Power System Reliability Assessment Research Articles

Determining the reliability function of the thermal power system in power plant 'Nikola Tesla, Block B1' using the Weibull distribution

The assessment of thermal power system reliability represents a key step towards maintaining the stability and efficiency of the system. Through the application of mathematical models, risk analysis, and maintenance strategies, it is possible to optimise system performance and reduce the risk of failures. This paper presents an assessment of the reliability of the thermal energy system in fossil fuel power plant ‘Nikola Tesla, Block B1’, based on a twelve-year failure database. By implementing the theory of reliability, based on statistics and theory of possibility, and using the simple and complex two-parameter Weibull distribution, the theoretical functions of the failure rate are determined. A significant advantage of such a study is the opportunity of an early and in-depth understanding of the logic and mechanisms of system risk behaviour, and a more precise assessment of its functioning throughout future exploitation.

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

Innovative Method for Reliability Assessment of Power Systems: From Components Modeling to Key Indicators Evaluation

Power systems comprise different electrical, electronic, electromechanical and electrochemical components. Adequacy, security, resilience and reliability represent essential requirements for grids functioning mode. The evaluation of such aspects can constitute a delicate task in the presence of heterogeneous components. Focusing on reliability assessment, several Reliability Prediction Models are available. They are suitably applied according to the type of component under evaluation. The lack of homogeneity of these models forbids the comparison of performance and identification of unreliable systems and grid section. This paper aims to face the mentioned issue proposing a unique reliability assessment methodology able to characterize different equipment connected to radial/meshed/ring grids and subjected to different stressing and ageing factors. It is customized for electrical lines, transformers, circuit breakers, converters and renewables plants. Component and systemic key indices are calculated. Furthermore, a novel “load feeding reliability“ indicator is evaluated for providing information about the supply reliability of a specific load. This index is meaningful for the identification of unreliable grids, microgrids and systems. Such an approach can contribute to improve power systems design, planning and control. The proposed method is integrated in a software application implemented for grid reliability assessment. The obtained results are reported for an urban grid including an underground transportation area.

An overview on reliability assessment in power systems using CI approaches

An overview on reliability assessment in power systems using CI approaches

Reliability Assessment of Power System based on Load Flow Analysis of the IEEE 57 Bus used in Micro Grid Applications

The complex power system consists of more interconnections; therefore, modelling such a type of network is a very difficult task in the commercialization area cycle. MATPOWER has all steady-state power system models, which are extensively used in academic research for power flow (PF) modeling. The PF architecture designed in this is extensible; it is easy to add or modify variables and constraints in the standard case structure. This paper presents the details of the mathematically scaled loads in IEEE 57 bus power system network modelling by using the standard test case data. Internally, all the bus voltages are expressed in p.u., and phase angles are expressed in radians, but the generators and loads are expressed in terms of power ratings. The scaling of these is defined based on the scaled voltage of the corresponding bus voltage. The scaling load procedure used in this paper is very useful for designing a low-voltage power system network for practical analysis purposes. Two simulations are performed in this paper for the analysis of the actual load flow (ALF) and scaled load flow (SLF) power system models. The voltage, phase angle, and power flow through the lines are compared to analyze the accuracy of both simulations. When compared with ALF, it has shown good accuracy, computational efficiency, and convergence properties.

Chronological simulation and Quantitative Evaluation of System Reliability in a Complex Restructured Power System

Ensuring the provision of high-quality and dependable power supply to customers stands as a paramount responsibility within the realm of power systems. The concept of restructuring the power system emerges as an efficient approach to deliver economically viable and uninterrupted power supply. The assessment of power system reliability hinges on various factors, with the reliability index serving as a pivotal metric, dependent on both system security and adequacy. To enhance the reliability index, strategically locating FACTS devices becomes essential, a task facilitated through power flow analysis with specified constraints, pinpointing the weakest points through Genetic Algorithm-driven fast-acting device placement. The correlation between DG establishment and the reliability index was meticulously calculated, further bolstered by the introduction of a Derated Forced Outage Rate for enhanced performance assessment. In the pursuit of heightened power system reliability, sequential simulations prove invaluable, particularly in minimizing Expected Energy Not Supplied (EENS) and improving overall system reliability. The results obtained through sequential simulations underscore the effectiveness of this approach within bulk electrical systems. The installation of multiple FACTS devices in the system's weakest areas facilitated EENS computation, coupled with the assessment of related reliability indices such as system frequency and system duration. This approach not only enhances system reliability but also promotes the economic operation of both transmission and distribution.The simulation results substantiate the alignment of power system reliability objectives in deregulated power systems with the required standards, advocating for the restructuring of power networks. The reliability assessments, driven by the optimal placement of DGs and FACTS devices, reveal substantial improvements in system reliability, underscoring the pivotal role of these technologies in enhancing overall power system dependability.

