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In this paper, a simplified model of voltage source converter-based high-voltage direct current (VSC-HVDC) link is proposed that is effective in the analysis of multi-machine systems, even when crucial applications of the link are involved. The model is derived by eliminating the DC dynamics, including the converter-related impedances as a part of the AC system transmission network and obtaining the converter currents in a straightforward manner. Case studies are conducted on 4-machine, 10-bus and 16-machine, 68-bus systems to prove the accuracy of the model. The study clearly indicates the model's ability to reproduce the influence of VSC controllers, impact of variable power levels and effects of multiple HVDC links in a system. It is further verified for significant VSC-HVDC applications. The model is effective at handling frequency support of asynchronous systems and can be applied to VSC-HVDC connected offshore wind farms feeding multi-machine systems. It is demonstrated that the proposed model can be efficiently used for analysis of large AC systems embedded with VSC-HVDC links with lesser modeling complexity and computation time. [ABSTRACT FROM AUTHOR]

The power system stabilizer (PSS) is vital to enhance the dynamic stability of the electrical grid. The conventional PSS, designed at a fixed operating point may fail to retain the system stability in real life due to varying system conditions. Hence, it is essential to develop a robust stabilizer that guarantees system stability and gives the best performance under wide-range scenarios. This research work presents a novel method to design a robust PID-PSS utilizing a decentralized approach. The extended multi-machine power system is divided into subsystems in this study. To depict broad operating conditions resulting from widespread system conditions, a transfer function with interval coefficients is developed for each subsystem. The necessary and sufficient constraints for robust stability are derived using a systematic way, and a performance index is devised to improve PSS performance. Global search and fmincon tool in MATLAB is employed to identify robust controller gains. The proposed method's efficacy is demonstrated using a two-area, 10-bus, four-machine system. The system is tested for short circuit faults that are both symmetrical and asymmetrical, as well as load disturbances. The proposed controller's robustness and superiority over the existing are demonstrated by nonlinear simulation results. [ABSTRACT FROM AUTHOR]

Modern power system networks are inherently complex and susceptible to a range of undesirable events, such as line and generator outages, transmission line faults, and power oscillations. These power oscillations arise following disturbances, causing generators to oscillate in relation to one another. Effectively damping these oscillations is crucial for ensuring the reliable operation of the overall system. In light of this, our research employs a convolutional neural network (CNN) approach to fine-tune the parameters of a conventional power system stabilizer (PSS). The goal is to enhance the damping of power oscillations triggered by abrupt disturbances in a multi-machine system. In the process of training the neural network, input vectors are transformed into image vectors and subsequently trained using a CNN architecture. This trained network is then utilized to derive optimized PSS parameters from test data. The proposed CNN-based PSS is rigorously evaluated across a variety of operational scenarios, demonstrating its effectiveness in enhancing power oscillation damping in both single-machine infinite bus and multi-machine systems. Time-domain simulations are conducted to analyze rotor speed and rotor angle deviations within the system equipped with the proposed CNN-based PSS. Comparative assessments are made against systems employing different conventional PSS configurations and systems without PSS. The advantages of CNN-based PSS are its ability to capture complex patterns and relationships in the data, enabling it to effectively learn and adapt to various operating conditions, resulting in improved damping performance. Remarkably, our proposed PSS exhibits superior performance in terms of damping power oscillations, effectively outperforming conventional PSS approaches. [ABSTRACT FROM AUTHOR]

The escalating risks to network security, stemming from the continual evolution of network infrastructure and application systems, have significantly impacted system availability, with distributed denial of service (DDoS) attacks standing out as a particularly disruptive threat capable of overwhelming and even incapacitating systems. In response, this study introduces a comprehensive system (named uitSDD) designed to detect and mitigate DDoS attacks within software-defined networks (SDNs). Our proposed solution involves a multi-modules architecture operating as an intrusion prevent system, strategically engineered for seamless integration into the Controller device. The system’s functionality includes the periodic extraction of multiple parameters from flow entries on the OpenFlow switch, complemented by machine learning models, enabling the identification of abnormal packets infiltrating the system. Upon detecting an attack, the controller promptly dispatches policies to the OpenFlow switch, initiating the removal of malicious packets. This autonomous system employs common machine learning algorithms and real-time processes to detect and counteract attacks on OpenFlow Switches. The study evaluates the system’s performance using four distinct algorithms such as Decision Tree, K-Nearest neighbors (KNN), Random Forest, and support vector machine (SVM). Notably, when utilizing the CIC-DDOS2019 dataset to evaluate machine learning algorithms in detecting DDoS attacks, the Decision Tree algorithm exhibits the highest F1-score at 99.87%. [ABSTRACT FROM AUTHOR]

