Academic literature on the topic 'Power quality perturbations'

Power is usually generated by various power plants and transmitted at high voltages to the end users. This is made possible through a sophisticated network of diverse components utilized in a given power system. Also, every day, domestic and commercial activities such as cooking, transportation, manufacturing, illumination of offices and homes, air conditioning, refrigeration, and the likes, makes use of electricity. It is thus impossible that the power quality (PQ) will remain the same given that most components utilized across these processes are mostly and highly non-linear in nature. What this implies is that there will always be power quality problems, and these issues cannot be totally eliminated but can be effectively reduced. In this study, firstly, the various PQ problems were categorized accordingly and their overview given. Then, the causes and effects of these issues were succinctly listed and lastly, the ways of reducing PQ perturbations were given. Industry experts, other researchers and enthusiasts will find this study very useful.

Power is usually generated by various power plants and transmitted at high voltages to the end users. This is made possible through a sophisticated network of diverse components utilized in a given power system. Also, every day, domestic and commercial activities such as cooking, transportation, manufacturing, illumination of offices and homes, air conditioning, refrigeration, and the likes, makes use of electricity. It is thus impossible that the power quality (PQ) will remain the same given that most components utilized across these processes are mostly and highly non-linear in nature. What this implies is that there will always be power quality problems, and these issues cannot be totally eliminated but can be effectively reduced. In this study, firstly, the various PQ problems were categorized accordingly and their overview given. Then, the causes and effects of these issues were succinctly listed and lastly, the ways of reducing PQ perturbations were given. Industry experts, other researchers and enthusiasts will find this study very useful.

Limitation of electromagnetic disturbances occurring in industrial processes and improving power quality are major concerns of professionals in the energy sector. The goal of the work consists in emphasizing the power quality problems incurred due to equipment, which causes disturbances in electricity distribution networks, and highlightes solutions for their effective functioning. The paper presents an application using specialized software to analyze power grids in order to create an overview of solutions for efficient operation of the distribution system in case when a user determines fluctuations of electrical supply voltage. The work aims verifying the connection of a STATCOM to limit disturbances, the data will be validated by experimental tests.

Measures for limiting environmental pollution by power plants using fossil fuel sources and for efficient use of local energy sources can cause significant changes in the structure of the electricity distribution networks through the development of micro-grids that can operate practically isolated or connected to the public electricity network, which ensures bilateral exchanges of energy. The microgrid requires a detailed analysis of the level of quality of electricity supplied to the microgrid. The detailed analysis of technical solutions is needed to ensure a necessary level of electric energy quality in practically isolated microgrid. Some of the specific conditions of microgrid operation are analyzed, which will ensure operation of the system at the action of disturbances.

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage perturbations is studied. In order to ensure the high-quality performance of LCL-SAF in the presence of voltage perturbations, the robust continuous second-order sliding mode controller (2-SMC), including twisting and super-twisting controllers, and continuous higher-order sliding mode controller (C-HOSMC)-based approaches are employed. These controllers, whose outputs are processed by pulse-width modulation (PWM), allow minimization of the phase shift and prevent the generation of discontinuous chattering commands, which can severely damage the VSI components. Moreover, an integration of a generalized instantaneous power identification algorithm with an advanced phase locked loop (PLL) was proposed and experimentally tested to validate the effective performances of SAF under severe perturbations. Additionally, the studied approaches were tested via simulations taking into account a conventional nonlinear industrial load in a real textile factory environment, using measurements provided by power quality analyzers. Finally, the effects of the measuring devices, including the current and voltage sensors, on the accuracy and reliability of the SAF and, consequently, on the PQ of the electric power grid were studied via simulations and experimentally. The results of this study support the validity of the recently published patent.

