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Abstract Power disturbances, defined as the waveform distortion of a power system under normal or abnormal conditions, contain considerable system and equipment state information. Obtaining equipment and system state information from power disturbance is very important to ensure the safety of power grids. To adapt to the development of power electronics, informatisation and digitisation of power systems, several applications with waveform‐recording devices have obtained large amounts of disturbance waveform data, laying an important foundation for the analysis and application of power disturbance waveform data. First, typical disturbance waveform monitoring devices and a disturbance trigger detection algorithm are introduced. Then, disturbances are classified as switching, fault, or abnormal operations, according to the cause. The characteristics of various typical disturbance waveform data were analysed by combining the simulation and measured data. This paper summarises the application analysis of power disturbance waveform data at both the system and equipment levels. Finally, the construction scheme of a power disturbance waveform data monitoring and analysis platform for two different application scenarios was proposed for commutation failure monitoring and medium‐voltage distribution network fault warning. The research conducted here is expected to support the construction of a power disturbance waveform analysis platform.

Developing countries are experiencing significant urban and population growth. This amplifies the crisis of governance of essential public services. This study analyses the electricity market in Lubumbashi, one of the Democratic Republic of Congo (DRC) municipalities. Surveys at different scales are conducted, assessing both access to electricity and the role of actors. 5270 households, 41 policymakers and 100 employees at different scales are interviewed. It has been found that the electricity market is deteriorated by illegal connections to the electricity grid, uncontrolled urban growth, corruption, and poverty. Besides, the quality and cost of electricity decrease with distance from the city centre. Therefore, privatization of the electricity sector would lead to low-cost electricity and high-quality service.

Cable incipient fault (CIF) is a potential fault which may recur over time and evolve into permanent fault eventually. The identification of CIF can help to reduce the likelihood of a permanent fault and enhance power supply reliability. This paper fully considers the randomness and uncertainty of CIF waveforms, and proposes a CIF identification method using power disturbance waveform feature learning. Firstly, the shallow features are extracted to characterize the transient components of different disturbance current waveforms, by conducting a stationary wavelet transform. Then, a dropout deep belief network (DDBN) is constructed using the extracted shallow features by pre-training and fine-tuning. Finally, the well- constructed DDBN model is used to identify CIF from other similar disturbance events. The performance of the proposed method is verified by simulation data and experimental data, for different disturbance events, such as sub-cycle cable incipient fault and multi-cycle cable incipient fault, as well as other over-current disturbance events such as capacitor switching and inrush current. Both the accuracy and generalization ability are higher than other methods. The proposed method provides new insights on possible applications on monitoring of cable status and timely warning of cable faults.

Abstract In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.

This paper presents a new method for the detection of voltage disturbances caused by various reasons in households and industries. The aim of the paper is to improve the power quality disturbance methods and implement it in real-time with a very high accuracy and use existing mitigation techniques after classification of the signal. The method involves a novel idea to use the quaternions for the purpose of signal tracking. The envelope formed by Hilbert envelope detection method is used as an input for the quaternions. The quaternions produce a spike whenever there is a deviation from the pure sine wave giving very accurate results in a very short period of time. Combination of SVM and DSmT classifier is used for recognizing and classification of different signals such as sags, swells, transients and other power quality disturbances, hence can be used to monitor the power quality. [ABSTRACT FROM AUTHOR]

In order to mitigate voltage sag related problems based on power quality monitoring data, it is meaningful to improve the efficiency of power quality data analysis. Due to depending on accurate models of voltage sag, the traditional methods are inadequate for complex problems with multiple uncertainty factors. Therefore, the spatial--temporal multi-granular attribute analysis of voltage sag data and a related knowledge discovery method were proposed in this paper. Inspired by the cognitive hierarchy of complex problems, a framework consisted of "data--characteristic--index--information--knowledge" was proposed as a general technical route for voltage sag related problems. Based on the framework, to solve the problem of information loss caused by single granular, sag information in different granular was extended by voltage sag spatial--temporal multi-granular analysis. The relationship between power system structure attribute and voltage sag was discovered by granular reduction. Then, knowledge about voltage sag severity and propagation was derived. The synthetic and measured data were used to validate the effectiveness of the proposed method. Results showed that the proposed method can describe and resolve complex problems with many uncertainty factors. [ABSTRACT FROM AUTHOR]

