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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 ...

The increasing number of low-frequency oscillation events has posed significant challenges to power system stability. It is imperative to accurately identify and properly control generators with poor or negative damping contribution for better oscillation damping. This paper proposes a hybrid energy and mode decomposition-based method for evaluating generators damping in multi-machine power systems. It is derived that the aggregate electric torque can be modeled as the superposition of electric torques in different modes. In each oscillation mode, the electric torque can be decomposed into the damping torque and the synchronizing torque. Moreover, a novel oscillation energy function is developed to evaluate generators’ damping effect in each oscillation mode. The result proves that in each oscillation mode, generators with attenuated oscillation energies have negative damping effects. Simulation results in single and multinegative damping generators cases, as well as the comparison with other methods, demonstrate the effectiveness and robustness of the proposed method for damping evaluation.

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 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 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.

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.

In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the “explosion of complexity” problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small $\mathcal {L}_{\infty }$ a tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly bounded b and can be made arbitrarily small. Simulation results show the validity of the proposed method. a Here, the $\mathcal {L}_{\infty }$ norm is defined as $\left \Vert{ x}\right \Vert _{\infty }\mathop{=}\limits^{\Delta } \sup \limits_{t\geq 0}\left \vert{ x(t)}\right \vert $ and we say $x\in \mathcal {L}_{\infty }$ when $\left \Vert{ x}\right \Vert _{\infty }$ exists. b Here, we say $x(t)$ is ultimately uniformly bounded if there exist positive constants $b$ and $c$ , independent of $t_{0}\geq 0$ , and for every $a\in (0,c)$ , there is $T=T(a,b)$ , independent of $t_{0}$ , such that $\left \Vert{ x(t_{0})}\right \Vert \leq a\Rightarrow \left \Vert{ x(t)}\right \Vert \leq b$ , $\forall t\geq t_{0}+T$ .

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 paper proposes a method to design power system stabilizers (PSSs) based on single-machine infinite-bus system models to mitigate the risk of low-frequency electro- mechanical power oscillations in an N-machine power system. First, models of N fabricated identical-machine power systems are established for the N-machine power system. Analysis in the paper indicates that the electromechanical oscillation mode of fabricated identical-machine power systems with the least damping is of less damping than the electromechanical oscillation modes of N-machine power system. Second, it is proved that models of fabricated identical-machine power systems are dynamically equivalent to the single-machine infinite-bus system models. Finally, it is suggested that the PSSs are designed using single-machine infinite-bus system models to enhance the least damped electromechanical oscillation mode of fabricated identical-machine power systems. This eventually improves the damping of electromechanical oscillation modes of N-machine power system, thus mitigating the risk of low-frequency electromechanical power oscillations. In the paper, the proposed method is demonstrated and evaluated by using three well-known example multi-machine power systems. Effectiveness of PSSs is confirmed by the results of modal computation and simulation.

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 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.

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.

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...

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.

This paper presents a sliding mode dynamic surface control strategy for multi-machine power systems with static var compensator (SVC) to design the controller of the generator excitation system. Fi...

This paper presents the design of a sliding-mode perturbation observer-based sliding-mode control for stability enhancement of multi-machine power systems. The combinatorial effect of nonlinearities, parameter uncertainties, unmodelled dynamics and time-varying external disturbances is aggregated into a perturbation, which is rapidly estimated by a sliding-mode state and perturbation observer and then fully compensated by a sliding-mode controller in real time. The attractiveness of the sliding surface is analysed theoretically in the context of the Lyapunov criterion. The proposed control does not require an accurate system model and only one state measurement is needed. In addition, an over-conservative control effort can be effectively avoided via perturbation compensation. Simulation results for a three-machine power system and the New England power system verify the effectiveness of the proposed approach.

The assembly scheduling problem is a common layout with many applications in real manufacturing scenarios. Despite the high number of studies dealing with this problem, no benchmark has been proposed up-to-now in the literature generating neither hard nor balanced instances. In this paper we present two extensive sets of instances for two variants of the 2-stage assembly scheduling problem. The first set is composed of 240 instances for the variant with one assembly machine in the second stage, while in the second set 960 instances are proposed for the variant with several assembly machines. An exhaustive experimental procedure, generating several preliminary testbeds with different processing times and number of jobs and machines, is carried out in order to identify the most representative instances of the problem under study. A total of 120,000 instances are generated and, among them, 1,200 are selected ensuring that the new benchmarks satisfy the desired characteristics of any benchmark: adequacy, empirical hardness, exhaustiveness, and amenity for statistical analysis. Finally, two computational evaluations are performed comparing and evaluating the existing heuristics in the literature, thus establishing the set of efficient heuristics for this assembly problem.

