This document discusses using a hybrid ARIMA-wavelet model for time series forecasting. It begins with an introduction to time series forecasting models including ARIMA. It then discusses the wavelet transform and how it can be used to decompose time series data. The document proposes a hybrid model where the time series is decomposed using wavelets, separate ARIMA models are fitted to the approximated and detailed signals, and then forecasts are produced. It applies this method to stock market data from the S&P BSE information technology index to predict future values. The hybrid model is found to improve forecasting accuracy compared to a standard ARIMA model.
The document discusses using Haar wavelets to solve optimal control problems numerically. It begins by explaining the limitations of indirect methods for solving optimal control problems and advantages of direct methods. It then provides background on wavelet theory, describing different types of wavelets and their properties. Haar wavelets are discussed as being useful for solving variational problems by reducing differential equations to algebraic equations. The document concludes that the Haar wavelet approach provides an effective numerical method for solving variational and optimal control problems compared to other existing techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using wavelet transforms to identify and classify faults in underground high voltage cables. It begins by introducing wavelet transforms and their advantages over Fourier transforms for analyzing non-stationary signals like those seen in power cables. The document then provides more details on discrete wavelet transforms and multi-resolution analysis. It describes simulating different fault conditions in underground cables using MATLAB and analyzing the resulting voltage signals using wavelet transforms. Key wavelet coefficients are examined to detect and classify the type of fault (e.g. line-to-ground) and its location along the cable. The results demonstrate the proposed wavelet-based technique can accurately identify and classify faults.
A Tactical Chaos based PWM Technique for Distortion Restraint and Power Spect...IJPEDS-IAES
The pulse width modulated voltage source inverters (PWM-VSI) dominate in the modern industrial environment. The conventional PWM methods are designed to have higher fundamental voltage, easy filtering and reduced total harmonic distortion (THD). There are number of clustered harmonics around the multiples of switching frequency in the output of conventional sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) inverters. This is due to their fixed switching frequency while the variable switching frequency makes the filtering very complex. Random carrier PWM (RCPWM) methods are the host of PWM methods, which use randomized carrier frequency and result in a harmonic profile with well distributed harmonic power (no harmonic possesses significant magnitude and hence no filtering is required). This paper proposes a chaos-based PWM (CPWM) strategy, which utilizes a chaotically changing switching frequency to spread the harmonics continuously to a wideband and to reduce the peak harmonics to a great extent. This can be an effective way to suppress the current harmonics and torque ripple in induction motor drives. The proposed CPWM scheme is simulated using MATLAB / SIMULINK software and implemented in three phase voltage source inverter (VSI) using field programmable gate array (FPGA).
Fault detection and diagnosis ingears using wavelet enveloped power spectrum ...eSAT Journals
Abstract In this work, automatic detection and diagnosis of gear condition monitoring technique is presented. The vibration signals in time domain wereobtained from a fault simulator apparatus from a healthy gear and an induced faulty gear. These time domain signals were processed using Laplace and Morlet wavelet based enveloped power spectrum to detect the faults in gears. The vibration signals obtained were filtered to enhance the signal components before the application of wavelet analysis. The time and frequency domain features extracted from Laplace wavelet based wavelet transform are used as input to ANN for gear fault classification. Genetic algorithm was used to optimize the wavelet and ANN classification parameters. The result shows the successful classification of ANN test process. Index Terms:Continuous wavelet transform, Envelope power spectrum, Wavelet, Filtering, ANN.
Comparative analysis on an exponential form of pulse with an integer and non-...IJERA Editor
This document presents a comparative analysis of using exponential pulses with integer and non-integer exponents for pulse compression in radar systems. Pulse compression aims to achieve better range and velocity resolution by extending the frequency spectrum of transmitted pulses while keeping pulse duration constant. The document simulates exponential pulses with different exponents in the time and frequency domains. It analyzes parameters like time-bandwidth product, peak sidelobe level, and main lobe width from the matched filter output and ambiguity function. The results show that differentiated non-integer exponential pulses modulated with a carrier signal provide better pulse compression performance compared to other pulse forms.
Performance of Modified S-Transform for Power Quality Disturbance Detection a...TELKOMNIKA JOURNAL
Detection and classification of power quality (PQ) disturbances are an important consideration to electrical utility companies and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. Power quality signal consists of stationary and non-stationary events which need a robust signal processing technique to analyse the signals. In this paper, Modified S-Transform (MST) was used to analyse single and multiple power quality signals. MST is a modified version of S-transform with improved time-frequency resolution. The power quality signals that are considered in this study are voltage swell, sag, interruption, harmonic, interharmonic, transient, sag plus harmonic and swell plus harmonics. The performance of the proposed method has been studied under noisy and unnoisy condition. Hard thresholding technique has been applied with MST while analysing noisy PQ signals. The result shows that MST is able to give higher classification rate with better time and frequency distribution (TFD) spectrum of the PQ disturbances.
