Power Quality Monitoring System Research Articles

Multi-source data driven harmonic spectrum estimation of substation feeder current

With the proliferation of non-linear loads and distributed power resources that connect through power electronic equipment, decentralized harmonic pollution in three-phase symmetric power system becomes increasingly serious. Despite this, monitoring the current harmonic of all substation feeders regardless of cost is unrealistic. To this end, a multi-source data driven method is proposed in this paper, utilizing data from the power quality monitoring system, dispatching system, and market business system, to estimate the harmonic spectrum of substation feeder currents and estimate the harmonic current components of each feeder with monitoring data of bus voltage harmonics and line harmonic currents. The objective function of the harmonic spectrum estimation model is constructed based on the analysis of current harmonic phasor and power relationship on the feeders. An improved particle swarm optimization (PSO) algorithm is proposed to solve the proposed estimation model thereby obtaining the current harmonic content of each feeder and the phase angle difference between the current harmonics of the feeders. The performance of the proposed model and algorithm is validated by numerical simulation with a test system and an actual system.

POWER QUALITY MONITORING FOR INDUSTRIAL LOAD

Power quality monitoring is critical in ensuring the efficient and reliable operation of industrial systems. This paper presents a MATLAB-based simulation approach for power quality monitoring, providing a cost-effective and scalable solution for industrial applications. The idea behind this paper is to design and simulate a comprehensive power quality monitoring system that includes voltage and current measurement, harmonic detection, waveform analysis, and microcontroller-based data processing. This simulation environment allows for the evaluation of various power quality parameters and the optimization of system parameters for accurate assessment. The abstracted system integrates sensor data acquisition, signal processing algorithms, and communication modules within the MATLAB framework. Through extensive simulations and parameter tuning, the system can effectively detect and mitigate power quality issues such as harmonics, voltage sags, swells, and transients. This paper significance lies in its ability to offer a robust, cost- effective, and customizable solution for power quality monitoring in industrial settings, ultimately ensuring the longevity and reliability of critical equipment and processes. The presentation will provide insights into the design, simulation, and potential real-world implementation of this MATLAB-based power quality monitoring system. Keywords –Harmonics, THD analysis, Fault correction, FFT analysis, Matlab simulation.

Design and implementation of smart meter for PQ-based tariff computation for LV distribution network consumers

The drastic rise of renewable energy, electric vehicles, and electronic loads is deteriorating distribution networks' power quality (PQ), affecting utility companies' profitability and consumer satisfaction. Therefore, it is imperative to implement a PQ monitoring system at the consumer level and enforce a quality-constrained tariff scheme to regulate the PQ issues. This article presents the design and development of a low-cost smart energy meter that accurately measures 25 essential PQ parameters every 0.2 s and transmits the data to a server at one-minute intervals. Further, these parameters are used for remote PQ monitoring and PQ-based tariff implementation to regulate reactive power and harmonics in the distribution network. The smart meter is calibrated and tested with standard meters and validated in laboratory-scaled diversified supply voltage and load scenarios, demonstrating accuracy, practicality, and affordability. Concludes it as a viable option to monetize and regulate the PQ issues in distribution networks or smart grids.

Introduction of state-of-the-art low-voltage distribution network devices

Trends towards the development of distributed generation involve a number of problems related to maintaining the quality of electric energy in medium and, especially, low-voltage networks. The article examines the impact of monitoring the quality of electricity of low voltage classes on the reliability of the electrical network. MSZ EN 50160 and Hungarian Energy and Utilities Regulatory Office (MEKH) standards are considered. In accordance with MSZ EN 50160, the main indicators of the quality of electric energy are considered, such as: frequency, voltage fluctuations, rapid voltage changes, Flicker dose, voltage dips, short-term voltage interruptions, long voltage interruptions, temporary overvoltages of industrial frequency, transient overvoltages between current-carrying conductors and voltage ground , harmonic voltage fluctuations, interharmonic voltage fluctuations. SAIFI and SAIDI are calculated according to the Hungarian Energy and Utilities Regulatory Office. The devices for monitoring the quality of electricity of low-voltage networks are analyzed. The factors affecting the choice of the place of installation of monitoring devices are considered. It is presented how power quality monitoring systems can reduce the number of malfunctions in the 0.4 kV network, and the influence of monitoring systems on the quality of electric power in 6-35 kV networks is considered.

