This paper investigates the load frequency control problem of real power systems equipped with the automatic generation control service under false data injection (FDI) attacks. The available methods either require the exact system model or extensive historical data. Moreover, they cannot distinguish load changes from cyber-attacks. To address these challenges, this paper proposes a model-free defense method comprising an observer and a detector. The observer estimates the targeted signal and compares the difference between the estimated and observed signals as the state estimation error with a predetermined threshold value. When the state estimation error exceeds the threshold value, the detector detects an attack. In this situation, the observer’s output is the state estimation error. This causes the estimated signal to be sent to the secondary controller to mitigate the attack’s effect. If the state estimation error is smaller than the threshold value, no attack has occurred on the system. In such conditions, the observed signal is sent to the secondary controller. The proposed method rebuilds the actual system output in real-time under cyber-attacks. The presented method is independent of the system’s mathematical model and historical data, and it has a simple design procedure. This method can successfully distinguish load changes from FDI attacks. Moreover, it is not dependent on the size and complexity of the power system. The simulation results on the IEEE 14-bus and 39-bus power systems in the DIgSILENT PowerFactory show that the proposed method can timely detect the attacks and completely mitigate the effects of attacks on the system’s dynamic performance. Also, the obtained false alarm and attack detection rates are zero and over 98%, respectively.

Load frequency control (LFC) application in power systems has an essential role in improving the system’s stability. However, the presence of the automatic generation control service incorporated into the LFC application, being a system dependent on communication networks, makes the LFC system susceptible to cyber threats. Falsifying measurement and control signals through communication networks, known as data integrity attacks (DIAs), can severely affect the system’s dynamic performance. This paper studies the frequency regulation issue of an interconnected power system under stealth DIAs. Accordingly, a novel identification scheme consisting of the dynamic multiplicative watermarking technique, an estimator, an anomaly detector, and a trigger mechanism is introduced to identify the DIAs. The watermarking concept is the intentional overlay of a watermark signal onto the source signal transmitted through the communication network. Achieving this requires using a specific watermarking filter, and the result is that operators gain greater flexibility in regulating the transmitted signals, which in turn provides improved signal integrity. In the proposed identification scheme, there is a multiplicative superimposition, where the watermark equalizing filter on the opposite end of the network can cancel out the impact of the watermarking, leading to the retrieval of the original signal. After identifying the attack, the proposed trigger mechanism blocks the manipulated ACE signal and submits the estimated ACE signal to the secondary controller. A model-free sliding mode control method is also implemented as the secondary frequency controller to regulate the system’s frequency under DIAs, load disturbances, and physical limitations. The Speedgoat-based real-time simulation results reveal that the developed defense method can timely identify stealth DIAs and significantly improve the system’s dynamic responses compared to the other techniques under these attacks.

This study proposes a two-level defense strategy to cope with false data injection (FDI) attacks applied to the control and measurement signals of a two-area load frequency control (LFC) system. In the first level, the combination of a recursive estimator, a residual signal-based detector, and a model-free observer is proposed to detect the attacks and mitigate the attacks’ impacts on the system’s measurement signals and secondary control commands. Simultaneously with the first level, a novel model-free independent defense strategy, fractional-order brain emotional learning (FOBEL), is developed in the second level to improve the first-level mitigation performance. The FOBEL controller can make robust and fast decisions in systems facing cyber-attacks. Selecting the appropriate values for the FOBEL controller is vital in improving its performance. To this end, a soft actor-critic deep reinforcement learning (SAC-DRL) algorithm is also employed to tune the FOBEL controller’s coefficients. The proposed two-level defense scheme efficiency is evaluated via real-time simulations in the OPAL-RT simulator and compared with the various detection and mitigation methods under different scenarios. The experimental results reveal that the presented defense strategy successfully detects FDI attacks and performs significantly better in mitigating them than the other strategies.