Reliability Assessment of Power Systems in High-Load Areas with High Proportion of Gas-Fired Units Considering Natural Gas Loss

The “dual-carbon” policy underscores the crucial importance of a secure and stable natural gas supply to ensure the reliable operation of power systems. In high-load areas with a high proportion of gas-fired units and no alternative energy supply, urgent attention needs to be paid to the impact of natural gas loss on power system reliability. Given this background, a method to evaluate power system reliability that considers natural gas supply fluctuations is proposed. In this method, a reliability model of the natural gas supply chain based on the minimal cut set theory is constructed and the influence of policy regulations and economic market factors on system components is quantified. Then, a reliability-evaluation model for a power system that considers gas loss is constructed, and a non-sequential Monte Carlo simulation is used to solve it. Afterward, a reliability-evaluation method considering the power system reserve capacity is proposed. Finally, case studies on a natural gas system with a 14-node power system of a certain area are performed to verify the effectiveness of the proposed method, and the simulation results demonstrate that the reliability of the energy supply directly affects the reliability of the power system.

An improved sequential importance sampling method for reliability assessment of renewable power systems with energy storage

Due to the temporal coupling constraints of energy storage, the reliability of modern renewable power systems can no longer be assessed by non-sequential methods. However, the existing sequential methods cannot be combined with variance reduction methods as easily as the non-sequential ones. Hence, the power system reliability assessment is faced with incredible computational burden. In this paper, we propose an improved sequential importance sampling method to accelerate the computation of power system reliability indices. The proposed method is composed of two stages, namely the pre-simulation and main simulation stages. In the pre-simulation stage, we design a customized probability distribution parameter optimization process for load curves in the cross entropy based importance sampling method. In the main simulation stage, the derived probability distribution parameters in the pre-simulation stage are converted and further used to carry out a sequential Monte Carlo sampling process to efficiently estimate the reliability indices of renewable power systems with energy storage. The proposed method is validated by numerical tests performed on the modified IEEE-RTS 79 and IEEE-RTS 96 test systems, both of which are integrated with three wind farms, one photovoltaic station and one battery energy storage power station. Results show that the proposed method is much faster than existing sequential importance sampling methods in both test systems because of exploiting the acceleration potential of the load. Therefore, the proposed method can be used to assess the reliability of the renewable power systems with more energy storage devices in the future.

Cyber- physical power system reliability assessment considering multi-state independent components

Cyber- physical power system reliability assessment considering multi-state independent components

Neural Networks Based Dynamic Load Modeling for Power System Reliability Assessment

The reliability of a power system is considered as a critical requirement in planning and operating the system due to the increasing demand for more reliable service with a lower frequency and duration of interruption. Hence, reliability is also considered as a major challenge in the development of future power systems as they become more advanced and complex, making the accuracy of the reliability assessment dependent on several factors such as supply and load modeling. Recent studies on power systems’ reliability and stability have focused on load modeling, where loads are either assumed to be static or dynamic, by introducing significant constraints. However, the emergence of new types of loads necessitates the development of models that can incorporate them with accuracy, as this would facilitate their effective use in flow and stability simulation studies, as well as reliability analyses. In this study, dynamic loads are modeled using a feed-forward neural network where a simulation test bed is developed in MATLAB/Simulink to generate operating data used during training and validating of the neural network model. Subsequently, Electrical Transient Analyzer Program (ETAP) software is used to verify the effect of load modeling on power system reliability assessment platform. Bus 2 of Roy Billinton Test System (RBTS) is employed as a case study to investigate the sensitivity of the reliability indices, such as System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI), on the load modeling technique with mixed loads (dynamics and statics).

Power Systems Reliability Assessment based on Load Shaping Consideration

The load shaping technique is an important key that has been widely implemented inelectrical power systems. The benefits of this activity involve system reliability enhancement and operation requirement satisfaction. In this study, the benefits of load shaping are investigated based on reliability impact. First, the reliability indices have been assessed with no original load. Secondly, the same indices are assessed when load shaping is considered. Results show that the proposed model yields higher accurate reliability assessment and, therefore, a more robust strategy to be implemented. Monte Carlo simulation is utilized to calculate predicted energy not supplied.The paper demonstrates IEEE reliability test system. It describes a comparison of reliability of generating systems based on real time assessment.

