Voltage Sag Detection Research Articles

Energy feature extraction and visualization of voltage sags using wavelet packet analysis for enhanced power quality monitoring

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring. The study decomposes voltage sag signals into different frequency bands to extract key features for disturbance detection. We compare six commonly used mother wavelets (db1, db4, db10, dmey, sym5, and coif5) to identify the most suitable wavelet for voltage sag detection. The energy distribution curve analysis is used to evaluate the energy characteristics of each wavelet’s decomposition, with a focus on identifying the most effective signal features for PQ monitoring. The paper presents a thorough error analysis and compares the energy values extracted by different wavelet functions to demonstrate the reliability and accuracy of the proposed method. The results show that wavelet packet analysis significantly improves the detection and classification of voltage sag disturbances, providing a robust and efficient tool for real-time PQ monitoring. This study contributes to the development of advanced PQ monitoring systems by offering a more precise and computationally efficient method for voltage sag analysis, ultimately helping to protect electrical systems from potential damage and reducing operational costs. Wavelet packet analysis is applied as a novel feature extraction method for voltage sag detection by offering improved time-frequency analysis over traditional methods. Energy features are extracted from wavelet packet coefficients for sag identification. This work utilizes wavelet packet analysis to extract energy-based features for voltage sag detection.

A method of voltage sag detection based on strong tracking SVD-UKF

Abstract Addressing issues of the traditional strong tracking unscented Kalman filtering (STUKF) algorithm in the process of voltage sag detection, such as inadequate precision, large computational complexity, and unstable iterative procedures, a method of voltage sag detection based on strong tracking SVD-UKF is proposed. Only one unscented transformation (UT) is used per iteration by establishing the linear state equation based on the voltage signal model, which simplifies the calculation procedure. Substituting singular value decomposition (SVD) for Cholesky decomposition averts the challenge of non-positive-definite error covariance matrix, ensuring stable execution of the algorithm. The action position of the fading factor is improved to further enhance detection precision. Through simulation experiments, we validate that the proposed method could effectively detect voltage sag characteristics, including amplitude, phase, and duration time.

Comparative Analysis of Various Methodologies for Voltage Swell and Sag Detection in Online and Offline Conditions

Comparative Analysis of Various Methodologies for Voltage Swell and Sag Detection in Online and Offline Conditions

Management strategy to mitigate voltage sags effects of a multi-motors system using ADALINE algorithm and cascade sliding mode control

Multi-motor systems (MMS) find widespread use in various industrial applications, including plastic, paper, textiles, and steel rolling mills, where synchronized speeds are crucial for optimal operation. However, a significant limitation of these systems is their susceptibility to voltage sags, resulting in speed and synchronization loss, along with peak currents and torques during voltage recovery. This paper presents a comprehensive multi-motors management strategy aimed at attenuating the adverse effects of voltage sags. The proposed technique is based on principles that involve recovering the system’s kinetic energy and leveraging the current reversibility of the converters. The control scheme comprises two main strategies: an adaptive linear neuron or later adaptive linear element (ADALINE)-based voltage sag detection algorithm utilizing least mean square (LMS) adaptation for rapid convergence using artificial neural networks, and a control scheme incorporating sliding mode speed controllers and indirect rotor field-oriented control (IRFOC). Additionally, a logic-based strategy for voltage sag attenuation completes the control framework. The effectiveness and efficiency of the proposed strategy are demonstrated through simulation results obtained using MATLAB/Simulink/SimPowerSystems.

Comparison of different voltage dip detection techniques

To accurately determine the beginning and the end of voltage sags is very important for the actuation of a system that is connected to the grid and must have ride-through capability. This paper analyses four voltage sag detection methods, comparing both detection speed and reliability, not presenting false detections and providing a consistent signal during all the gap duration. In particular, it has been analysed the behaviour of the detection methods when voltage waveforms present harmonics due to power electronics switching.

A new method for the characterization and type detection of voltage sag using active voltage component

ABSTRACT In this paper, a new power quality (PQ) index, namely active voltage component (AVC), has been developed using the help of symmetrical component analysis (SCA). Furthermore, the developed PQ index, i.e., AVC, has been used for the identification of the faulted phase and characterization of the voltage sags. The effectiveness and accuracy of the developed technique have been revealed by considering the voltage sag signals from IEEE database. Through the outcomes, as attained with the AVC, it is revealed that the sag duration and type are exactly same as the real recorded test signals. Also, it is shown that the developed index, i.e. AVC, is sufficient for the simultaneous characterization, classification, and detection of the voltage sag. The error in sag duration with the proposed index is found to be zero while the other existing methods either under-or over-estimate the sag duration. Finally, the shortfalls of the existing methods, based on SCA and space vector (SV), have also been discussed. Through the comparative studies, it is shown that the existing methods, based on SCA and SV, provide the erroneous characterization due to oscillation and noise present in the voltage sag signals while the proposed index provides accurate characterization, classification, and detection.

