Online Power Quality Research Articles

Intelligent transformer tap controller for harmonic elimination in photovoltaic interfaced micro-grid network

The photovoltaic (PV) system is interfaced to the microgrid through an on-load tap changer (OLTC) transformer. Pulse width modulation (PWM) AC choppers are used to control these OLTC but create harmonics owing to their non-optimal firing angles. In this paper, Differential Evolution (DE) and Grey Wolf Optimizer (GWO) techniques are used for optimal control of an AC chopper to keep the harmonic distortion of voltage and current within limits. An IEEE-13 node network is adapted as a micro-grid with PV integration through transformers. The simulated results are validated by a laboratory set-up. A dSPACE experimental set-up is used for the real-time implementation of optimization algorithms. The results show that the DE gives firing angles that improve power quality, particularly a reduction in total harmonic distortion and improvement in the root mean square voltage profile. The research is particularly useful for the on-line power quality improvement in PV interfaced microgrid networks.

Research about a fast identification method for intermittent single Sub-synchronous Resonance phenomenon in large scale doubly-fed wind farm

In this paper, a synchronous resonance (SSR) phenomenon caused by a large scale doubly-fed wind farm group after putting in series compensation device is analysed. By analysing the changing trends of the indicators during period of SSR phenomenon, 11 power quality indicators are chosen for calculated, then divided all the 11 indicators into 3 source data and 8 preliminary stage operational data by the observability of value. After divided the two kinds of data, a Pearson correlation coefficient between this two kind data was calculated to judge the relationship of them, and the characteristic indexes with stronger correlation during the resonance period were analysed. The maximum value of the above 8 characteristic indexes in the normal power generation process is used as the basic data for comparison, so as to realize the rapid and accurate judgement of the SSR moment. In the process of SSR judgement, the method has the characteristics of faster speed and simple determination, and it does not need to reform the existing power quality online monitoring device, and can realize real-time analysis and judgement of SSR phenomenon only by increasing extraction of the existing data function from the monitoring system. Finally, by testing data of two actual SSR events, the characteristic index determination method proposed in this paper is analysed and verified, and the feasibility of this method is proved.

Online Identification of Underlying Causes for Multiple and Multi-Stage Power Quality Disturbances Using S-Transform

ABSTRACT The proposed methodology aims at the identification of multiple and multi-stage power quality (PQ) disturbances along with their underlying causes. To this end, a computationally efficient S-Transform with decision tree (DT) based online PQ monitoring algorithm has been presented in this paper. This online algorithm provides a sample-based analysis of 40-dB noisy multiple and multi-stage PQ disturbance signals simulated in MATLAB environment with the different combinations of underlying causes as per the IEEE-1159 standard. The features like residual voltage, instantaneous phase-angle jump (IPAJ), and number of zero crossings (NZC) have been extracted from the transformed contours, viz., maximum voltage amplitude versus time (MVAVT) and phase-angle versus time (PAVT). Henceforth, the accurate recognition of most common underlying causes like three-phase fault, induction motor starting, and capacitor bank energizing has been achieved. For the first time, an online PQ monitoring algorithm is effectively assessing the type and underlying cause of a multiple or multi-stage disturbance simultaneously. The proposed methodology has also been experimentally validated with real-time PQ disturbance signals acquired in the laboratory.

Design and Implementation of a Data-Driven Approach to Visualizing Power Quality

Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio–temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.

A Sequential Bayesian Approach to Online Power Quality Anomaly Segmentation

Increased observability of power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this article, an online model is proposed. Our Bayesian Changepoint power quality anomaly segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices.

