Nitin Singh

Optimal gain tuning of PI current controller with parameter uncertainty in DC motor drive for speed control

This paper presents a framework to carry out a simulation of Simulink model of dc drive system to optimize the controller gains for known inputs. The objective is to find the optimal controller gain (proportional and integral) in a closed loop system with parameter uncertainty. The optimization results have been obtained by three algorithms. These algorithms are gradient descent, pattern search and simplex search based optimization algorithm. The uncertainties of parameters have been also included with the case study. These uncertainties are Monte Carlo (random) and grid type uncertainty. The study has been conducted on a model of three phase converter controlled direct current (DC) drive with current control strategy. The results show a comparison between proposed two algorithms with uncertainty.

Modelling and simulation of matrix converter based DC-DC converter

This paper presents modelling and simulation of DC-DC converter with matrix converter as its basic topology. The proposed converter performs the four quadrant chopper operation. This topology can perform many different converter functions. This DC-DC converter can be utilized in speed control of dc motor in electric automobiles and traction where supply is dc voltage. The simulation model was developed in MATLAB/Simulink environment. Pulse width modulation technique is used for output synthesis of the converter. The analysis of converter was done considering resistive and inductive both loads separately. Finally, state space model is presented.

Sensitivity based capacitor placement: A comparative study

This paper presents a comparative study of two sensitivity based methods, namely loss sensitivity method and bus sensitivity method, for the optimal capacitor placement problem. The sizing of capacitors is done using the particle swarm optimization (PSO). The performance of these two methods is compared on the basis of the active losses and voltage profile of the bus systems after optimal capacitor placement. The methods have been implemented on IEEE 9-bus and IEEE 69-bus radial distribution systems. The results obtained clearly indicate that bus sensitivity method based capacitor placement gives better results in comparison to the loss sensitivity method.

Harmonic compensation of HVDC rectifier using shunt active filter

In recent years, the issue of harmonic compensation has got considerable attention. This paper presents harmonic compensation of HVDC rectifier using shunt active filter. The filtering technique reduces harmonics in current and voltage and also improves total harmonic distortion of system. Simulation model was developed in MATLAB/ Simulink environment. Results are obtained for a resistive and inductive load and analysis of harmonic distortion is done at different firing angle values of thyristor used in HVDC rectifier. Frequency domain analysis is also shown to achieve optimum results.

Compensation technique and power quality improvement in multiphase distribution system

The degradation in power quality is an important issue in these days drawing attention of industry also. Multiphase distribution systems are better solution as compared to single and three phase systems due to so inherent advantage as loads are split over multiple phases. As most of the loads in real life are inductive in nature, reactive power is generated and thus requires compensation. In this paper compensation has been done in six phase using modification in theories used for three phase systems. Results are obtained for resistive inductive load and case of unbalanced system is also considered. Simulation model is developed in MATLAB / Simulink environment.

Model Order Reduction Using Fuzzy C-Means Clustering and Particle Swarm Optimization

The hybrid method which combines the evolutionary programming technique, i.e., based on the swarm optimization algorithm and fuzzy c-means clustering method is used for reducing the model order of high-order linear time-invariant systems in the presented work. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The reduction of the numerator of original high-order model is done using the particle swarm optimization algorithm, and fuzzy c-means clustering technique is used for reducing the denominator of the higher-order model. The stability of the model is also verified using the pole zero stability analysis, and it was found that the obtained reduced-order model is stable. Further, the transient and steady state response of the obtained lower-order model as compared to the other existing techniques are better. The output of the obtained lower-order model is also compared with the other existing techniques in the literature in terms of ISE, ITSE, IAE, and ITAE.

Model order reduction using factor division algorithm and fuzzy c-means clustering technique

This paper proposes a novel hybrid approach that combines factor division algorithm and fuzzy c-means clustering technique for reducing the model order of high-order linear time invariant system. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The numerator of the higher order model is reduced using the factor division algorithm and the denominator of the higher order model is reduced using the fuzzy c-means clustering technique. The stability of the model is also verified using the pole zero stability analysis and it was found that the obtained reduced order model (ROM) is stable. Further, the steady state and transient response of the ROM is found to be better than the other existing techniques. The performance of the ROM is compared to other existing techniques in terms of integral square error, integral of time multiply squared error, integral absolute error and integral time-weighted absolute error.

A DPSO-Based NN-PID Controller for MIMO Systems

The neural networks are generally trained using the standard back propagation (BP) algorithm and its variants. In the BP algorithm, the initial weights are generated randomly which affects the convergence of algorithm, and hence, the algorithm is prone to the problem of local optima. In the proposed work, dynamic particle swarm optimization (DPSO) has been used to initialize the weights of the NN-PID controller for multiple input multiple output (MIMO) systems. The results obtained using the proposed DPSO-based NN-PID controller were compared with the other existing NN-PID control techniques. Simulation results show that the performance of the BP algorithm was significantly improved with the use of DPSO algorithm for initializing the weights.

Short-Term Electricity Price Forecasting Using Hybrid SARIMA and GJR-GARCH Model

The liberalization of the power markets gained a remarkable momentum in the context of trading electricity as a commodity. With the upsurge in restructuring of the power markets, electricity price plays a dominant role in the current deregulated market scenario which is majorly influenced by the economics being governed. Electricity price has got great affect on the market and is used as a basic information device to evaluate the future markets. However, highly volatile nature of the electricity price makes it even more difficult to forecast. In order to achieve better forecast from any model, the volatility of the electricity price need to be considered. This paper proposes a price forecasting approach combining wavelet, SARIMA and GJR-GARCH models. The input price series is transformed using wavelet transform and the obtained approximate and detail components are predicted separately using SARIMA and GJR-GARCH model respectively. The case study of New South Wales electricity market is considered to check the performance of the proposed model.

A PSO-Based ANN Model for Short-Term Electricity Price Forecasting

In the last few decades, electricity markets around the world have gradually transformed from highly regulated to deregulated and competitive markets. Prior knowledge of electricity demand and price is needed by the generation companies and market operators for getting best return of investment and for maintaining the real-time balance between demand and supply, respectively. Although, the nonlinear and black box structure of the forecasting models based on artificial intelligence techniques have made them popular among the researchers, their inherent limitations posed due to their structure can be overcome by using evolutionary optimization techniques along with them for achieving better forecasting accuracy. The proposed work presents artificial neural network-based short-term electricity price forecasting model. In the presented work, dynamic particle swarm optimization technique is used to adjust the weights of the neural network model to the optimal values. The electricity price of New South Wales electricity market is forecasted using the proposed model in order to verify the performance of the proposed model.