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This paper deals with the stability problem of multimachine power systems with transfer conductances via a Lyapunov function. We present easily tested criteria for the existence of this function. Using an example with four-machine power systems studied in previous literature we show the efficiency of these criteria in applications.

This paper presents an online learning recurrent neural network stabilization controller to improve the transient stability of a power system. Since the proposed recurrent neural network is of the online tuning type, it can stabilize the power system for different system parameters, operating conditions, and fault point. The proposed controller robustness and effectiveness in damping power system oscillations are illustrated through simulations.

This study presents the transient stability enhancement capability of Unified power flow controller (UPFC) as an effective Flexible AC Transmission System (FACTS) device in a multi-machine power system. The test system was a reduced Nigerian 330kV power system and the focus was on the effect of disturbances on the largest generating unit (Egbin) in the system. The analysis was conducted by simulating a 3-phase fault at two locations; on the terminal of the largest generator unit at Egbin bus and the bus with the largest load at Ikeja–west. The response of the system in both cases was compared with and without the device in operation. Simulations were carried out using the Power System Simulation for Engineering (PSS/E) software. Results showed that, with the UPFC in the network, system transient stability was enhanced considering that critical clearing time of the system was increased from 380ms to 590ms when the fault was at Egbin generator terminal and from 470ms to 510ms following the fault at Ikeja-west. In addition, the device was able to damp power oscillations resulting from the disturbance created by the faults.

This paper proposed a radical basis function neural networks (RBFNNs) based decentralized adaptive dynamic surface control scheme is proposed for the multi-machine power system. The main advantages of the proposed scheme are 1) by introducing the error transformed function, the tracking error of the power angle can be prescribed making the transient performance of the tracking error is guaranteed; 2) by using RBFNNs and the dynamic surface controller design method, the structure and the interactions between each subsystem can be totally unknown; 3) by estimating the norm of the optimal weighted vector of the neural networks, the computational burden is greatly reduced. It is proved that all the signals in the closed-loop power system are semi-globally uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.

Particle filters provide a general framework for dynamic state estimation (DSE) in nonlinear systems. The scope for particle filter-based DSE can be significantly enhanced by exploiting data from phasor measurement units (PMUs) when available at higher sampling frequencies. In this paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The filter relies on typical output measurements assumed available from PMUs stationed at generator buses. The performance of the proposed filter is illustrated with dynamic simulations on IEEE 14-bus system including: 1) generators models with subtransient dynamics, 2) excitation units (IEEE DC1A, DC2A, AC5A), and 3) turbine-governor models (steam and hydro). The estimation accuracy of the proposed filter is assessed for three classes of disturbances assuming noisy PMUs' measurements and comparative results are presented with the unscented Kalman filter (UKF). The accuracy-computational burden trade-off is also analyzed and the results strengthen the feasibility of using particle filters for dynamic state estimation.

This paper presents the results from a laboratory experiment of a single machine islanded area operated as a synchronous island. The paper also presents how this was realized as well as explaining the control theory that was utilized. The methodology given in this paper serves as a platform on which multi-machine islanding will be based. The laboratory setup consist of a generator model realized by a DC machine coupled with a synchronous machine rated at 5 kVA / 4 kW nominal. The generator is coupled with a load bank rated 5 kW nominal. The laboratory experiment consist of the generator coupled with the load bank. The island controller is implemented with Python code running on a Raspberry Pi.

This paper presents a critical scenario of voltage stability study on a multi-machine system when subjected to different loading patterns. High Voltage Direct Current (HVDC) links control and the effects of its compensating devices' effects on voltage stability are also investigated. A static approach with the use of P-V (Power-Voltage) and Q-V (Reactive power-Voltage) curve was used in this study. The simulation results show that with the HVDC line, more power can be transmitted with lower line losses and improved voltage profile. The transformer tap changers and the switchable HVDC compensating devices also play a major role in stabilizing the voltage profile under different loading conditions. Simulations were run using DigSILENT PowerFactory, a software package for power systems simulation and analysis.