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

Nonlinear loads and grid side disturbances in the power distribution network cause harmonic rich currents and voltage perturbations such as voltage harmonics, voltage sags and voltage swells. The unified power quality conditioner (UPQC) with shunt inverter and series inverter are usually employed for elimination of current and voltage related power quality issues. However, elimination of voltage and current quality issues in a grid connected Photovoltaic (PV) system is essential. Therefore, PV integrated UPQC topology is proposed in this paper with a novel controller for power quality improvement. The control algorithm based on notch filter Phase Locked Loop (PLL) is utilized for proper phase detection and elimination of voltage and current disturbances. PV tied UPQC controller based on notch filter PLL has the capability to avoid multiple zero crossing during highly distorted grid voltage condition. Conventional control algorithms such as synchronous reference frame theory, unit vector controller based on traditional PLL are also analyzed for the control of PV tied UPQC. The proposed system is developed in Matlab/Simulink and the results obtained are evaluated to measure the performance. A real-time hardware prototype is developed using dSPACE processor. The obtained experimental results validate the efficiency of the proposed controller for PV tied UPQC.

Forecasting random perturbations allows improving the control quality in intelligent transport systems and ensuring the efficient operation of diagnostic systems. Several works are known where extrapolator models based on Chebyshev polynomials orthogonal on equidistant points are presented. These models use a predictive polynomial whose coefficients are computed using the least squares criterion. Additionally, an analysis of forecast errors for random stationary input signals has been conducted. At the same time, in the case of non-stationary input signals, singular perturbations may occur, the influence of which on the extrapolator leads to significant forecast errors.
 This article presents an example of the occurrence of additive perturbations that arise in automatic train control systems. An analytical expression has been derived, and calculations of forecast error magnitudes in the presence of singular perturbations have been conducted. The analysis of the calculation results allows determining the influence of extrapolator parameters on the forecast error magnitude, highlighting the necessity of detecting singular perturbations, and excluding their influence on the forecast error magnitude.
 The article discusses an algorithm for detecting singular perturbations and their exclusion during the forecasting process. The conclusion is drawn about the effectiveness of using extrapolators for random perturbations with the exclusion of singular perturbations in intelligent systems for automatic train control in subway transportation.

La reconnaissance des perturbations survenant sur les réseaux HTA est une problématique essentielle pour les clients industriels comme pour le gestionnaire du réseau. Ces travaux de thèse ont permis de développer un système d'identification automatique. Il s'appuie sur des méthodes de segmentation qui décomposent de manière précise et efficace les régimes transitoires et permanents des perturbations. Elles utilisent des filtres de types Kalman linéaire ou anti-harmoniques pour extraire les régimes transitoires. La prise en compte des variations harmoniques et de la présence de transitoires proches se fait à l'aide de seuils adaptatifs. Des méthodes de correction du retard a posteriori permettent d'améliorer la précision de la décomposition. Des indicateurs adaptés à la dynamique des régimes de fonctionnement analysés sont utilisés pour caractériser les perturbations. Peu sensibles aux erreurs de segmentation et aux perturbations harmoniques, ils permettent une description fiable des phases des perturbations. Deux types de systèmes de décision ont également été étudiés : des systèmes experts et des classifieurs SVM. Ces systèmes ont été mis au point à partir d'une large base de perturbations simulées. Leurs performances ont été évaluées sur une base de perturbations réelles : ils déterminent efficacement le type et la direction des perturbations observées (taux de reconnaissance moyen > 98%).

