Practical Issues for Implementation of Online Dynamic Security Assessment Systems

To prevent or suppress the low-frequency oscillation in real time, the modal sensitivity with respect to operating parameters is calculated. It is applied to generate the effective control strategy that can improve the system damping. Initially, Fuzzy ISODATA clustering method is used to obtain the coherent generation group, from which candidate generators for rescheduling are formed and further refined by using participation factors. With the efficient parallel computation of eigen-sensitivity, the relationship between modes of interest and operating parameters can be obtained and used to provide system operators with real-time system operation strategies to improve the system damping. The proposed control strategy satisfies many constraints, such as power output limits and real time operational constraints. The feasibility and applicability of the proposed method has been tested using an actual large scale power system of China with more than 20000 nodes. The time taken to solve this large system is just over 2 minute with parallel computation. The proposed method has been implemented in the on-line dynamic security assessment and early warning system of State Grid Corporation of China (SGCC).

In this paper, implementation of power system dynamic security assessment (DSA) into a time domain simulation environment is described. The paper discusses general characteristics of DSA in time domain and presents a framework for software implementation. The implemented DSA system is user oriented and enables simple investigation of power system security in normal or contingency provoked system conditions. Using continuous processing, DSA is fully automated and can process any number of cases without user interaction, however, with some user established security constraints. If a security violation is detected, the contingency is documented and more detailed case study is possible. The paper presents methodology of implementation of DSA into well established simulator PSStradeNETOMAC, an overview of the considered power system security constraints and visualization options developed. The paper concludes with a case study. The DSA system was tested for performance on a generic single processor computer.

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.

On-line dynamic security assessment offers several significant engineering and financial benefits. However, Current EMS systems can not perform online dynamic security assessment (DSA) to ensure the ability of the power system to withstand some contingencies. We have designed and implemented a reliable and effective system, called BCU-DSA, for online dynamic security assessments and energy margin calculations of practical power systems. BCU-DSA system achieves high level of reliability and accuracy by effective exploration of the merits of both the improved BCU classifiers and the detailed time-domain simulation program. The system has been evaluated on practical power system models with promising results. The BCU-DSA system gives exact stability/instability assessments and accurate energy margin for every contingency of practical power systems.

The paper reports on the activity of an EU funded research project named OMASES that concerns a preventive approach to dynamic security assessment (DSA) and control of large power systems. The aims of the project are presented. A short description of the hardware and software architecture for implementing on-line DSA are recalled. Some hints about the theoretical background for the specific applications within OMASES (TSA-transient stability assessment, VSA-voltage stability assessment, TS-training simulator and MS-market simulator) are described. Preliminary results of the prototyping phase are commented upon.

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.

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

In the following work, A machine learning-based Online Dynamic security assessment (DSA) scheme is proposed for which Energy-based Kinetic energy terms are applied as input features. The aim is to develop simple pre-processed input features that are easier to calculate and when applied as inputs, require fewer samples of data to develop a trained model. The model is trained using the current system topological condition. The proposed scheme is implemented using a Bayesian neural network (BNN), which is developed by the Bayes-by-Backprop algorithm. The performance of the proposed input features is demonstrated with IEEE 39 bus test system. When the Energy-based kinetic energy terms are applied as input features it is shown that the model can be trained with fewer samples of data.

In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data mining-based model for DSA, the Bayesian neural network is learned by Bayes by Backprop to detect the security status in real-time and to inform the system operators with the confidence of the prediction. For dynamic insecurity prevention, the security-constrained optimal power flow (SCOPF) model incorporating the Bayesian neural network based security constraints is proposed, which is solved by the derivation-free Bayesian optimization. Case studies on the IEEE 39-bus system show that the Bayesian neural network is able to detect the security status of the power system correctly and reliably and is able to avoid the over-fitting problem compared with the traditional point estimation neural network. Also, Bayesian optimization is able to solve the non-analytical SCOPF model efficiently to mitigate the dynamic insecurity by preventive generation rescheduling.

In recent years, as power systems have become larger, power oscillations have become multifaceted, and so online Dynamic Security Assessment (DSA) has become essential to estimate these low-frequency electromechanical modes. To use this information for stabilization control of angle stability, it is important to obtain information on not only the frequency and damping of those modes but also the relative phase relation between each generator. This paper introduces a technique to estimate the relative phase relation of generators from time series data. The proposed method is applied to a two-variable ARMA model to estimate the low-frequency modes, and is applied to the cross spectrum to estimate the eigenvector efficiently. The proposed technique is verified by numerical examples with the IEEE 39-Bus system and the West 30-machine system model (115-Bus) which simulates the power system of Japan, and is compared with the eigenvector estimated by eigenvalue analysis.

With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses.

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.

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 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.

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.