With the rapid development of wind power technology, the size and number of grid-connected wind farms increase rapidly. Equivalent modeling of wind farms is the basis of the research on grid operating characteristics after wind power is connected to the grid. Long-distance transmission of large-scale wind power has become an inevitable trend, it seems that it is meaningful to study the equivalent model of wind farm. This paper build a multi machine equivalent model of wind farm By using the power system time domain simulation software-PSCAD/EMTDC. After debugging the model and running simulation, it build a specific equivalent model of wind farm and the results after the simulation of equivalent model.

This article aims to present the performance of a B-spline neural network scheme for tuning both the reactive power provision controller from synchronous machines and power system stabilizers in multi-machine power systems. The B-spline neural network is an efficient tool to implement the adaptive control voltage and power system stabilizer coordination, with the possibility of carrying out this task on-line, taking into account the systems' non-linearities. One of the main tasks within this context is to estimate the best power system stabilizer's parameters and adjust the proportional-integral parameters for reactive power provision. In this article, this is solved by a neural network algorithm. The applicability of the proposal is demonstrated by simulation on two test systems. Results show that the proposed coordination scheme is comparable to that obtained by a conventional design, without requiring a strict model analysis.

In this paper the problem of designing excitation controllers to improve the transient stability of multi-machine power systems is addressed adopting two new perspectives. First, instead of the standard formulation of stabilization of an equilibrium point, we aim here at the more realistic objective of keeping the difference between the generators rotor angles bounded and their speeds equal—which is called synchronization in the power literature—and translates into a problem of stabilization of a set. Second, we adopt the classical viewpoint of power systems as a set of coupled nonlinear pendula, and express our control objective as ensuring that some suitable defined pendula dynamics are (asymptotically) immersed into the power system dynamics. Our main contribution is the explicit computation of a control law for the two–machine system that achieves global synchronization. The same procedure is applicable to the n–machine case, for which the existence of a locally stabilizing solution is established.

In this paper, damping of the low frequency oscillations of multi-machine multi-UPFC power systems is investigated based on adaptive input-output feedback linearization control (AIFLC) approach. Considering a three-phase symmetrical fault, ignoring the subtransient states of the synchronous machines, the nonlinear state equations of the system are derived in order to obtain the UPFC reference control signals as well as the system parameters estimation laws. The stability of the system controller is proved by Lyapunov theory. Moreover using the six reduced order model of synchronous machine, some simulation results are presented in order to verify the validity and effectiveness of the proposed control approach.

A new robust power system stabilizer (PSS) design using Quantitative Feedback Theory (QFT) for damping electromechanical modes of oscillations and enhancing power system stability is proposed in this paper. The design procedure is carried out on a multi-input–multi-output (MIMO), non-minimum phase and unstable plant. A multi-machine electric power system with system parametric uncertainties is considered as a case study. To show the effectiveness of the QFT technique, the proposed method is compared with a conventional PSS (CPSS) whose parameters are tuned using the classical lead-lag compensation and genetic algorithms. Several nonlinear time-domain simulation tests indicate that the suggested control scheme is robust to the changes in the system parameters and also to successfully reject the disturbances. The results also show that the performance of the QFT method given in this paper is more desirable than CPSS and genetic algorithm (GA).

In this paper, a multi-product multi-machine serial production line operated under a constant-work-in-process protocol is considered. A mathematical model for the system is first presented, and then an artificial bee colony optimization algorithm is applied to simultaneously find the optimal work-in-process inventory level as well as job sequence order in order to minimize the overall makespan time. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, the proposed method does not use a linearized or simplified model of the system. A production line simulator implemented on MATLAB is, instead, employed to model the highly nonlinear dynamics of the production line and is used to evaluate the candidate solutions. The efficiency of the proposed approach, even for systems of large sizes, is validated via numerical simulations.

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.

This article presents the application of Taguchi robust design techniques for the tuning of power system stabilizers in a multi-machine power system. The power system stabilizer tuning is performed in two phases. In the first pre-elimination phase, the compound factor method is used to identify the significant and insignificant power system stabilizer parameters. In the second phase, only the significant power system stabilizer parameters are fine-tuned iteratively using the Taguchi optimization procedure. The efficacy of the proposed design technique has been evaluated on widely used a 10-generator, 39-bus New-England power system.

Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.

