Power System Disturbances Research Articles

Sensing of Power System Disturbances Using CNN-Aided Tailored BiLSTM Model

Sensing of Power System Disturbances Using CNN-Aided Tailored BiLSTM Model

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Safe dynamic optimization of automatic generation control via imitation-based reinforcement learning

IntroductionThe increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcement learning (RL), in automatic generation control (AGC). However, traditional RL cannot ensure constraint safety during training and frequently violates the constraints (e.g., frequency limitations), further threatening the safety and stability of grid operation.MethodsTo address the safety issue, we propose a novel safe RL framework that combines expert experiences with the RL controller to achieve imitation-based RL. This method allows an initialized safe policy by imitating expert experiences to prevent random explorations at the beginning. Specifically, we first formulate the AGC problem mathematically as a Markov decision process. Then, the imitation mechanism is developed atop a soft actor–critic RL algorithm.Results and discussionFinally, numerical studies are conducted with an IEEE 39-bus network, which show that the proposed method satisfies the frequency control performance standard better and improves the RL training efficiency.

Decentralized Robust Power System Stabilization Using Ellipsoid-Based Sliding Mode Control

Power systems are naturally prone to numerous uncertainties. Power system functioning is inherently unpredictable, which makes the networks susceptible to instability. Rotor-angle instability is a critical problem that, if not effectively resolved, may result in a series of failures and perhaps cause blackouts (collapse). The issue of state feedback sliding mode control (SMC) for the excitation system is addressed in this work. Control is decentralized by splitting the global system into several subsystems. The effect of the rest of the system on a particular subsystem is considered a disturbance. The next step is to build the state feedback controller with the disturbance attenuation level in mind to guarantee the asymptotic stability of the closed-loop system. The algorithm for SMC design is introduced. It is predicated on choosing the sliding surface correctly using the invariant ellipsoid approach. According to the control architecture, the system motion in the sliding mode is guaranteed to only be minorly affected by mismatched disturbances in power systems. Furthermore, the proposed controllers are expressed in terms of Linear Matrix Inequalities (LMIs) using the Lyapunov theory. Lastly, an IEEE test system is used to illustrate how successful the suggested approach is.

Estimation of aggregated inertia constant and load damping: A PMU-based analytical approach

A precise, straightforward, and efficient data-driven methodology is introduced to estimate the most important aggregated generation-load parameters of the power system frequency response model i.e., the inertia constant and the load damping. The estimation method depends on defining specific base systems characterized by particular frequency features. A sensitivity analysis is carried out to determine the most suitable base system for each real-time application. The data collected from PMUs is then compared to the attributes of the base system to make accurate estimations of the parameters of interest. The effectiveness of the proposed methodologies is assessed using datasets that include both simulated data from the New York-New England benchmark and actual measurements collected from six historical power system disturbances within a national power grid.

Decentralized Sampled-Data Control for Stochastic Disturbance in Interconnected Power Systems With PMSG-Based Wind Turbines.

The presented work is concerned with the stability performance of frequency response for interconnected power systems (IPSs) with permanent magnet synchronous generator (PMSG)-based wind turbines (WTs) via a decentralized control scheme against external and stochastic disturbances. To do this, initially, the state-space model is derived when a PMSG is penetration into IPSs. Differing from existing works, IPSs with PMSG-based WTs, including the stochastic disturbance, are modeled as a stochastic state-space model. Then, decentralized sampled-data load frequency control is designed to regulate the frequency response of the proposed model against the deterministic and stochastic noises. By constructing bilateral looped Lyapunov functional and using Itôs formula, stochastic sufficient conditions are derived, which ensure that the closed-loop form of the proposed model is asymptotically stable in the mean square with H∞ performance index γ . Finally, three-area IPSs with PMSG-based WTs are validated with derived sufficient conditions. The simulation results confirmed the better-stability performance of frequency response for the proposed stochastic IPSs with PMSG-based WTs.

