Iranian Journal of Electrical and Electronic Engineering

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Dealing with uncertainty is one of the most critical problems in complicated

pattern recognition subjects. In this paper, we modify the structure of a useful Unsupervised

Fuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of

fuzzy neurons and its associated self organizing supervised learning algorithm. This

improved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used for

classification and identification of shifted and distorted training patterns. It is generally

useful for those flexible patterns which are not certainly identifiable upon their features. To

show the identification capability of our proposed network, we used fingerprint, as the most

flexible and varied pattern. After feature extraction of different shapes of fingerprints, the

pattern of these features, “feature-map”, is applied to the network. The network first

fuzzifies the pattern and then computes its similarities to all of the learned pattern classes.

The network eventually selects the learned pattern of highest similarity and returns its

specific class as a non fuzzy output. To test our FNN, we applied the standard (NIST

database) and our databases (with 176×224 dimensions). The feature-maps of these

fingerprints contain two types of minutiae and three types of singular points, each of them

is represented by 22×28 pixels, which is less than real size and suitable for real time

applications. The feature maps are applied to the FNN as training patterns. Upon its setting

parameters, the network discriminates 3 to 7 subclasses for each main classes assigned to

one of the subjects.

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A fast low-power 1-bit full adder circuit suitable for nano-scale CMOS implementation is presented. Out of the three modules in a common full-adder circuit, we have replaced one with a new design, and optimized another one, all with the goal to reduce the static power consumption. The design has been simulated and evaluated using the 65 nm PTM models.

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This paper presents a new framework for the day-ahead reactive power market based on the uniform auction price. Voltage stability and security have been considered in the proposed framework. Total Payment Function (TPF) is suggested as the objective function of the Optimal Power Flow (OPF) used to clear the reactive power market. Overload, voltage drop and voltage stability margin (VSM) are included in the constraints of the OPF. Another advantage of the proposed method is the exclusion of Lost Opportunity Cost (LOC) concerns from the reactive power market. The effectiveness of the proposed reactive power market is studied based on the CIGRÉ-32 bus test system.

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Congestion management in electricity markets is traditionally done using deterministic values of power system parameters considering a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties. The uncertainty sources that are modeled in the proposed stochastic framework consist of contingencies of generating units and branches as well as load forecast errors. The Forced Outage Rate of equipment and the normal distribution function to model load forecast errors are employed in the stochastic programming. Using the roulette wheel mechanism and Monte-Carlo analysis, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. Scenario reduction is adopted as a tradeoff between computation time and solution accuracy. After scenario reduction, stochastic congestion management solution is extracted by aggregation of solutions obtained from feasible scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with the deterministic solution by a reasonable uncertainty cost. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.

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In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM) is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.

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Wireless Sensor Networks (WSNs) comprising of tiny, power-constrained nodes are getting very popular due to their potential uses in wide applications like monitoring of environmental conditions, various military and civilian applications. The critical issue in the node is energy consumption since it is operated using battery, therefore its lifetime should be maximized for effective utilization in various applications. In this paper, a game theory based hybrid MAC protocol (GH-MAC) is proposed to reduce the energy consumption of the nodes. GH-MAC is combined with the game based energy efficient TDMA (G-ETDMA) for intra-cluster communication between the cluster members to head nodes and game theory based nanoMAC (G-nanoMAC) protocol used for inter-cluster communication between head nodes. Performance of GH-MAC protocol is evaluated in terms of energy consumption, delay and compared with conventional MAC schemes. The results obtained using GH-MAC protocol shows that the energy consumtion is enormously reduced and thereby the lifetime of the sensor network is enhanced.

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This paper proposes a method for extending the ability of rotary power flow controller (RPFC) using tap-changer of the RPFC’s transformers. A detailed model of the device is presented to analyze the effects of the tap changer operation on the performance of the RPFC. To evaluate the results, the RPFC model is simulated using PSCAD/EMTDC software. Dynamic operation of the RPFC on a 400 kV transmission line is studied. Based on the results, using tap-changer of transformers can extend the RPFC ability to control the active power of the transmission line about 25%.

