Dynamic security contingency ranking and generation reallocation using time domain simulation based severity indices

In this paper, a novel approach for generation rescheduling as a preventive control for enhancing dynamic security using neural network is presented.. Critical clearing time (CCT) associated with each fault including the effect of system controllers and limitation, is adopted as dynamic security criteria. A Dynamic Security Analyzer Neural Network (DSANN) is trained to estimate CCTs associated with different system faults. For each given operating point, DSANN evaluate system CCTs by using steady state pre fault operating condition as input pattern. The most interesting feature of the proposed neural network application is evaluation of sensitivity of CCT with respect to generation pattern. These sensitivities are derived from the information stored in the weighting factors of trained DSANN. The sensitivity of CCT is used as a guideline for selecting the most effective pair of generators to reschedule their MW generation in the process of generation rescheduling aimed security enhancement. The proposed method has been demonstrated on the IEEE-39 bus system with promising results for enhancing dynamic security by generation rescheduling using sensitivity characteristic of neural network.

In this paper, a new method is proposed for dynamic security dispatch. The idea of coherent behavior of generators is used to find a new generation configuration with better transient stability behavior. Rescheduling the generation in the power system improves system security by increasing the critical clearing time or the transient energy margin without changing total generation. The calculation in the proposed method is simple and direct; the generation rescheduling depends only on the original generation and inertia constants of the machines, and their rotor speeds at the fault clearing time. In this work, the transient energy function (TEF) method is used to check the dynamic security of the system by analyzing all credible three phase faults with single line outages. If the worst energy margin is not acceptable, the proposed rescheduling is done, and the security is checked again. The method has been tested on the Ontario Hydro 55-bus 11-generator system, the IEEE 145-bus 50-generator system and the IEEE 162-bus 17-generator system. Results on several loading levels of the 162-bus 17-generator system are also shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power systems dynamic security assessment tools are required. One of the core elements of near real-time dynamic security assessment tools is contingency screening and ranking. Most of the commercially available tools screen and rank contingencies by using the traditional numerical integration or Transient Energy Functions (TEFs) or hybrid methods. The traditional numerical integration method is accurate but computationally intensive and has a slow assessment speed which makes it difficult to identify any insecure contingency before it happens. Despite the TEF method of transient stability analysis being relatively fast, it develops less accurate results due to models simplification and assumptions. This paper introduces transient stability based on fast and robust contingency screening and ranking using an Adaptive step-size Differential Transformation (AsDTM) method. Based on the most current snapshot from Supervisory Control and Data Accusation (SCADA) data, the proposed method triggers AsDTM-based transient stability simulation for each credible contingency and evaluates Transient Stability Indices (TSI) as the normalized weighted sum of squares of errors derived from state variables and complex bus voltages at every simulation time step. Finally, contingencies are ranked based on these TSI and the worst contingency is identified for the next detail assessment. The method is tested on IEEE 9 bus and 39 bus test systems. Test results reveal that the proposed method is faster, robust, and can be used in near real-time dynamic security assessment sessions.

The Transient Energy Function (TEF) method has been intensely investigated over the last decade as a reliable and accurate tool for transient stability assessment of multimachine power systems. In this paper we propose the Potential Energy Boundary Surface (PEBS) based method for direct analysis of transient stability. The critical value of transient energy and the Critical Clearing Time (TCCT) are evaluated without the need for successive calculation and memorization of the system kinetic and potential energy values along the sustained fault trajectory. The method is used for determination of output features in the training phase of the neural‐net based transient security assessment. The proposed approach is tested on the well‐known ten‐generator New England test system demonstrating the validity and accuracy of obtained results.

This paper presents a new decision tree (DT) based approach for fast voltage contingency screening and ranking for on-line applications in energy management systems. The hybrid decision tree model is developed to learn all the selected contingencies simultaneously, therefore fewer DTs are required. To reduce the size and improve the accuracy of the decision tree, the K-class problem is converted into the set of K two-class problems, and separate decision tree modules are trained for each of the two class problems. All the selected contingencies are presented to the filter module, which is trained to separate them in critical and non-critical contingency classes, which reduces the burden on ranking modular DT. The critical contingencies screened out by the filter module are presented to the ranking modular decision tree for their further ranking. To measure the severity of contingencies, bus voltage violation based scalar performance index is used. Full AC load flow is performed to generate the training and testing patterns for the proposed hybrid decision tree, under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, a hybrid decision tree method gives fast and accurate screening and ranking of contingencies for unknown load patterns.

Dynamic analysis is a technique that is used to fully understand the internals of a system at runtime. On Android, dynamic security analysis involves real-time assessment and active adaptation of an app’s behaviour, and is used for various tasks, including network monitoring, system-call tracing, and taint analysis. The research on dynamic analysis has made significant progress in the past years. However, to the best of our knowledge, there is a lack in secondary studies that analyse the novel ideas and common limitations of current security research. The main aim of this work is to understand dynamic security analysis research on Android to present the current state of knowledge, highlight research gaps, and provide insights into the existing body of work in a structured and systematic manner. We conduct a systematic literature review (SLR) on dynamic security analysis for Android. The systematic review establishes a taxonomy, defines a classification scheme, and explores the impact of advanced Android app testing tools on security solutions in software engineering and security research. The study’s key findings centre on tool usage, research objectives, constraints, and trends. Instrumentation and network monitoring tools play a crucial role, with research goals focused on app security, privacy, malware detection, and software testing automation. Identified limitations include code coverage constraints, security-related analysis obstacles, app selection adequacy, and non-deterministic behaviour. Our study results deepen the understanding of dynamic analysis in Android security research by an in-depth review of 43 publications. The study highlights recurring limitations with automated testing tools and concerns about detecting or obstructing dynamic analysis.