Mathematical Morphology-Based Sensing of Power System Disturbances Using PCA-Aided Support Vector Machine

Mathematical Morphology-Based Sensing of Power System Disturbances Using PCA-Aided Support Vector Machine

Power System Disturbance Classification Using CWT Guided Customized AlexNet CNN

Power System Disturbance Classification Using CWT Guided Customized AlexNet CNN

A User - Friendly Signal-Processing App For Harmonics

Harmonics, especially the 3^rd,5^th, and 7^th components, are significant disturbances in power systems, occurring simultaneously and at varying time intervals. Measurements of these components depend on factors such as fundamental frequency deviation, event duration, amplitude values, and noise levels. Accurate detection and measurement are crucial for effective harmonic mitigation and preventive strategies. This study introduces a harmonic signal analysis application utilizing methods like FFT, STFT, CWT, EMD, and HHT, with individual user interfaces for in-phase and time-variant harmonic signals. The application provides results for amplitude, frequency, event time intervals, and noise effects simultaneously. Test results at a 60 dB signal-to-noise ratio (SNR) reveal that FFT achieves precise frequency and amplitude results due to its high resolution, whereas other methods offering time-frequency domain results exhibit lower resolution. EMD, in particular, demonstrates high errors in frequency and amplitude responses, reducing four frequency components to three. HHT, utilizing EMD results, yields higher accuracy with minimal errors compared to other methods. This application, combined with test results, facilitates signal synthesis and comparative analysis in time and time-frequency domains.

Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging

Power quality is a major issue in the power transfer process. This is caused by disturbances such as voltage sags, voltage spikes, and harmonics. Voltage sag is the most common disturbance in the electric power system. However, the dynamic voltage restorer (DVR) is the most effective device for voltage sags. This research uses the DVR to overcome voltage sags using fuzzy logic controller (FLC) and battery energy storage system (BESS) to improve the performance of the DVR. The results showed that DVR using FLC improved the quality of voltage recovery compared to BESS because FLC injected a greater voltage of 0.0991 pu than BESS.

Multifunctional Superconducting Magnetic Energy Compensation for the Traction Power System of High-Speed Maglevs

With the global trend of carbon reduction, high-speed maglevs are going to use a large percentage of the electricity generated from renewable energy. However, the fluctuating characteristics of renewable energy can cause voltage disturbance in the traction power system, but high-speed maglevs have high requirements for power quality. This paper presents a novel scheme of a high-speed maglev power system using superconducting magnetic energy storage (SMES) and distributed renewable energy. It aims to solve the voltage sag caused by renewable energy and achieve smooth power interaction between the traction power system and maglevs. The working principle of the SMES power compensation system for topology and the control strategy were analyzed. A maglev train traction power supply model was established, and the results show that SMES effectively alleviated voltage sag, responded rapidly to the power demand during maglev acceleration and braking, and maintained voltage stability. In our case study of a 10 MW high-speed maglev traction power system, the SMES system could output/absorb power to compensate for sudden changes within 10 ms, stabilizing the DC bus voltage with fluctuations of less than 0.8%. Overall, the novel SMES power compensation system is expected to become a promising solution for high-speed maglevs to overcome the power quality issues from renewable energy.

Power systems disturbance classification using modular neural networks with multilayers experts

This work evaluates the effectiveness of a new hybrid algorithm in the training of modular neural networks with multilayers specialists. The algorithm is applied on the classification of electric disturbances previously characterized and pre-processed, making possible the comparison between its performance with multilayers perceptrons networks. It presents the algorithm’s development and inclusion of delta-bar-delta rule, such as the data acquisition methodology and its preprocessing.

