Biblio
This paper presents some of our first experiences and findings in the ARPA-E project ReNew100, which is to develop an operator support system to enable stable operation of power system with 100% non-synchronous (NS) generation. The key to 100% NS system, as found in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. In this paper, we demonstrate in Electro-Magnetic-Transient (EMT) simulations, based on Hawai'i big island system with 100% NS capacity, that a system can be operated stably with the help of GFM inverters and appropriate controller parameters for the inverters. The dynamic security optimization (DSO) is introduced for optimizing the inverter control parameters to improve stability of the system towards N-1 contingencies. DSO is verified for five critical N-1 contingencies of big island system identified by Hawaiian Electric. The simulation results show significant stability improvement from DSO. The results in this paper share some insight, and provide a promising solution for operating grid in general with high penetration or 100% of NS generation.
This paper puts forward a dynamic reduction method of renewable energy based on N-1 safety standard of power system, which is suitable for high-voltage distribution network and can reduce the abandoned amount of renewable energy to an ideal level. On the basis of AC sensitivity coefficient, the optimization method of distribution factor suitable for single line or multi-line disconnection is proposed. Finally, taking an actual high-voltage distribution network in Germany as an example, the simulation results show that the proposed method can effectively limit the line load, and can greatly reduce the line load with less RES reduction.
This article reports results about the development of the algorithm that allows to increase the information security of OFDM communication system based on the discrete-nonlinear Colpitts system with dynamic chaos. Proposed system works on two layers: information and transport. In the first one, Arnold Transform was applied. The second one, transport level security was provided by QAM constellation mixing. Correlation coefficients, Shannon's entropy and peak-to-average power ratio (PAPR) were estimated.
This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.
The correctness of security control system strategy is very important to ensure the stability of power system. Aiming at the problem that the current security control strategy verification method is not enough to match the increasingly complex large power grid, this paper proposes a cyclic verification method of security control system strategy table based on constraints and whole process dynamic simulation. Firstly, the method is improved based on the traditional security control strategy model to make the strategy model meet certain generalization ability; And on the basis of this model, the cyclic dynamic verification of the strategy table is realized based on the constraint conditions and the whole process dynamic simulation, which not only ensures the high accuracy of strategy verification for the security control strategy of complex large power grid, but also ensures that the power system is stable and controllable. Finally, based on a certain regional power system, the optimal verification of strategy table verification experiment is realized. The experimental results show that the average processing time of the proposed method is 10.32s, and it can effectively guarantee the controllability and stability of power grid.
Aiming at the problems of imperfect dynamic verification of power grid security and stability control strategy and high test cost, a reliability test method of power grid security control system based on BP neural network and dynamic group simulation is proposed. Firstly, the fault simulation results of real-time digital simulation system (RTDS) software are taken as the data source, and the dynamic test data are obtained with the help of the existing dispatching data network, wireless virtual private network, global positioning system and other communication resources; Secondly, the important test items are selected through the minimum redundancy maximum correlation algorithm, and the test items are used to form a feature set, and then the BP neural network model is used to predict the test results. Finally, the dynamic remote test platform is tested by the dynamic whole group simulation of the security and stability control system. Compared with the traditional test methods, the proposed method reduces the test cost by more than 50%. Experimental results show that the proposed method can effectively complete the reliability test of power grid security control system based on dynamic group simulation, and reduce the test cost.
This contribution provides the implementation of a holistic operational security assessment process for both steady-state security and dynamic stability. The merging of steady-state and dynamic security assessment as a sequential process is presented. A steady-state and dynamic modeling of a VSC-HVDC was performed including curative and stabilizing measures as remedial actions. The assessment process was validated by a case study on a modified version of the Nordic 32 system. Simulation results showed that measure selection based on purely steady-state contingency analysis can lead to loss of stability in time domain. A subsequent selection of measures on the basis of the dynamic security assessment was able to guarantee the operational security for the stationary N-1 scenario as well as the power system stability.
In this work, the algorithm of increasing the information security of a communication system with Orthogonal Frequency Division Multiplexing (OFDM) was achieved by using a discrete-nonlinear Duffing system with dynamic chaos. The main idea of increasing information security is based on scrambling input information on three levels. The first one is mixing up data order, the second is scrambling data values and the final is mixing symbols at the Quadrature Amplitude Modulation (QAM) plot constellation. Each level's activities were made with the use of pseudorandom numbers set, generated by the discrete-nonlinear Duffing system with dynamic chaos.
In dynamic control centers, conventional SCADA systems are enhanced with novel assistance functionalities to increase existing monitoring and control capabilities. To achieve this, different key technologies like phasor measurement units (PMU) and Digital Twins (DT) are incorporated, which give rise to new cyber-security challenges. To address these issues, a four-stage threat analysis approach is presented to identify and assess system vulnerabilities for novel dynamic control center architectures. For this, a simplified risk assessment method is proposed, which allows a detailed analysis of the different system vulnerabilities considering various active and passive cyber-attack types. Qualitative results of the threat analysis are presented and discussed for different use cases at the control center and substation level.
In this work, an asymmetric cryptography method for information security was developed, inspired by the fact that the human body generates chaotic signals, and these signals can be used to create sequences of random numbers. Encryption circuit was implemented in a Reconfigurable Hardware (FPGA). To encode and decode an image, the chaotic synchronization between two dynamic systems, such as Hopfield neural networks (HNNs), was used to simulate chaotic signals. The notion of Homotopy, an argument of topological nature, was used for the synchronization. The results show efficiency when compared to state of the art, in terms of image correlation, histogram analysis and hardware implementation.
Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.
Security evaluation of diverse SDN frameworks is of significant importance to design resilient systems and deal with attacks. Focused on SDN scenarios, a game-theoretic model is proposed to analyze their security performance in existing SDN architectures. The model can describe specific traits in different structures, represent several types of information of players (attacker and defender) and quantitatively calculate systems' reliability. Simulation results illustrate dynamic SDN structures have distinct security improvement over static ones. Besides, effective dynamic scheduling mechanisms adopted in dynamic systems can enhance their security further.
Enhanced situational awareness is integral to risk management and response evaluation. Dynamic systems that incorporate both hard and soft data sources allow for comprehensive situational frameworks which can supplement physical models with conceptual notions of risk. The processing of widely available semi-structured textual data sources can produce soft information that is readily consumable by such a framework. In this paper, we augment the situational awareness capabilities of a recently proposed risk management framework (RMF) with the incorporation of soft data. We illustrate the beneficial role of the hard-soft data fusion in the characterization and evaluation of potential vessels in distress within Maritime Domain Awareness (MDA) scenarios. Risk features pertaining to maritime vessels are defined a priori and then quantified in real time using both hard (e.g., Automatic Identification System, Douglas Sea Scale) as well as soft (e.g., historical records of worldwide maritime incidents) data sources. A risk-aware metric to quantify the effectiveness of the hard-soft fusion process is also proposed. Though illustrated with MDA scenarios, the proposed hard-soft fusion methodology within the RMF can be readily applied to other domains.