Cette thèse porte sur la surveillance des perturbations de la qualité de l’énergie d’un réseau électrique via des techniques paramétriques de traitement du signal. Pour élaborer nos algorithmes de traitement du signal, nous avons traité les problèmes d’estimation des différentes grandeurs du réseau électrique triphasé et de classification des perturbations de la qualité d'énergie. Pour ce qui est du problème d’estimation, nous avons développé une technique statistique basée sur le maximum de vraisemblance. La technique proposée exploite la nature multidimensionnelle des signaux électriques. Elle utilise un algorithme d’optimisation pour minimiser la fonction de vraisemblance. L’algorithme utilisé permet d’améliorer les performances d’estimation tout en étant d’une faible complexité calculatoire en comparaison aux algorithmes classiques. Une analyse plus poussée de l’estimateur proposé a été effectuée. Plus précisément, ses performances sont évaluées sous un environnement incluant entre autres la pollution harmonique et interharmonique et le bruit. Les performances sont également comparées aux exigences de la norme IEEE C37.118.2011. La problématique de classification dans les réseaux électriques triphasés a plus particulièrement concerné les perturbations que sont les creux de tension et les surtensions. La technique de classification proposée consiste globalement en deux étapes : 1) une pré-classification du signal dans l’une des 4 préclasses établis et en 2) une classification du type de perturbation à l’aide de l’estimation des composants symétriques.Les performances du classificateur proposé ont été évaluées, entre autres, pour différentes nombre de cycles, de SNR et de THD. L’estimateur et le classificateur proposés ont été validés en simulation et en utilisant les données d’un réseau électrique réel du DOE/EPRI National Database of Power System Events. Les résultats obtenus illustrent clairement l’efficacité des algorithmes proposés quand à leur utilisation comme outil de surveillance de la qualité d’énergie<br>This thesis deals with electric grid monitoring of power quality (PQ) disturbances using parametric signal processing techniques. The first contribution is devoted to the parametric spectral estimation approach for signal parameter extraction. The proposed approach exploits the multidimensional nature of the electrical signals.For spectral estimation, it uses an optimization algorithm to minimize the likelihood function. In particular, this algorithm allows to improve the estimation accuracy and has lower computational complexity than classical algorithms. An in-depth analysis of the proposed estimator has been performed. Specifically, the estimator performances are evaluated under noisy, harmonic, interharmonic, and off-nominal frequency environment. These performances are also compared with the requirements of the IEEE Standard C37.118.2011. The achieved results have shown that the proposed approach is an attractive choice for PQ measurement devices such as phasor measurement units (PMUs). The second contribution deals with the classification of power quality disturbances in three-phase power systems. Specifically, this approach focuses on voltage sag and swell signatures. The proposed classification approach is based on two main steps: 1) the signal pre-classification into one of 4 pre-classes and 2) the signature type classification using the estimate of the symmetrical components. The classifier performances have been evaluated for different data length, signal to noise ratio, interharmonic, and total harmonic distortion. The proposed estimator and classifier are validated using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed techniques for PQ monitoring purpose

