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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 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 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 ...

In the paper a variant of cyclic production with setups and two-machine cell is considered. One of the stages of the problem solving consists of assigning each operation to the machine on which it will be carried out. The total number of such assignments is exponential. We propose a polynomial time algorithm finding the optimal operations to machines assignment.

Predstavlja se metoda brzog reguliranja predviđanja uzbude za elektro-energetski sustav s više strojeva. Ta metoda predviđanja u nekoliko koraka ostvaruje se dinamičkim modelom sustava. Neka ograničenja neujednačenosti stanja, ulaza i izlaza razmatraju se u optimizaciji valjanja. U svrhu uštede vremena optimizacije otvorene petlje prediktivnog upravljanja modela primijenjuje se Gramian metoda balansirane redukcije i poboljšani algoritam optimizacije. Za provjeru učinkovitosti tog pristupa koristi se elektro-energetski sustav s 50 strojeva. U usporedbi sa simuliranim rezultatima uz različite regulatore, ovom se metodom može znatno reducirati vrijeme računanja. Naponi terminala generatora zadržani su u zadanim točkama. Poboljšana je stabilnost elektroenergetskog sustava., A fast excitation predictive control method for multi-machine power system is presented. The multi-step prediction technique is realized via system dynamic model. Some inequality constraints on states, inputs and outputs are considered in rolling optimization. The Gramian balanced reduction technique and the improved optimization algorithm are used in order to save the time of open-loop optimization in model predictive control. A 50-machine power system is used to verify the effectiveness of this approach. Compared with simulated results under different controllers, this method can greatly reduce the calculating-time. The voltages of generator terminals are maintained within the set points. The stability of power system is improved.

Wind energy is one of the fastest growing energy resources of alternative power generation across the globe. Maximum number of generators in wind farm is provided with Doubly Fed Induction Generator (DFIG) due to its many advantages. The effects on small signal stability of system with distributed additional load, equipped with DFIG based wind farms, is presented here. The additional load added in the existing system is made same in value as the power generated by the wind farm. Different distributions of additional load are considered to determine the small signal stability of system with varying wind power penetration. The position of the negative real part of eigenvalues of system matrix helps to determine the best location for the integration of wind power for particular load distribution of additional load among different load buses. A new sensitivity based index has been introduced here to determine the maximum penetration level in terms of the system small signal stability. WSCC 3-machine 9-bus system is used for the study which is carried out in MATLAB.

With the penetration of renewable generation, electric vehicles and other random factors in power systems, the stochastic disturbances are increasing significantly, which are necessary to be handled for guarantying the security of systems. A novel stochastic optimal control strategy is proposed in this paper to reduce the impact of such stochastic continuous disturbances on power systems. The proposed method is effective in solving the problems caused by the stochastic continuous disturbances and has two significant advantages. First, a simplified and effective solution is proposed to analyze the system influenced by the stochastic disturbances. Second, a novel optimal control strategy is proposed in this paper to effectively reduce the impact of stochastic continuous disturbances. To be specific, a novel excitation controlled power systems model with stochastic disturbances is built in the quasi-generalized Hamiltonian form, which is further simplified into a lower-dimension model through the stochastic averaging method. Based on this It&ocirc, equation, a novel optimal control strategy to achieve the intra-region probability maximization is established for power systems by using the dynamic programming method. Finally, the intra-region probability increases in controlled systems, which confirms the effectiveness of the proposed control strategy. The proposed control method has advantages on controlling the fluctuation of system state variables within a desired region under the influence of stochastic disturbances, which means improving the security of stochastic systems. With more stochasticity in the future, the proposed control method based on the stochastic theory will play a novel way to relieve the impact of stochastic disturbances.

This research paper presents a technique based on the non- linear optimization method for parameters of the power system stabilizers in multimachine power system. For the optimization or tuning of the parameters of PSS, the objective function is the maximization of the damping ratio of the critical modes of electro mechanical rotor oscillations. The objective of the coordinated parameter tuning is to globally optimize the overall system damping performance by maximize the damping of all both local and inter area modes of oscillations. The time domain simulation results of the multi-machine power system validate the effectiveness of the proposed approach. In this paper, the 10 machine 39bus New England system is used as the test system.

Optimal tuning of Power System Stabilizers (PSSs) parameters using Grey Wolf Optimization (GWO) algorithm is presented in this paper. Selection of the parameters of power system stabilizers which simultaneously stabilize system oscillations is converted to a simple optimization problem which is solved by a Grey Wolf Optimization (GWO) algorithm. The efficiency of the proposed method has been tested on two cases of multi-machine systems include 3-machine 9 buses system. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and at the same time guarantees a robust acceptable performance over a wide range of operating and system condition.

