to thermal limit then one or more healthy lines may face congestion, if some line is out of operation due to fault or maintenance. For a given voltage difference between two end buses of a congested line, the line current can be reduced to normal value or congestion can be mitigated by increasing the reactance of that line. The reactance of the line can be suitably increased with the help of series TCSR FACTS Controller. The reactance of the Controller can be suitably increased by firing the SCRs at the appropriate firing angles. TCSR FACTS Controller has been implemented to mitigate the congestion in IEEE 5-Bus and Modified IEEE 14-Bus Test systems. The TCSR FACTS Controller is successful in mitigation of congestion of any level.

This paper concerns the development of a sensitivity method for the optimization of multi-machine system performance by which the estimated values of the parameter settings are optimal. This method does not renujre a mathematical description of the system under control except for the controllers which need to be identified. The method is suitable for implementation on model system and on real control systems in service. Also it is capable of producing a control law which is derived only from the machine signals independent on other machine signals.

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

This paper proposes a new controller for stability and voltage improvement of power networks equipped by wind turbine which optimize the dynamical response of power systems performances (voltage and transient stability) after fault. The proposed control algorithm based on new Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to enhance the mechanical power of the synchronous machine into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better performances over wide range of disturbances for three considered scenarios studied.

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT) and tools simultaneous scheduling in a multi-machine flexible manufacturing system (FMS) to minimize makespan ...

Turbine generator shaft torsional oscillations is an interdisciplinary power system dynamic problem as it involves mechanical and electrical engineering. Torsional oscillations occur in the mechanical for electrical reasons. Torsional oscillations cause fatigue life expenditure of the mechanical shaft system. There have been great motivations to mitigate the shaft torsional oscillations especially when unrestricted high speed reclosure (HSR) is utilized on the overhead transmission lines emanating from a generation station. Mitigation of torsional oscillation compromises between the use of HSR and preserving the mechanical integrity of the involved turbine generator set. Therefore, braking resistor (BR) controlled by fuzzy logic controller is presented in this paper as a low cost, reliable mean for torsional oscillations mitigation. BR was first utilized for the system transient stability enhancement. It serves as an extra load capable of dissipating extra generated power in case of system severe faults close to a generation station consequently prevents generator pole slipping conditions. IEEE 3 machine 9 bus system is adopted in this paper to test the effects of BR on shaft torsional oscillations mitigation in interconnected power system. Comparative simulation studies between the unsuccessful reclosure with and without fuzzy controlled BR prove the effectiveness of the scheme for mitigation of torsional oscillations significantly.

This paper proposes a novel nonlinear decentralized control method for a class of multi‐machine power systems. The aim is to construct a suitable decentralized feedback control law so as t...

The execution of production processes in real manufacturing systems is associated with the occurrence of numerous disruptions, which predominantly revolve around technological machine failure. Therefore, various maintenance strategies are being developed, many of which tend to emphasise effective preventive measures, such as the Time-Based Maintenance (TBM) discussed in this paper. Specifically, this publication presents the time-based machine failure prediction algorithm for the multi-machine manufacturing environment. The Introduction section outlines the body of knowledge related to typical strategies applied in maintenance. The next part describes an approach to failure prediction that treats processing times as makespan and is followed by highlighting the key role of historical data in machine failure management, in the subsequent section. Finally, the proposed time-based machine failure prediction algorithm is presented and tested by means of a two-step verification, which confirms its effectiveness and further practical implementation

As part of ITPA-Integrated Operational Scenario activities, this contribution reviews recent experimental characterizations of radio-frequency (RF)-induced scrape-off layer (SOL) modifications of various tokamaks worldwide and of the LArge Plasma Device at UCLA. The phenomenology, as observed using a large variety of measurement techniques, is consistent with expectations from RF sheath rectification. Emphasis is then put on the complex three-dimensional (3D) spatial patterns of the RF–SOL interaction, in relation to the magnetic topology and the spatial distribution of RF currents over the metallic structures surrounding the RF wave launchers. Dependence on the local plasma parameters in the antenna vicinity is also briefly addressed. The final part discusses implications for future devices.