Design of a new method for a robust, nonlinear and intelligent stabilizer based on fuzzy logic combining a conventional controller, lead-lag power system stabilizer (PSS), is suggested in this paper for single- and multi-machine power systems. The proposed strategy takes into account the stability of the system by damping the oscillations separately of the operating point and this even for several operating conditions. Simulation of two power systems Kundur made it possible to determine the performance of the proposed fuzzy logic power system stabilizer (FPSS) versus the various quotas commonly encountered in the network. The implementation of the proposed FPSS is examined for trapezoidal, Gaussian and triangular membership functions and compared to the conventional PSS. Increasing of speed deviation and acceleration are taken like inputs. To show the efficacy of the proposed FPSS, the simulation results of the framework are implemented in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The optimal design of Power System Stabilizer (PSS) parameters is a significant optimization problem in power systems. Metaheuristic search (MHS) algorithms are among the most commonly used methods for optimizing PSS parameters. The preferred MHS algorithm must have strong exploration capability and an effective exploitation-exploration balance. The Fitness-Distance Balance (FDB) is a novel method for MHS algorithms, and it is featured by balanced search and effective diversity capabilities. In this paper, the exploration and balanced search capabilities of the Social Network Search (SNS) have been developed by using the FDB method. The FDB method guides the search process in the SNS and enables a more efficient selection of solution candidates in the search space. The performance of FDB-based SNS (FDBSNS) has been tested in the CEC-2014 and CEC-2017 benchmark test suits, and its superiority against SNS has been verified. Moreover, the effect of the FDBSNS was investigated in WSCC 3-machine 9-bus and 10-machine 39-bus New England test systems in the optimal PSS design problem. The obtained results and statistical analyses demonstrated that FDBSNS is a robust MHS algorithm compared to the algorithms in the literature for solving the CEC benchmark suits and optimal PSS design problem. [ABSTRACT FROM AUTHOR]

This paper addresses the joint optimization model of production scheduling, work-in-process (WIP) inventory control and group preventive maintenance (PM) planning in a multi-machine system with multi-components. The objective is to obtain optimum production sequence, PM intervals and grouping of components, which minimize the total expected cost per unit time of the system. A new meta-heuristic named Jaya algorithm and two popular algorithms viz. simulated annealing (SA) and particle swarm optimization (PSO) are applied to optimize the objective function. Initially, the optimum group of components is obtained based on the integer multiples of individual PM intervals. Secondly, the job permutation sequence incorporating group PM intervals is identified with the largest order value (LOV) rule. The shift in optimum PM intervals is realized with an advanced-postpone balancing approach. Computational results reveal that the proposed integrated model along with group PM yields up to 25% cost reductions when compared to the integrated model with individual maintenance as well as 37% savings while no integration is performed. Furthermore, the performance of algorithms is evaluated with large-sized problems. The obtained results show that Jaya and SA yielded comparable results, however, PSO is least productive. Thus, the proposed approach yields better economic performance and brings more improvised solutions as compared to the conventional methods of integrated scheduling and maintenance optimization problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multimachine systems are complex power systems consisting of multiple generators, loads, and transmission lines. In these kinds of systems, maintaining stability is a difficult task. Different damping controllers, like the power system stabilizer and FACTS controllers, are used to maintain stability. The design of the control scheme ensures that the PSS and SSSC work together in a coordinated manner. The controller parameter is optimized by using metaheuristic methods. In this work, a salp swarm algorithm (SSA) is proposed to tune the controller parameters for the multi-machine system. The statistical analysis is carried out to assess the performances of the SSA method against some other promising methods. The objective of this work is considered as minimization of rotor speed deviation. The rotor speed deviation is suppressed by using SSA tuned damping controllers. The mean rotor speed deviation obtained using SSA is 0.000601 pu. Using MATLAB simulation, the performance of multi-machine system is examined in terms of speed deviation between inter-area and local-area, injected voltage, and tie-line power. The simulation result suggests the supremacy of SSA over other methods considered for comparative study. [ABSTRACT FROM AUTHOR]

