Power System Network Research Articles

Dataset on Complex Power Systems: Design for Resilient Transmission Networks using a Generative Model

Abstract Interdependent critical infrastructures are prone to be a target of disruptions from either natural disasters or deliberate attacks that can cause a great magnitude of negative consequences on the system. To withstand and reduce the impacts of disruptive events, it is beneficial to consider them in the early stage of design to create a configuration that can maintain its performance as much as possible. However, due to the complexity of modern systems, it is difficult to optimize the large-scale system design with complicated constraints directly. To overcome this issue, it is advantageous to tackle the problem with a state-of-the-art data-driven approach. However, the problem arises due to the limited amount of data for power systems available to the public. Hence, synthesized power systems that resemble existing networks are collected and processed, which can be utilized for implementing machine-learning models. The power system networks are represented as graphs with features holding information on the nodes and edges that relate to electrical components. Using the generated dataset, this research implements a generative approach based on Wasserstein GAN to create intelligent designs. Afterwards, performance measures are employed to induce the generation of a population with more appealing characteristics. To demonstrate the practicality of the dataset on data-driven design, it was applied to train the generative model for the 57-bus system. The results have shown that the model could generate feasible designs of higher resilience.

Research on Automatic Identification of Power Big Data Anomalies Based on Improved Decision Tree

With the advancement of smart grid technology, the issue of power system network security has become increasingly critical. To fully utilize the power grid’s vast data resources and enhance the efficiency of anomaly detection, this paper proposes an improved decision tree (DT)-based automatic identification approach for anomalies in electric power big data. The method employs six-dimensional features extracted from the dimensions of volatility, trend, and variability to characterize the time series of power data. These features are integrated into a hybrid DT-SVM-LSTM framework, combining the strengths of DTs, support vector machines, and long short-term memory networks. Experimental results demonstrate that the proposed method achieves an accuracy of 96.8%, a precision of 95.3%, a recall of 94.8%, and an F1-score of 95.0%, outperforming several state-of-the-art methods cited in the literature. Moreover, the approach exhibits strong robustness to noise, maintaining high detection accuracy even under low signal-to-noise ratio conditions. These findings highlight the effectiveness of the method in efficiently detecting anomalies and addressing noise interference.

Passivity‐based event‐triggered frequency control in power system using dynamic pricing

AbstractThe integration of renewable energy resources in modern power systems promotes flexible demand response but poses challenges to power balance and frequency stability due to their intermittent generation. To address this problem, this study deals with the frequency control of power system in the presence of demand responses. The dynamic electricity pricing scheme enabling both demand response participants and suppliers to contribute to the frequency regulation via their own decision‐making process is proposed. The main concern in the controller design is the integration of physical and human systems on the same timescale, encompassing controllers based on frequency dynamics and dynamic pricing. Therefore, event‐triggered conditions are proposed to decrease the communication frequency while ensuring system stability, leveraging the passivity property of the system. Under the proposed event‐triggered conditions, the authors clearly demonstrate the asymptotic stability around the equilibrium point of the entire system. Furthermore, a numerical simulation using a four areas power network system is performed, confirming the effectiveness of the proposed control scheme and the stability of the system.

ელექტროსისტემის გადაცემის ქსელში უეცრად წარმოქმნილი რეაქტიული სიმძლავრის უბალანსობა

The paper discusses the issue of sudden imbalance of reactive power in transmission network. The article provides a formula based on which it is possible to estimate change of nodal voltages in transmission network during a sudden imbalance of reactive power. The mentioned problem is discussed on the example of transmission network of Georgian power system, where source of sudden imbalance of reactive power is emergency shutdown of shunt reactors. In the article there is calculated own and mutual impedances of nodes of Georgian transmission network and is evaluated change of nodal voltages during the sudden imbalance of reactive power.

