Multivariate Analysis on System Reliability and Security

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

The constant failure in cyber security in power systems and substation automation that has crippled business activities that mostly depend on power to run their routine activities are caused by Lack of Adequate Security Measures, Human Error, Outdated Software and Hardware, Insufficient Training and Awareness, Insider Threats, Weak Access Control Mechanisms, Inadequate Network Segmentation, Failure to Patch Vulnerabilities, Complexity of the System, Third-Party Vendor Risks and Denial of Service (DoS) Vulnerabilities. This is surmounted by introducing improved cyber security in power systems and substation automation using intelligent based system. To achieve this, it is done in this procedure characterizing and establishing the causes of failure in cyber security in power systems and substation automation, designing a SIMULINK model for cyber security in power systems and substation automation, designing a rule base that will reduce the causes of failure in cyber security in power systems and substation automation,training ANN in the rule base for effective reduction of causes of failure in cyber security in power systems and substation automation, developing an algorithm for the process, designing a SIMULINK model for improving cyber security in power systems and substation automation using intelligent based system and validating and justifying the percentage of improvement in the reduction of causes of failure in cyber security in power systems and substation automation. The results obtained were the conventional Lack of Adequate Security Measures that cause failure in improving cyber security in power systems and substation automation was 25%. On the other hand, when an intelligent based system was incorporated into the system, it drastically reduce the cause failure in improving cyber security in power systems and substation automation to 21.53%, the conventional outdated software and hardware that cause failure in improving cyber security in power systems and substation automation was 15%. Meanwhile, when an intelligent based system was inculcated in the system, it became reduced to 12.92% thereby enhancing the performance of cyber security in power systems and substation automation by 2.08%, the conventional Weak Access Control Mechanisms that cause failure in improving cyber security in power systems and substation automation was 8%. However, when an intelligent based system was integrated in the system, it was reduced to 6.9% and the conventional Denial of Service (DoS) Vulnerabilities that cause failure in improving cyber security in power systems and substation automation was 2% while when an intelligent based system was incorporated in the system, it automatically reduced to 1.7%. Finally, with these results obtained, It vehemently shows that the percentage improvement in cyber security in power systems and substation automation was 0.3%.

The escalating frequency and intensity of wildfires, driven by extreme climate events and anthropogenic activities, pose severe threats to the security and reliability of electrical power systems. Wildfires in proximity to high-voltage transmission lines can lead to a critical reduction in air insulation strength, caused by elevated temperatures, smoke particulate matter, and highly conductive flames. These conditions often precipitate insulation breakdown, line tripping, and potentially widespread power outages. This paper proposes an integrated physical-Bayesian modeling framework for assessing the susceptibility of transmission lines to wildfire-induced tripping. The framework combines a Bayesian network model, which estimates wildfire ignition probability by incorporating geographic, meteorological, fuel, and anthropogenic variables, with a physics-based model that evaluates the risk of insulation failure under wildfire conditions, accounting for both flame bridging and smoke effects. Key influencing features are identified through the Relief algorithm for feature selection. Applied to the Guangdong Power Grid in China, the model identifies distance to settlements, vegetation type, and monthly rainfall as the dominant factors affecting ignition probability. Spatial analysis reveals strong agreement between high-risk zones and historical fire events, and predicted tripping risks are consistent with actual outage records. The approach supports differentiated wildfire prevention strategies and enhances grid resilience, offering a scientifically robust and practical tool for proactive risk management in power systems exposed to wildfire hazards.

The reliability of power system will obviously be influenced by wind farms connected because of the volatility of wind power. With the increase of wind power penetration, it's necessary to research the influence of wind power integration on the reliability level of system and how much reserve capacity should we increase in order to stabilize the influence of wind power on power system reliability. In this paper a reliability evaluation model is built by Monte Carlo method and also a reserve optimization model, which can be used to analyze changes of system reliability in conditions of different grid-connected wind farms, and to find out the optimum of system reserve to achieve unifying the maximum utilization of new energy and meanwhile high reliability of power system.

The increase of extremely hot days globally has led to more frequent wildfires and frequent power outages directly and indirectly caused by the wildfires. This has sought an urgency to investigate the power system security issues associated with wildfires and the implementation of risk mitigation strategies. This article provides a comprehensive review of power system security in relation to wildfires, exploring methods to enhance the system security. The article starts with the multi-scale aspect of power system security and resiliency associated with wildfires. Additionally, it investigates the direct and indirect impacts of wildfires on renewable energy generation and the overall security of the system. Furthermore, the emerging technologies to enhance the system security against wildfires are discussed. To conclude, this paper sheds light on the industry, academic research gaps, and future direction.

The information security of the electric power system is increased as a result of constant control of the measurement information quality. The paper presents a method developed for detecting rough and systematic errors in the measurements related to a wind farm that operates under the conditions of low information redundancy. The method involves a comparison of the estimates of injections that are obtained using the PQ and RX models of an induction generator in the state estimation problem. Provided there are no errors in the measurement of the wind farm output and in the measurement of the flow in the line going from this wind farm, these estimates coincide.

