Reliability Of Modern Power Systems Research Articles

Simulation-based post-fault analysis in WECS using DFIG systems under an unbalanced fault scenario

Fault analysis in an integrated Wind Energy Conversion System (WECS) is the highest priority factor to be taken into account for distributed generation (DG). The contribution of renewable energies (like solar and wind) to the power system is increasing continuously. New challenges are being faced while integrating such systems because of the no inertial response provided by renewable resources. So, for a reliable integration further exploration, development, and even new strategies are needed to improve the reliability, power quality, control and protection of renewable energy generation. Disintegration in distributed generation is still a huge question mark on the reliability of modern power systems. Disintegration has many causes and faults are one of them, that cause severe voltage profile distortions, which makes WECS a complex system in terms of control and protection of power system. In this work, focus has been paid to the analysis of asymmetrical faults in wind farms and their impacts are evaluated on the overall generation system. A 30 MW wind power plant has been developed in MATLAB to study the faults. Since wind farms are Low Voltage (LV) networks, therefore, fault impedance method (IEC 60909) and the Fort-cue Method have been used for the fault analysis. The fault current response and behaviour of the DG system under asymmetrical fault conditions at different locations in and around the wind farm have been obtained. It is found that fault loop impendence, transformer grounding type, and fault location largely affect the dynamic behaviour of the co-generation system under asymmetrical faults. In this work dynamic behaviour of the wind generation system has been analysed for the different asymmetrical faults induced at different locations of the system. The response of the wind generator has been evaluated based on the different fault simulations considering the sequence component method.

Integration of Wind Systems with SVC and STATCOM during Various Events to Achieve FRT Capability and Voltage Stability: Towards the Reliability of Modern Power Systems

FACTS tools in modern power systems provide a vital solution to the problems of voltage deviation and fault ride-through (FRT) capability in electrical power systems especially during the integration of wind power. Many distinct journals highlight that the wind-driven squirrel cage generator (SCIG) still accounts for around 15% of operating wind generators so far. To enhance voltage stability and FRT capacity, this paper recommends a cost-effective static VAR compensator (SVC) which has a size rating of six MVAR, and this improves the efficiency of the electrical power system. Different events are considered in this study such as high turbulent wind speed, low turbulent wind speed, unsymmetrical faults, and symmetrical faults to validate the suggested option. Moreover, the suggested solution is compared with the static synchronous compensator (STATCOM) and a fixed capacitor to ensure that during the studied wind speed profiles and faults, voltage stability, reactive power consumption, and FRT capability are realized. An overall comparison among them is performed under all studied scenarios to summarize their benefits and impacts. The simulated results show the effectiveness and superiority of SVC in improving the operation of an integrated wind system based on a grid-linked SCIG and the performance of the power system. The modeling of SCIG, SVC, and STATCOM is designed by MATLAB/Simulink toolbox.

An Overview of Flexible Current Control Strategies Applied to LVRT Capability for Grid-Connected Inverters

Distributed power generation plays a critical role in the stability and reliability of modern power systems. Due to the rapid growth of renewable energy generation, the requirements of the transmission and distribution system operators are becoming more stringent. Among these requirements, one of the most important is the Low-Voltage Ride-Through (LVRT) capability, which demands that the inverters remain connected to the grid and provide support during voltage sags. For this purpose, flexible current control algorithms stand out because they can manage unbalanced voltages and simultaneously achieve other control objectives. With the aim of presenting a concrete document focused on describing fundamental principles and discussing common design guidelines, this paper presents an overview of flexible current control strategies applied to LVRT capability. The operation features and design aspects of experiences reported in the literature are reviewed. Moreover, the paper proposes a general methodology to design LVRT flexible current control algorithms. Finally, current and future trends are discussed.

Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks

Monitoring centers in the smart grid exchange the collected data by sensors and smart meters to monitor the current conditions and performance of electric power components. Distribution Power Transformers (DPTs) have a key role in maintaining the integrity of power flow in the smart grid. Online monitoring of DPTs to detect possible faults can potentially increase the reliability of modern power systems. Mechanical defects of DPTs are the major issues in their proper operation that must be detected in their early stage of occurrence. One of the most effective solutions for diagnosing mechanical defects in DPTs is Frequency Response Analysis (FRA). In this study, an appropriate condition monitoring scheme for DPTs is developed to identify even minor winding defects. Disk-Space Variation (DSV), a common DPT windings fault, is applied to the 20 kV-winding of a 1.6 MVA DPT in various locations and with different severity. Their corresponding frequency responses are then computed, and all four components of the frequency responses, i.e., amplitude, argument, and real and imaginary parts, are evaluated. Different data-driven-based indices are implemented to extract appropriate feature vectors in the preprocessing stage. Group Method of Data Handling (GMDH) Artificial Neural Networks is proposed to assist monitoring centers in interpreting FRA signatures and identifying DPT defects at primary stages. GMDH has a data-dependent structure, which gives high flexibility to modeling nonlinear characteristics of FRA test results with different data sizes. It is demonstrated that the proposed approach is capable of accurately determining the fault location and fault severity. The proposed Artificial Intelligence (AI)-based approach is used to extract essential features from frequency response traces in order to detect the position and degree of Disk-Space Variation (DSV) in the DPT windings. The experimental results verify the effectiveness of the proposed methods in determining the severity and location of DSV defects.

Mitigating Cascading Failures in Power Grids via Markov Decision-Based Load-Shedding With DC Power Flow Model

Despite the reliability of modern power systems, large blackouts due to cascading failures (CFs) do occur in power grids with enormous economic and societal costs. In this article, CFs in power grids are theoretically modeled proposing a Markov decision process (MDP) framework with the aim of developing optimal load-shedding (LS) policies to mitigate CFs. The embedded Markov chain of the MDP, established earlier to capture the dynamics of CFs, features a reduced state-space and state-dependent transition probabilities. We introduce appropriate actions affecting the dynamics of CFs and associated costs. Optimal LS policies are computed that minimize the expected cumulative cost associated with CFs. Numerical simulations on the IEEE 118 and IEEE 300 bus systems show that the actions derived by the MDP result in minimum total cost of CFs, compared to fixed and random policies. Moreover, the optimality of derived policies is validated by a CF simulation based on dc power flow for the IEEE 118 bus system. Therefore, such actions developed by the proposed theoretical MDP framework can serve as a baseline for devising optimal LS strategies to mitigate CFs in power grids.

A novel long-term power forecasting based smart grid hybrid energy storage system optimal sizing method considering uncertainties

With the penetration of renewable generation, the reliability of modern power systems is increasingly challenged. This is especially true for power systems with comparatively low inertia, such as smart grids. To mitigate the impact of renewable intermittency on smart grid operation, a hybrid energy storage system (HESS) is widely employed. Nonetheless, the proper sizing of HESS in smart grids remains a technical challenge. Most of the existing energy storage sizing methods rely on historical data or deterministic renewable generation/load forecasting. Their results can be unconvincing with the presence of uncertainties. To figure out the optimal size of HESS in smart grids using probability-based long-term forecasting, this paper proposes a novel sizing approach with an improved forecasting accuracy. It considers the impact of uncertainties and the life cycle cost of HESS.

Energy Storage System Analysis Review for Optimal Unit Commitment

Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

New strategy based modified Salp swarm algorithm for optimal reactive power planning: a case study of the Algerian electrical system (114 bus)

Optimal reactive power planning is an important task for experts and industrials to ensure the reliability of modern power systems. Actually, the structure of practical power systems becomes dynamic and characterised by uncertainty in load and non-linear characteristic of various elements of power systems such as constraints associated to thermal units, constraints associated with FACTS devices and renewable sources. This study introduces an intelligent strategy based new metaheuristic named Salp swarm algorithm (SSA) to improve the solution of reactive power dispatch by optimising the total power loss, the total voltage deviation individually and simultaneously considering static VAR compensators (SVCs). To improve the efficiency of the original algorithm in solving large test systems, a sub SSA is formed to optimise the various objective functions based on a grouped control variable. In this study, four grouped swarms named SSA_PG for active power, SSA_VG for voltages, SSA_Ti for Tap transformers, and SSA_SVC for SVCs are formed to operate in a flexible structure to minimise a specified objective function. The proposed intelligent planning strategy validated on the IEEE 30 bus and to the large electrical test system 114 Bus of the Algerian network at normal condition and considering critical situations such as margin loading stability and contingency. Results found using the proposed strategy compared to those cited recently in the literature proves its particularity in terms of solution quality and convergence characteristics.

