A simple predictive method of critical flicker detection for human healthy precaution

Interharmonics and flickers have an interrelationship between each other. Based on International Electrotechnical Commission (IEC) flicker standard, the critical flicker frequency for a human eye is located at 8.8 Hz. Additionally, eye strains, headaches, and in the worst case seizures may happen due to the critical flicker. Therefore, this paper introduces a worthwhile research gap on the investigation of interrelationship between the amplitudes of the interharmonics and the critical flicker for 50 Hz power system. Consequently, the significant findings obtained in this paper are the amplitudes of two particular interharmonics are able to detect the critical flicker. In this paper, the aforementioned amplitudes are detected by adaptive linear neuron (ADALINE). After that, the critical flicker is detected by substituting the aforesaid amplitudes to the formulas that have been generated in this paper accordingly. Simulation and experimental works are conducted and the accuracy of the proposed algorithm which utilizes ADALINE is similar, as compared to typical Fluke power analyzer. In a nutshell, this simple predictive method for critical flicker detection has strong potential to be applied in any human crowded places (such as offices, shopping complexes, and stadiums) for human healthy precaution purpose due to its simplicity

Harmonic Estimation Of Distorted Power Signals Using PSO – Adaline

In recent times, power system harmonics has got a great deal of interest by many Power system Engineers. It is primarily due to the fact that non-linear loads comprise an increasing portion of the total load for a typical industrial plant. This increase in proportion of non-linear load and due to increased use of semi-conductor based power processors by utility companies has detoriated the Power Quality. Harmonics are a mathematical way of describing distortion in voltage or current waveform. The term harmonic refers to a component of a waveform occurs at an integer multiple of the fundamental frequency. Several methods had been proposed, such as discrete Fourier transforms, least square error technique, Kalman filtering, adaptive notch filters etc; Unlike above techniques, which treat harmonic estimation as completely non-linear problem there are some other hybrid techniques like Genetic Algorithm (GA), LS-Adaline, LS-PSOPC which decompose the problem of harmonic estimation into linear and non-linear problem. The results of LS-PSOPC and LS-Adaline has most attractive features of compactness and fastness. . Our new proposed technique tries to reduce the pitfalls in the LS-PSOPC, LS-Adaline techniques. With new technique we tried to estimate the Amplitudes by Least square estimator, frequency of the signal by PSOPC and phases of the harmonics by Adaline technique using MATLAB program. Harmonic signals were estimated by using LS-PSOPC, PSOPC-Adaline. Errors in estimating the signal by both the techniques are calculated and compared with each other

Harmonics and Phasor Estimation for a Distorted Power System Signal

The controlling, operating and monitoring of electric devices has been possible because of the knowledge of power system parameters. The relay functionality in power systems is influenced by the two vital power system parameters which are frequency and harmonics. Hence in power systems, phasor estimation is of utmost importance. These computations not only facilitate realtime state estimation, but also improve protection schemes. However, in the presence of power frequency deviation, the phasor undergoes rotation in the complex plane. Interconnection of power grids and distributed generation systems becomes difficult because of this phenomenon. Hence, in this report different algorithms are studied and implemented for the estimation of phasor. The parameters estimated are limited to voltage amplitude and phase, change of frequency and rate of change of frequency. In this thesis, Singular Value Decomposition (SVD) technique and Recursive Least Square (RLS) algorithms are used to estimate the amplitude and phase for different harmonics present in a distorted power system signal. Simple DFT algorithm is used to estimate the phasor variation, change of frequency and rate of change of frequency when deviated from the nominal frequency

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

Power system frequency estimation using linear and nonlinear techniques

In an electrical power system frequency is an important parameter. The frequency of operation is not constant but it varies depending upon the load conditions. In the operating, monitoring and controlling of electric device power system parameters are having great contribution. So it is very important to accurately measure this slowly varying frequency. Under steady state conditions the total power generated by power stations is equal to system load and losses. Frequency can deviate from its nominal value due to sudden appearance of generation-load mismatches. Frequency is a vital parameter which influences different relay functionality of power system. This study was made to estimate the frequency of measuring voltage or current signal in presence of random noise and distortion. Here we are first using linear techniques such as complex least mean square (LMS), least square (LS) and recursive least square (RLS) algorithm for measuring the frequency from the distorted voltage signal. Then comparing these results with nonlinear techniques such as nonlinear least mean square (NLMS), nonlinear least square (NLS), nonlinear recursive least square (NRLS) algorithms. The performances of these algorithms are studied through simulation

Analysis and Mitigation of Power Quality Issues in Distributed Generation Systems Using Custom Power Devices

This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults

A Novel AI-driven Hybrid Method for Flicker Estimation in Power Systems

This paper introduces a novel hybrid method using a combination of an H-infinity filter and artificial neural network (ANN) to estimate flicker components within power distribution system voltages. The H-infinity filter first extracts the estimated envelope of the applied voltage waveforms, incorporating a new voltage fluctuation model that realistically accounts for both harmonic and flicker components. Furthermore, an ADALINE (adaptive linear neuron) extracts the specific flicker components within the envelope. The hybrid process decouples prediction states, enhancing convergence behavior. Additionally, it showcases robust flicker component tracking even in the presence of power harmonics and noise, offering advantages over traditional signal processing methods. The algorithm’s performance in flicker estimation is validated through statistical analysis using Monte Carlo (MC) simulations and real world dat

ADALINE-based synchronous detection for enhanced shunt APF performance

Power quality issues caused by current harmonics from nonlinear and unbalanced loads are a growing concern. This paper presents a novel control strategy for four-wire shunt active power filters (SAPF) that surpasses existing conventional methods in mitigating harmonics and power factor correction. The strategy employs an improved synchronous detection method (SDM) enhanced by an adaptive linear neural network (ADALINE) trained using the least mean square (LMS) algorithm. This approach accurately estimates harmonic frequencies, enabling the SAPF to generate precise compensation currents. The effectiveness of the proposed method is validated through MATLAB-Simulink simulations under balanced supply conditions, encompassing diverse load scenarios. These simulation results are compared with those obtained using instantaneous power theory (IPT). They demonstrate the ability of the proposed method to achieve excellent harmonic identification and elimination, to comply with IEEE 519 harmonic limits, to ensure sinusoidal and balanced line currents, and to compensate for reactive power and neutral current. Furthermore, its simple architecture and noise robustness make it a promising solution for enhancing power quality