An image-based deep transfer learning approach to classify power quality disturbances

• Pre-trained deep learning models can be effectively used to detect power quality disturbances. • A new approach is proposed and validated using real-time data. • This approach is based on the use of Transfer Learning and adopts images of voltage waveforms. • The proposed approach allows reducing the complexity of the training process and is suitable for real-time deployment. Power quality disturbances (PQDs) consist in deviation of voltage and current waveforms from the ideal sinusoid at fundamental frequency, and need to be monitored to ensure a reliabile electrical supply. While, traditionally, power quality monitoring has been performed using signal processing techniques, coupled with shallow Machine Learning classifiers or wave change detection methods, more recently, new approaches, based on Deep Learning, have been proposed. These methods have the potential to achieve high classification accuracy and to remove the need of extensive data pre-processing, hence being more suitable for real-time deployments. However, high classification performance has been only demonstrated using synthetically generated data. In order to address limitations related to processing time and accuracy, this paper proposes a novel end-to-end framework for automated detection of PQDs based on Deep Transfer Learning. The proposed approach uses a small set of images of voltage waveforms to train the model and classify different types of PQDs. This method leverages on the high performance of existing pre-trained models for image classification and shows consistent high accuracy for data with varying resolution. The proposed methodology provides a pathway towards effective deployment of Deep Learning in power quality monitoring systems and real-time applications.

Fault location method of Distributed Generation Sources in Distribution Networks based on optimal configuration of voltage sag monitoring points

In order to evaluate the fault location performance under the optimal configuration scheme of Power Quality Monitoring System(PQMS) monitoring points with Distributed Generation(DG) in, a fault location method based on successive binary search was proposed. Firstly, the influence of DG on traditional optimal configuration is analyzed, and the optimal configuration improvement strategy for DG access is proposed. The function relation between node voltage and fault distance under DG access is analyzed, and the ratio variation relation caused by voltage variation between monitoring points is used to reflect the characteristics of slight variation of electrical quantity, and the ranging evaluation function is established. In order to avoid the setting of convergence threshold, the disadvantage of setting fixed threshold is eliminated. Based on the mean ranging error, several feasible monitoring point configuration schemes are evaluated for ranging performance and the optimal configuration scheme for fault location performance is obtained. The 4-node system verifies that the ranging method does not need to set fixed convergence threshold. In the improved IEEE14 node system, this feature can be used to evaluate the positioning performance of four different monitoring point configuration schemes, and finally obtain the optimal positioning performance configuration scheme.

A Data-Driven Self-Learning Evaluation Method of Voltage Sag Severity

Lightning is one of the main causes of voltage sags, and it is of great significance for subsequent analysis and governance to accurately evaluate the severity of the sag events caused by lightning. There are many uncertain factors between lightning fault events and voltage sag events. To evaluate the severity of voltage sag events caused by lightning, a data-driven self-learning evaluation method for voltage sag severity is proposed. According to a large number of online monitoring data of lightning positioning system and power quality monitoring system, the association rule mining algorithm based on incremental learning is used. And the rules are kept updating through the accumulation of historical data, which may give it the abilities of self-learning. The empirical analysis is carried out based on the monitoring data of a regional power grid. The results show that the method in this paper can accurately mine more valuable rules in reality and solve the problem of low efficiency of mining algorithm when the database changes dynamically.

Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data

One of the common power quality (PQ) problems in transmission and distribution systems is the voltage sag that affects the sensitive loads. Losses and problems caused by the voltage sag in the power system can be reduced by correctly determining the relative location of the voltage sag. This paper proposes a novel algorithm to classify voltage sag relative location and fault type, which is the main root cause of voltage sag, based on the actual voltage and current data before and during the voltage sag. The performance of the algorithm is investigated by performing a numerical simulation utilizing MATLAB/Simulink. Moreover, the proposed algorithm is integrated into the power quality monitoring system (PQMS) of the real distribution system and tested. The results show that the performance of the proposed method is satisfactory.

Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid

Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.

