Power System Security Research Articles

A review of multistage solar driven photovoltaic–thermal​ components with cascade energy storage system for tri-generation

Energy demand has exponentially increased in recent times, mainly because of population and economic growth as well as high standards of living in commercial and residential buildings. Conventional power systems have been undergoing great transformation owing to the integration of renewable energy sources (RESs) and demand side management programs. Solar Photovoltaic–Thermal (PV/T) is one of the most growing Renewable Energy Resources (RERs) because of its low carbon emission, high energy efficiency, cost effectiveness, readily available, among other benefits. Several researches have been explored to enhance the performance of different components in the building integrated systems distributed solar energy for tri-generation: heating, cooling and electricity generation. The Photovoltaic–Thermal (PV/T) energy system may be enhanced using various advanced control schemes and cooling technologies. Energy storage systems have shown outstanding benefits by improving the reliability and security of modern power systems. Ice Thermal Energy Storage (ITES) systems have been used for cooling loads, while Heat Storage Water Tank (HSWT) are used for hot water demand. Alternatively, battery banks have been used for storing chemical energy and releasing electricity to the demand side. This reviewed work highlights various components of a solar driven grid-connected PV/T energy system, considering the ITES, HSWT and a battery bank under-price based Demand Response (DR) in terms of the optimal sizing and operational control.

Dynamic Security Assessment For Power System Using Attribute Selection Technique

The evaluation of the dynamic security of the electrical power system after the occurrence of disturbances in the network is one of the most important tools that the control center uses to maintain the system in a safe operating mode, as well as prevent cases of system out of control and cases of complete shutdown. With the annual increase in the size of the electrical system and its distribution over a very wide geographical area, this led to a new challenge to assess dynamic security assessment (DSA), which is dealing with a huge and varied amount of data that requires processing in a very short time. To address these challenges, this study presented a new technique of artificial intelligence, which is the attribute selection technique, to reduce the size of this data and thus improve the accuracy and speed of results. This method relied on the combination of decision tree algorithms and a technique (Attribute selection) in the data obtained from the test system (IEEE-30Bus). The results of this method showed a significant reduction in the number of data used, which amounted to (45.55%) of the total data, Which led to an improvement in the classification accuracy, as the classification accuracy reached (97.27%). This reduction is very important when dealing in the online operating environment, as it saves the time necessary to reach the most accurate evaluation decision and thus issue gives a greater opportunity to take the appropriate decision in the event of disturbances and keep the electrical system in a secure state.

Protecting Power Transmission Systems against Intelligent Physical Attacks: A Critical Systematic Review

Power systems are exposed to various physical threats due to extreme events, technical failures, human errors, and deliberate damage. Physical threats are among the most destructive factors to endanger the power systems security by intelligently targeting power systems components, such as Transmission Lines (TLs), to damage/destroy the facilities or disrupt the power systems operation. The aim of physical attacks in disrupting power systems can be power systems instability, load interruptions, unserved energy costs, repair/displacement costs, and even cascading failures and blackouts. Due to dispersing in large geographical areas, power transmission systems are more exposed to physical threats. Power systems operators, as the system defenders, protect power systems in different stages of a physical attack by minimizing the impacts of such destructive attacks. In this regard, many studies have been conducted in the literature. In this paper, an overview of the previous research studies related to power systems protection against physical attacks is conducted. This paper also outlines the main characteristics, such as physical attack adverse impacts, defending actions, optimization methods, understudied systems, uncertainty considerations, expansion planning, and cascading failures. Furthermore, this paper gives some key findings and recommendations to identify the research gap in the literature.

Optimal Location for FACTS Devices: Hybrid Algorithm to Improve Power Flow and System Security

In this study, an efficient and reliable algorithm known as Jaya Algorithm-enabled Flower Pollination Algorithm (JA-FPA) is introduced for enhancing the Optimal Power Flow (OPF) and system security by a multi objective model. The multi-objective model is first derived based on entropy of individual objectives such as the Voltage Stability Index (VSI), Power Loss Index (PLI), Line Collapse Proximity Indexes (LCPI), severity and TCSC cost. Multi- objectives are achieved by optimizing the location as well as size of Flexible Alternating Current Transmission System (FACTS) devices in the test bus system. Consequently, the most popular and ubiquitous component of the FACTS equipment category, the Thyristor Controlled Series Capacitor (TCSC), is utilized in this study. The best solution with optimal sizing and allocation of TCSC devices for achieving the aforesaid objectives is chosen from the JA-FPA optimized outputs. The proposed model is performed on two benchmark test bus systems. The adopted model is distinguished with conventional models, and the results are obtained to analyze its effectiveness. In particular, the computational time of proposed model is is 1.74%, 2.52%, 1.13%, 0.63%, 50.15%, 2.36%, 3.83%, 6.00%, 2.79%, and 7.57% superior to existing GWO, WOA, DA, FPA, JA, PSO + GSA, PSO, Henry gas, GA, and TLBO approaches.