Instantaneous Frequency Estimation Based On Time-Varying Auto Regressive Mode...CSCJournals
Time-varying autoregressive (TVAR) model is used for modeling non stationary signals, Instantaneous frequency (IF) and time-varying power spectral density are then extracted from the TVAR parameters. TVAR based Instantaneous frequency (IF) estimation has been shown to perform very well in realistic scenario when IF variation is quick, non-linear and has short data record. In TVAR modeling approach, the time-varying parameters are expanded as linear combinations of a set of basis functions .In this article, time poly nominal is chosen as basis function. Non stationary signal IF is estimated by calculating the angles of the roots (poles) of the time-varying autoregressive polynomial at every sample instant. We propose modified covariance method that utilizes both the time varying forward and backward linear predictors for estimating the time-varying parameters and then IF estimate. It is shown that performance of proposed modified covariance method is superior than existing covariance method which uses only forward linear predictor for estimating the time-varying parameters. The IF evaluation based on TVAR modeling requires efficient estimation of the time-varying coefficients by solving a set of linear equations referred as the general covariance equations. When covariance matrix is of high order, usual approach such as Gaussian elimination or direct matrix inversion is computationally incompetent for solving such a structure of equations. We apply recursive algorithm to competently invert the covariance matrix, by means of Wax-Kailath algorithm which exploits the block-Toeplitz arrangement of the covariance matrix for its recursive inversion, which is the central part of this article. The order determination of TVAR model is addressed by means of the maximum likelihood estimation (MLE) algorithm.
The document discusses using Haar wavelets to solve optimal control problems numerically. It begins by explaining the limitations of indirect methods for solving optimal control problems and advantages of direct methods. It then provides background on wavelet theory, describing different types of wavelets and their properties. Haar wavelets are discussed as being useful for solving variational problems by reducing differential equations to algebraic equations. The document concludes that the Haar wavelet approach provides an effective numerical method for solving variational and optimal control problems compared to other existing techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using wavelet transforms to identify and classify faults in underground high voltage cables. It begins by introducing wavelet transforms and their advantages over Fourier transforms for analyzing non-stationary signals like those seen in power cables. The document then provides more details on discrete wavelet transforms and multi-resolution analysis. It describes simulating different fault conditions in underground cables using MATLAB and analyzing the resulting voltage signals using wavelet transforms. Key wavelet coefficients are examined to detect and classify the type of fault (e.g. line-to-ground) and its location along the cable. The results demonstrate the proposed wavelet-based technique can accurately identify and classify faults.
A Tactical Chaos based PWM Technique for Distortion Restraint and Power Spect...IJPEDS-IAES
The pulse width modulated voltage source inverters (PWM-VSI) dominate in the modern industrial environment. The conventional PWM methods are designed to have higher fundamental voltage, easy filtering and reduced total harmonic distortion (THD). There are number of clustered harmonics around the multiples of switching frequency in the output of conventional sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) inverters. This is due to their fixed switching frequency while the variable switching frequency makes the filtering very complex. Random carrier PWM (RCPWM) methods are the host of PWM methods, which use randomized carrier frequency and result in a harmonic profile with well distributed harmonic power (no harmonic possesses significant magnitude and hence no filtering is required). This paper proposes a chaos-based PWM (CPWM) strategy, which utilizes a chaotically changing switching frequency to spread the harmonics continuously to a wideband and to reduce the peak harmonics to a great extent. This can be an effective way to suppress the current harmonics and torque ripple in induction motor drives. The proposed CPWM scheme is simulated using MATLAB / SIMULINK software and implemented in three phase voltage source inverter (VSI) using field programmable gate array (FPGA).
Fault detection and diagnosis ingears using wavelet enveloped power spectrum ...eSAT Journals
Abstract In this work, automatic detection and diagnosis of gear condition monitoring technique is presented. The vibration signals in time domain wereobtained from a fault simulator apparatus from a healthy gear and an induced faulty gear. These time domain signals were processed using Laplace and Morlet wavelet based enveloped power spectrum to detect the faults in gears. The vibration signals obtained were filtered to enhance the signal components before the application of wavelet analysis. The time and frequency domain features extracted from Laplace wavelet based wavelet transform are used as input to ANN for gear fault classification. Genetic algorithm was used to optimize the wavelet and ANN classification parameters. The result shows the successful classification of ANN test process. Index Terms:Continuous wavelet transform, Envelope power spectrum, Wavelet, Filtering, ANN.
Comparative analysis on an exponential form of pulse with an integer and non-...IJERA Editor
This document presents a comparative analysis of using exponential pulses with integer and non-integer exponents for pulse compression in radar systems. Pulse compression aims to achieve better range and velocity resolution by extending the frequency spectrum of transmitted pulses while keeping pulse duration constant. The document simulates exponential pulses with different exponents in the time and frequency domains. It analyzes parameters like time-bandwidth product, peak sidelobe level, and main lobe width from the matched filter output and ambiguity function. The results show that differentiated non-integer exponential pulses modulated with a carrier signal provide better pulse compression performance compared to other pulse forms.
Performance of Modified S-Transform for Power Quality Disturbance Detection a...TELKOMNIKA JOURNAL
Detection and classification of power quality (PQ) disturbances are an important consideration to electrical utility companies and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. Power quality signal consists of stationary and non-stationary events which need a robust signal processing technique to analyse the signals. In this paper, Modified S-Transform (MST) was used to analyse single and multiple power quality signals. MST is a modified version of S-transform with improved time-frequency resolution. The power quality signals that are considered in this study are voltage swell, sag, interruption, harmonic, interharmonic, transient, sag plus harmonic and swell plus harmonics. The performance of the proposed method has been studied under noisy and unnoisy condition. Hard thresholding technique has been applied with MST while analysing noisy PQ signals. The result shows that MST is able to give higher classification rate with better time and frequency distribution (TFD) spectrum of the PQ disturbances.