A Cascaded Multilevel Battery Energy Storage Based Parallel Dynamic Voltage Compensator for Medium Voltage Industrial Distribution Systems

This paper proposes a cascaded multilevel battery energy storage based parallel dynamic voltage compensator (DVC) for medium voltage industrial distribution systems. In this parallel DVC, fast switch is series-connected for voltage sag detection, and cascaded multilevel battery energy storage is parallel-connected for voltage support during the voltage sag period. According to the grid-side voltage sags or not, corresponding circuits and three-layer control strategies are developed. While the feedforward proportional–integral (PI) control method is adopted in the outer layer to track the ideal compensation voltage when the grid-side voltage sags, the ideal compensation current when the grid-side voltage does not sag is obtained through battery charging current as well as reactive power, harmonics, and negative sequence load current components. In order to cope with parameter differences in cascaded multilevel battery energy storage, the middle layer interphase and the inner layer intermodular state of charge (SOC) equalization control are adopted in two scenarios. Furthermore, the parallel DVC is quantitatively compared with series DVC in the aspect of required battery capacity, and their advantages and disadvantages are systematically analyzed. Case studies are performed to verify the effectiveness and superiority of the proposed methodology on voltage support.

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system.

A novel voltage sag detection method for analyzing charging quality of electric vehicle

Electric car charging quality is severely impacted by voltage sag in the distribution network. Voltage sag events frequently occur in conjunction with other power quality disturbances, which leads to signal waveform distortion, making it difficult to identify voltage sag characteristics in composite power quality disturbance signals. The voltage sag amplitude, phase angle jump, and duration are all accurately detected in the composite power quality disturbance signal thanks to the method's two-stage waveform decomposition model, which eliminates the interference of the transition segment by pinpointing the beginning and ending points of the voltage sag using DQ transform. Moreover, the designed simulation experiments further verify that our proposed method outperforms other mainstream approaches, and it can be well used to effectively detect voltage sags of different causes under various signals that disturb the composite power quality, with an average accuracy rate of over 98.86 %.

Voltage Sag Detection and Compensation Signal Extraction for Power Quality Mitigation Devices

The importance of voltage quality is continuously increasing in electrical networks due to the rising manufacturing costs resulting from system faults and disturbances in utility dynamics. Researchers generally prefer reference-frame transformation-based methods to detect and mitigate these disturbances. However, these methods are adversely affected during unbalanced loading and disturbances due to their direct dependence on system dynamics (currents and voltages). In this study, a new and simple method based on Clarke transformation is proposed to detect disturbances and generate compensation signals for Power Quality Mitigation Devices. The aim is to address the deficiencies of existing approaches. Firstly, the Clarke transformation is introduced through the vector presentation. Then, the mathematical derivation of the proposed method is provided to enhance readers’ understanding. The voltage sag detection and compensation signal extraction of its control algorithm for a Dynamic Voltage Restorer is illustrated graphically. Subsequently, a simple power system is created using a simulation program. Balanced and unbalanced voltage disturbances are applied to the test system to demonstrate the validation of the proposed method under distorted system conditions. The results of voltage sag detection and compensation signal extraction for both the proposed and existing methods are compared at the end of the case studies.

Research on Voltage Sag Detection Algorithm based on Regional Transmission System

Voltage sag is the most common power quality issue and often brings huge economic losses. To achieve voltage sag prevention and compensation, it is necessary to effectively detect voltage sag characteristics. An IEEE-39 node system model is built using Simulink, combined with common aer transformation methods and αβ. The transformation method accurately detects voltage sag accidents and incorporates a derivative algorithm, greatly reducing the detection delay and meeting the real-time and accuracy requirements of actual systems.

Rancang Bangun Alat Deteksi Sag Tegangan Menggunakan Sistem Data Logger

-This research discusses the design and manufacture of a voltage sag detection device using a data logger system. Voltage sag detection device to detect the presence of voltage sag which is one of the power quality problems in industrial electrical systems. Voltage sag can cause disturbances in electronic components that are sensitive to changes in voltage values. In this device, the voltage sag is detected using a voltage sensor by reading the RMS voltage from the PLN 220V AC source, then it is converted into a digital signal. The digital signal is sent to the ESP32 module to be processed and outputs a voltage value which is then displayed on the LCD Display. The voltage value data collection is carried out every <10ms and the data taken is the voltage value <90% of the rms voltage. The data taken is then saved to a micro SD card and displayed on the LCD Display. Several tests were carried out to ensure the performance of the voltage sag detector as designed. By switching the 110 / 220VAC transformer connected to the voltage sensor for 10 seconds, 12 seconds and 17 seconds. Based on these tests, the voltage sag detector can read and store the voltage sag data when the voltage read is below 90% of the rms voltage.