This paper presents a validation of selection process for selecting the most effective stabilizing signal to improve damping of inter area oscillations in a multi-machine power system by different signal selection methods. This paper also deals with wide area damping controller scheme compensating time latency of feedback signal in order to damp low frequency inter area oscillations in large power system. Pade approximation to time delay is used with controller synthesis. Eigenvector based coherent machine identification method has been adapted in this research for coherent area identification in multi-machine power system. The selected control signal is tested on the 4 machine 11 bus system. Nonlinear simulations are carried out in order to evaluate the performance of different approaches of signal selection under study.

The problem of disturbances compensation in multi-machine power systems under parametric uncertainties and perturbed mechanical input power is considered. The solution is proposed for the case when only relative speed of each electrical generator is available for measurement. The proposed control algorithm synchronizes the multi-machine power system with the required accuracy in the normal mode and under symmetrical 3-phase short circuit faults which occur on transmission lines. Efficiency of the proposed scheme is illustrated by modeling of the three-machine power system.

Power system operates under secure state. Any disturbance in terms of fault or major load change introduces oscillations in the power system. These oscillations cause stability problems, if not damped in short span of time. In a multi machine power system the rotor swing between generators makes the problem more complicated. In this paper Fuzzy Logic based Power System Stabilizer (FLPSS) is designed to damp the low frequency oscillations arising out of small and large disturbances in multi machine system. FLPSS is designed with speed deviation and rotor acceleration as input signals. The novelty about the work reported in this paper is that nine linguistic variables are considered to design the FLPSS. A four machine, two-area system is considered to validate the design of the proposed FLPSS. The superiority of the FLPSS is demonstrated by comparing its performance with that of a Conventional PSS (CPSS) by carrying out nonlinear simulation on the multi machine.

The paper demonstrates the possibility to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) based adaptive supervisory controller (ASC) which considers the wide-area signal transmission delays. The paper uses an LMI-based iterative nonlinear optimization algorithm to establish a method of designing state-feedback controllers for power systems with a time-varying delay. This method is based on the delay-dependent stabilization conditions obtained by the improved free weighting matrix (IFWM) approach. In the stabilization conditions, the upper bound of feedback signal’s transmission delays is taken into consideration. Combining theoriesof state feedback control and state observer, the ASC is designed and time-delay output feedback robust controller is realized for power system. The ASC uses the input information from Phase Measurement Units (PMUs) in the system and dispatches supplementary control signals to the available local controllers. The design of the ASC is explained in detail and its performance validated by time domain simulations on a New England test power system (NETPS).

Scheduling packaging lines in the process industry is a well known hard problem. The problem becomes even more complicated when each line consists of several machines and an order has to be manufactured on different lines. In this paper we consider the case where a number of orders has to switch from one line to another. This means that an order-run is sometimes manufactured first on the first part of a line and then has to switch to the second part of another packaging line. In this situation the second part of the first line and the first part of the second line cannot be used simultaneously for another run. This situation is studied for the case of the tobacco company Royal Kabat BV. In order to solve the complicated planning problem, the process is partitioned into a Pre-scheduling Level (PSL) and a Scheduling Level (SL). At the PSL an aggregated plan for a number of successive weeks is made that takes care of the purchasing of packaging materials and the balancing of the capacities. The PSL plan for ...

This paper proposes the use of fuzzy applications to a 4-machine and 10-bus system to check stability in open conditions. Fuzzy controllers and the excitation of a synchronous generator are added. Power system stabilizers (PSSs) are added to the excitation system to enhance damping during low frequency oscillations. A fuzzy logic power system stabilizer (PSS) for stability enhancement of a multi-machine power system is also presented. To attain stability enhancement, speed deviation (Δω) and acceleration (Δ?) of the Kota Thermal synchronous generator rotor are taken as inputs to the fuzzy logic controller. These variables have significant effects on the damping of generator shaft mechanical oscillations. The stabilizing signals are computed using fuzzy membership functions that are dependent on these variables. The performance of the fuzzy logic PSS is compared with the open power system, after which the simulations are tested under different operating conditions and changes in reference voltage. The simulation results are quite encouraging and satisfactory. Similarly, the system is tested for the different defuzzification methods, and based on the results, the centroid method elicits the best possible system response.

