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The small signal stability analysis has got much importance for a single machine infinite bus system (SMIB). Conventional controllers are not smart to give the faster response. Hence advanced and intelligent controllers like Fractional order PID (FOPID) and State space analysis methods are used to obtain the better responses. In this article, corresponding SMIB system is analyzed using simulation studies and are carried out in MATLAB 2017a.

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.

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems. CC BY 4.0Corresponding author: Alexander Karlsson (alexander.karlsson@his.se)This work was supported by grant 2019-00789 at Vinnova, Project: Predictive Maintenance using Advanced Cluster Analysis (PACA). Predictive Maintenance using Advanced Cluster Analysis (PACA)

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.

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 ...

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.

A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

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 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

This paper describes an application of nonlinear adaptive Hamilton minimax control to multi-machine power systems with steam valve controllers. The controllers are designed explicitly to maintain transient stability of systems. First, the feedback link is introduced to transform the traditional power system model into Hamilton structure. It is worth mentioning that in the process of building the system model and designing the controllers, this paper considers the problem of interconnection interference. The proposed controllers enable the system to have L2 interference suppression characteristics in the case of maximum disturbance. Finally, the simulation results show that the adaptive Hamilton minimax method has better transient performance.

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 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...

This article presents a detailed mathematical modelling and integration of a substantial 300-MW wind farm in a New England 39-bus system for stability investigations. Machine modelling is achieved using the differential algebraic equations from the two-axis model, in the form of a flexible network function. An aggregated wind farm model is developed with the order of hundreds of wind turbines, driving an aggregate doubly fed induction machine feeding the multi-machine system. Stability studies are performed on the non-linear model in MATLAB-Simulink platform, with exogenous disturbances of a continuous disturbed wind and a three-phase short circuit with two different clearing times. Stability retention of the wind farm–fed system is affirmed with the extensive power balance, frequency supervision and profuse voltage levelling.

This paper introduces a new robust controller with cascaded fuzzy blocks as a power system stabilizer (CFPSS) to enhance damping during low-frequency oscillations. This CFPSS is designed to act as a nonlinear lead–lag PSS with a given number of compensation blocks. To demonstrate the efficiency and the robustness of this proposed stabilizer, simulation results performed on the IEEE three-generator nine-bus multi-machine power system subjected to a three-phase short-circuit fault have been carried out. The parameters of the proposed PSS and those of the conventional IEEE linear lead–lag PSS have been tuned by a recently developed optimization technique (krill herd algorithm). The robustness of this novel CFPSS is proved, by optimizing the parameters of the two PSSs for one operating point (normally loaded system) and applying them to other operating points (case of heavy and low loads) with some key parameters variation. The obtained results have shown the superiority and the robustness of the CFPSS comparatively to the conventional IEEE lead–lag PSS in terms of oscillations damping over a wide range of operating conditions and against parametric variation. The same conclusions have been drawn in the case of a large power system (IEEE 16-machine, 68-bus test system) characterized by its local and inter-area oscillations modes.

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.

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.

In this paper, a novel energy flow analysis (EFA) is proposed based on the signal reconstruction and decomposition to investigate the electromechanical oscillations. In contrast to the conventional EFA, the connection between the proposed EFA and modal analysis (MA) can be quantitatively revealed for arbitrary models of synchronous generators in multi-machine power systems. Firstly, the time-domain implementation (TDI) of the proposed EFA is designed. Specifically, the measurements at the terminal of a local generator are reconstructed through an exponential operator and then decomposed with respect to an angular frequency. Then, the mode-screened damping torque coefficient is defined to extract the damping feature with respect to an electromechanical oscillation mode. After that, the frequency-domain implementation (FDI) is derived. Specifically, the Parsevals Theorem is applied to transform the proposed EFA from the time domain to frequency domain. On this basis, the consistency between the proposed EFA and MA is strictly proved, which is applicable for arbitrary models of synchronous generators in multi-machine power systems. Additionally, the application procedure of the proposed EFA in investigating electromechanical oscillations is given. Finally, the proposed EFA is substantially demonstrated in multiple case studies.

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.

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.

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.

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.

The system frequency response (SFR) model describes the average network frequency response after a disturbance and has been applied to a wide variety of dynamic studies. However, the traditional literature does not provide a generic, analytical method for obtaining the SFR model parameters when the system contains multiple generators; instead, a numerical simulation-based approach or the operators’ experience are the common practice to obtain an aggregated model. In this paper, an analytical method is proposed for aggregating the multi-machine SFR model into a single-machine model. The verification study indicates that the proposed aggregated SFR model can accurately represent the multi-machine SFR model. Furthermore, the detailed system simulation illustrates that the SFR model can also accurately represent the average frequency response of large systems for power system dynamic studies. Finally, three applications of the proposed method are explored, with system frequency control, frequency stability, and dynamic model reduction. The results show the method is promising with broad potential applications.

This paper derives the initial value calculation of a multi-machine power system with a detailed sixth-order model of synchronous generator equipped with exciter and turbine systems. The method is generalized that can be used for any model including the reduced model of synchronous generators such as two-axes, one-axis, and classical model. A Matlab-based package was developed for calculating the initial values and simulating a multi-machine power system. The test system used in the study is IEEE 16-machine 68-bus system.

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.

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.

This paper is devoted to the topic of robust stabilization for uncertain multi-machine power systems (MMPSs) using input delay-based sampled-data control. The study explores the sampled-data control for a nonlinear MMPS with parametric uncertainties exacerbated with sector saturating actuators. A saturated controller is considered for the system to recover the loss of stability in the continuous time domain. An approach, comprising linear matrix inequality technique and average dwell time method, is exploited, employing proper Lyapunov–Krasovskii functional, to show that the proposed saturated sampled-data control renders exponential stability. More precisely, the existence condition of sampled-data control law is developed in form of linear matrix inequalities. In order to simplify the derivation in main results, Schur complement and Wirtinger inequalities are used. Through the simulation tests on a two-machine infinite bus system model, the effectiveness and robustness of the proposed controller over the time delays and parameter uncertainties are verified.

This paper deals with simultaneous scheduling of machines, automated guided vehicles (AGVs) and tools in a multi-machine flexible manufacturing system (FMS) considering the transfer times of jobs to minimize makespan (MSN). 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. Jobs and tools among machines are carried by AGVs and tool transporter (TT). This simultaneous scheduling problem is highly complex in nature as it involves job operations sequencing on machines, allocation of tools and AGVs to job operations and associated trip operations including the times of dead heading trip and loaded trip of AGVs. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines, AGVs and tools and a flower pollination algorithm (FPA) built on the flowers’ pollination process for solving this simultaneous scheduling problem. The results indicate the proposed FPA is superior in terms of final solution quality, efficiency, convergence rate and robustness.