МОДЕЛЬ ТА МЕТОД АНАЛІЗУ ПРАЦЕЗДАТНОСТІ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ ПРИ НАНЕСЕННІ НЕПРОЕКТНИХ ЗБУРЕНЬ

Abstract. Information and simulation models are important tools in the modern world for analyzing, designing, and optimizing various systems. One effective approach to their construction is the use of graph theory. In this article, we will examine the fundamental principles of constructing an information model using graphs and the method for analyzing the operability of this model under non-projected disturbances. In amidst increasing demands for reliability in the energy sector, the concept of developing an energy system in the form of a layered graph is becoming increasingly relevant and promising. A layered graph simulation model can provide an effective tool for optimizing energy supply and ensuring the stability and reliability of the system. Using a layered graph, it is possible to visualize the hierarchy of different energy sources, including renewable sources, traditional fuel types, and energy-efficient technologies. This will facilitate understanding the interaction between various components of the system and allow for the improvement of their integration. Keywords: electric power system, operability of the electric power system, assessment of the operability of the power supply system, non-project disturbances in the electric power system, generation, transformer substations, electricity consumers.

Resilient Operation of Grid-Forming Inverters under Large-scale Disturbances in Low Inertia Power System

Resilient Operation of Grid-Forming Inverters under Large-scale Disturbances in Low Inertia Power System

Application of noise-filtering techniques to data-driven analysis of electric power systems based on higher-order dynamic mode decomposition

A transition to renewable energy is increasing the long-distance export of power, with reduced spinning inertia and small stability margins. In this work, we apply higher-order variants of a data-driven technique, the dynamic mode decomposition (DMD), for stability analysis and short-term prediction of power systems with renewable sources of energy. In the present paper we study the sampling duration for extraction of physically relevant dynamics suitable for prediction using noisy historical measurements of power system disturbance events. Trends in behaviour of the dominant mode are studied to estimate the singular value threshold and the delay embedding for prediction. In a sampling window around ten times the dominant periodic interval, the higher-order Kalman filtering DMD and extended Kalman filtering DMD (filtering for system identification) are newly applied to predict an historical resonance and a possible near-resonance events of single dominant frequency. In turn, multiple frequencies of a wide-area oscillatory disturbance with low-level non-Gaussian noise are best captured and predicted using the total-least-squares higher-order DMD.

A multi‐variables loss of excitation protection scheme to improve the factors of accuracy and speed of detection than the conventional impedance relay

AbstractThe most common disadvantages of loss of excitation (LOE) impedance relay are its improper operation in the conditions of power system disturbances (PSD) and its long operation time. In this regard, to solve the mentioned problems and as a result improve the accuracy and speed of detecting the LOE events, four key variables including active power (Psg), reactive power (Qsg), load angle (δsg), and terminal voltage (Vsg) are selected, on the basis of an in‐depth theoretical‐ and simulation studies. These variables have been implemented in a precise combinational logic. In the developed LOE detection logic of this paper, the dependent parameters of Vsg and Qsg as well as Psg and δsg parameters are examined simultaneously in an AND logic cluster. If both the defined combinations detect the occurrence of LOE, the generator trip command is issued. Otherwise, the normal operation of the under‐study machine will continue. The results of the performed simulations in MATLAB/Simulink environment (2017b) during the detailed scenarios of LOE and PSD events on the IEEE 39‐bus standard system confirm the efficiency of the outlined LOE protection model. Meanwhile, its superiority from the viewpoints of accuracy, swiftness, and simplicity and cost of implementation are comprehensively compared with the other schemes.

