1,720,972 research outputs found
Extensive assessment of virtual synchronous generators in intentional island mode
No full textVirtual Synchronous Generators (VSGs) are considered one of the most effective cutting-edge technologies for seamlessly integrating Renewable Energy Systems (RESs) into power grids. While various VSG control schemes have been proposed and analysed for different technical aspects, not many experimental demonstrations have been published about VSGs operating in Intentional Island Mode (IIM). Inversely, the IIM has become increasingly attractive, nowadays, to energize those remote areas of the world where entire districts are served by a small number of RESs operating off-grid. In this scenario, the main objective of this paper is to present an extensive performance assessment of the VSG operating in IIM. Different working conditions are considered, with special focus on: black start capability, grid-connected-to-IIM transition, grid reconnection, feeding of non-linear and asymmetric local loads, load sharing between multiple VSGs operating in parallel. In order to achieve this objective, an improved structure of VSG is implemented on 8kVA three-phase converters and significant experimental results are presented. Algorithms for black start capability and grid synchronization are illustrated in detail as well. Meaningful information is provided for engineers involved in activity of designing and testing islanded VSGs
Experimental evaluation of virtual synchronous generators with high regulation performance to enhance AC–DC grid interconnections
No full textIn the recent years there has been a significant increase in the interest surrounding the interconnection of AC and DC networks due to its potential to improve the power systems reliability. Despite the potential advantages, however, the AC and DC grid coexistence can pose challenges if not appropriately managed. In this scenario, this paper presents an enhanced control scheme enabling the use of Virtual Synchronous Generators (VSGs) as elements of interconnection between AC and DC networks. The proposed solution enables VSG operation with both fast dynamics for DC voltage regulation and slow dynamics for AC inertia emulation. This approach accommodates the contrasting requirements arising from the distinct mechanisms of DC voltage regulation and AC frequency regulation. Simulation and experimental results obtained with a 8 kVA three-phase converter prototype are provided to validate the effectiveness of the proposed technique and prove its better performances with respect to the solutions currently presented in literature
Coordinated Control of the Volt-Var Optimization Problem Under PV-Based Microgrid Integration into the Power Distribution System: Using the Harmony Search Algorithm
No full textHighlights: What are the main findings? Power management: Peak demand reduction and DER utilization via PV-based microgrid. Active power loss and voltage fluctuations reduction. What is the implication of the main finding? Coordinated control of Volt-Var devices with grid-connected microgrid. Effective utilization of system components, and determining the optimum working conditions of the system. A coordinated control for the volt-var optimization (VVO) problem is presented using load tap changer transformers, voltage regulators, and capacitor banks with the integration of a PV-based microgrid. The harmony search (HS) algorithm, which is a metaheuristic-based optimization algorithm, was used to determine global optimum settings of related devices to operate efficiently under changing conditions. The major objectives of volt-var optimization were to reduce power losses, peak power demands, and voltage variations in the distribution circuit while maintaining voltages within the permitted range at all nodes and under all loading conditions. The problem was a mixed integer nonlinear problem with discrete integer variables; binary variables for the capacitor status on/off, voltage regulator taps as integers, and continuous variables; the current output of the microgrid; and nonlinear electric circuit equations. The simulations were verified using the IEEE 13-node test circuit. Daily load profiles of the main power system grid and the microgrid’s PV were used with a 15 min resolution. Power flow solutions were produced using the OpenDSS (version 9.5.1.1, year 2022) power distribution system solver. It can be applied to operational and planning purposes. The results showed that active power loss, peak power demand, and voltage fluctuation were significantly reduced by the coordinated control of the volt-var problem
Flatness-based control in successive loops for VSI-fed PM synchronous motors and induction motors
No full textElectric traction systems consisting of inverter controlled three-phase motors are widely used in electric vehicles. In this article the control problem for the nonlinear dynamics of Voltage Source Inverter-fed synchronous and asynchronous motors is solved with the use of a flatness-based control approach which is implemented in successive loops. The state–space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. In this chain of (Formula presented.) subsystems, the state variables of the subsequent (i + 1th) subsystem become virtual control inputs for the preceding (ith) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N−1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a Voltage Source Inverter-fed Permanent Magnet Synchronous Motor (VSI-fed PMSM), (ii) control of a Voltage Source Inverter-fed Induction Motor (VSI-fed IM)