The dependence was studied of the level of density fluctuations recorded at different devices on the minor and major radii of the tokamak. It is known that a high level of density fluctuations can negatively affect the operation of diagnostics, in particular, the plasma reflectometry diagnostic. The increase of density fluctuations decreases the quality of raw data by increasing the error of measuring the density profile and, when density fluctuations exceed the threshold value, it renders such measurements impossible. Based on experimental data obtained on devices with substantially different sizes, a dependence of the density fluctuation level on the major and minor tokamak radii was proposed. Since the main experiments were carried out in round limiter tokamaks in ohmic (OH) heating regimes, the extrapolation result is applicable, generally speaking, to installations of larger size with the same configuration and regimes. However, experiments with electron cyclotron heating at the T-10 tokamak also allow one to extend the obtained dependence to regimes with auxiliary heating. It was shown that the obtained dependence is applicable to limiter tokamaks Tore Supra and TFTR of larger size. The applicability of the dependence to installations with diverter configuration is discussed and the possible level of density fluctuations is extrapolated to the international reactor tokamak ITER that is being constructed in France. [ABSTRACT FROM AUTHOR]

In the field of seismic signal analysis, it is of utmost importance to accurately differentiate between earthquakes and underground nuclear explosions. As a contribution for the verification regime of the Comprehensive Nuclear Test Ban Treaty (CTBT), Various methods have been employed for this purpose, including Complexity, Spectral ratio, mb—Ms (body wave and surface wave magnitudes), and corner frequency of P and S waves. These discrimination techniques have been examined to manually identify natural seismic events from nuclear explosions across different regions worldwide, such as China, India, Pakistan, North Korea, and the United States. To gather the necessary data, a comprehensive dataset comprising nuclear explosions and earthquakes of the same magnitude range (4 ≤ mb ≤ 6.5) of 35 seismic events from 1945 to 2017 has been compiled from the International Research Institute for Seismology (IRIS) using broadband and long period seismic stations. The objective of this study is to employ a range of linear and nonlinear Machine Learning (ML) models with the aim of automatically distinguishing between underground nuclear explosions and large earthquakes to enhance the accuracy of manual feature extraction. For this purpose, time domain waveforms and different classifier techniques focused on feature extraction have been used. The ML models employed include logistic regression, K-nearest neighbours classifier, decision tree classifier, random forest classifier, voting classifier, and Naive Bayes. The outcomes of the ROC and AUC analyses were employed to validate the validity of our proposed discrimination algorithm. The results show that the Random Forest Classifier is the most effective model, obtaining 100% accuracy in the case of feature extraction, while the best model for the time domain waveform classifier that achieved 75.5% accuracy is the voting classifier. [ABSTRACT FROM AUTHOR]

Purpose: The present paper proposes to explore the fundamental natural frequencies of a porous functionally graded material (FGM) plates. The effect of porosity in an FGM structure is studied in an uncertain quantification domain. Method: A finite element model is developed considering isoperimetric quadratic element and evaluating the first three natural frequencies for the porous FGM plate. Hereafter, a stochastic approach is explored by incorporating Monte Carlo simulation (MCS) and application of machine learning (ML) models. A detailed comparison is conducted to evaluate the predictive efficiency of five machine learning models viz., radial basis function (RBF), linear regression (LR), Gaussian progression regression (GPR), artificial neural network (ANN), and support vector machining (SVM). Results and Conclusion: The authenticity of the models is evaluated based on the mean and standard deviation error analysis. The error analysis provides adequate confidence on the authenticity of the ML models. The predicted results are depicted in the form a three-dimensional probability density function and scatter plots. The effective results and discussion portray an efficient stochastic model to quantify the uncertainty in the porous FGM structure. [ABSTRACT FROM AUTHOR]

