Abstract
This paper offers an efficient and robust concept for a decentralized bad data processing, able to supply in real-time a power system state estimator with a repaired measurement set. Corrupted measurement vectors are funneled through a denoising auto-associative neural network in order to project the biased vector back to the data manifold learned during an offline training process. In order to improve accuracy, a maximum similarity with the solution manifold, measured with Correntropy, is searched for by a meta-heuristic. The extreme robustness and scalability of the process is demonstrated in multiple characteristic case studies.

Abstract
The power produced by wind power plants has an extremely random character due to the intermittency of wind. This leads to problems in balancing the power production and demand in the power systems. To overcome this problem, wind power forecast is used. However, as in any prediction tasks, wind power forecasting does not offer perfect results. It is the purpose of this paper to propose a method based on Monte Carlo simulations and artificial intelligence techniques to assess the impact of the deviation of the generated wind power from the predicted values on the power systems when no corrective measures are taken. The method is tested on an IEEE network as well as on a real electric network from the Romanian power system and the results and drawn conclusions are presented here.

Abstract
This paper presents a new concept for an approach to deal with measurements contaminated with gross errors, prior to power system state estimation. Instead of a simple filtering operation, the new procedure develops a screen-and-repair process, going through the phases of detection, identification and correction of multiple gross errors. The method is based on the definition of the coverage of the measurement set by a tiling scheme of 3-overlapping autoencoders, trained with denoising techniques and correntropy, that produce an ensemble-like set of three proposals for each measurement. These proposals are then subject to a process of fusion to produce a vector of proposed/corrected measurements, and two fusion methods are compared, with advantage to the Parzen Windows method. The original measurement vector can then be recognized as clean or diagnosed with possible gross errors, together with corrections that remove these errors. The repaired vectors can then serve as input to classical state estimation procedures, as only a small noise remains. A test case illustrates the effectiveness of the technique, which could deal with four simultaneous gross errors and achieve a result close to full recognition and correction of the errors.

Abstract
This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L-1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.

Abstract
This paper presents a new stochastic programming model for PAR/PST definition and location in a network with a high penetration of wind power, with probabilistic representation, to maximize wind power penetration. It also presents a new optimization meta-heuristic, denoted DEEPSO, which is a variant of EPSO, the Evolutionary Particle Swarm Optimization method, borrowing the concept of rough gradient from Differential Evolution algorithms. A test case is solved in an IEEE test system. The performance of DEEPSO is shown to be superior to EPSO in this complex problem. © 2014 IEEE.

Abstract
Optimization problems in electric power systems under high levels of uncertainty have been solved using stochastic programming methods for years. This is especially the case for medium-term problems and systems with a large share of hydro storages. The increased uncertainty in power system operation coming from volatile renewables has made the stochastic techniques interesting in shorter time frames as well. In the stochastic models the uncertainty is typically included by a discretized set of scenarios. This increases the computational burden significantly so a common approach is to preprocess and reduce the number of scenarios. Scenario reduction methods have been shown to function relatively well in expected value stochastic optimization. However, such setting of stochastic optimization is often criticized as being risk-prone so other risk-averse models exist. The evolutionary computation algorithms' flexibility permits the implementation of these risk models with relative simplicity. In this paper, a population-based evolutionary computation algorithm is applied to unit commitment problem under uncertainty and the paper illustrates several approaches to including risk policies in an evolutionary algorithm fitness function and illustrates its flexibility along with the link between scenario reduction similarity metric and risk policy.