New Rules Generation From Measurement Data Using an Expert System in a Power Station

Sai, T. K.; Reddy, K. Ashoka

doi:10.1109/tpwrd.2014.2355595

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…The blue line represents the instantaneous power of the loads, the activation time, the peak value of the requested power and, finally, the duration time depending on the loads; indeed, all these parameters are outside the control of the supervisor. For the MILP definition, the load nominal power has been considered, as shown with the red dotted line in the same figure; as a consequence, the p LK (t) can be calculated from (13) and it is shown as a yellow line in the figure. Finally, p LK (t) is used to define the available power P AV (t) in ( 14), as indicated by the blue line in Figure 11a.…”

Section: The Loads L1 and L2 Have Power Depending On The Time Tmentioning

confidence: 99%

“…Other authors have investigated the use of expert systems applied to the power flow, to optimize the objective function when the operating points and/or the grid change [11][12][13]; in deed, the expert system approach works well when all states of the grid are known, thus moving the complexity to the design phase. A different approach considers instead the power balancing as a slack definition of power flow; this allows us to solve the OPF problem as MILP [14].…”

Section: Introductionmentioning

confidence: 99%

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Design of a Power System Supervisory Control with Linear Optimization for Electrical Load Management in an Aircraft On-Board DC Microgrid

Rubino

Conti

2021

Sustainability

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In modern aircraft, energy supply management has become a critical matter, since many aboard electrical loads have to be supplied, especially those related to flight safety. However, at the same time, the size and weight of electrical generators must be limited because of their on-board installation. In this paper, the Mixed Integrated Linear Programming (MILP) methodology has been used to formulate the Supervisor definition of the direct current (DC) microgrid (MG) on-board system with an extension for the programmable loads. Due to the problem of dimension increase, two methods have been presented and tested to perform optimal energy management (EM) aboard an aircraft: the Branch and Bound (B&B) and the Linear Regression Approximation (LRA). Finally, numerical simulations and results have been provided to validate the proposed optimization methodologies, according to the dimensions and the complexity of the problem.

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Section: The Loads L1 and L2 Have Power Depending On The Time Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a Power System Supervisory Control with Linear Optimization for Electrical Load Management in an Aircraft On-Board DC Microgrid

Rubino

Conti

2021

Sustainability

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“…The existing classifiers mainly include: artificial neural network (Haddad et al, 2018), Support Vector Machine (SVM) (Yong et al, 2015), decision tree (Huang et al, 2015;Long et al, 2018), expert system (Sai et al, 2015) and Bayesian classifier (Zhou et al, 2011), etc. For step 2): SVM has a high classification accuracy, but the amount of calculation in the process of parameter optimization is relatively large, and the real-time performance is not good.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Liu

Deng-ke

et al. 2021

Front. Energy Res.

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The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

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“…Knowledge, in a rule-based knowledge approach, is represented in terms of condition statements, like it-then rules. There are a number of applications of rule-based expert systems [7], [8]. Expert systems come under applied artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Designing a Rule Based Expert Systems for Contact Lenses Patients

Aydilek¹,

Gümüşçü²

2018

IJITCS

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Abstract-Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.

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New Rules Generation From Measurement Data Using an Expert System in a Power Station

Cited by 6 publications

References 4 publications

Design of a Power System Supervisory Control with Linear Optimization for Electrical Load Management in an Aircraft On-Board DC Microgrid

Design of a Power System Supervisory Control with Linear Optimization for Electrical Load Management in an Aircraft On-Board DC Microgrid

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Designing a Rule Based Expert Systems for Contact Lenses Patients

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