Instantaneous Frequency Estimation Based On Time-Varying Auto Regressive Mode...CSCJournals
Time-varying autoregressive (TVAR) model is used for modeling non stationary signals, Instantaneous frequency (IF) and time-varying power spectral density are then extracted from the TVAR parameters. TVAR based Instantaneous frequency (IF) estimation has been shown to perform very well in realistic scenario when IF variation is quick, non-linear and has short data record. In TVAR modeling approach, the time-varying parameters are expanded as linear combinations of a set of basis functions .In this article, time poly nominal is chosen as basis function. Non stationary signal IF is estimated by calculating the angles of the roots (poles) of the time-varying autoregressive polynomial at every sample instant. We propose modified covariance method that utilizes both the time varying forward and backward linear predictors for estimating the time-varying parameters and then IF estimate. It is shown that performance of proposed modified covariance method is superior than existing covariance method which uses only forward linear predictor for estimating the time-varying parameters. The IF evaluation based on TVAR modeling requires efficient estimation of the time-varying coefficients by solving a set of linear equations referred as the general covariance equations. When covariance matrix is of high order, usual approach such as Gaussian elimination or direct matrix inversion is computationally incompetent for solving such a structure of equations. We apply recursive algorithm to competently invert the covariance matrix, by means of Wax-Kailath algorithm which exploits the block-Toeplitz arrangement of the covariance matrix for its recursive inversion, which is the central part of this article. The order determination of TVAR model is addressed by means of the maximum likelihood estimation (MLE) algorithm.
Heuristic based adaptive step size clms algorithms for smart antennascsandit
This document summarizes research on heuristic-based adaptive step size complex least mean square (CLMS) algorithms for smart antennas. It presents Benveniste and Mathews algorithms, which use heuristics with CLMS to speed up convergence. Simulations show these algorithms outperform CLMS and augmented CLMS in convergence rate while achieving similar performance in terms of half power beamwidth and side lobe level of the array factor. The key advantage of these algorithms is faster convergence for the output signal of smart antenna systems compared to baseline algorithms like CLMS.
A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Contr...IAES-IJPEDS
This paper proposes a novel approach for obtaining a closed loop control
scheme based on Fuzzy Logic Controller to regulate the output voltage
waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and
control the inverter to synthesize a stepped output voltage waveform with
reduced harmonics. In this paper, three different intelligent soft-computing
methods are used to design a fuzzy system to be used as a closed loop control
system for regulating the inverter output. Gravitational Search Algorithm
and Genetic Algorithm are used as optimization methods to evaluate
switching angles for different combination of input voltages applied to MLI.
Wavelet Transform is used as synthesizing technique to shape stepped output
waveform of inverter using orthogonal wavelet sets. The proposed FLC
controlled method is carried out for a wider range of input dc voltages by
considering ±10% variations in nominal voltage value. A 7-level inverter is
used to validate the results of proposed control methods. The three proposed
methods are then compared in terms of various parameters like
computational time, switching angles and THD to justify the performance
and system flexibility. Finally, hardware based results are also obtained to
verify the viability of the proposed method.
Shunt Faults Detection on Transmission Line by Waveletpaperpublications3
Abstract: Transmission line fault detection is a very important task because major portion of power system fault occurring in transmission system. This paper represents a fast and reliable method of transmission line shunt fault detection. MATLAB Simulink use for modeled an IEEE 9-bus test power system for case study of various faults. In proposed work Daubechies wavelet is applied for decomposition of fault transients. The application of wavelet analysis helps in accurate classification of the various fault patterns. Wavelet entropy measure based on wavelet analysis is able to observe the unsteady signals and complexity of the system at time-frequency plane.
The result shows that the proposed method is capable to detect all the shunt faults.
Short term load forecasting using hybrid neuro wavelet modelIAEME Publication
The document presents a hybrid neuro-wavelet model for short term load forecasting. The model uses a cascaded feedforward backpropagation neural network approach. It first applies discrete wavelet transform to decompose historical electricity load data into wavelet coefficients. These coefficients are then used as input to train multiple neural networks. The neural network outputs are recombined using wavelet reconstruction to produce the final forecast. The model is tested on half-hourly electricity load data from Queensland. It achieves a mean square error of 2.07x10-3, indicating an accurate forecast.
This document summarizes a research paper that proposes a dynamic approach to improving the k-means clustering algorithm. The proposed approach aims to address two weaknesses of the standard k-means algorithm: its requirement of prior knowledge of the number of clusters k, and its sensitivity to initialization. The approach determines initial cluster centroids by segmenting the data space and selecting high-frequency segments. It then uses the silhouette validity index to dynamically determine the optimal number of clusters k, rather than requiring the user to specify k. The approach is compared to the standard k-means algorithm and other modified approaches, and is shown to improve initial center selection and reduce computation time.
IRJET- Fitness Function as Trust Value using to Efficient Multipath Routi...IRJET Journal
This paper proposes an energy efficient multipath routing protocol for mobile ad hoc networks. The protocol considers transmission power and remaining energy of nodes as energy metrics to select energy efficient paths and extend network lifetime. It is implemented using the NS-2 simulator. Simulation results show that the proposed protocol increases network lifetime and performance compared to the conventional AOMDV routing protocol by reducing energy consumption of mobile nodes. Key contributions are using transmission power control and residual energy calculation to select paths, and modifying the AOMDV route discovery process to include these energy metrics in route selection.
