Bio: Pampa Sinha is an academic researcher from Netaji Subhash Engineering College. The author has contributed to research in topics: Computer science & AC power. The author has an hindex of 4, co-authored 8 publications receiving 76 citations.
TL;DR: In this paper, the authors presented a new method based on discrete wavelet transform for the identification of prevailing disturbing loads in power systems downstream or upstream from the metering section, which is based on the analysis of detail active power quantities which are based on IEEE Standard 1459-2000 approach.
Abstract: Harmonic pollution is one of the most important power quality problems in power system. There is an urgent requirement for the proper identification of the location of the source of harmonic pollution in order that remedial measures can be taken for the improvement of power quality at the consumer premises. This paper presents a new method based on discrete wavelet transform for the identification of prevailing disturbing loads in power systems downstream or upstream from the metering section. It is based on the analysis of detail active power quantities which are based on IEEE Standard 1459-2000 approach. The detail active powers at the lowest level along with the details pollution factor have been used to clearly identify the location of the harmonic source. The simulation results presented here show that the proposed method can effectively indicate the presence of nonlinear load under both sinusoidal and distorted supplies and can also detect the dominant source of harmonic pollution in power system network.
TL;DR: In this paper, a wavelet decomposition of voltage and current signals at the point of measurement is used to identify the location and nature of the harmonic generating sources in a distribution system.
Abstract: SUMMARY A new method to identify the location and nature of the harmonic generating sources in a distribution system has been proposed. The method is based on wavelet decomposition of voltage and current signals at the point of measurement. Detail reactive power at level 1 of wavelet decomposition has been used. A harmonic generating load is identified by extracting its characterizing harmonics in the power system signals. The pseudo-frequency for the wavelet decomposition at level 1 is set at the characteristic frequency of the load, and the sampling frequency is so chosen that the desired harmonic information can be extracted at the detail level 1. Power systems signals are, however, captured at a higher sampling frequency. For the identification of the disturbing source, the required sampling frequency for level 1 decomposition is obtained using downsampling on the captured data. Applicability of the method has been demonstrated for both radial and non-radial power system networks.
TL;DR: In this article , an improved CSA (ICSA) has been formulated to minimize the congestion cost in a deregulated power system, where the generator sensitivity factors (GSF) have been considered to select the most sensitive generators that would participate in CM.
Abstract: Congestion management is one of the most critical issues in the operation of deregulated power systems. This research work proposes a Congestion Management (CM) approach considering the optimal real power rescheduling of power system generators. The Generator Sensitivity Factors (GSF) have been considered to select the most sensitive generators that would participate in CM. An Improved Crow Search Algorithm (ICSA) has been formulated to minimize the congestion cost. The stages of exploration and exploitation of the Crow Search Algorithm (CSA) have been modified with the incorporation of the dynamic awareness probability and Lévy flight approach for the formulation of ICSA in CM. The CM cost optimization problem was validated using the standard 39-bus new England system and the IEEE-118 bus system. A comparison analysis with other optimization techniques has also been carried over to further assess the performance of the proposed ICSA approach. The obtained results showed that the congestion cost achieved with ICSA has been reduced by 1.84%, 4.01%, 6.25%, when compared to the Differential Evolution (DE) Algorithm, Grey Wolf Optimization (GWO), and Crow Search Algorithm (CSA), respectively, for the 39-bus system. For the IEEE 118-bus system, the application of ICSA has curtailed the congestion cost by 2.50%, 8.55%, and 12.50%, when compared to the congestion cost achieved with DE, GWO, and CSA, correspondingly. It is observed that the performance of ICSA for CM cost has superseded the other approaches adopted for CM.
TL;DR: A wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching, and with the help of neural network classifier, the transient signals are effectively classified.
Abstract: Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.
••01 Dec 2009
TL;DR: In this article, the power components under nonsinusoidal conditions have been calculated using discrete wavelet transform and fuzzy logic and has been implemented for the determination of power system components and power quality factor under non sinusoidal condition.
