Research progress in dynamic security assessment

The increasing complexity of power systems as well as the rapid expansion of renewable energy resources and deregulation of electricity industry lead to continual demand for operating power systems at higher efficiencies. The modern power systems are pushed to operate close to their secure limits. When subjected to a severe contingency, the stressed power systems may lose its dynamic security, resulting in catastrophic consequences such as cascading failure and even wide-spread blackout. Under this new reality, online and real-time power system stability assessments have great impacts on avoiding blackout events. The traditional stability methods are mainly based on time-domain simulation, which are not fast enough to satisfy the online and real-time application requirements. In recent years, with the development of advanced real-time system monitoring and communication techniques, the data-driven methods have been identified as powerful tools to help power systems counteract large disturbance and avoid instability. To solve above threats, this Ph.D. research aims to develop data-analytics methodologies from power grid measurement data to recognize the system stability, covering both pre-fault dynamic security assessment (DSA) and post-fault real-time short-term voltage stability (STVS) assessment areas under some extreme scenarios and practical issues. Specific objectives of this Ph.D. research are to develop an intelligent system for the future smart grid. Based on several kinds of promising advanced data-analytics technologies, including randomized learning, incremental broad learning (BL), transfer learning (TL), adversarial machine learning (ML), and generative adversarial network (GAN), etc, an intelligent system will be developed to interpret the power grid stability characteristics, and to support stability-constrained operation control actions. Hence, it can make power systems achieve seven goals, including extensibility, efficiency, robustness, security, personalization, reliability, and effectiveness. The outcomes of this Ph.D. research will enable a more secure, intelligent, and knowledge-transparent future of the next-generation smart grid. The main contributions through this Ph.D. research are as follows: Firstly, the temporal-adaptive data-driven methods are proposed based on optimized hybrid ensemble learning mechanism for online STVS assessment. Compared to the traditional data-driven stability methods, the proposed methods can balance the tradeoff between the stability accuracy and speed and select the most suitable Pareto set. Secondly, in the area of DSA, an online updating DSA method is developed by using incremental BL technique. The incremental BL method only needs to calculate the pseudoinverse of new state matrix without the computations of the whole output weights matrix. In this way, it does not need re-training the whole model to continuously maintain and improve the online DSA accuracy. Besides, the proposed method can achieve under three different increment scenarios, including increment of enhancement hidden nodes, features, and new training instances. Thirdly, in the area of missing phasor measurement unit (PMU) data, two different fully robust data-driven stability methods are developed, respectively for pre-fault DSA and post-fault STVS assessment. The former one is based on GAN without observability-constrained feature subsets to ensure its availability under any PMU missing scenario. The latter one is based on super-resolution perception and incremental BL, which can achieve both satisfactory stability accuracy and speed. Fourthly, in order to accurately quantify the robustness of the ML-based model under adversarial perturbations on safety-critical power system stability problem, an adversarial robustness verification method for data-driven stability models is proposed. Besides, a model-free and attack-independent robust index are defined to evaluate the ability of the ML-based stability models against adversarial examples, which can be used to select the candidate model and provide formal robustness guarantee for stability application in practice. Rigorous mathematic proof is provided for the robust index under both linear and non-linear binary scenarios. Moreover, the proposed robust index can be used to mitigate the adversarial examples via adversarial training. Finally, three different TL-based DSA methods are proposed to use one known fault to evaluate other related but different unknown faults. The first two methods aim to reduce the distribution differences between the trained data and unknown data with the complete unlearned faults. The third one considers the different faults with incomplete data scenario. Through applying the adversarial training process, the DSA model trained by one fault can work for a different but related fault by confusing domain discriminator. With the property of feature extractor network, the proposed integrated TL-based method can solve the missing data issue for the unknown faults. All the methodologies have been tested on benchmark power systems to verify their effectiveness, and comparative and comparative studies with existing methods in the literature are conducted where applicable.

