On-line determination of salient-pole hydro generator parameters by neural network estimator using operating data (PEANN)

A novel application of Artificial Neural Network (ANN) to estimate and track Hydro Generator Dynamic Parameters using online disturbance measurements is presented within this paper. The data for training ANN are obtained through off-line simulation of the generators modelled in a one-machine-infinite-bus environment using the parameters sets that are representative of practical data. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANN are organized in coordination with the results obtained from the observability analysis of synchronous generator dynamic parameters in its dynamic behaviour. A collection of 10 ANNs with similar input patterns and different outputs are developed to determine a set of dynamic parameters. The trained ANNs are employed in a real-time operational environment for estimating generator parameters using online measurements acquired during disturbance conditions. The ANNs are employed and tested to identify generator parameters using online measurements obtained during different disturbances. Simulation studies demonstrate the ability of the ANNs to accurately estimate dynamic parameters of hydro-generators. The results also show the impact of test conditions on the accuracy degree of estimation for these parameters. The optimal structure of ANNs is also determined to minimize the error in estimating each dynamic parameter

Improving thermal characteristics and energy absorption of concrete by recycled rubber and silica fume

The two conventional disposal methods as landfilling and incineration of waste rubber raise environmental concerns. It is while burying rubber in concrete enhances flexibility and thermal properties due to its inherent ductility and insulation properties. In this study, a comprehensive approach has been adopted to investigate the effects of the amount and size of rubber particles combined with silica fume on the thermal characteristics, energy absorption, and mechanical properties of concrete, including its workability, specific weight, thermal conductivity, thermal performance, compressive strength, flexural strength, and flexural toughness. In addition, statistical analyses were conducted to evaluate the experimental results. It was observed that in concrete containing 50% fine or 50% coarse rubber aggregates combined with 15% silica fume, the thermal conductivity decreases by 45% and 43%, respectively. The energy absorption of concrete samples with 100% fine or 100% coarse rubber particles increased by 2.8 and 4.5 times, respectively, compared to those with 20% rubber. Appropriate equations were developed to estimate the compressive and flexural strengths of concrete with rubber particles while statistical analyses exhibited error levels of 11% and 15%, respectively, for such equations. The compressive and flexural strengths decreased by an average of 68% and 62%, respectively, in samples containing 100% crumb rubber. Likewise, these factors experienced an average reduction of 73% and 71%, respectively, in specimens with 100% rubber powder compared to those with 20% rubber

Development and implementation of neural network observers to estimate synchronous generators' dynamic parameters using on-line operating data

This paper illustrates a new application of artificial neural network (ANN) observers in identifying and estimating synchronous generator dynamic parameters via time-domain, on-line disturbance measurements. To prepare the training database for an ANN observer, the transient behaviours of synchronous generators have been determined through off-line simulations of a generator operating in a one-machine-infinite-bus environment. The Levenberg–Marquardt optimization utilising very fast back propagation algorithm has been adopted for training feed-forward neural networks. The inputs of ANNs are organized in coordination with the data from the observability analysis of synchronous generator parameters in its dynamic behaviour. A collection of ANNs with same inputs but different outputs is developed to determine a set of the parameters. The ANNs are utilized to estimate the above parameters by the measurements for every kind of fault separately. The robustness tests are executed by on-line measurements to identify the parameters. Simulation studies not only indicate that the observer is capable to identify the dynamic parameters of synchronous generator but also show that the tests which have given better results in identification of each dynamic parameter can be acquired

An integrated method for under frequency load shedding based on hybrid intelligent system-part ii: UFLS design

The first part of this two part paper has proposed a novel strategy for frequency response modelling of modern power system, as it recommended a new application of artificial neural network in assessment of power system dynamic performance. The intelligent methods have shown a high ability in estimation and optimisation problems, as the recent advances in computer systems and intelligent methods have created new opportunities. The current paper proposes an integrated under frequency load shedding system based on genetic algorithm which is able to consider all effective factors at the same time. It also presents a new hybrid artificial neural network-genetic algorithm basis system for under frequency load shedding which is a quick, simple and applied method of UFLS. This assessment includes a review of significant researches on under frequency load shedding design and application

An integrated method for under frequency load shedding based on hybrid intelligent system-part i: dynamic simulation

Security is one of the most vital requirements in the operation of power systems. Frequency is a reliable indicator to determine instability condition in power system, i.e. the stability of power system is closely dependent on the value of system frequency. Under Frequency Load Shedding (UFLS) is one of the most important protection systems as in many cases it is the last action taken to prevent a system blackout after a serious disturbance occurs in power system. The first part of this two part paper presents various factors in modern power systems which have significant contribution on Under Frequency Load Shedding (UFLS). A high-order multi-machine frequency response model is utilized as it the best strategy of power system dynamic simulation. Classification of modern power system components and using an equal unit for each class is proposed in this work. The results show that ANN models can also be implemented as well as a fast dynamic simulator of electric power system. This assessment includes a review of significant researches on power system dynamic simulation and frequency response model leading to an integrated UFLS system design