Power System Disturbances Research Articles

Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks

This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score.

A novel protection scheme for doubly fed induction generator‐based wind turbines during the fault occurrence in the rotor‐circuit

AbstractThe rotor‐circuit (RC) of the doubly fed induction generator‐based wind turbine (DFIG‐WT) consists of various equipment in which faulty conditions must be immediately identified, because this faulty status leads to the loss‐of‐excitation (LOE) phenomenon and may cause some serious damages. In this regard, this paper develops a novel solution to protect the DFIG‐WT during such a condition. For this purpose, a comprehensive investigation on behavior of the DFIG‐WT during the conditions of the RC failures has been carried out. The results show that, the output active power of the machine (Pdfig) can be considered as a suitable initial indicator for detecting the RC failures. However, to reliable detection of the partial‐LOE (PLOE), complete‐LOE (CLOE) and discrimination from the other power system disturbance (PSD), a directional over‐current (DOC) and an under voltage (UV) relays are suggested to be installed in the RC, at both ends of the back‐to‐back converters. Consequently, by detecting the Pdfig decrement, the provided DOC and UV relays in the RC will be triggered to classify the occurred events. The outlined protection scheme is exhaustively investigated on a typical power system which the obtained simulation results show the high reliability in discriminating the RC failures and PSD events with an acceptable detection time.

A Conspectus of PQD Analysis

The sustainability of a stable voltage with consistent amplitude and frequency is paramount for power systems. Various standards, including IEC 61000-4-7, -15, and -30, and IEEE 1159, 1459, address power quality disturbances (PQDs). These standards establish rules and limitations for different voltage parameter values to ensure power system stability. Nevertheless, disturbances in the electrical power systems can cause fluctuations in voltage amplitude and fundamental frequency. These disturbances, such as sag, flicker, interruption, harmonics, swell, and interharmonics, may emerge in different combinations, yielding unpredictable and variable effects on the system components. Hence, conducting a comprehensive analysis and examination of these disturbances is crucial. Studying PQDs faces challenges in obtaining real datasets due to the randomness of power system events. As a result, there is an increasing need for experimental investigations to explore the complexities of these disturbances. The objective of this paper is to assist and enrich research on PQDs by delivering some knowledge of data and experimental arrangements. This paper will make easy further examination and exploration of PQDs. This will empower researchers to acquire profound understandings and devise efficient approaches to tackle these matters, contributing to improved power system performance.

Need for Load Modeling in Short Circuit Analysis of an Inverter-Based Resource-Dominated Power System

Short circuit models of transmission networks have not commonly included loads. The reason is that the fault current from synchronous generators are many times higher than the load current and highly reactive, and hence omission of load current does not reduce the accuracy of short circuit results. Nevertheless, this modeling practice may no longer be appropriate to an inverter-based resource (IBR)-dominated grid or grid region; the fault current of an IBR is comparable to load current and, depending on inverter terminal voltage, can have a considerable active component. This paper studies the impact of load modeling on fault analysis of IBR dominated grids. Specifically, the paper identifies potential simulation inaccuracy issues caused by lack of load modeling under IBRs and provides considerations to reduce the error and increase the fidelity of short circuit results. If these inaccuracies are not addressed, they may impact the behavior of protection relays in the simulation and risk incorrect protection relay configurations. This is turn could create risks of relay misoperation or failure to operate during power system disturbances.

FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years. However, cyberattack detection in smart grids now faces new challenges, including privacy preservation and decentralized power zones with strategic data owners. To address these technical bottlenecks, this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC, that enables Discrimination between power System disturbances and Cyberattacks. Specifically, we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data. Secondly, we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency. Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information. Furthermore, we find that the gradient quantization scheme utilized improves communication efficiency by 40% when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

An intelligent mitigation of disturbances in electrical power system using distribution static synchronous compensator

