Anti-windup Schemes for Proportional Integral and Proportional Resonant Controller

Problems like windup or rollover arise in a PI controller working under saturation. Hence anti-windup schemes are necessary to minimize performance degradation.Similar situation may occur in a Proportional Resonant(PR)controller in the presence of a sustained error input.Several methods can be employed based on existing knowledge on PI controller to counter this problem.In this paper few such schemes are proposed and implemented in FPGA and MATLAB and from the obtained results their possible use and limitations have been studied.

Wavelength/Time Multiple-Pulses-per-Row codes: Construction and verification

Two dimensional Optical Orthogonal Codes (OOCs) named Wavelength/Time Multiple-Pulses-per-Row (W/T MPR) codes suitable for use in incoherent fiber-optic code division multiple access (FO-CDMA) networks are reported in [6]. In this paper, we report the construction of W/T MPR codes, using Greedy Algorithm (GA), with distinct 1-D OOCs [1] as the row vectors. We present the W/T MPR codes obtained using the GA. Further, we verify the correlation properties of the generated W/T MPR codes using Matlab.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Incipient motion design of sand bed channels affected by bed suction

Seepage through sand bed channels in a downward direction (suction) reduces the stability of particles and initiates the sand movement. Incipient motion of sand bed channel with seepage cannot be designed by using the conventional approach. Metamodeling techniques, which employ a non-linear pattern analysis between input and output parameters and solely based on the experimental observations, can be used to model such phenomena. Traditional approach to find non-dimensional parameters has not been used in the present work. Parameters, which can influence the incipient motion with seepage, have been identified and non-dimensionalized in the present work. Non-dimensional stream power concept has been used to describe the process. By using these non-dimensional parameters; present work describes a radial basis function (RBF) metamodel for prediction of incipient motion condition affected by seepage. The coefficient of determination, R-2 of the model is 0.99. Thus, it can be said that model predicts the phenomena very well. With the help of the metamodel, design curves have been presented for designing the sand bed channel when it is affected by seepage. (C) 2010 Elsevier B.V. All rights reserved.

A non-cooperative game-theoretic approach to channel assignment in multi-channel multi-radio wireless networks

Channel assignment in multi-channel multi-radio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to consider the interference characteristics of the nodes in the network especially when nodes are in different collision domains. This work views the problem of channel assignment in multi-channel multi-radio networks with multiple collision domains as a non-cooperative game where the objective of the players is to maximize their individual utility by minimizing its interference. Necessary and sufficient conditions are derived for the channel assignment to be a Nash Equilibrium (NE) and efficiency of the NE is analyzed by deriving the lower bound of the price of anarchy of this game. A new fairness measure in multiple collision domain context is proposed and necessary and sufficient conditions for NE outcomes to be fair are derived. The equilibrium conditions are then applied to solve the channel assignment problem by proposing three algorithms, based on perfect/imperfect information, which rely on explicit communication between the players for arriving at an NE. A no-regret learning algorithm known as Freund and Schapire Informed algorithm, which has an additional advantage of low overhead in terms of information exchange, is proposed and its convergence to the stabilizing outcomes is studied. New performance metrics are proposed and extensive simulations are done using Matlab to obtain a thorough understanding of the performance of these algorithms on various topologies with respect to these metrics. It was observed that the algorithms proposed were able to achieve good convergence to NE resulting in efficient channel assignment strategies.

A current error space vector based hysteresis controller with constant switching frequency and simple online boundary computation for VSI fed IM drive

This paper proposes a simple current error space vector based hysteresis controller for two-level inverter fed Induction Motor (IM) drives. This proposed hysteresis controller retains all advantages of conventional current error space vector based hysteresis controllers like fast dynamic response, simple to implement, adjacent voltage vector switching etc. The additional advantage of this proposed hysteresis controller is that it gives a phase voltage frequency spectrum exactly similar to that of a constant switching frequency space vector pulse width modulated (SVPWM) inverter. In this proposed hysteresis controller the boundary is computed online using estimated stator voltages along alpha and beta axes thus completely eliminating look up tables used for obtaining parabolic hysteresis boundary proposed in. The estimation of stator voltage is carried out using current errors along alpha and beta axes and steady state model of induction motor. The proposed scheme is simple and capable of taking inverter upto six step mode operation, if demanded by drive system. The proposed hysteresis controller based inverter fed drive scheme is simulated extensively using SIMULINK toolbox of MATLAB for steady state and transient performance. The experimental verification for steady state performance of the proposed scheme is carried out on a 3.7kW IM.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Adaptive Filtering Technique and DSP Based Implementation for High-Speed Distance Protection

The paper presents an adaptive Fourier filtering technique and a relaying scheme based on a combination of a digital band-pass filter along with a three-sample algorithm, for applications in high-speed numerical distance protection. To enhance the performance of above-mentioned technique, a high-speed fault detector has been used. MATLAB based simulation studies show that the adaptive Fourier filtering technique provides fast tripping for near faults and security for farther faults. The digital relaying scheme based on a combination of digital band-pass filter along with three-sample data window algorithm also provides accurate and high-speed detection of faults. The paper also proposes a high performance 16-bit fixed point DSP (Texas Instruments TMS320LF2407A) processor based hardware scheme suitable for implementation of the above techniques. To evaluate the performance of the proposed relaying scheme under steady state and transient conditions, PC based menu driven relay test procedures are developed using National Instruments LabVIEW software. The test signals are generated in real time using LabVIEW compatible analog output modules. The results obtained from the simulation studies as well as hardware implementations are also presented.

