Dynamic security assessment using time-domain simulator

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.

The increasing complexity of power systems due to their growth, operation close to technical limits, economical constraints and new regulations, calls for the inclusion of dynamic security assessment (DSA) into energy management systems where the overall system control is performed. Recent researches on DSA have shown a significant interest in the accuracy of tools based on hybrid methods, a combination of time domain simulation and transient energy functions. A synthetic review of a hybrid method based procedure, implemented by the authors using a suitably adapted simulation code, is first recalled. Successively the possible application of a tool based on artificial neural networks (ANN) for operating a contingency pre-screening by identifying stable and unstable situations is proposed. The ANN capability of identifying the stable and unstable clusters which may originate after a perturbation is also investigated. The proposed ANN structure has been trained and validated by simulating properly arranged scenarios from a test system constituted by a real size HV network of the Mediterranean area.

With the development of synchronised measurement technique, online dynamic security assessment (DSA) is of great significance to prevent power system blackout. Recently, based on the phasor measurement unit (PMU) data, intelligent data‐driven techniques have been rapidly developed for online DSA owing to their fast decision speed, less data requirement, and decision rule discovery ability. The interpretable decision rules provide useful information for preventive control and post‐event auditing. However, in case of incomplete measurement events, such as PMU failure, communication loss, and phasor data concentrator failure, the accuracy and the transparency of the intelligent models can be significantly impaired by such incomplete data. To overcome this problem, this paper proposes a robust white‐box model that can sustain DSA accuracy and survive the model interpretability and transparency against incomplete PMU data. The proposed model is tested on New England 39‐bus system and demonstrates higher robustness over existing methods.

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.

A dynamic state estimator for power system dynamic security assessment

This chapter discusses the issues associated with the successful design and implementation of an online dynamic security assessment system. The need for, the benefits of, and the current application status of such systems are presented. The discussions are primarily focused on the practical issues for implementing an online Dynamic Security Assessment (DSA) system, based on the experiences gained from recent applications. Finally, an overview of some future directions on the online DSA technologies is provided.

This paper presents a system-wide approach for contingency severity screening intended as a practical and scalable precursor to full dynamic security assessment (DSA). DSA is the standard practice used by Transmission System Operators (TSOs) to ensure that power systems remain stable following disturbances, with key variables—such as voltage, frequency, and thermal loading—kept within defined dynamic performance limits. However, applying DSA comprehensively across all credible contingencies and operating points is prohibitive, making a pre-screening stage indispensable. Commonly used static-security filters offer limited insight to transient dynamics, motivating the development of alternative screening strategies. To support early-stage analysis and prioritization, three static indicators are devised as proxies for dynamic behaviour, grounded in the power–angle characteristics determining transient stability. For eachn−1 contingency, these indicators provide a severity estimate and serve as inputs to a supervised learning model trained on labels derived from time-domain simulation. This approach aims to reflect how each disturbance might impact system stability, consistent with the objectives of DSA. Contingency rankings derived from these estimates serve to guide further analysis, enabling TSOs to focus dynamic simulations on the most critical scenarios.

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.

The increasing penetration of wind power can alter the dynamic security characteristic of a power system. To accommodate rapid and volatile wind power variations, dynamic security assessment (DSA) against foreseeable disturbances is required to be carried out online and provide security monitoring results within sufficiently small time frame. Based on soft computing (SC) technologies, this paper develops an intelligent framework for real-time DSA of power systems with large penetration of wind power. It consists of a DSA engine whose role is to perform real-time DSA of the power system, a wind power and load demand (W&LF) forecasting engine for offline and online predicting wind power generation and electricity load demand, a database generation (DBG) engine for generating instances to train the DSA engine, and a model updating (MU) engine for online updating the DSA engine. Case studies are conducted on two benchmark systems where high DSA efficiency and accuracy are obtained. This framework can be an ideal candidate for advanced security monitoring in the future SmartGrid control centres.

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.

Areas discussed are power system modeling, state estimation, structure decomposition, state forecasting, clustering and security measure development. A detailed dynamic model of a multi-machine power system has been developed. A process state estimator was developed to estimate the long-term dynamic behavior of the power system. The algorithm is identical to the extended Kalman filter but has a modified process noise driving term. A two-stage structure estimation technique was proposed for identifying the power system network configuration. Two approaches to structure decomposition were investigated. A time-scale decomposition of the system equations, based on a singular perturbation approach, was evaluated using a detailed model of a generating system. Spatial decomposition was examined by applying an optimal network decomposition technique to a 39-bus test system. Stochastic approximation based approaches to estimator simplification were examined. Explicit expressions were obtained for the evolution of the first and second moments of the system state. Research into security measures proceeded in three directions. The first area involves viewing the security assessment problem as a hyperplane crossing problem for a stochastic process. The second approach examined the stability of an unforced linear system where the system coefficients are subject to future jumps. The third area of research has led to the formulation of a security measure suitable for on-line assessment of transient stability.

The liberalization of the electricity market induces a large variety of scenarios that may lead power systems close to their operation limits. This supports the need for on-line dynamic security assessment (DSA) of the grids, in order to provide operators with a clear insight of the current network state. The on-line application of DSA to a realistic network needs adequate methods to screen the large amount of contingencies to be examined by DSA tools. This paper proposes some practical heuristic indices for Transient Stability contingency pre-filtering and ranking in an on-line DSA session.

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.

A new dynamic security assessment framework based on semi-supervised learning and data editing

A major drawback of current dynamic security assessment (DSA) software used to analyze the transient and dynamic responses to a large number of potential power system disturbances in a transient time frame is the long simulation time required to complete the calculations, making it impractical for real-time applications. This paper describes an attempt to dramatically reduce the time for DSA calculations so that DSA analyses can be converted from offline studies to routine, online use to aid grid operators in their real-time controller analyses. Using a new distributed computational architecture among other techniques, a modified version of DSA is developed which performs calculations fast enough for real-time applications to energy management systems (EMS) that operate today's grid systems.