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Zhang, Yanjun, Zhao, Peng, Han, Ziyang, Yang, Luyu, Chen, Junrui.  2022.  Low Frequency Oscillation Mode Identification Algorithm Based on VMD Noise Reduction and Stochastic Subspace Method. 2022 Power System and Green Energy Conference (PSGEC). :848–852.
Low-frequency oscillation (LFO) is a security and stability issue that the power system focuses on, measurement data play an important role in online monitoring and analysis of low-frequency oscillation parameters. Aiming at the problem that the measurement data containing noise affects the accuracy of modal parameter identification, a VMD-SSI modal identification algorithm is proposed, which uses the variational modal decomposition algorithm (VMD) for noise reduction combined with the stochastic subspace algorithm for identification. The VMD algorithm decomposes and reconstructs the initial signal with certain noise, and filters out the noise signal. Then, the optimized signal is input into stochastic subspace identification algorithm(SSI), the modal parameters is obtained. Simulation of a three-machine ninenode system verifies that the VMD-SSI mode identification algorithm has good anti-noise performance.
Sen, Ömer, Eze, Chijioke, Ulbig, Andreas, Monti, Antonello.  2022.  On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach. 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :380–386.
While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.
Kumar, T. Ch. Anil, Dixit, Ganesh Kumar, Singh, Rajesh, Narukullapati, Bharath Kumar, Chakravarthi, M. Kalyan, Gangodkar, Durgaprasad.  2022.  Wireless Sensor Network using Control Communication and Monitoring of Smart Grid. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). :1567—1570.
For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.
Wu, Haijiang.  2022.  Effective Metrics Modeling of Big Data Technology in Electric Power Information Security. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). :607—610.
This article focuses on analyzing the application characteristics of electric power big data, determining the advantages that electric power big data provides to the development of enterprises, and expounding the power information security protection technology and management measures under the background of big data. Focus on the protection of power information security, and fundamentally control the information security control issues of power enterprises. Then analyzed the types of big data structure and effective measurement modeling, and finally combined with the application status of big data concepts in the construction of electric power information networks, and proposed optimization strategies, aiming to promote the effectiveness of big data concepts in power information network management activities. Applying the creation conditions, the results show that the measurement model is improved by 7.8%
Guili, Liang, Dongying, Zhang, Wei, Wang, Cheng, Gong, Duo, Cui, Yichun, Tian, Yan, Wang.  2022.  Research on Cooperative Black-Start Strategy of Internal and External Power Supply in the Large Power Grid. 2022 4th International Conference on Power and Energy Technology (ICPET). :511—517.
At present, the black-start mode of the large power grid is mostly limited to relying on the black-start power supply inside the system, or only to the recovery mode that regards the transmission power of tie lines between systems as the black-start power supply. The starting power supply involved in the situation of the large power outage is incomplete and it is difficult to give full play to the respective advantages of internal and external power sources. In this paper, a method of coordinated black-start of large power grid internal and external power sources is proposed by combining the two modes. Firstly, the black-start capability evaluation system is built to screen out the internal black-start power supply, and the external black-start power supply is determined by analyzing the connection relationship between the systems. Then, based on the specific implementation principles, the black-start power supply coordination strategy is formulated by using the Dijkstra shortest path algorithm. Based on the condensation idea, the black-start zoning and path optimization method applicable to this strategy is proposed. Finally, the black-start security verification and corresponding control measures are adopted to obtain a scheme of black-start cooperation between internal and external power sources in the large power grid. The above method is applied in a real large power grid and compared with the conventional restoration strategy to verify the feasibility and efficiency of this method.
Wredfors, Antti, Korhonen, Juhamatti, Pyrhönen, Juha, Niemelä, Markku, Silventoinen, Pertti.  2021.  Exciter Remanence Effect Mitigation in a Brushless Synchronous Generator for Test-field Applications. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. :1–6.
Brushless synchronous generators (BSG) are typically used to produce an island network whose voltage is close to the nominal voltage of the generator. Generators are often used also in test-field applications where also zero output voltage is needed. The exciter construction and magnetic remanence may lead to a situation where the non-loaded generator terminal voltage cannot be controlled close to zero but a significant voltage is always generated because the exciter remanence. A new brushless synchronous generator excitation and de-excitation converter topology for test applications is proposed. The purpose is to achieve full voltage control from zero to nominal level without modifications to the generator. Insulated-gate bipolar transistor (IGBT) and Field-Programmable Gate Array (FPGA) technology are used to achieve the required fast and accurate control. In the work, simulation models were first derived to characterize the control performance. The proposed converter topology was then verified with the simulation model and tested empirically with a 400 kVA brushless synchronous generator. The results indicate that the exciter remanence and self-excitation can be controlled through the exciter stationary field winding when the proposed converter topology controls the field winding current. Consequently, in highly dynamical situations, the system is unaffected by mechanical stresses and wear in the generator.