A cutting-plane approach is developed for the problem of optimally scheduling jobs with arbitrary precedence constraints on unrelated parallel machines to minimize weighted completion time. While the single machine version of this problem has attracted much research efforts, enabling solving problems with up to 100 jobs, not much has been done on the multiple machines case. A novel mixed-integer programming model is presented for the problem with multiple machines. For this model, many classes of valid inequalities that cut off fractional linear programming solutions are developed. This leads to an increase of the linear programming lower bound from 89.3 to 94.6% of the corresponding optimal solution, and a substantial reduction in the computational time of an optimal branch-and-bound algorithm for this problem. This enables us to report optimal solutions for problem instances with up to 25 jobs and 5 machines, which is more than twice the size of problems for which optimal solutions have been reported in the literature thus far. For a special case of the problem—that of minimizing makespan—application of our model helps solve 18 of 27 previously unsolved problem instances to optimality. [ABSTRACT FROM AUTHOR]

Stabilization of wind farm (WF) is considered one of the major serious issues for developing power systems based on renewable energy sources. A comparison of dynamic performances for FACTS devices like STATCOM and SVC is presented in this paper. Such devices are employed to stabilize WF integrated with multi-machine power system (WFMPS). Fuzzy logic controller (FLC) has been employed for controlling FACTS devices to enhance WFMPS performance under different abnormal conditions. Three-phase to ground short circuit fault has been taken into consideration in different locations of system. MATLAB/Simulink is used to perform the modeling and simulation of WFMPS. The obtained results demonstrate the effect of both SVC- and STATCOM-based FLC on damping the system oscillations and improving the system dynamic performance in the post-fault duration. The comparison confirms superior dynamic performance and fast fault recovery of STATCOM-based FLC through different fault conditions compared with SVC-based FLC. [ABSTRACT FROM AUTHOR]

A system development case study problem based on a set of aircraft landing gear is examined in Hybrid Event-B (an extension of Event-B that includes provision for continuously varying behaviour as well as the usual discrete changes of state). Although tool support for Hybrid Event-B is currently lacking, the complexity of the case study provides a valuable challenge for the expressivity and modelling capabilities of the Hybrid Event-B formalism. The size of the case study, and in particular, the number of overtly independent subcomponents that the problem domain contains, both significantly exercise the multi-machine and coordination capabilities of the modelling formalism. These aspects of the case study, vital in the development of realistic cyberphysical systems in general, have contributed significant improvements in the theoretical formulation of multi-machine Hybrid Event-B itself. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are generally used to solve the low-frequency oscillation problem. To overcome this type of oscillation, this paper presented a new method by using the genetic algorithm (GA) to identify PSS settings. The tuning of PSS parameters was formulated relying on an eigenvalue-based objective function aiming at maximizing the stability margin. This was achieved by increasing the sum of the squares of negative real parts of the system eigenvalues. The small disruptions in the form of load change take place routinely; the controller parameters should be adjusted to the changing conditions. To remedy this defect, an adjustment of parameters as a function of the load was required. Thus, a neuronal model by using a historical database determined by the GA solutions for various load levels can approximate the simulation studies, since it could estimate the stabilizer parameters in real time after the learning phase. The validity of the proposed technique was checked through the evolution simulation of the regulator parameters for a load forecast curve. These concepts were discussed in details on a multi-machine network Western System Coordinating Council (WSCC) comprising three generators and nine nodes. The eigenvalue analysis and time domain simulation results were presented at different operating conditions and under various disturbances in order to show the effectiveness of this study. [ABSTRACT FROM AUTHOR]

Recently, the flow shop scheduling problem under blocking has gained broad attention in academic fields. Various papers have been devoted to investigate this issue and have been mostly restricted to the treatment of single objective at a time. Nevertheless, in practice the scheduling decisions often involve simultaneous consideration of multiple objectives (usually contradicting) to give more realistic solutions to the decision maker. In this study, we deal with a bi-objective blocking permutation flow shop scheduling problem where the makespan and total completion time are considered as objective functions. Both measures lead to an NP-hard problem. Our interest is to propose for the first time a Genetic Algorithm based on NSGA-II for searching locally Pareto-optimal frontier for the problem under consideration. The individuals in the algorithm are represented as discrete job permutations. Some specific versions of the NEH heuristic are used to generate the initial population. Non-dominated solutions and differences among parents are taken advantage of when designing the selection operator. The efficiency of the proposed algorithm, based on various metrics, is compared against the multiobjective evolutionary algorithm SPEA-II. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present research focuses on transient stability of multi-machine power systems in a full consideration regarding the performances of the Takagi-Sugeno fuzzy-based sliding mode control approach in association with the conventional sliding mode and also the optimal control approaches to improve the last finding outcomes in this area. Hereinafter, concerning the robustness of the sliding mode control approach toward parametric uncertainties and environment perturbations, in fact, a couple of different sliding mode control approaches are designed for mutual comparison, after a number of state-of-the-art technique considerations. To increase the control performance, the Takagi-Sugeno fuzzy-based approach is devised to provide the appropriate coefficients. Finally, the three control approaches are all carried out in the six-machine power system under the same condition and the investigated results are correspondingly provided to be analyzed. The results indicate that the proposed fuzzy-based control approach is well behaved with respect to other related ones. [ABSTRACT FROM AUTHOR]