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Phasor Measurement Units (PMUs) are in growing attention in recent power systems because of their paramount abilities in state estimation. PMUs are placed in existing power systems where there are already installed conventional measurements, which can be helpful if they are considered in PMU optimal placement. In this paper, a method is proposed for optimal placement of PMUs incorporating conventional measurements of zero injection buses and branch flow measurements using a permutation matrix. Furthermore, the effect of single branch outage and single PMU failure is included in the proposed method. When a branch with a flow measurement goes out, the network loses one observability path (the branch) and one conventional measurement (the flow measurement). The permutation matrix proposed here is able to model the outage of a branch equipped with a flow measurement or connected to a zero injection bus. Also, measurement redundancy, and consequently measurement reliability, is enhanced without increasing the number of PMUs this implies a more efficient usage of PMUs than previous methods. The PMU placement problem is formulated as a mixed-integer linear programming that results in the global optimal solution. Results obtained from testing the proposed method on four well-known test systems in diverse situations confirm its efficiency.

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This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke's transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter (not complex) which can be efficiently implemented by DSP. In unbalanced situations, simulation experiments show the advantages and drawbacks of the proposed algorithm in comparison to Complex LMS (CLMS) and Augmented Complex LMS (ACLMS) methods

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Evaluation of the reliability parameters in micro-grids based on renewable energy sources is one of the main problems that are investigated in this paper. Renewable energy sources such as solar and wind energy, battery as an energy storage system and fuel cell as a backup system are used to provide power to the electrical loads of the micro-grid. Loads in the micro-grid consist of interruptible and uninterruptible loads. In addition to the reliability parameters, Forced Outage Rate of each component and also uncertainty of wind power, PV power and demand are considered for micro-grid. In this paper, the problem is formulated as a nonlinear integer minimization problem which minimizes the sum of the total capital, operational, maintenance and replacement cost of DERs. This paper proposes PSO for solving this minimization problem.

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The use of wavelets in denoising, seems to be an advantage in representing well the details. However, the edges are not so well preserved. Total variation technique has advantages over simple denoising techniques such as linear smoothing or median filtering, which reduce noise, but at the same time smooth away edges to a greater or lesser degree. In this paper, an efficient denoising method based on Total Variation model (TV), and Dual-Tree Complex Wavelet Transform (DTCWT) is proposed to incorporate both properties. In our method, TV is employed to refine low-passed coefficients and DTCWT is used to shrink high-passed noisy coefficients to achieve more accurate image recovery. The efficiency of our approach is firstly analyzed by comparing the results with well-known methods such as probShrink, BLS-GSM, SUREbivariate, NL-Means and TV model. Secondly, it is compared to some denoising methods, which have been reported recently. Experimental results show that the proposed method outperforms the Steerable pyramid denoising by 8.5% in terms of PSNR and 17.5% in terms of SSIM for standard images. Obtained results convince us that the proposed scheme provides a better performance in noise blocking among reported state-of-the-art methods.

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Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising and destroys the flatness of homogenous area. Wavelets are not very effective in dealing with multidimensional signals containing distributed discontinuities such as edges. This paper develops an effective shearlet-based denoising method with a strong ability to localize distributed discontinuities to overcome this limitation. The approach introduced here presents two major contributions: (a) Shearlet Transform is designed to get more directional subbands which helps to capture the anisotropic information of the image; (b) coefficients are divided into low frequency and high frequency subband. Then, the low frequency band is refined by Wiener filter and the high-pass bands are denoised via NeighShrink model. Our framework outperforms the wavelet transform denoising by %7.34 in terms of PSNR (peak signal-to-noise ratio) and %13.42 in terms of SSIM (Structural Similarity Index) for ‘Lena’ image. Our results in standard images show the good performance of this algorithm, and prove that the algorithm proposed is robust to noise.

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Circuit Breakers (CBs) are critical components in power system for reliability and protection. To assure their accurate performance, a comprehensive condition assessment is of an imminent importance. Based on dynamic resistance measurement (DRM), this paper discusses a simple yet effective fuzzy approach for evaluating CB’s electrical contacts condition. According to 300 test results obtained from healthy and three defected electrical contacts, the authors describe the special effect of common failures on DRM characteristics and propose seven deterioration indicators. Using these parameters, a fuzzy classifier is suggested to accurately determine contact sets condition. The salient advantage of the proposed model is its capability to recognize the type of contact failure. The feasibility and effectiveness of the proposed scheme has been validated through 40 real life recorded data of some electrical contacts. 

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The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subfilter changes according to the largest decrease in mean square deviation. The simulation results show that the proposed VSS-WTDLMS has faster convergence rate and lower misadjustment than ordinary WTDLMS.

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