Power System Network Research Articles

Critical Infrastructures at Risk Addressing Information Security in Power Systems

According to the power system network security risks and problems, this paper presents the work of information security and the general principles of information security from a technical and safety management put forward the idea of addressing security issues and methods of analysis of local area network security management related to issues, and according to their own experience of a relatively effective way of thinking.

Finite frequency H ∞ control for singular systems with application to a 2-machine 6-bus power network

This paper is concerned with the H ∞ control problem for singular systems over finite frequency ranges. The disturbance is assumed to be within low-/middle-/high-frequency ranges. Both the state feedback and static output feedback controllers are designed. First, by combining the Lyapunov function method and the generalized Kalman–Yakubovich–Popov lemma, a new finite frequency bounded real lemma (BRL) is proposed, which ensures the resulting closed-loop system to be regular, impulse free and stable with given finite frequency H ∞ performance bound. It is shown by theoretical analysis and numerical simulation that the proposed BRL has less conservatism than the existing ones in the literature. With the aid of the Finsler's Lemma, two improved finite frequency BRLs are further presented for the design of controllers. Then, by utilizing convexification techniques, sufficient conditions for solving the desired state feedback and static output feedback gains are presented in terms of linear matrix inequalities, respectively. Finally, the applicability and effectiveness of the proposed control methods are verified by a 6-bus power network system.

The Effect Pso/Abc, Abc/Bfo, Pso/Bfo and Cs/Bfo Hybrid Techniques on Power System Buses for Voltage Stability Margin Improvement

This paper presents and investigates new approaches inspired by nature to determine the optimal placement of UPFC for enhancing voltage stability margin of power system network. Voltage collapse phenomenon has remained an operational problem yet to be solved by power system engineers. To improve the voltage stability of the power system network, new optimisation techniques viz: PSO/ABC, ABC/BFO, PSO/BFO AND CS/BFO hybrids are investigated for their performance on power system buses for voltage stability improvement. The performance of the proposed hybrid methods are examined on the practical Nigerian 56 - bus network for voltage stability improvement. The simulation results has shown that ABC/BFO technique is better than BFO/PSO by 3.44 in improving voltage stability of margin of power system, gives additional improvement of 4.07, totalling 7.51% and lastly BFO/CS technique improved voltage stability margin of power system further by 5% totalling 12.51%. Also this research revealed that the smaller the gamma value the better the UPFC contribution to voltage stability enhancement. BFO/CS technique yielded the smallest gamma values of 0. 74532 and 0.830913 in both Nigerian practical network and IEEE – 14 bus network as shown in the analysis.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Distributed-Based Rapid Assessment of Carbon Emission Factors for Power Grid

As the size of the grid has gradually increased, there is a decentralization among power systems. Traditional carbon flow tracking models based on the principle of proportional sharing, combined with power system networks, are able to analyse the carbon emissions and the direction of carbon flows at individual nodes. With a multi-node power system network, the data cannot be collected accurately, and the method is calculated over a long period of time, making it hard to address the demands of online analysis. To address the above situation, a rapid assessment of carbon emission factors was presented for power grid for power system nodes based on distributed approach. Unlike solving a system of equations, the carbon emissions of each node in this method are calculated sequentially according to the carbon flow direction of the node. In addition, each node only collects information from nodes connected to itself. In this way the computational efficiency is both improved and the stability of the computation is ensured. Simulation results validate the effectiveness of the method. The experiments also show the computational advantages and evaluation accuracy of the method, which can be used for the carbon flow tracking analysis of power system nodes.

