Journal of Intelligent & Fuzzy Systems - Volume 42, issue 2

Authors: Goel, Sonia | Tushir, Meena

Article Type: Research Article

Abstract: Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering …is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods. Show more

Keywords: Semi-supervised clustering, labeled and unlabeled data, incomplete data, and interpolation

DOI: 10.3233/JIFS-189744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 727-739, 2022

Authors: Azeem, Abdul | Malik, Hasmat | Jamil, Majid

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), artificial neural network (ANN) and J48 algorithm of machine learning for real-time harmonics analysis of digital substation’s equipment based on IEC-61850 using explanatory input variables based on laboratory proto-type real-time recorded database. In the proposed hybrid model, these variables are first extracted then diagnostic of power transformer harmonics of digital substation is evaluated/analyzed to perform the long term as well as the short term goal and planning in the electrical power network. In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using …FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). Then, features are extracted from experimental dataset using EMD (empirical mode decomposition) method. The IMFs (intrinsic mode functions) have generated from EMD, which are used as an input variable to the two different diagnostic models, i.e., ANN and J48 algorithm. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using ANN and J48 method (with and without EMD method) and the results are compared. Obtained results shows that the proposed hybrid diagnostics approach for harmonics analysis has outperformance characteristics. Show more

Keywords: ANN, explanatory feature, J48 algorithm, EMD, IEC-61850, feature extraction, digital substation, real-time, harmonics, power transformer, diagnosis, incipient level

DOI: 10.3233/JIFS-189745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 741-754, 2022

Authors: Ray, Papia | Salkuti, Surender Reddy | Biswal, Monalisa

Article Type: Research Article

Abstract: In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target Distributed Generation (DG) location of the distribution network under study. In one of the schemes, Discrete Wavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of …loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9 MW wind farm driven by six 1.5 MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based approach provides a better resolution and quick detection of islanding than the wavelet transform approach. Show more

Keywords: Artificial neural network, islanding detection, wavelet transforms, distributed generation, S-transform

DOI: 10.3233/JIFS-189746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 755-766, 2022

Authors: Gautam, Abhinav K. | Tariq, Mohd | Verma, Kripa Shankar | Pandey, Jai Prakash

Article Type: Research Article

Abstract: A Maximum Power Tracking Technique (MPPT) for Photovoltaic Powered e-Vehicles via Black Widow Optimization Technique is introduced. The proposed system addresses the problems of conventional MPPT methods via a black widow spider-inspired optimization approach. As a result, the design would require fewer iterations to achieve prime conditions, thus increasing the complete efficiency of the proposed system. Field-oriented control (FOC) is used for speed control of the BLDC engine (e-vehicle). The proposed model was first designed, and then simulated in MATLAB environment. The simulink results run in parallel with the Typhoon HIL 402 setup. The results obtained the superior performance of …the BWO-based MPPT technique. Details of the modeling of a new MPPT used for PV-driven BLDC-based e-vehicles are also discussed in this paper. There are many factors involved in a real situation for poor efficiencies, such as shade, irregular sunlight, and weather conditions, which show the non-linear characteristics of PV. The MPPT approach discussed in this article may be used to increase overall productivity and minimize costs for the operation of e-vehicles based on the PV framework. Show more

Keywords: MPPT, BWO, electric vehicle, BLDC, battery, VSI, boost converter

DOI: 10.3233/JIFS-189747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 767-777, 2022

Authors: Fatema, Nuzhat | Farkoush, Saeid Gholami | Hasan, Mashhood | Malik, H

Article Type: Research Article

Abstract: In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. …The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. Show more

Keywords: Gravitational search algorithm, back-propagation algorithm, neural network, machine learning, optimization, occupancy, smart building

DOI: 10.3233/JIFS-189748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 779-791, 2022

Authors: Anees, Mohd. Anas | Tariq, Mohd | Lodi, Kaif Ahmed | Alam, Mahetab | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time …emulator. Show more

Keywords: Model predictive control, packed-U-Cell, reactive power compensation, multilevel inverter

DOI: 10.3233/JIFS-189749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 793-806, 2022

Authors: Pervez, Imran | Sarwar, Adil | Alam, Afroz | Tariq, Mohd | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: Due to its clean and abundant availability, solar energy is popular as a source to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the …power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations. Show more

Keywords: Maximum power point tracking, metaheuristic algorithms, partial shading, photovoltaic

DOI: 10.3233/JIFS-189750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 807-816, 2022

Authors: Riyaz, Ahmed | Sadhu, Pradip Kumar | Iqbal, Atif | Tariq, Mohd

Article Type: Research Article

Abstract: The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, …selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). Show more

Keywords: Renewable energy sources (RES), packed E-Cell (PEC), genetic algorithm (GA), total harmonic distortion (THD), selective harmonic elimination (SHE)

DOI: 10.3233/JIFS-189751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 817-825, 2022

Authors: Malik, Hasmat | Ahmad, Md Waseem | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But …PMUs are not economical, so PSN operator needs to install a minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus systems. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared. Show more

Keywords: Condition monitoring, PMU, placement, wide area monitoring, smart grid

DOI: 10.3233/JIFS-189752

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 827-839, 2022

Authors: kaur, Surinder | Chaudhary, Gopal | Dinesh kumar, Javalkar

Article Type: Research Article

Abstract: Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping …the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment. Show more

Keywords: Biometrics, deep learning, feature level fusion, fusion, score level fusion

DOI: 10.3233/JIFS-189753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 841-849, 2022