4,182 research outputs found

    Neural Network-Based Stabilizer for the Improvement of Power System Dynamic Performance

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    This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs) to improve the oscillation damping and dynamic performance of interconnected multimachine power system. The scheme was based on the use of a neural network which identifies online the optimal controller parameters. The inputs to the neural network include the active- and reactive- power of the synchronous generators which represent the power loading on the system, and elements of the reduced nodal impedance matrix for representing the power system configuration. The outputs of the neural network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing system configuration and operating condition. For a representative power system, the neural network has been trained and tested for a wide range of credible operating conditions and contingencies. Both eigenvalue calculations and time-domain simulations were used in the testing and verification of the performance of the neural network-based stabilizer

    Design of Power System Stabilizer

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    A power system stabilizer (PSS) installed in the excitation system of the synchronous generator improves the small-signal power system stability by damping out low frequency oscillations in the power system. It does that by providing supplementary perturbation signals in a feedback path to the alternator excitation system. In our project we review different conventional PSS design (CPSS) techniques along with modern adaptive neuro-fuzzy design techniques. We adapt a linearized single-machine infinite bus model for design and simulation of the CPSS and the voltage regulator (AVR). We use 3 different input signals in the feedback (PSS) path namely, speed variation(w), Electrical Power (Pe), and integral of accelerating power (Pe*w), and review the results in each case. For simulations, we use three different linear design techniques, namely, root-locus design, frequency-response design, and pole placement design; and the preferred non-linear design technique is the adaptive neuro-fuzzy based controller design. The MATLAB package with Control System Toolbox and SIMULINK is used for the design and simulations

    A Heuristic Dynamic Programming Based Power System Stabilizer for a Turbogenerator in a Single Machine Power System

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    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique

    Advantages of versatile neural-network decoding for topological codes

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    Finding optimal correction of errors in generic stabilizer codes is a computationally hard problem, even for simple noise models. While this task can be simplified for codes with some structure, such as topological stabilizer codes, developing good and efficient decoders still remains a challenge. In our work, we systematically study a very versatile class of decoders based on feedforward neural networks. To demonstrate adaptability, we apply neural decoders to the triangular color and toric codes under various noise models with realistic features, such as spatially-correlated errors. We report that neural decoders provide significant improvement over leading efficient decoders in terms of the error-correction threshold. Using neural networks simplifies the process of designing well-performing decoders, and does not require prior knowledge of the underlying noise model.Comment: 11 pages, 6 figures, 2 table

    A Heuristic-Dynamic-programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System

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    Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS\u27s performance is presented

    Advanced Control of Small-Scale Power Systems with Penetration of Renewable Energy Sources

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    Stability, protection, and operational restrictions are important factors to be taken into account in a proper integration of distributed energy. The objective of this research is presenting advanced controllers for small-scale power systems with penetration of renewable energy sources resources to ensure stable operation after the network disturbances. Power systems with distributed energy resources are modeled and controlled through applying nonlinear control methods to their power electronic interfaces in this research. The stability and control of both ac and dc systems have been studied in a multi-source framework. The dc distribution system is represented as a class of interconnected, nonlinear discrete-time systems with unknown dynamics. It comprises several dc sources, here called subsystems, along with resistive and constant-power loads (which exhibit negative resistance characteristics and reduce the system stability margins.) Each subsystem includes a dc-dc converter (DDC) and exploits distributed energy resources (DERs) such as photovoltaic, wind, etc. Due to the power system frequent disturbances this system is prone to instability in the presence of the DDC dynamical components and constant-power loads. On the other hand, designing a centralized controller may not be viable due to the distance between the subsystems (dc sources.) In this research it is shown that the stability of an interconnected dc distribution system is enhanced through decentralized discrete-time adaptive nonlinear controller design that employs neural networks (NNs) to mitigate voltage and power oscillations after disturbances have occurred. The ac power system model is comprised of conventional synchronous generators (SGs) and renewable energy sources, here, called renewable generators (RGs,) via grid-tie inverters (GTI.) A novel decentralized adaptive neural network (NN) controller is proposed for the GTI that makes the device behave as a conventional synchronous generator. The advantage of this modeling is that all available damping controllers for synchronous generator, such as AVR (Automatic Voltage Regulator) + PSS (Power System Stabilizer), can be applied to the renewable generator. Simulation results on both types of grids show that the proposed nonlinear controllers are able to mitigate the oscillations in the presence of disturbances and adjust the renewable source power to maintain the grid voltage close to its reference value. The stability of the interconnected grids has been enhanced in comparison to the conventional methods

    Control techniques for power system stabilisation

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    The conventional PSS was first proposed earlier based on a linear model of the power system to damp the low frequency oscillations in the system. But they are designed to be operated under fixed parameters derived from the system linearized model. Due to large interconnection of power system to meet the load demand brings in deviations of steady-state and non-linearity to power system. The main problem is that PSS includes the locally measured quantities only neglecting the effect of nearby generators. This is the reason for the advent of Wide area monitoring for strong coupling between the local modes and the inter-area modes which would make the tuning of local PSSs for damping all modes nearly impossible when there is no supervisory level controller. Wide area control addresses these problems by proposing smart topology changes and control actions. Dynamic islanding and fast load shedding are schemes available to maintain as much as possible healthy transmission system. It is found that if remote signals from one or more distant locations of the power system can be applied to local controller design, system dynamic performance can be enhanced. In order to attain these goals, it is desirable to systematically build a robust wide area controller model within an autonomous system framework
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