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

The Impact of Solar Distributed Generation on Distribution Networks: Case Study of Chipata Distribution Network

The installation of solar PV systems in distribution networks is slowly increasing. The study focused on identifying the challenges and benefits of installing Distributed Solar Generation in the Chipata 33kV and 11kV distribution Networks. Chipata Town is in the Eastern Part of Zambia, and it has a peak demand of about 23.3MW. The 2023 version Power Factory Dig Silent Software was used to model the Chipata 33kV and 11kV distribution network and different Solar penetration levels were used to understand the impact of Distributed Solar. Chipata Distribution Network has four major stations operating at 33kV and 11kV voltages and four main distribution lines. The main objective of the study was to determine the impact of distributed solar PV in the Chipata Distribution Network. One of the specific objectives was to determine the hosting capacity of the Chipata Distribution Network. The hosting capacity gives the maximum amount of PV power integrated in a power system network without violating the set operation limits. The study analysed the impact of solar Distributed Generation on network losses, fault levels, voltage stability and line loading. The study revealed that an increase in penetration level resulted in a general increase in the Bus Bar voltages on both 11kV and 33kV voltage levels. Different Penetration levels of solar ranging from 6.66MW to 132MW were studied. Simulations revealed that the hosting capacity for Chipata Distribution Network is 96MW (412% of Solar PV Penetration Level). The voltages in the distribution network at hosting capacity were ranged from 0.93p.u to 1.02p.u. Short Circuit analysis simulations showed negligible impact on the fault level after integration of solar in the distribution network. Some circuits showed both increased and reduced power losses with increased solar penetration. The obtained results indicated that increased installation of distributed Solar PV systems in the Distribution Network impacts voltage stability, line losses and line loading.

Long-term Load Forecasting for Optimal Power System Planning and Decision-Making

Forecasting the future load growth of an area based on its load demand is often a proactive measure to ensure a steady electricity power supply to that area. The study focused on long-term load forecasting for power system planning, specifically examining the electric load demand from consumers on distribution transformers within Port Harcourt City, located in Rivers State, Nigeria. The study encompassed a comprehensive review of both statistical and artificial intelligence-based approaches. Historical load data for distribution transformer readings spanning 2008 to 2017 were acquired from the Port Harcourt Electricity Distribution Company (PHEDC) and subjected to analysis using the curve-fitting technique. For the period between 2015 and 2030, a yearly load forecast simulation was conducted using the Fourier Series model, implemented with MATLAB software. This simulation aimed to provide insights into future load demand, facilitating careful and informed decision-making in the investment, operation, and maintenance of power system equipment. The effectiveness of the forecasting investigation was assessed using the Root Mean Square Error (RMSE), confirming the efficiency, reliability, and validity of the employed model. The study's forecasted results are presented as a valuable guide and practical tool for policymakers and the utility company (PHEDC) to enhance proper planning and decision-making processes. Considering the observed trend in the results, it is suggested that installing additional transformer units in the region would be necessary to alleviate the loads on existing overloaded transformer units within the power system network.

Structural Deformation and Displacement of a Disc Winding Due to Standard Switching Impulse Voltage via Finite Element Method

Switching operations in a power system network can lead to transient overvoltage in the high voltage (HV) winding of distribution transformers that causes high-stress build-up. This paper presents the relationship between electromagnetic force due to a standard switching impulse (SSI) and mechanical deformation/displacement behaviours for a disc-type transformer. The analysis was carried out based on a three-dimensional (3D) modelling of a continuous HV disc winding configuration whereby it is subjected to the switching transient voltage and force excitations through the finite element method (FEM). The electric transient solver analysed the static and dynamic aspects of the electromagnetic forces associated with the variation of forces versus time. The transient structural solver evaluated the structural behaviours of the disc winding related to the axial height and radial width of the winding under electromagnetic forces. It is found that the positively dominant axial force generated in the winding with a magnitude of 8.7 N causes the top and bottom layers of disc winding to tilt and displace. In addition, the positive average radial force of 1.4 N causes the circumference of the winding to experience hoop tension and outwardly stretch.

Design and Analysis of a Low Voltage Simulink Model (LVSM) of IEEE 57 Bus

The present power system is gaining momentum towards designing equivalent circuit models. The Ward and REI methods involve the admittance reduction method as well as being merged with the EMS model to derive boundary parameters, but these methods are limited and valid for a predefined condition. Therefore, it is required to design an equivalent circuit that adopts the real power system for analysis. In this paper, a new method is proposed to design a scaled-down power system model without changing the impedance of components. In this regard, a Low Voltage Simulink Model (LVSM) of the IEEE 57 bus network was designed in MATLAB/ Simulink so that it could be useful for laboratory model design purposes. The main objectives of this paper are to propose a mathematical procedure to scale down the network parameters and design a 3-phase LVSM of an IEEE 57 bus power system network within the Simulink platform. The performance of LVSM was analyzed with no-load, balanced load, and unbalanced load models. These simulation studies were validated and compared with the theoretical results to prove that the proposed LVSM modeling has good mathematical accuracy, robustness, and validity for practical model implementation.

