Devices In Power Systems Research Articles

Maintenance of RZA equipment according to technical condition

Subject of research: methods of maintenance and adjustment of relay protection and automation devices (RPA) in electrical power systems. Purpose of research: analysis of existing maintenance and adjustment methods, development of proposals for their optimization to increase the efficiency and reliability of electrical power systems. Methods of research: the research used methods of analysis of regulatory documents, survey of specialists and analysis of publications. Main results of research: identification of a lack of standardization of procedures, problems with personnel qualifications and the discrepancy of some devices with the declared characteristics. The findings highlight the need to develop unified approaches to maintenance and commissioning, as well as improve the professional training of specialists. The results of the study can be used to improve the practices of maintenance and adjustment of relay protection and automation systems, which helps to increase the reliability of electric power systems.

Constructing superparaelectric state for NaNbO3-based ceramics electrostatic supercapacitors

A crucial prerequisite for developing high-performance energy storage devices for high pulsed power systems is the urgent necessity to acquire dielectric ceramics with high recoverable energy density (Wrec) and ultrahigh energy efficiency (η), particularly through an environmentally friendly lead-free approach. Here, superparaelectric state with ultra-small nanodomains in NaNbO3-based ceramics is successfully induced by implementing a superparaelectric modulation strategy. This innovation not only reduces the curvature value of the polarization–electric field (P-E) curve, but also produces negligible remanent polarization (Pr) and a linear-like P-E curve, obtaining outstanding efficiency (η) of 92.2 % and high energy storage density (Wrec) of 7.05 J/cm3. This achievement not only opens new avenues for enhancing the energy storage performance of dielectric ceramics but also heralds extensive applications of this material in high-pulse power electronic devices.

Development of a fuzzy logic-based model for assessing the reliability of relay protection systems

This article presents a reliability assessment model for relay protection devices using fuzzy logic. It introduces an algorithm for simulating these devices, employing the Mamdani model with an "if-then" rule base. Inputs include the percentage of correct operation and the frequency of correct and incorrect operations. Implemented in Matlab with 21 rules, the fuzzy logic model uses triangular membership functions for input and output variables, with defuzzification via the center of gravity method. The model was tested using statistical data from SIEMENS SIPROTEC terminals, specifically distance protection and differential protection devices for transformers and autotransformers. The assessment considers operation frequencies for 1 to 3 devices, combining statistical and simulation data for a comprehensive analysis. Results show that including operation frequencies improves evaluation accuracy. The proposed model not only assesses current reliability but also predicts future behavior, aiding in the planning and optimization of relay protection systems. This model is valuable for professionals in generating companies, grid organizations, and operational dispatch control entities, helping them analyze relay protection performance and develop strategies to ensure reliable operation. The research focuses on the reliability of relay protection devices in power systems, addressing the need for a more accurate and comprehensive evaluation method. Traditional methods may not fully account for the complexities in modern microprocessor-based protections. This study aims to enhance reliability assessments through a model that integrates statistical data and simulation techniques, ultimately supporting better planning and optimization of relay protection systems

Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

Improving cyber-physical-power system stability through hardware-in-loop co-simulation platform for real-time cyber attack analysis

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyber-physical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed.

Physics-Informed Neural Network-Based VSC Back-to-Back HVDC Impedance Model and Grid Stability Estimation

With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant impedance measurement data from power electronic devices. In this paper, we propose a method for constructing impedance models of equipment with fewer impedance measurement data in voltage-source converter (VSC) back-to-back high-voltage direct current (HVDC) systems using physics-informed neural networks. Furthermore, given the power system states, we present a neural network approach to estimate grid stability at different operating points. Validation via PSCAD/EMTDC simulations and a PyTorch neural network confirmed the adequacy of these models.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

Study of operating modes of the AION tanker electrical power system

THE PURPOSE. Consider the issue of automation of modern sea vessels with a high degree of electrification. The purpose of the study is to analyze the electrical power system of the AION tanker to determine the possible occurrence of abnormal phenomena during parallel operation of generator sets and methods for eliminating them. METHODS. The article analyzes the electrical power system and automatic control devices of the AION tanker. Particular attention is paid to the study of systems that ensure the production and distribution of electrical energy between consumers. The widespread use of modern computer-based automatic control systems in ship power plants has been noted. The extensive possibilities for controlling a ship's electrical power plant in various daily or emergency situations are shown. The software and hardware of the ship's Power Management System allows you to reduce the consumption of fuel, oil and other important resources by automatically regulating the load on diesel generators, timing the supply of fuel to the cylinders, and controlling exhaust and starting valves. RESULTS. Experimental oscillograms of studies of the parallel operation of diesel generator sets were obtained, in which power exchange oscillations were recorded. The amplitude of power exchange oscillations reaches 40 % of the steady-state value, and their period is 600 - 800 ms, depending on the operating mode of the ship's electrical power system. During thecollection of these oscillograms, the ship's load is turned on and no starts or stops of any powerful consumers are made, that is, a quasi-steady process takes place. CONCLUSION. It is proposed to introduce a function for eliminating power exchange oscillations into the ship power plant control system. To implement it, it is necessary to add a block that receives information from all parallel operating diesel generator sets and adaptively adjusts the settings of the frequency regulators.