Oscillatory instability in modern power system has increased due to increasing complexity and integration of dynamic loads. Stability analysis of such interconnected power system is very prominent ...

Generally, the mathematical formulation of the dynamics governing multi-area power systems with Unified Power Flow Controller (UPFC) is a challenging task owing to the presence of both differential and algebraic sub-systems. The proposed research work attempts to integrate the two subsystems by replacing the algebraic subsystem with a differential approximant that leads to a non-linear system of differential equations. Solution of the proposed model with a properly chosen Lyapunov function produce a nonlinear control signal which damps inter-area oscillations effectively. The non-linear control signal is realised using the backstepping method. Moreover, the new formulation enables utilisation of the law for uncertain parameters using the standard parametric feedback form, such that the advantage of such a controller is unaffected by these parameters. In addition to this major contribution, full utilisation of UPFC, by using a lone multi-variable PI controller which eliminates negative interaction between the controllers, is also achieved. Empirical verification of the proposed approach is done by simulating various scenarios with varying degrees of complexity – from dual area power networks to 39 buses New England system. The results of the experiments indicate the efficacy of the method.

In terms of power generation, Automatic Generation Control (AGC) has taken care of the interchange power flow deviations in order to sufficiently alleviate both the frequency and tie-line power dev...

Many disturbances and various faults can occur in power systems so in this article the control purpose is to increase the stability and enhance the robustness of power system against different faults by combining fractional calculus with adaptive sliding mode controller (ASMC). In this article a new Power System Stabilizer (PSS) based on an adaptive fractional order variable structure controller is proposed. At first, the system equations are transformed to the canonical form and then an Adaptive Fractional order Sliding Mode Control (AFrSMC) is designed and its system stability is shown. Simulations are carried out to indicate the efficiency of the proposed scheme. The proposed method is compared with the conventional ASMC.

We investigate an adaptive neuro-control approach, namely goal representation heuristic dynamic programming (GrHDP), and study the nonlinear optimal control on the multi-machine power system. Compared with the conventional control approaches, the proposed controller conducts the adaptive learning control and assumes unknown of the power system mathematic model. Besides, the proposed design can provide an adaptive reward signal that guides the power system dynamic performance over time. In this paper, we integrate the novel neuro-controller into the multi-machine power system and provide adaptive supplementary control signals. For fair comparative studies, we include the control performance with the conventional heuristic dynamic programming (HDP) approach under the same conditions. The damping performances with and without the conventional power system stabilizer (PSS) are also presented for comparison. Simulation results verify that the investigated neuro-controller can achieve improved performance in terms of the transient stability and robustness under different fault conditions.

This paper proposes a novel framework that enables the simultaneous coordination of the controllers of doubly-fed induction generators, synchronous generators, and static compensator devices. The proposed adaptive zero dynamics based approach aims at enhancing the transient stability and improving the voltage regulation of multi-machine power systems under a wide range of operating conditions. The proposed approach was implemented to Kundur's four-machine two-area and the IEEE 39-bus power systems. Its performance was assessed in the presence of various faulty scenarios and disturbances. Eigen-value analysis, time domain simulations, and transient stability margin analysis (measured by critical clearing time) were carried out in the performance assessment. Comparison analysis with other control approaches was also conducted to highlight the effectiveness of the proposed approach. Enhancing the power and voltage stability of large scale multi-machine power systems, damping inter-area power oscillations, and providing faster post-fault recovery time are among the positive features of the proposed approach.

This paper proposes a new nonlinear control approach to coordinate doubly fed induction generator (DFIG)-based wind turbines and static synchronous compensator (STATCOM) controllers in multi-machine power systems. Its main objectives are to improve the transient and voltage stability of the inter-connected multi-machine power system via simultaneous design of the voltage of the DFIGs’ rotors and reference current of STATCOM. The nonlinear approach is based on a multi input-multi output (MIMO) zero dynamics (ZD) technique augmented by a super twisting sliding mode control (STSMC) to reduce the effects of the linearization error. The proposed approach was implemented to the Kundur’s four-machine two-area power system and the IEEE 39-bus power system. Its performance was assessed in the presence of various faulty scenarios and disturbances. The effectiveness of the proposed approach (ZD-STSMC) was evaluated via eigenvalue analysis, transient simulations and transient stability margin analysis (by critical clearing time (CCT)). The merits of the proposed design are further compared to those of the exact feedback linearization (EFL) approach. Enhancing the power and voltage stability of large scale power systems, damping power oscillations and providing faster post-fault recovery time are among the positive features of the proposed approach.