Optimal placement of BESS in a power system network for frequency support during contingency

In this work, a strategy is proposed for the optimal placement of a Battery Energy Storage System (BESS) in a power system network for frequency support during a power system contingency. It is an optimization algorithm that considers the best location for the integration of a BESS for a frequency support as the place that will result in a minimum rate of change of frequency (RoCoF) during a power system disturbance. The formulation (which aims at determining the minimum RoCoF during contingency) was based on the inertia constant contributions and active power injections (during contingency) from a mixed power generation sources of conventional power plants (CPPs), wind power plants (WPPs) and a battery energy storage system (BESS). Three different optimization solvers, particle swarm optimization (PSO), Fmincon MATLAB solver, and genetic algorithm (GA)) were used in solving the optimization problem for the purpose of comparing the results obtained in order to choose the minimum RoCoF value among the three optimization solvers. The proposed methodology was tested using two network models namely a modified 12-bus and 53 -bus (Western Cape) test systems each consisting of CPPs, WPPs and BESS as energy sources. Simulation results show that when BESS is placed on its optimal location of bus 61 (using the modified 53-bus Western Cape Network model) the power imbalance due to contingency was reduced to about 38 % of its maximum value of 4791 MW and consequently the system frequency nadir was improved from 49.49 Hz to 49.60 Hz. This is very necessary in order to keep the system frequency from falling beyond the limit that may activate the underfrequency load shedding relays.

Simulasi Analisis Kualitas Daya Menggunakan Logika Fuzzy Untuk Meningkatkan Efisiensi Sistem Tenaga Listrik

This study aims to simulate power quality analysis using fuzzy logic to increase the efficiency of the power system. Poor power quality can cause disturbances in the electric power system and reduce the efficiency of the energy used. Therefore, improving power quality is one of the main focuses in the electric power industry.The fuzzy logic method is used in this study to evaluate power quality based on relevant parameters, such as voltage, current, frequency and harmonics. Fuzzy logic is able to overcome the uncertainty and complexity of data related to electric power systems, and provide more adaptive and intelligent solutions. In this study, simulations were carried out using special software that implements fuzzy logic to analyze power quality. The input data obtained from the electric power system is used to build a fuzzy logic model. Then, the simulation results are used to identify existing power quality problems and propose appropriate improvement strategies.The simulation results show that the use of fuzzy logic can significantly increase the efficiency of the power system by minimizing disturbances and optimizing power quality. In addition, fuzzy logic also provides the ability to predict the future conditions of the electric power system and take the necessary precautions. This research has important practical implications in the field of power systems. By using fuzzy logic in power quality analysis, power companies and grid operators can optimize power system operations, reduce power losses, improve energy efficiency, and ensure reliable electricity supply to consumers.

Machine Learning to Detect Cyber-Attacks and Discriminating the Types of Power System Disturbances

This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including dataset preprocessing, feature selection, model creation, and evaluation. To validate our approach, we used a dataset used, consist of 15 separate datasets obtained from different PMUs, relay snort alarms and logs. Three machine learning models: Random Forest, Logistic Regression, and K-Nearest Neighbour were built and evaluated using various performance metrics. The findings indicate that the Random Forest model achieves the highest performance with an accuracy of 90.56% in detecting power system disturbances and has the potential in assisting operators in decision-making processes

Hardware Realization of an Innovative Disturbance Detection Algorithm for Control Strategy of Solid-State Transfer Switch

An accurate and fast Solid-State Transfer Switch (SSTS) has the potential to automate conventional utility network. Presently, It consumes most of the transfer time in detecting the condition for initiating the transfer process. Recent developments in the power semiconductor devices technology have reduced the switching time to the tune of nanoseconds. Hence, time consumed by the disturbance detection is significant in SSTS. IEEE Std. 1100-2005 and IEEE Std. 446-1995 requires fast load transfer for SSTS. Realization of an innovative machine learning technique is implemented on three AVR microcontrollers for each of three phases. The microcontroller executes segmentation of acquired signal and calculates derivative of each segment. From each pre-processed 10-sample segment, mean absolute deviation (MAD) and energy (E) features are extracted. On the basis of feature values, SVM classifier with the linear kernel detects disturbance. Logical OR gate generates transfer enable signal to initiate the transfer process. Results of case studies appreciate the ability of proposed hardware to detect common disturbances in power system under different operating conditions. The hardware provides a feasible solution for solid state transfer switch with required accuracy and speed.