Multi-apartment residential microgrid monitoring system based on kernel canonical variate analysis

Ciabattoni, Lucio; Comodi, Gabriele; Ferracuti, Francesco; Fonti, Alessandro; Giantomassi, Andrea; Longhi, Sauro

doi:10.1016/j.neucom.2015.04.099

Cited by 21 publications

(13 citation statements)

References 33 publications

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“…A limited number of studies in the existing literature have focused on CVA with the kernel extension . Theoretically, KCVA maps the original data space into a high dimensional linear space.…”

Section: Preliminary Methodsmentioning

confidence: 99%

“…However, when the normal distribution assumption is invalid, the probability density function (PDF) estimated directly by a non‐parametric approach is usually adopted. The kernel density estimation is considered because it is a non‐parametric method for estimating the PDF of

T^{2}

and Q statistics . Assuming

x_{1}

is a random variable and

p false(x_{1} false)

denotes the density function:

P false(x_{1} < h false) = \int_{- \infty}^{h} p (x_{1}) {dx}_{1} .

…”

Section: Erkcva‐based Fault Detection Algorithmmentioning

confidence: 99%

“…A limited number of studies in the existing literature have focused on CVA with the kernel extension. [18] Theoretically, KCVA maps the original data space into a high dimensional linear space. It assumes a nonlinear transformation based on kernel function, which maps the past and future matrices into the feature space.…”

Section: Kernel Cvamentioning

confidence: 99%

“…The kernel density estimation is considered because it is a non-parametric method for estimating the PDF of T 2 and Q statistics. [18,37] Assuming x 1 is a random variable and p(x 1 ) denotes the density function:…”

Section: Erkcva-based Fault Detection Algorithmmentioning

confidence: 99%

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Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes

2017

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Kernel canonical variate analysis (KCVA) cannot be adopted for monitoring nonlinear time-varying processes because of changes in variance, mean, and correlation between variables. Efficient recursive kernel canonical variate analysis (ERKCVA) is thus proposed to monitor the nonlinear time-varying processes. In a high-dimensional feature space, the covariance matrix can be updated recursively by the exponentially weighted moving average approach. The first-order perturbation theory is introduced to obtain the recursive singular value decomposition of the Hankel matrix, which can significantly reduce the computational cost of the proposed method. Prediction errors and state variables are non-Gaussian; thus, upper control limits can be derived from the estimated probability density function by kernel density estimation. The proposed method is demonstrated by simulating a continuous stirred tank reactor. Simulation results indicate that ERKCVA could efficiently capture the predefined normal and natural changes in nonlinear time-varying processes. In addition, ERKCVA can also identify 4 types of sensor faults.

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Section: Preliminary Methodsmentioning

confidence: 99%

T^{2}

and Q statistics . Assuming

x_{1}

is a random variable and

p false(x_{1} false)

denotes the density function:

P false(x_{1} < h false) = \int_{- \infty}^{h} p (x_{1}) {dx}_{1} .

…”

Section: Erkcva‐based Fault Detection Algorithmmentioning

confidence: 99%

Section: Kernel Cvamentioning

confidence: 99%

Section: Erkcva-based Fault Detection Algorithmmentioning

confidence: 99%

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Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes

2017

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“…There are many ways for theft detection and minimization [25,26]. One simple approach is that whenever a line to ground fault occurs or some theft attempt is made, there is a change of total current of house loads.…”

Section: Tfdmentioning

confidence: 99%

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

et al. 2016

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Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model.

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Citizen-Oriented Technologies in the Cities of Tomorrow

Arnesano

Casaccia

Perna

et al. 2019

The First Outstanding 50 Years of “Università Politecnica Delle Marche”

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Multi-apartment residential microgrid monitoring system based on kernel canonical variate analysis

Cited by 21 publications

References 33 publications

Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes

Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

Citizen-Oriented Technologies in the Cities of Tomorrow

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