A common approximation used in the derivation of the Transient Energy Functions (TEFs) for AC/DC power systems is the omission of HVDC dynamics. The differential equations that represent HVDC dynamics are used to find the fault-on trajectory but not to derive the TEF. This leads to an error in TEF-based stability prediction. In this paper, a method is presented to derive the TEF with HVDC dynamics included. By using this improved TEF, the accuracy of the stability prediction in AC/DC systems is improved. In addition, due to the inclusion of differential equations that represent HVDC dynamics in the TEF, the calculation of the post-fault Stable Equilibrium Point (SEP) must be done only once. This reduces the CPU time required to find the Transient Energy Margin (TEM). The method is validated using several test systems by comparing results with those obtained from time simulation. The new transient energy function is used in a number of studies to establish its usefulness.

As the electric power demand increases, power systems become larger and more complicated, and the dependence on electric power has ever been increasing. Power outages have more social and economic influences. It is very important that we grasp the state of power systems and operate the power systems stably. Therefore, preventive control is more desirable in power system operations. In this paper we propose a generation rescheduling method for transient stability to solve this problem. At first, using the contingency screening, we select severe contingencies for transient stability. If severe contingencies are found, we compute the kinetic energy change at the fault clearing time. Using this information and the linear relationship between the critical clearing time and the generator output at the faulted bus for the critical contingency, we determine the ratio of the generation rescheduling. The proposed method is demonstrated using a sample 4 machine 7 bus system.

A method is presented to integrate the dynamics of DC power flow into calculations of transient energy functions of AC/DC power systems. This method treats the generator input power as a function of the parameters and the input, output variables of the DC damping power controller. By using this transient energy function, the accuracy of the stability prediction of AC/DC power systems is improved. The new transient energy function is validated using 12-bus and 30-bus test systems by comparing the stability results with those obtained from time simulation. The new transient energy function is used in a number of studies to establish its effectiveness.

In this paper, a transient energy function for AC/DC power systems is used to evaluate critical clearing times with consideration of the DC dynamics. The generator angular speeds, rotor angles and voltages at the HVDC link's AC buses are explicit variables in the transient energy function. A method for computing the transient energy function numerically is proposed. In the method, the angular speeds and rotor angles deemed as slow dynamics are integrated along sustained fault trajectories, and the integral path of the DC dynamics treated as fast dynamics are acquired by some modifications. The problems in applying potential energy boundary surface (PEBS) method to the computation of critical clearing time are also discussed. The critical clearing times for 4-generator and 10-generator AC/DC systems obtained using the transient energy function approximate well to those with the step-by-step time simulation.

This paper suggests a sparse feedback dynamic control scheme with unified power flow controller (UPFC) to prevent transmission system overload. An transient energy function (TEF) is developed to measure the security of the grid in dynamic. Firstly, a TEF are constructed based on AC dynamic model with transmission over-current tripping model. Then a power flows feedback control law is proposed to exploits the non-positive time derivative of the TEF with the accommodated admittance, to mitigate dynamic current violation. The accommodated admittance are obtained by TEF stability margin sensitivity analysis. With the proposed control method, the system can be driven to a stable state without current violation even if part of the UPFCs fails. Finally, the simulation results of 14 bus test case demonstrate the effectiveness, as well as the resilience of our proposed method.

Renewable power systems dynamic security using a new coordination of frequency control strategy based on virtual synchronous generator and digital frequency protection

Preventive and corrective control applications in power systems via Big Bang–Big Crunch optimization

This paper presents a methodology based on Artificial Neural Network (ANN) structures for the dynamic security assessment (DSA) of power systems. Proposed methodology involves, ANN approach for fast and accurate estimation of critical clearing time (CCT) values of credible faults occurring in the system, considering changes in the loading conditions and system topology. CCT is an important indicator that measures the transient stability of the system against critical contingencies. Offline trained ANNs can monitor online DSA without suffering from excessive computational burden of time domain simulations (TDS). Decision Trees are used as a feature selection tool to reduce the training time and ANN complexity, increasing the CCT estimation performance of the ANN applications studied in this work, Multi-Layer Perceptron, Radial Basis Neural Network, Generalized Regression Neural Network and Adaptive Neuro-Fuzzy Inference Systems. The proposed approach is applied to 16 generator-68 bus test system operating at various loading conditions and system topologies.

The real time transient stability assessment is the important steps of the dynamic security evaluation of power systems. To do this, intelligent systems (ISs) such as neural networks as an effective method to accurately and quickly estimate the critical clearing time (CCT) has been widely used. But, choosing the proper inputs for these systems remains a major challenge for researchers, still. Variables related to energy functions such as minimum kinetic energy, the slope of the variation of the minimum kinetic energy and maximum potential energy contain useful information in estimating CCT. Accordingly, in this paper, these variables are used as the IS inputs. However, the time-domain simulation of the power system response to obtain these inputs is time consuming. To be able to use the energy function-based inputs for real time stability assessment, in addition to the main IS used to estimate CCT, another ISs are used. By those ISs, a very limited period of system response is simulated to obtain proper inputs of the main IS. The method is simulated on the 10-generator New England test system. Simulation results show, the CCT can be found by simulation just 0.05 s of the considered power grid.