The power quality of an electrical system is critical for industrial, commercial, and housing applications, and with the increasing use of sensitive loads, customers and utilities are beginning to pay more attention to it. A distribution static synchronous compensator (D-STATCOM) represents one of the best custom power devices (CPDs) for improving the power quality of a distribution system. The performance of this device relies upon the algorithm and strategy used for its control. Artificial intelligence was utilized to overcome these shortcomings, while a response optimizer tool was used for the tuning process. An adaptive controller design was also proposed, based on the integration of fuzzy logic with traditional proportional-integral (PI) controller. The fuzzy logic controller system was designed using the adaptive neuro fuzzy interference system (ANFIS) editor. In this work, a D-STATCOM controller was used to mitigate sag and swell problems, while the ANFIS together with the optimization method was used to improve the system response. This study was carried out using MATLAB/Simulink, and the results showed a superior and adaptive performance in mitigating voltage sag and swell problems at different loading conditions compared to the traditional PI.

Unexpected Consequences: Global Blackout Experiences and Preventive Solutions

Power system blackouts result in complete interruption of the electricity supply to all consumers in a large area. Systems need to be planned and engineered in a way that minimizes exposure to and prevents cascading blackouts. Blackouts are caused by a sequence of cascading outages caused by a combination of multiple low-probability events (e.g., a transmission line sagging into a tree, hidden failures in equipment protection, the loss of multiple generation units because of a weather event, and aging equipment failure) occurring in an unanticipated or unintended sequence. The likelihood for power system disturbances to escalate into a large-scale cascading outage increases when the grid is already under stress. This stress can be caused by lower operational margins, overloaded equipment, and other factors, which are discussed in detail in this article.

A fused CNN‐LSTM model using FFT with application to real‐time power quality disturbances recognition

AbstractWith the progress of renewable energy generation and energy storage technologies, more and more renewable sources and devices are integrated into the power system. Due to the complexity of the power system, single and multiple power quality disturbances (PQDs) occur more frequently. Hence, real‐time detection of PQDs is the primary issue to mitigate the risk of distortions. This study presents the real‐time PQDs classification using fused convolutional neural networks (CNN) combined with long short‐term memory (fused CNN‐LSTM) architecture based on time and frequency domain features. The frequency‐domain features were obtained from time‐series data using fast Fourier transform. The original time‐domain and frequency‐domain features are extracted by respective CNN‐LSTM structures. The extracted time and frequency domain features are concatenated to classify the PQD through fully connected layers. Our proposed method was trained and tested using 16 types of synthetic noise PQDs data generated by mathematical models, in accordance with the standard IEEE‐1159. Moreover, to further verify the performance of our approach, a simulation distributed power system is carried out to detect various PQDs. We compared three advanced neural network approaches: Deep CNN, CNN‐LSTM, and multifusion CNN (MFCNN). The fused CNN‐LSTM model takes only 0.64 ms to classify each PQDs signal and achieves an accuracy of 98.95% and 98.89% in synthetic data and simulated power systems which indicates our proposed method outperformed compared methods.

Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

A Data-Driven Under Frequency Load Shedding Scheme in Power Systems

This paper presents a measurement-based under-frequency load shedding scheme that considers time delays, measurement uncertainties, and communication network faults. Using a moving first-degree polynomial function, an iterative approach to estimate the frequency nadir and the associated frequency nadir time is proposed. Then, a novel method to simultaneously estimate the power imbalance and the system inertia constant is developed. According to an analytical basis, a base system is selected from a set of pre-defined base systems to guarantee accurate power imbalance estimation. Finally, a mathematical index is investigated to calculate a threshold for the generation-load power imbalance beyond which loads should be shed. Further, a systematic approach for specifying the amount of load to be shed in each transmission bus of the system is proposed. The effectiveness of the proposed methodologies is examined on both simulated and actual measured data from 9 historical power system disturbances in a national power grid.