Les énergies renouvelables, l’énergie sous la forme électrique et son transport à l’aide de réseaux électriques intelligents représentent aujourd’hui des enjeux majeurs car ils ont de grands impacts environnementaux et sociétaux. Ainsi, la production, le transport et la gestion de l’énergie électrique, continuent toujours à susciter un intérêt croissant. Pour atteindre ces objectifs, plusieurs verrous technologiques doivent être levés. Au-delà des questions liées aux architectures des réseaux électriques, aux modèles, aux outils de dimensionnement, à la formalisation de caractéristiques et d’indicateurs, aux contraintes et aux critères, à la gestion et à la production décentralisée, la qualité de la puissance électrique est centrale pour la fiabilité de l’ensemble du système de distribution. Les perturbations affectent la qualité des signaux électriques et peuvent provoquer des conséquences graves sur les autres équipements connectés au réseau. Les travaux de cette thèse s’inscrivent dans ce contexte et de fait ils sont orientés vers le développement de modèles, d’indicateurs et de méthodes de traitement des signaux dédiés à la surveillance en temps-réel des performances des réseaux de distribution électrique.Cette thèse analyse la qualité de la puissance électrique, en prenant en compte plusieurs caractéristiques bien connues ainsi que leur pertinence. Les modèles des systèmes électriques et les méthodes de traitement du signal pour estimer leurs paramètres sont étudiés pour des applications en temps-réel de surveillance, de diagnostic et de contrôle sous diverses conditions. Parmi tous, la fréquence fondamentale est l’un des paramètres les plus importants pour caractériser un système de distribution électrique. En effet, sa valeur qui est censée être une constante, varie en permanence et reflète la dynamique de l’énergie électrique disponible. La fréquence peut également être affectée par certaines productions d’énergie renouvelable et peut être influencée par des mauvaises synchronisations de certains équipements. En outre, la puissance absorbée par les charges ou produite par des sources est généralement différente d’une phase à l’autre. Évidemment, la plupart des installations électriques existantes avec plusieurs phases, qu’elles soient résidentielles ou industrielles, travaillent dans des conditions déséquilibrées. Identifier les composantes symétriques de tension est dans ce cas un moyen pertinent pour quantifier le déséquilibre entre les phases d’un système électrique.De nouvelles représentations de type espace d’état et modélisant des systèmes électriques sont proposées pour estimer la fréquence fondamentale et pour identifier les composantes symétriques de tension des systèmes électriques triphasés et déséquilibrés. Le premier modèle d’espace d’état proposé considère la fréquence fondamentale comme connue ou obtenue par un autre estimateur. En contrepartie, il fournit les autres paramètres caractérisant le système électrique. Un second modèle d’état-espace est introduit. Il est original dans le sens où il ne nécessite aucune connaissance de la fréquence fondamentale. Une de ses variables d’état est directement reliée à la fréquence et permet donc de la déduire. En outre, ce nouvel espace d’état est parfaitement capable de représenter des systèmes électriques à trois phases équilibrés et non équilibrés. [...]<br>Renewable energy, electricity and smart grids are core subjects as they have great environmental and societal impacts. Thus, generating, transporting and managing electric energy, i.e., power, still continue to drive a growing interest. In order to properly achieve these goals, several locks must be removed. Beyond issues related to the distribution architecture, the formalization of models, sizing tools, features and indicators, constraints and criteria, decentralized generation and energy management, power quality is central for the whole grid’s reliability. Disturbances affect the power quality and can cause serious impact on other equipment connected to the grid. The work of this thesis is part of this context and focuses on the development of models, indicators, and signal processing methods for power quality monitoring in time-varying power distribution systems.This thesis analyzes the power quality including several well-known features and their relevance. Power system models and signal processing methods for estimating their parameters are investigated for the purpose of real-time monitoring, diagnostic and control tasks under various operating conditions. Among all, the fundamental frequency is one of the most important parameters of a power distribution system. Indeed, its value which is supposed to be a constant varies continuously and reflects the dynamic availability of electric power. The fundamental frequency can also be affected by renewable energy generation and by nasty synchronization of some devices. Moreover, the power absorbed by loads or produced by sources is generally different from one phase to the other one. Obviously, most of the existing residential and industrial electrical installations with several phases work under unbalanced conditions. Identifying the symmetrical components is therefore an efficient way to quantify the imbalance between the phases of a grid. New state-space representations of power systems are proposed for estimating the fundamental frequency and for identifying the voltage symmetrical components of unbalanced three-phase power systems. A first state-space representation is developed by supposing the fundamental frequency to be known or to be calculated by another estimator. In return, it provides other parameters and characteristics from the power system. Another original state-space model is introduced which does not require the fundamental frequency. Here, one state variable is a function of the frequency which can thus be deduced. Furthermore this new state-space model is perfectly are able to represent a three-phase power system in both balanced and unbalanced conditions. This not the case of lots of existing models. The advantage of the proposed state-space representation is that it gives directly access to physical parameters of the system, like the frequency and the amplitude and phase values of the voltage symmetrical components. Power systems parameters can thus be estimated in real-time by using the new state-space with an online estimation process like an Extended Kalman Filter (EKF). The digital implementation of the proposed methods presents small computational requirement, elegant recursive properties, and optimal estimations with Gaussian error statistics.The methods have been implemented and validated through various tests respecting real technical constraints and operating conditions. The methods can be integrated in active power filtering schemes or load-frequency control strategies to monitor power systems and to compensate for electrical disturbances

Higher-order statistics demonstrate their innovative features to characterize power quality events, beyond the traditional and limited Gaussian perspective, integrating time-frequency features and within the frame of a Higher-Order Neural Network (HONN). With the massive advent of smart measurement equipment in the electrical grid (Smart Grid), and in the frame of high penetration scenarios of renewable energy resources, the necessity dynamic power quality monitoring is gaining even more importance in order to identify the suspicious sources of the perturbation, which are nonlinear and unpredictable in nature. This eventually would satisfy the demand of intelligent instruments, capable not only of detecting the type of perturbation, but also the source of its origin in a scenario of distributed energy resources.

