Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

Abstract

Several kinematic techniques for the trajectory optimization of redundant manipulators control the gripper using the pseudoinverse of the Jacobian. Nevertheless, these algorithms lead to a kind of chaotic motion with unpredictable arm configurations. This paper presents a new technique for solving the inverse kinematics problem for redundant manipulators that combines the closed-loop pseudoinverse method with genetic algorithms.

Increase of the delivered power probability in distribution networks using Pareto DC programming

“…CASE STUDY A realistic distribution network [12,13] with 30kV, 1 substation, 180 buses, 90 loads, 163 overhead lines and 16 cable, is used for the application of the proposed methodology. This distribution network presents 8.984MVA of mean peak load (summer season from 2008 to 2010).…”

Multi‐criteria optimisation approach to increase the delivered power in radial distribution networks

“…Moreover, maximising the reliability in distribution power systems involves minimising the unserved energy, and therefore load curtailment. There are two types of uncertainties in distribution power systems: randomness and fuzziness [2][3][4][5]. It is well known that interruption frequencies or interruption probabilities of distribution overhead lines are related to weather conditions [2,3,[5][6][7].…”

“…Each weather condition (rain, wind, storm etc.) can be categorised into different degrees (such as heavy, medium or light rain) [2][3][4][5]. This classification is obviously vague or fuzzy.…”

Optimal Approach for Reliability Assessment in Radial Distribution Networks

“…In addition, in this work, the reliability indexes [System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI)] are optimized. Canizes et al [19] proposed a methodology that aims to increase the probability of delivering power to any load point of distribution networks by identifying new investments in distribution components to improve the failure rate and the repair time without considering the network technical constraints. This methodology uses a fuzzy set approach [20] to estimate the outage parameters, and the investments aim to reduce the components failure rates and the repair times.…”

“…This methodology uses a fuzzy set approach [20] to estimate the outage parameters, and the investments aim to reduce the components failure rates and the repair times. Unlike [16] and [17], [19] uses actions to improve the failure rate and the repair time, i.e., considering a discrete optimization. Taking into account the increasing penetration of DG and the importance of reliability of supply, [21] presents method for DG optimum location in order to maximize the reliability.…”