Pravat Kumar Rout

A Fast Gauss-Newton Algorithm for Islanding Detection in Distributed Generation

The paper presents a new Fast Gauss-Newton algorithm (FGNWA) for the detection of islanding condition in distributed generation systems (DGs) when they are disconnected from the main supply system or there are small load unbalances in the distribution network. During islanding conditions power system parameters like frequency, voltage magnitude, phase change, total harmonic distortion, and various sequence voltage, current, and power components do change and hence by monitoring these changes accurately, an islanding condition can be detected. A forgetting factor weighted error cost function is minimized by the well known Gauss-Newton (GN) algorithm and the resulting Hessian matrix is approximated by ignoring the off-diagonal terms to yield the new FGNW algorithm to estimate, in a recursive and decoupled manner, all the above voltage and current signal parameters accurately for realistic power systems even in the presence of significant noise. A number of test cases considering both islanding and nonislanding, for realistic, hybrid distribution networks has demonstrated the reliability and accuracy of the islanding detection scheme, when a fuzzy expert system (FES) is used in conjunction with the proposed FGNW algorithm.

Modified differential evolution optimization algorithm for multi-constraint optimal power flow

In this paper presents an algorithm for solving optimal power flow problem through the application of a modified differential evolution algorithm(MDE). The objective of an optimal Power Flow(OPF) is to find steady state operation point which minimizes total generating unit (thermal) fuel cost and total load bus voltage deviation from a specified point while maintaining an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, transformer taps, output of various compensating devices and power flow of transmission lines. Differential Evolution (DE) is one of evolutionary algorithms, which has been used in many optimization problems due to its simplicity and efficiency. The proposed MDE is in the framework of differential evolution owning new mutation operator and selection mechanism. To test the efficacy of the algorithm, it is applied to IEEE 30-bus power system with two different objective functions. The optimal power flow results obtained using MDE are compared with other evolutionary methods. The simulation results reveal that the MDE optimization technique provides better results compared to other methods recently published in the literature as demonstrated by simulation results.

Environmental economic load dispatch using multi-objective differential evolution algorithm

The economic and environmental dispatch problem is formulated as a Non-linear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost and emission. This paper presents a new multi-objective differential evolution algorithm. Initially, a non dominated sorting genetic algorithm is employed to obtain a set of pareto solutions followed by Multi-objective differential evolution algorithm and its corresponding set of pareto solution. The proposed algorithm has been tested on a forty unit test system to illustrate the analysis. The results demonstrate the capabilities of the proposed Multi-objective differential evolution technique to generate the set of well-distributed Pareto-optimal solutions and also reflects its superiority in terms of diversity of the pareto-optimal set. The simulation results obtained from the proposed approach are compared with the NSGA-II method.

Optimal placement and sizing of distributed generation in radial distribution system using differential evolution algorithm

Integration of renewable energy based distributed generation (DG) units provides potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in energy losses and emissions, and reliability improvement. Therefore, the allocation of DG units should be carefully determined with the consideration of different planning incentives. Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. This paper proposes a Differential Evolution Algorithm (DEA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real power loss and improve the voltage profile within the frame work of system operation and security constraints. The proposed DE algorithm is also used to determine optimal sizes and locations of multi-DGs. The proposed method is tested on standard IEEE 69-bus test system and the results are presented and compared with different approaches available in the literature. The proposed method has outperformed than the other methods in terms of the quality of solution and computational efficiency.

Modified invasive weed optimization with dual mutation technique for dynamic economic dispatch

Dynamic economic dispatch (DED) is one of the main functions of power system operation and control. It determines the optimal operation of units with predicted load demands over a certain period of time with an objective to minimize total production cost while the system is operating within its ramp rate limits. This paper presents DED based on Invasive Weed Optimization (IWO) technique for the determination of the global or near global optimum dispatch solution. In the present case, load balance constraints, operating limits, valve-point loading, ramp constraints, and network losses using loss coefficients are incorporated. Numerical results for a sample test system (10-unit) have been presented to demonstrate the performance and applicability of the proposed method.

A solution to economic load dispatch problem with non-smooth cost function using Self-Realized Differential Evolution optimization algorithm

This paper proposes a new differential evolution optimization (DE) strategy namely, Self-Realized Differential Evolution (SRDE) for solving the economic dispatch (ED) problem with non-smooth cost functions in power systems. The proposed SRDE is in the structure of differential evolution owning new mutation operation and selection mechanism. An effective constraint handling method is presented in the suggested stochastic search technique. The proposed approach has been examined and tested with the numerical results of ED problems with forty-generation units including ramp rate limits, prohibited operating zones and valve-point loading effects also ten-generation units with multiple fuel options. The results of the proposed technique are compared with that of other techniques reported in the literature. For both the cases, the proposed algorithm outperforms the solution reported for the existing algorithms. In addition, the promising results show the robustness, fast convergence and efficiency of the proposed technique.