This chapter displays a control strategy for a photovoltaic system (PV) linked to the network with two phases of a PWM converter, where the first phase is a DC-DC converter linked among the photovoltaic source and the DC-AC converter. The second phase is a DC-AC converter linked to the grid. The maximum power point (MPP) is tracked by DC-DC converter, which increases the DC bus voltage. The P&amp;O (perturbation and observation) technique is utilized as a direct current (DC-DC) converter controller to make the PV arrays work at greatest value of power under changing weather conditions. The DC-AC converter transfers the maximum power extracted from the PV cell into the grid. To improve the energy quality produced by the photovoltaic field other than the performance of the pulse width modulation (PWM) inverter, direct power control (DPC) is used to achieve these improvements. The simulation results showed a good performance of the suggested controller. Decoupled power control is achieved successfully, and a good power quality with low harmonic distortion rate (THD) is obtained.

We introduce meta-reflect-arrays composed of magnetic dipole guided-mode resonances whose high quality factors enable wavefront shaping with vanishingly small structural perturbations (&lt;2.6% volume fraction). This brings low-power, efficient, and high-resolution spatial light modulation into view.

With the advent of lean premixed gas turbine combustors, research in the area of thermo-acoustic instabilities and active combustion control came into the limelight. To be able to predict and control these instabilities, it is required that both the acoustics of the system, and a frequency-resolved response of the combustion process to velocity perturbations be understood. Experimental techniques developed by the Virginia Active Combustion Control Group at Virginia Tech, to obtain an open loop flame transfer function were applied to both fully and partially premixed swirl stabilized turbulent gaseous flames using commercial grade methane as fuel. A frequency-resolved fluctuating velocity was applied at the inlet of the combustor within the frequency range of 20–400 Hz, and the OH* chemiluminescence was used as a measure of the fluctuating heat release rate within the flame. Experiments were conducted at atmospheric pressure for two swirl numbers of 0.79 and 1.19, and three equivalence ratios of 0.55, 0.60 and 0.65. The flow rates studied resulted in Reynolds numbers of 14,866 and 19,821. The results show that for the linear range, the magnitude of the FRF is primarily dependent on the premixing quality and the mean energy content of the mixture, while the phase of the FRF is quite sensitive to Φ′ oscillations and to the variations in the species concentration across the cross-section of the flow.

The accurate measurement of water content in the turbine oil has great significant for guiding steam turbine safety and economy running. A method of the on-line measurement of the water content in the turbine oil based on microwave resonant cavity perturbation is presented in this paper, The author deduced the formula of the volume proportion of water content in turbine oil, the resonant cavity and the measuring system of this method is simple. The precision of measurement can be improved by selecting work pattern with high quality factor, advancing the measurement precision of the relative frequency deflection and adopting the lower thermal expansion coefficient material and the especial resonant cavity configuration. Ten PPM water content in turbine oil can be distinguished by this method.