In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Engineering Science / Gongcheng Kexue Yu Jishu is the property of Advanced Engineering Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the recent years, electrical, electronics, and telecommunications have far-famed a rare improvement, the quantity of nonlinear loads has inflated. Many electric power consumption devices are sensitive to magnetic attraction disturbances, created through nearby devices, which might have an effect on the power quality for several industry units or may be domestic users. Poor quality of power leads to fault of devices and instrumentation instability in a short period. Systematically, some research and analysis are required to monitor the disturbances of power quality which increases quality of power. This paper consists of power quality analysis on RLC circuits with voltage and current magnification values through Hyperspectral signal processing. The voltage changes and harmonic disturbances can be simulated finely with the MATLAB–Simulink simulation results and the theoretical analysis display. The simulated outcome and the Hyperspectral processing offer data and identification of power quality variances and additional control measures accurately. [ABSTRACT FROM AUTHOR]

On-line cable insulation condition monitoring can significantly reduce the outages caused by insulation failure. This paper proposes a new method to achieve this by utilizing the natural power disturbances originating from outside of the cable, such as capacitor switching and fault transients. In this method, the relative permittivity of the cable insulation is used as the indicator to be monitored, which is closely associated with cable tanδ. The relative permittivity is extracted from the power disturbances and their responses, which are of sufficient strength and a wide range of frequencies. This method is beneficial to the on-line monitoring for it does not need the accurate remote synchronized phase angle information. Case studies and sensitivity studies demonstrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances.

Electrical energy is the essential resource for modern society. Recently, the demand for power was significantly increased. Increase of power demand has lead to a decrease in the power quality. Power quality in modern society has been an important factor that can cause a major problem throughout the home and general industry. Therefore, we need a system for preventing the power quality problems. To avoid power issues, it is important that the measurement of the power quality and initial response. In this paper, we propose real-time power quality measurement system and harmonics monitoring system. We design the system using DM240001 board include dsPIC33FJ256GP710A of microchip. This system can adapt three-phase three-wire system. And optimized the algorithm, we can measure momentary changes of the power system. In addition, designed system can measure harmonics distortion like to VTHD, ITHD and ITDD for 31th harmonics. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique, generalized morphological open-closing and close-opening undecimated wavelet (GMOCUW), based on which a power disturbance identification scheme is developed. In order to extract features of power disturbances, the proposed scheme employs undecimated wavelet transform for its advantage in retaining information and reducing waveform distortion, multiscale morphological analysis for its ability in frequency analysis, and generalized morphological open-closing (GMOC) and generalized morphological close-opening (GMCO) operations for their advantages in information preserving. Power system computer aided design/electro-magnetic transient in dc system (PSCAD/EMTDC) was employed to construct a test power system to simulate eight types of power disturbances. Additionally, a laboratory platform was established to generate power quality (PQ) signals under real operating conditions. The performance of GMOCUW has been compared with that of morphological gradient wavelet (MGW), new dual neural-network-based methodology (NDNM), S-transform (ST), and Daubechies 4 wavelet (DB4W). Comparison results have proved that, in power disturbance detection, GMOCUW is more accurate and faster than these methods. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) power disturbance from massage may hurt operators even living environment. EM disturbance from power, transformation, indicator and MOS units in massage were discussed, using on-line positioning and analyzing methods. Test results show that the EMI power emission from massage is reduced, and below the limit values of the relevant standard, by promoting relevant circuits, which will supply some valuable reference further improving electrical and electronic products design. [ABSTRACT FROM AUTHOR]