Bento, Murilo E. C., Ferreira, Daniela A. G., Grilo-Pavani, Ahda P., Ramos, Rodrigo A..  2021.  Combining Strategies to Compute the Loadability Margin in Dynamic Security Assessment of Power Systems. 2021 IEEE Power & Energy Society General Meeting (PESGM). :1–5.
The load margin due to voltage instability and small-signal instability can be a valuable measure for the operator of the power system to ensure a continuous and safe supply of electricity. However, if this load margin was calculated without considering system operating requirements, then this margin may not be adequate. This article proposes an algorithm capable of providing the power system load margin considering the requirements of voltage stability, small-signal stability, and operational requirements, as limits of reactive power generation of synchronous generators in dynamic security assessment. Case studies were conducted in the 107-bus reduced order Brazilian system considering a list of contingencies and directions of load growth.
Hakim, Mohammad Sadegh Seyyed, Karegar, Hossein Kazemi.  2021.  Detection of False Data Injection Attacks Using Cross Wavelet Transform and Machine Learning. 2021 11th Smart Grid Conference (SGC). :1—5.
Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique.
Tahirovic, Alma Ademovic, Angeli, David, Strbac, Goran.  2021.  A Complex Network Approach to Power System Vulnerability Analysis based on Rebalance Based Flow Centrality. 2021 IEEE Power & Energy Society General Meeting (PESGM). :01—05.
The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57- and 118-bus test system, outperforming relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.
Luo, Weifeng, Xiao, Liang.  2021.  Reinforcement Learning Based Vulnerability Analysis of Data Injection Attack for Smart Grids. 2021 40th Chinese Control Conference (CCC). :6788—6792.
Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme.
Wang, Zhiwen, Zhang, Qi, Sun, Hongtao, Hu, Jiqiang.  2021.  Detection of False Data Injection Attacks in smart grids based on cubature Kalman Filtering. 2021 33rd Chinese Control and Decision Conference (CCDC). :2526—2532.
The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.
Yang, Ruxia, Gao, Xianzhou, Gao, Peng.  2021.  Research on Intelligent Recognition and Tracking Technology of Sensitive Data for Electric Power Big Data. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :229–234.
Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.
Chen, Zhaohui, Karabulut, Emre, Aysu, Aydin, Ma, Yuan, Jing, Jiwu.  2021.  An Efficient Non-Profiled Side-Channel Attack on the CRYSTALS-Dilithium Post-Quantum Signature. 2021 IEEE 39th International Conference on Computer Design (ICCD). :583–590.
Post-quantum digital signature is a critical primitive of computer security in the era of quantum hegemony. As a finalist of the post-quantum cryptography standardization process, the theoretical security of the CRYSTALS-Dilithium (Dilithium) signature scheme has been quantified to withstand classical and quantum cryptanalysis. However, there is an inherent power side-channel information leakage in its implementation instance due to the physical characteristics of hardware.This work proposes an efficient non-profiled Correlation Power Analysis (CPA) strategy on Dilithium to recover the secret key by targeting the underlying polynomial multiplication arithmetic. We first develop a conservative scheme with a reduced key guess space, which can extract a secret key coefficient with a 99.99% confidence using 157 power traces of the reference Dilithium implementation. However, this scheme suffers from the computational overhead caused by the large modulus in Dilithium signature. To further accelerate the CPA run-time, we propose a fast two-stage scheme that selects a smaller search space and then resolves false positives. We finally construct a hybrid scheme that combines the advantages of both schemes. Real-world experiment on the power measurement data shows that our hybrid scheme improves the attack’s execution time by 7.77×.
Raab, Alexander, Mehlmann, Gert, Luther, Matthias, Sennewald, Tom, Schlegel, Steffen, Westermann, Dirk.  2021.  Steady-State and Dynamic Security Assessment for System Operation. 2021 International Conference on Smart Energy Systems and Technologies (SEST). :1—6.

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.

Danilczyk, William, Sun, Yan Lindsay, He, Haibo.  2021.  Smart Grid Anomaly Detection using a Deep Learning Digital Twin. 2020 52nd North American Power Symposium (NAPS). :1—6.