Proposing novel power system stabilizers (PSSs) to damp effectively low frequency oscillations (LFOs) in multimachine power systems is an interesting topic which has attracted many attentions in recent years. Fractional order controllers (FOCs) have found new applications in power system stability issues, recently. In current paper, fractional order proportional integral derivative (FOPID) controller is employed in design of a practical stabilizer, which is based on integral of accelerating power. The authors have proposed an FOPID-based PSS2B stabilizer to improve the damping of LFOs in a multimachine power system. The design of FOPID-PSS2B is formulated as a nonlinear optimization problem in which adjustable parameters are optimized simultaneously via a modified group search optimization (MGSO) algorithm by minimizing integral of time multiplied squared error (ITSE) performance index. The damping performance of the proposed stabilizer is compared with the earlier stabilizers of PID-PSS2B and FOPID-PSS. The results of time domain simulations on a 3-machine 6-bus test system in MATLAB/Simulnik environment reveal that the proposed FOPID-PSS2B achieves superior damping performance in comparison with the former stabilizers. Besides, sensitivity analysis is carried out to evaluate the robustness of the proposed stabilizer. FOMCON toolbox is used for fractional order modeling and control.

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 Braking Resistor (BR) or Dynamic Brake known as one of the effective practices to enhance the transient stability of synchronous generators and power systems, following a significant disturbance. In this paper, two methods for calculating the value of BR, one by using a one per unit value of BR, and the other one by using calculating criteria or equation to find the value of BR which will give a cost-wise analysis. Also, by using the Thyristor Controlled Braking Resistor (TCBR) with a PID Controller for the control of the insertion of the braking resistor to enhance the transient stability of the proposed power system, the so-called IEEE9Bus System (or Two Machines to Infinite Bus System). Comparison is made in terms of the speed deviation and load angle of the synchronous generators while using the calculated BR and the one per unit BR which will give us a cost-wise analysis when choosing the brake and the effectiveness of the brake to enhance the transient stability of the poposed power system.

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.

Harvest operations carried out by a single logger operating more than one machine are common, yet little is understood of their productivity and costs. A case study of three single-logger operations (SLO) located in New York, USA was conducted to assess their harvest costs and productivity. Daily activity and production data were collected from each harvest operation and analyzed in conjunction with machine rate calculations to estimate system productivity and costs for each SLO. Based on these data, deterministic simulations were conducted to assess how the addition of second and third loggers affected harvest system efficiency. Operator and equipment utilization rates were calculated at the systems level and compared among the observed SLOs and simulated two- and three-logger simulations. In most cases, the introduction of additional loggers reduced operator utilization due to the introduction of system delays, and therefore, increased labor costs. However, this investment in labor often led to ...

This paper proposes a coordinated switching power system stabilizer (SPSS) to enhance the stability of multimachine power systems. The SPSS switches between a bang-bang power system stabilizer (BPSS) and a conventional power system stabilizer (CPSS) based on a state-dependent switching strategy. The BPSS is designed as a bang-bang constant funnel controller (BCFC). It is able to provide fast damping of rotor speed oscillations in a bang-bang manner. The closed-loop stability of the power system controlled by the SPSSs and the CPSSs is analyzed. To verify the control performance of the SPSS, simulation studies are carried out in a 4-generator 11-bus power system and the IEEE 16-generator 68-bus power system. The damping ability of the SPSS is evaluated in aspects of small-signal oscillation damping and transient stability enhancement, respectively. Meanwhile, the coordination between different SPSSs and the coordination between the SPSS and the CPSS are investigated therein.

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 study presents stability analysis using a new approach to model the disturbances that occur in multi-machine systems connected with wind turbines. Consideration of stability and operability of conventional electric power systems planning and developing facing problems with the integration of installations in wind power plants (WPP) is needed because the current system is connected with renewable energy. This study highlighting the effects of WPP connected to a multi-engine 9 bus system from the effect of changes in disturbances occurring on several buses when operating. Disruption in multi-machine systems connected with wind turbines can affect system stability. The noise changes from bus to bus in the voltage drop effect on the system bus. The effect of wind turbine when a fault has a small moment of inertia can make the system unstable on the bus. The results of simulations carried out using ETAP to observe system stability have been carried out. The simulation results show that a system disruption that occurs in a multi engine system connected to the wind turbine does not result in a decrease in the bus near the fault then the stability decreases.

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.

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 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.

Aiming at the uncertainties of wind turbines clustering results and equivalent model caused by the changes of operating scenarios in wind farms, an aggregate modeling method for wind farms based on multi-machine representation is proposed. Firstly, based on the Markov property of wind speed, the probability transfer matrix is established to characterize the seasonal and daily characteristics of wind speed. Secondly, the two-step method is used to aggregate and classify wind turbines according to wind speed and operation characteristics of front-end speed-regulating wind turbines. According to Fisher criterion, the clustering results were analyzed by probability analysis and regression test in order to merge the groups with higher correlation. Finally, the highest probability of each quarter as the final clustering results, so as to get the wind farm aggregation model. Compared with the output characteristics of the detailed model, the proposed modeling method and the aggregation model can more comprehensively characterize the operation characteristics of wind farm. Thus, the adaptability of the equivalent model is improved effectively, and the uncertainty of the clustering results of the equivalent model is solved.