Serial transfer lines with parallel machines in each stage are now a fairly common manufacturing system. However, its analysis and predictive-reactive control during transients remains mostly unexplored. This manufacturing system consists of a series of stages where in each stage a finite number of machines work concurrently. A two multi-state stage transfer line is studied in this paper. To describe the probability of one part being completed from the several machines in a stage, an equivalent multi-state, perfectly reliable machine is proposed. This multi-state model enables variance in the processing time between job completions to be captured stochastically. Based on historic data or engineering experience, an equivalent multi-state machine is simulated from the cumulative distribution function of the aggregated behavior of the parallel machines. In this paper, a serial production line with two equivalent, multi-state machines is used to analyze transient performance. Analytical closed-form expressions are derived to evaluate system transient behaviors. This system is also used with a controller to implement practical production control. Using model predictive control, system control parameter values provide decision support for managers to control short-term production. The proposed methodology can be applied to improve resource efficiency in other operation management problems. [ABSTRACT FROM AUTHOR]

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions. [ABSTRACT FROM AUTHOR]

Modeling of unified power flow control (UPFC) into multi-machine power systems by Wang, published by IET, has been widely quoted by many researchers. However, the correctness of the derivation of its equations has not been strictly scrutinized. The model cannot properly represent power system networks in its current form and need to be corrected. In this paper, the model proposed by Wang is revisited and the equations re-derived obtaining different results. The corrected model was developed and tested on three-machine and New England power systems installed with a UPFC. The DC link capacitor voltage is controlled in UPFC by modulating the phase angle of the excitation transformer voltage. The effect of the controller in damping of the low-frequency oscillations is demonstrated by using the proposed correct model and the results are compared with model presented by Wang. [ABSTRACT FROM AUTHOR]

In the previously published articles "Features of strong excitation control of synchronous generators in a complex power system" (Élektricheskie Stantsii, 2019, No. 7) and "Improving power system stabilization in response to grid disturbances" (Électrichestvo, 2020, No. 5), the author experimentally showed that the method conventionally used in the Unified Energy System (UES) of Russia for stabilizing the operating modes of complex power systems under external disturbances is not always efficient. This stabilization method uses signals from automatic excitation regulators (AER) based on the deviation and voltage frequency derivative on the generator terminals. In oscillating transient modes, however, these signals differ significantly in phase from the deviation and rotation frequency derivative of the generators. Instead of damping the occurring oscillations, this process can amplify their amplitude and duration. The author proposed a method for stabilizing the operating modes of power systems by the signals of deviation difference between the rotation frequency of each generator and the voltage frequency on the busbars, as well as the derivative of mismatch between them. The conducted full-scale tests confirmed the efficiency of the proposed method. Following the publication of the above articles, the efficiency of the method was confirmed in a critical discussion presented in the Élektricheskie Stantsii journal. This article discusses the physical difference between the proposed and conventional methods for stabilizing the operating modes of power systems, and explains the efficiency of the proposed method under the operating conditions of complex power systems. Experimental oscillograms of transient processes that have not been published before are presented. [ABSTRACT FROM AUTHOR]

This paper investigates a multi-product multi-machine production-inventory system, characterized by job shop routings and stochastic demand interarrival times, set-up times and processing times. The inventory points and the production system are controlled integrally by a centralized decision maker. We present a heuristic that minimizes the relevant costs by making near-optimal production and inventory control decisions while target customer service levels are satisfied. The heuristic is tested in an extensive simulation study and the results are discussed. [ABSTRACT FROM AUTHOR]

