K. R. Krishnanand

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.

Brain storming incorporated teaching-learning-based algorithm with application to electric power dispatch

This paper intends to incorporate a brain storming mechanism into the existing Teaching---Learning---Based Optimization (TLBO) algorithm. The potential solutions of TLBO evolve using the primitive steps that are maintained between the acts of teaching and learning. Another novel algorithm, Brain Storm Optimization (BSO) sticks to the philosophy of interchange of ideas by a team to develop as a whole. The brain storming methods from BSO are introduced into the working of TLBO and applied to a well---studied electric power dispatch problem of high intricacy. The results are compared to best of the existing solutions to demonstrate the efficacy of the proposed hybrid 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.

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.

Economic load dispatch using hybridized Differential Evolution and invasive weed operation

This article presents an efficient optimization approach to solve constrained Economic Load Dispatch (ELD) problem using a hybridized Differential Evolution (DE) and Invasive Weed Optomization (IWO) algorithm. The proposed method is found to give near optimal results while working with different operational constraints in the ELD, arising due to practical limitations like dynamic operation constraints (ramp rate limits) and prohibited zones. Both the convex characteristic and the non-convex characteristics of the fuel cost of the thermal generators have been considered. Simulations performed over various systems with different number of generating units with the proposed method and the results have been compared with other existing relevant approaches.

Solution to Non-convex Electric Power Dispatch Problem Using Seeker Optimization Algorithm

This paper presents the application of Seeker Optimization Algorithm (SOA) to constrained economic load dispatch problem. Independent simulations were performed over separate systems with different number of generating units having constraints like prohibited operating zones and ramp rate limits. The performance is also compared with other existing similar approaches. The proposed methodology was found to be robust, fast converging and more proficient over other existing techniques.

A hybrid multi-objective improved bacteria foraging algorithm for economic load dispatch considering emission

In this paper, a hybrid multiobjective improved bacterial foraging algorithm with fuzzy dominance sorting FSIBF is proposed and used to solve environmental friendly economic load dispatch problem. The fuzzy dominance-based sorting procedure is used to select the non-dominated solutions in the Pareto front. The proposed algorithm is applied to nonlinear constrained multiobjective environmental friendly economic load dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system with fuel cost and emission as two conflicting objectives to be optimised simultaneously satisfying the systems operational constraints. The constraints imposed from the operation point of view are the limits on generator real power and reactive power outputs, bus voltages and power flow in the transmission lines. The proposed work also includes the effect of having non-smooth cost characteristics of the thermal generators which arises because of the valve point loading effect. The practical generator constraints such as ramp rate limits and prohibited operating zones are also incorporated in this work suitably.

Optimal coordination of overcurrent relay using an enhanced discrete differential evolution algorithm in a distribution system with DG

Interconnecting a distributed generation (DG) to an existing distribution system causes an improved utilization of the local resources, prominently the renewable energy sources. Penetration of a DG into an existing distribution system has so many impacts on the system, despite the benefits a DG will provide; it has a negative impact on one of the most important aspects of the system, one of which is the power system protection. Installation of DG to the existing system will produce additional fault current which may disturb the previously coordinated relays and lead to miscoordination of overcurrent relays. In this paper, an enhanced discrete differential evolution is introduced to perform the overcurrent relay coordination required to protect the radial distribution network with distributed generators. The proposed DE generates quantized vectors and uses time varying scaling factors for enhanced operation. The case studies have been carried out to identify the effectiveness of the proposed algorithm. The results show that the optimized relay settings can improve the supply reliability especially in distributed generation cases.

Optimal short-term hydrothermal generation scheduling using modified seeker optimisation algorithm

This paper presents a new evolutionary optimisation algorithm to solve the short-term hydrothermal generation problem with operational constraints using the modified seeker optimisation algorithm. Seeker optimisation algorithm is a recently developed empirical gradient based, meta-heuristic optimisation algorithm, which draws inspiration from the random process of human search strategy. In this paper, we improvise the step length determination strategy in the classical seeker optimisation method by considering an optimistically contracting step length calculation. The proposed methodology easily takes care of solving non-linear hydrothermal generation problem along with different constraints like power balance, capacity limits, valve-point loading and prohibited operating zones. Simulations were performed over various standard test cases and a comparative study is carried out with other existing relevant approaches. The result obtained reveals the robustness and ability of the proposed methodology over other existing techniques.