This paper deals with simultaneous scheduling of machines and tools with alternate machines in a multi machine flexible manufacturing system (FMS) to minimize makespan (MS). Only one copy of each type of tools is made available due to economic restrictions and the tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to select machines from alternate machines for job-operations, allocation of tools to job-operations and job-operations’ sequencing on machines for MS minimization. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines and tools with alternate machines and a symbiotic organisms search algorithm (SOSA) built on the symbiotic interaction strategies that organisms employ to continue to exist in the ecosystem for solving the scheduling of machines and tools with alternate machines. The results have been tabulated, analyzed. It is observed that there is a reduction in MS when the alternate machines are considered for job-operation.

The paper studies the robust control for multi-machine power systems under parametric uncertainties, perturbed mechanical input power and unknown variable communication time-delay. Only relative speed of each electrical generator is available for measurement. The theoretical investigations show that the proposed algorithm synchronizes the multi-machine power system with the required accuracy in the normal mode and under symmetrical 3-phase short circuit faults which occur on ransmission lines. Numerical investigations illustrate the efficiency of the proposed scheme for the three machines and the New England Power system benchmark.

This paper lays down a formal framework for simultaneous scheduling of machines, automated-guided vehicles (AGVs) and tools in a multi-machine flexible manufacturing system (FMS) while acco...

Manifold consensus in the multi-objective sense of frequency synchronism, torque angle specification, power swing reduction, and inter-generator dynamics decoupling in multi-machine power systems i...

The role of Power System Stabilizer (PSS) in the power system is to provide necessary damping torque to the system in order to suppress the oscillations caused by a variety of disturbances that occur frequently and maintain the stability of the system. In this paper, a PSS design technique is proposed using Whale Optimization Algorithm (WOA) by considering eigenvalue objective function. Two bench mark multi machine test systems: three- generator nine- bus system, two- area four- generator inter connected system working on various operating conditions are considered as case studies and tested with the proposed technique. Extensive simulation results are obtained and effectiveness of proposed WOA-PSS are compared with well - known PSO and DE based stabilizers under several disturbances.

Design of power system stabilizer (PSS) withstanding critical situations is a challenging task. In this paper, an effective method for the design of a coordinated multiple PSS in power systems are offered and weaknesses of the existing methods are disclosed. The setup includes the nonlinear model of the system, a specific multi-objective function and a newly developed meta-heuristic Hyper-Spherical Search (HSS) optimization algorithm. The objective function includes both eigenvalue stability index (SI) and nonlinearity index (NI). The employed new NI is the second order modal interaction of the lightly damped electromechanical modes. The performance of the proposed Stability-Nonlinearity index (SN) based PSS design by HSS (SNPSS-HSS) is compared with SNPSS-GA, SI based PSS (SPSS) and the conventional design method (CPSS) under various operating conditions and disturbances. The investigations are conducted over both a single lead PSS and PSS2B. The performance is detailed using time domain simulations, SI and NI analysis. A four-machine two-area system and the IEEE 39-bus system are utilized for test purpose. The results of extensive simulations indicate the superiority of SNPSS-HSS design with respect to the others. The obtained relative stability is higher and the nonlinearity index is lower than the product of the other designs. Besides, as it is expected, using a high degree of freedom PSS2B improves the performance of the aforementioned algorithms.

Aiming at the problems of asymmetric information and unreasonable emergency allocation schemes in the current cross-regional emergency operation, the emergency deployment process of multi-machine and multi-task is analyzed, and the emergency allocation model with the goal of minimizing the allocation cost and loss is established in the paper. Emergency allocation algorithm based on rule of nearest-distance-first, which allocate machinery for the nearest farmland firstly, and emergency allocation algorithm based on rule of max-ability-first, by which machinery with maximum ability to farmland is allocated firstly, are proposed. The operational data of farmland and agricultural machinery generated randomly are calculated and analyzed. The results show that when the amount of agricultural machinery is sufficient, the algorithm based on the maximum contribution capacity priority is better. When the agricultural machinery is insufficient, the calculation results of the emergency allocation algorithm based on the nearest distance priority are better. When the number of farmland is not more than 30, the average operation time of the two algorithms in this paper is not more than 3.8 seconds, and both two algorithm have good performance.