This document summarizes a research article that proposes using a novel clustering-based fuzzy controller for speed control of DC motors. The controller uses a hybrid kernel-based clustering algorithm called KPFCM to identify fuzzy rules and membership functions from motor data. This approach provides an efficient way to model the nonlinear dynamics of DC motors for control purposes. Simulation results show the proposed fuzzy controller achieves better performance than a conventional fuzzy logic controller in terms of rise time, overshoot, settling time, and error metrics. The clustering-based approach reduces computational time compared to traditional fuzzy design methods.
This document presents a two-stage approach for optimal capacitor placement in distribution systems to minimize losses using fuzzy logic and bat algorithm. In the first stage, fuzzy logic is used to determine optimal capacitor locations based on power loss index and voltage levels. In the second stage, the bat algorithm is used to determine the optimal capacitor sizes at the identified locations to minimize losses. The methodology is tested on 15-bus and 34-bus test systems and results are presented. Capacitor placement helps improve power factor, voltage profile, reduces power losses and increases feeder capacity of distribution systems.
IRJET - Energy Efficient Enhanced K-Means Cluster-Based Routing Protocol for WSNIRJET Journal
This document proposes an energy efficient routing protocol for wireless sensor networks called Enhanced K-Means Cluster-based Routing Protocol. It uses K-means clustering to divide nodes into clusters. Unlike other protocols, it considers an optimal fixed packet size based on radio parameters and channel conditions to decrease energy consumption. It also uses varying power levels for communication between cluster heads and members. Simulation results show it performs better than the conventional K-means based energy aware clustering protocol in terms of network lifetime and overall throughput.
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.
Kalman Filter Algorithm for Mitigation of Power System Harmonics IJECEIAES
The maiden application of a variant of Kalman Filter (KF) algorithms known as Local Ensemble Transform Kalman Filter (LET-KF) are used for mitigation and estimation power system harmonics are proposed in this paper. The proposed algorithm is applied for estimating the harmonic parameters of power signal containing harmonics, sub-harmonics and interharmonics in presence of random noise. The KF group of algorithms are tested and applied for both stationary as well as dynamic signal containing harmonics. The proposed LET-KF algorithm is compared with conventional KF based algorithms like KF, Ensemble Kalman Filter (En-KF) algorithms for harmonic estimation with the random noise values 0.001, 0.05 and 0.1. Among these three noises, 0.01 random noise results will give better than other two noises. Because the phase deviation and amplitude deviation less in 0.01 random noise. The proposed algorithm gives the better results to improve the efficiency and accuracy in terms of simplicity and computational features. Hence there are less multiplicative operations, which reduce the rounding errors. It is also less expensive as it reduces the requirement of storing large matrices, such as the Kalman gain matrix used in other KF based methods.
Smart Antenna is a device with signal processing
capability combining multiple antenna elements to optimize its
radiation and reception patterns as per designed specifications.
Smart antennas basically comprise of two functionalities, i.e.,
Direction of Arrival and Beamforming. This paper explains the
estimation of Direction of Arrival using MLM method and a
novel approach called MUltiple Signal Classification which takes
advantage of orthogonal property and performs subspace
computation. With a comparative study of both the algorithms,
we shall prove the advantages of MUltiple Signal Classification
over the MLM method with the aid of MATLAB
Sliding Discrete Fourier Transform (SDFT) is very efficient regarding computational load and it possesses a very fast phase angle detection with excellent harmonic rejection at nominal frequency. However, at off-nominal frequency, SDFT generates errors in both magnitude and phase angle due to spectral leakage. This paper introduces a workaround for Fourier Transform to handle this disability under off-nominal frequency while avoiding variable-rate sampling. Sliding Fourier Transform (SFT) is used as a phase detector for a phase-locked loop whose output frequency is used to drive the SFT. The paper revisits the mathematics of Fourier Transform (FT) in a three-phase setting via a time-domain approach to show a newly proposed filtering technique for the double-frequency oscillation just by summing the FT sine/cosine filter outputs of the three individual phases. Also, the analysis aims to determine and correct the phase and magnitude errors under offnominal frequency operation. The proposed technique (SFT-PLL) is tested in real time on dSPACE DS1202 DSP using voltage vectors that are pregenerated to simulate the most adverse grid conditions. The testing scenarios compare the performance of the SFT-PLL with that of the Decoupled Stationary Reference Frame PLL (dαβPLL). The results prove that SFT-PLL is superior to dαβPLL.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document summarizes electricity load forecasting techniques for power system planning. It discusses using curve fitting algorithms to forecast electricity load based on analyzing past load data from 2012. Specifically, it proposes using a Fourier series curve fitting model to predict future load based on factors like temperature, humidity, and time of day or year. The document also briefly describes other common load forecasting techniques including multiple regression, exponential smoothing, and neural networks.
