The relevance of power quality (PQ) issues has recently augmented because of the increased use of power electronic equipment, which results in a voltage deviation and current waveforms. The PQ monitoring is covered by two main subjects: the development of PQ indices to quantify the power supply quality and the electrical disturbances detection such as harmonics, sags, swells etc., which allows knowing the conditions of the electric power systems. In this study a review of techniques and methodologies developed for PQ analysis and power disturbance classification is presented in order to show their major characteristics.

Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review

The proposed power quality detection scheme. Different types of power quality disturbances are discriminated from each other.VMD and ST are used for extraction of dominant features.SVM with simple structure and few adjustable parameters is used as the classifier core.The generalization capability and detection accuracy of the proposed method are increased by elimination of redundant features by using different feature selection methods.The start and end points of PQ events can be detected accurately. Power quality (PQ) issues have become more important than before due to increased use of sensitive electrical loads. In this paper, a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems. The proposed method is constructed based on four main steps: simulation of PQ events, extraction of features, selection of dominant features, and classification of selected features. By using two powerful signal processing tools, i.e. variational mode decomposition (VMD) and S-transform (ST), some potential features are extracted from different PQ events. VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains. In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure, sequential forward selection (SFS) and sequential backward selection (SBS) as wrapper based methods and Gram-Schmidt orthogonalization (GSO) based feature selection method as filter based method are used for elimination of redundant features. In the next step, PQ events are discriminated by support vector machines (SVMs) as classifier core. Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions. Moreover, the start and end points of PQ events can be detected with high precision.

Combined VMD-SVM based feature selection method for classification of power quality events

Detection of Power Quality Event using Histogram of Oriented Gradients and Support Vector Machine

Accuracy of Frozen Section in Diagnosis of Ovarian Mass Siriwan             Tangjitgamol            MD* Somneuk        Jesadapatrakul         MD** Sumonmal      Manusirivithaya        MD* *Department of Obstetrics and Gynecology, BMA Medical College and Vajira Hospital **Department of Pathology, BMA Medical College and Vajira Hospital Objective: To determine the accuracy of frozen section compared to permanent section according to status of malignancy and histologic cell type. Study design: Diagnostic study. Subjects: Frozen and permanent pathological reports of 212 ovarian masses from 212 women who underwent ovarian resection at BMA Medical College and Vajira Hospital from January 1, 1992 to December 31, 2002 were studied. Methods: All frozen and subsequent permanent pathological reports of ovarian masses from the studied group were reviewed.  The data of age, tumor size, number of frozen and permanent sections of the ovarian masses, the results of frozen and permanent section were taken from the pathological report.  The pathological slides were reviewed when there was discrepancy of frozen and permanent section diagnosis, in deferred diagnosis for status of malignancy and/ or histologic cell type. Main outcome measures: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value of frozen section. Results: From 212 ovarian masses, there were 13 deferred cases which frozen section diagnosis for status of malignancy could not be given. Statistical analysis was then performed in 199 cases..  The overall accuracy was 90.9%.  Sensitivity of frozen section diagnosis was highest in the benign group at 99.1%, next was in the malignant group at 86.1% and lowest in borderline group at 50%.  The specificity were 90.7, 94.6 and 100.0% in the benign, borderline and malignant groups respectively.  Positive and negative predictive values of benign, borderline and malignant groups were 93.3, 41.2, 100.0% and 98.7, 96.2, 92.7%, respectively.  All inaccurate diagnosis were in the common epithelial group.  When the statistical data were analyzed according to histology subgroup of common epithelial tumor, sensitivity in diagnosis malignant and borderline mucinous were lower than non-mucinous tumor, 62.5% and 41.7% compared to 96.9% and 50.0% respectively.  For diagnosis of the histologic cell type, frozen section had 91.9% accuracy.  Thirteen out or 16 incorrect histologic diagnosis cases (81.3%) were in the common epithelial group while only three cases were germ cell tumor. Conclusion: The accuracy of frozen section in diagnosis of frozen section in diagnosis of ovarian mass was generally high according to both status of malignancy and histologic cell type.  There were exception in tumors with large size, mucinous or borderline tumor which yielded lower accuracy, sensitivity, specificity and positive predictive value than the other groups.  The surgeon should recognize these limitations and appraise the test result together with other clinical data.  This is to achieve for the correct final diagnosis and the proper management for the patient. Key word: frozen section, ovarian mass, accuracy, malignancy, histologic cell type Vajira Med J 2003 ; 47 : 17 - 28

Accuracy of Frozen Section in Diagnosis of Ovarian Mass

Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA

Application of lifting based wavelet transforms to characterize power quality events

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.

Detection of Mechanical Imbalances of Induction Motors with Instantaneous Power Signature Analysis

Stator interturn faults are one of the most common faults occurring in induction motors. Early detection of interturn short circuit is important to reduce repair costs. Axial leakage monitoring, zero-sequence components, negative sequence current, and motor current signature analysis have been used for fault detection in early states. In the paper, the instantaneous power signature analysis technique is used to detect these faults, and experimental results for healthy and faulty motors are shown and discussed.

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Detection of stator winding fault in induction motor using instantaneous power signature analysis

Photovoltaic (PV) systems produce a significant amount of electrical energy used around the world. The performance of a PV array is affected by temperature, solar insolation, shading and array configuration. Obtainable maximum power generation from PV based energy production systems is only possible with Maximum Power Point Tracking (MPPT) methods. In order to maximize the efficiency of PV energy conversion systems, solar panels and arrays should be operated at maximum power points. At maximum power point, solar arrays generate the electric energy at maximum efficiency and minimum losses. This paper discusses the following issues: (1) what is the effect of MPPT unit in PV systems, (2) two connection methods between solar arrays and buck-boost DC/DC converter with MPPT unit, (3) determine behaviours of PV power generation systems in cloudy and sunny conditions. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13761

/pdf/efficiency-in-centralized-dc-systems-compared-with-50pu3xcuoi.pdf

Efficiency in Centralized DC Systems Compared with Distributed DC Systems in Photovoltaic Energy Conversion

The continuity of electrical energy is the main subject among all others affecting operational sustainability in industrial facilities. Therefore electrical system and its extensions shall be controlled, supervised, monitored and protected by intelligent systems. Hence, it is necessary to incorporate into electrical system required resources and hardware that will enhance the safety and sustainability of electrical energy by means of utilizing all available economical and technical means. In this regard; industrial facilities, along getting fed by national grid, install standalone production units too. However, electricity systems involving multiple energy production resources need to be run and controlled by smart systems that do not require technical manpower interference in order to comply with economical constraints, governing laws, regulations, etc. and be able to take prompt and correct decisions. Therefore, electricity systems should be enhanced with necessary prompt intelligent decision-maker and performer subsystems, which will continuously monitor, supervise, control and record electrical system and extensions and ensure system sustainability in case of occurrence significant change in the whole system. This article aims to illustrate all required smart systems that ensure sustainability and safety of electrical energy, complying with governing laws and regulations and an application regarding supporting systems in an industrial facility.

Mehmet Bayrak

Papers

Application of lifting based wavelet transforms to characterize power quality events

Detection of Mechanical Imbalances of Induction Motors with Instantaneous Power Signature Analysis

Detection of stator winding fault in induction motor using instantaneous power signature analysis

Efficiency in Centralized DC Systems Compared with Distributed DC Systems in Photovoltaic Energy Conversion

Load management of industrial facilities electrical system using intelligent supervision, control and monitoring systems