A multi-machine train consists of several complex shafts, rotors, or crankshafts and relatively flexible shaft extensions and couplings. It may also include gear unit, clutch, or flywheel. As connected machines function as spring systems in series, the potential exists for torsional resonance and torsional fatigue damage. Couplings are often modified to tune the overall system. Practical torsional analysis method, useful recommendations, and optimum coupling selection are addressed. Case studies for rigid connection (flange-to-flange connection without coupling), high torsional stiffness coupling, and flexible coupling are also investigated.

In part I of this paper, based on the extended Phillips-Heffron model of a single-machine infinite-bus power system installed with a FACTS-based stabilizer, the conventional frequency-domain phase compensation method is applied to design the FACTS-based stabilizer. Three types of FACTS-based stabilizers, the SVC-based, TCPS-based and TCSC-based stabilizer, are investigated. Examples of successful applications are presented. In part II the frequency domain phase compensation method's application in multi-machine power systems to design FACTS-based stabilizers is presented. An example of three-machine power system is demonstrated, where a TCSC-based stabilizer is successfully designed by the phase compensation method to damp system oscillations.

Superconducting magnetic energy storage (SMES) units can be used to enhance the stability of power systems. The key to make good use of the SMES units is to design effective controllers. This paper presents a number of results on the analysis and control of multi-machine power systems with such units via Hamiltonian function method. It has been shown that the multi-machine power systems with SMES units can be made to be a port-controlled Hamiltonian (PCH) system by using a state feedback control, and that the stability of the resulting system can be established. Furthermore, this paper proposes a novel energy-based adaptive L"2 disturbance attenuation control scheme for the multi-machine systems with SMES units. The control scheme is a decentralized one and consists of two parts: one is an L"2 disturbance attenuation excitation controller for the generators, and the other is an adaptive L"2 disturbance attenuation controller for the SMES units. Simulations on a six-machine system with one SMES unit show that the proposed control scheme is very effective.

A Hamiltonian multi-machine formulation is utilized for designing non-linear controllers with the purpose of helping to alleviate the electromechanical transient behavior after large disturbances. The controllers' design is based on a tracking problem, where the excitation voltage and the mechanical torque are employed as inputs. It is possible to utilize an observer for no accessible signals. The combined action of voltage and speed controllers achieves the objectives. Results are exhibited on two power systems in the open literature.

The proposed method enables the modeling and integration of synchronous machinery models in accurate and efficient simulation of power systems over diverse time scales that cover electromagnetic and electromechanical transients. The underlying models make use of frequency-adaptive simulation of transients (FAST) where analytic signals are used since they lend themselves to the shifting of the Fourier spectra. The shift frequency appears as a simulation parameter in addition to the time step size. When setting the shift to the common carrier frequency of either 50 or 60 Hz, the method emulates phasor-based simulation that is very suitable for extracting envelope information at relatively large time step sizes. At zero shifting, instantaneous values are being tracked. It is shown how this shifting plays a critical role in integrating synchronous machine models that are represented using the Park transformation with the network model. In a further step, it is illustrated how the modeling approach is modified if the Park transformation is not applied. For illustrative purposes, the integration is first validated for a single-machine-infinite-bus system. In a following multi-machine test case involving four machines in two areas, the added value of the proposed methodology becomes clear as both electromagnetic transients and electromechanical transients are emulated accurately and efficiently within one simulation run.

This paper presents a design procedure for a robust and adaptive fuzzy neural network-based power system stabilizer (RAFNNPSS) and investigates the robustness and adaptive feature of the RAFNNPSS for a single machine connected to an infinite bus system and multi-machine power systems in order to enhance the dynamic stability (small signal stability of the system). The parameters of RAFNNPSS are tuned by adaptive neural network (NN). This RAFNNPSS uses adaptive network-based fuzzy inference system (ANFIS) network, which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFNNPSS. Speed deviation of synchronous generator and its derivative are chosen as the input signals to the RAFNNPSS. The dynamic performance of single-machine infinite bus (SMIB) system, a two-area, five-machine, eight-bus power system and a large power system (10-machine, 39-bus New England system) with the proposed RAFNNPSS under different operating conditions and change in system parameters have been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic-based PSS (FPSS) are compared with the proposed RAFNNPSS. The simulation results demonstrate that the proposed RAFNNPSS performs well in damping and quicker response when compared with the other two PSSs. Copyright © 2008 John Wiley & Sons, Ltd.

