Real-time Power Quality Monitoring Research Articles

Monitoring-Based Localization of Unbalances and Root Cause Analysis in Low-Voltage Distribution Systems

The dynamic nature of power systems operation, lack of sufficient monitoring initiatives, and the continuous nature of the power quality phenomena are the primary reasons behind a distribution system operator's inability to localize and understand the root cause behind unbalance. Additionally, there is a deficiency of algorithms that can leverage the grid monitoring data, even if limited, to present a sufficiently useful overview of unbalance propagation which can lead to appropriate corrective measures. This article presents: a new unbalance index derived from feeder active power measurements, an algorithm for localization of unbalanced contributions, and an algorithm for root cause analysis that can distinguish whether the unbalance problem is loading, asymmetry, and/ or fault-oriented. The end goals of this work are twofold: to present a holistic view of the grid unbalanced situation using EN50160 type monitoring, and to assist the distribution system operators in unbalance reduction. The usability of the proposed algorithms and indices is shown using a hybrid European MV-LV model simulated in OpenDSS and on two actual LV distribution systems with real-time power quality monitoring.

Design of Real-Time Power Quality Monitoring System for Active Distribution Network Based on Computer Monitoring

The power system is continuously optimized and advanced, and then various equipment auxiliary systems are produced. This paper studies the real-time power quality monitoring system of active distribution network based on computer monitoring. The system can monitor the real-time parameters of electric energy and the running status of equipment, and design the memory function and fault processing function. When some equipment or parts of the power grid failure, the system can produce alarm signals in a timely manner, notify the system monitor, the staff can immediately deal with the problems caused by the signal, reduce the loss caused by failure in time. The memory function of the system is to carry on the memory to the fault signal, then can quickly find, is advantageous to the fault study and the prevention.

Conjugate gradient back-propagation based artificial neural network for real time power quality assessment

This paper proposes conjugate gradient back-propagation based artificial neural network for real time power quality assessment. The novel real time voltage sag and swell detection, classification scheme using artificial neural network is presented. The ANN is trained in MATLAB using voltage sampled signal data and corresponding parameters of the neural network are utilized to implement in LabVIEW in real time sag swell detection and classification. The Levenberg Marquardt, the resilient back-propagation and conjugate back-propagation algorithm performance is evaluated in MATLAB to find the best neural network for real time application. Among these three back-propagation algorithms, conjugate gradient algorithm has better performance for real time power quality monitoring. The mathematical model of Conjugate gradient back-propagation neural network is implemented in LabVIEW. Real time voltage signals of sag and swell for different time duration and magnitude, intensity are acquired from hardware experimental setup. Hardware setup mainly consists of single phase 230V voltage source, microcontroller, dimmerstat and solid state relays. Voltage signals of sag and swell are sensed using high precision voltage sensor. Data acquisition system is used to acquire the signal from voltage sensor. The output of data acquisition system is given to the personal computer with LabVIEW. The proposed monitoring system also detects odd and even harmonic components in the voltage signal acquired using FFT. Real time hardware results obtained using proposed power quality monitoring system for detection of voltage sag, swell and harmonics claims the suitability, robustness and adaptability to monitor power quality issues.

Cooperative Change Detection for Voltage Quality Monitoring in Smart Grids

This paper considers the real-time power quality monitoring in power grid systems. The goal is to detect the occurrence of disturbances in the nominal sinusoidal voltage/current signal as quickly as possible such that protection measures can be taken in time. Based on an autoregressive (AR) model for the disturbance, we propose a generalized local likelihood ratio (GLLR) detector which processes meter readings sequentially and alarms as soon as the test statistic exceeds a prescribed threshold. The proposed detector not only reacts to a wide range of disturbances, but also achieves lower detection delay compared to the conventional block processing method. Then we further propose to deploy multiple meters to monitor the power signal cooperatively. The distributed meters communicate wirelessly to a central meter, where the data fusion and detection are performed. In light of the limited bandwidth of wireless channels, we develop a level-triggered sampling scheme, where each meter transmits only one-bit each time asynchronously. The proposed multi-meter scheme features substantially low communication overhead, while its performance is close to that of the ideal case where distributed meter readings are perfectly available at the central meter.

