Cogeneration Application of Induction Generators

Parsons, John Richard

doi:10.1109/tia.1984.4504443

Cited by 27 publications

(11 citation statements)

References 2 publications

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“…Fowler (1983) attempts to find the optimum location for cogeneration facilities in Chicago. Parsons (1984) introduces the concept of induction generators to cogeneration as an alternative to synchronous generators and brings out its economic advantages. Mouostafa et al (1984) offers a new design for a solar power plant in Kuwait.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Prediction of Self-produced Energy

Altay

Turkoglu

2015

Sustainable Future Energy Technology and Supply Chains

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The need for energy has been aggressively increasing since the industrial revolution. An exponential growth of industrial and residential power use is encountered with the technological revolution. Cogenerated and self produced energy is a solution that allows the reuse of heat produced, decreases transmission investments, and reduces carbon emissions and decreases dependency on energy resource owners. The mass production sites, health centers, big residential sites and more can use the system. In this chapter, the focus is given to industrial autoproducers. Power market balance is based on the day-ahead declarations; therefore, the production is to be planned in detail to avoid penalties. A recurrent Artificial Neural Network model is constructed in order to predict the day ahead energy supply. The model considers energy resource price, demand from multiple sites, production cost, the amount of energy imported from the grid and the amount of energy exported to the grid. In order to achieve the energy production rate with the least error rate possible, an energy demand forecasting model is constructed for a paper producing company, using a Nonlinear Autoregressive Exogenous Model (NARX) network implemented in Matlab. Three parameters of the forecasting model are tuned using the Particle Swarm Optimization (PSO) algorithm: the number of layers, the number of nodes in hidden layers and the number of delays in the network. Error level is measured using the Minimum Absolute Percentage Error between the predictions and the actual output. Results indicate that NARX is an appropriate tool for forecasting energy demand and the algorithm yields better results when the system parameters are tuned.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Prediction of Self-produced Energy

Altay

Turkoglu

2015

Sustainable Future Energy Technology and Supply Chains

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“…Although most induction generators in operation are employed in wind power plants [3], [6], such machines have also been used in medium-size hydro and thermal plants [3], [7]- [9]. Therefore, in order to keep the results as generic as possible, the mechanical torque was considered constant, i.e., the regulator and prime mover dynamics were neglected.…”

Section: A Induction Generatorsmentioning

confidence: 99%

Comparative Analysis Between Synchronous and Induction Machines for Distributed Generation Applications

Freitas

Vieira

Morelato

et al. 2006

IEEE Trans. Power Syst.

157

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This paper presents a detailed comparative analysis between synchronous and induction machines for distributed generation applications. The impacts of these generators on the distribution network performance are determined and compared by using computational simulations. The technical factors analyzed are steady-state voltage profile, electrical power losses, voltage stability, transient stability, voltage sags during unbalanced faults, and short-circuit currents. The results showed that the best technical choice depends on the network characteristics, i.e., the main factors that may limit the penetration level of distributed generation.

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“…In this context, a crescent number of Induction Generators (IGs) have been connected to distribution systems, commonly through small generation plants using hydro, thermal, solar or wind energy sources (Jenkins et al, 2000;Akhmatov et al, 2003;Parsons, 1984;Barbi, 2008;Lubis et al, 2009;Santos et al 2010). The analysis of the impacts of the installation of these induction generators in the distribution network and also of their operational characteristics under normal and fault conditions, becomes crucial for the network operation, as described in (Akhmatov, 2003).…”

Section: Introductionmentioning

confidence: 99%

A Simple Algorithm to Assess the Speed Stability of Induction Generators

Mota¹,

Mota²

2011

Journal of Computer Science

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Problem statement:The speed stability if Induction Generators (IG) can be defined as its ability to remain connected to the electric network, operating at a mechanical speed close to the speed associated to the actual system frequency, after being subjected to a perturbation. This IG behavior is normally studied using dynamical methods that are determined through time-consuming simulations and are difficult to implement in simpler computational devices, such as microcontrollers, that are the basic component of electronic protection relays. In this way, the possibility to assess the speed stability of IGs through a simpler method becomes very desirable. Approach: This paper proposes a simple analytical method for assessing the speed stability of induction generators connected to distribution systems that can be easily implemented in microcontrollers, based on the determination of its critical speed and critical time. The formulation of the proposed method is founded on the electricalmechanical equilibrium equations associated to the steady-state operation condition in the classical induction machine model. Results: The developed method has four simple steps, based on mathematical equations obtained from the classical IG model and presented a mean difference less than 5% when compared with results obtained by dynamical simulations, tested in the context of a distribution electrical network that considers generator parameters, line parameters and capacitor bank variation. Conclusions/Recommendations: This analytical approach can be used as a guide to significantly reduce the simulation efforts, required to assess the induction generators stability in distribution networks and permits its implementation in less complex computational contexts, like microcontrollers. The reliability of the method has been attested by extensive simulation results.

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Cogeneration Application of Induction Generators

Cited by 27 publications

References 2 publications

An Intelligent Prediction of Self-produced Energy

An Intelligent Prediction of Self-produced Energy

Comparative Analysis Between Synchronous and Induction Machines for Distributed Generation Applications

A Simple Algorithm to Assess the Speed Stability of Induction Generators

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