Reliability measures how well a system can be expected to perform its intended purpose; it is expressed as a probability function with time and environment as variables. This paper discusses the mechanics of component failure and repair and shows that power system behavior follows a Markov process. The reliability of simple system configurations is evaluated analytically by solving the Markov equations. The reliability of complex systems is more easily evaluated by the use of digital computer simulation. The simulation method is described.

The article assesses the application of distributed generation to meet the customer’s own needs for electricity and heat. Currently, the electric power industry is faced with many problems of functioning of electric power systems, which have a negative impact on the processes of transmission and distribution of electric energy to end users. These problems lead to decrease in reliability of power supply and economic efficiency of operation of enterprises. In order to solve the problems of increasing the reliability and economic efficiency of electric power systems the introduction of distributed generation is effective. The modes of electric power generation are considered. The paper also analyzes and identifies the features of the application of distributed generation to achieve the required indicators of reliability and economic efficiency of electric power systems.

FACTS technologies can have major positive impacts on power system reliability performance and the actual benefits obtained can be assessed using suitable models and practice. Emerging techniques for composite power system reliability evaluation mainly focus on conventional generation and transmission facilities. In this paper, improvement of Reliability in composite electric power system is examined by incorporating Unified Power Flow Controller (UPFC). Two test systems, Reliability Test System (RTS) and Roy Billinton Test System (RBTS) are considered to show major improvement in reliability. A state space reliability model of multi-module UPFC has been developed and incorporated in the system. Load point & System indices performances are presented to examine the impact of UPFC on the composite electric power system reliability. Investigation results show a significant improvement in the Load point & system indices when utilizing UPFC.

Supply-demand balance is imperative for the reliability of the power system. Inability to maintain this balance results in frequency deviation and system failure. The recent integration of renewable energy sources such as wind and solar have reduced inertia and variable output which leaves the power system at risk of disturbance while also reducing the controllability of generators. However, the latter-day demand response is coming across as an economical and effective way of adding to the reliability and security of power systems by managing the electricity demand of customers at times of severe power imbalance. This chapter carries out a detailed literature review of centralized and decentralized demand control approaches. As well as presents a novel demand control approach for providing frequency regulation by using domestic refrigerators as control loads. This chapter also carries out a detailed study of large-scale appliance level interval meter consumption data from Australia's largest network provider Ausgrid. Appliance level data is used in combination with household-level data to study the contribution of air-conditioners in summer peak demand. Clustering is performed on air-conditioner data to identify various air-conditioner load profile patterns. These patterns are then used with demand control strategies to study the possible load reductions from residential air-conditioner control across the Australian State of New South Wales.

Recently, wide concern of extreme risk events in power system has been aroused due to several big blackouts at home and abroad. The present study is designed to assess the extreme risk of power system considering cyber attacks, which can not be reflected by using traditional risk assessment method. Based on the theory of value at risk(VaR) and extreme value theory(EVT) in financial terms, a method to evaluate and quantize extreme risk of power system is proposed to determine the potential risk resulted from extreme events with low probabilities and serious consequences. The method is applied to evaluate the extreme risk of cyber attacks in power system and comparisons between evaluation results of commom risk and extreme risk are conducted. Statistically significant results have justified the validity of extreme risk assessment method, which could be regarded as a complement and improvement to traditional risk assessment. It is recommended that further study is needed to be conducted for risk prevention so as to enhance the operation security and reliability of power system.

Power system is one of the most complex systems ever designed. Determining its reliability is therefore a demanding process, which is mostly dealt with in a way that partial problems are stated and solved. A reliability method of loss of load expectation is selected and improved and the model of conventional power system is considered as a standpoint. Reliability of the initial conventional power system is evaluated. Then, the conventional power system is changed, where one conventional power plant is abandoned and new wind power plants stand to its place. The reliability of the initial and the changed power system is calculated and compared. Discussion shows the problems of decreased reliability of the power system with increasing the percentage of wind power plants within the power system replacing conventional plants, if not enough reserve is added to the power system at the same time. The other related problems are discussed in addition, to show that the problem is very complex and one cannot only consider a limited number of influencing parameters when deciding on power system configurations.

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.

This article examines the actual problem of optimizing the operating modes of power plants, taking into account the criteria for reliability and efficiency, using the example of the power system of the Kaliningrad region, which may function in isolation from the unified energy system (UES) of Russia. The modes of operation of power plants are analyzed from the point of view of their optimality according to the economic criterion and the criteria of the reliability of the electric power system. The target optimization function is specified, the choice of an optimization algorithm for a software tool that allows achieving the specified criteria is described. Examples of the results of calculations of a specially developed program using the selected method of optimization of power plant modes for hourly, daily, monthly and annual planning are given. Taking into account these examples, recommendations are given on the withdrawal of power equipment for repair, which can be used by generating companies and dispatch centers of isolated power systems.

The possibility to improve the operation regime and the reliability of electric power systems using superconductor devices is considered.