Two-dimensional convolution-based power system reliability assessment

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

Reliability Assessment of Interconnected Power Systems with HVDC Links Considering Frequency Regulation Process

With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the inter- connected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.

Multi-agent optimization in solving the problem of optimal power system control and reliability assessment

The paper proposes a method for distributed optimal control of EPS with the presence of stochastic elements. The proposed technique is based on distributed optimization methods. The forecast of electric mode parameters is used as input data for optimization. The forecast horizon is divided into separate time steps at equal intervals. Distributed optimization methods are extended to the dynamic optimization problem, when not just independent states for each moment of time, but the process of their change in time is considered.

Machine learning application for power systems reliability assessment

The paper presents the principles and features of the use of machine learning methods to assess the power system reliability. Based on the analysis of publications, the main approaches to the application of machine learning methods are given. A prototype of an automatic system has been developed to identify in real time potentially dangerous power system states, the occurrence of which can lead to the power system failures.

Reliability assessment of cyber-physical power systems considering the impact of predicted cyber vulnerabilities

This paper presents a reliability assessment technique for cyber-physical power systems (CPPSs) that incorporates cybersecurity issues by considering non-normal random variables with non-linear dependencies. Our model uses bidirectional encoder representations from transformers (BERTs) to predict the severity of cyber vulnerabilities by analyzing their textual description. This practical model allows the engineer to manually enter text that describes threats to predict the probability of cyber vulnerability. We use a Bayesian attack graph to simulate the attack paths and a Markov model to demonstrate the consequences of cyber attacks on the CPPS. Furthermore, a hybrid approximate-analytical approach is employed to assess the overall reliability of the CPPS when considering the load and wind generation uncertainties, given their non-linear dependencies. The proposed method is implemented on the modified IEEE Reliability Test System. The results demonstrate that the reliability indices for a simultaneous cyber attack on corporate networks and wind farm control centers are more significant than those obtained for other attack scenarios. The proposed prediction method is highly accurate, and the predicted vulnerability scores align with those provided by the National Vulnerability Database. The results are compared with sequential Monte Carlo simulation, subset simulation, and cross-entropy-based importance sampling methods. The comparison shows that the proposed method outperforms other methods in terms of convergence speed and accuracy.

A Modified Multiparameter Linear Programming Method for Efficient Power System Reliability Assessment

Power systems face adequacy risks because of the high integration of renewable energy. It is urgent to develop efficient methods for power system operational reliability assessment. Conventional power system reliability assessment methods cannot achieve real-time assessment of system risk because of the high computational complexity and long calculation time. The high computational complexity is mainly caused by a large number of optimal power flow (OPF) calculations. To reduce the computational complexity, this paper transfers the optimal power flow model as a multiparameter linear programming model. Then, the optimal power flow can be obtained by linear calculations. Furthermore, this paper proposes a state reduction method considering the importance index of transmission lines for further improving the calculation efficiency. Case studies are carried out on IEEE standard systems and a provincial power grid in China. Compared with the conventional reliability assessment method, the reliability assessment efficiency of the proposed method increases by 10–40 times, and the assessment error is less than 1%.

Power System Reliability Assessment— A Review on Analysis and Evaluation Methods

The structures of power systems and their capacity have been updated significantly from time to time. Therefore, a reliability analysis is an essential issue in the planning, designing, and operation of electric power systems. Thus, a number of methods have been proposed. These are grouped as analytical based methods, simulation-based methods, and hybrid methods. Some methods like Monte Carlo, Markov etc., developed and some indices such as interruption indices, energy-oriented indices, are used for the evaluation. The purpose of this review study is to investigate the reliability analysis approaches, methods and difficulties, and to report importance of the reliability analysis in power systems. Therefore, reliability indices and evaluation methods and models of evaluation of power system are listed and explained. Besides, modeling and computational burden and complexity and problems are discussed. The importance of reliability analysis for emerging power systems is examined and explained.

Reliability assessment of modern distribution networks embedded with renewable and distributed resources

With the rapid depletion of fossil fuel based resources and growing concerns related to climate change, significant development of renewable energy has been observed around the world. Reliability assessment of power systems is a key aspect to measure the impact of renewable distributed generation (DG) units on modern distribution networks. This paper proposes a Monte Carlo simulation based approach to investigate the influence of the stochastic nature of renewable and distributed resources including solar PV, battery energy storage, and electric vehicles on the reliability of distribution systems. A modified Roy Billinton test system (RBTS) is used to demonstrate the findings and calculate customer side reliability indices including SAIDI, SAIFI, and CAIDI with the consideration of capacity factors.