A Comparative Study of the Prediction of Voltage Collapse in Power System Network

A Comparative Study of the Prediction of Voltage Collapse in Power System Network

Asymmetric LKF approach for stability analysis of multi-area load frequency control systems with time-varying delays

ABSTRACT The widespread use of communication networks in power systems to send and receive control signals causes time delays in the feedback loop. Improper handling of these delays might degrade controller performance or cause system instability. This work presents new stability criteria that are less conservative in analysing the influence of delays on the stable operation of PI controller-based multi-area power systems with uncertainties and disturbances. An asymmetric Lyapunov-Krasovskii functional technique is used to reduce conservatism by relaxing the condition that all matrices associated with the LKF formulation need not be symmetric or have positive definiteness. In contrast, the symmetric LKF requires all matrices to be symmetric. Two tight-bound integral inequalities are implemented to compute the quadratic integral terms included in the derivative of asymmetric LKF. The less conservative stability conditions are derived for multi-area LFC systems subjected to delays, parameter uncertainties, and disturbances by utilising asymmetric LKF. The superiority of the proposed stability criterion is determined both theoretically and through simulation. According to the analysis, the proposed stability criterion provides higher delay margins than existing techniques.

Stochastic assessment of voltage sags and techno-economic optimization

Power quality has become an essential concern for industries and domestic energy consumption with the development of sophisticated equipment like micro-electronic devices. When a power quality event happens, such as voltage sag, financial losses for industries increase tremendously. Therefore, voltage sag evaluation becomes crucial for predicting the number of events and severity of voltage sag events of the electrical network. Industries can determine the mitigation investment needed for optimal operation using the knowledge obtained through voltage sag analysis method. Thus, the first part of the study addressed the development of a stochastic assessment method for voltage sag at an interested bus in the power system network, which was an initial objective of the study. The second part of the research investigates dynamic voltage restorer (DVR) control techniques, as well as the necessary capacitance and energy storage dimensions. The studies also recommend a technique for economic evaluation that emphasizes the expense of voltage sag events as well as the cost of investment in DVR with and without voltage sag events. The size of DVR and economic assessment model established in these studies enables industries to rate the cost of voltage sag and the total investment in mitigation devices.

A modelling method for overvoltage and overload fault propagation in distribution cyber-physical systems

Abstract The introduction of communication networks in modern power systems also brings new security issues into traditional power grids. Compared with cascading failures in a single power network, the analysis of cascading failures in information-physical coupling networks is more complex. To analyze the complex cyber-physical coupling network cascading faults, this paper proposes a multi-layer distribution network cyber-physical system. Based on this model, the possible impact on the communication side nodes after overvoltage and overload faults occur on the physical side distribution network is analyzed, forming a cyber-physical-cyber cascading fault propagation analysis method.

Design of a microservices-based architecture for residential energy efficiency monitoring

With the significant advancement of electrical infrastructure in the context of smart buildings and smart homes, the need arises to overcome the limitations of the traditional energy efficiency control system based on service-oriented architecture (SOA). To address these challenges, this study proposes a distributed architecture based on microservices, with the main objective of improving the performance and stability of these systems. This proposal seeks to enable end users to effectively monitor and control their electrical devices while effectively integrating them into a wide network of power systems. The proposed architecture relies on a series of cloud services that enable better performance and control in energy efficiency management, highlighting key features of microservices such as fault tolerance, performance, and scalability. Using a structural methodology centered on pre-existing components and an iterative approach, a versatile and scalable architecture was designed that addresses current challenges in energy efficiency management. The results show a significant impact on key performance indicators such as demand response, energy savings, and power quality, highlighting the resilience and scalability of the proposed architecture. The conclusions highlight the importance of energy efficiency in reducing the environmental impact and costs associated with electric power, suggesting future improvements in data access and the implementation of advanced machine learning algorithms.