Fractional order Sprott chaos synchronisation‐based real‐time extension power quality detection method

It is known that real-time power quality monitoring is usually rather difficult to achieve due to the requirement of expensive equipments and necessary of more signals to be captured. To solve this problem, this study combines fractional order Sprott chaos synchronisation system with extension theory to detect power signal disturbance including voltage swell, sag, interruption, and harmonics for real-time power quality monitoring. The use of fractional order chaotic systems can significantly improve the situations of misjudgement due to the dynamic error is too large by using general integer order chaotic systems. Otherwise, the three-dimensional (3D) Sprott error dynamics can be reduced to 2D error system. It will become easier and cheaper to implement this scheme in the portable device. The results of numerical simulation show that the detection accuracy rate is 100% and it is better than the methods in previous studies. It should be able to get a very high diagnostic accuracy if this method can be applied to the actual power system.

电能质量监测点优化配置综述

随着国民经济的飞速发展，我国的电网模式正在向智能化发展，然而要实现电网的智能化，其中一项很重要的技术就是电网监测和控制技术，加上近年来各电力行业对电能质量问题的关注度日益提高，所以构建电能质量实时监测网络对全网电能质量问题进行实时监测具有重要的现实意义。为了同时满足经济效益并且具有足够的监控能力，国内外学者相继提出了一系列解决电能质量监测点优化配置问题的方法，并为电能质量监测系统的发展做出了巨大贡献。本文对这些方法进行了综述，并对未来电能质量监测系统的发展进行了展望。 With the rapid development of national economy, electric power industry pays more and more attention to the power quality problems day by day. In order to minimize the harm of the power quality problems, building a real-time power quality monitoring network for real-time monitoring of the power quality problems in the entire network has important practical significance. At the same time, in order to meet the economic benefits, domestic and foreign researchers have proposed a series of methods to solve the optimum measuring positions for power quality monitoring, and made a great contribution to the development of power quality monitoring system. In this paper, these methods were summarized, and the future development of power quality monitoring system was discussed.

안드로이드 앱과 MCU를 이용한 저가형 원격 전원품질이상 감시 시스템

This paper presents a low-cost remote power-quality-failure monitoring system (RPMS) using Android App and TI MCU (micro-controller unit), which is appliable to a micro-grid. The designed RPMS testbed consists of smart nodes, a server, and Android APPs. Especially, the C2000-series MCU-based RPMS smart node that is low-cost compared to existing monitoring systems has both a signal processing function for power signal processing and a data transmission function for power-quality monitoring data transmission. The signal processing function implements both a wavelet-based power failure detection algorithm including sag, swell, and interruption, and a FFT-based power failure detection algorithm including harmonics such that reliable and real-time power quality monitoring is guaranteed. The data transmission function implements a low-complexity RPMS transmission protocol and defines a simple data format (msg_Diag) for power monitoring message transmission. We may watch the monitoring data in real time both at a server and Android phone Apps connected to the WiFi network (or WAN). We use RS-232 (or Bluetooth) as the wired (or wireless) communication media between a server and nodes. We program the RPMS power-quality-failure monitoring algorithm using C language in the CCS (Code Composer Studio) 3.3 environment.

Power quality diagnosis using time frequency analysis and rule based techniques

Diagnosing a power quality disturbance means identifying the type and cause of the disturbance. Fast diagnosis of power quality disturbances is important so as to assist network operators in performing counter measures and implementing suitable power quality mitigation actions. In this study a novel method for performing power quality diagnosis is presented by using the S-transform and rule based classification techniques. The proposed power quality diagnosis method was evaluated for its functionality in detecting the type of short duration voltage disturbances and identifying the cause of the disturbances which may be due to permanent or non permanent faults. Based on the results, this new method has the potential to be used in the existing real time power quality monitoring system in Malaysia to expedite the diagnosis on the recorded voltage disturbances.

Two-Stage Improved Recursive Newton-Type Algorithm for Power-Quality Indices Estimation

In the paper a new improved recursive Newton type algorithm (IRNTA) suitable for various measurement applications in the electric power systems is presented. Here it is applied for power quality indices estimation according to the IEEE Standard 1459-2000. Basically the algorithm is a recursive nonlinear estimator, improved with a strategy of sequentially tuning of the forgetting factor. This approach generally improves the algorithm performance: immunity to random noise, accuracy and the speed of convergence. The IRNTA consider the power frequency as unknown model parameter and takes into account distortion of voltages and currents. Therefore, it is suitable for the real-time power quality monitoring. The algorithm has two stages. In the first stage the input signal spectra and fundamental frequency are estimated, whereas in the second stage the unknown power quality indices are calculated. To demonstrate the efficiency of the proposed algorithm, the results of computer simulations and laboratory testing are presented.