Development of Real Time Power Quality (RTPQ) Analyzer using Lab-VIEW

Power Quality (PQ) issues and its alleviation techniques are becoming one of the vital research topics, soon after the integration of power electronic devices in power system. The non-linear nature of equipment’s introduces PQ issues such as voltage sag, swell, harmonics, unbalance and transients etc. and cause damage of end user equipment’s. Poor PQ has become a vital issue which affects production and profit of both utilities and consumers. Therefore continuous monitoring and assessment of PQ is essential for finding optimal solution to analyze and mitigate various PQ problems and also maintain quality of power within the prescribed limit. For PQ analysis, it is very difficult to generate power quality events in real life in order to observe the effects or to study their characteristics. Now days, various physical PQ analyzers are available in market to continuously monitor, analyze and record PQ problems. The major drawback of this analyzer is its hardware design, complexity and its cost which limits its usage to many places. Thus online PQ monitoring is a very big challenge for researches. This paper presents modeling and simulation of virtual PQ analyzer using Lab VIEW. Lab VIEW supports a lot of library functions for an acquisition, analysis of data and also the control of instruments. This software is mainly used to model standard real instruments used in laboratories by providing more flexibility. The proposed PQ analyzer is capable of generating, identifying various PQ problems and also measuring PQ parameters RMS Voltage (Vrms), RMS Current (Irms), Total Harmonic Distortion (THD), Real, Reactive and Apparent Power from the test system. Using Lab VIEW software, a complete quality analysis of electric energy can be done online at the user's location. So that the selection of custom power devices and mitigation methods for PQ problems can be done in a perfect manner. This RTPQ analyzer is tested using real time data of hardware are acquired by the construction of test systems such as rectifier with various resistive and inductive loads. ALL PQ parameters measured using proposed PQ Analyzer are also compared with the parameters measured using fluke PQ Analyzer. The response attained has proven that the accuracy and precision of the proposed analyzer can also be used as a very good cost effective tool for online PQ study.

FPGA-Based Online Power Quality Disturbances Monitoring Using Reduced-Sample HHT and Class-Specific Weighted RVFLN

This paper proposes a new signal segmentation method, reduced-sample empirical mode decomposition, to extract the highly correlated monocomponent mode of oscillations. The two efficacious power quality indices are extracted from the Hilbert transformed (HT) array of the first three intrinsic mode functions. A novel class-specific weighted random vector functional link network (CSWRVFLN) classifier is proposed to recognize the complex power quality disturbances (PQDs). The performance of reduced sample Hilbert–Huang transform (RSHHT) combined with CSWRVFLN (RSHHT-CSWRVFLN) method is tested and compared with tunable-Q Wavelet transform associated with HT and CSWRVFLN and empirical wavelet transform along with HT and CSWRVFLN methods. The short event detection, lesser computational complexity, superior classification accuracy, and robust antinoise performance are the major advantages of the proposed RSHHT-CSWRVFLN method. Furthermore, a field-programmable gate array embedded processor is used to test and validate the feasibility of the proposed method for online monitoring the PQDs.

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Harmonic emission level assessment method based on parameter identification analysis

In this study, a new method for harmonic emission level assessment is proposed. The method is based on parameter identification analysis. The quantitative relationship between standard deviation values of equivalent parameters and harmonic emission levels is studied and discussed through theoretical derivations. The complete harmonic emission level assessment method is then proposed on the basis of quantitative analysis and validated by means of simulation studies and field tests. The method has the ability of distinguishing the active injection contents in load harmonic currents and determining the actual harmonic responsibilities of the customers in power systems. On-line power quality monitoring applications and incentive management schemes can be designed by the proposed method.

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real time. Empirical mode decomposition is used to decompose the nonstationary PQEs into the monocomponent mode of oscillation, known as intrinsic mode functions (IMFs). The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs. An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine, least-square support vector machine, extreme learning machine, and bidirectional extreme learning machine. The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform associated with HT and WBELM method, and tunable-Q wavelet transform along with HT and WBELM method. The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system. Finally, a hardware prototype is developed based on the digital signal processor to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.

FPGA-based online power quality monitoring system for electrical distribution network

In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.

Publicly accessible online power quality monitoring databases

While performing power system studies, it can be difficult to find real-world data to supplement and/or reinforce experimental data. This study provides information on how four databases can be obtained through a new online power quality (PQ) monitoring system accessible via the Internet and how the data in these databases can be used. The available data includes National Oceanic and Atmospheric Administration Space Weather, National Renewable Energy Laboratory Wind, the Department of Energy (DOE)/Electric Power Research Institute Disturbance Library, and the PQSoft PQ Waveform Library. The cloud-based databases are available at no cost to educational institutions worldwide for the purpose of training electric power engineering students.

Research on classification of voltage sag sources based on recorded events

In recent years, with the wide application of power electronic devices and stringent requirement of industrial process, voltage sag has caused much attention in many countries. To design effective and efficient mitigation and management strategies, it is essential to have accurate classification of voltage sag sources. In this study, based on the analysis of massive voltage sag events in the grid recorded by online power quality monitoring systems in several different regions in China, a more extensive and practical classification of voltage sag sources is given. The voltage sags are divided into eight categories due to short-circuit faults with symmetric and asymmetric faults included, transformer energising, induction motor starting, lightning faults, self-extinguishing faults, the combined action of short-circuit faults and heavy load starting, upgrade faults and multi-stage voltage sags. For each category of the voltage sag sources, both the instantaneous value and the root mean square value of the waveform of the typical recorded events are given. In addition, the reasons are analysed and the waveform characteristics are summarised for each category of the voltage sag sources. Finally, the frequencies of eight categories under different voltage levels and the statistical result analysis are given.