Integrating non-dispatchable renewable energy sources (RES) such as wave farms into electric power systems presents inherent challenges. Specifically, the variability of the resource and its non-dispatchable) nature introduces power imbalances and power system stability problems in the system over both short and medium terms. This issue is particularly relevant for isolated power systems, such as those found on several European islands, where interconnection with the mainland is lacking. Therefore, although wave energy has significant potential for integration into island power systems due to resource availability, determining the maximum RES penetration that such systems can sustain while maintaining stability is not straightforward. Power system stability is closely tied to frequency. A well-established solution for improving the stability of isolated systems when integrating non-dispatchable RES is to install devices capable of responding to over- or under-frequency events, such as energy storage systems (ESS). The literature extensively discusses ESS control techniques for frequency regulation, with one of the most straightforward approaches being to operate the ESS according to a droop control strategy (similar to a statism). In this method, the power dispatched or stored by the ESS increases proportionally to the deviation from the frequency reference value. This paper presents a case study analyzing the frequency stability of an island power system equipped with an energy storage system (ESS) when integrating a wave farm. Frequency stability is evaluated based on two variables: wave energy penetration and the ESS droop control parameter. The case study is based on the island of El Hierro (Canary Islands, Spain), an isolated electrical system with a very high penetration of RES. The island’s electrical generation system consists of a wind farm, a pumped-storage hydroelectric power plant, and conventional generation from a diesel power plant. The analysis is conducted through simulations in MATLAB Simulink. A wave farm is modeled to evaluate the generated power and its associated oscillations, based on the wave energy resource at various locations along El Hierro’s coast. Additionally, an aggregated inertial dynamic model of the electrical power system is employed to assess the impact of the wave energy farm's generation on the system’s frequency and the aging that the frequency excursions introduce in the elements of the system. The Spanish Grid Code is considered to ensure compliance with frequency regulation mechanisms in isolated systems. Simulations are performed across different scenarios, varying both the nominal power of the wave farm and the ESS droop control parameter, in order to evaluate the ESS capacity. The results identify the optimal droop control parameter for each wave farm size to ensure frequency remains within acceptable thresholds. Furthermore, the minimum ESS capacity required for each wave farm size is determined from the simulations. These findings provide a basis for ESS and wave farm dimensioning, which could be translated into economic terms to maximize revenue (this aspect is beyond the scope of the present work).

Contingency ranking is one of the methods used in power system operations and planning to foresee the behavior of a system when there is a large disturbance in the system. Several indices have been proposed in the literature for estimating the severity of a particular contingency but mostly in the area of static security assessment. Therefore, in order to effectively capture the performance of a large power system under contingencies, dynamic security assessment (DSA) indices would be more desirable. In this paper, a score is presented with relatively less computational burden and used to determine the severity of a large disturbance in the power system for the purpose of contingency ranking. This score is formulated from the algebraic state variables that are related to the machines in a multi-machine power system modeled with differential algebraic equations (DAE). The proposed score is used with the angle stability criteria and applied on the IEEE 14-Bus test system in Matlab environment to determine the critical transmission lines in the system.

Artificial neural networks (ANN) based pattern recognition for steady state (SSA), transient (TSA) and dynamic security assessment (DSA) of the power systems is presented. Conventional methods for security assessment take lot of time to assess the security of power systems when all contingencies are considered. Artificial neural networks based pattern recognition is used to solve SSA, TSA and DSA and its effectiveness against conventional methods is presented. In addition contingency ranking and generator severity ranking is used to measure the severity of various contingencies which allows an operator to take preventive control (generator rescheduling) or to be ready for corrective actions. Direct methods are fast compared to numerical methods but proper choice of Lyapunov function is difficult. ANN based PR for SSA, TSA and DSA effectiveness is compared with other conventional methods like pattern recognition which confirms that ANN not only gives accurate results but also is adaptive to the topology changes of the power system. In addition contingency ranking and generator severity ranking has been addressed which allows the operator to take preventive control actions like generator rescheduling.

Guest Editorial: Situational awareness of integrated energy systems

A Dynamic simulation Model (DynMo-CBC) is developed and demonstrated for a power system comprised of a gas cooled reactor and three independent CBC loops. The reactor core is divided into three sectors that are hydraulically independent, but thermally and neutronically coupled. Each reactor sector is directly coupled to a CBC loop with a Brayton Rotating Unit designated UNM-BRU-1 and NaK-78 secondary loop with water heat pipes radiator panels. The results presented are for the startup transient to steady-state operation of the space power system at a UNM-BRU-1 shaft speed of 45 krpm, turbine and compressor inlet temperatures of 1149 K and 400 K, and reactor thermal power of 471 kWth. At these conditions, the electrical power and thermal efficiency of the S^4-CBC power system are 130.8 kWe and 27.8%.

This paper presents an on-going research project to develop an intelligent system (IS) that can be used together with existing power system dynamic security assessment tools in order to enhance the capabilities of online security assessment for power system operations. The use of intelligent systems in this application is expected to improve the speed, accuracy, and robustness of security assessment. A prototype system named POSSIT, POwer System Security using Intelligent Technologies, is presently being developed. An overview of the functional design of this prototype is given here and shows how this approach is applied to the Hydro-Quebec and BC Hydro power systems.

Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm

Preface. Contributors. 1 The Real-Time and Study-Mode Data Environment in Modern SCADA/EMS ( Sudhir Virmani and Savu C. Savulescu ). 1.1 Introduction. 1.2 SCADA/EMS Architectures. 1.3 Integrating Stability Applications with the SCADA/EMS. 1.4 References. 2 Overview of Key Stability Concepts Applied for Real-Time Operations ( Savu C. Savulescu ). 2.1 Introduction. 2.2 In Search of the Stability Limits. 2.3 Transient and Voltage Stability Limits. 2.4 Steady-State Stability Limits. 2.5 Concluding Remarks. 2.6 References. Annex 1-1. Reactive Power Steady-State Stability Criterion dDELTAQ / dV. 3 LIPA Implementation of Real-Time Stability Monitoring in a CIM Compliant Environment ( Loris Arnold, Janos Hajagos, Susan M. Manessis, and Anie Philip ). 3.1 Introduction. 3.2 Static and Dynamic Security Assessment at LIPA. 3.3 Benchmarking the Real-Time Stability Application. 3.4 Practical Experience and Outlook. 3.5 References. 4 Real-Time Stability Monitoring at the Independent System Operator in Bosnia and Herzegovina ( Dusko Vickovic and Roland Eichler ). 4.1 Introduction. 4.2 Interim Implementation of Real-Time Stability Assessment at NOS BiH. 4.3 Real-Time Stability Assessment in the New SCADA/EMS Environment. 4.4 Conclusions and Recommendations. 4.5 References. Annex 4-1. TSL, TTC, and the Stability Envelope. Annex 4-2. Siemens Implementation of the Continuation Power Flow. 5 Experience with Real-Time Stability Assessment at Transelectrica ( Horia S. Campeanu, Cornel Erbasu, and Cornel Aldea ). 5.1 Introduction. 5.2 Security Assessment Philosophy and Criteria. 5.3 Real-Time Steady-State Stability Assessment and Monitoring. 5.4 Off-Line Stability Tools in Support of System Operations. 5.5 Conclusions and Outlook. 5.6 References. 6 Implementation of Online Dynamic Security Assessment at Southern Company ( Kip Morison, Lei Wang, Fred Howell, James Viikinsalo, and Alan Martin ). 6.1 Introduction. 6.2 DSA Implementation Fundamentals. 6.3 Transient Security Assessment Implementation at Southern Company. 6.4 Conclusions. 6.5 References. Annex 6-1. Further Details of the DSA Software and Hardware Architecture. Description of the Core DSA Software. Online DSA Implementation Using DSATools. 7 Online Security Assessment for the Brazilian System A Detailed Modeling Approach ( Jorge L. Jardim ). 7.1 Introduction. 7.2 Security Criteria and Functions. 7.3 Solution Methods and Architecture. 7.4 Practical Implementation Aspects. 7.5 User Interface And Performance. 7.6 Concluding Remarks. 7.7 Acknowledgments. 7.8 References. 8 Dynamic Network Security Analysis in a Load Dispatch Center ( Guenther Beissler, Olaf Ruhle, and Roland Eichler ). 8.1 Introduction. 8.2 Siemens Approach to Dynamic Security Assessment. 8.3 Case Studies: Challenges, Implementation Approach, and Solution Features. 8.4 References. Annex 8-1. Further Dynamic Simulation Capabilities. Time Frame for Dynamic Simulations. Simulation in the Frequency Domain. Eigenvalue and Modal Analysis. 9 Real-Time Transient Security Assessment in Australia at NEMMCO ( Stephen J. Boroczky ). 9.1 Introduction. 9.2 Transient Security Assessment at NEMMCO. 9.3 Performance and Reliability. 9.4 Experience, Benefits, and Outlook. 9.5 References. 10 Online Voltage Security Assessment in the Hellenic Interconnected System ( Costas Vournas, George Christoforidis, and Thierry Van Cutsem ). 10.1 Introduction. 10.2 The Control Center of HTSO. 10.3 Online VSA in the Hellenic System. 10.4 Use of Online VSA For Arming Load-Shedding Protection. 10.5 Conclusion. 10.6 References. Annex 10-1. Quasi-Steady-State Simulation. Principle of the QSS Approximation. Handling of Frequency in QSS Simulation. QSS Model of the Synchronous Machine and its Regulations. Numerical Integration of the QSS Model. 11 The Real-Time Supervision of Transmission Capacity in the Swedish Grid ( Lars Sandberg and Klas Rouden ). 11.1 Introduction. 11.2 Prior and Current Application Development at SVK. 11.3 Voltage Security Assessment with SPICA. 11.4 Benefiting from the Knowledge of the Current Transmission Capacity. 11.5 Additional SPICA Functionality. 11.6 Summary. Appendix A Dimo's Approach to Steady-State Stability Assessment: Methodology Overview, Numerical Example, and Algorithm Validation ( Roberto D. Molina Mylius, Martin Cassano, and Savu C. Savulescu ). A.1 Methodology Overview. A.2 Numerical Example Independent Testing of Algorithm Implementation. A.3 Benchmarking the Methodology. A.4 Conclusions. A.5 References. Appendix B SIME: A Comprehensive Approach to Transient Stability ( Mania Pavella, Daniel Ruiz-Vega, and Mevludin Glavic ). B.1 Introduction. B.2 Basic Formulation. B.3 Preventive SIME. B.4 Emergency SIME. B.5 Postface. B.6 References. Notation. Abbreviations and Acronyms. Appendix C Detection and Evaluation of Stability Constrained ( Marius Pomarleanu and Savu C. Savulescu ). C.1 Introduction. C.2 Approach. C.3 Conclusions. C.4 References. Index.

