Distorted Power System Signals Research Articles

Novel Frequency Estimator for Distorted Power System Signals Using Two-Point Iterative Windowed DFT

Novel Frequency Estimator for Distorted Power System Signals Using Two-Point Iterative Windowed DFT

Optimization of harmonics with active power filter based on ADALINE neural network

Mostly the power quality issues in the distribution line system happen due to the presence of harmonics. Especially, the nonlinear loads such as power electronic converters, high-speed semi-conducting switches, and solid state drives were the major causes for harmonics in distorted power system signals. Moreover, the estimation of magnitude and the phase of this harmful harmonic interference are necessary. By taking in to consideration of all the above factors, this paper develops an efficient technique for harmonic estimation and detection of the renewable wind energy resources and elimination of these harmonics will also be done accordingly for getting desired output from wind energy. 8 bit inputs (4 + 4) are collected and used to generate the intended input set for ANN training. The proposed work develops an Adaptive Linear Neural Network (ADALINE) for the estimation of harmonics which is the novelty of this work. For making the harmonics content more negligible and to enhance the load power quality, an Active Power Filter (APF) is used. The novel control design is developed with a Pulse Width Modulation (PWM) control. In addition, feed forward networks (trained by back propagation algorithm) works like a hysteresis band comparator. An APF control design is developed with ADALINE network in which the load and current along with voltage will be analyzed and then the controller will be calculating the control signal by considering the reference compensation current. Afterwards, the power system is injected with compensating current. The simulation is carried out with Matlab-Simulink laboratory prototype is developed with Xilinux 3E Spartan FPGA board to verify the proposed control designs. The proposed work is compared with exiting method comprising Shunt Active Power Filters (SAPF) with ADALINE for the performance perspectives. This method was found to be effective in terms of many parameters such as load voltage, load current, voltage, reactive power, real power and especially THD value than those of the existing works which are considered.

A simple algorithm for amplitude estimation of distorted power system signal

Abstract This paper presents development and implementation of a novel digital signal processing algorithm for on-line estimation of peak value of the fundamental component of a non-sinusoidal power system signal. The algorithm relies on stable band-pass first and second degree digital integrator based rigorous mathematical deduction and recombines itself with zero-crossing avoiding technique. Compared with the well-established technique such as the enhanced phase-locked-loop (EPLL) based system, the proposed algorithm provides higher degree of immunity and insensitivity to harmonics and noise, and faster response. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented and its accuracy and response time have been compared with the EPLL systems. Moreover, a simple laboratory setup implemented by MATLAB and dedicated hardware is built to verify the performance of the proposed algorithm in real time applications. The framework of laboratory prototype has been given out and the necessary modification in proposed algorithm for on-line implementation has been discussed.

Real time harmonics estimation of distorted power system signal

Abstract Power system harmonics should be estimated properly for the maintenance of power quality. So, there is a need for development of more accurate and computationally efficient estimation technique. In this context, this paper presents a harmonics estimation technique using a Variable Leaky Least Mean Square algorithm. In this proposed method drifting of parameters is avoided using a leak adjustment method. To improve the rate of convergence, step size is also adapted in this method. Real time power system is also imitated using different cases through simulation, which enables the superiority of the proposed method over the other existing techniques. Studies made on experimental data, processed in arduino due microcontroller, also support its superiority.

Neuro-evolutionary approaches to power system harmonics estimation

This paper focuses on exploiting two computational intelligence techniques such as artificial neural network and evolutionary computation techniques in estimation of harmonics in power system. Accurate estimation of harmonics in distorted power system current/voltage signal is essential to effectively design filters for eliminating harmonics. No standard design is available for handling of local minima and training of NN but Evolutionary Computation (EC) techniques are capable of resolving local minima. Neural Network and Evolutionary Computing (Bacterial Foraging Optimization (BFO)) are combined to achieve accurate estimation of different harmonics components of a distorted power system signal. First estimation of unknown parameters are carried out using BFO, then optimized output of BFO are taken as initial values of the unknown parameters for Adaline. Amplitude and phase of fundamental and harmonics components are determined from final updated values of unknown parameters using Adaline. This Adaline based Bacterial Foraging Optimization (Adaline-BFO) hybrid estimation algorithm addresses the problems of slow convergence and reduction of time generation of off-springs happening in Genetic Algorithm (GA), and to avoid local minima in Particle Swarm Optimization (PSO). The proposed Adaline-BFO algorithm has been applied for estimation of harmonics of the voltage obtained across the inverter terminals of a prototype Photovoltaic (PV) system. From the obtained results, it is confirmed that the proposed Adaline-BFO algorithm provides superior estimation performance in terms of improvement in % error in estimation, processing time in computation and performance in presence of inter and sub-harmonic components when compared with the Discrete Fourier Transform (DFT), Kalman Filter (KF) and BFO algorithms.