Integrated control algorithm for fast and accurate detection of the voltage sag with low voltage ride-through (LVRT) enhancement for doubly-fed induction generator (DFIG) based wind turbines

Low voltage ride-through (LVRT) is one of the essential aspects of grid codes for integrating doubly-fed induction generators (DFIG) to achieve reliable and uninterrupted electrical power generation. The detection time of the voltage sag is one of the crucial aspects for the LVRT improvement of the DFIG. This paper introduces a second-order generalized integrator (SOGI), quadrature signal generator (QSG) with a frequency locked loop (FLL) based algorithm to detect the symmetrical voltage sag fast and accurately. The proposed SOGI-QSG-FLL-based voltage sag detection algorithm (VSDA) works under a second-order band-pass filter with an alfa-beta stationary reference frame. The proposed VSDA is integrated with the novel feed-forward transient current compensation (FFTCC) scheme with an advanced interval type-2 fuzzy logic-proportional integral (IT2-FLC-PI) controller to enhance the LVRT capability of the DFIG. This FFTCC scheme is activated after the detection of voltage sag by using the proposed VSDA. The proposed FFTCC feeds the uninterrupted active and reactive power to the grid and reduces the torque ripple of the DFIG. The detection time of voltage sag by using the proposed VSDA is compared with existing algorithms. The experimental results are also presented to validate the proposed algorithm.

A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems

Several strategies have been developed for identifying power quality issues, monitoring them, and compensating for relevant disturbances. In this field, online estimate of amplitudes and phase angles of network voltages and currents is commonly used. The adaptive linear neuron (ADALINE)-based voltage sag detection algorithm with least mean square (LMS) adaptation allows for rapid convergence of estimate techniques based on artificial neural networks (ANN). This approach has the advantage of being straightforward to implement on hardware and based on simple calculations (essentially multiply and accumulate "MAC"). This paper gives a comparison of the performance of two ADALINE approaches ("with" and "without" error supervision) for detecting and estimating voltage dips. The described techniques and models of a two-coupled motor system were implemented in MATLAB/Simulink/SimPowerSystems to run simulations under various fault scenarios in order to create the three-phase voltage sag alarm signal. The simulation outcomes are presented and debated.

Methodology for the Surveillance the Voltage Supply in Public Buildings Using the ITIC Curve and Python Programming

This paper proposes an easy-to-implement method for detecting and assessing two of the most frequent PQ (Power Quality) problems: voltage sags and swells. These can affect sensitive equipment such as computers, programmable logic controllers, contactors, etc. Therefore, it is of great interest to implement it in any laboratory, not only for protection reasons but also as a safeguard for claims against the supply company. Thanks to the actual context, in which it is possible to manage big volumes of data, connect multiple devices with IoT (Internet of Things), etc., it is feasible and of great interest to monitor the voltage at specific points of the network. This makes it possible to detect voltage sags and swells and diagnose which points are more prone to this type of problems. For the detection of sags and swells, a program written in Python is in charge of crawling all the files in the database and target those RMS values that fall outside the established limits. Compared to LabVIEW, which might have been the most logical alternative, being the acquisition hardware from the same company (National Instruments), Python has a higher computational performance and is also free of charge, unlike LabVIEW. Thanks to the libraries available in Python, it allows a hardware control close to what is possible using LabVIEW. Implemented in MATLAB, the ITIC (Information Technology Industry Council) power acceptability curve reflects the impact of these power quality disturbances in electrical power systems. The results showed that the combined action of Python and MATLAB performed well on a conventional desktop computer.

Detection of Three-Phase Voltage Sag under Multiple Power Quality Disturbances

Voltage sag is an unavoidable short-term power quality disturbance problem in the power system. Rapid and accurate detection of voltage sag characteristic quantities is the premise of dealing with the sag problem. Based on the principle of d-q transformation and ISOGI-QSG structure, this paper proposes a method for fast detection and identification of three-phase voltage sag. It describes the working principle of the improved SOGI in detail. In addition, the structure of three-phase voltage sag independent detection is analyzed, and a fast determination method for sag fault types is proposed. The advantages of this method compared with traditional three-phase voltage detection are verified by simulation comparison, and it still has good anti-interference ability and better dynamic performance under various power quality disturbances. Moreover, the detection speed can meet the time requirements of sensitive loads for dual power switching.