One of the most important engineering problems, with numerous uses in the applied sciences, is the synchronization of chaos dynamical systems. This paper introduces a dynamic‐free T‐S fuzzy sliding mode control (TSFSMC) method for synchronizing the different chaotic fractional‐order (FO) systems, when there is input saturation. Using a new definition of fractional calculus and the fractional version of the Lyapunov stability theorem and linear matrix inequality concept, a Takagi–Sugeno fuzzy sliding mode controller is driven to suppress and synchronize the undesired behavior of the FO chaotic systems without any unpleasant chattering phenomenon. Finally, an example of synchronization of complex power grid systems is provided to illustrate the theoretical result of the paper in real‐world applications.

The study pioneers in proposing a hybrid cloud-based framework for designing a totally stand-alone, green residential house, stable over load variations and fluctuations of renewable energy sources (RES). The framework uses wind turbine (WT) and photovoltaic panels (PV) as the main power supplies, while employing a fuel cell (FC), fed with an electrolyzer, as the secondary source of energy. A battery is also used, which together with the FC and electrolyzer, constitute the compensation system in the proposed framework. Taking into account the various types of residential electrical loads, including an electrical vehicle (EV), and applying a deep learning method, the proposed framework makes the day-ahead scheduling of all components in the house based on the forecasted profile of load demand and the energy generated by the RES. The compensation system comes into use to balance the real-time scheduling error caused by the uncertainties of the main sources of power. To ascertain the practicality of the proposed framework for real-life implementation, it is examined on a residential house considering components with authentic technical features. The real-time operation of the suggested residential system is also tested on the SpeadGoat real-time simulator, whose results corroborate the practicability of both the real-time and day-ahead operation of the proposed framework.

This article is dedicated to model order reduction of linear time-invariant passive systems, i.e., positive real (PR) systems, based on the balanced truncation (BT) concept. The main feature of this article is that we have used the phase angle of the transfer function,$G(s)$, in model order reduction for the first time in the proposed method, which results in more accurate reduced-order models. It is worth noting that this hypothesis has been never made before in any other model order reduction techniques. Furthermore, new gramians are calculated corresponding to their associated new Riccati equations and Lur’e equations, which are in turn achieved by using Kalman–Yakubovic–Popov (KYP) lemma and linear matrix inequalities (LMI). Thereafter, a novel passivity preserving model order reduction algorithm, which takes the phase angle of the transfer function,$G(s)$, into account, is presented. It is demonstrated that the proposed method is a generalization of the positive real balanced truncation (PRBT) method, and it is perfectly capable of providing more accurate approximation error compared to PRBT. Finally, numerical examples are included to figure out the effectiveness of the presented method.

This study proposes a new application for delay-dependent stability analysis of a shipboard microgrid system. Gain and phase margin values are taken into consideration in delay dependent stability analysis. Since such systems are prone to unwanted frequency oscillations against load disturbances and randomness of renewable resources, a virtual gain and phase margin tester has been incorporated into the system to achieve the desired stabilization specification. In this way, it is considered that the system provides the desired dynamic characteristics (e.g. less oscillation, early damping, etc.) in determining the time delay margin. Firstly, the time delay margin values are obtained and their accuracy in the terms of desired gain and phase margin values are investigated. Then, the accuracy of the time delay margin values obtained by using the real data of renewable energy sources and loads in the shipboard microgrid system is shown in the study. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested method, from a systemic perspective, for the load frequency control problem in the shipboard microgrid.

Increasing concerns related to fossil fuels have led to the introducing the concept of emission-free ships (EF-Ships) in marine industry. One of the well-known combinations of green energy resources in EF-Ships is the hybridization of fuel cells (FCs) with energy storage systems (ESSs) and cold-ironing (CI). Due to the high investment cost of FCs and ESSs, the aging factors of these resources should be considered in the energy management of EF-Ships. This article proposes a nonlinear model for optimal energy management of EF-Ships with hybrid FC/ESS/CI as energy resources considering the aging factors of the FCs and ESSs. Total operation costs and aging factors of FCs and ESSs are chosen as problem objectives. Moreover, a stochastic model predictive control method is adapted to the model to consider the uncertainties during the optimization horizon. The proposed model is applied to an actual case test system and the results are discussed.