Congestion Management using Optimal Choice and Allocation of FACTS Controllersidescitation
This paper concerns the optimal choice and
allocation of FACTS (Flexible AC Transmission Systems)
devices in multi-machine power system using genetic
algorithm. The objective is to improve the system loadability
and the voltage stability. Using the proposed method, the
locations of the FACTS devices, their types and rated values
are optimized simultaneously. Different kinds of FACTS
devices are simulated in this study: Thyristor Controlled
Series Compensator (TCSC) and Static Var Compensator
(SVC). The proposed algorithm is an effective and practical
method for the choice and allocation of FACTS devices in
large power systems.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Survey on Routing Protocols in Wireless Sensor Network Using Mobile SinkIJEEE
Wireless sensor network (WSN) is collection of large number of sensor nodes which senses the physical conditions of environment and send the data to sink. WSN can be classified as static and mobile WSN. In static routing protocol, energy consumption is not uniformly distributed. To avoid this problem, wireless sensor network with mobile sink can be used, where mobile sink gathers data from other nodes using 1-hop communication. In this paper, we presented the various types of WSN. At last, we compared the various routing protocol of WSN with mobile sink based on parameter no. of sinks, mobility of CH and mobility pattern.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Enhancing Performance for Orthogonal Frequency Division Multiplexing in Wirel...IRJET Journal
The document discusses enhancing the performance of Orthogonal Frequency Division Multiplexing (OFDM) in wireless systems. It proposes using a technique called Selective Level Mapping (SLM) to reduce the peak-to-average power ratio (PAPR) of OFDM signals. PAPR reduction is important for OFDM systems to improve power amplifier efficiency. The document describes a "Class-III SLM scheme" that can generate multiple alternative OFDM signal sequences using only one inverse fast Fourier transform, helping to reduce complexity. It proposes a selection method for rotation values that can achieve optimal PAPR reduction while balancing the load across components. Simulation results show the proposed method achieves better PAPR reduction performance than conventional methods
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Heuristic based adaptive step size clms algorithms for smart antennascsandit
This document summarizes research on heuristic-based adaptive step size complex least mean square (CLMS) algorithms for smart antennas. It presents Benveniste and Mathews algorithms, which use heuristics with CLMS to speed up convergence. Simulations show these algorithms outperform CLMS and augmented CLMS in convergence rate while achieving similar performance in terms of half power beamwidth and side lobe level of the array factor. The key advantage of these algorithms is faster convergence for the output signal of smart antenna systems compared to baseline algorithms like CLMS.
A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Contr...IAES-IJPEDS
This paper proposes a novel approach for obtaining a closed loop control
scheme based on Fuzzy Logic Controller to regulate the output voltage
waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and
control the inverter to synthesize a stepped output voltage waveform with
reduced harmonics. In this paper, three different intelligent soft-computing
methods are used to design a fuzzy system to be used as a closed loop control
system for regulating the inverter output. Gravitational Search Algorithm
and Genetic Algorithm are used as optimization methods to evaluate
switching angles for different combination of input voltages applied to MLI.
Wavelet Transform is used as synthesizing technique to shape stepped output
waveform of inverter using orthogonal wavelet sets. The proposed FLC
controlled method is carried out for a wider range of input dc voltages by
considering ±10% variations in nominal voltage value. A 7-level inverter is
used to validate the results of proposed control methods. The three proposed
methods are then compared in terms of various parameters like
computational time, switching angles and THD to justify the performance
and system flexibility. Finally, hardware based results are also obtained to
verify the viability of the proposed method.
Shunt Faults Detection on Transmission Line by Waveletpaperpublications3
Abstract: Transmission line fault detection is a very important task because major portion of power system fault occurring in transmission system. This paper represents a fast and reliable method of transmission line shunt fault detection. MATLAB Simulink use for modeled an IEEE 9-bus test power system for case study of various faults. In proposed work Daubechies wavelet is applied for decomposition of fault transients. The application of wavelet analysis helps in accurate classification of the various fault patterns. Wavelet entropy measure based on wavelet analysis is able to observe the unsteady signals and complexity of the system at time-frequency plane.
The result shows that the proposed method is capable to detect all the shunt faults.
Short term load forecasting using hybrid neuro wavelet modelIAEME Publication
The document presents a hybrid neuro-wavelet model for short term load forecasting. The model uses a cascaded feedforward backpropagation neural network approach. It first applies discrete wavelet transform to decompose historical electricity load data into wavelet coefficients. These coefficients are then used as input to train multiple neural networks. The neural network outputs are recombined using wavelet reconstruction to produce the final forecast. The model is tested on half-hourly electricity load data from Queensland. It achieves a mean square error of 2.07x10-3, indicating an accurate forecast.
This document summarizes a research paper that proposes a dynamic approach to improving the k-means clustering algorithm. The proposed approach aims to address two weaknesses of the standard k-means algorithm: its requirement of prior knowledge of the number of clusters k, and its sensitivity to initialization. The approach determines initial cluster centroids by segmenting the data space and selecting high-frequency segments. It then uses the silhouette validity index to dynamically determine the optimal number of clusters k, rather than requiring the user to specify k. The approach is compared to the standard k-means algorithm and other modified approaches, and is shown to improve initial center selection and reduce computation time.
IRJET- Fitness Function as Trust Value using to Efficient Multipath Routi...IRJET Journal
This paper proposes an energy efficient multipath routing protocol for mobile ad hoc networks. The protocol considers transmission power and remaining energy of nodes as energy metrics to select energy efficient paths and extend network lifetime. It is implemented using the NS-2 simulator. Simulation results show that the proposed protocol increases network lifetime and performance compared to the conventional AOMDV routing protocol by reducing energy consumption of mobile nodes. Key contributions are using transmission power control and residual energy calculation to select paths, and modifying the AOMDV route discovery process to include these energy metrics in route selection.