Sliding Mode Control of Energy Storage Systems for Reshaping the Accelerating Power of Synchronous Generators

The rotor angle stability has been a key concern in the stable operation of power systems, especially in the renewable energy age. However, to improve the power system rotor angle stability, none of the existing methods considered achieving it through reshaping the accelerating power of synchronous generators (SGs) and decoupling them after grid disturbances. In this paper, the sliding mode control (SMC) theory is utilized to tackle the coupling among SGs. After careful modeling and controller design, a SMC-based energy storage system (ESS) controller is proposed to temporarily decouple the interacted SGs through reshaping the accelerating power of SGs to the desired value. The proposed controller can: 1) drive the SG’s accelerating power to follow the reference in infinite time; 2) decouple the interacted SG dynamics and stabilize the disturbed SG exponentially. The proposed controller achieves the above two goals without using the communication and hard-to-estimate SG rotor angles. Case studies are carried out on the 2-Area 4-Generator System and the Australian 14-Generator Equivalent System to demonstrate the effectiveness of the proposed controller. The results also show that the proposed controller stabilizes the disturbed power systems with better performance over existing controllers.

A novel protection strategy for microgrid based on estimated differential energy of fault currents

• This article has presented a protection algorithm based on energy measurement of the current signal using TKEO. • TKEO shows that the energy of a signal can be realized in terms of its instantaneous amplitude and frequency. • The non-zero value of the energy difference index triggers the fault detection algorithm. • This class of operators is an efficient tool for analysing amplitude-modulated-frequency-modulated signals due to its simplicity and excellent time-resolution capability. • This approach can efficiently detect both low resistance and high impedance faults, with excellent distinguishable capability between faults with other power system disturbances. The significant benefits of distributed generation (DG) integration in the emerging microgrid are compromised due to substantial challenges associated with the protection. Due to DG penetration, fault current becomes dynamic, leading to make conventional protection methods vulnerable. In order to address this serious issue, a simple and fast protection algorithm, based on Teager-Kaiser Energy Operator (TKEO) is proposed in this paper. This scheme extracts the energy of current signal, retrieved from both ends of the line using TKEO. For fault detection, energy difference of the current signals is used. In order to validate the effectiveness of this proposed scheme, 33 bus test microgrid system operating in both grid-connected and autonomous mode is presented. For evaluating the performance of proposed scheme, high impedance fault cases are also examined. The distinctiveness of the proposed algorithm is that it is principally relying on the energy difference of current signals, therefore it can subdue the difficulties associated with dynamic fault current. Moreover, it does not suffer from computational complexity; thus, inherently hastening the detection process. The test system is designed and simulated in PSCAD-EMTDC software , while MATLAB programming interface is used for developing and testing the proposed algorithm.

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model’s high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.

Chaotic Harris hawks optimisation based fuzzy lead lag TCSC and PSS with coordinated control design for enhancement of power system transient stability

This paper investigated the application of Chaotic Harris Hawks optimisation (CHHO) technique for the tuning of a coordinated control of fuzzy lead lag based TCSC controller with fuzzy based PSS power system. The eigenvalues and simulation results of proposed CHHO optimised TCSC controller are presented and compared with a coordinated control of lead lag TCSC controller with PSO, DE and GSA optimised lead lag controller under various cases of operating conditions and disturbances in SMIB power system. The CHHO optimised of proposed fuzzy coordinated controller is compared with CHHO based lead lag TCSC coordinated control of same power system to check the effectiveness and robustness analysis. Finally, the proposed design approach is extended to multi-machine test model system to demonstrate the coordinated control of fuzzy lead lag TCSC damping controller with fuzzy power system stabilises to damp out oscillations in power system to improve transient stability performances.