A robust control strategies to improve transient stability in VSC-HVDC based interconnected power systems

This paper presents a robust non linear sliding mode controller(SMC) for VSC-HVDC transmission link in parallel with an AC transmission line connecting a synchronous generator to infinite bus. A non linear sliding mode controller has been proposed to provide voltage support by means of reactive control at both ends; to damp power oscillations and improve transient stability by controlling either active or reactive power, and to control the power flow through the HVDC link. The proposed control scheme is tested under several system disturbances like changes in short-circuit ratio, faults on the converter and inverter buses. Based upon the time domain simulations in MATLAB/SIMULINK environment, the proposed controller is tested and its better performance is shown compare with the PI controllers whose gains are optimized with teaching learning optimization technique. The proposed controller improves voltage stability, damping of power oscillations and transient stability.

Structural and electrical characteristics of barium modified bismuth-sodium titanate (Bi 0.49 Na 0.49 Ba 0.02 )TiO 3

The lead-free polycrystalline ceramic, barium titanate modified bismuth–sodium titanate 0.98 (Bi0.5Na0.5TiO3) + 0.02 BaTiO3 (BNT–BT-2) was synthesized by using a mixed oxide route at high temperature. The structural (crystal structure, microstructure, molecular structure), and electrical (dielectric constant and loss, impedance, conductivity) characteristics of the parent compound (Bi0.5Na0.5TiO3) have greatly been influenced by the addition of even a small amount of barium titanate in it. Preliminary structural study of BNT–BT-2 using X-ray diffraction data clearly shows the formation of a single-phase perovskite structure with the coexistence of hexagonal (major phase) and tetragonal (minor) phases. The existence of diffuse phase transtition and the relaxor behaviour in the material is confirmed by analysis of temperature-frequency dependence of dielectric parameters. The composition (0.98BNT − 0.02BT) of the material shows non-Ohmic conduction in its J–E characteristics. In this lead-free ceramic, permittivity anomalies are matched to modulus anomalies. The highlighted features are determined by structural phase transitions of the ferroelectric-like.

Non-linear control and stabilisation of VSC-HVDC transmission system based on Type-2 fuzzy sliding mode control

This paper presents a robust non-linear hybrid controller based on Type-2 fuzzy and sliding mode control strategy for VSC-HVDC transmission link, which inherits the benefits of these two methods. The interval Type-2 fuzzy (IT2FSMC) is used to handle the uncertainties to enhance the real time performance. The sliding mode control combines with the fuzzy system to compensate the influence of un-modelled dynamics and chattering phenomena on the system response, and also to improve the robustness of the reaching phase of the sliding mode control (SMC). The effectiveness of the proposed controller is demonstrated through digital simulation studies using MATLAB/SIMULINK software package. The simulation results show that the proposed controller contribute significantly towards improving the damping behaviour of the VSC-HVDC system with regard to parameter variations and external load disturbances compared to conventional PI controller under a wide range of operating conditions.

Optimal control of islanded microgrid with adaptive fuzzy logic & PI controller using HBCC under various voltage & load variation

This paper presents a robust & astute voltage control avenue for micro grid system operating in autonomous mode. The proposed Adaptive Fuzzy Logic Controller contains two Mamdani based FLC which features in self adjustment of scaling factors, Membership Functions & control gains during uncertainties regardless of any change in the operating conditions. A Hysteresis Band Current Controller (HBCC) has been used which helps in generating the gate pulses for the Voltage Source Converters (VSCs) and also helps in controlling the current instantaneously. The HBCC used in this system limits the device current & gives a faster response irrespective of the controllers & the control outputs used. The proposed controller shows exceptional results as compared to the traditional PI controller. Irrespective of load variation, voltage variation & load unbalance the proposed technique holds good. The transient response time is exclusively reduced, power oscillations are wiped out and faster concurrence is obtained. The uncomplicated design & upgraded dynamic behavior of the A-FLC makes it assuring contestant for Voltage control in autonomous micro grid. Apart from all these disturbances the system was modeled, validated and simulated under the simulation studies in MATLAB environment.