The design of excitation controllers to improve transient stabilization of power systems is a topic of renewed interest in the control community. Existence of a state-feedback stabilizing law for multi-machine aggregated reduced network models has recently been established. In this paper we extend this result in two directions: first, in contrast with aggregated models, we consider the more natural and widely popular structure-preserving models that preserve the identity of the network components (generators, loads and lines) and allow for a more realistic treatment of the loads. Second, we explicitly compute a control law that, under a detectability assumption, ensures that all trajectories converge to the desired equilibrium point, provided that they start and remain in the region where the model makes physical sense.

In this paper, the algorithm we developed previously (1991) and its application to power system stabiliser design for single-machine infinite-bus systems is extended to multi machine systems. This is based on: 1) the pseudo global system models introduced in this paper; 2) a frequency-response based model-order reduction technique; and 3) the pole assignment algorithm for the single-input multi-output control systems presented by us previously (1991). The design method is further illustrated by an application to a model of a practical 10 machine power system.

A new indirect adaptive type-2 fuzzy sliding mode controller as a power system stabilizer (PSS) is proposed for damping low frequency power oscillation of interconnected power system and capable of performing well for a wide range of variations in system parameters and/or operating conditions. The proposed controller design is based on the integration of sliding mode control (SMC) and Adaptive type-2 fuzzy control. The type-2 fuzzy logic system is used to approximate the unknown system function and PI control term is used to eliminate chattering action in the design of sliding mode control. Using Lyapunov stability theory, the adaptation laws are developed to make the controller adaptive to take care of the changes due to the different operating conditions occurring in the power system and guarantees stability converge. The robustness of the proposed stabilizer has been tested on a two-area four machine power system. Nonlinear simulation studies show the best performance of the proposed stabilizer and confirm its superiority over adaptive type-2 fuzzy synergetic (AFSPSS), adaptive type-2 fuzzy (AFPSS) and the conventional (CPSS) stabilizers.

For the two area interconnected power system with SVC, it is equivalent to the two machine system. A new stability controller for SVC which is designed to use the improved adaptive backstepping method and adaptive sliding mode control. The derivative of the virtual control is viewed as bounded uncertainty. In order to avoid the problem of “explosion of terms” of the backstepping method, the continuous sliding mode term is adopted to compensate the uncertainty. In this paper, the mathematical model of the system does not use the linearization method, so that the nonlinear of the system can be kept. The simulation results show that the controller can improve the transient stability of interconnected power system.

Excitation control is one of the most effective means to improve the dynamic performance of power systems. This paper investigate the switched excitation control of power systems with generator tripping operation under emergency. First, we discuss the dynamics of the generators under different operating conditions and put forward a switched power system model. Then, the switched dynamic model is transformed to its equivalent switched dissipative Hamiltonian system via a proper pre-feedback control, based on which an energy-based switched excitation controller is constructed. Using the Hamiltonian functions of the subsystems as multiple Lyapunov functions, the stability of the closed-loop system is analyzed. Simulation results show that the proposed switched excitation controller can effectively improve the transient stability of the power systems.

This paper presents the application of spider monkey optimization based PI controller to a VSC-HVDC based four machine power system. A PI controller, whose gains are selected by spider Monkey optimization technique, is used to minimize the power oscillations and to recover the dynamic stability of VSC-HVDC based four machine power systems. The proposed optimized PI controller is tested under different faults like three phase LLLG fault, Change of torque angle, etc. The comprehensive simulations are carried out in time domain in MATLAB/Simulink environment. The following figures depicts that optimized PI controller is more efficient than conventional PI controller.

Excitation supplementary control is used in power systems to enhance the damping of low frequency oscillations due to the mechanical modes of the generators. Traditionally, frequency deviation is fed back to the exciter through phase lag-lead compensators to form the supplementary control. This supplementary control is known as power system stabilizer (PSS). An optimal power system stabilizer (OPSS) based on linear quadratic regulator design and utilizing the conventional phase lag-lead structure of PSS is presented in this paper. Furthermore, a suboptimal power system stabilizer (SOPSS) is proposed by neglecting the coupling gains between machines and feeding back only the speed deviation and the PSS states. The OPSS and SOPSS are compared with the conventional phase lag-lead power system stabilizer and simulation results of several power system examples are presented.