Abstract: Reliability and quality are the two important facets of power system delivery. A power distribution system is considered to be reliable if all its customers get interruption free power throughout the year. The term power quality may be referred to as maintaining near sinusoidal voltage at rated frequency at the consumers end. In recent times the issues involved with power quality have generated a tremendous amount of interest among power system engineers. In sinusoidal conditions the definitions of power components are unique and expressive. But the traditional definitions of power components become unsuitable under non sinusoidal conditions. The power component definitions under nonsinusoidal conditions have been proposed in IEEE Standard 1459–2000 both for single phase and three phase unbalanced systems. These power component definitions are based on Fourier Transform. In this paper the power components under nonsinusoidal conditions have been calculated using discrete wavelet transform. Then fuzzy logic and has been implemented for the determination of power system components and power quality factor under non sinusoidal condition.
TL;DR: A comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances and a comparison of various classification systems is presented in tabular form.
Abstract: The increasing trend towards renewable energy sources requires higher power quality (PQ) at the generation, transmission and distribution systems. The PQ disturbances are produced due to the nonlinear loads, power electronic converters, system faults and switching events. The utilities and consumers of electric power are expected to acquire ideal voltage and current waveforms at rated power frequency. The development of new techniques for the automatic classification of PQ events is at present a major concern. This paper presents a comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances. Various signal processing techniques used for the feature extraction such as Fourier transform, wavelet transform, S-transform, Hilbert transform, Gabor transform and their hybrids have been reviewed. The artificial intelligent techniques used for the pattern recognition such as artificial neural network, fuzzy logic, support vector machine are reviewed in detail. The optimization techniques used for the optimal feature selection such as genetic algorithm, particle swarm optimization and ant colony optimization are also reviewed. A comparison of various classification systems is presented in tabular form which highlights the important techniques used in the field of PQ disturbance monitoring. The comparison of research works carried out on the classification of PQ disturbances points out that many researchers have focussed on the feature extraction and classification techniques. Only few authors have used the feature selection techniques for selecting the best suitable features. This review may be considered a valuable source for researchers as a reference point to explore the opportunities for further improvement in the field of PQ classification.
TL;DR: In this article, the authors provide a comprehensive review on the DVR topologies, control strategies and applications, and some comparative conclusions are also provided for the researchers and engineers, who want to do investigations on DVRs.
Abstract: During the last half century the Power Quality (PQ) related problems have become important issues. Many solutions have been proposed to address the PQ problems. The most attractive and flexible way is to use power electronic converter based devices such as Dynamic Voltage Restorer (DVR), Distribution Static Compensator (DSTATCOM), Unified Power Quality Conditioner (UPQC), Uninterruptible Power Supply (UPS), and other devices usually called custom power devices. Among custom power devices, the DVR is the most economical solution to overcome the voltage-related PQ problems. Intensive research has been done in the field of DVR and the field is rather mature now. But, a survey on the published papers showed that there is not any published paper that reviews the DVR technology. This paper tends to provide a comprehensive review on the DVR topologies, control strategies and applications. We will consider all of the fast voltage compensators, i.e. the devices called Static Series Compensator (SSC), sag corrector, Dynamic Sag Corrector (DySC), and other similar devices are also considered. All of these devices will be called DVR since they operate almost in the same way. Some comparative conclusions are also provided. This paper can be beneficial for the researchers and engineers, who want to do investigations on DVRs.
TL;DR: A critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration is presented, to provide various concepts utilized for extraction of the features to detect and classify the P Q disturbances even in the noisy environment.
Abstract: The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.
TL;DR: A novel approach for power quality disturbance classification using Hidden Markov Model (HMM) and Wavelet Transform (WT) and the results obtained for practical data prove the capability of the proposed method for implementing in experimental systems.
Abstract: A novel approach for power quality disturbance classification using Hidden Markov Model (HMM) and Wavelet Transform (WT) is proposed in this paper. The energy distributions of the signals are obtained by wavelet transform at each decomposition level which are then used for training HMM. The statistical parameters of the extracted disturbance features are used to initialize the HMM training matrices which maximize the classification accuracy. Fifteen different types of power quality disturbances are considered for training and evaluating the proposed method. The Dempster–Shafer algorithm is also used for improving the accuracy of classification. In addition, the effect of the noise is studied and the performance of a denoising method is also investigated. Simulation results in a 34-bus distribution system verify the performance and reliability of the proposed approach. Also the results obtained for practical data prove the capability of the proposed method for implementing in experimental systems.