Towards formal verification of analog mixed signal designs using SPICE circuit simulation traces

An extension to a formal verification approach of hybrid systems is proposed to verify analog and mixed signal (AMS) designs. AMS designs can be formally modeled as hybrid systems and therefore lend themselves to the formal analysis and verification techniques applied to hybrid systems. The proposed approach employs simulation traces obtained from an actual design implementation of AMS circuit blocks (for example, in the form of SPICE netlists) to carry out formal analysis and verification. This enables the same platform used for formally validating an abstract model of an AMS design, to be also used for validating its different refinements and design implementation; thereby, providing a simple route to formal verification at different levels of implementation. The feasibility of the proposed approach is demonstrated with a case study based on a tunnel diode oscillator. Since the device characteristic of a tunnel diode is highly non-linear with a negative resistance region, dynamic behavior of circuits in which it is employed as an element is difficult to model, analyze and verify within a general hybrid system formal verification tool. In the case study presented the formal model and the proposed computational techniques have been incorporated into CheckMate, a formal verification tool based on MATLAB and Simulink-Stateflow Framework from MathWorks.

Magnetic arm-switch-based three-phase series-shunt compensated quality AC power supply

This study presents a novel magnetic arm-switch-based integrated magnetic circuit for a three-phase series-shunt compensated uninterruptible power supply (UPS). The magnetic circuit acts as a common interacting field for a number of energy ports, viz., series inverter, shunt inverter, grid and load. The magnetic arm-switching technique ensures equivalent series or shunt connection between the inverters. In normal grid mode (stabiliser mode), the series inverter is used for series voltage correction and the shunt one for current correction. The inverters and the load are effectively connected in parallel when the grid power is not available. These inverters are then used to share the load power. The operation of the inverters in parallel is ensured by the magnetic arm-switching technique. This study also includes modelling of the magnetic circuit. A graphical technique called bond graph is used to model the system. In this model, the magnetic circuit is represented in terms of gyrator-capacitors. Therefore the model is also termed as gyrator-capacitor model. The model is used to extract the dynamic equations that are used to simulate the system using MATLAB/SIMULINK. This study also discusses a synchronously rotating reference frame-based control technique that is used for the control of the series and shunt inverters in different operating modes. Finally, the gyrator-capacitor model is validated by comparing the simulated and experimental results.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Damage limit states of reinforced concrete beams subjected to incremental cyclic loading using relaxation ratio analysis of AE parameters

This paper presents an experimental study on damage assessment of reinforced concrete (RC) beams subjected to incremental cyclic loading. During testing acoustic emissions (AEs) were recorded. The analysis of the AE released was carried out by using parameters relaxation ratio, load ratio and calm ratio. Digital image correlation (DIC) technique and tracking with available MATLAB program were used to measure the displacement and surface strains in concrete. Earlier researchers classified the damage in RC beams using Kaiser effect, crack mouth opening displacement and proposed a standard. In general (or in practical situations), multiple cracks occur in reinforced concrete beams. In the present study damage assessment in RC beams was studied according to different limit states specified by the code of practice IS-456:2000 and AE technique. Based on the two ratios namely load ratio and calm ratio and when the deflection reached approximately 85% of the maximum allowable deflection it was observed that the RC beams were heavily damaged. The combination of AE and DIC techniques has the potential to provide the state of damage in RC structures.

Deterministic seismic hazard macrozonation of India

Earthquakes are known to have occurred in Indian subcontinent from ancient times. This paper presents the results of seismic hazard analysis of India (6 degrees-38 degrees N and 68 degrees-98 degrees E) based on the deterministic approach using latest seismicity data (up to 2010). The hazard analysis was done using two different source models (linear sources and point sources) and 12 well recognized attenuation relations considering varied tectonic provinces in the region. The earthquake data obtained from different sources were homogenized and declustered and a total of 27,146 earthquakes of moment magnitude 4 and above were listed in the study area. The sesismotectonic map of the study area was prepared by considering the faults, lineaments and the shear zones which are associated with earthquakes of magnitude 4 and above. A new program was developed in MATLAB for smoothing of the point sources. For assessing the seismic hazard, the study area was divided into small grids of size 0.1 degrees x 0.1 degrees (approximately 10 x 10 km), and the hazard parameters were calculated at the center of each of these grid cells by considering all the seismic sources within a radius of 300 to 400 km. Rock level peak horizontal acceleration (PHA) and spectral accelerations for periods 0.1 and 1 s have been calculated for all the grid points with a deterministic approach using a code written in MATLAB. Epistemic uncertainty in hazard definition has been tackled within a logic-tree framework considering two types of sources and three attenuation models for each grid point. The hazard evaluation without logic tree approach also has been done for comparison of the results. The contour maps showing the spatial variation of hazard values are presented in the paper.

Updating reliability models of statically loaded instrumented structures

The study extends the first order reliability method (FORM) and inverse FORM to update reliability models for existing, statically loaded structures based on measured responses. Solutions based on Bayes' theorem, Markov chain Monte Carlo simulations, and inverse reliability analysis are developed. The case of linear systems with Gaussian uncertainties and linear performance functions is shown to be exactly solvable. FORM and inverse reliability based methods are subsequently developed to deal with more general problems. The proposed procedures are implemented by combining Matlab based reliability modules with finite element models residing on the Abaqus software. Numerical illustrations on linear and nonlinear frames are presented. (c) 2012 Elsevier Ltd. All rights reserved.