Generation of Single and Multiple Power Quality Disturbances Using Integral Mathematical Models

Today, generation based on renewable energy sources is employed more and more to meet the rising demand for electricity. Power electronic converters are needed for this generation's grid integration. The main causes of Power Quality Disturbances are these power electronic converters, switching activities, non-linear loads, and network problems (PQDs). The end-delicate user's equipment is severely harmed by these PQDs. As a result, Power Quality (PQ) emerges as the primary concern for both utilities and customers. We require a precise PQ monitoring system that can identify and categorize PQDs in order to reduce the PQ issue. There is a tone of study being done in this area. This research's initial stage is to collect some artificial data to train and test the detection and classification algorithms is the initial stage in this project. In order to create single and many PQDs, it is currently popular to use integral mathematical models that are expressed as parametric equations. This study demonstrates the use of MATLAB programming to create integral mathematical models of single and multiple PQDs, including voltage sag, swell, interruption, harmonics, voltage flicker, voltage notch, impulsive transient, oscillatory transient, and voltage sag with harmonics. This will give the researcher working in this field a quick and automatic technique to generate PQDs.

Homology detection method for voltage sags based on space vector and maximum mean discrepancy

A single voltage sag source may trigger multiple power quality monitors to record waveforms resulting in the redundancy of voltage sag monitoring data. To address this problem, a homology detection method for voltage sags based on a space vector and maximum mean discrepancy was proposed, which is conducive for locating accurate voltage sag sources and inverting sag event propagation paths. First, a waveform transformation method based on a space vector was proposed to eliminate the influence of the transformer on voltage sag propagation. Second, a two-sample test method for voltage sags based on the maximum mean discrepancy was proposed to detect whether any two voltage sag waveforms are caused by the same voltage sag source. The connected graph method was used to identify the number of sag sources; this avoids the difficulty in accurately selecting the number of clusters. The measured and simulation data of the IEEE 30-bus system were used for verification, and the results verified the correctness and effectiveness of the proposed method. The proposed method can be implemented in a power quality monitoring system as a pre-processing step to support related research activities.

Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine

Accurate detection and classification of power quality (PQ) disturbances is an essential prerequisite for PQ mitigation. To address this issue, a new PQ assessment framework based on modified empirical wavelet transform (MEWT) and light gradient boosting machine (LightGBM) is proposed here. First, the frequency estimation mechanism and band segmentation rule of empirical wavelet transform (EWT) is modified, which makes EWT suitable for analysing complex PQ signals. Second, based on the PQ disturbance analysis results of MEWT, 8 characteristic curves are defined and statistical features are extracted in both the time and frequency domain. Third, binary relevance-based LightGBM (BR-LightGBM) is designed for the multilabel classification of massive PQ events. Considering the impact of input features and model structure, feature selection and hyper parameter optimization are conducted for achieving better classification performance. Finally, extensive experiments based on synthetic data and two groups of measured data show the effectiveness of the proposed method on 48 types of complex PQ disturbances. Compared with other algorithms for multiple PQ signals, the proposed method is fast for computation and performs better in classification accuracy and robustness, which is a prospective alternative for the PQ monitoring system.

Research and implementation on high-precision power quality monitoring system of EAST power supply

Implementing power quality analysis of the Experimental Advanced Superconducting Tokamak (EAST) power supply is great significance to the stable operation of the power system and the future development of nuclear fusion power stations. Due to the limited channels and fixed resolution of the existing monitoring equipment in the industry, it is difficult to cover the whole requirements of EAST power quality monitoring. And customized highly personalized equipment will be very expensive. Until now, the EAST power system lacks a multi-functional, high-precision power quality monitoring system. So that it cannot solve the impact fault caused by harmonics and inter-harmonics. Thus, a set of high-precision, multi-channel and real-time harmonic monitoring system with automatic trigger storage is established in this paper. Based on the tri-spectrum line interpolation method and the optional frequency-spectrum function, the spectral resolution can be improved to 1.25Hz. Meanwhile, the horizontal comparison of the data can more accurately locate and measure the characteristics of harmonics and inter-harmonics. Finally, the measurement data of the monitoring system is compared and verified by standard sources.

Novelty Detection on Power Quality Disturbances Monitoring

Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.

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.