Power System Harmonics Estimation using Signal Processing Techniques

This paper proves the effectiveness of Least Mean Square (LMS) estimation algorithm for estimation of power system harmonics, inter-harmonics and sub-harmonics of distorted signal. Accurate estimation can be achieved by this technique for harmonics, sub-harmonics and inter-harmonics estimation including possible variation in the signal such as change in amplitude when applied to a distorted power system signal. This technique is compared with Least square based algorithm and Recursive least square (RLS) Algorithm simulation and experimental studies are carried out for the above three algorithm and it is observed that the proposed LMS approach to estimation of harmonics, inter-harmonics and sub-harmonics in a distorted power system signal exhibits superior estimation performance in terms of tracking time and accuracy as compared to performances Least Square based algorithm i.e Recursive Least Square (RLS) algorithm.

Harmonic estimation of distorted power system signals employing two hybrid strategies

This paper deals with two hybrid strategies for harmonic estimation of distorted power system signal. Harmonics estimation for a signal distorted with additive noise is an important aspect while dealing with power quality. For this reason, the two proposed methods, Kalman filter-modified genetic algorithm (KF-modified GA) and Kalman filter-bacterial foraging optimisation (KF-BFO), are employed for promoting the efficiency in estimation of harmonics. Simulation results are presented and compared in order to exemplify the effectiveness and robustness of the proposed approach. Improvement in both convergences for solution as well as processing time is demonstrated from algorithm.

Amplitude and frequency detection of power system signals with chaotic distortions using independent component analysis

In this paper independent component analysis (ICA) method for amplitude and frequency estimation of highly distorted power system signals is presented. In a power system the accuracy of amplitude and frequency detection is strictly related to distortion strength of the system signal. Distorted system signal may contain chaotic (ferroresonance) and non-chaotic (white, pulse, harmonic etc.) noise. In this study, chaotic distortions are used to contaminate system signal, which is the worst case scenario for a power system. The proposed method is employed to eliminate the noise and hence to improve the efficiency of the estimation. To simulate chaotic distortions Duffing Oscillator solutions are used. Computer simulations have been carried out for the performance analysis of the proposed method and the comparison of the results of the proposed method with the conventional filters are presented.

Estimation of time varying signal parameters using an improved Adaline learning algorithm

This paper presents an improved adaptive linear combiner (Adaline) structure for fast estimation of time varying power signal parameters corrupted by noise. Unlike the conventional Adaline approach, the new algorithm minimizes an objective function based on weighted square of the error and uses a modified recursive Gauss Newton (MRGN) method. The Hessian matrix, obtained by minimizing the objective function, was simplified using certain approximations. A weight adjustment procedure for the Adaline is defined in a decoupled manner for direct current (DC), fundamental, harmonic components and system frequency. The new improved Adaline, thus produces a faster convergence and tracking accuracy for the time varying distorted power system signals. To test the effectiveness of the algorithm, several time varying power network signals were simulated with abrupt change in system frequency, harmonics, decaying dc components with low signal to noise ratio (SNR), and the changing parameters were estimated. The performance of proposed Adaline structure is compared with the standard Adaline structure in terms of accuracy.

Ensemble-Kalman-Filter-Based Power System Harmonic Estimation

The growing use of power-electronics-based components and nonlinear loads is increasing the presence of harmonics in power system signals. In this scenario, proper estimation of such harmonics is intended to maintain power quality and improved operation of the system. It is also desirable that the estimation technique should be computationally efficient while being accurate. From this viewpoint, this paper proposes a nonlinear state estimation technique based on ensemble Kalman filtering for estimation of harmonics, interharmonics, and subharmonics, all using a single framework and at a time, from distorted power system signal. The proposed technique is computationally efficient compared to conventional Kalman filtering leading to less computational cost and hardware requirement. It is observed from both simulation and experimental studies that the proposed ensemble Kalman filter (KF) approach to estimation of harmonics, interharmonics, and subharmonics in a distorted power system signal exhibits superior estimation performance in terms of tracking time and accuracy as compared to performances of some of the existing techniques such as recursive least square, recursive least mean square, and KF algorithms. The proposed technique is also found to be robust and gives accurate estimates even in the presence of amplitude variations in the measured signal.

BFO optimized RLS algorithm for power system harmonics estimation

In this paper a new estimation approach combining both Recursive Least Square (RLS) and Bacterial Foraging Optimization (BFO) is developed for accurate estimation of harmonics in distorted power system signals. The proposed RLS–BFO hybrid technique has been employed for estimating the fundamental as well as harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive by using RLS that sequentially updates the unknown parameters of the signal. Simulation and experimental studies are included justifying the improvement in performance of this new estimation algorithm.