This document summarizes a research article that proposes using a novel clustering-based fuzzy controller for speed control of DC motors. The controller uses a hybrid kernel-based clustering algorithm called KPFCM to identify fuzzy rules and membership functions from motor data. This approach provides an efficient way to model the nonlinear dynamics of DC motors for control purposes. Simulation results show the proposed fuzzy controller achieves better performance than a conventional fuzzy logic controller in terms of rise time, overshoot, settling time, and error metrics. The clustering-based approach reduces computational time compared to traditional fuzzy design methods.
This document presents a two-stage approach for optimal capacitor placement in distribution systems to minimize losses using fuzzy logic and bat algorithm. In the first stage, fuzzy logic is used to determine optimal capacitor locations based on power loss index and voltage levels. In the second stage, the bat algorithm is used to determine the optimal capacitor sizes at the identified locations to minimize losses. The methodology is tested on 15-bus and 34-bus test systems and results are presented. Capacitor placement helps improve power factor, voltage profile, reduces power losses and increases feeder capacity of distribution systems.
IRJET - Energy Efficient Enhanced K-Means Cluster-Based Routing Protocol for WSNIRJET Journal
This document proposes an energy efficient routing protocol for wireless sensor networks called Enhanced K-Means Cluster-based Routing Protocol. It uses K-means clustering to divide nodes into clusters. Unlike other protocols, it considers an optimal fixed packet size based on radio parameters and channel conditions to decrease energy consumption. It also uses varying power levels for communication between cluster heads and members. Simulation results show it performs better than the conventional K-means based energy aware clustering protocol in terms of network lifetime and overall throughput.
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.
Kalman Filter Algorithm for Mitigation of Power System Harmonics IJECEIAES
The maiden application of a variant of Kalman Filter (KF) algorithms known as Local Ensemble Transform Kalman Filter (LET-KF) are used for mitigation and estimation power system harmonics are proposed in this paper. The proposed algorithm is applied for estimating the harmonic parameters of power signal containing harmonics, sub-harmonics and interharmonics in presence of random noise. The KF group of algorithms are tested and applied for both stationary as well as dynamic signal containing harmonics. The proposed LET-KF algorithm is compared with conventional KF based algorithms like KF, Ensemble Kalman Filter (En-KF) algorithms for harmonic estimation with the random noise values 0.001, 0.05 and 0.1. Among these three noises, 0.01 random noise results will give better than other two noises. Because the phase deviation and amplitude deviation less in 0.01 random noise. The proposed algorithm gives the better results to improve the efficiency and accuracy in terms of simplicity and computational features. Hence there are less multiplicative operations, which reduce the rounding errors. It is also less expensive as it reduces the requirement of storing large matrices, such as the Kalman gain matrix used in other KF based methods.
Smart Antenna is a device with signal processing
capability combining multiple antenna elements to optimize its
radiation and reception patterns as per designed specifications.
Smart antennas basically comprise of two functionalities, i.e.,
Direction of Arrival and Beamforming. This paper explains the
estimation of Direction of Arrival using MLM method and a
novel approach called MUltiple Signal Classification which takes
advantage of orthogonal property and performs subspace
computation. With a comparative study of both the algorithms,
we shall prove the advantages of MUltiple Signal Classification
over the MLM method with the aid of MATLAB
Sliding Discrete Fourier Transform (SDFT) is very efficient regarding computational load and it possesses a very fast phase angle detection with excellent harmonic rejection at nominal frequency. However, at off-nominal frequency, SDFT generates errors in both magnitude and phase angle due to spectral leakage. This paper introduces a workaround for Fourier Transform to handle this disability under off-nominal frequency while avoiding variable-rate sampling. Sliding Fourier Transform (SFT) is used as a phase detector for a phase-locked loop whose output frequency is used to drive the SFT. The paper revisits the mathematics of Fourier Transform (FT) in a three-phase setting via a time-domain approach to show a newly proposed filtering technique for the double-frequency oscillation just by summing the FT sine/cosine filter outputs of the three individual phases. Also, the analysis aims to determine and correct the phase and magnitude errors under offnominal frequency operation. The proposed technique (SFT-PLL) is tested in real time on dSPACE DS1202 DSP using voltage vectors that are pregenerated to simulate the most adverse grid conditions. The testing scenarios compare the performance of the SFT-PLL with that of the Decoupled Stationary Reference Frame PLL (dαβPLL). The results prove that SFT-PLL is superior to dαβPLL.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document summarizes electricity load forecasting techniques for power system planning. It discusses using curve fitting algorithms to forecast electricity load based on analyzing past load data from 2012. Specifically, it proposes using a Fourier series curve fitting model to predict future load based on factors like temperature, humidity, and time of day or year. The document also briefly describes other common load forecasting techniques including multiple regression, exponential smoothing, and neural networks.