Development of grid-side converter-based FRT control and protection in a grid-connected solar photovoltaic park system under different control modes

In a grid-connected solar photovoltaic system, voltage dips on the grid side, increased grid current, and overshoot in the inverter’s dc-link voltage are all noticed during grid disturbances. The grid code stipulates that in order to protect the cascaded power converter systems from high currents and overvoltages, the solar PV system disconnects from the grid anytime the voltage level at PCC falls below a particular standard nominal value. This study proposes combined GSC-based fault ride-through (FRT) and protection control strategies which can provide independent real and reactive power control for the inverter for effective FRT in photovoltaic parks interconnected with a large power system during grid faults. The proposed approach operates in three modes—voltage control, power factor control, and reactive power control modes. The developed protection modules in the PV system consist of over/undervoltage protection, voltage sag detection, and overcurrent detection. The inverter-fed real–reactive power control technique limits grid overcurrent by modifying active power injection into the grid and stabilizes grid voltage during faults by injecting reactive current. The performance of the proposed FRT and protection control strategy is studied through the simulation of 75 MW PV park in the EMTP platform and the experimental setup of a 5 kVA grid-connected inverter-fed solar PV system. The simulation results and hardware discussion presented in this study validate the performance of the proposed FRT scheme in comparison with conventional control schemes during grid faults.

A fast voltage sag detection method based on second order generalized integrator and delay transform method

Voltage sag is an important problem that causes power quality degradation, and the premise of solving the problem of voltage sag is to detect the depth of voltage sag accurately and quickly. Based on the past research on voltage sag detection, this paper proposes a voltage sag detection method that combines second order generalized integrator and delay transform method. Use the second order generalized integrator as the phase-locked loop and filter to extract the grid voltage phase angle and grid voltage fundamental wave, and then construct the quadrature component of the grid voltage fundamental wave by delaying a small angle. Square and square root calculation to quickly obtain voltage sag information. At the same time, the voltage sag information is used as the control signal of the second order generalized integrator to solve the problem of long response time of the second order generalized integrator. In this way, the interference of harmonics, zero-sequence and negative-sequence voltages on sag detection is excluded, and the rapidity of sag detection is ensured. Simulation and experiments show that the method can accurately and quickly determine the voltage sag depth.

A Novel Voltage Sag Detection Method Based on a Selective Harmonic Extraction Algorithm for Nonideal Grid Conditions

Voltage sag detection is utilized to capture the sag occurrence moment and calculate the sag depth of power grid voltage in real time, so as to generate reference voltage for controlling voltage interactive equipment such as dynamic voltage restorers (DVRs). However, the traditional voltage sag detection methods based on synchronously rotating frames (SRFs) are unable to acquire high-precision sag information under nonideal grid conditions such as unbalance or harmonic interference. In order to enhance the immunity of the sag detection, a method based on a selective harmonic extraction algorithm (SHEA) is proposed in this paper. Firstly, the state-space model of SHEA is established using discrete orthogonal basis to decouple and separate the signal of target frequency and the signal of interference frequency. The controllability, stability and convergence of SHEA are analyzed theoretically and serve as the criteria for parameter tuning. Moreover, a gain compensator (GC) is used to improve the low and middle frequency gains of the voltage sag detection method based on SHEA so that the dynamic response speed for sag judgment can be optimized quantitatively. The simulation results indicate that the proposed voltage sag detection method has good dynamic and steady-state performance under nonideal power grid conditions such as unbalanced sag, frequency drift, phase variation and harmonic interference.

Mother wavelet selection method for voltage sag characterization and detection

Wavelet-based techniques are strongly recommended as a good alternative for the fast detection and characterization of voltage sags. However, the accuracy and effectiveness of these techniques greatly depend on selecting an appropriate mother wavelet. Therefore, in this work, a wavelet correlation-based technique has been developed to select the most appropriate mother wavelet for the characterization and detection of voltage sag. The efficacy and accuracy of the proposed method are tested with twenty different mother wavelets on various voltage sag signals, namely, recorded industrial, multi-stage, and synthetic signals under different conditions of unbalanced. It is shown that the mother wavelet having the highest similarity with a voltage sag provides the best results for its characterization and detection. Further, the various performance parameters of voltage sags, namely magnitude, duration, sag initiation, recovery, are evaluated with the proposed method and results are compared with Independent Component Analysis (ICA), hybrid wavelet, dq-transformation, Enhanced Phase Locked Loop (EPLL), Fast Fourier Transform (FFT) methods showing that the performance of the proposed method is better than other existing methods for sag detection. In addition, the proposed method can also be used for estimating the magnitude of voltage sags.