Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

A survey on applications of Alternating Direction Method of Multipliers in smart power grids

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

One of the basic requirements of today's sophisticated world is the availability of electrical energy, and neglect of this matter may have irreparable damages such as an extensive blackout. The problems which were introduced about the traditional power grid, and also, the growing advances in smart technologies make the traditional power grid go towards smart power grid. Although widespread utilisation of telecommunication networks in smart power grid enhances the efficiency of the system, it will create a critical platform for cyber attacks and penetration into the system. Automatic generation control (AGC) is a fundamental control system in the power grid, and it is responsible for controlling the frequency of the grid. An attack on the data transmitted through the telecommunications link from the sensors to the AGC will cause frequency deviation, resulting in disconnection of the load, generators and ultimately global blackout. In this study, by using a Kalman filter and a proposed detector, a solution has been presented to detect the attack before it can affect the system. Contrary to existing methods, this method is able to detect attacks that are stealthy from the area control error signal and χ 2 ‐detector. Simulations confirm the effectiveness of this method.

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.

Guest Editorial: Situational awareness of integrated energy systems

Due to climate change challenges, countries are diversifying their energy sources to reduce carbon emissions and adopt cleaner alternatives. However, integrating these new energy sources into existing power grids poses challenges, such as increased intermittency. Prior studies have shown that active control of the power grid's topology can address these issues. This research aims to demonstrate the effectiveness of the Augmented Random Search (ARS) algorithm as a faster alternative to neural network-based reinforcement learning algorithms. The ARS algorithm can achieve comparable results to neural networks in significantly less time, enabling a broader range of tests and reducing computational training costs.

Developments in information and communication technologies (ICT) achieved new trends such as smart grid (SG). Innovations in ICT bring challenges to our security and expectations of privacy. ICT has such a big attack surface that possess increased risks from cyber-attacks in smart grid. Actually, as a critical infrastructure, the smart grid is more sensitive to cyber-attacks, as monitoring and control of smart grid can be realized over standard internet-based protocols. Therefore, an attacker may achieve financial cost to the service and create loss of electric resources, through breaching the real-time balance between energy production and consumption, by controlling data produced by the smart entities. Therefore, in this study, bagging ensemble classifier is evaluated for intrusion detection in a smart grid with a benchmark data. Experimental results have revealed the practicality of Bagging ensemble classifier by realizing a better performance for the cyber security threats in a smart grid environment. The results indicated that the suggested Bagging ensemble classifier with REP Tree achieved a total classification accuracy of 99.94%.

This paper presents a comprehensive review of advanced control and communication technologies of smart grid (SG). Various func-tions/features of SG include real-time pricing, voltage/VAr control, frequency control, power quality, demand response management, distri-bution system restoration, and reconfiguration, distributed generation dispatch and storage. There is a strong requirement to control these functions, as well as to communicate within themselves. Suppose, if two-way communication is not possible then it cannot be considered as a function of SG. When trying to track or monitor a function, one must be able to control, command, and communicate with it. In order to control observation ability and measure of some type of numerical quantity that is obtained from a real time measuring device being moni-tored is necessary. Some of these measurable quantities are: overvoltage limits, security measures and contingency analysis. This paper addresses various challenges of control and communication techniques of SG.

In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, distributed computational intelligence (CI)-based techniques can provide a much better near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in smart grids. Therefore, in this paper, a consensus-based dimension-distributed CI technique is proposed for real-time optimal control in smart distribution grids in which a large number of DGs and CLs are present. The proposed approach considers each DG or CL as a separate private entity, which is more relevant from the perspective of smart grid optimization. In the proposed consensus-based framework, each DG or CL is associated with an agent, and each agent is allowed to communicate only with its neighboring agents. The effectiveness of the proposed approach in terms of convergence, adaptability, and optimality with respect to a centralized algorithm and a benchmark algorithm is shown through simulations on 30-node and 119-node distribution test systems.

IoT Based Wireless Sensor Network for Power Quality Control in Smart Grid

The BlackEnergy malware targeting critical infrastructures has a long history. It evolved over time from a simple DDoS platform to a quite sophisticated plug-in based malware. The plug-in architecture has a persistent malware core with easily installable attack specific modules for DDoS, spamming, info-stealing, remote access, boot-sector formatting etc. BlackEnergy has been involved in several high profile cyber physical attacks including the recent Ukraine power grid attack in December 2015. This paper investigates the evolution of BlackEnergy and its cyber attack capabilities. It presents a basic cyber attack model used by BlackEnergy for targeting industrial control systems. In particular, the paper analyzes cyber threats of BlackEnergy for synchrophasor based systems which are used for real-time control and monitoring functionalities in smart grid. Several BlackEnergy based attack scenarios have been investigated by exploiting the vulnerabilities in two widely used synchrophasor communication standards: (i) IEEE C37.118 and (ii) IEC 61850-90-5. Further, the paper also investigates protection strategies for detection and prevention of BlackEnergy based cyber physical attacks.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy

Review on smart grid control and reliability in presence of renewable energies: Challenges and prospects

System state estimation and control are important issues to ensure the stability and reliability of the smart grid system. In this paper, the problem of how to securely estimate the system state and control the smart grid is studied. In the setup studied, the sensor(s) and the controller communicate with each other through a wireless channel subjected to monitoring by an eavesdropper. The channel capacity requirement that ensures negligible information leakage to the eavesdropper about the system state and control messages is studied from the information theoretic perspective. Two scenarios with single sensor or multiple sensors are studied. Numerical simulations are used to evaluate the capacity requirement in typical configurations of the smart grid.