In recent years, power systems have become more dependent on new technological advancement in the communication network to send and receive data and commands through the wide area power network. This dependence has created a new threat to the network, known as a cyber-attack. Such attacks could lead to blackouts and the consequences on the security of the power system would be severe. This study presents a new approach, which aims at assessing the dynamic behavior of the test system’s model under a cyber-attack contingency. The methodology used in this study was based on the scenarios of cyber-attacks on the protection relay devices to generate the database for dynamic security assessment. Then, a data mining framework was used for the database preparation and classification via feature selection algorithm and decision tree classifier. The results of the modified IEEE 30-bus test system model in this study showed a high accuracy of 99.537%, and a short time frame that makes this application suitable for real-time application to protect the power network from an insecure state and ensure that the power system remains reliable.

This paper presents an efficient security assessment system for validating power system operation planning, which has been used in Henan provincial power grid in China. Daily operation planning is to schedule unit commitment and to dispatch generator output economically considering next day generators and transmission components' maintenance planning. The security assessment for validating the daily operation planning includes static security assessment and dynamic security assessment which are done for all the 96 power flow snapshots for next day. It is obviously a big challenge in reducing exhausting calculation burden. To speed up the calculation, a flexible and distributed parallel computing platform is designed which is based on a PC cluster and Multi-Agent system. The framework of the security assessment system for daily operation planning validation is introduced. And the technologies to implement the high performance computing are given. Some calculations for daily operation planning validation for control centre of Henan power grid are done and the results show that the parallel computing platform is efficient in computation.

Power system stability has become an increasing concern in renewable power integration because the active power generated by renewable energy varies randomly. Thus, we focus on random outside excitations in this study. Since the power obeys Non‐Gaussian distribution in most case, a novel small disturbance stochastic stability analysis method based on entropy is presented. First, the dynamic model of power system with random outside excitation is established by stochastic differential equation. Then, the values of mean and mean square stability are efficiently derived by transforming the stochastic differential equation into a deterministic mean or variance equation. For non‐Gaussian disturbance, entropy stability is presented by this transformation. At last, the numerical simulations are presented on a WSCC three‐generator nine‐bus power system with a wind farm © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

The growth of large interconnected electricity networks requires a high degree of security for normal operation. This paper attempts to overview several available techniques for assessing the Transient Security Assessment (TSA) of a power system. Different algorithms are explained in details (Static and Dynamic Security Assessment) with the uses of Artificial Intelligent (AI) method. Transient effects can be roughly described as undesired voltage/ current that may result a contingencies in the power system. However, it is only considered lightning and switching as the main causes of TSA. In this proposed method, Artificial Neural Network (ANN) and Fuzzy techniques are able to use in term of classification, prediction and to determine the system security status. Time domain analysis is performed for each credible contingency using signal processing method; than an AI model is proposed for the TSA analysis. The novelty of the proposed approach is that the fast ability to detect and classify any disturbance (lightning (or) switching) in the electric power system using AI techniques.

In this paper, the nonlinear dynamic model of a multi machine power system incorporated with Interline Power Flow Controller [IPFC] has been developed for improvement in damping of power system oscillations and transient stability. The IPFC performance is tested with PI controllers in comparison with fuzzy logic based controller under healthy and abnormal operating conditions. The IPFC fuzzy controller is design to coordinate two control inputs: change in voltage and change in capacitor voltage to improve the transient stability of the multimachine system. The Interline Power Flow Controller [IPFC] with fuzzy logic controller is designed with simple fuzzy rules to coordinate the additional damping signal. The proposed controller for IPFC is able to achieve improved designed performance of the power system. Digital simulations are carried out in MATLAB environment.

The nonlinear dynamic model of a multi machine power system incorporating an Interline Power Flow Controller [IPFC] was developed in this paper to improve damping of power system oscillations and transient stability. Under normal and abnormal operating conditions, the IPFC performance with PI controllers is compared to that of a fuzzy logic-based controller. Fuzzy Logic is a form of many-valued logic.   The IPFC fuzzy controller is intended to coordinate two control inputs: voltage change and capacitor voltage change, in order to improve the transient stability of the multimachine system. To coordinate the additional damping signal, the Interline Power Flow Controller [IPFC] with fuzzy logic controller is designed with simple fuzzy rules. The proposed IPFC controller can improve the designed performance of the power system. The digital simulations are performed in the MATLAB environment.