An approach for real-time implementation of cyber security in power system network

Abstract In this era of Web World-3, usage of the Internet of Things (IoT) in power system networks has attained scalable altitude. By using this technology, data can be transferred and monitored from the far end. Further, the suggested corrective actions are also executed. However, recently, many cyber-attacks have been observed on the power system in which activities such as injection of false data, unwanted switching operations, formation of sub-system layers, and false indication of failure of cyber-physical components (CpC) are experienced. In the worst case, it leads to invite cascade tripping of the power system. Utmost care is taken for the selection of CpC for the power system. However, it is observed that the cyber attackers mostly take entry into the network using the CpC of the power system. Cyber attackers perform false switching operations and false data injection. This article suggests a cyber security concept to minimize the false switching operations and false data injection in the power system network. A hardware model is prepared and the working scheme is implemented using IoT technology. The hardware result suggests that the un-authentic attempt/cyber-attack has been identified and the alarm is generated. It also does not permit the un-authentic person to access the real-time data. A wide range of applicability of the suggested scheme has been verified by hardware results. In addition to this, the power factor correction algorithm also works satisfactorily in the hardware along with the cyber security constraints. In order to prove wide range of applicability of the suggested scheme, additional features such as controlling of multiple switching devices and interlock between CB and earthing switch has been successfully implemented in developed hardware.

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

Design and implementation of a control system for multifunctional applications of a Battery Energy Storage System (BESS) in a power system network

This work proposes a design and implementation of a control system for the multifunctional applications of a Battery Energy Storage System in an electric network. Simulation results revealed that through the suggested control approach, a frequency support of 50.24 Hz for the 53-bus system during a load decrease contingency of 350MW was achieved. Without the control system, the frequency was 50 .38Hz. Such a high frequency if not addressed, may result in a loss of synchronization among interconnected synchronous machines which could result in a decrease in voltage stability of the studied network. Besides, a reduction of about 2.05 MW in the active power losses was accomplished and a reactive power support of 3.63Mvar was realised. Thus, through the proposed strategy, Battery energy storage system has been enabled for frequency regulation, power loss minimization and voltage deviation mitigation resulting in an overall enhancement of the power quality of the electric power delivered in the studied networks.

Optimal sizing of battery energy storage system (BESS) for multiple applications using regression analysis and deep sleep optimizer algorithm

The multifunctional applications of battery energy storage system in a power system network will reduce the significant slack time of use as evident in many single-based applications. In order to deploy BESS for multiple applications, it is of utmost importance that the optimal size for the desired multiple functions, firstly be determined. This work proposes a novel methodology for the optimal sizing of battery energy storage system for frequency support, power loss minimization and voltage deviation mitigations. The suggested sizing methodology takes into account the level of penetration of the renewable energy sources in the power network. Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size. The robustness of the proposed method was tested by using IEEE modified 39-bus system. Simulation results show that with the BESS optimal size integrated into the network, voltage deviations were mitigated by about 20 % and power losses were reduced from 65.3 MW to 59.68 MW. Also, the system frequency nadir during the outage of the largest single generating, was sustained at 59.60 Hz whereas, without BESS it was 59.15 Hz. This is critical because such a frequency decline may activate the underfrequency load shedding relays.

Fiber‐Integrated Diamond Quantum Sensor for High‐Voltage Current Measurements

AbstractIn power network systems, there is an urgent demand for highly accurate and miniaturized sensors, owing to their high safety level and limited installation space. Current sensors in high‐voltage grids are required to accommodate harsh environments and provide accurate measurements of several kiloamperes. Thus, this study proposed an integrated quantum diamond sensor to facilitate high‐accuracy, large‐dynamic‐range current measurements. The design incorporated optical fiber and directional microwave (MW) antennas to drive the diamond sensor, which significantly reduced the size and power consumption on the high‐voltage side. Remote‐control and demodulation systems are installed more than 10 m away from the low‐voltage side. The proposed approach achieved zero power consumption on the high‐voltage side and ensured efficient signal transmission. A passive diamond probe manufactured using microfabrication processes facilitated miniaturization and practical deployment. Through parameter optimization, a magnetic detection sensitivity of 4.86 nT·Hz−1/2 is achieved at a safe distance of 11 m, which can be further optimized to 0.77 nT·Hz−1/2 with enhanced MW power. This sensor achieved a current measurement error of ±0.4% in the 1000 A measurement range. Thus, this study provides a new solution for the application of diamond quantum sensors in power systems.

Economic Load Dispatch on a 132 kV Line with Service Potential Transformer Substations: A Case Study of Juja-Rabai Line.

