Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy

Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок(ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором

Mitigation of Power Quality Problems Using Custom Power Devices: A Review

Electrical power quality (EPQ) in distribution systems is a critical issue for commercial, industrial and residential applications. The new concept of advanced power electronic based Custom Power Devices (CPDs) mainly distributed static synchronous compensator (D-STATCOM), dynamic voltage restorer (DVR) and unified power quality conditioner (UPQC) have been developed due to lacking the performance of traditional compensating devices to minimize power quality disturbances. This paper presents a comprehensive review on D-STATCOM, DVR and UPQC to solve the electrical power quality problems of the distribution networks. This is intended to present a broad overview of the various possible DSTATCOM, DVR and UPQC configurations for single-phase (two wire) and three-phase (three-wire and four-wire) networks and control strategies for the compensation of various power quality disturbances. Apart from this, comprehensive explanation, comparison, and discussion on D-STATCOM, DVR, and UPQC are presented. This paper is aimed to explore a broad prospective on the status of D-STATCOMs, DVRs, and UPQCs to researchers, engineers and the community dealing with the power quality enhancement. A classified list of some latest research publications on the topic is also appended for a quick reference

A Comprehensive Survey on Different Control Strategies and Applications of Active Power Filters for Power Quality Improvement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Power quality (PQ) has become an important topic in today’s power system scenario. PQ issues are raised not only in normal three-phase systems but also with the incorporation of different distributed generations (DGs), including renewable energy sources, storage systems, and other systems like diesel generators, fuel cells, etc. The prevalence of these issues comes from the non-linear features and rapid changing of power electronics devices, such as switch-mode converters for adjustable speed drives and diode or thyristor rectifiers. The wide use of these fast switching devices in the utility system leads to an increase in disturbances associated with harmonics and reactive power. The occurrence of PQ disturbances in turn creates several unwanted effects on the utility system. Therefore, many researchers are working on the enhancement of PQ using different custom power devices (CPDs). In this work, the authors highlight the significance of the PQ in the utility network, its effect, and its solution, using different CPDs, such as passive, active, and hybrid filters. Further, the authors point out several compensation strategies, including reference signal generation and gating signal strategies. In addition, this paper also presents the role of the active power filter (APF) in different DG systems. Some technical and economic considerations and future developments are also discussed in this literature. For easy reference, a volume of journals of more than 140 publications on this particular subject is reported. The effectiveness of this research work will boost researchers’ ability to select proper control methodology and compensation strategy for various applications of APFs for improving PQ.publishedVersio

Distribution System Disturbance Analysis and Outage Management Using Hybrid Data-Driven and Physics-Based Approaches

Securing cyber-power distribution systems (DS) against malicious events is critical with the integration of distributed energy resources (DERs), supporting automation and increasing vulnerabilities. Situational awareness utilizing power data (e.g., data from distribution phasor measurement units (D-PMUs)) and cyber data (e.g., network packets data) is the main focus of this dissertation by means of which, an opportunity for real-time monitoring and decision-making is provided. To further improve the DS’s reliable and resilient operation, in this Ph.D. work, the aim has been put towards the development of an automated tool consisting of multiple modules to precisely investigate any type of data anomalies, followed by root cause finding. For this purpose, a data aggregation scheme is developed to synchronize the resolution and time stamp of multiple metering sources throughout the DS, using an enhancement of the conventional Kalman Filter, named Ensemble Extended Kalman Filter (EEKF). EEKF is implemented as an automated module by exploiting the real-time measurements as well as deriving the system physics. Furthermore, this dissertation develops online cyber-physical event detection and classification as well as proposal of the novel Outage Root Cause Analysis (ORCA) system. Different sections of the work have been tested on IEEE and OPAL-RT test systems as well as real-filed measurements from installed actual hardwares

Multi-parameter optimisation of quantum optical systems

Quantum optical systems are poised to become integral components of technologies of the future. While there is growing commercial interest in these systems---for applications in information processing, secure communication and precision metrology---there remain significant technical challenges to overcome before widespread adoption is possible. In this thesis we consider the general problem of optimising quantum optical systems, with a focus on sensing and information processing applications. We investigate four different classes of system with varying degrees of generality and complexity, and demonstrate four corresponding optimisation techniques. At the most specific end of the spectrum---where behaviour is best understood---we consider the problem of interferometric sensitivity enhancement, specifically in the context of long-baseline gravitational wave detectors. We investigate the use of an auxiliary optomechanical system to generate squeezed light exhibiting frequency-dependent quadrature rotation. Such rotation is necessary to evade the effect of quantum back action and achieve broadband sensitivity beyond the standard quantum limit. We find that a cavity optomechanical system is generally unsuitable for this purpose, since the quadrature rotation occurs in the opposite direction to that required for broadband sensitivity improvement. Next we introduce a general technique to engineer arbitrary optical spring potentials in cavity optomechanical systems. This technique has the potential to optimise many types of sensors relying on the optical spring effect. As an example, we show that this technique could yield an enhancement in sensitivity by a factor of 5 when applied to a certain gravitational sensor based on a levitated cavity mirror. We then consider a particular nanowire-based optomechanical system with potential applications in force sensing. We demonstrate a variety of ways to improve its sensitivity to transient forces. We first apply a non-stationary feedback cooling protocol to the system, and achieve an improvement in peak signal-to-noise ratio by a factor of 3, corresponding to a force resolution of 0.2fN. We then implement two non-stationary estimation schemes, which involve post-processing data taken in the absence of physical feedback cooling, to achieve a comparable enhancement in performance without the need for additional experimental complexity. Finally, to address the most complex of systems, we present a general-purpose machine learning algorithm capable of automatically modelling and optimising arbitrary physical systems without human input. To demonstrate the potential of the algorithm we apply it to a magneto-optical trap used for a quantum memory, and achieve an improvement in optical depth from 138 to 448. The four techniques presented differ significantly in their style and the types of systems to which they are applicable. Successfully harnessing the full range of such optimisation procedures will be vital in unlocking the potential of quantum optical systems in the technologies of the futur

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring