Browsing by Author "Bakdi, Azzeddine"

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    A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection
    (Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Bakdi, Azzeddine
    Principal Component Analysis (PCA) is the most common Multivariate Statistical Process Control (MSPC) method that is widely used for Fault Detection and Diagnosis (FDD). Since early abnormality detection with high accuracy is required for safe and reliable process operation, False Alarms Rate (FAR), Missed Detection Rate (MDR) and the detection time delay are the major factors that must be taken into consideration when developing any process monitoring scheme. Unfortunately, the PCA performance, with fixed limits, is weak in terms of the stated factors. In contrast, conventional Moving Window PCA (MWPCA) is an adaptive technique which updates both the PCA model and the thresholds once a new normal observation is available. Yet, MWPCA methodology still does not reduce the MDR and the detection delay. In this paper, a Modified MWPCA (MMWPCA) with Fuzzy Logic Filter (FLF) is proposed to enhance the monitoring performance of PCA. It is an adaptive approach with a fixed model that combines both aforementioned techniques. The aim of using FLF is to ensure robustness to false alarms without affecting the Fault Detection (FD) performance. The application of the proposed method has been carried out on the Tennessee Eastman Process (TEP). Hold-one and hold-five MMWPCA with FLF are applied and compared to recent FDD work in the literature. The obtained results demonstrate the superiority of the proposed technique in detecting different types of faults with high accuracy and with shorter time delay
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    Comprehensive learning bat algorithm for optimal coordinatedtuning of power system stabilizers and static VAR compensator inpower systems
    (Taylor and Francis Ltd, 2019) Bousaadia, Baadji; Bentarzi, Hamid; Bakdi, Azzeddine
    This article presents a novel comprehensive learning bat algorithm (CLBAT)for the optimal coordinated design of power system stabilizers (PSSs)and static VAR compensator (SVC) for damping electromechanical oscilla-tions in multi-machine power systems considering a wide range of oper-ating conditions. The CLBAT incorporates a new comprehensive learningstrategy (CLS) to improve microbat cooperation; location updating is alsoimproved to maintain the bats’ diversity and to prevent premature con-vergence through a novel adaptive search strategy based on relative trav-elled distance. In addition, the proposed elitist learning strategy speedsup convergence during the optimization process and drives the globalbest solution towards promising regions. The superiority of the CLBATover other algorithms is demonstrated via several experiments and com-parisons through benchmark functions. The developed algorithm ensuresconvergence speed, credibility, computational resources and optimal tun-ing of PSSs and SVCs of multi-machine systems under different operatingconditions through eigenanalysis, nonlinear simulation and performanceindices.
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    Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
    (Elsevier, 2021) Belagoune, Soufiane; Bali, Noureddine; Bakdi, Azzeddine; Baadji, Bousaadia; Atif, Karim
    Fault detection, diagnosis, identification and location are crucial to improve the sensitivity and reliability of system protection. This maintains power systems continuous proper operation; however, it is challenging in large-scale multi-machine power systems. This paper introduces three novel Deep Learning (DL) classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP). These novel models explore full transient data from pre- and post-fault cycles to make reliable decisions; whereas current and voltage signals are measured through Phasor Measurement Units (PMUs) at different terminals and used as input features to the DRNN models. Sequential Deep Learning (SDL) is employed herein through Long Short-Term Memory (LSTM) to model spatiotemporal sequences of high-dimensional multivariate features to achieve accurate classification and prediction results. The proposed algorithms were tested in a Two-Area Four-Machine Power System. Training and testing data are collected during transmission lines faults of different types introduced at various locations in different regions. The presented algorithms achieved superior detection, classification and location performance with high accuracy and robustness compared to contemporary techniques
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    Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme
    (Elsevier, 2017) Bakdi, Azzeddine; Kouadri, Abdelmalek; Bensmail, Abderazak
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    Fault detection and diagnosis of nonlinear dynamical processes through correlation dimension and fractal analysis based dynamic kernel PCA
    (Elsevier, 2020) Bounoua, Wahiba; Bakdi, Azzeddine
    A novel Dynamic Kernel PCA (DKPCA) method is developed for process monitoring in nonlinear dynamical systems. Classical DKPCA approaches still exhibit vague linearity assumptions to determine the number of principal components and to construct the dynamical structure. The optimal Static PCA (SPCA) and Dynamic PCA (DPCA) structures are constructed herein through the powerful theory of the nonlinear Fractal Dimension (FDim). While DKPCA offers a generic data-driven modelling of nonlinear dynamical systems, the fractal correlation dimension provides an intrinsic measure of the data complexity counting for the nonlinear dynamics and the chaotic behaviour. The proposed Fractal-based DKPCA (FDKPCA) integrates the two strategies to overcome SPCA/DPCA/DKPCA shortcomings, FDim allows verifying the degree of fitting and ensures optimal dimensionality reduction. The novel fault detection and diagnosis method is validated through seven applications using the Process Network Optimization (PRONTO) benchmark with real heterogeneous data, FDKPCA showed superior performance compared to contemporary approaches
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    Improved process monitoring using PCA methods and adaptive threshold scheme
    (2017) Bakdi, Azzeddine; Kouadri, Abdelmalek