Congestion Management using Optimal Choice and Allocation of FACTS Controllersidescitation
This paper concerns the optimal choice and
allocation of FACTS (Flexible AC Transmission Systems)
devices in multi-machine power system using genetic
algorithm. The objective is to improve the system loadability
and the voltage stability. Using the proposed method, the
locations of the FACTS devices, their types and rated values
are optimized simultaneously. Different kinds of FACTS
devices are simulated in this study: Thyristor Controlled
Series Compensator (TCSC) and Static Var Compensator
(SVC). The proposed algorithm is an effective and practical
method for the choice and allocation of FACTS devices in
large power systems.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Survey on Routing Protocols in Wireless Sensor Network Using Mobile SinkIJEEE
Wireless sensor network (WSN) is collection of large number of sensor nodes which senses the physical conditions of environment and send the data to sink. WSN can be classified as static and mobile WSN. In static routing protocol, energy consumption is not uniformly distributed. To avoid this problem, wireless sensor network with mobile sink can be used, where mobile sink gathers data from other nodes using 1-hop communication. In this paper, we presented the various types of WSN. At last, we compared the various routing protocol of WSN with mobile sink based on parameter no. of sinks, mobility of CH and mobility pattern.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Enhancing Performance for Orthogonal Frequency Division Multiplexing in Wirel...IRJET Journal
The document discusses enhancing the performance of Orthogonal Frequency Division Multiplexing (OFDM) in wireless systems. It proposes using a technique called Selective Level Mapping (SLM) to reduce the peak-to-average power ratio (PAPR) of OFDM signals. PAPR reduction is important for OFDM systems to improve power amplifier efficiency. The document describes a "Class-III SLM scheme" that can generate multiple alternative OFDM signal sequences using only one inverse fast Fourier transform, helping to reduce complexity. It proposes a selection method for rotation values that can achieve optimal PAPR reduction while balancing the load across components. Simulation results show the proposed method achieves better PAPR reduction performance than conventional methods
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document provides an introduction to analyzing signals in the time, frequency, and modal domains. It discusses:
1) The time domain directly records how a parameter varies over time, while the frequency domain represents a signal as the sum of its sine wave components.
2) Analyzing signals in the frequency domain can make it easier to detect small signals masked by large ones, as components are separated. Instruments like dynamic signal analyzers are useful for frequency domain analysis.
3) Examples show that a sine wave has a single frequency component, while square waves and transients have multiple components. The spectrum of an impulse is flat, containing all frequencies.
IRJET- Design and Implementation of Performance Evaluation of Routing Protoco...IRJET Journal
This document summarizes a research paper that evaluates the performance of different routing protocols under various mobility models in mobile ad hoc networks (MANETs). It discusses reactive, proactive, and hybrid routing protocols and mobility models including chain, disaster area, small world in motion, and probabilistic random walk. The research aims to design and implement a performance evaluation of routing protocols under different mobility models in MANETs using the NS-2 simulator. Key performance metrics analyzed include packet delivery ratio, end-to-end delay, jitter, and throughput. The research will provide insights into how different protocols perform under diverse mobility scenarios in MANETs.
The document compares different ANN algorithms for detecting the saturation level in the magnetic core of a welding transformer. Four algorithms are evaluated: Resilient Backpropagation, Gradient Descent, Levenberg-Marquardt, and Bayesian Regularization. The algorithms are assessed based on computational time, error, gradient, and complexity. Detecting saturation is important to prevent current spikes that could shut down the welding system. An ANN uses the primary current as input to identify spikes and control the flux density to prevent saturation and overcurrent.
Classification of vehicles based on audio signalsijsc
The document describes a method for classifying vehicles based on their audio signals using quadratic discriminant analysis. Feature vectors containing short-time energy, average zero-crossing rate, and pitch frequency are extracted from periodic segments of vehicle audio signals. The method achieves better classification accuracy by only considering feature vectors with high energy, as these correspond better to the vehicle sounds and exclude low energy background noise regions. Simulation results show the proposed method of separating high energy vectors based on thresholds of average energy and zero-crossing rate improves classification performance compared to considering all vectors.
This report discusses the planning Associate in
nursing the implementation of an OFDM system
in several information module schemes like MQPSK,
M-QAM. First, a short introduction is
provided by explaining the background and the
specification of the project. Then the report deals
with the system model, every block of the OFDM
system is represented (IFFT, FFT, Cyclic prefix,
modulation / reception, Channel estimation, bit
error rate). System design is analyzed. The
transmission techniques, further because the
system parameters for transmission and reception
are explained well. Finally, the results are
provided.
This document discusses techniques for predicting traffic patterns to evaluate the probability of channel availability for spectrum sharing using cognitive radio. It reviews related work on traffic models and prediction methods, including ARIMA, SARIMA, neural networks, and mean square approaches. The goals are to increase channel utilization and reduce call blockage and interference for secondary users accessing licensed channels when primary users are not using them. Accurately predicting primary user traffic patterns is important to avoid interference and allow secondary users to evaluate channel availability before transmission.
This document proposes using the discrete wavelet transform (DWT) with truncation for privacy-preserving data mining. The DWT decomposes data into approximation and detail coefficients. Truncating the detail coefficients distorts the data while maintaining statistical properties. An experiment shows the method effectively conceals sensitive information while preserving data mining performance after distortion.
This document discusses the discrete wavelet transform (DWT) and its implementation in MATLAB. It begins with an introduction to DWTs, explaining that they decompose signals into different frequency bands using low-pass and high-pass filters. It then describes how 2D DWTs are implemented by applying 1D transforms separately to rows and columns, producing subbands that emphasize different types of edges. The document provides steps for performing DWTs in MATLAB, including single-level and multi-level decomposition and reconstruction. It concludes by showing decomposed images and discussing how DWTs separate approximation and detail information.