Value of Interconnectors Operating in Simultaneous Energy-Frequency Response Markets

This paper investigates the potential contribution that interconnectors can provide to efficiently support the security of interconnected power systems. The proposed modelling setup introduces a radical paradigm shift in the operation of the interconnectors and in their interactions with multiple markets. To the best of our knowledge, this is the first paper that models a simultaneous allocation of the interconnector capacity for the exchange of energy and of inertia-dependent primary frequency response. The benefits and impact of this new methodology are evaluated with typical market indicators (e.g., social welfare and interconnector revenues) under two different paradigms: a centralized approach where the interconnectors are operated to minimize the system operational cost and a market-based framework where the interconnectors are privately-owned assets with self-interested objectives. By modelling the interconnectors as “price-maker”, the proposed work quantifies the potential inefficiencies of market solutions while considering key elements such as capacity withdrawing. A case study of the GB-France systems assesses the value of interconnectors on system efficiency and security under the considered paradigms.

Indonesian load prediction estimation using long short term memory

Prediction of electrical load is important because it relates to the source of power generation, cost-effective generation, system security, and policy on continuity of service to consumers. This paper uses Indonesian primary data compiled based on data log sheet per hour of transmission operators. In preprocessing data, detrending technique is used to eliminate outlier data in the time series dataset. The prediction used in this research is a long-short-term memory algorithm with stacking and time-step techniques. In order to get the optimal one-day forecasting results, the inputs are arranged in the previous three periods with 1, 2, 3 layers, 512 and 1024 nodes. Forecasting results obtained long short-term memory (LSTM) with three layers and 1024 nodes got mean average percentage error (MAPE) of 8.63 better than other models.

Pathways to Secure and Stable Island Power Systems [Guest Editorial

In a fast-changing world, island power systems face urgent environmental, economic, sustainability, and social challenges. Historically, island power systems were built to rely on oil-fired generation, causing carbon-intensive power generation, high costs to consumers, and dependance on market price volatilities, thus hindering their economic development and social stability. Reducing or eliminating the dependence on costly imported fossil-fuels has many benefits but also presents significant challenges when shifting to local but variable and power electronic-based sources, such as wind and solar. Island power systems are at the forefront of integrating extremely high shares of variable renewable generation and face related reliability challenges that are yet to be seen in larger interconnected power systems on the continental mainland. This is because island power systems operate in isolation and rely on their local grid resources to ensure stable operation.

Probabilistic Day-Ahead Inertia Forecasting

Power system inertia is declining and is increasingly variable and uncertain in regions where the penetration of non-synchronous generation and interconnectors is growing. This presents a challenge to power system operators who must take appropriate actions to ensure the stability and security of power systems relying on short-term forecasts of the system’s inertial response. Existing models to forecast inertia fail to quantify uncertainty, which may prevent their utilization given the risk aversion of the system operators when handling stability issues. This paper is the first to develop a model to produce calibrated, data-driven probabilistic forecasts of the inertia contribution of transmission-connected synchronous generators. The model provides a necessary tool for system operators to quantify forecast uncertainty, allowing them to manage the risk of frequency instability cost-effectively. The paper demonstrates that the assumption of a Gaussian distribution of uncertainty applied in existing models is not acceptable to accurately forecast the inertial response and provides a satisfactory forecast model by combining non-parametric density forecasting with parametric tail distributions. Moreover, the paper shows that satisfactory predictive performance can only be achieved by adopting a rolling horizon forecast approach to deal with the rapidly changing characteristics of the inertial response in power systems.

Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid

As one of the most dangerous cyber attacks in smart grids, the false data injection attacks pose a serious threat to power system security. To detect the false data, the traditional residual method and other improved methods, such as the Kalman-filter-based detector, have been proposed. However, these methods often have defects, especially in a very complex networked system with noises. By investigating the tolerance to the uncertainty in the residual detection method and properties of noises, the attack magnitude planning has been presented to hide the attack behind noises, which can bypass the residual detection method. As to the Kalman-filter-based detector, this article designs a specific attack strategy that can successfully deceive the Kalman-filter-based detector. Under this strategy, the false data injected at each step are used to balance the anomalies caused by previous false data, making the system look quite normal in monitoring, while deviating the system from normal operation eventually.

