COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2016 | Volume:7 | Issue:3|Papers Count:9

In an electricity market, all market players, generating companies and large customers, submit their bids to market operator. Then using the aggregated supply/demand curve, the market operator determines the market prices and the schedules the supply of generating companies and the demand level of customers within the planning horizon to maximize the social profit. In this paper, a new hybrid optimization algorithm based on bacterial foraging and differential evolution algorithm is presented to solve the bid-based environmental-dynamic economic dispatch. The hybrid algorithm performs search through a stochastic gradient search with adaptive movement operation that has been coupled with differential evolution mutation and crossing over of the optimization agents. Simulation results on different case studies show that the performance of the purposed method is better than previous methods in convergence speed, stability and precision.

Sudden Cardiac Death (SCD) is caused by loss of heart function which ultimately stops heart from pumping blood throughout the body and therefore, claims the patient’s life within few minutes. Once detected, sudden cardiac deaths could substantially decrease through applying medical procedures or instrumentations such as defibrillators. Nonetheless, effective approaches to SCD prediction, based on which doctors can make informed decisions, are yet to be discovered. This research aims to propose a novel approach to local feature selection with the assistance of the most accurate methodologies, which have formerly been developed in previous works of this team, for extracting features from nonlinear, time-frequency and classic processes. Furthermore, taking into consideration the existence of different features from different areas, the Mixture of Experts is put forward as a means of classification. The suggested methods enable us to select features that differ from one another in each minute before the incidence through the agency of optimal feature selection in each one-minute period of the signal. Not only will this facilitate increasing the prediction time from 4 minutes to 12 with a high level of accuracy, but it also will provide us with an opportunity to interpret clinical signs considering the plurality of features in each minute. Additionally, applying the Mixture of Experts classification proceeds to ensure a precise decision-making on the output of different areas processes. The results indicate to the superiority of the proposed method to those mentioned in similar studies.

In this paper a cyclostationarya-a based wideband spectrum sensing method is proposed. The received signal passes through a rough and flexible filter with its effective band tuned to a specific part of the received signal spectrum. This part belongs to a target signal which potentially exists in the received signal. Some cyclic frequencies of the target signal are employed to derive a normalized least mean square (NLMS) adaptive algorithm that estimates the output of the filter from its frequency shifted samples. If the target is absent in the received signal, the norm of the weights of the NLMS algorithm is almost zero. On the other hand, in the case of presence of the target, the norm of the weights will be greater than a certain threshold. The procedure is repeated to cover the entire band of the received signal and therefore it detects all cyclostationary signals with known cyclic frequencies in the received signal. The overall system is very easy to implement and fast and its performance is comparable to other spectrum sensing counterparts.

Fuzzy systems are a useful means that are applied to various problems, including decision making, taxonomy, modeling, prediction, and control. The major challenge in using such systems is designing a fuzzy rule base with optimized parameters to maintain a desirable system performance. In this paper, a hybrid particle swarm optimization and opposition-based differential evolution training method is proposed and used to optimize the Gaussian membership function parameters of the rule base in a fuzzy system of type Takagi-Sugeno-Kang (TSK). In this dissertation, the effect of soft computing methods, e.g. evolution computing, on a zero-order TSK fuzzy system is investigated to control two non-linear plants. This paper considers a hybrid computing approach consisting of: opposition-based differential evolution (ODE) and particle swarm optimization (PSO). Results of training a zero-level TSK fuzzy system used to control two non-linear plants indicate that the proposed hybrid algorithm has a better classification accuracy in comparison to other training approaches. Moreover, this study uses heuristic opposition-based differential evolution (ODE) and particle swarm optimization (PSO) algorithms (HODEPSO) and applies them to two accuracy-oriented fuzzy system (FS) design problems. For these two models, all free parameters of a first-level Takagi-Sugeno-Kang (TSK) system are also optimized using the HODEPSO algorithm. The models used in our experiments are the Mackey- Glass chaos time series and a real-world economic problem whose future values are predicted using the proposed algorithm. Finally, results of these experiments also show that HODEPSO has the minimum average training and test error in comparison to other training methods.

In this paper, an innovative model for managing load demand based on the amount of power generated and market clearing predicted price, considering uncertainty parameters, is presented for non-dispatch able resources, and load demand. Furthermore, a bidding strategy based on cooperative game theory is presented by taking into account the price uncertainties for determining the players’ pay-off function. The methodology presented for determining optimal supply and consumer resource bidding strategy is affected by the pricing behavior of other players, and is also based on maximizing their profit. The proposed framework for calculating the Nash equilibrium in a structure with several players in the constrained electricity market has been presented. The proposed framework is generally implementable over different game conditions in the electricity market and is based on cooperative game among participating players in the market, with discrete bidding strategies.

Wind speed prediction can be regarded as significant factor in control of wind turbines, schedule of the connection/disconnection of turbines and stability guarantee of power grids which is commonly carried out in various approaches. In this paper, a chaos based approach by analyzing only the previous measured data is proposed. For this purpose, in addition of evaluating the chaotic nature of wind speed data, the chaos theory with Neural Network techniques in forecasting session are combined in order that we can propose a method for wind speed prediction. For this regard, at first the correlation dimension and largest lyapunov exponent of wind speed time series are computed to prove that wind data generator process is chaotic. Then phase space of data generator dynamic is reconstructed. In this regard, we use the False Nearest Neighbors (FNN) algorithm to determine the embedding dimension and Average Mutual Information (AMI) approach to measure time delay for phase space reconstruction. Afterwards, Multi Layers Perceptron (MLP) neural networks and Radial Basis Function (RBF) neural networks are proposed to predict the wind speed which its structure is designed based on time delay and embedding dimension data. At the end, proposed methods apply on real data and results are expressed.

The purpose of unmixing in hyperspectral images is extraction of the end members spectral signatures and estimation of their related abundance fractions. Most algorithms used for endmember extraction (EE) process, are established on spectral information without any attention to spatial context and correlation of image pixels. Recently, several algorithms have been developed which utilize spatial and spectral information with the aim of improving EE and unmixing accuracy. In this paper, a novel spatial spectral preprocessor is proposed which exploits class map obtained by unsupervised clustering technique and 8th neighborhood window in order to identify pixels located in border regions between two or more clusters and discards not spatially homogenous regions. Afterwards, it calculates spectral purity weight of not border pixels in order to look for spatially homogenous and spectrally pure ones using otsu threshold. End members can be extracted rapidly and accurately by means of coupling our proposal with EEs. Our distinct scheme can reduce RMSE of reconstructed image and EE processing time as well as improve a new criterion known as Efficiency regarding the state-of-the-art preprocessors on real hyperspectral images.

Nowadays power system works with a lower stability boundary due to increasing energy consumption. Extensive blackouts in recent years show that the risk of the blackout has been increased. Cascading failures are the main reason of occurrence extensive blackouts. This paper develops a control strategy of optimal corrective actions for Isfahan-Khuzestan power system by using the standard blackout OPA model. SSSC, generation reallocation and load shedding are used in this structure for preventing cascading failures and blackout. Optimal SSSC installation place is determined Improved PSO (IPSO) algorithm to eliminate overload and improving power system voltage profile. Simulation results show the remarkable effect of SSSC on decreasing the risk of cascading failures and the ability of the proposed method for preventing blackout in Isfahan-Khuzestan network.