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    Improvement of an adaptive thresholding scheme for fault detection in complex systems
    (2018) Bakdi, Azzeddine
    Data-based analysis methods received an increasing attention for fault detection (FD) and diagnosis in large-scale and complex systems so as to improve the overall operation by detecting when abnormal system operations exist and diagnosing their sources. Common methods based on multivariate statistical analysis (MSA) are widely used and particularly principal component analysis (PCA). Fault detection indices used along with PCA including the Hoteling ..2 statistic and the squared prediction error (SPE) known as the .. statistic can be used to identify faults. However in industrial applications, process data is noisy in general with imprecise measurements and errors, in addition to the fact that acquired data doesn’t follow particular patterns and thus doesn’t have an exact representation. As a direct drawback, MSA methods and their extensions fail to achieve their desired outcomes due to data defections causing inaccurate features extraction and erroneous monitoring. Meanwhile these methods have their performance controlled through fixed control limits, which also control the degree of trade-off between robustness and detection sensitivity and thus produce a large amount of false alarms and missed detections, and consequently compromise the reliability of the process monitoring scheme. These shortcomings form the basic motivation of this work to develop an adaptive threshold algorithm to be integrated with MSA methods to overcome their limitations towards a more reliable and widespread applications
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    A modified Kullback divergence for direct fault detection in large scale systems
    (Elsevier, 2017) Hamadouche, Anis; Kouadri, Abdelmalek; Bakdi, Azzeddine
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    A new adaptive PCA based thresholding scheme for fault detection in complex systems
    (Elsevier, 2017) Bakdi, Azzeddine; Kouadri, Abdelmalek
    For large scale and complex processes, data-driven analysis methods are receiving increasing attention for fault detection and diagnosis to improve process operation by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. Common methods based on multivariate statistical analysis are widely used and particularly principal component analysis (PCA), fault detection indices used along with PCA including the Hotelling T² statistic and the sum of squared prediction error (SPE) known as the Q statistic can be used to identify faults. This paper develops a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic, which is effective in detecting small changes and abrupt shifts in the process operation. The aim is to enhance the performance of PCA methods for process monitoring, while maintaining a low false alarm rate with good sensitivity of anomalies. The performance of the developed scheme is compared to a conventional fixed thresholding technique by evaluating the detection performance across various types of faults that occurred in the Tennessee Eastman Process, The results demonstrate the promising capabilities of our proposed scheme
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    Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV
    (Elsevier, 2019) Bakdi, Azzeddine; Bounoua, Wahiba; Mekhilef, Saad; Halabi, Laith M.
    In parallel to sustainable growth in solar fraction, continuous reductions in Photovoltaic (PV) module and installation costs fuelled a profound adoption of residential Rooftop Mounted PV (RMPV) installations already reaching grid parity. RMPVs are promoted for economic, social, and environmental factors, energy performance, reduced greenhouse effects and bill savings. RMPV modules and energy conversion units are subject to anomalies which compromise power quality and promote fire risk and safety hazards for which reliable protection is crucial. This article analyses historical data and presents a novel design that easily integrates with data storage units of RMPV systems to automatically process real-time data streams for reliable supervision. Dominant Transformed Components (TCs) are online extracted through multiblock Principal Component Analysis (PCA), most sensitive components are selected and their time-varying characteristics are recursively estimated in a moving window using smooth Kernel Density Estimation (KDE). Novel monitoring indices are developed as preventive alarms using Kullback-Leibler Divergence (KLD). This work exploits data records during 2015–2017 from thin-film, monocrystalline, and polycrystalline RMPV energy conversion systems. Fourteen test scenarios include array faults (line-to-line, line-to-ground, transient arc faults); DC-side mismatches (shadings, open circuits); grid-side anomalies (voltage sags, frequency variations); in addition to inverter anomalies and sensor faults
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    Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control
    (Elsevier, 2017) Bakdi, Azzeddine; Hentout, Abdelfetah; Boutami, Hakim; Maoudj, Abderraouf; Hachour, Ouarda; Bouzouia, Brahim
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    Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
    (Elsevier, 2021) Bakdi, Azzeddine; Bounoua, Wahiba; Guichi, Amar; Mekhilef, Saad
    This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual estimations from Micro Phasor Measurement Unit (Micro-PMU). The high-dimensional high-frequency multivariate characteristics are nonlinear time-varying where computational efficiency becomes crucial to realize online adaptive FD. The adaptive assumption-free method is developed through Principal Component Analysis (PCA) for dimension reduction and feature extraction with reduced complexity. Novel fault indicators and discrimination index are developed using Kullback–Leibler Divergence (KLD) for an accurate evaluation of Transformed Components (TCs) through recursive Smooth Kernel Density Estimation (KDE). The algorithm is developed through extensive data with measurements from a GPV system under Maximum PPT (MPPT) and Intermediate PPT (IPPT) switching modes. The validation scenarios include seven faults: open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements