This paper proposes a novel active power redispatch sequential approach to solve the transient stability-constrained optimal power flow (TSC-OPF) problem for the preventive control of transient stability. Based on an independent transient stability assessment of the power system for a given contingency scenario, two proposed power redispatch constraints are formulated and embedded into a conventional OPF formulation. Since these two new constraints only depend on steady-state variables, the dimension of the resulting TSC-OPF model is similar to that of a conventional OPF model and can be solved by standard optimization methods to perform a nonheuristic active power redispatch. The stabilization process is performed by sequentially solving the optimization and the transient stability problems until a suitable generation dispatch that provides a transiently stable equilibrium point is assessed. The validity and the effectiveness of the proposed method are numerically demonstrated in the WSCC three-machine, nine-bus system and an equivalent model of the Mexican power system.

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab^® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

Induction motors are key elements of every industrial process. A faulty motor produces interruptions on production lines, with consequences in cost, product quality, and safety. The relevance in induction motor monitoring is the ability to detect faults in incipient state. Many proposed methods consider direct connection of motors to the power supply; however, the common practice in industry is to connect them through variable speed drives (VSD), which introduce harmonics into the current supply signal that make the fault identification extremely difficult. This work proposes a statistical analysis through mean, variance, and information entropy computation, combined with sensorless rotating speed estimation for classifying different faults in induction motors using an artificial neural network. The proposed methodology examines the voltage and current signals provided by an industrial VSD that ensures a high certainty on identifying the treated faults at different rotational speed. A field programmable gate array‐based implementation is developed to offer an online, system‐on‐chip solution for real‐time condition monitoring. © 2014 The Authors. International Transactions on Electrical Energy Systems published by John Wiley & Sons Ltd.

The objective of this study is to present a voltage compensator based on a matrix converter without energy storage, which can cope with common power quality problems presented in power distribution systems. The proposed scheme acquires from the grid the necessary energy during the disturbance, which eliminates the drawbacks imposed by the use of a dc‐link and the need of energy storage components. A matrix converter study under unbalanced input voltage conditions is exposed, as well as a detailed explanation of the proposed modified direct space vector modulation (MDSVM). It has been verified that even when the supply voltage exhibits unbalanced conditions and harmonic distortion, the control strategy does not exhibit difficulties to synthesise the compensation voltages, provided the restrictions imposed by the formulation are fulfilled. Numerical simulations and experimental results from a laboratory scale prototype are presented to validate the performance of the compensator.