This article addresses the problem of a robust decentralised observer based guaranteed cost control for nonlinear interconnected systems with norm bounded time varying parameter uncertainties. Sufficient conditions for the robust stabilisation and the guaranteed cost control performances of the closed-loop system are formulated in an optimisation problem in terms of linear matrix inequalities. Moreover, the designed problem is solved by a proposed iterative algorithm to compute the observer and the control gain matrices, to minimise the upper bound of a prescribed performance index and to maximise the nonlinearity domain, which leave the augmented system stable, despite the emerged uncertainties on the subsystems and on the interconnections between them. An extensive application example of a power system with three interconnected machines is discussed to demonstrate the reliability of the proposed control approach.

In many manufacturing environments, costly job inspection provides information about the random deterioration of the machines. The resulting maintenance and inspection problem is extensively studied for a single machine system by using the framework of Partially Observable Markov Decision Processes (POMDPs). In this work, this concept is extended to multiple operations and multiple job types by considering two process flow topologies: (i) re-entrant flow, (ii) hybrid flow. The resulting (significantly large sized) POMDPs are solved using a point based method called PERSEUS, and the results are compared with those obtained by conventionally used periodic policies.

The design of excitation controllers to improve transient stabilization of power systems is a topic of renewed interest in the control community. Existence of a state– feedback stabilizing law for multi–machine aggregated reduced network models has recently been established. In this paper we extend this result in two directions: first, in contrast with aggregated models, we consider the more natural and widely popular structure–preserving models (SPM) that preserve the identity of the network components and allow for a more realistic treatment of the loads. Second, we explicitly compute a control law that, under a detectability assumption, ensures that all trajectories converge to the desired equilibrium point, provided that they start and remain in the region where the model makes physical sense.

The intent and purpose of this work was to investigate and demonstrate cooperative behavior among a group of mobile robot machines. The specific goal of this work was to build a small swarm of identical machines and control them in such a way as to show a coordinated movement of the group in a flocking manner, similar to that observed in nature. Control of the swarm`s individual members and its overall configuration is available to the human user via a graphic man-machine interface running on a base station control computer. Any robot may be designated as the nominal leader through the interface tool, which then may be commanded to proceed to a particular geographic destination. The remainder of the flock follows the leader by maintaining their relative positions in formation, as specified by the human controller through the interface. The formation`s configuration can be altered manually through an interactive graphic-based tool. An alternative mode of control allows for teleoperation of one robot, with the flock following along as described above.

This paper presents a method for the coordination of fixed-parameter robust damping controllers, such as power system stabilizers (PSS), FACTS-based stabilizers, in multimachine power systems. The robustness of the controllers is ensured by the selection of a robust operating condition, at which controllers are designed. An example three-machine power system is demonstrated in the paper, where a TCSC-based stabilizer and PSS are designed in coordination to damp two-mode oscillations.

In this paper, a new mathematical programming model is developed to address common issues relating to single-stage CONstant-Work-In-Process based production lines. A Ge-netic Algorithm (GA) approach is then proposed to directly solve the model in order to simultaneously determines the optimal job sequence and WIP level. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, our proposed method does not rely on a linearized or simplified model of the system. results from a comprehensive numerical example indicate computational efficiency and validation of our method.

An indirect Adaptive fuzzy excitation control (IAFLC) of power systems based on multi-input-multi-output linearization technique is developed in this paper. The power system considered in this paper consists of two generators and infinite bus connected through a network of transformers and transmission lines. The fuzzy controller is constructed from fuzzy feedback linearization controller whose parameters are adjusted indirectly from the estimates of plant parameters. The adaptation law adjusts the controller parameters on-line so that the plant output tracks the reference model output. Simulation results shown that the proposed controller IAFLC, compared with a controller based on tradition linearization technique can enhance the transient stability of the power system.