Modeling and Simulation of Voltage Sag Assessment for Distribution Network Based on Total Coverage of Nodes

The evaluation of voltage sag of power grid is to analyze the situation of voltage sag, to guide the planning, construction and transformation of power grid, and further improve the power quality. It is an important content in the current power quality research. In this paper, the fault point is regarded as a new virtual node, and the fault location is described by fault location parameters λ. The system node impedance matrix is expanded by calculating the self impedance and mutual impedance between the virtual node and the concerned node. Through the modeling and simulation of distribution network, short-circuit faults are set to analyze the voltage sag. According to the three-phase voltage sag waveform recorded by power quality monitoring system (PQMS), the fault type can be identified, and the fault line can be determined according to the PDR of SCADA system. Without considering the fault resistance, the fault type and fault line are known, so the fault location can be found only by solving the fault location parameters λ. Because of its parallel processing and good robustness, particle swarm optimization algorithm can find the optimal solution of the problem with large probability and fast convergence speed, so this paper chooses to use particle swarm optimization algorithm to solve the fault location parameters λ.

Research on Distributed Power Quality Monitoring System of Large Centralized Charging Station

The power quality problems of large-scale centralized charging stations are prominent, affecting the operation of the station equipment and the power grid, and a simple and efficient distributed power quality monitoring system is required. Based on the analysis of large-scale centralized charging stations for distributed power quality monitoring system requirements, this paper studies the overall structure of the distributed monitoring system, and analyzes the functional requirements of each part of the monitoring system, and gives the voltage/current power quality Monitoring terminal functions and key device design; by comparing and analyzing the characteristics of various communication technologies, an optimal solution for the communication of the distributed monitoring system in the charging station is given. The proposed system provides a basis for real-time monitoring of multiple monitoring terminals in large-scale centralized charging stations, analysis and timely release of relevant power quality assessment information, and in-depth study of the power quality of various charging piles in the charging station and their mutual influence. At the same time, the formed “point-line-surface” charging station monitoring system can provide a data basis for smart operation and maintenance of charging stations.

Power quality monitoring system based on IEC61850

Power quality monitoring system is an important means to find and evaluate power quality problems in time. A service-oriented browser / server (B / S) mode active power quality real-time monitoring system is designed and implemented, which provides the real-time monitoring function of power quality in the process of active power distribution network absorbing distributed power generation layer by layer, and focuses on observing the impact of the change of power flow direction on the level of power quality. Based on IEC 61850 standard, the system establishes the power quality information model, and uploads the real-time monitoring data of power quality on site to the master station for analysis and storage in the real-time database through MMS. The client communicates with the server through windows communication base (WCF) service to obtain real-time monitoring data, and realizes the global real-time monitoring function of power quality of active distribution network combined with the main wiring diagram, so as to achieve the integrated display effect of data graph.

Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

This paper proposed a novel method for allocating power quality meters with the main objective of ensuring a completely observable power distribution system. The method was designed to establish the quantity, location and type of measurement to be performed (voltage and/or current) for a given distribution system by employing genetic algorithms and the principles of state estimation based on the singular value decomposition. Moreover, a limitation in the number of current measuring channels was inserted in the mathematical formulation as an alternative for reducing costs. The method has been validated by running a three-phase harmonic state estimation using the IEEE 34 and 37 bus distribution test feeders. The results demonstrated the effectiveness of the method for designing power quality monitoring systems in distribution grids, ensuring full observability for state estimation purposes.

Construction of a Power Quality Monitoring System Based on the Wavelet Transform Change-Point Detection Algorithm on the Virtual Instrument Platform

It is aimed to carry out the investigation on power quality detection, promote the realization of efficient transmission of network data, and expand the application of wavelet transform change-point detection algorithm in the monitoring system. The voltage deviation is used as a starting point to explore the detection of power quality. First, it describes the harmonics of the public power grid and the limits of harmonic voltage. Second, based on the virtual instrument platform, the power quality monitoring system based on wavelet transform change-point detection algorithm is completed. Finally, by adding a monitoring terminal and a service terminal, the design of the monitoring system server is completed. Through the analysis of the experimental results, it is found that in the monitoring system, the current waveform and the three-phase voltage can be accurately displayed. The combined design of the networked monitoring system and the system server enables the system to complete the rapid transmission of data related to power quality, while having a good monitoring effect. For the optimization of networked monitoring experienceof the server, the application of wavelet transform in power quality measurement is realized. The power quality monitoring system proposed has a strong practicality in power quality monitoring.