Identification Of Electrical Appliances Research Articles

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Non-intrusive load monitoring is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian summation angular field, Gramian difference angular field, and Markov transition field into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the squeeze -and-excitation module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.

Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring

The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conducted with up to 12 appliances operating at the same time in the single power supply circuit. The measurement setup was developed and used to record appliances’ switching on/off events. During tests, 2300 events for devices were recorded. Collected data were processed to identify particular devices with the accuracy of 98.8% and macro-averaged F-score measure of 0.9874. High identification accuracy was achieved despite the high number of devices operating in the background.

Identification of Electrical Appliances Using Their Virtual Description and Data Selection for Non-Intrusive Load Monitoring

The proper pattern of electric energy management on the part of consumers is a key element of the system enabling its effective use. This pattern can be developed by providing consumers with information obtained from non-invasive load monitoring. Improvement electric energy management can be achieved by turning off unnecessary appliances and by shifting some of them to off-peak hours. A rational benefit for consumers is the reduction of electricity consumption costs. In this paper, energy disaggregation was abandoned by introducing virtual electrical devices and by using optimal selection of explanatory data obtained from high-frequency measurements. The concept of virtuality results from the simultaneous operation of many physical devices, and especially for which it is difficult to distinguish the state of load, e.g., small, medium or large. The explanatory data was selected using the integral information capacity indicator of individual features. The determination of this indicator is based on the correlations between the explanatory variables, the explained variable and the mutual correlations between the explanatory variables. The parameters selected in this way constitute input data for various artificial neural network architectures identifying the electrical appliances. The obtained research results are very promising and confirm the validity of the approach used.

Selection of statistical wavelet features using a wrapper approach for electrical appliances identification based on a KNN classifier combined with voting rules method

This work is an extended version of a paper presented in the International Conference on Intelligent Systems and Patterns Recognition where the authors have proposed a compact features representation based on the estimation of statistical features using discrete wavelet transform for electrical appliances identification based on a K nearest neighbour classifier combined with voting rule strategy. The results have shown that the wavelet cepstral coefficients (WCC) descriptor presents highest performance with 98.13% classification rate (CR). In this work, we propose many extensions: 1) The logarithm energy (LOG_E) is used as additional descriptor; 2) The relevance of the wavelet-based features combined with LOG_E descriptor is investigated using feature selection based on wrappers approach; 3) Deep performances evaluation is carried out using five additional metrics. The results show that the selection of four features of WCC combined with LOG_E improves the CR at 98.51%.

Selection of statistical wavelet features using a wrapper approach for electrical appliances identification based on a KNN classifier combined with voting rules method

Selection of statistical wavelet features using a wrapper approach for electrical appliances identification based on a KNN classifier combined with voting rules method

Multi-state household appliance identification based on neural network

In the current non-intrusive load monitoring process, the states switching of some multi-state appliances is difficult to be correctly identified by the switch event detection method, and the energy consumption statistics due to poor multi-state identification of the electrical appliances are not accurate. This paper proposes a multi-state household appliance load identification method based on convolutional neural network and clustering model. First, a convolutional neural network is used to construct an appliance type identification model. Then, a multi-state identification model is established to identify the state of different multi-state appliances. The experimental results show that the proposed method achieves multi-state identification of electrical appliances and has good engineering and practical value.

Relevant harmonics selection based on mutual information for electrical appliances identification

Relevant harmonics selection based on mutual information for electrical appliances identification

Relevant harmonics selection based on mutual information for electrical appliances identification

Many appliance identification systems use harmonics of current signals as features. However, the choice of the order and number of relevant harmonics for appliance identification task has never been demonstrated. Here, we propose to tackle this issue by analysing the relevance and redundancy of harmonics for this task using feature selection algorithms based on mutual information criterion. Six heuristic strategies based on this criterion were implemented and their selection results from high-dimensional feature vectors were compared. For the choice of a minimal subset of relevant harmonics, we propose a stopping criterion in the selection procedure. In order to validate the selected subset of harmonics, a hidden Markov model based-classifier was used and evaluated on PLAID dataset. Results highlight odd order harmonics relevance. Furthermore, the feature subset {1,2,3,4,5,7,9} was selected by three strategies as strongly relevant since this minimal subset is sufficient for essentially explaining appliances' signatures.

Extraction and selection of statistical harmonics features for electrical appliances identification using k-NN classifier combined with voting rules method

In this paper, we propose a novel framework for electrical appliances identification using statistical harmonic features of current signals and the use of the k-NN classifier combined with a voting rule strategy. Harmonic coefficients are computed over time using short-time Fourier series of the current signals. From these sequences of coefficients, the mean, standard deviation, skewness, and kurtosis are computed, which provide the statistical harmonic features. This framework has three novelties: (i) selecting the best combination of statistical measures in the sense of classification rate (CR); (ii) combining the k-NN classifier with the voting rule method in order to search for the best number of voting vectors; and (iii) selecting relevant features for the task of appliances identification by using one of the relevant feature selection algorithms based on mutual information. Results evaluated on the Plaid dataset clearly show that the mean and standard deviation statistics combination gives the best CR of 92% with 500 features and gives the minimal computing time compared to the system based on HMM models. Moreover, combining the k-NN classifier with the voting rule using the above features increases the CR up to 95%. Using this combination, the results also show that an increase of the training dataset size further improves identification performance results in terms of precision, sensitivity, and F-score. A feature selection procedure based on joint mutual information strategy shows that using a selected subset of five features is sufficient, giving similar CR results to those obtained using the total number of features, whatever the training dataset size.

Non-intrusive electrical appliances identification using Wavelet Transform analysis

The paper presents the application of the Wavelet Transform (WT) analysis to identify electrical appliances operating in the end-user household. The generic approach to the non-intrusive devices identification, i.e. using aggregated current and voltage signals is described. Next, the novel approach to extract features from the signals using WT coefficients is introduced. This allows for monitoring the appliance in the transient state, during the on-off event or while changing its mode (in the multi-state devices). This approach may replace or support the traditional steady-state analysis. Features extracted during switching the specific appliance in the laboratory conditions are then used to train the artificial intelligence-based classifier. It is further responsible for identifying the devices in the on-line mode. As the classifier, the random forest was applied. Its evaluation on the available data proves the usefulness of the proposed approach.

Approach for home appliance recognition using vector projection length and Stockwell transform

Recognition of residential electrical appliances has been a highlighted area of research in recent years due to increasing energy consumption in residential sectors around the world. The use of instant current from 17 commonly presented electrical appliances in homes is proposed. The Stockwell transform, a time–frequency-domain transform, is applied to current collected data to be used as a load signature. Then, a novel method for identification of electrical appliances was employed to analyse the data. This method is an image pattern recognition technique, called vector projection length (VPC). In this case, the idea is to recognise patterns in appliances transformed into a 2D image. An extensive analysis is presented, analysing the trade-off between the training set database, the number of current cycles used and the variable k from the VPC projection equation, searching for optimal parameters for this application. Results have shown successful identification employing VPC, close to 90% of devices being identified.

Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach

The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.

The Design of Remote Monitoring System for Indoor Electrical Safety

Causes of electrical fires have great variety. In view of the main, this paper explores the algorithm of fault arc detection and the method of electric appliance identification, and briefly shows the overall design of the monitoring system. The judging threshold of fault arc can be carried out through building an AR model of normal current signal and calculating the coefficients of the AR model with the third-order Burg algorithm. The existence of fault arc can be determined by the squared Euclid distance between the threshold and the coefficients of current signal. The pattern recognize based on the Fourier series of current signal as characteristic values can be used as Electrical appliances identification methods combined with the power of appliance.