In this study, the penetration effect of wind generation on voltage stability in a weak multi machine power system has been investigated. In terms of static and dynamic voltage stability, the most critical bus of IEEE 10-machine 39-bus system has been determined by using sensitivity analysis. The comparison has been made for various wind power penetration levels. It is shown if a large amount of wind power is integrated into power system, the system stability will be deteriorated. In the paper, a static synchronous compensators (STATCOM) is proposed to improve the stability. The calculations and simulations have been conducted by DIgSILENT Power Factory 15.0. Comparative simulation results show that the STATCOM can effectively increase voltage stability margin of the AC system which was considerably decreased due to wind power's penetration.

In this paper, Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are two optimization algorithms used for tuning of Proportional–Integral–Derivative (PID) controller parameters in designing a multi-machine power system network. These are popular evolutionary algorithms which are generally used for tuning of PID controllers. The proposed approach is easy for implementation as well as it has superior features. The computational techniques enhance the performance of the system, and the convergence characteristics obtained are also stable. The system scheduling BFO-PID and GA-PID controller is modeled using MATLAB platform. When a comparison in the performance of optimal PIDs with conventional PID controller is carried out, the performance of BFO-PID and GA-PID controller is better as it improves the speed, loop response stability, and steady-state error; the rise time is also minimized. The results after simulation show that the controller developed using the BFO algorithm achieves a faster response as compared to GA.

A decentralized adaptive quantized dynamic surface control scheme is proposed for the large scale multi-machine power systems with static var compensator (SVC) and the unknown time delays. The ‘explosion of complexity’ problem in backstepping method, the uncertainties and complexities introduced by SVC are overcome. By estimating the weight vector norm of neural networks, the number of the parameters need to be estimated in each step is greatly reduces. It is proved that all the signals in the closed-loop system are ultimately uniformly bounded. Simulation results illustrate the validity of the proposed control scheme.

Power system stabilizers (PSS) are utilized to mitigate power system oscillations by excitation control. This paper proposes a PSS design based on a new population-based algorithm named Moth-flame optimization (MFO) algorithm in a multi-machine power system. A conventional speed-based lead-lag PSS is used for design purposes. The parameters of the sine-cosine algorithm based PSS are tuned using MFO to shift the weakly damped electromechanical mode eigenvalues towards the left half of S plane. Results so obtained has been extensively compared with those obtained by Bacteria Foraging (BF) and firefly algorithm (FA) which show the effectiveness of MFO in enhancing overall system stability over an extensive range of loading conditions and fault scenario.

In this article, power system stability enhancement by SSSC based damping controller using modified local input signal is considered. The locally measurable active power is used to obtain the input signal to the SSSC based damping controller for both single and multi machine power system. The proposed optimal controller parameters are tuned by using MOL based optimizing algorithm. The dynamics performance of the proposed controller is obtained under different loading condition to show the effectiveness and robustness of the controller under different disturbance and operating condition. The performance of the controller is compared by taking remote signal, local signal and modified local signal as input signal to the controller. It is observed that when modified local signal is considered it gives better damping performance as compared to local and remote input signal.

A new optimization technique known as the flower pollination algorithm is suggested in this article for robust tuning of a static VAR compensator to mitigate power system oscillations. The flower pollination algorithm is based on the properties of flowering plants. The tuning of a static VAR compensator controller is established as an optimization process. The flower pollination algorithm is applied to find out the optimal parameters of a static VAR compensator by diminishing certain performance index. The behavior of the suggested algorithm has been appreciated with the behavior of the genetic algorithm and bacteria foraging to certify the notability of the suggested flower pollination algorithm in designing a static VAR compensator. Simulation results of the flower pollination algorithm based static VAR compensator provide greater attenuation for various loading conditions than the optimized static VAR compensator by genetic algorithm and the optimized static VAR compensator by bacteria foraging...

This paper presents techniques which base on the concept of flows thinning together with the identification techniques. These methods are proposed to determine the expected number of failures to assess the efficiency of technical diagnostics of instruments. Additionally, this research focuses on the improvement of multi-machine troubleshooting systems, based on the ‘AND-OR’ graphs. Respective algorithms are presented. The majority principle uses the input information to check the correctness of the decision regarding identification of faulty machines. In this paper we base on the complete testing algorithm for elements of multi-computer complexes searching by criteria of failed element.

This article proposes a volumetric error generalized model and a distributed error compensation technique for multi-machine tools. A detailed generalized kinematic model for three- and five-axis machine tools is built. It can be applied to 4 types of three-axis machine tools and 12 types of five-axis machine tools. By simply inputting the translational and rotary type numbers of the specific machine tool, the final kinematic model can be obtained without further human interventions. A new compensation technique based on an Ethernet distributed numerical control system is proposed. Ethernet distributed numerical control software integrated with the generalized kinematic model is developed for machine tools with Fanuc and Siemens systems. Based on the Ethernet distributed numerical control system, the real-time volumetric errors of multi-machine tools are simultaneously compensated for. The proposed generalized kinematic model and Ethernet distributed numerical control systems are experimentally verified on three machine tools, whose volumetric errors are significantly reduced after compensation.