In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power System is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the System. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus System. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the System, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the System due to better utilization of System resources.

In this paper, a new method based on Adaptive Neuro-Fuzzy Inference System ((ANFIS)) is proposed for locating the switched capacitor banks in distribution Systems. To train the proposed (ANFIS) model, an index based on current transient is introduced, which is calculated either offline by using data or online by real time simulation. The proposed method uses only current transient waveforms, immediately before and after the switching instant. Since only the current signal is used which is available in several locations, the method is simple and can be applied online. The method uses wavelet to determine the capacitor switching instant, which is needed for the (ANFIS) model to locate the switching capacitor. The method is simulated using PSCAD. Through various simulations, it is shown that other power quality disturbances such as voltage dip, unbalances and harmonics cannot disturb the method. Moreover, the size and connection type of the capacitor bank do not affect the method accuracy. The proposed algorithm is validated by simulating the IEEE 13-bus distribution System. According to the simulation results, the method is reliable enough to be applied to real Systems.

In recent years, using Fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and (ANFIS) which are based on Fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that (ANFIS) was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The (ANFIS) was also more precise in peak discharges simulation than FIS.

Limitations on freshwater resources have caused researchers and water resources managers to focus an increasing attention over the past few decades on water quality protection. Surface water quality management in such resources as rivers, seas, lakes, and estuaries is of a greater importance than other water resources and a greater number of studies have been conducted on them as they are more accessible and, therefore, more directly exposed to a variety of contaminants and pollutants. Application of appropriate and efficient mathematical models for river water quality simulation is essential for the formulation of comprehensive guidelines used in evaluating measures that are employed for river pollution control and management. The non-linear equations dominating pollutant transfer phenomena in rivers, the complexity of their simultaneous solution, and the multiplicity of kinetic constants and coefficients have made it difficult, or at times impossible, to use physically-based models and methods for this purpose. Therefore, most of these models can only be applied to simplified cases or to situations where the models are strictly calibrated and validated, with no adequate accuracy when applied to unrestricted conditions. The uncertainties in water quality problems have made Fuzzy Inference Systems, especially as combined with adaptive neural networks, to be used as a novel approach. The main objective of the present study is to exploit the capabilities of the adaptive neuro-Fuzzy Inference System ((ANFIS)) for river quality predictions with emphasis on DO and BOD. In the case study carried out on the Zayandehroud River, BOD predictions were obtained by the proposed System with a correlation coefficient of 0.953 in the calibration stage and 0.931 in the validation stage and DO predictions were obtained with a correlation coefficient of 0.921 in the calibration stage and 0.904 in the validation stage. Comparison of the results provided by the adaptive neuro-Fuzzy Inference System and the measured values reveals the high accuracy level of the proposed model.

With the advent of hybrid compensators, it has become possible to improve energy transfer by the least investment cost and the rapid control of power System problems. In this paper, Adaptive Neuro-Fuzzy Inference System ((ANFIS)) method is applied to control Rotary Hybrid Flow Controller parameters as a new member of hybrid compensators. Also, the proposed control strategy is simulated to determine the total fuel cost, power losses and System loadability as objective functions in IEEE 14 bus test System using Matlab software. Furthermore, in order to highlight the ability of (ANFIS) method, the results are compared to OPF model from economical and technical points of view. The results demonstrate the effectiveness of the intelligent method to improve the power System operation.

This paper presents a new online power System Stabilizer (PSS) design based on Fuzzy wavelet network (FWN) to damp the multi-machine power System low frequency oscillations. The FWN, inspired by the wavelet theory and Fuzzy concepts, is used to simultaneous design of two PSSs, in which error between System desired output and output of control object is directly utilized to tune the network parameters. The orthogonal least square (OLS) algorithm is used to determine network dimension, purify the wavelets for selecting efficient wavelets, and determine the number of sub- wavelet neural networks and Fuzzy rules. In this paper, Shuffled Frog Leaping Algorithm (SFLA) is employed for learning of FWN parameters and to find the optimal values of the controller parameters. To illustrate the capability of the proposed approach, some numerical results are presented on a 2-area 4-machine System. To show the effectiveness and robustness of the designed supplementary controllers, a line-to-ground fault and also a three phase fault are applied at a bus. Furthermore, to make a comparison, two conventional PSSs are designed in which a lead-lag structure is considered for each PSS and its parameters are tuned using SFLA. The simulation results show the superiority and capability of the FWN based PSSs.

The aim of this study is to address a new feature extraction method in the area of the heart arrhythmia classification based on a metric with simple mathematical calculation called Curve-Length Method (CLM). In the presented method, curve length of the under study excerpted segment of signal is considered as an informative feature in which the effect of important geometric parameters of the original signal can be found. To show merits of the presented method, first the original electrocardiogram (ECG) in lead I is preprocessed by removing its baseline wander then by scaling it in the [-1,1] interval. In the next step, using a' trous method, discrete wavelet scales 23 and 24 and smoothing function scale 22 are extracted. Afterwards, segments including samples of the QRS complex, P and T waves are estimated via an approximation criterion and CLM is implemented to extract corresponding features from aforementioned scales, smoothing function and also from each original segment. The resulted feature vector (including 12 components) is used to tune an Adaptive Network Fuzzy Inference System ((ANFIS)) classifier. The presented strategy is applied to classify four categories found in the MIT-BIH Arrhythmia Database namely as Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC) and average values of Se = 99.81%, P+ = 99.80%, Sp = 99.81% and Acc = 99.72% are obtained for sensitivity, positive predictivity, specifity and accuracy respectively showing marginal improvement of the heart arrhythmia classification performance.

One of the most significant threats of a national economy is the bankruptcy of its firms. Assessment of bankruptcy provides valuable information on which governments, investors and shareholders can base their financial decisions in order to prevent possible losses. The aim of this study was to model bankruptcy by using Adaptive Neuro Fuzzy Inference System ((ANFIS)). Statistical society for performing of this research is companies which were listed at Tehran Stock Exchange since 2001 up to 2010 and according to article 141 of commercial code, including 40 bankrupt companies and 40 non bankrupt companies. These companies were divided randomly in three sets: train set for creating model, test set and check set for validating model. financial ratios of the companies in the year before bankruptcy were considered as input variables (ANFIS). The result of this study points out that percentage of success predictions one year before bankruptcy is 83.75. Finally, according to this study, the (ANFIS) selection is helpful to predict the financial distress situation for companies which were listed at Tehran Stock Exchange.