The recent advances in power electronics have led to the development of the Flexible Alternating Current Transmission Systems (FACTS). FACTS devices require fully rated capacitor and reactor banks in order to supply/absorb reactive power. These requirements can be escaped by employing solid state synchronous voltage sources. The family of FACTS devices based on Voltage Source Converters (VSC) consists of STATCOM, SSSC and UPFC. The high cost of these VSC based FACTS controllers is found to be the major hindrance to their widespread use. Novel and cost effective converter based FACTS topologies have been proposed which make use of presumably existing passive components also and thus regarded as hybrid in nature. The different HPFC configurations devised by the investigator have been implemented on a Multi-Machine system and simulated using MATLAB/ SIMULINK. The comparison of simulation results shows that HPFC is a better option for damping power system oscillations and thus enhancing the stability of the system.

Static phase shifters (SPSs) are new FACTS devices. They can offer both static and dynamic controls of power flows in power systems. This paper reports the build-up of an SPS mathematical model that can be applied to the transient stability analysis of a multimachine system. The simulation results reveal the potential of using SPS for enhancing the transient stability of power systems.

This paper proposes the parallel resonance type fault current limiter (PRFCL) to enhance the transient stability of multi-machine power systems. The transient stability performance of the parallel resonance type FCL is compared with that of the bridge type fault current limiter (BFCL). The total kinetic energy (TKE) of the generators in the system is used to determine the transient stability enhancement index. Also, the critical clearing time has been presented as a stability limit. Simulations are performed by using the Matlab/Simulink software. Simulation results of both permanent and temporary faults at different points of the IEEE 39 bus power system indicate that the parallel resonance type FCL can enhance the transient stability of the system well. Also, the performance of the proposed parallel resonance type FCL is better than that of the bridge type FCL.

This paper proposes a decentralized output feedback control scheme for the multi-machine power system. The synthesis problem is formulated as scaled H ∞ control problem and a novel LMI-based algorithm is developed to compute the decentralized output feedback controller. The proposed control law provides robustness with regard to parametric uncertainties and attenuates bounded exogenous disturbances in the sense of L 2 -gain. Simulation results clearly show the effectiveness of the proposed decentralized robust output feedback control scheme.

With the advent of smart-grid technology, the wide application of FACTS (Flexible AC Transmission Systems)- based damping controllers exerts influences on both small signal stability and transient stability. Traditional Modal Analysis (MA) method can hardly meet the requirements of online applications. According to the inter-area oscillation mode to be studied, the multi-machine system can be equivalent to a two-machine system, and the Wide-Area Measurement System (WAMS) supports real-time dynamic equivalents. This paper explores the electric torque analysis (ETA) method in the two-machine system and the criteria of stability. An online coordination scheme of SVC-based Wide-Area Damping Controller (WADC) to maximize the damping ratio of the inter-area mode with the transient stability constraints is presented in this paper. The scheme is treated as an optimization issue and a plant growth simulation algorithm (PGSA) is employed to search for the optimal controller parameters. A 4-machine-2-area benchmark is used to verify the effectiveness and advantages of the proposed scheme for online applications.

In this paper, a dynamic algorithm for load sharing in multi-machine systems is proposed based on a principle of hierarchical operation of the motors. The motors share the load cooperatively according to the load variations and their power rating in order to save energy and optimize the performance of the drive system and the motors. The proposed algorithm demonstrates superiority in optimizing the drive system performance, and provides a significant saving of energy consumption and minimizing losses as compared to equal torque sharing.

Fractional-order proportional-integral-differential (FOPID) controller is a generalization of PID controller employing fractional calculus. In this paper, FOPID power system stabilizer (FOPID-PSS) called PIλDμ-PSS is introduced to improve damping of electromechanical (EM) low frequency oscillations (LFOs) in a multi-machine power system. FOPID-PSS design is formulated as a nonlinear optimization problem in which adjustable parameters are optimized simultaneously via an improved particle swarm optimization (IPSO) algorithm by minimizing integral of time multiplied squared error (ITSE) performance index. Dynamic stability of 3-machine 6-bus test system is evaluated by eigenvalue analysis and time domain simulations in MATLAB/SIMULINK environment considering FOPID-PSS. Results are compared with both classical integer-order PID-PSS (IOPID-PSS) and lead-lag conventional PSS (CPSS) from standpoints of dynamic response and system robustness with respect to different uncertainties. The investigations using various performance indices demonstrate that proposed FOPID-PSS achieves superior dynamic performance and more robustness against uncertainties in comparison with IOPID-PSS and CPSS.