Robust Mitigation Strategy Against Dummy Data Attacks in Power Systems

The constant failure in cyber security in power systems and substation automation that has crippled business activities that mostly depend on power to run their routine activities are caused by Lack of Adequate Security Measures, Human Error, Outdated Software and Hardware, Insufficient Training and Awareness, Insider Threats, Weak Access Control Mechanisms, Inadequate Network Segmentation, Failure to Patch Vulnerabilities, Complexity of the System, Third-Party Vendor Risks and Denial of Service (DoS) Vulnerabilities. This is surmounted by introducing improved cyber security in power systems and substation automation using intelligent based system. To achieve this, it is done in this procedure characterizing and establishing the causes of failure in cyber security in power systems and substation automation, designing a SIMULINK model for cyber security in power systems and substation automation, designing a rule base that will reduce the causes of failure in cyber security in power systems and substation automation,training ANN in the rule base for effective reduction of causes of failure in cyber security in power systems and substation automation, developing an algorithm for the process, designing a SIMULINK model for improving cyber security in power systems and substation automation using intelligent based system and validating and justifying the percentage of improvement in the reduction of causes of failure in cyber security in power systems and substation automation. The results obtained were the conventional Lack of Adequate Security Measures that cause failure in improving cyber security in power systems and substation automation was 25%. On the other hand, when an intelligent based system was incorporated into the system, it drastically reduce the cause failure in improving cyber security in power systems and substation automation to 21.53%, the conventional outdated software and hardware that cause failure in improving cyber security in power systems and substation automation was 15%. Meanwhile, when an intelligent based system was inculcated in the system, it became reduced to 12.92% thereby enhancing the performance of cyber security in power systems and substation automation by 2.08%, the conventional Weak Access Control Mechanisms that cause failure in improving cyber security in power systems and substation automation was 8%. However, when an intelligent based system was integrated in the system, it was reduced to 6.9% and the conventional Denial of Service (DoS) Vulnerabilities that cause failure in improving cyber security in power systems and substation automation was 2% while when an intelligent based system was incorporated in the system, it automatically reduced to 1.7%. Finally, with these results obtained, It vehemently shows that the percentage improvement in cyber security in power systems and substation automation was 0.3%.

Spinning reserve (SR) is one of the most important ancillary services which are needed for satisfying reliability requirements, including the desired level of security in power systems. By definition, spinning reserve is the unloaded section of synchronized generation that is able to respond immediately to serve load. This paper proposes a hybrid DCOPF and harmony search (HS) optimization method as a novel heuristic approach to the joint dispatch of energy and reserve considering the bottling of reserve due to transmission limits. The effectiveness of the proposed method is examined by applying it to the IEEE 30-bus test system. The results obtained using the proposed method are presented and compared with those of particle swarm optimization (PSO).

Power system security and stability control is heavily dependent on the reliability of communication network. The communication interruption and latency may cause security and stability issues in power system. Thus, studying the dynamic procedure of the interaction between power system and communication network in fault situation becomes more and more important. In this paper, by analyzing the weakness of the existing co-simulation frameworks, a new co-simulation framework, Global Status Event-Based Iterative Co-simulation Framework, is proposed, in which a global event scheduler manages the communication simulator and power simulator, and power simulator will run iterative simulation as a new event is identified and added to the global event scheduler. Then, a co-simulation platform based on the proposed framework is introduced which integrates the power system simulator and communication network simulator. Finally, a case is studied and reveals the procedure of the cascading failures in both power system and communication network, which cannot be captured by the simulation of a single system.

Operational research methods, and particularly flow and transportation methods have many applications in the power system field: optimization of hydroelectric power system operation, seasonal or short-time planning of a hydro-thermal system and finally power system security. The purpose of this paper is to specify the network simplex method of the load-flow calculation in a power system. This study is not limited to the DC-flow calculation but particularly presents a model for the full AC load-flow calculation. The proposed model is an imaginary and real current one. It is based on a simplifying assumption which will allow decoupling of the problem and which is the basis of the algorithm called FLF (Flow Load-Flow algorithm). Power system elements such as lines, transformers, generators and voltage control busses are represented with adapted models. Systematic tests have been performed on some existing power systems, such as the IEEE 24-bus Reliability system. The performance of this method is compared with the results of a decoupled Newton-Raphson load-flow. The accuracy of the FLF algorithm is competitive with the Newton-Raphson method, but its speed still has to be improved. However the load-flow calculation is only a part of the power system security problem, where properties and characteristics of flow and transportation algorithms will offer greater perspective.