Application of SSSC for Voltage Stability Improvement in the Nigerian 330 kV Transmission System using Particle Swarm Optimization Technique

Load imbalance has always been a major cause of voltage instability of the power system network and increase in voltage instability affects the power system reliability. In this paper, static synchronous series capacitor (SSSC) FACTS device was implemented to improve the voltage stability in the Nigerian 330 kV transmission network using particle swarm optimization (PSO) technique. Modelled power system network in the power system analysis toolbox (PSAT) is simulated to determine the existing power flow analysis and the level of instability in the power system generation plants situated in the Northern region. The outcome of the power flow is thereafter, optimized with PSO by generating the relationship with a high prediction accuracy between voltage at each location and an inter-linked transmission line distance of the 330kV. The model was generated with least square method and the high prediction accuracy determination was carried out with R-square value of the polynomial order. The best model was optimized and the entire computations were carried out in MatLab resulting in the determination of the optimal location which was between Jebba transmission and Shiroro generation stations. SSSC FACTS controller was implemented on the optimized transmission line and the simulation results showed that the introduction of SSSC improved the voltage stability of the power system network.

Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters

Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.

Robust Tuning of Optimized Tuned Controller for Wind Power System Network to Improve Power Quality

Robust Tuning of Optimized Tuned Controller for Wind Power System Network to Improve Power Quality

Machine learning for power outage prediction during hurricanes: An extensive review

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN.

Optimal location of FACTS devices with EVCS in power system network using PSO

The appeal of Electric Vehicles (EVs) is steadily growing, driven by the numerous advantages they offer to both owners and the environment. However, this surge in EV adoption places an increasing demand on the power system network for electric charging. The consequential strain on the power system may lead to instability and potential contingencies within the network. To mitigate these challenges, this paper recommends the installation of Flexible AC Transmission System (FACTS) devices. Specifically, two types of FACTS devices, Static Var Compensator (SVC) and Thyristor-Controlled Series Capacitor (TCSC), are employed to enhance the stability and loading conditions of the power system network. The study focuses on an IEEE 39 bus system network chosen for the installation of Electric Vehicle Charging Stations (EVCS). The objective is to assess the maximum loading factor of each bus within the network. This paper introduces a methodology for determining the optimal locations for both EVCS and FACTS devices. Notably, a Particle Swarm Optimization (PSO) algorithm is employed to optimize the size of FACTS devices and associated installation costs, ensuring an efficient and cost-effective enhancement to the power system's stability and loading conditions.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Opportunity Assessment of Virtual Power Plant Implementation for Sustainable Renewable Energy Development in Indonesia Power System Network

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.

Reliability Assessment of Integrated Power and Road System for Decarbonizing Heavy-Duty Vehicles

With the continual expansion of urban road networks and global commitments to net zero, electric vehicles (EVs) have been considered to be the most viable solution to decarbonize the transportation sector. In recent years, the electric road system (ERS) has been introduced and piloted in a few countries and regions to decarbonize heavy-duty vehicles. However, little research has been carried out on its reliability. This paper fills the gap and investigates the reliability of electric truck power supply systems for electric road (ETPSS–ER), which considers both the power system and truck traffic networks. First, a brief introduction of electric roads illustrates the working principle of EV charging on roads. Then, an optimized electric truck (ET) travel pattern model is built, based on which the corresponding ET charging load demand, including both static charging and dynamic charging, is conducted. Then, based on the new ET travel pattern model, a daily travel-pattern-driven Monte Carlo simulation-based reliability assessment method for ETPSS–ER system is presented. Case studies based on the IEEE RBTS system shows that ETs driving on ERS systems can meet the daily travel demands. The case studies also examine the impacts of increasing number of ETs, extra wind power, and battery energy storage systems (BESS) on the reliability of ERS power systems.