C.K. Babulal

Adaptive neuro-fuzzy inference system based representative quality power factor for power quality assessment

Under sinusoidal operating conditions of electric power system, the classical definitions of apparent power and power factor work well as long as the loads are linear and the source voltage waveform is sinusoidal. Increase in use of power electronic devices, adjustable speed drives and other nonlinear loads cause the voltage and current waveforms to become non-sinusoidal and highly distorted. A new adaptive neuro-fuzzy inference system based representative quality power factor (ANFIS RQPF) is proposed in this paper to represent the existing different power factors-displacement power factor, transmission efficiency power factor and oscillation power factor. The ANFIS RQPF can represent an essential module for evaluating and amalgamating the three power factors. The ANFIS RQPF was applied to different cases-linear, nonlinear, sinusoidal and non-sinusoidal considering lagging and leading power factors. It is shown that the ANFIS RQPF is expressive and accurately represents the existing power factors in all cases and in all situations. Taking into consideration the advantages of the ANFIS such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties, this factor can be useful for power quality assessment, cost-effective analysis of power quality mitigation techniques, as well as billing purposes, in these situations.

Adaptive neuro-fuzzy inference system based total demand distortion factor for power quality evaluation

An adaptive neuro-fuzzy inference system based total demand distortion factor (ANFIS TDDF) is proposed in this paper. When considering a single range of short circuit level, the values of total demand distortion (TDD) are enough to quantify harmonic distortion in a certain current waveform. When considering multiple ranges of short circuit levels the TDD is unable to determine whether the distortion is within the acceptable limits or not. The ANFIS TDDF indicates the level of distortion in the current waveform or how close is the waveform to a pure sinusoidal wave shape and also allows deciding whether the distortion contained in the current is within the acceptable limit or not. Moreover, the use of an adaptive neuro-fuzzy inference system (ANFIS) has the advantages of being simple, easy to implement and contains its knowledge base. The proposed ANFIS TDDF is sensitive to the TDD and short circuit level changes in all distortion cases in sinusoidal and non-sinusoidal situations. Therefore it will be very useful for many applications such as power-quality (PQ) evaluation, cost-benefit analysis of PQ mitigation techniques and setting penalty tariffs for customers generating harmonics.

Assessment of voltage stability margin by comparing various support vector regression models

Voltage stability assessment and prediction of loadability margin are the major concerns in real-time operation of power systems. This paper proposes a support vector machine (SVM) regression network for the voltage stability assessment for normal condition as well as for contingency cases. The loadability margin of any given operating conditions is obtained for pre-contingency and post-contingency based on the computation of a stability index. SVM takes real and reactive power at all buses of the system and gives the loading margin. The validity of the proposed SVM-based index is tested on IEEE 30 and Indian 181 bus systems. The results of the proposed method are compared with neural network, extreme learning machine, online sequential extreme learning machine and extreme support vector machine regression methods. The feasibility of application of the proposed SVM regression network for real-time stability assessment is discussed. Also, FACTS devices are produced to improve the system loadability and their results are discussed.

Static security enhancement using fuzzy particle swarm optimization

Purpose – The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines. Design/methodology/approach – Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation. Findings – The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different...

Optimal siting and sizing of unified power flow controller using covariance matrix adapted evolution strategy in grid integrated wind energy conversion systems

This paper discusses the application of covariance matrix adapted evolution strategy (CMAES) algorithm on wind energy conversion systems. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. Modified IEEE 14 bus system is considered for simulation purpose. The critical evaluation of maximum loadability of the system is determined. Statistical performance of CMAES algorithm reveals that the best value of maximum loadability is obtained when compared to primal dual interior point method. Even though CMAES takes higher computation time, this method gives the best loadability margin.

Static Loadabilty Assessment Using Covariance Matrix Adapted Evolution Strategy

This paper discusses application of Covariance Matrix Adapted Evolution Strategy (CMAES) algorithm for maximizing loadability margin of power system. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. IEEE 14 bus , 30 bus and 118 bus systems are considered for simulation purpose. For comparison of the results, primal dual interior point (PDIP), continuation power flow (CPF), Particle swarm optimization algorithms are considered. Statistical performance of CMAES algorithm reveals that even the mean value of maximum loadability is better than maximum loadability obtained in other methods. Even though CMAES takes higher computation time due to the determination of covariance matrix, only this algorithm gives maximum loadability margin.