Calculation of the hydraulic coupling in the single penstock multi-machine (SPMM) is one of the main obstacles for control design of the hydro turbine governor. In order to resolve this problem, a multi-machine differential equation model suitable for control design and stability analysis is established in this study. First, the multi-machine differential equation with the hydraulic coupling model is then established to facilitate building the joint model of the hydro turbine with the hydraulic system and the generator considering the hydraulic-mechanic-electric coupling. Second, the dynamic head in the common penstock satisfying the superposition principle is revealed. This means that the dynamic head in the common penstock can be calculated by using the state variables of the branch pipe. Third, simulation reveals that the differential item in the hydraulic coupling is the main factor affecting the computational convergence. As such, the approximate method ignoring the differential item in the hydraulic coupling is proposed to solve the problem of computational convergence of the SPMM. Finally, the classical method of characteristic (MOC) is employed to verify the proposed model. The results show that the proposed model has higher accuracy and is easy to connect with the non-linear model of the generator.

In this research, an effective application and performance assessment of the Neuro-Fuzzy Controller (NFC) damping controller is designed to replace a single machine infinite bus (SMIB) power system stabilizer (PSS), and coordinated multi PSSs in large interconnected power systems are presented. The limitation of the conventional PSSs on SMIB and interconnected multi-machine test power systems are exposed and disclosed by the proposed NFC stabilizer. The NFC is a nonlinear robust controller which does not require a mathematical model of the test power system to be controlled, unlike the conventional PSSs&rsquo, damping controller. The Proposed NFC is designed to improve the stability of SMIB, an interconnected IEEE 3-machine, 9-bus power system, and an interconnected two-area 10-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The proposed NFC damping controller performance is compared with the conventional PSS damping controller to confirm the capability of the proposed stabilizer and realize an improved system stability enhancement. The conventional PSSs&rsquo, design problem is transformed into an optimization problem where an eigenvalue-based objective function is developed and applied to design the SMIB-PSS and the interconnected multi-machine PSSs. The time-domain phasor simulation was done in the SIMULINK domain, and the simulation results show that the transient responses of the system rise time, settling time, peak time, and peak magnitude were all impressively improved by an acceptable amount for all the test system with the proposed NFC stabilizer. Thus, the NFC was able to effectively control the LFOs and produce an enhanced performance compared to the conventional PSS damping controller. Similarly, the result validates the effectiveness of the proposed NFC damping controller for LFO control, which demonstrates more robustness and efficiency than the classical PSS damping controller. Therefore, the application and performance of the NFC has appeared as a promising method and can be considered as a remarkable method for the optimal design damping stabilizer for small and large power systems.

Dynamic interactions introduced by grid-connected wind farms are multivariable. This is why wind farms’ models need to be simplified when the impedance-model-based analysis is applied to examine whether negative resistance is supplied by the wind farms to cause subsynchronous oscillations (SSOs). In this paper, we examine and reveal the mechanism of torsional SSOs caused by grid-connected wind farms from the standpoint of near strong modal resonance (NESMOR). The examination and revelation consider fully the multivariable dynamic interactions introduced by wind farms in a complex multi-machine power system without simplifying the models of wind farms. We carry out the analysis based on the multi-input multi-output (MIMO) closed-loop model with two interconnected subsystems. The analysis indicates that the open-loop modal proximity (OLMP) of interconnected subsystems is a special case of NESMOR. When the NESMOR happens, growing torsional SSOs may be excited by the grid-connected wind farms. Hence, the analysis unveils the connection between the modal condition of OLMP and NESMOR, shedding a deeper insight into the mechanism of torsional SSOs caused by wind farms. In addition, we propose a method to detect the NESMOR. An example multi-machine power system with two grid-connected wind farms is presented to demonstrate the analysis and method.

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.

This paper combines the characteristics of windshield glass production process and compares various intelligent algorithms. It proposes to start from the windshield parallel multi-machine manufacturing process and establish the simulation unit of windshield manufacturing process. The method considers the windshield manufacturing process. The sequence of the process and the factors affecting the tempo of each process are transformed into genomic information. This paper establishes a simulation model of the two-chrome parallel multi-machine production line to optimize the scheduling of the windshield. This method increases the production efficiency of the windshield manufacturing process while achieving line balance with minimal processing time. Based on the Plant Simulation simulation platform, the effectiveness of the proposed method is verified by optimizing the production line of parallel multi-machine.