Power outages have created significant challenges for power system networks, particularly in developing countries where the electricity demand continues to rise without a corresponding increase in power generation or the expansion of transmission and distribution networks. In Kenya, while there is a well-established transmission line network, the distribution infrastructure remains inadequate for supplying electricity to end consumers. This paper examines the economic load dispatch (ELD) of power system networks utilizing Service Potential Transformer (SPT) substations to provide electricity to villages located near high voltage (HV) lines. The ELD analysis was conducted to identify the optimal economic power output from the Kipevu, Rabai, and Thika thermal power plants, addressing the demand for both conventional and non-conventional substations. A gradient method was employed to calculate the ELD for these three generating units, and the results were validated using the PowerWorld simulator. Findings indicated that the three generators supplied 20 MW, 37.5 MW, and 12.5 MW, respectively. The results obtained from the gradient method are consistent with those obtained from PowerWorld software. Additionally, this study projected an annual fuel cost savings of USD 17,695.20 when ELD was implemented, compared to a scenario of equal load distribution among generating units. Over a ten-year period, these savings would be sufficient to establish a conventional distribution substation to meet the power demands of villages located further away from high voltage lines.

Physical model learning based false data injection attack on power system state estimation

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

VOLTAGE FLICKER MITIGATION USING GTOBASED STATCOM TO IMPROVE POWER QUALITY OF A MULTI MACHINE SYSTEM

Transmission networks of modern power systems are becoming increasingly stressed because of growing demand and restrictions on building new lines. One of the consequences of such a stressed system is the threat of losing stability following a disturbance. Flexible ac transmission system (FACTS) devices are found to be very effective in a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. FACTS such as Static Synchronous Compensator (STATCOM), employ the latest technology of power electronic switching devices in electric power transmission systems to control voltage and power flow. The STATCOM adjusts voltage at its terminal by managing the amount of reactive power injected into or absorbed from the power supply. When the system voltage is low, STATCOM creates reactive power; when the system voltage is high, STATCOM absorbs reactive power. In this paper STATCOM controllers are designed for improving transient stability of multi machine systems. Proposed controllers are implemented under MATLAB/SIMULNK environment. Results of PI based controllers installed with multi machine system is found to be better on comparison with conventional system.

Review on the Evaluation and Improvement Measures of the Carrying Capacity of Distributed Power Supply and Electric Vehicles Connected to the Grid

With the increasing number of distributed power sources such as photovoltaic power and wind power and electric vehicles connected to the grid, the structure and operation state of the traditional distribution network have undergone great changes. Therefore, through the establishment of a distributed power grid-connected evaluation system, it has become an important research topic to evaluate the access of distributed power and the carrying capacity of electric vehicles in the distribution network of new power systems. Firstly, the situation of distributed power supply and electric vehicles and the impact of grid connection are introduced. Secondly, the traditional distribution network carrying capacity evaluation method is studied and introduced. Then, the distribution network carrying capacity evaluation considering uncertainty is reviewed and investigated. Finally, in view of the lack of existing research, further research is needed, aiming to provide a reference for the realization of distributed power supply and grid-connected EV carrying capacity evaluation and a scientific basis for the future operation planning of distributed power supply and EV integration into the distribution network.

Identifying interaction boundary of inverter-based generation in assessing system strength of power grids using relative electrical distance concept

The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.

PENERAPAN MANAJEMEN ARSIP ELEKTRONIK DALAM OPTIMALISASI PENGELOLAAN ARSIP DI PT KREATIVITAS GANESHA SEJAHTERA

In the digital era, archives management is evolving with challenges and opportunities. This research evaluates the use of electronic archives for process optimization. Traditional methods often consume time and are vulnerable to physical risks. This study focuses on the application of electronics for archiving. Primary data was obtained from interviews with managers at PT Kreativitas Ganesha Sejahtera. Findings include: (i) Digital archive storage through softcopy and hardcopy (ii) Grouping based on institution and time (iii) Classification of archives based on subject, form, and function (iv) Use of ERMS and EDMS systems (v) Important information technology infrastructure such as power supply, network, and backup systems.