PQ Event Detection After Noise Removal Using Fuzzy Transform and Hilbert Spectral Analysis on EMD

In this paper, a noise removal technique from Power Quality (PQ) signals is proposed which is based on a soft thresholding method. Thresholding is accomplished by using Fuzzy Membership Function (MF) and Empirical Mode Decomposition (EMD). To assess the denoising performance, the proposed technique is applied to detect PQ disturbances from the denoised power system signals. The EMD is applied to decompose the signal into different components. Next, the Intrinsic Mode Functions (IMFs) obtained from the EMD are utilized to determine the IMF dependent thresholding parameters, which are used into the modified fuzzy MF for the noise removal. For improved performance, several random versions of the first IMF are created, independently soft thresholded, and averaged to yield the final denoised version of the first IMF. A separate measurement is used to decide how many IMFs are required to be thresholded. For the evaluation of the noise removal performance, 1) the proposed method is compared with a wavelet-transform-based state of art technique, and 2) an application is presented on the denoised signal for the detection of PQ events from real and simulated power system disturbance data. The denoised and processed IMFs are subjected to Hilbert spectral analysis for the identification of the localized features, which are then utilized for the detection of PQ events in smart grid signal.

Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances

Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances

Studi Kelayakan Sistem Pentanahan pada Gedung Teknik Elektro Universitas Andalas

The grounding system is one part of the electric power system that has an important role in flowing overcurrent from the electric power system to the ground due to electrical power system disturbances or lightning strikes that can endanger the building and its surroundings. This grounding system aims to protect humans, electrical equipment, and electrical installations from the danger of electrical short circuits. In compliance with official regulations, safety standards and according to general electrical installation regulatory standards (PUIL) to avoid the danger of lightning strikes in buildings, it is necessary to have grounding resistance value of less than five ohms and is made as small as possible. In this study, the measurement of grounding resistance seen from the depth of the grounding and the grounding resistance are arranged in parallel. The results of this study indicate that the parallel grounding resistance method produces a more significant reduction in the value of grounding resistance compared to the grounding resistance in a certain depth From the calculation results, the resistance value of the Electrical Engineering Building at Andalas University using a rod electrode with a diameter of 0.016 meters is 24.5 Ω with an electrode depth of 0.5 meters and at a depth of 1 meter a grounding value of 17.52 Ω is obtained. The deeper the depth of the electrode, the smaller the resulting resistance value, and installing a grounding system with the multirod method, namely arranging electrodes in parallel with a certain depth, obtains less resistance compared to not parallelizing the electrodes

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.

Load Angle Based Loss of Excitation Protection Scheme for Parallel Connected Synchronous Generators

The main weaknesses of impedance-based loss-of-excitation (LOE) relay are its maloperation during the power system disturbances (PSDs) and its long detection time during LOE events. In this article, to tackle the mentioned problems, a new LOE protection scheme is proposed based on load angle (δ) variation of parallel synchronous generators (SGs) connected to a point of connection (POC). It will be shown that during a PSD, the variation of δ for all parallel SGs is similar such that their δ increases or decreases simultaneously. However, it will be shown that when the LOE occurs for one of the parallel SGs, the δ of faulty SG increases while other SGs will experience reduction in their δ. Hence, when more than one SG is connected to a POC, the LOE event can be discriminated from PSDs. In the proposed scheme, when one SG is connected to the grid, the impedance-based relay is employed to enable the developed method to detect LOE/PSD. Since in a real-world application, the outage rate of the parallel SGs in a power plant during the emergency situations, periodic services, etc., is very low on a yearly basis, the contribution of the impedance relay in the proposed scheme is quite limited to some special circumstances. Comprehensive simulation studies have been conducted in MATLAB/Simulink environment (R2017b); the results prove high efficiency of the proposed scheme.

주파수 변화율 측정 기반 에너지저장장치 효과적인 제어 전략

The paper describes an effective control strategy for energy storage system (ESS) according to the severity of occurring in power system. ESS is generally used to mitigate the frequency oscillation against the power system disturbances. However, ESS can frequently operate even in situation where it is not necessary to operate due to small load change or small fluctuations in renewable energy. It can cause the frequency variations of the state of charge (SOC) of ESS and shorten the lifespan of the ESS. Therefore, the paper proposes effective control method to reduce the unnecessary operation of ESS by categorizing three control modes based on measured rate of change of frequency (ROCOF). The performance of the proposed method is verified through time domain simulation by applying it to the microgrid system.