Trabalho apresentado em 53rd International Universities Power Engineering Conference, 4-7 setembro, Glasgow, Escócia In regards to SR machines design, there are a multiplicity of feasible topologies, differentiated by the properties of the electrical and magnetic circuits, and their relative location. A feasible SRG topology for an energy converter can comprise a series of n-SR machines assembled on a common axis. Comparing with a monolithic SR machine, that mono-axial multi-machine topology shows a higher fault tolerance and a simpler maintenance. The proposed scale models methodology makes it easy to incorporate in comparison of SR topologies other physical phenomena such as thermal effect and magnetic saturation by introducing some constraints. With the comparison of topologies, the issues surrounding the design of low speed SR multi-machine topology are discussed, leading into consideration monolithic topologies. The monolithic topology can optimize the efficiency taking benefits from the gain of power per unit of copper mass and lower losses. N/A

In this paper, we address the two-stage multi-machine assembly scheduling problem, a layout consisting of several dedicated parallel machines in the first stage and identical parallel machines in the second stage. The objective considered is the minimisation of the total completion time. Despite its relevance in practice and its NP-hard nature, this problem has not received much attention so far in the literature. In our paper, we propose two new efficient heuristics. The first heuristic constructs a solution taking into account some specific knowledge of the problem domain. This algorithm is embedded into a beam search-based constructive heuristic and its behaviour when the beam width takes different values is analysed. The computational experiments carried out show that the proposals are more efficient than the existing heuristics for the problem and also the adaptations of heuristics from related problems.

A new method for studying the transient asymmetrical shunt and series faults in multi-machine multi-unified power flow controller (multi-UPFC) power systems is proposed. Considering an arbitrary shunt or series fault in a power system, the method uses a combination of space phasors-based sequence networks corresponding to the fault. Synchronous machines and UPFCs are modelled via injecting sequence current phasors. The quasi-static Y-matrix equations of the network are solved first, to obtain the sequence voltages of the terminal bus-bars of synchronous machines. Then, these voltages are used to solve the non-linear equations of the machines (in the rotors d - q reference frames) in the subsequent step time (Δt). This procedure is repeated until a steady-state condition is reached in the post-fault condition. The proposed method for fault analysis has high processing speed and acceptable accuracy, which is very important for online protection and control computations. Based on the proposed method, a computer program has been developed that is applicable for the fault analysis of modern or conventional power systems with any size. The performance of the proposed method is studied by some simulation results and compared with other similar works. The simulation results very well support the accuracy and efficiency of the method.

Transmission faults caused by recent wildfires in California have induced the disconnection of utility-scale converters in photovoltaic (PV) power plants. Postmortem investigations reported that tripping commands were caused by phase-locked loops (PLLs) and dc-side dynamics, which are typically unmodeled in classical transient stability studies. Since existing simulation packages rely on simplified models that neglect these dynamics, they have a limited capability to predict converter behavior during faults. To address this shortcoming, we set forth a positive-sequence model for PV power plants that is derived from physics and controls first principles. As seen on utility-scale three-phase converters, the model includes PLLs, dc-side dynamics, and closed-loop controllers. Instances of the developed model are integrated into illustrative power systems containing conventional generators. Numerical simulations of the obtained multi-machine multi-converter power systems are assessed via a suitable set of stability and performance metrics.

Based on a resistive-type superconducting fault current limiter (SFCL) as well as a unified power flow controller (UPFC), this paper researches the combined utilization of them to improve a multi-machine power system’s transient stability. The SFCL is firstly to provide a fault current-limitation. Then, the UPFC is to realize an efficient power flow adjustment and a precise stabilizing control. Related modeling and control are presented, and a four-machine two-area test system including a SFCL and a UPFC is simulated in MATLAB. Different simulation conditions are applied, and it can be concluded that: (i) The SFCL-UPFC enables to more directly and powerfully refrain the fault current surge, mitigate the bus voltage sag and alleviate the inter-area oscillation. (ii) As the increase of fault clearance time, the power system’s demand for the SFCL-UPFC’s stabilizing effect would be very critical. (iii) Compared to the asymmetric fault, the SFCL-UPFC owns a better ability to handle the symmetric fault.

In this paper, the effects of three FACTS controllers, static synchronous series compensator (SSSC), thyristor-controlled series compensator (TCSC), and static synchronous compensator (STATCOM), on the transient stability of a multi-machine power system in the presence of two 200 MW wind farms based on a doubly fed induction generator (DFIG) and a 120 MW photovoltaic (PV) solar plant are studied. Also, power system stabilizers (PSSs) are installed on four synchronous machines. It is generally accepted that there is a threat to the stability of power systems with the penetration of wind farm and PV plant. Therefore, in order to improve transient stability in the power system, this paper proposes the application of PI controllers in PV and wind farm controllers and a combination of PSS and FACTS controllers, whose coordination is very important. The design problems of the PSS, FACTS, and PI controllers are formulated as an optimization problem, and the adaptive velocity update relaxation particle swarm optimization (AVURPSO) algorithm, gravitational search algorithm (GSA), and genetic algorithm (GA) are employed to search for the optimal controller parameters. These three approaches identify the solution to a given objective function, but they employ different strategies and computational efforts. Therefore, a comparison of their performance is required.