Harmonics estimation using KF-Adaline algorithm and elimination with hybrid active power filter in distorted power system signals

Harmonics estimation and its elimination for a signal distorted with additive noise is an interdisciplinary area of interest for many researchers. This paper presents Kalman filter – adaptive linear neural network (KF-Adaline) approach for harmonics estimation and hybrid active power filter (HAPF) with modified PWM control technique for its elimination in distorted power system signals. In the estimation process, the weight of the Adaline is updated using Kalman filter algorithm. Harmonics components are estimated from the updated weights of the Adaline. In order to mitigate these harmonics, HAPF with modified PWM control is proposed. The modified PWM control technique is based on comparing simultaneously a triangular high frequency carrier signal with a slow varying regulation signal and it is opposite. A laboratory prototype for HAPF with modified PWM technique is built for harmonics elimination in distorted power system signals. Simulation and experimental results are presented to verify the good behav...

New Algorithms for the Estimation of Distorted Time-varying Power System Signal Parameters in Noise

This article proposes an improved two-step augmented Gauss-Newton type algorithm for the estimation of distorted power system signals. The proposed algorithm is capable of accurately estimating the amplitude, phase, and frequency of the harmonics and interharmonics present in the power system signal in the presence of high additive white Gaussian noise. The estimation of all the parameters is divided into two steps. In the first step, the frequency of the signal is computed using an improved recursive Newton-type algorithm; in the second step, the amplitude and phase of the power system voltage or current are computed using a recursive Gauss-Newton-type algorithm. Further, to reduce the computational complexity of the standard Gauss-Newton algorithm, certain approximations are carried out, making the algorithm suitable for on-line applications. Also, a variable forgetting factor is used to improve the speed of convergence and accuracy of the algorithm. To demonstrate the robustness and accuracy in the estimation of the distorted fundamental and harmonic phasors using the proposed algorithm, the results obtained from computer simulations are presented.

Bandpass Second-Degree Digital-Integrator-Based Power System Frequency Estimation Under Nonsinusoidal Conditions

A novel digital signal processing algorithm for online estimation of the fundamental frequency of the distorted power system signals is presented. The basic algorithm relies on the development of an efficient variance reduction algorithm; and design of a new stable bandpass infinite impulse response (IIR) second-degree digital integrator (SDDI) with reduced approximation error. Compared with the well-established technique such as the enhanced-phase-locked-loop (EPLL) system, the proposed algorithm provides the following: 1) higher degree of immunity and insensitivity to harmonics and noise and 2) faster response during step frequency change. Structural simplicity, wide range of application, controls over speed and accuracy, and parameter robustness are other salient features of the method. The only limitation as compared with the EPLL system is its slower transient response during step change in signal magnitude. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented, and its accuracy and response time have been compared with the EPLL systems.

Design and implementation of a high accuracy sampling wattmeter under non-sinusoidal and off-nominal frequency conditions

Errors may creep in when measuring active power under non-sinusoidal and off-nominal frequency conditions by a conventional regularly spaced sampling digital wattmeter (RSSDW) due to the existence of higher order voltage and current harmonics, and asynchrony between the signal fundamental frequency and the sampling rate. This paper presents design and implementation of a modified regularly spaced sampling digital wattmeter, called M-RSSDW, immune to above mentioned drawbacks of the conventional RSSDW. The proposed method utilizes a modified/ first degree digital integrator design technique, suitable for the distorted power system signals. The harmful truncation errors of asynchronous sampling have been reduced using numerical compensation method. Compared with the well-established technique such as Liangs’ method, the proposed algorithm provides higher degree of immunity and insensitivity to harmonics and frequency deviation. Structural simplicity, wide range of application and reduced computational load are other salient features of the proposed method. Based on simulation studies, the performance and validity of the proposed algorithm at different operating conditions have been presented and its accuracy and response time have been compared with the Liangs’ approach.

Frequency Estimation of Distorted Power System Signals Using a Robust Algorithm

This paper proposes a robust algorithm based on an extended complex Kalman filter (ECKF) for improving frequency estimation of distorted power system signals. In general, abnormal data obtained from measurements may cause noises and disturbances of power systems as well as affect the accuracy of frequency estimation. For this reason, the proposed algorithm is employed to suppress the abnormalities from abnormal data of measurements for promoting the efficiency in frequency estimation. Moreover, not only can it perform the ECKF without changing any form of it, but it can also save the execution time. Results of frequency estimation for distorted signals using the proposed method are compared with the ECKF.

Frequency estimation of distorted power system signals using extended complex Kalman filter

The paper proposes an extended complex Kalman filter and employs it for the estimation of power system frequency in the presence of random noise and distortions. From the discrete values of the 3-phase voltage signals of a power system, a complex voltage vector is formed using the well known /spl alpha//spl beta/-transform. A nonlinear state space formulation is then obtained for this complex signal and an extended Kalman filtering approach is used to compute the true state of the model iteratively with significant noise and harmonic distortions. As the frequency is modeled as a state, the estimation of the state vector yields the unknown power system frequency. Several computer simulations test results are presented in the paper to highlight the usefulness of this approach in estimating near nominal and off-nominal power system frequencies.