Prakash Chittora

Harmonic current extraction and compensation in three phase three wire system using notch filter

This paper presents a notch filter based extraction of harmonic current and compensation technique in a three phase three wire distribution system using a voltage source converter (VSC). A notch filter is a band reject (band stop) filter which allows most part of frequencies to pass unchanged but attenuates other frequencies to very low levels in a certain range. A notch filter is developed to extract the fundamental component from the polluted load current and VSC provides the necessary compensation. The proposed technique is simple and can easily detect and mitigate harmonic current using shunt compensation. The technique is analyzed and simulated using MATLAB and verified on a prototype three phase three wire system developed in the laboratory. The dSPACE 1104 is used as a digital signal processor to generate necessary gating pulses for VSC. The performance of the proposed technique is found to be quite satisfactory for various aspects of power quality improvement of system.

Chebyshev Functional Expansion Based Artificial Neural Network Controller for Shunt Compensation

Three-phase four-wire (TPFW) distribution systems are prone to various power quality (PQ) issues, such as voltage fluctuations, poor power factor, unbalanced load conditions, and the presence of harmonics in current. Mitigation of these PQ problems using appropriate shunt compensator requires advanced control algorithms for control of three-phase voltage source converters (VSC) in a distribution system. In this paper, Chebyshev functional expansion based artificial neural network (ChANN) algorithm for shunt compensation using distribution static compensator (DSTATCOM) is proposed. The parameters of ChANN are trained in real time. Implementation results with linear and nonlinear loads are demonstrated on a prototype hardware designed and developed using dSPACE 1104, current and voltage sensors for the realization of DSTATCOM for TPFW system. A zigzag transformer is used along with conventional three-phase, three-wire (TPTW) DSTATCOM to reduce its overall rating. Suitable comparisons with conventional control techniques are also mentioned.

Application of Hopfield Neural Network for Harmonic Current Estimation and Shunt Compensation

Abstract Harmonics generated by nonlinear loads pollute the power system and affect the operation of equipment connected to it. Hence, harmonic mitigation is of prime concern to a power system engineer. Artificial Neural Network (ANN) is a nonlinear signal processing technique, which is built from interconnected elementary processors called neurons. In this article, a Hopfield Neural Network (HNN) based control algorithm for shunt compensator in a power distribution system is realized. The Hopfield network is modeled using energy minimization principle and consists of “n” interconnected neurons. The HNN is used to estimate different harmonic components present in distribution system operating with nonlinear loads. It also provides suitable control signals to the shunt compensator for compensation of various power quality issues such as power factor correction, load balancing, and harmonic reduction in the distribution system. Detailed experimental results are presented along with simulation studies on the prototype model developed in the laboratory and these results demonstrate the feasibility of the proposed method of control in DSTATCOM. The comparison of the HNN-based compensation technique with a popular and effective control algorithm based on Least Means Square (LMS) is also presented in this article.

Performance evaluation of a new Kalman filter based least mean square algorithm for power quality improvement

This paper discusses an innovative control strategy for controlling distribution static compensator (DSTATCOM) in a three-phase; three-wire distribution network. It is based on the combination of Kalman filtering (KF) and least mean square (LMS) algorithm for closed loop control. It incorporates the simplicity of the LMS algorithm and the advantages of Kalman filtering. The learning rate for LMS algorithm is governed by Kalman gain (Kk). The control algorithm is simulated in MATLAB Simulink and hardware results are taken on a prototype developed in the laboratory using dSPACE 1104. The results are verified for several power quality improvement features such as power factor correction (PFC); harmonic reduction and load balancing.

Modeling and analysis of power quality problems in electric arc furnace

This paper presents a comprehensive analysis of power quality problems created by Electrical arc furnaces (EAF). An EAF plays an important role in melting process of metals. However due to its highly nonlinear characteristics, it creates lot of power quality problems such as harmonics generation, poor power factor, load unbalancing, voltage flicker, change in reactive power demand etc. In this paper, some of these aspects are studied using mathematical modeling of EAF. The Analysis of EAFs power quality problems are carried out through two different simulator studies in MATLAB/SIMULINK. First model is developed based on linear approximation of EAF during arc ignition and arc extinction period. The second model is based on Cassie and Mayrs equations. Both the mathematical models of EAF are developed, studied, analyzed, compared and validated the actual characteristics of EAF. Study of voltage flicker is also carried out.