Intelligent Monitoring of Protection Devices in Power system with Enhanced Faster R-CNN

Relay protection devices are necessary to guard the power system safety and stability. With the significant number of substations and relay protection devices, the maintenance workload has become difficult due to limited manpower, posing hidden dangers to the reliable operation of protection devices. In view of this, this article proposes an enhanced Faster R-CNN algorithm to diagnose the relay protection devices based on image monitoring. The proposed algorithm uses RestNet50 as the main tool to recognize features from input images to generate feature maps. Meanwhile, to improve the image detection accuracy, the Non-Maximum Sup-pression (NMS) algorithm in regional suggestion network (RPN) is optimized from solid thresh-old to soft threshold. By combining the images captured by the surveillance camera, intelligent inspections are conducted on the status of the pressing plate, fiber bending, external object retention, and other items in the relay protection device. A great number of samples are used to train networks for different tasks. The results indicate that the enhanced Faster R-CNN has a higher image recognition rate, and the recognition accuracy of each inspection item for the protection device is higher than 95%.

Enhancement of ATC with FACTS Devices in Deregulated Power System Considering Various Contingency and Benefit Margins

The global electric power systems are under stressed condition because of increased per capita power consumption and ever-growing load demand. It is difficult to modify existing infrastructure of transmission systems to meet increasing load demand. The volume of electric power transmitted depends on the real and reactive power supply and the availability of margin in transmission system. Available Transfer Capability is an estimation of additional power transfer capability of the existing transmission system for a further market activity over and above the already committed power transactions. Factors such as weather, line and generator outages must be taken into account when computing the rigorous value of ATC. This paper focuses on enhancing the ATC limit of a system by employing STATCOM and Whale Optimization Algorithm for tuning system variables and to determine optimal size and location. An investigation has been carried out to understand the impacts of ATC by implementing line and generator outages in the test system. Additionally, an investigation has been done to realise the various benefit margins like ETC, TRM and CBM. The competence of the proposed methodology is validated by conducting different case studies on IEEE-30 bus test system using MATLAB for various bilateral and multilateral transactions.

Broad Learning System Using Rectified Adaptive Moment Estimation for Harmonic Detection and Analysis

The extensive application of nonlinear components and power electronic devices in power systems has led to increasingly deteriorating harmonic pollution. The rapid and high-precision harmonic detection and analysis technology will set the prerequisite for mitigating the harmonic crisis. This paper presents a robust, stable and real-time approach based on broad learning system (BLS) for detecting and analyzing the amplitudes of fundamental and harmonic components, which only requires half or even quarter cycle input. Additionally, a fast-converging adaptive parameter learning strategy using rectified adaptive moment estimation (RAdam) for BLS is proposed. To obtain the actual distorted signals, the signal acquisition system is built on the basis of the designed multi-pulse rectifier power supply (6/18 pulse). With the fundamental frequency deviation, interharmonics and noise, etc., the simulated signals synthesized by MATLAB tool and the actual signals acquired from the built system are attained to verify the performance of methods. The proposed approach differentiates from other existing methods not only due to its superior precision and robustness in the presence of multitudinous interference factors but also its real-time performance because of low average computing time.

Comparison of photoconductive semiconductor switch parameters with selected switch devices in power systems

Comparison of photoconductive semiconductor switch parameters with selected switch devices in power systems

Power flow analysis and control of distributed FACTS devices in power system

Power flow analysis and control of distributed FACTS devices in power system

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

A Framework for Analyzing System Loadability With Multiple VSCs Using a Hybrid Model

Due to the increasing number of power electronics-based devices in modern power systems, there are concerns about the impact of higher-frequency interactions on system stability. The typically-used algebraic representation of networks fails to capture these newly emergent issues, but dynamically modelling the entire network with differential equations will lead to a prohibitively long simulation time. This paper proposes a computationally-efficient analysis framework to determine the system loadability in power electronics-rich, large networks with the consideration of multiple types of stabilities. The framework developed in this paper identifies the critical network elements based on the eigenvalue sensitivity in order to exploit a hybrid modelling approach. Within the hybrid model, only the critical network portion is modelled dynamically with the rest of the network represented by algebraic equations. Bifurcation theory is used to simultaneously analyze both small-disturbance rotor angle and small-disturbance voltage stability. The results obtained show that the high-frequency interactions are accurately captured with the proposed framework. Additionally, applying this methodology results in a small dimension for the system matrix and a reduction in the computational burden. Two test networks, a three-bus system and the IEEE 39-bus system, are used to illustrate and verify the analysis framework.

