Neural networks for real-time estimation of parameters of signals in power systems

Fast determination of parameters of the fundamental waveform of voltages and currents is essential for the control and protection of electrical power systems. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. New parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural networks principles, are proposed. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least absolute value, the minimax, the least-squares and the robust leastsquares criteria are developed and compared. The networks process samples of observed noisy signals (voltages or currents) and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithms and neural network realizations. The proposed methods seem to be particularly useful for real-time, high-speed estimation of parameters of sinusoidal signals in electrical power systems

Neural networks for real-time estimation of parameters of signals in power systems

The purpose of this paper is to present new algorithms and along with them new architectures of analogue neuron-like adaptive processors for online estimation of parameters of sinusoidal signals, which are distorted by higher harmonics and corrupted by noise. For steady-state conditions we have developed neural networks which enable us to estimate the amplitudes and the frequency of the fundamental component of signals. When estimating the basic waveform of currents during short circuits the exponential DC component distorts the results. Assuming the known frequency, we have developed adaptive neural networks which enable us to estimate the amplitudes of the basic components as well as the amplitudes and the time constant of a DC component. The problem of estimation of signal parameters is formulated as an unconstrained optimization problem and solved by using the gradient descent continuous-time method. Basing on this approach we have developed systems of nonlinear differential equations that can be implemented by analog adaptive neural networks. The solution of the optimization problem bases on some principles given by Tank and Hopfield [ 4 ] as well as by Kennedy and Chua. The developed networks contain elements which are similar to the adaptive threshold elements of the perceptron presented by Widrow

Adaptive Neural Networks for Robust Estimation of parameters of Noisy Harmonic Signals

In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural network principles. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least-squares (LS), the total least-squares (TLS) and the robust TLS criteria are developed and compared. The networks process samples of observed noisy signals and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithm

Computation of Voltage Sag Initiation with Fourier based Algorithm, Kalman Filter and Wavelets

Dynamic Voltage Restorers (DVR) have been successfully applied for voltage dip mitigation in the last years. Especially in systems with nonlinear loads and wind turbine generation DVR units support the Power Quality enhancement. The reliability and quality of DVR operation depends mostly on fast and accurate voltage dip detection. Detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation using carefully selected advanced signal processing methods such as Fourier based algorithm, Kalman filtering and Wavelets. Additionally, the traditional and common technique of RMS value tracking has been mentioned. The algorithms have been tested under different conditions: voltage dip with phase jump, noise, frequency variations

“Pleasure stolen from the poor”: community discourse on the ‘theft’ of a Banksy

The removal of street art from community walls for private auction is a morally problematic yet legal action. This paper examines community reactions to the removal of Banksy’s No Ball Games for private auction. 500 unique reader comments on online newspaper articles reporting this controversial event were collected and analysed. An emerging set of urban moral codes was used to position street art as a valuable community asset rather than as an index of crime and social decay. An older discourse depicted No Ball Games as unlawful graffiti that was rightfully removed. Here, the operations of ‘the police’ (Rancière, 1999) in the distribution of the sensible are evident in the assertions that validate and depoliticize the removal of No Ball Games. This repertoire was used to attribute responsibility for the work’s removal to deterministic external forces, while reducing the accountability attributable to those responsible for the removal of the work. A contrasting anti removal repertoire depicted street art as a gift to the community, and its removal as a form of theft, and a source of harm to the community. The pro-removal repertoire incorporates and depoliticizes elements of the anti-removal repertoire, by acknowledging the moral wrong of the removal, but yielding to the legal rights of the wall owners to sell the work; and by recognizing the status of street art as valuable, but asserting that the proper place for art is a museum. The anti-removal repertoire counters elements of the pro-removal repertoire, by acknowledging the illegality of street art, but containing this to the initial act of making unsanctioned marks on a wall, after which point the work becomes the property of the community it is located within. This analysis reveals an emergent set of urban moral codes that positions a currently legal action as a form of criminal activity

Laser spectroscopy for breath analysis : towards clinical implementation

Detection and analysis of volatile compounds in exhaled breath represents an attractive tool for monitoring the metabolic status of a patient and disease diagnosis, since it is non-invasive and fast. Numerous studies have already demonstrated the benefit of breath analysis in clinical settings/applications and encouraged multidisciplinary research to reveal new insights regarding the origins, pathways, and pathophysiological roles of breath components. Many breath analysis methods are currently available to help explore these directions, ranging from mass spectrometry to laser-based spectroscopy and sensor arrays. This review presents an update of the current status of optical methods, using near and mid-infrared sources, for clinical breath gas analysis over the last decade and describes recent technological developments and their applications. The review includes: tunable diode laser absorption spectroscopy, cavity ring-down spectroscopy, integrated cavity output spectroscopy, cavity-enhanced absorption spectroscopy, photoacoustic spectroscopy, quartz-enhanced photoacoustic spectroscopy, and optical frequency comb spectroscopy. A SWOT analysis (strengths, weaknesses, opportunities, and threats) is presented that describes the laser-based techniques within the clinical framework of breath research and their appealing features for clinical use.Peer reviewe

Software and hardware needs assessment for a numerical capability at IMD/NRC

The first phase of evaluation of several commercial computer codes has been completed. The objective of the review was to select existing tools to develop numerical capabilities of IMD for both short and long term goals.NRC publication: Ye

Adaptive Neural Networks for Robust Estimation of parameters of Noisy Harmonic Signals

In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural network principles. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least-squares (LS), the total least-squares (TLS) and the robust TLS criteria are developed and compared. The networks process samples of observed noisy signals and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithm

Renewable Energy and Power Quality Journal (RE&PQJ) Assessment of PV generation based on spherical irradiation measurements

Abstract. In this paper spherical irradiation measurements are exploited and compared to power output in order to assess PV system generation potential. Not the spherical irradiation as a sum is measured, but irradiation referred to a specific solid angle. This approach enhances generation forecasting for predefined panel orientations within urban areas. In urbanised regions sophisticated shadowing and reflexion patterns result generally in complicated generation predictions