The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.

Tang, Fei, Jia, Hao, Shi, Linxin, Zheng, Minghong.  2021.  Information Security Protection of Power System Computer Network. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :1226–1229.
With the reform of the power market(PM), various power applications based on computer networks have also developed. As a network application system supporting the operation of the PM, the technical support system(TSS) of the PM has become increasingly important for its network information security(NIS). The purpose of this article is to study the security protection of computer network information in power systems. This paper proposes an identity authentication algorithm based on digital signatures to verify the legitimacy of system user identities; on the basis of PMI, according to the characteristics of PM access control, a role-based access control model with time and space constraints is proposed, and a role-based access control model is designed. The access control algorithm based on the attribute certificate is used to manage the user's authority. Finally, according to the characteristics of the electricity market data, the data security transmission algorithm is designed and the feasibility is verified. This paper presents the supporting platform for the security test and evaluation of the network information system, and designs the subsystem and its architecture of the security situation assessment (TSSA) and prediction, and then designs the key technologies in this process in detail. This paper implements the subsystem of security situation assessment and prediction, and uses this subsystem to combine with other subsystems in the support platform to perform experiments, and finally adopts multiple manifestations, and the trend of the system's security status the graph is presented to users intuitively. Experimental studies have shown that the residual risks in the power system after implementing risk measures in virtual mode can reduce the risk value of the power system to a fairly low level by implementing only three reinforcement schemes.
Alrubei, Subhi, Ball, Edward, Rigelsford, Jonathan.  2021.  Securing IoT-Blockchain Applications Through Honesty-Based Distributed Proof of Authority Consensus Algorithm. 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–7.
Integrating blockchain into Internet of Things (IoT) systems can offer many advantages to users and organizations. It provides the IoT network with the capability to distribute computation over many devices and improves the network's security by enhancing information integrity, ensuring accountability, and providing a way to implement better access control. The consensus mechanism is an essential part of any IoT-blockchain platform. In this paper, a novel consensus mechanism based on Proof-of-Authority (PoA) and Proof-of-Work (PoW) is proposed. The security advantages provided by PoW have been realized, and its long confirmation time can be mitigated by combining it with PoA in a single consensus mechanism called Honesty-based Distributed Proof-of-Authority (HDPoA) via scalable work. The measured results of transaction confirmation time and power consumption, and the analyses of security aspects have shown that HDPoA is a suitable and secure protocol for deployment within blockchain-based IoT applications.
ZHU, Guowei, YUAN, Hui, ZHUANG, Yan, GUO, Yue, ZHANG, Xianfei, QIU, Shuang.  2021.  Research on Network Intrusion Detection Method of Power System Based on Random Forest Algorithm. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :374–379.
Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.
Zhou, Xiaojun, Wang, Liming, Lu, Yan, Dong, Zhiwei, Zhang, Wuyang, Yuan, Yidong, Li, Qi.  2021.  Research on Impact Assessment of Attacks on Power Terminals. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). :1401–1404.
The power terminal network has the characteristics of a large number of nodes, various types, and complex network topology. After the power terminal network is attacked, the impact of power terminals in different business scenarios is also different. Traditional impact assessment methods based on network traffic or power system operation rules are difficult to achieve comprehensive attack impact analysis. In this paper, from the three levels of terminal security itself, terminal network security and terminal business application security, it constructs quantitative indicators for analyzing the impact of power terminals after being attacked, so as to determine the depth and breadth of the impact of the attack on the power terminal network, and provide the next defense measures with realistic basis.
Zadsar, Masoud, Abazari, Ahmadreza, Ansari, Mostafa, Ghafouri, Mohsen, Muyeen, S. M., Blaabjerg, Frede.  2021.  Central Situational Awareness System for Resiliency Enhancement of Integrated Energy Systems. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). :1–6.
In integrated gas and electricity energy systems, a catastrophic outage in one system could propagate to other, resulting in severe service interruption like what happened in 2021 Texas Blackout. To alleviate detrimental effects of these events, a coordinated effort must be adopted between integrated energy systems. In this paper, a central situational awareness system (CSAS) is developed to improve the coordination of operational resiliency measures by facilitating information sharing between power distribution systems (PDSs) and natural gas networks (NGNs) during emergency conditions. The CSAS collects operational data of the PDS and the NGN as well as data of upcoming weather condition, extracts the most vulnerable lines and pipelines, and accordingly obtains emergency actions. The emergency actions, i.e., optimal multi-microgrid formation, scheduling of distribution energy resources (DERs), and optimal electrical and gas load shedding plan, are optimized through a coupled graph-based approach with stochastic mixed integer linear programming (MILP) model. In the proposed model, uncertainties of renewable energy resources (RESs) is also considered. Numerical results on an integrated IEEE 33-bus and 30-node NGNs demonstrate the effectiveness of proposed CSAS.