In this paper, we propose a safety-driven cost effective scheduling controller to arbitrate and operate the energy resources of a maritime hybrid energy application. The proposed control algorithm enables efficient energy management to dynamically schedule the energy sources to supply the realtime power requests so that 1) we maintain the system safety by not overloading or heating up an energy source; 2) reduce the operation cost by considering the cheapest energy source in a real-time manner. The efficiency and safety of our scheduling algorithm have been examined using Uppaal model checker. The experiment outputs show that our controller maintains the system safety and guarantees the lowest operation cost.

This paper proposes a novel robust controller design for a class of nonlinear systems including hard nonlinearity functions. The proposed approach is based on Takagi-Sugeno (TS) fuzzy modeling, nonquadratic Lyapunov function, and nonparallel distributed compensation scheme. In this paper, a novel TS modeling of the nonlinear dynamics with signum functions is proposed. This model can exactly represent the original nonlinear system with hard nonlinearity while the discontinuous signum functions are not approximated. Based on the bounded-input-bounded-output stability scheme and L1performance criterion, new robust controller design conditions in terms of linear matrix inequalities are derived. Three practical case studies, electric power steering system, a helicopter model and servo-mechanical system, are presented to demonstrate the importance of such class of nonlinear systems comprising signum function. Furthermore, to show the superiorities of the proposed approach, it is applied to these systems; and, the experimental real-time hardware-in-the-loop results are compared with the published literature with the same topic.

With the recent development of power electronic equipment in marine industry, the deployment of pulse power loads in the Shipboard power systems (SPSs) is constantly expanding. During the usage of a pulse power load (PPL), a huge amount of energy is consumed within a short period of time which brings new threats to the reliability and stability of the SPSs. Technically, the negative effects of PPLs to SPSs can be alleviated by powering specialized energy storage systems (ESSs). On the other hand, the challenges of the PPL accommodation on SPSs become more intensified when the systems are coupled with the communication networks. In this paper, an intelligent controller is developed for counteracting the effect of PPLs problem on a Cyber-Physical Shipboard Microgrid (CPSMG). This study presents an optimal general type-2 fractional order fuzzy P + fuzzy I + fuzzy D (GT2FOFP+FI+FD) controller for the secondary load frequency control (LFC) of the CPSMG. In order to boost the output performance of the LFC, an enhanced JAYA algorithm (EJAYA) is utilized for the online setting of the GT2FO-FP+FI+FD controller coefficients. Finally, a real-time CPSMG hardware-in-the-loop (HIL) is adopted to investigate the applicability of the suggested scheme in dealing with the impacts of PPLs and communication degradations from a systemic perspective.

The stability of DC microgrids (DC MG) that are connected to constant power loads (CPL) is of prime importance for the operation of shipboard power systems (SPS). To control the DC MG of SPS effectively, the instantaneous power value of the time-varying uncertain CPLs is necessary. Since the integration of current sensors is costly and the resistance of these sensors degrades ripple filtering, instantaneous power estimation of CPLs is proposed. This paper investigates the development of an extended Kalman filter (EKF) to estimate the power of time-varying uncertain CPLs alongside estimation of CPLs' and sources' currents in a DC MG. By augmenting the CPLs powers into the DC MG states, a joint estimation problem is presented to effectively estimate the currents of the DC MG as well as the CPLs powers values. The proposed approach is applied to a DC MG that feeds one CPL. Experimental results show the effectiveness of the proposed EKF in the estimation of instantaneous CPLs powers and the currents of the CPLs and the source.

In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in market's performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants' attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.