Simulation test of transformer short-circuit impedance based on equivalent model at different frequencies

A distribution transformer is one of the most important equipments in the power grid, and its operational status is directly related to the security and stability of the entire power system. The performance of transformer windings determines the service life of the transformer. It is of great significance to evaluate the performance of transformer windings accurately, reliably, and effectively after the short-circuit test of the transformer. Aiming at the problem that the natural vibration frequency of the transformer measured before and after the short circuit may not be the same under the impact of the short circuit of the transformer, this paper puts forward the evaluation method for the performance evaluation of the winding of the transformer after the short circuit test and adopts the method of combining the analytic hierarchy process with the fuzzy evaluation method to quantitatively obtain the score for the performance evaluation of the winding. And that evaluation language is given according to the score. Finally, the evaluation of the transformer winding performance after the short-circuit test is realized. The evaluation results show that this method can effectively get the performance index of the transformer winding, without the need for hanging cover inspection, and solve the subjectivity of human eye observation to objectively, accurately, and quantitatively describe the performance state of the transformer winding. The method is favorable for power grid monitoring and maintenance personnel to accurately evaluate the operation stability of the transformer and has very positive significance for improving the short-circuit impact resistance of the transformer and ensuring the safety and stability of the operation of a power system.

Over current protection of distribution system with impact of solar and wind generation using DIgSIlent power factory

The utilization of renewable resources has been growing very fast worldwide recently to manage the increasing energy gap, but it also raises some challenges like protection issues, transient stability issues and security issues in the power system operation. Mainly, wind and solar photovoltaic renewable power generation sources are account for bulky renewable energy share. The transients in power systems including renewables are reduced and have recently attracted wide attention. The impact of renewables generation on power system transients should be effectively analyzed and evaluated to improve power system reliability, stability, operation and security. DIgSILENT Power Factory software is more powerful and useful for providing phasor of fundamental power frequency components better than other existing software’s; therefore, DIgSILENT Power Factory is proposed for modeling and analysis of the system.

Characterising the security of power system topologies through a combined assessment of reliability, robustness, and resilience

Electricity has a prominent role in modern economies; therefore, ensuring the availability of electricity supply should be a top priority for policymakers. The joint assessment of reliability, robustness, and resilience can be a useful criterion to characterise different topologies and improve the security of supply. This paper proposes a novel integrated analysis of these three attributes to quantify the security of power grid topologies. Hence, eight case studies with different topologies created using the IEEE 24-bus reliability test system were analysed. Reliability was evaluated by applying the sequential Monte Carlo approach, robustness was evaluated by simulating cascading failures, and resilience was evaluated by analysing recovery curves. The different indicators associated with each of the three evaluations were then calculated. The results obtained were discussed both graphically and quantitatively in a novel three-dimensional representation, where the importance of joint analysis was also highlighted. The proposed method can serve as an additional tool for planners to identify possible investments or improvements in power system topologies.

Detailed Transients Simulation of a Doubly Fed Induction Generator Wind Turbine System with the EMTP-Type OVNI Simulator

Doubly fed induction generator wind turbines are increasingly used in new wind turbine installations all over the world. Growing concerns about the impact of a large number of these generators on transient and voltage stability of power system networks has led engineers to revisit modelling and simulation practices used for system stability analyses. In this paper, the latest advancements in design of the general purpose power system simulator OVNI developed at the University of British Columbia are presented, and its application to the simulation of a doubly fed induction generator (DFIG) wind turbine system is shown. Because OVNI is based on the EMTP methodology for accurate detailed modelling, and the Multilevel MATE (Multi-Area Thévenin Equivalent) concept, which, combined with hardware solutions, allows for fast simulation of large power system networks, it represents an ideal tool for testing and developing benchmark models of different wind turbine installations. Using the EMTP approach for modelling of a DFIG wind turbine system and its feeding power network we were able to study the responses of the wind turbine generator to different network events. The ultimate goal of our investigations is the development of a benchmarking process for testing different models of wind turbine generators and determining the range of validity of various degrees of approximations normally used for stability simulation purposes. Due to the rapid development of wind generation technology, it is essential to determine the minimum requirements for dynamic modeling of wind turbine generators for assessing impacts of their installations on the dynamic security and stability of power systems.

A risk-based machine learning approach for probabilistic transient stability enhancement incorporating wind generation

ABSTRACT Power systems are becoming more complex than ever and are consequently operating close to their limit of stability. Considering its significance in power system security, it is important to propose a novel approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables. Moreover, the time-domain simulation approach for transient stability evaluation can be very computationally intensive, especially for a large-scale system. The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this paper proposes a risk-based, machine learning decision-making approach, for probabilistic transient stability enhancement, by replacing circuit breakers, including the impact of wind generation. The IEEE 14-bus test system was used to test and validate the effectiveness of the proposed approach. DIgSILENT PowerFactory and MATLAB were utilised for transient stability simulations (for obtaining training data for machine learning), and applying machine learning algorithms, respectively.