This document discusses a method for detecting, classifying, and locating faults on 220kV transmission lines using discrete wavelet transform and neural networks. Fault detection is performed by calculating the energy of detail coefficients from wavelet transformation of phase current signals. A neural network is then used for fault classification and location. The neural network is trained using patterns generated by simulating different fault conditions, including varying fault location, type, and resistance. The proposed method aims to classify 10 different fault types and locate faults occurring at different points along the transmission line.
This paper presents a discrete wavelet transform and neural network approach to fault
detection and classification and location in transmission lines. The fault detection is carried out by
using energy of the detail coefficients of the phase signals and artificial neutral network algorithm
used for fault type classification and fault distance location for all the types of faults for 220 KV
transmission line. The energies of the all three phases A, B, C and ground phase are given in put to
the neural network for the fault classification. For each type of fault separate neural network is
prepared for finding out the fault location. An improved performance is obtained once the neutral
network is trained suitably, thus performance correctly when faced with different system parameters
and conditions.
A Survey on Classification of Power Quality Disturbances in a Power SystemIJERA Editor
This document provides a survey of techniques for classifying power quality disturbances in a power system. It discusses various power quality issues and types of disturbances such as transients, interruptions, sags, swells, waveform distortions, and frequency variations. It then describes several signal processing techniques used for feature extraction, including Fourier transform, short-time Fourier transform, S-transform, Hilbert-Huang transform, Kalman filter, and wavelet transform. Finally, it reviews various classification methods such as artificial neural networks, fuzzy expert systems, adaptive neuro-fuzzy systems, genetic algorithms, and support vector machines that have been applied to classify power quality disturbances.
IRJET- Efficient and Secure Communication In Vehicular AD HOC NetworkIRJET Journal
The document discusses efficient and secure communication in vehicular ad hoc networks (VANETs). It proposes a Cluster based reliable routing (CRR) protocol. Vehicles are clustered based on their velocity, and a Cluster Controller (CC) is elected based on transmitter heights and position to manage communication among cluster members. The CRR protocol aims to address the challenging routing issues posed by the highly dynamic topology of VANETs.
The document discusses and compares different approaches to filtering time series data from gas turbine sensors to remove noise while preserving trends that indicate faults. It introduces two non-linear filtering approaches called analysis and synthesis and compares them to a classical linear exponentially weighted moving average (EWMA) filter. The analysis approach models the signal as piecewise constant or linear while the synthesis approach reconstructs the signal as a combination of predefined basis signals. Both incorporate prior assumptions about the signal in different ways. The document then evaluates the noise reduction and trend preservation capabilities of the different filters on simulated step and ramp signals representative of gas turbine sensor data.
Classification of Vehicles Based on Audio Signals using Quadratic Discriminan...ijsc
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method, we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
This paper discusses the time-frequency transform based fault detection and classification of STATCOM (Static synchronous compensator) integrated single circuit transmission line. Here, fast-discrete S-Transform (FDST) based time-frequency transformation is proposed for evaluation of fault detection and classification including STATCOM in transmission line. The STATCOM is placed at mid-point of transmission line. The system starts processing by extracting the current signals from both end of current transformer (CT) connected in transmission line. The current signals from CT’s are fed to FDST to compute the spectral energy (SE) of phase current at both end of the line. The differential spectral energy (DSE) is evaluated by subtracting the SE obtained from sending end and SE obtained from receiving end of the line. The DSE is the key indicator for deciding the fault pattern detection and classification of transmission line. This proposed scheme is simulated using MATLAB simulink R2010a version and successfully tested under various parameter condition such as fault resistance (Rf),source impedance (SI), fault inception angle (FIA) and reverse flow of current. The proposed approach is simple, reliable and efficient as the processing speed is very fast to detect the fault within a cycle period of FDT (fault detection time).
This document provides an overview of the contents and structure of a Digital Signal Processing lab manual for an undergraduate course. The manual covers experiments and programs that will be implemented in both software (using MATLAB, LabVIEW, or C programming) and hardware (using DSP boards from TI, Analog Devices, or Motorola). The experiments are intended to help students learn digital signal processing concepts and gain practical skills in implementing DSP algorithms and applications. Some example topics mentioned include discrete time signals and systems, Fourier analysis, FIR and IIR filter design, and speech/image processing. The manual provides a framework for students to complete their DSP lab assignments throughout the semester.
MRA Analysis for Faults Indentification in Multilevel InverterIRJET Journal
This document proposes using wavelet analysis to detect and identify switch faults in a diode-clamped multilevel inverter feeding an induction motor drive. A wavelet-based multi-resolution analysis is used to analyze voltage and current signals from the system under normal and faulty conditions. Signatures extracted from the wavelet analysis at different resolution levels can be used to develop a feature vector to discriminate between healthy and faulty systems, and identify the type of fault. The analysis is able to detect switch shorts, opens, increased load, and line-to-line faults based on variations in the wavelet transform details of signals like phase voltage, line current, and switch voltages.
Similar to IRJET-Forecasting of Time Series Data using Hybrid ARIMA Model with the Wavelet Transform. (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.