Optimal power flow (OPF) is a major task in power system economics and operation. In OPF power real power outputs from the generators of a power system are so adjusted that the total production cost is minimum. Security constraint OPF (SC-OPF) is minimizing the cost keeping line flows within their respective limits for security reasons. Real power output from generators, generator bus voltage m...

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work i...

This paper describes the Obje Perception Framework (OPF ), a distributed software architecture for context sensing and inference in ubiquitous computing environments. OPF provides flexibility, scalability, and robustness even as the sensor configuration changes. For flexibility, OPF supports many context inference tasks, ways of achieving those tasks, and heterogeneity in sensor types. With res...

The AC Optimal power flow (OPF) problem is one of the most fundamental problems in power systems engineering. For the past decades, researchers have been relying on unproven heuristics to tackle OPF. The hardness of OPF stems from two issues: (1) non-convexity and (2) combinatoric constraints (e.g., discrete power injection/ejection constraints). The recent advances in providing sufficient cond...

We have recently shown that the optimal power flow (OPF) problem with a quadratic cost function can be solved in polynomial time for a large class of power networks, including IEEE benchmark systems, due to their physical properties. In this work, our previous zero-duality-gap result is extended to OPF with arbitrary convex cost functions, and then it is proved that adding phase shifters to the...

In this work, Particle Swarm Optimizationand Genetic algorithm for the solution of the optimal power flow (OPF) is studied. Traditionally, classical optimization methods were used to effectively solve OPF. But more recently due to incorporation of Flexible A.C. Transmission System (FACTS) devices and deregulation of a power sector, the traditional concepts and practices of power systems are sup...

This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples a...

Article history: Received 9 April 2014 Accepted 8 October 2014 Available online 9 November 2014 This paper presents efficient chaotic invasive weed optimization (CIWO) techniques based on chaos for solving optimal power flow (OPF) problems with non-smooth generator fuel cost functions (non-smooth OPF) with the minimum pollution level (environmental OPF) in electric power systems. OPF problem is...

The solution of an optimal power ow (OPF) problem in rectangular form by an interior-point method (IPM) for nonlinear programming is described. When formulated in rectangular form, some OPF variants have quadratic objective and quadratic constraints. Such quadratic features allow for ease of matrix setup and inexpensive incorporation of higher-order information in a predictor-corrector procedur...

The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost, loss etc. or maximizes social welfare, loadability etc. while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, line flow limits, output of various compensating devices etc. Traditionally, classical optimization me...