Sukumar Mishra

A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation

Harmonic estimation for a signal distorted with additive noise has been an area of interest for researchers in many disciplines of science and engineering. This work presents a new algorithm based on the foraging behavior of E. coli bacteria in our intestine to estimate the harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive, through a Takagi-Sugeno fuzzy scheme, depending on the operating condition to make the convergence faster. Besides, the harmonic estimation is linear in amplitude and nonlinear in phase. As the proposed algorithm does not rely on Newton-like gradient descent methods, this is used for phase estimation whereas the linear least square scheme estimates the amplitude, thereby presenting the hybrid method. The improvement in %error, as well as the processing time compared with the conventional discrete Fourier transform and genetic algorithm method is demonstrated in this paper. Besides, the performance is quite acceptable even in the presence of decaying dc component as well as to change in amplitude and phase angle of harmonic components.

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.

Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control

A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (KIi) for the secondary control, governor speed regulation parameters (Ri) for the primary control and frequency bias parameters (Bi), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of KIi, Ri, and Bi parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of Ri (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized KIi, Ri, and Bi to wide changes in inertia constant (H), reheat time constant (Tr), reheat coefficient (Kr), system loading condition, and size and position of step load perturbation.

Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation

The conventional method of obtaining the coefficients of proportional plus integral (PI) controllers for the active power filter utilizes a linear model of the PWM inverter. The values so obtained may not give satisfactory results for a wide variation in operating conditions. This paper presents a new algorithm based on the foraging behavior of E-coli Bacteria in the human intestine, to optimize the coefficients of the PI controller. Through the simulation results, it is observed that the dynamic response of the bacterial foraging PI (BF-PI) controller is quite satisfactory. The proposed BF technique is compared with the genetic algorithm (GA) and found to converge faster than GA to reach the global optimum solution

Bacteria Foraging-Based Solution to Optimize Both Real Power Loss and Voltage Stability Limit

Summary form only given. Optimal location and control of an unified power flow controller (UPFC) along with transformer taps are tuned with a view to simultaneously optimize the real power losses and voltage stability limit (VSL) of a mesh power network. This issue is formulated as a non-linear equality and inequality constrained optimization problem with an objective function incorporating both the real power loss and VSL. A new evolutionary algorithm known as bacteria foraging is applied for solving the multi-objective multi-variable problem, with the UPFC location, its series injected voltage and the transformer tap positions as the variables. For a single objective of only real power loss, the same problem is also solved with interior point successive linearization program (IPSLP) technique using the LINPROG command of MATLAB. A comparison between the two suggests the superiority of the proposed algorithm. A cost effectiveness analysis of UPFC installation vis-a-vis loss reduction is carried out to establish the benefit of investment in an UPFC.

Permanent Magnet Synchronous Generator-Based Standalone Wind Energy Supply System

In this paper, a novel algorithm, based on dc link voltage, is proposed for effective energy management of a standalone permanent magnet synchronous generator (PMSG)-based variable speed wind energy conversion system consisting of battery, fuel cell, and dump load (i.e., electrolyzer). Moreover, by maintaining the dc link voltage at its reference value, the output ac voltage of the inverter can be kept constant irrespective of variations in the wind speed and load. An effective control technique for the inverter, based on the pulsewidth modulation (PWM) scheme, has been developed to make the line voltages at the point of common coupling (PCC) balanced when the load is unbalanced. Similarly, a proper control of battery current through dc-dc converter has been carried out to reduce the electrical torque pulsation of the PMSG under an unbalanced load scenario. Based on extensive simulation results using MATLAB/SIMULINK, it has been established that the performance of the controllers both in transient as well as in steady state is quite satisfactory and it can also maintain maximum power point tracking.

A radial basis function neural network controller for UPFC

This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either a single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with a BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation.

Transmission loss reduction based on FACTS and bacteria foraging algorithm

An optimal location and parameters of an UPFC along with values of OLTC taps are tuned with a view to minimize the real power losses of a mesh power network. This issue is formulated as a non-linear equality and inequality constrained optimization problem with an objective function incorporating power loss. A new evolutionary algorithm known as Bacteria Foraging is applied for solving, the optimum location and the amount of series injected voltage for the UPFC, and the best values of the taps present in the system. The same problem is also solved with Interior Point Successive Linearization technique using the LINPROG command of MATLAB. A comparison between the two suggests the superiority of the proposed algorithm.

TS-fuzzy-controlled active power filter for load compensation

Summary form only given. The paper describes the application of Takagi-Sugeno (TS) type fuzzy logic controller to three-phase shunt active power filter (APF) for the power quality improvement and reactive power compensation required by a non-linear load. The advantage of fuzzy logic control is that it does not require a mathematical model of the system. The application of Mamdani type fuzzy logic controller to three-phase shunt active power filter have been investigated earlier but it has the limitation of more number of fuzzy sets and rules. Therefore, it needs to optimize large number of coefficients, which increases the complexity of the controller. On the other hand, TS fuzzy controllers are quite general in that they use arbitrary input fuzzy sets, any type of fuzzy logic, and the general defuzzifier. Moreover, TS fuzzy controller could be designed by using less number of rules and classes. Further, in this paper hysteresis current control mode of operation is implemented for PWM switching signal generation. Computer simulation results show that the dynamic behavior of TS fuzzy controller is better than the conventional Pl controller and is found to be more robust to changes in load and other system parameters compared to the conventional Pl controller

Classification of power system disturbances using a fuzzy expert system and a Fourier linear combiner

This paper presents a hybrid scheme using a Fourier linear combiner and a fuzzy expert system for the classification of transient disturbance waveforms in a power system. The captured voltage or current waveforms are passed through a Fourier linear combiner block to provide normalized peak amplitude and phase at every sampling instant. The normalized peak amplitude and computed slope of the waveforms are then passed on to a diagnostic module that computes the truth value of the signal combination and determines the class to which the waveform belongs. Several numerical tests have been conducted using EMTP programs to validate the disturbance waveform classification with the help of the new hybrid approach which is much simpler than the recently postulated ANN or wavelet based approaches.