Abstract In reservoir engineering, history matching and calibration process yields nonunique plausible outcomes due to the inherited uncertainty of earth models. The process is carried on with the ultimate objectives of providing reliable predictive reservoir models with the highest possible quality at minimal computational overhead. This work capitalizes on the development of a tightly-coupled Surrogate AI model with Ensemble Iterative algorithm (Alturki et. al, 2024) to devise the relationships of uncertainty variables and physics model's responses with minimal full-physics simulations of the reservoir model. Surrogate AI models are supervised machine learning models that are driven by physical model responses to the changes in uncertainty variables. They are widely used methods in different engineering disciplines when the outcomes are hard to be quantified, measured or computational expensive to carry on using full physics models. Models’ calibration and history matching process involves dealing with large volumes of data, exploring vast solution space, and quantification of uncertainty in modeling. Coupling of Surrogate AI models with the power of Ensemble iterative methods allows for proper quantification of uncertainties with much less computational requirements and minimal full-physics simulation runs. In this work, modified NORNE and BRUGGE benchmark models were used to demonstrate the efficiency of the developed hybrid model to the traditionally compute-intensive and time-consuming history matching process. An initial equally probable ensemble size of 50 and 60 for NORNE and BRUGGE models, respectively, were generated to capture the influential uncertain reservoir properties (e.g., permeability tensor, transmissibility tensor, etc.). The efficiency of the tightly coupled Surrogate AI and Ensemble Iterative optimization algorithm is demonstrated by carrying on history matching on the modified NORNE and BRUGGE benchmark models. The objective function consists of a set of vectors (i.e., responses) as a result to the perturbations of the uncertainty variables (i.e., reservoir properties). The hybrid model starts with running the full-physics simulation runs for all the ensembles. The Surrogate AI model then, iteratively, evaluates the misfit and computes the responses as a result of updating uncertain reservoir parameters in searching for minima in the solution space to satisfy the minimization objective function. Once convergence is reached, full-physics simulations are run once for the ensembles to validate the updates. The results show faster convergence rate in just few iterations of the Surrogate AI model without the need for the intermediate full-physics simulation runs. This translates to eliminating about 60% of the full-physics simulation run that would normally be required by an iteration Ensemble algorithm. In addition, it is observed that the Surrogate AI convergence rate and solution quality is directly proportional to the representation of the uncertainty by the initial ensemble. As a sanity check, the history matched ensembles were run in prediction with full-physics simulation with "No Further Action" scenario to evaluate the models‘ predictive capabilities and ensure that uncertainty is well-represented in prediction. Hybrid tightly-coupled Surrogate AI model with the iterative Ensemble algorithm, drastically, reduced the number of needed full-physics simulations. That is with a faster convergence rate, remarkable computational, good quality history match. The cumulative oil production from the prediction runs indicate splendid quantification of uncertainty, measure of history match quality, and predictive capabilities.

Micro electrical mechanical systems inherently tend to behave nonlinearly in high power handling. Such nonlinearities arise from mechanical and electrical characteristics of the systems. In this study, the mechanical nonlinearities are focused and the longitudinal mode shape of a microbeam resonator with high quality factor is considered. The mechanical nonlinearities for this system are assumed to be cubic and quadratic. the method of multiple scale is used for perturbation analysis which leads us to obtain the function of displacement with respect to frequency which is beneficial to set an upper dynamic limit to avoid instability and, also, facilitates to make the system so-called almost linear.

Phased-locked arrays of diode lasers have been reported as early as 1978.(1) However, very rarely have the beam patterns of arrays displayed the desired result: a single, diffraction-limited beam in both cw and pulsed operation. Initially, the problem has been that arrays were made of gain-guided devices, that is, devices for which lateral optical-mode confinement is solely provided by the injected-carrier profile. Gain-guided devices are thus intrinsically vulnerable to gain-profile changes due to drive condition changes (i. e. cw or pulsed), spatial hole-burning at high drive levels above threshold, and/or temperature variations across the laser chip. Revealingly, recent studies of arrays of gain-guided lasers(2) show that such devices are more appropriately modeled as broad-area devices with gain perturbations rather than coupled waveguides. Thus, even though output powers as high as 5W have been reported from gain-guided arrays, their beam quality has generally been poor, which removes them as candidates for applications requiring beam-forming optics. While harder to make, arrays of index-guided lasers,(3) that is devices with "built-in" dielectric waveguides, have demonstrated both stable-beam operation under cw and pulsed drive conditions as well as single-longitudinal mode operation. Most recent work has concentrated on the fabrication and mode control of index-guided devices.