The due date assignment has become a challenging issue related to the interaction between various participants of the supply chain. In this paper, we consider the total earliness and tardiness penalty scheduling simultaneously with the deterioration effects and maintenance activities on an unrelated parallel machine setting. We aim to determine jointly the optimal maintenance activity positions, the optimal common due date for all the jobs, and the optimal schedule to minimize the sum of the earliness and tardiness costs. If the number of machines is fixed, we introduce a polynomial time algorithm for solving the problem. We also propose an efficient polynomial time algorithm with a lower order time complexity for finding the optimal solution of a special case of the problem. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems. [ABSTRACT FROM AUTHOR]

This paper propose, a robust excitation controller designed by a coordination of the optimal H tracking control and the proportional integral derivative (PID) controller optimized by the hybrid differential evolution and teaching-learning based optimization algorithm (DE-TLBO). These two controllers are used in order to guarantee the transient stability during a change in the operating conditions and the uncertainties in parameters. We have applied a method based on the modified tracking error by using the optimized exponential function, to avoid the compromise between the high gain in the control input and the H tracking performance with the variation in the system parameter. A new hybrid algorithm (DE-TLBO) is employed in this study to adjust optimally the parameters of the (PID-PSS) controller and the exponential form of the tracking error modified. The purpose of the suggested approach is to ensure a good tracking accuracy and to enhance the level of the oscillations damping in the multi-machine power system with an optimal choice of the parameters of all proposed controllers. The results of simulation demonstrate the efficient, and the robustness of the proposed approach (H and DE-TLBO-PID-PSS) under the different operation conditions. [ABSTRACT FROM AUTHOR]

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park (LPP) on small-signal stability of a power network and design of hybrid controller for these units. A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP. For damping of oscillations focusing on inter-area oscillatory modes, a hybrid controller for LPP was proposed. The performance of the suggested controller was tested using a 16-machine 5-area network. The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions. The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP. Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP. [ABSTRACT FROM AUTHOR]

Using an energy-based Hamiltonian function method, this paper investigates the robust excitation control of multi-machine multi-load power systems described by a set of uncertain differential algebraic equations. First, we complete the dissipative Hamiltonian realization of the power system and adjust its operating point by the means of pre-feedback control. Then, based on the obtained Hamiltonian realization, we discuss the robust excitation control of the power system and put forward an H excitation control strategy. Simulation results demonstrate the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW · h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling. [ABSTRACT FROM AUTHOR]

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments. [ABSTRACT FROM AUTHOR]

Thyristor controlled series capacitor (TCSC) can regulate line impedance and therefore increase transferred power of the system. On the other hand power system stabilizer (PSS) increases dynamic stability of generator. To enhance the stability, combination of TCSC and PSS can be applied, and in such a case coordination of TCSC and PSS is essential. This paper applies this combined controller in order to enhance the stability of multi-machine system. Parameters of these controllers are optimized by velocity update relaxation particle swarm optimization (VURPSO) algorithm and Genetic algorithm (GA). The simulation results show that the combination of VURPSO algorithm and GA leads to a better design and stability. [ABSTRACT FROM AUTHOR]

This paper presented a novel wide-area nonlinear excitation control strategy for multi-machine power systems. A simple and effective model transformation method was proposed for the system's mathematical model in the COI (center of inertia) coordinate system. The system was transformed to an uncertain linear one where deviation of generator terminal voltage became one of the new state variables. Then a wide-area nonlinear robust voltage controller was designed utilizing a LMI (linear matrix inequality) based robust control theory. The proposed controller does not rely on any preselected system operating point, adapts to variations of network parameters and system operation conditions, and assures regulation accuracy of generator terminal voltages. Neither rotor angle nor any variable's differentiation needs to be measured for the proposed controller, and only terminal voltages, rotor speeds, active and reactive power outputs of generators are required. In addition, the proposed controller not only takes into account time delays of remote signals, but also eliminates the effect of wide-area information's incompleteness when not all generators are equipped with PMU (phase measurement unit). Detailed tests were conducted by PSCAD/EMTDC for a three-machine and four-machine power systems respectively, and simulation results illustrate high performance of the proposed controller. [ABSTRACT FROM AUTHOR]