Flatness-based control in successive loops for VSI-fed induction motors
No full textThe control problem for the nonlinear dynamics of Voltage Source Inverter-fed asynchronous motors is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. In this chain of i1,2,...,N subsystems, the state variables of the subsequent (ith) subsystem become virtual control inputs for the preceding (i-th) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N-1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in case of a Voltage Source Inverter-fed Induction Motor (VSI-fed IM). The article's results can be used in electric vehicles and their electric traction systems which often comprise inverter controlled three-phase motors
Power quality disturbance signal segmentation and classification based on modified BI-LSTM with double attention mechanism
No full textThis paper proposes a recurrent neural network based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanisms is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, an attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. This attention gate applied in the LSTM cells improves effective and decisive key information extraction from future and past states of the PQD signal and the addition of attention layer improves the overall decision capability of the model, saving computation time, and increasing PQD classification accuracy. Finally, a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The proposed BI-LSTM model with attention gate and attention layer mechanism is compared to different baseline models based on recurrent neural network and convolution neural network (CNN). From the simulation study, it is inferred that with the proposed method, the multiple combined PQD signals are easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time as compared to popular signal processing based approaches for which classification of multiple PQDs is computationally difficult
Nonlinear optimal control for the five-axle and three-steering coupled-vehicle system
No full textTransportation of heavy loads is often performed by multi-axle multi-steered heavy duty vehicles In this article a novel nonlinear optimal control method is applied to the kinematic model of the five-axle and three-steering coupled vehicle system. First, it is proven that the dynamic model of this articulated multi-vehicle system is differentially flat. Next. the state-space model of the five-axle and three-steering vehicle system undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization is based on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the five-axle and three-steering vehicle system a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and minimal dispersion of energy by the propulsion and steering system of the five-axle and three-steering vehicle system
Noisy and non-stationary power quality disturbance classification based on adaptive segmentation empirical wavelet transform and support vector machine
No full textThe empirical wavelet transform (EWT) has demonstrated better performance in signal noise removal compared to other threshold techniques based on the conventional wavelet transform (WT), achieved by generating an adaptive filter bank. However, the enhanced EWT (EEWT), the most advanced form of EWT, has limited practical applications since it requires previous knowledge of the spectral components present in the superposed signal. This work introduces a novel adaptive empirical wavelet transform (AEWT) technique designed to segment the signal spectrum and adaptively find out the count of signal components, eliminating the need for manual intervention as required by the traditional EEWT technique. The proposed AEWT technique is particularly well-suited for practical applications, including power quality disturbance (PQD) classification. It is observed that the proposed AEWT technique offers better border detection and denoising performance for PQD signal. In this paper, AEWT is proposed to extract robust features from noisy and non-stationary combined PQD signals, resulting in high accuracy in PQD classification based on these robust features. The proposed AEWT method utilizes a smaller dataset for feature extraction compared to Deep Learning (DL)-based methods, yet achieves higher accuracy than traditional signal processing techniques
Nonlinear optimal and multi-loop flatness-based control of induction motor-driven desalination units
No full textNonlinear Optimal Control of an H-Type Gantry Crane Driven by Dual PMLSMs
No full textGantry cranes of the H-type with dual electric-motor actuation are widely used in industry. In this article, the control problem of an H-type gantry crane which is driven by a pair of linear permanent magnet synchronous motors (dual PMLSMs) is considered. The integrated system that comprises the H-type gantry crane and its two LPMSMs is proven to be differentially flat. The control problem for this robotic system is solved with the use of a nonlinear optimal control method. To apply the nonlinear optimal control method, the dynamic model of the H-type gantry crane with dual LPMSM undergoes approximate linearization at each sampling instant with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization point is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the nonlinear optimal control method are proven through Lyapunov analysis. The proposed control scheme achieves stabilization of the H-type gantry crane with dual LPMSMs without the need of diffeomorphisms and complicated state-space model transformations
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