This paper analyzed the multi-machine repairable system with one unreliable server and one repairman. The machines may break at any time. One server oversees servicing the machine breakdown. The server may fail at any time with different failure rates in idle time and busy time. One repairman is responsible for repairing the server failure, the repair rate is variable to adapt to whether the machines are all functioning normally or not. All the time distributions are exponential. Using the quasi-birth-death(QBD) process theory, the steady-state availability of the machines, the steady-state availability of the server, and other steady-state indices of the system are given. The transient-state indices of the system, including the reliability of the machines and the reliability of the server, are obtained by solving the transient-state probabilistic differential equations. The Laplace–Stieltjes transform method is used to ascertain the mean time to the first breakdown of the system and the mean time to the first failure of the server. The case analysis and numerical illustration are presented to visualize the effects of the system parameters on various performance indices.

This study presents a framework to detect malicious windows commands by using both statistical and deep learning algorithms. Our method consists of two stages. In the first stage, malicious windows commands are identified using a benign profile with the Euclidean distance. In the second stage, we recognize the malicious part in the Windows commands by using Natural Language Processing and proposed a scoring mechanism. Finally, we used two real cases to verify our method's effectiveness in detecting malicious commands and malicious parts. We also verify that the factor affecting the precision, recall and demonstrated our approach was effective through experiments.

In the paper we consider an NP-hard problem of tasks scheduling with due dates and penalties for the delay in a flexible production cell. Each task should be assigned to one of the cell’s machines and the order of their execution on machines should be determined. The sum of penalties for tardiness of tasks execution should be minimized. We propose to use the tabu search algorithm to solve the problem. Neighborhoods are generated by moves based on changing the order of tasks on the machine and changing the machine on which the task will be performed. We prove properties of moves that significantly accelerate the search of the neighborhoods and shorten the time of the algorithm execution and in result significantly improves the efficiency of the algorithm compared to the version that does not use these properties.

In this study, a methodology for partial eigenstructure assignment (PEVA) is applied to dampen electromechanical oscillations in electrical multi-machine power systems. The approach is anchored in allocating a small number of undesirable eigenvalues, for example, which are poorly damped, preserving the other eigenvalues in the system - the so-called no-spillover spectrum. The new position of the selected eigenvalues is carried out based on the partial controllability analysis of the system, in order to minimize the control effort. Simulation examples using a system with 68 buses, 16 generators and five areas showed that the presented methodology is efficient in dampening the local and inter-area oscillation modes when compared to the classic power system stabilizers (PSS). The quality of the solution is illustrated through computer simulations, eigenvalues tables and mode-shapes.

In this paper, a decentralized model predictive controller (MPC) is designed for three machine infinite bus system. The objective of the MPC controller is to mitigate low frequency oscillation of power system. To validate the effectiveness of the controller a three machine power system is considered here. The MPC controller is placed as a decentralized controller. The behaviour of the MPC is observed with multi machine environment using both DC and AC excitation system. The behaviour of the MPC is also compared with centralized and decentralized control technique.

Most of large scale systems are described by their complex mathematical model’s structures which caused by the overlapping between its inputs and outputs variables. In many cases, the complexity of the model may lead to lose some part of the input-output signals. Besides, it obscures study and analysis of such type of systems for researchers. Many studies have been discussed the design of simpler controller based on the mathematical structure of the systems’ model, wherein the aim was to decrease control complexity. The main objective of this manuscript is to benefit from the multi-overlapping structure of complex systems in order to design a simpler controller using loop approach; the latter allows highlighting the advantages of applying the overlapping decomposition strategy in order to improve the robustness of the designed controller. Therefore, it can be considered as an efficient strategy suitable for such type of large scale systems. A multi-machine power system with loop structure is considered in order to demonstrate the usefulness of the proposed design.

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms.

This article coordinated control control of SVC and Power system stabilizer(PSS) is considred in power system stability analysis. The Remote signal(Δω) based SVC with PID controller coordinated with Δω based PSS is compared with Δω based SVC with LL controller with same Δω based PSS in the purposed analysis. To findout the optimal coordinated controller parameters the Pattern Search (PS) evolunatinary algorithm is used in both SMIB and Multi machine power system. The performance analysis can be checked under different location of fault and loading condtion is considred to show the effectiveness and robustness of the system.

Summary Focusing on solving the control problem of the multimachine excitation systems with static var compensator (SVC), this paper proposes a decentralized neural adaptive dynamic surface control...