Cui, Ajun, Zhao, Hong, Zhang, Xu, Zhao, Bo, Li, Zhiru.  2021.  Power system real time data encryption system based on DES algorithm. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :220–228.
To ensure the safe operation of power system, this paper studies two technologies of data encryption and digital signature, and designs a real-time data encryption system based on DES algorithm, which improves the security of data network communication. The real-time data encryption system of power system is optimized by the hybrid encryption system based on DES algorithm. The real-time data encryption of power system adopts triple DES algorithm, and double DES encryption algorithm of RSA algorithm to ensure the security of triple DES encryption key, which solves the problem of real-time data encryption management of power system. Java security packages are used to implement digital signatures that guarantee data integrity and non-repudiation. Experimental results show that the data encryption system is safe and effective.
Chen, Lu, Dai, Zaojian, CHEN, Mu, Li, Nige.  2021.  Research on the Security Protection Framework of Power Mobile Internet Services Based on Zero Trust. 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA). :65–68.
Under the background of increasingly severe security situation, the new working mode of power mobile internet business anytime and anywhere has greatly increased the complexity of network interaction. At the same time, various means of breaking through the boundary protection and moving laterally are emerging in an endless stream. The existing boundary-based mobility The security protection architecture is difficult to effectively respond to the current complex and diverse network attacks and threats, and faces actual combat challenges. This article first analyzes the security risks faced by the existing power mobile Internet services, and conducts a collaborative analysis of the key points of zero-trust based security protection from multiple perspectives such as users, terminals, and applications; on this basis, from identity security authentication, continuous trust evaluation, and fine-grained access The dimension of control, fine-grained access control based on identity trust, and the design of a zero-trust-based power mobile interconnection business security protection framework to provide theoretical guidance for power mobile business security protection.
Wang, Zhiwen, Hu, Jiqiang, Sun, Hongtao.  2020.  False Data Injection Attacks in Smart Grid Using Gaussian Mixture Model. 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). :830–837.
The application of network technology and high-tech equipment in power systems has increased the degree of grid intelligence, and malicious attacks on smart grids have also increased year by year. The wrong data injection attack launched by the attacker will destroy the integrity of the data by changing the data of the sensor and controller, which will lead to the wrong decision of the control system and even paralyze the power transmission network. This paper uses the measured values of smart grid sensors as samples, analyzes the attack vectors maliciously injected by attackers and the statistical characteristics of system data, and proposes a false data injection attack detection strategy. It is considered that the measured values of sensors have spatial distribution characteristics, the Gaussian mixture model of grid node feature vectors is obtained by training sample values, the test measurement values are input into the Gaussian mixture model, and the knowledge of clustering is used to detect whether the power grid is malicious data attacks. The power supplies of IEEE-18 and IEEE-30 simulation systems was tested, and the influence of the system statistical measurement characteristics on the detection accuracy was analyzed. The results show that the proposed strategy has better detection performance than the support vector machine method.
Qu, Yanfeng, Chen, Gong, Liu, Xin, Yan, Jiaqi, Chen, Bo, Jin, Dong.  2020.  Cyber-Resilience Enhancement of PMU Networks Using Software-Defined Networking. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–7.
Phasor measurement unit (PMU) networks are increasingly deployed to offer timely and high-precision measurement of today's highly interconnected electric power systems. To enhance the cyber-resilience of PMU networks against malicious attacks and system errors, we develop an optimization-based network management scheme based on the software-defined networking (SDN) communication infrastructure to recovery PMU network connectivity and restore power system observability. The scheme enables fast network recovery by optimizing the path generation and installation process, and moreover, compressing the SDN rules to be installed on the switches. We develop a prototype system and perform system evaluation in terms of power system observability, recovery speed, and rule compression using the IEEE 30-bus system and IEEE 118-bus system.
Wang, Chenguang, Tindemans, Simon, Pan, Kaikai, Palensky, Peter.  2020.  Detection of False Data Injection Attacks Using the Autoencoder Approach. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). :1—6.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.