Fast Procedure for Characterization of Voltage Sag for Medium Voltage (MV) Distribution Network without Online Monitoring Using Fault and Regression Analyses

Power Quality (PQ) Management undertaken by most utilities including TNB involves monitoring/online measurement, data processing & analysis, reporting and customers’ complaints management. The Malaysian regulator requires proactive communication and PQ disturbance report covering specific details such as percentage remaining voltage, duration, and cause of voltage sag/dip events to be shared with affected customers. The above stipulated requirement can be easily met for substations/exposed areas with online PQ measurements, which capture voltage magnitude and duration that could be corroborated with tripping events. However, it is not practical to have online monitoring facilities as the majority of medium voltage (MV) substations are not equipped with voltage and current transformers where measurements could be tapped. Current procedures of characterizing voltage sag for substations without measurements facilities is time consuming. In this paper, a fast procedure using combinations of fault simulations and regression analysis to characterize voltage sag is proposed. Extensive results from 160 simulation cases comprising 4 network operating configurations, 20 fault distances, and 2 fault types are used in the regression formulation. The results based on the proposed fast procedure for MV distribution network without online monitoring is found to be fairly accurate.

Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System.

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

A power quality online monitoring system oriented ZigBee routing optimization strategy

For ZigBee Cluster-Tree routing protocol in the power system applications existing not optimal routing and not real-time problems, the actual demand from on-line monitoring power quality of substation point of view, considering hops, link busy status and the residual energy, a ZigBee Cluster-Tree improved routing algorithm is proposed, calculating the hops of all neighbor nodes to the destination node and introducing an alternative node. Several NS2.34 simulation experiments show that the improved routing optimization algorithm reduces the number of hops and end to end delay, improves power quality monitoring in real time, saves overall network energy consumption, and prolongs the network life cycle.

Development of New Multi-Function Power Quality On-Line Monitoring Device

A power quality on-line monitoring device based on two CPUS is designed and implemented in this paper, which separated the data analysis from the peripheral control completely, used CPLD (Complex Programmable Logic Device) to generate control timing of peripheral devices, made full use of the DSP, so as to meet the requirements of power quality: monitoring precision and real time. The paper firstly introduces the monitor's sampling circuit, communication module circuit, storage module circuit and the flow chart of software. Then, the functions, features and designations of each module are described in detail.

Research on China's First Residential Grid-Connected Photovoltaic System

Along with the continuous expansion of photovoltaic (PV) power generation, different capacity of grid connected PV system is gradually increased. China's first residential grid-connected PV system has interconnected successfully in QingDao and operated normally. This document analyzed electrical connected diagram of grid-connected PV system. Using power quality analyzer, the online power quality is monitored and analyzed. When PV power generation is low efficiency of operating state, harmonic current is over distortion limits. Monitoring data was simulated through electrical power standard source. To ensure power metering accuracy under harmonic, the watt-second method is proposed. Testing results show that smart electrical meter can meter accurately within 20 times harmonics.

Design and Realization of an Online Power Quality Monitoring System Based on DSP and GPRS

In order to evaluate power quality more accurately and synthesize the characteristics of the distribution, which has nodes in large quantity and area, an online power quality monitoring system (PQMS) is developed by using the digital signal processor (DSP) and the general packet radio service (GPRS). This PQMS consists of two parts: the power quality monitor (PQM) which is designed on the structure of DSP and the monitoring software (MS) in upper computer which is designed on the platform of LabVIEW. GPRS network provides the communication channel for these two parts. Therefore, supervisors can monitor and analyze the power quality from a long distance. In this paper, the design and realization of each part are described in detail. Experimental results show that the proposed system is fast in response with high accuracy and has the ability of real-time processing.

A Power Quality Online Monitoring Device Based on PowerPC and DSP

According to the practical requirements of setting up large scale power quality monitoring systems and monitoring power quality in digital substations, an embedded power quality online monitoring device based on PowerPC and DSP is realized. The device structure, the overall hardware design, the PowerPC and the DSP systems are introduced. The design scheme based on PowerPC and DSP can take full advantages of the fast data calculation speed of DSP and the strong network processing ability of PowerPC. The test result of the measurement accuracy shows that the device can meet the power quality indicators measuring requirements of national standards.