Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Li, Yahui; Li, Yang; Sun, Yuanyuan

doi:10.3390/app8091442

Cited by 32 publications

(27 citation statements)

References 42 publications

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“…As one of the decision methods, GRP method is especially suitable for evaluating the solutions with grey relationships in the MaOPs, which is based on grey system theory and vector projection [23]. The projection   ( ) l V of the lth solution in the ideal scheme can be written as [57,58]:…”

Section: Grp Methodsmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

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Section: Grp Methodsmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

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“…Features are selected based on a Single Ranking and Correlation Coefficient approach. In [133], the authors propose a neural network trained with PSO, while, in [134], they use a LASSO (Least Absolute Shrinkage and Selection Operator) method.…”

Section: A Prediction Of Power Flowsmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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“…A different set of indices has been proposed in Reference 30 which consider alarm and security alarms of the voltage and line flow, simultaneously:

\begin{matrix} lefttrue \\ PI = {([], \sum_{i} {(q_{U_{i}}^{\max})}^{4} + \sum_{i} {(q_{U_{i}}^{\min})}^{4} + \sum_{j} {(q_{P_{j}})}^{4})}^{\frac{1}{4}}, \\ q_{U_{i}}^{\max} = \{\begin{matrix} center \\ \frac{U_{i} - H_{i}^{\max}}{A_{i}^{\max} - H_{i}^{\max}} & U_{i} > H_{i}^{\max} \\ 0 & otherwise \end{matrix}, \\ q_{U_{i}}^{\min} = \{\begin{matrix} center \\ \frac{H_{i}^{\min} - U_{i}}{H_{i}^{\min} - A_{i}^{\min}} & U_{i} < H_{i}^{\min} \\ 0 & otherwise \end{matrix}, \\ q_{P_{j}} = \{\begin{matrix} center \\ \frac{|P_{j}| - P_{H_{j}}}{P_{H_{j}} - P_{A_{j}}} & |P_{j}| > P_{H_{j}} \\ 0 & otherwise \end{matrix}, \end{matrix}

where A i max , A i min , H i max and H i min are upper and lower alarm and security limits of voltage ( U i ) and

P_{H_{j}}

and

P_{A_{j}}

are alarm and security limitations of the line power flow ( P j ).

q_{U_{i}}^{\max}

q_{U_{i}}^{\min}

and

…”

Section: Categorization Based On Power System Implementationmentioning

confidence: 99%

“…The output of the proposed structure is the secure/insecure status of the grid and the type of violation in insecure cases, that is, line overload or voltage violation. A new classification method named LASSO has been used in Reference 30. The name of the algorithm is the abbreviation of the least absolute shrinkage and selection operator.…”

Section: Categorization Based On Power System Implementationmentioning

confidence: 99%

Static security assessment of power systems: A review

Gholami

Sanjari

Safari

et al. 2020

Int Trans Electr Energ Syst

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Summary The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.

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Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Cited by 32 publications

References 42 publications

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Recent Developments in Machine Learning for Energy Systems Reliability Management

Static security assessment of power systems: A review

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