A Robust Economic Region Considering Load Uncertainty for Security and Economic Operation Optimization in Power Systems

Electricity serves as the backbone and essential energy source for various sectors, including transportation, residential areas, manufacturing, and industry. As engineering and technology advance, the demand for electricity continues to rise. Expanding the electricity grid to meet transmission needs and provide high-quality service has become a fundamental challenge in the power system domain. However, load expansion introduces issues such as line overloads when demand surges, compromising power quality, system security, and reliability during operation, potentially leading to system failures. Addressing these load-related problems is crucial for enhancing power system stability, reducing troubleshooting expenses, and improving operational efficiency. This study proposes the utilization of thyristor-controlled series compensator (TCSC) as a solution to enhance power system efficiency. Furthermore, to optimize TCSC placement and determine the appropriate compensation level for devices on transmission lines, the research suggests employing the JAYA optimization algorithm. MATLAB software is utilized to investigate the IEEE standard 30-node transmission lines case. The obtained results have demonstrated the effectiveness of the solution in enhancing electrical transmission capacity, improving stability, and reducing energy losses within the system at a low operational cost.

Power system security is the power system’s capability to maintain its stability when a disturbance occurs. Transient instability is a phenomenon that threatens the security of the power system and synchronous generators. When rotor angle instability occurs in a synchronous generator, it is capable of driving the generator O.S. If the O.S operation of a generator sustains even for a brief period of time, it may result to serious mechanical and thermal damages to the machine. Therefore, faster identification of such conditions and in time isolation of the unstable generator from the power system is necessary for keeping the power system safe. Conventional methods for the identification of the generator O.S is based on the use of impedance relays. The purpose of this chapter is the prediction of the generator O.S with dynamic data of generator and power system, which can be gathered by PMUs in the fault and clearance period. Generator dynamic information includes variations of voltage phasor, active and reactive power, rotor angle, and speed of generator as well as the data of voltage and line flows in the adjacency of the generator that are collected in different time intervals. The measured information is continually updated in the moving window and with a special algorithm such as a three-step DT that performs the detection fault occurrence and clearance and prediction of O.S of generators. The proposed algorithm is working constantly as a relay, and the proposed algorithm recognizes the stability of the generator whenever changes occur in the generator parameter according to the severity of variations. Since training data perform an effective role in DT learning or other machine learning methods, to increase the learning ability of DT, training samples are prepared for having complete coverage of almost all load levels and different operation points. The proposed scheme is implemented on the generator of the IEEE 39-bus system with promising results.

Deep learning (DL) has seen tremendous recent successes in many areas of artificial intelligence. It has since sparked great interests in its potential use in power systems. However, success from using DL in power systems has not been straightforward. Even with the continuing proliferation of data collected in the power systems from, e.g., synchrophasors and smart meters, how to effectively use these data, especially with DL techniques, remains a widely open problem. This chapter shows that the great power of DL can be unleashed in solving many fundamentally hard high-dimensional real-time inference problems in power systems. In particular, DL, if used appropriately, can effectively exploit both the intricate knowledge from the nonlinear power system models and the expressive power of DL predictor models. This chapter also shows the great promise of DL in significantly improving the stability, resilience, and security of power systems.

The power system is a complex network which is expected to operate with consumption of minimal resources giving the maximum security and reliability. The optimal power flow (OPF) problem is an important tool to help the operator to achieve these goals by providing the optimal setting of all control variables. Due to its significant influence on security and economic operation of power systems, one of the OPF problems namely optimal reactive power dispatch (ORPD), has received an ever-increasing interest from electric utilities. This paper proposes a recent nature inspired algorithm called moth-flame optimizer (MFO) to solve the ORPD problem. MFO is utilized to find the best combination of control variables such as the real power generation to be generated, generator voltages, tap changing transformers' ratios and the size of reactive power compensation devices so that the total minimum loss in the network can be obtained. The effectiveness of MFO is tested on IEEE-30 bus system with 25 control variables and the comparison with other recent algorithms such as grey wolf optimizer (GWO), ant-lion optimizer (ALO) and multi-verse optimizer (MVO) also will be presented in this paper.

