Sources Of Power Quality Disturbances Research Articles

Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction That Incorporates Weather Conditions

Power quality (PQ) disturbance in power systems has been a concern for operators and customers. The purpose is to locate and forecast the presence of PQ disturbances to suppress or avoid their negative effects on power grid and appliances. This study, using a multi-hidden Markov model (MHMM), motivates data-driven tools to achieve situation awareness of PQ disturbance. We first design a modified adaptive-sorted neighborhood method that consists of blocking and merging phases to locate PQ disturbance sources from a large volume of PQ records. We then group and discretize the data on disturbance sources and weather conditions according to regions. The capability of MHMM-based tools to predict future PQ disturbance level can be improved by clustering the training set of time series of PQ disturbance levels based on weather conditions. A Hadoop-based PQ analysis framework is proposed to reduce computational times, considering the volume of PQ data in a realistic power grid is large. We utilize numerical case studies that use real data collected from a power system of a Chinese city to investigate the correctness and feasibility of the proposed method.

A Neural-Network-Based Data-Driven Nonlinear Model on Time- and Frequency-Domain Voltage–Current Characterization for Power-Quality Study

An accurate model of nonlinear load is important for the evaluation of power quality (PQ). Among different PQ disturbance sources, alternating current electric arc furnace (AC EAF) is one of most complicated and serious loads. To provide effective operation prediction of AC EAF, a data-driven modeling approach based on time- and frequency-domain voltage-current (v -i) characterization is proposed in this paper. With the prediction of the proposed model in the time domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasistationary and long-term features of AC EAF would be extracted via the frequency-domain phase of the proposed model. From the comparison on the field measurement data, the performance of the proposed model can be verified in the applications of PQ studies.

Power Quality Early Warning Based on Anomaly Detection

Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.

Power quality disturbance source identification using self-organising maps

Power quality (PQ) is becoming increasingly important owing to the increasing use of power electronic devices, coupled with the increasing penetration of loads, which are sensitive to voltage disturbances. As a result of the problems caused by the confluence of these two trends, there is an increasing need for PQ to be monitored in order to diagnose its nature and locate the source of the disturbance, allowing remedial measures to be taken. While automated systems for diagnosis of PQ events have been developed, identifying the location of the source of a disturbance is a problem, which has not been fully addressed to date; in particular the question of locating a non-stationary disturbance. In this study, a novel approach to identify the location of the source of a PQ disturbance is described, using a form of artificial neural network known as a self-organising map. The proposed technique is verified via simulation of the IEEE 14-bus model in PSCAD and an experimental system based on the IEEE 6-bus model. This approach provides a mean of locating the source of PQ events, including transient disturbances.

State Estimation for the Localization of Harmonic Sources in Electric Distribution Systems

Throughout the last years, with the liberalization of the energy market, the localization of the sources of power quality disturbances has become a nodal point for operators and customers. In the liberalized market, knowledge of the system status, along with the uncertainty affecting the estimated data, is required to operate management and control issues such as energy dispatching and protection coordination. In distribution systems, too many measurement instruments would be needed to obtain a complete observation of the electric quantities. Thus, model-based state-estimation techniques can be implemented to exploit the few real-time measurements from the field and all the other available information to estimate the desired quantities. In this paper, a novel approach to the estimation of the harmonic sources by means of a Bayesian approach is proposed. The procedure also allows the ldquoqualityrdquo of the obtained estimates to be assessed. The theoretical fundamentals of the method are discussed, and results relevant to a simple distribution network are presented.

Virtual PQ troubleshooter [power quality

The authors describe a prototype instrument that locates the sources of power quality disturbances and is geared toward power system troubleshooting and management.