Mesoscopic conductivity and trap distribution characteristics in the interfacial region of polypropylene/MgO nanocomposites with high energy storage density

Polymer dielectric materials are crucial for storage devices in power and electronic systems, as well as electric vehicles, due to their high energy density and fast discharge rates. Polymer nanocomposites (PNCs) possess superb electrical and energy storage properties, making them highly promising as next-generation thin-film dielectrics for energy storage. The excellent performance of PNCs derive from the interfacial region introduced by nano-doping. However, the current measurement equipment and technology make it difficult to accurately obtain the mesoscopic scale distribution characteristics of the conductivity of the interfacial region, and are also impossible to clarify the formation mechanism of deep traps in the interfacial region. We employed high-throughput simulation and experiment to investigate the mesoscopic conductivity and trap distribution properties of the interfacial region in PNCs. Furthermore, we developed a three-dimensional mesoscopic conductivity distribution simulation model of PNCs containing polymer matrix, nanoparticles, and interfacial region. The interfacial region size and conductivity distribution of the PNCs were determined by comparing with the experimental results, and the mesoscopic distribution characteristics of the interfacial traps in PNCs were obtained. It is proved that the nanofillers form a condensed structure with high order and compactness by restraining the movement of molecular chains, which deepens the trap energy in the interfacial region and restricts the free volume expansion. This results in the formation of interfacial regions with low conductivity, contributing to enhancing energy storage performance.

Intelligent controller based WECS fed unified power flow conditioner for PQ enhancement

<p>The dominance of non-linear loads, sustainable energy sources and power electronic devices in modern power system has in turn compromised the system stability by introducing numerous power quality (PQ) issues. A wind energy conversion system (WECS) fed unified power flow conditioner (UPFC) architecture is suggested in this work since maintaining optimal PQ is important for both the supply authorities and customers. The proposed approach supports both clean energy generation and PQ enhancement. The UPFC aids with accomplishing control over reactive power flow, active power flow, phase angle, impedance and voltage. The reference current for the UPFC is generated using DQ theory. Moreover, a shunt and series compensators of an UPFC are controlled using cascaded fuzzy logic controller (CFLC). The stability of the DC voltage obtained from doubly fed induction generator (DFIG) based WECS is also controlled using a CFLC. The simulation model for the presented WECS fed UPFC is developed in MATLAB and its performance is evaluated.</p>

Precise emergency load shedding approach for distributed network considering response time requirements

Emergency load shedding (ELS) is a vital measure for power systems to manage extreme events, ensuring the safety, stability, and economic operation of the grid. The integration of distributed energy resources and controllable devices in modern power systems has bolstered grid flexibility. Consequently, developing precise load shedding strategies to balance economic and security goals has emerged as a prominent subject in power system optimization. However, existing methods exhibit inadequacies, including overlooking practical operability, privacy concerns, and a lack of adaptability to response time requirements. To address these gaps, this paper introduces a precise ELS approach for distributed networks with a focus on response time needs. Contributions encompass designing load shedding processes for various response times, integrating demand response, and partitioning networks for optimized load shedding. Through validation using standard test cases, the proposed approach effectively utilizes response time and demand-side resources for precise ELS control in distribution networks. It accommodates different scenarios, offering a robust solution for rapid and accurate load shedding during emergencies.

An adaptive data compression technique based on optimal thresholding using multi-objective PSO algorithm for power system data

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07.

Lazy deep Q networks for unified rotor angle stability framework with unified time-scale of power systems with mass distributed energy storage

The addition of increasing numbers of new energy sources can lead to challenges to the security and stability of power systems. The combined rotor angle stability (RAS) framework of power systems with multiple time-scales could lead to uncoordinated stability control commands for numerous stability control devices of power systems. To mitigate this deficiency, this study proposes a unified RAS framework to replace the RAS with three time-scales, which consist of small-disturbance, large-disturbance, and long-term RASs. This study proposes lazy deep Q networks for the unified RAS framework of power systems. The lazy deep Q networks approach, which is based on the Markov decision process, contains lazy learning, deep Q networks, a state-buffer, a selector operation, and a limiter operation. The unified stability framework and the conventional combined stability framework of power systems are compared under four cases, i.e., the power system with four generators, the power system based on IEEE 39-bus system, the European high voltage 89-bus system, and the European high voltage 1354-bus system. These four case studies verify that the unified stability framework based on the lazy deep Q networks approach is more stable than the conventional combined stability framework based on the combination of optimization algorithm of long-term dynamic stability, control method of middle-term transient stability, and control method of short-term static stability. The error indices for the proposed lazy deep Q networks algorithm are as follows: the AAE is at least 4.7% lower than the other compared algorithms; the IAE is at least 4.8% lower than the other compared algorithms; the ISE is at least 3.9% lower than the other compared algorithms; the ITAE is 2.3% lower than the other compared algorithms.