A Review on Data-Driven Security Assessment of Power Systems: Trends and Applications of Artificial Intelligence

Mehrzad, Alireza; Darmiani, Milad; Mousavi, Yashar; Shafie‐khah, Miadreza; Aghamohammadi, Mohammad Reza

doi:10.1109/access.2023.3299208

Cited by 7 publications

(3 citation statements)

References 94 publications

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“…Such increasing demand could not be solely met by conventional solutions, and novel alternatives have also come into operation for this purpose [4,5]. One economic and energy-efficient method, multi-generation systems, has been widely employed [6][7][8]. As the name suggests, in a multi-generation unit, the given energy is utilized for producing more than one product [9,10].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Energy and Economic Advantages of Using Solar Stills for Renewable Energy-Based Multi-Generation of Power and Hydrogen for Residential Buildings

Bahrami,

Soltanifar,

Fallahi

et al. 2024

Buildings

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The multi-generation systems with simultaneous production of power by renewable energy, in addition to polymer electrolyte membrane electrolyzer and fuel cell (PEMFC-PEMEC) energy storage, have become more and more popular over the past few years. The fresh water provision for PEMECs in such systems is taken into account as one of the main challenges for them, where conventional desalination technologies such as reverse osmosis (RO) and mechanical vapor compression (MVC) impose high electricity consumption and costs. Taking this point into consideration, as a novelty, solar still (ST) desalination is applied as an alternative to RO and MVC for better techno-economic justifiability. The comparison, made for a residential building complex in Hawaii in the US as the case study demonstrated much higher technical and economic benefits when using ST compared with both MVC and RO. The photovoltaic (PV) installed capacity decreased by 11.6 and 7.3 kW compared with MVC and RO, while the size of the electrolyzer declined by 9.44 and 6.13%, and the hydrogen storage tank became 522.1 and 319.3 m3 smaller, respectively. Thanks to the considerable drop in the purchase price of components, the payback period (PBP) dropped by 3.109 years compared with MVC and 2.801 years compared with RO, which is significant. Moreover, the conducted parametric study implied the high technical and economic viability of the system with ST for a wide range of building loads, including high values.

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Section: Introduction 1backgroundmentioning

confidence: 99%

Energy and Economic Advantages of Using Solar Stills for Renewable Energy-Based Multi-Generation of Power and Hydrogen for Residential Buildings

Bahrami,

Soltanifar,

Fallahi

et al. 2024

Buildings

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“…Recently, with the advancement of technology, widespread deployment of phasor measurement units (PMUs) and smart meters have enabled system operators and utilities to continuously monitor the grid to identify potential problems and improve the system's efficiency and reliability. A wealth of data measured by these devices has embraced the utilization of deep neural networks (DNNs) and other machine learning (ML) methods in various applications in power system data analytics such as load forecasting [16], [17], demand-side modeling [18], [19], wind and solar forecasting [20], [21], state estimation [22], [23], cyberpower anomaly detection [24], [25], security assessment [26], and PF calculations [27]. In particular, these methods are applied to power systems to achieve more accurate and reliable power flow calculation methods for more efficient power grid operations [27], [28], [29], [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Tiwari,

Zideh,

Talreja

et al. 2024

IEEE Access

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Most power systems' approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as forward-backward sweep (FBS) and Newton-Raphson require a high number of iterations to solve non-linear PF equations making them computationally very intensive. PF is the most important study performed by utility, required in all stages of the power system, especially in operations and planning. This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids. Three deep neural networks (DNNs); Radial Basis Function Network (RBFnet), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN), are proposed in this paper to predict PF solutions. The strength of the proposed DNN models over the traditional iterative-based PF solvers is that these models can capture the nonlinear relationships in PF calculations to accurately predict the solutions. The PF problem is formulated as a multi-output regression model where two or more output values are predicted based on the inputs. The training and testing data are generated through the OpenDSS-MATLAB COM interface. These methods are completely data-driven where the training relies on reducing the mismatch at each node without the need for the knowledge of the system. The novelty of the proposed methodology is that the models can accurately predict the PF solutions for the unbalanced distribution grids with mutual coupling and are robust to different R/X ratios, topology changes as well as generation and load variability introduced by the integration of distributed energy resources (DERs) and electric vehicles (EVs). To test the efficacy of the DNN models, they are applied to IEEE 4-node and 123-node test cases, and the American Electric Power (AEP) feeder model. The PF results for RBFnet, MLP, and CNN models are discussed in this paper which demonstrate that all three DNN models provide highly accurate results in predicting PF solutions.INDEX TERMS Power flow analysis, deep neural networks, radial basis function networks, multi-layer perceptron, convolutional neural networks, unbalanced power distribution grids.The associate editor coordinating the review of this manuscript and approving it for publication was Ehab Elsayed Elattar .

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“…The development of artificial intelligence facilitated new solutions to online risk assessment of a power system. Based on the training of a large number of samples, machine learning methods can directly establish the mapping between the input and output features, thereby eliminating the complex intermediate process and reducing the computation time; this is highly suitable for the problem of online risk assessment; therefore, in recent years, there have been some research studies on machine learningbased risk assessment of power systems (Alimi et al, 2020;Gholami et al, 2020;Mehrzad et al, 2023;Prusty et al, 2023). Yun used the static voltage stability margin as a severity index of the possible state of a power system for voltage safety risk assessment and introduced a support vector machine (SVM) to achieve a rapid calculation of severity while optimizing the parameters of the SVM by the genetic algorithm (Yun et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Online load-loss risk assessment based on stacking ensemble learning for power systems

Wang,

Sun,

Dan

et al. 2023

Front. Energy Res.

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Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliability of power systems. This paper presents an online load-loss risk assessment method for power systems based on stacking ensemble learning. First, a traditional load-loss risk assessment method based on power flow analysis was constructed to generate risk samples. The label of the sample is load-loss risk assessment index and the features are multiple operational variables of the power system. And the recursive feature elimination using cross validation (RFECV) was adopted for feature selection. Second, four different machine learning models, including support vector regression (SVR), extremely randomized trees (ET), extreme gradient boosting (XGBoost) and elastic network (EN) were used to form a stacking ensemble learning model for sample training. Moreover, to further improve the model performance, the particle swarm optimization (PSO) algorithms was used for parameter optimization. Finally, based on this model, the online load-loss risk assessment of a power system was realized. The application of the proposed method on IEEE test systems demonstrated that the proposed method was more accurate than methods based on individual machine learning models, from which the stacking was designed, while still maintaining a significant advantage in terms of runtime compared to the traditional risk assessment method.

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A Review on Data-Driven Security Assessment of Power Systems: Trends and Applications of Artificial Intelligence

Cited by 7 publications

References 94 publications

Energy and Economic Advantages of Using Solar Stills for Renewable Energy-Based Multi-Generation of Power and Hydrogen for Residential Buildings

Energy and Economic Advantages of Using Solar Stills for Renewable Energy-Based Multi-Generation of Power and Hydrogen for Residential Buildings

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Online load-loss risk assessment based on stacking ensemble learning for power systems

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