Resilient and event‐triggered sliding mode load frequency control for multi‐area power systems under hybrid cyber attacks

The attacks on power systems have been happening all the time, and gradually changing from a single attack to hybrid multiple cyber attacks. Such hybrid attacks may cause more catastrophic damages to the security and safety of multi-area power systems. Here, the security control problem is analyzed for multi-area power systems under hybrid cyber attacks. Two Lyapunov–Krasovskii functionals are constructed, and by utilizing the second-order canonical Bessel–Legendre inequality, sufficient conditions are derived for stability criteria under resilient event-triggered mechanism. A bound with H ∞ $H_{\infty }$ security performance is obtained under hybrid cyber attacks. A proper controller is synthesized to ensure that the trajectory of the multi-area power systems can be driven onto the prescribed sliding surface. Finally, numerical examples and simulated results demonstrate the effectiveness and correctness of the proposed scheme.

Investigating Various Severity Factor Behaviors for Operational Risk Assessment

Operational risk assessment is a stochastic approach to quantify the operational security of power systems. In this study, the interrelation between severity factor and operational risk, two important parameters in operational risk assessment, is analyzed. Four different definitions of severity factor, based on the results from network studies, are proposed and applied, resulting in different values for the operational risk indices. The behavior of these indices is analyzed under varying operating conditions and compared with the behavior of the severity factor for individual contingencies. This study confirms the importance of the severity factor definition and shows that multiple operational risk indices are required to obtain a clear picture of the operational security of a transmission system. One risk index may increase while another decreases.

RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT

The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.

Fault‐tolerant modular multilevel converter for a seamless transition between stand‐alone and grid‐connected microgrid

Recently, renewable distributed generation (DG) systems have emerged to improve the power system security and reliability while reducing the environmental incompatibility of fossil fuel power plants. DG could operate either as a standalone and/or as a grid-connected power system. The interfacing circuits of DG and their controls have to comply with utility quality regulations and afford a seamless transition from online to offline and vice versa. This article advises a simple and robust control scheme for a modular multilevel converter (MMC) that interfaces with a photovoltaic (PV) generator. The proposed control method ensures high-quality output voltage/current waveform in online and/or offline operating modes. Moreover, the proposed control provides a seamless and swift transition from standalone to grid-connected and vice versa. The exhibited control scheme is simulated with MATLAB/Simulink. Real-time simulator OPAL-RT OP4510 is used to verify the displayed simulation results. The simulation results determine the accomplishment of the proposed control technique for a seamless transition from the grid-connected mode to the standalone mode and vice versa via MMC.

Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

It is necessary to predict solar photovoltaic (PV) output and load profile to guarantee the security, stability, and reliability of hybrid solar power systems. Severe frequency fluctuations in hybrid solar systems are expected due to the intermittent nature of the solar photovoltaic (PV) output and the unexpected variation in load. This paper proposes designing a PID controller along with the integration of a battery energy storage system (BESS) and plug-in hybrid electric vehicle (PHEV) for frequency damping in the hybrid solar power system. The solar PV output is predicted with high accuracy using artificial neural networks (ANN) given that solar irradiance and cell temperature are inputs to the model. The variation in load is also forecasted considering the factors affecting the load using ANN. Optimum values of the PID controller have been found using genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm considering integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE) objective functions. IAE, ISE, ITAE, Rise time, settling time, peak overshoot and maximum frequency deviation have been measured for comparison and effectiveness. The transient behavior has been further improved by utilizing the power from BESS/PHEV to the power system. The results demonstrate the efficacy of the suggested design for frequency control using the genetic algorithm method along with ISE objective function compared with those obtained from the conventional, particle swarm optimization, artificial bee colony, and firefly algorithm techniques.

Sparse Dictionary Learning for Transient Stability Assessment

Transient stability assessment (TSA) has always been a fundamental and challenging problem for ensuring the security and operation of power systems. With more power electronic interface resources integrated into the grid and large renewable energies, the stability of the power system is jeopardized. Therefore, TSA of the power system should be considered in advance to keep the system running stable. In recent years, with the development of artificial intelligence (AI) technologies such as artificial neural network (ANN), support vector machine (SVM), and Markov decision process, TSA has improved dramatically. In this study, a sparse dictionary learning approach is proposed to improve the precision of the classification accuracy of transient stability assessment in power systems. Case studies of TSA using multi-layer support vector machine (ML-SVM) and long short-term memory network–based recurrent neural network (LSTM-RNN) are discussed as benchmarks to validate the proposed method. The stable and unstable dictionary learnings are designed based on datasets obtained by simulating thousands of different time-domain simulation (TDS) scenarios performed on the New-England 39-bus system in the PSAT (power system analysis toolbox) toolbox. Stable and unstable dictionaries are developed based on the K-SVD approach. The testing dataset contains both stable and unstable samples which steps into the sparse coding process to obtain the indexes. Compared with the indexes, the system’s final TSA is targeted. The proposed method exhibits satisfactory classification accuracy in transient stability prediction and provides the ability to reduce false alarms both in positives and negatives of the power system.