In recent years, cyber-physical systems (CPSs) have attracted intense attention due to their potential applications in many areas. However, the strong reliance on communication networks makes CPSs vulnerable to intentional cyberattacks. Therefore, a great number of attack detection methods have been proposed to enforce security of CPSs. In this article, various false data injection attack detection methods presented for CPSs are investigated and reviewed. According to the knowledge of control information, the controllers of CPSs are categorized as centralized and distributed controllers. Existing centralized attack detection approaches are discussed in terms of i) linear time-invariant systems, ii) actuator and sensor attacks, iii) nonlinear systems, and iv) systems with noise. Furthermore, the development of distributed attack detection is reviewed according to different decoupling methods. Some challenges and future research directions in the context of attack detection approaches are provided.

Brief Survey on Attack Detection Methods for Cyber-Physical Systems

This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases.

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A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System Using PSO-GSA Optimized Cascade PI-PD Controller for Automatic Load Frequency Control

This paper focuses on the load frequency control (LFC) for a multi-area interconnected microgrid power system with the introduction of communication networks, a robust sliding mode control strategy based on the adaptive event-triggered mechanism is proposed against the frequency deviation caused by power unbalance or time delays. First, a three-area power system attached to renewable energies and energy storage is considered, the corresponding LFC model is established. Second, the networked control is introduced into the LFC scheme, and the adaptive trigger mechanism which can adaptively adjust the event-triggered threshold is designed to improve the data transmission efficiency, the LFC scheme with network induced delays is formulated. Third, the Luenberger observer is designed to estimate the state errors and to facilitate the design of the sliding surface, the overall closed-loop system asymptotically stable and robust performance are analyzed by solving linear matrix inequalities. Then the control law is proved that the system state trajectory can be driven into the designed sliding surface in a limited time. Finally, some simulation experiments are given, and the results show that the proposed method is effective and has excellent robust performance.

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Adaptive Event-Triggered Load Frequency Control for Interconnected Microgrids by Observer-Based Sliding Mode Control

Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities

The large-scale penetration of intermittent Renewable Energy (RE) sources such as wind and solar power generation may cause a problem of frequency aberration of interconnected Hybrid Power System (HPS). This occurs when the load frequency control of interconnected system is unable to compensate the power balance between generation and load demand. Also owing to the enhancement of future transport, the Plug-in Electric Vehicle (PEV) plays a significant role to customer at demand side. Thus, the PEV can act as a power control to compensate the power balance in Renewable Energy integrated power system. This paper presents a physics inspired Atom Search Optimization (ASO) algorithm for tuning the parameters of Fractional Order Proportional-Integral-Derivative (FOPID) controller for Automatic Load Frequency control of HPS. In this proposed work, an attempt has been made to analyze the frequency stability of HPS using Matignon's theorem. The interconnected HPS consists of reheat thermal power system, RE sources such as wind and solar thermal power generation associated with energy storage devices namely aqua electrolyzer, fuel cell and electric vehicle. The gain and fractional terms of the controller were obtained by minimizing the Integral Time Absolute Error of interconnected system. The robustness of ASO-tuned FOPID controller is tested on two-area HPS that was modelled using MATLAB/Simulink. The results obtained were then compared with other fractional order and classical integer order controllers. From the simulation results, it is inferred that the proposed ASO-tuned FOPID controller gives superior transient and steady-state response compared with other controllers. Moreover, the self-adaptiveness and robustness of the controller was validated to account for the change in RE power generations and system parameters. Furthermore, the effectiveness of the method is proved by comparing its performance with the recent literature works. The real-time applicability of proffered controller is validated in hardware-in-the-loop simulation using Real Time Digital Simulator.

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A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

Solar irradiance forecasting based on direct explainable neural network

Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulator-based real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.

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Variable Universe Fuzzy Logic-Based Hybrid LFC Control With Real-Time Implementation

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

Accompanied by the rising fashion of distributed energy resources requiring distributed optimization is becoming more prevalent among power system. However, research for distributed optimization to determine smooth operation point (SOP) has attracted less attention in hybrid multi-terminal high voltage direct current (MTDC) and thus, it is still a core issue. The proposed method mainly designed to deal with SOP, and this goal can be mainly achieved by taking minimal line losses and smoothness objectives (MLLSO), which is formulated as an MLLSO optimization model. Then, the MLLSO optimization is transformed to single-objective distributed optimization through weight normalization of both objective functions. A minimal line loss objective and smoothness objective-based optimal power flow (MLLOSOBOPF) model is built at first to effectively handle prevailing constraints for the power systems parameters. Then, the alternating direction method of multipliers (ADMM) is utilized to solve this model by breaking it into smaller pieces based on OPF, each of which is easier to handle. In addition, different sized power systems are integrated with the 14-bus and 30-bus hybrid MTDC-AC systems to increase the complexity of test systems. Finally, based on 14-bus and 30-bus hybrid MTDC-AC systems, the computational output and comparison of the proposed method with centralized optimization centralized MTDC show that the proposed method is feasible and effective for determining hybrid MTDC grid SOP.

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ADMM-Based Distributed Optimization of Hybrid MTDC-AC Grid for Determining Smooth Operation Point

With the development of science and technology, wind speed prediction plays an important role in smart grid. In order to improve the efficiency of wind power and ensure the safety of the new smart grid, the accurate prediction of wind power is very important. Because the variation of wind speed is nonlinear, and wind speed is influenced by many factors, Scholars all over the world have proposed a variety of forecasting methods to achieve a relatively accurate prediction of wind speed.In this paper, a novel method of wind speed prediction based on long short-term memory network with peephole (peephole LSTM) and wavelet decomposition is proposed. Firstly, the wind speed data are processed by wavelet dynamic decomposition, using the peephole LSTM structure to analyze the data sequence, after that a new model of peephole network is established. Through the forward training and the back-propagation algorithm update the weights iteratively, as a result get the optimal parameters.Selecting one month’s data and simulating with MATLAB. High prediction accuracy is obtained by simulation. Predict the next 5 days through the first 25 days of training, and the MAPE is used to analyze prediction data. The feasibility of this method is proved by simulation results.

Saddam Aziz

Papers

Solar irradiance forecasting based on direct explainable neural network

Variable Universe Fuzzy Logic-Based Hybrid LFC Control With Real-Time Implementation

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

ADMM-Based Distributed Optimization of Hybrid MTDC-AC Grid for Determining Smooth Operation Point

A Novel Method of Wind Speed Prediction by Peephole LSTM