Quantifying Power System Operational Reliability

Rising uncertainty in power systems due to ongoing system and operational changes has led to increasing risks in operating a system in a reliable and economic manner. There are growing reliability concerns with the widely used methods, such as the N-1 criterion, to determine operating reserve requirement during unit commitment, and the economic load dispatch method to allocate regulating margin to respond to disturbances. These deterministic methods do not consider the uncertainty or stochastic nature of power systems and are often inadequate to maintain the required operating reliability of modern power systems. This paper introduces a reliability index, designated as the committed generators’ response risk (CGRR) that can be used to maintain a specified level of operating reliability. A probabilistic method to evaluate the CGRR is presented and validated using a Monte Carlo simulation technique. An application of the new index and the methodology is illustrated using the IEEE Reliability Test System. The CGRR provides a comprehensive operating risk measurement of the committed generation until further assistance is available to support the system, and therefore helps operators in decision making for unit commitment and dispatch of the generating units to meet the projected load in the short future time.

A novel approach for the identification of critical nodes and transmission lines for mitigating voltage instability in power networks

Voltage collapse is a major issue combating the effectiveness and optimal operation of modern power systems in recent times. This is a great threat to the security and reliability of modern power systems and, in recent times, it has been a growing concern for power system engineers, researchers and utilities. A prompt identification of transmission lines whose outage could lead to a cascading failure and the sets of nodes where voltage collapse could erupt, during critical outages, is therefore a vital issue for a reliable and secure power system operation. An alternative approach to solving these problems is therefore presented in this paper. The problem is viewed from the graph-theoretical perspective, considering the topological properties of power networks. Application of the fundamental circuit theory is employed and the bus-to-line matrix (BLM) is formulated. This matrix provides clearer insights into the interconnections of the components within the network. This valuable information is captured and used for identifying the critical elements where a suitable location for reactive power support could be placed to avoid voltage collapse of the network. The effectiveness of the proposed approach is tested using a simple 10-bus power network. The results obtained are compared with those obtained from the existing approaches. The results obtained show a strong correlation and agreement between the proposed and the existing approaches.

Sequential Service Restoration for Unbalanced Distribution Systems and Microgrids

The resilience and reliability of modern power systems are threatened by increasingly severe weather events and cyber-physical security events. An effective restoration methodology is desired to optimally integrate emerging smart grid technologies and pave the way for developing self-healing smart grids. In this paper, a sequential service restoration (SSR) framework is proposed to generate restoration solutions for distribution systems and microgrids in the event of large-scale power outages. The restoration solution contains a sequence of control actions that properly coordinate switches, distributed generators, and switchable loads to form multiple isolated microgrids. The SSR can be applied for three-phase unbalanced distribution systems and microgrids and can adapt to various operation conditions. Mathematical models are introduced for three-phase unbalanced power flow, voltage regulators, transformers, and loads. The SSR problem is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the modified IEEE 123 node test feeder.

Generation Adequacy Analysis of Multi-Area Power Systems With a High Share of Wind Power

There is growing concern regarding generation adequacy within the power system industry. The ever-increasing injection of intermittent renewable resources makes it harder than before to estimate the reliability of modern power systems using traditional approaches. This paper develops a framework for estimating the reliability of modern power systems that have considerable levels of wind power generation. Monte Carlo simulation is applied using a very efficient importance sampling technique based on the cross-entropy method as well as the Copula theory. Tailor-made importance sampling functions for conventional generation, load, and wind power generation drastically reduce the number of samples required to estimate reliability parameters of interest. The methodology enables simulation of multi-area power systems with considerable amount of correlated wind power generation in each of the different areas. Simulation results confirm the efficiency as well as the accuracy of the proposed method and show that it is orders of magnitude faster than crude Monte Carlo simulation.

Reliability Assessment of Smart Grid Considering Direct Cyber-Power Interdependencies

Smart grid initiatives are becoming more and more achievable through the use of information infrastructures that feature peer-to-peer communication, monitoring, protection and automated control. The analysis of smart grid operation requires considering the reliability of the cyber network as it is neither invulnerable nor failure free. This paper quantitatively evaluates the reliability of modern power systems, which incorporates the impact of cyber network failures on the reliability of the power network. In this paper, four types of interdependencies are defined and a new concept of state mapping is proposed to map the failures in the cyber network to the failures of the power network. Furthermore, in order to evaluate the impact of direct cyber-power interdependencies on the reliability indices, two optimization models are introduced to maximize the data connection in the cyber network and minimize the load shedding in the power network. The effectiveness of proposed reliability evaluation method is shown by a smart microgrid application. The methodology presented in this paper is a start point to optimize the future power grid which has increasingly interdependencies between cyber and power networks.