Supply-demand balance is imperative for the reliability of the power system. Inability to maintain this balance results in frequency deviation and system failure. The recent integration of renewable energy sources such as wind and solar have reduced inertia and variable output which leaves the power system at risk of disturbance while also reducing the controllability of generators. However, the latter-day demand response is coming across as an economical and effective way of adding to the reliability and security of power systems by managing the electricity demand of customers at times of severe power imbalance. This chapter carries out a detailed literature review of centralized and decentralized demand control approaches. As well as presents a novel demand control approach for providing frequency regulation by using domestic refrigerators as control loads. This chapter also carries out a detailed study of large-scale appliance level interval meter consumption data from Australia's largest network provider Ausgrid. Appliance level data is used in combination with household-level data to study the contribution of air-conditioners in summer peak demand. Clustering is performed on air-conditioner data to identify various air-conditioner load profile patterns. These patterns are then used with demand control strategies to study the possible load reductions from residential air-conditioner control across the Australian State of New South Wales.

The necessity of Distributed Generation (DG) in the modern power system has increased greatly. Optimal placement and sizing of DGs have a significant impact on the objectives of voltage stability enhancement, real and reactive power loss minimization and power system security and reliability. Different types of DGs such as Type I, Type II, Type III and Type IVare placed. The optimal placement of these DGs is found using Voltage Stability Index (VSI). The optimal sizing of these DGs is done using Differential Evolution (DE) algorithm. MATLAB simulation is carried out in standard IEEE 33-bus test system. The test is performed for various combinations of different types of DG. The results show that the voltage stability index along with Differential Evolution algorithm provides better voltage profile and power loss minimization as compared to the system without DG.

Congestion in transmission corridors are the major bother for deregulated power system’s operation. Generator rescheduling along with demand alteration is a traditional remedy for transmission line congestion. According to market clearing process, the system operator (SO) has to pay a certain amount of cost to the market participants for rescheduling the generation and demand. This kind of redispatch related congestion management (CM) procedure is mainly carried out to reduce the congestion cost, but they are failing to provide an attention in power systems security. The risky generator’s power shifts may diminish the voltage and transient stability of the power system. So power system security should be included in the congestion management procedure. In this proposed multi objective congestion management procedure, rescheduling of active power is carried out to improve/retain the power systems security along with a congestion cost reduction. Voltage security margin (λ) and corrected transient energy margin (CTEM) provides a measure for power system security level. Multi Objective Dragonfly Algorithm (MODA) is employed to trace the non dominated solutions for three conflicting objectives. Fuzzy decision making principle is applied to select the best Pareto solution depends on the objective’s significances. The goodness of the MODA optimization approaches is experimented in congestion alleviation of New England 39 bus systems and solutions are compared with some reputed methods.

The increase of extremely hot days globally has led to more frequent wildfires and frequent power outages directly and indirectly caused by the wildfires. This has sought an urgency to investigate the power system security issues associated with wildfires and the implementation of risk mitigation strategies. This article provides a comprehensive review of power system security in relation to wildfires, exploring methods to enhance the system security. The article starts with the multi-scale aspect of power system security and resiliency associated with wildfires. Additionally, it investigates the direct and indirect impacts of wildfires on renewable energy generation and the overall security of the system. Furthermore, the emerging technologies to enhance the system security against wildfires are discussed. To conclude, this paper sheds light on the industry, academic research gaps, and future direction.

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.

In this paper, a new technique based on particle swarm optimization (PSO) is proposed to incorporate voltage stability limits into traditional optimal power flow (OPF) formulations. Two objective functions such as fuel cost and voltage stability margin (loadability) are considered to be optimized. In proposed method systempsilas maximum loading margin (loadability) is calculated by continuation power flow (CPF). Particle swarm optimization (PSO) is employed to solve the proposed multi-objective OPF problem. The proposed method allows system operators in order to control the desired level of system security via adjusting the weighting factors of the different objective functions directly. Simulation results for the IEEE 30-bus test system demonstrate the effectiveness of the PSO in solving multi-objective OPF problems.