324 research outputs found

    Controlling Techniques for STATCOM using Artificial Intelligence

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    The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence

    Power System Loading Margin Enhancement by Optimal STATCOM Integration:a case study

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    Safe and secure network operation with acceptable voltage level has become a challenging task for utilities requiring corrective measures to be implemented. Network upgrades using Flexible Alternating Current Transmission System devices are being considered to serve this purpose. To this end, static loading margin enhancement by optimal static synchronous compensator (STATCOM) allocation to enhance the power transfer capability with minimal voltage variation is presented. Maximum loadability is formulated as an optimization problem, subjected to voltage and small-signal stability constraints. Stability indices are presented and incorporated with the optimization problem to ensure secure operation under maximum loading. The scheme is executed with the IEEE system and an Indian utility network. Improved voltage regulation with different loading condition was achieved for both test networks, with the service rendered by the optimally placed STATCOM. Moreover, it facilitates an additional 50% capacity release in both test systems for hosting the active power and loads

    A survey on fopid controllers for lfo damping in power systems using synchronous generators, facts devices and inverter-based power plants

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    In recent decades, various types of control techniques have been proposed for use in power systems. Among them, the use of a proportional–integral–derivative (PID) controller is widely recognized as an effective technique. The generalized type of this controller is the fractional-order PID (FOPID) controller. This type of controller provides a wider range of stability area due to the fractional orders of integrals and derivatives. These types of controllers have been significantly considered as a new approach in power engineering that can enhance the operation and stability of power systems. This paper represents a comprehensive overview of the FOPID controller and its applications in modern power systems for enhancing low-frequency oscillation (LFO) damping. In addition, the performance of this type of controller has been evaluated in a benchmark test system. It can be a driver for the development of FOPID controller applications in modern power systems. Investigation of different pieces of research shows that FOPID controllers, as robust controllers, can play an efficient role in modern power systems

    Swarm Intelligence for Transmission System Control

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    Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems

    Particle swarm optimization-based superconducting magnetic energy storage for low-voltage ride-through capability enhancement in wind energy conversion system

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    This article presents a novel application of the particle swarm optimization technique to optimally design all the proportional-integral controllers required to control both the real and reactive powers of the superconducting magnetic energy storage unit for enhancing the low-voltage ride-through capability of a grid-connected wind farm. The control strategy of the superconducting magnetic energy storage system is based on a sinusoidal pulse-width modulation voltage source converter and proportional-integral-controlled DC-DC converter. Control of the voltage source converter depends on the cascaded proportional-integral control scheme. All proportional-integral controllers in the superconducting magnetic energy storage system are optimally designed by the particle swarm optimization technique. The statistical response surface methodology is used to build the mathematical model of the voltage responses at the point of common coupling in terms of the proportional-integral controller parameters. The effectiveness of the proportional-integral-controlled superconducting magnetic energy storage optimized by the proposed particle swarm optimization technique is then compared to that optimized by a genetic algorithm technique, taking into consideration symmetrical and unsymmetrical fault conditions. A two-mass drive train model is used for the wind turbine generator system because of its large influence on the fault analyses. The systemic design approach is demonstrated in determining the controller parameters of the superconducting magnetic energy storage unit, and its effectiveness is validated in augmenting the low-voltage ride-through of a grid-connected wind farm

    NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

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    The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented

    Adaptive Control for Power System Voltage and Frequency Regulation

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    Variable and uncertain wind power output introduces new challenges to power system voltage and frequency stability. To guarantee the safe and stable operation of power systems, the control for voltage and frequency regulation is studied in this work. Static Synchronous Compensator (STATCOM) can provide fast and efficient reactive power support to regulate system voltage. In the literature, various STATCOM control methods have been discussed, including many applications of proportional–integral (PI) controllers. However, these previous works obtain the PI gains via a trial and error approach or extensive studies with a tradeoff of performance and applicability. Hence, control parameters for the optimal performance at a given operating point may not be effective at a different operating point. To improve the controller’s performance, this work proposes a new control model based on adaptive PI control, which can self-adjust the control gains during disturbance, such that the performance always matches a desired response in relation to operating condition changes. Further, a new method called the flatness-based adaptive control (FBAC), for STATCOM is also proposed. By this method, the nonlinear STATCOM variables can easily and exactly be controlled by controlling the flat output without solving differential equations. Further, the control gains can be dynamically tuned to satisfy the time-varying operation condition requirement. In addition to the voltage control, frequency control is also investigated in this work. Automatic generation control (AGC) is used to regulate the system frequency in power systems. Various control methods have been discussed in order to design control gains and obtain good frequency response performances. However, the control gains obtained by existing control methods are usually fixed and designed for specific scenarios in the studied power system. The desired response may not be obtained when variable wind power is integrated into power systems. To address these challenges, an adaptive gain-tuning control (AGTC) for AGC with effects of wind resources is presented in this dissertation. By AGTC, the PI control parameters can be automatically and dynamically calculated during the disturbance to make AGC consistently provide excellent performance under variable wind power. Simulation result verifies the advantages of the proposed control strategy

    A Comparison of PSO and Backpropagation for Training RBF Neural Networks for Identification of a Power System with STATCOM

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    Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method

    Toward Fault Adaptive Power Systems in Electric Ships

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    Shipboard Power Systems (SPS) play a significant role in next-generation Navy fleets. With the increasing power demand from propulsion loads, ship service loads, weaponry systems and mission systems, a stable and reliable SPS is critical to support different aspects of ship operation. It also becomes the technology-enabler to improve ship economy, efficiency, reliability, and survivability. Moreover, it is important to improve the reliability and robustness of the SPS while working under different operating conditions to ensure safe and satisfactory operation of the system. This dissertation aims to introduce novel and effective approaches to respond to different types of possible faults in the SPS. According to the type and duration, the possible faults in the Medium Voltage DC (MVDC) SPS have been divided into two main categories: transient and permanent faults. First, in order to manage permanent faults in MVDC SPS, a novel real-time reconfiguration strategy has been proposed. Onboard postault reconfiguration aims to ensure the maximum power/service delivery to the system loads following a fault. This study aims to implement an intelligent real-time reconfiguration algorithm in the RTDS platform through an optimization technique implemented inside the Real-Time Digital Simulator (RTDS). The simulation results demonstrate the effectiveness of the proposed real-time approach to reconfigure the system under different fault situations. Second, a novel approach to mitigate the effect of the unsymmetrical transient AC faults in the MVDC SPS has been proposed. In this dissertation, the application of combined Static Synchronous Compensator (STATCOM)-Super Conducting Fault Current Limiter (SFCL) to improve the stability of the MVDC SPS during transient faults has been investigated. A Fluid Genetic Algorithm (FGA) optimization algorithm is introduced to design the STATCOM\u27s controller. Moreover, a multi-objective optimization problem has been formulated to find the optimal size of SFCL\u27s impedance. In the proposed scheme, STATCOM can assist the SFCL to keep the vital load terminal voltage close to the normal state in an economic sense. The proposed technique provides an acceptable post-disturbance and postault performance to recover the system to its normal situation over the other alternatives
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