Ultra-spectra communication system (USCS): a promising way to energy-efficient green communication networks

Li, Bin; Zhou, Zheng; Zou, Weixia

doi:10.1186/1687-1499-2012-47

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Signal processing-based techniques include Fourier transform, s-transform, Hilbert Huang transform, wavelet transform, and tt-transform. The explanation of these signal processing methods is used in islanding finding approaches are deliberated in the next segment [19].…”

Section: Signal Processing Methods For Islanding Detectionmentioning

confidence: 99%

Machine Learning Based Islanding Detection During Blackout

Ghafoor

2022

IJSET

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In modern era, demand of electricity is increasing rapidly due to fast development in industrial sector, communication technologies, commercial and domestic usage. The need of the hour is to protect the power system from blackouts during abnormal conditions, like voltage collapse and frequency disturbance etc between the generation and consumption. The sudden change in weather conditions, loading of power system, and over ageing of transmission and distribution lines becomes the cause of blackouts. Islanding detection and protection is one of the best possible techniques to improve the power quality, reliability, system security, transmission congestion relief and efficient energy utilization. Distribution generators (DGs), if properly placed have many positive impacts on the operation of power system. The islanding can improve the reliability by counting the operation of electrical power to keep the balance between consumption and generation. The main problem is sustained islanding with fast timing. Plant should sustain its island mode condition when fault occurs in transmission lines. Most islanding detection algorithms suffer from large NDZ and Non detection zone must be minimized to protect system properly and avoid blackouts. This study presents a technique to develop a reliable system to prevent complete blackouts, while maintaining the stability of the power system with high variability and uncertainty under the abnormal conditions. It is also needed to present the period all the DGs need to connect and disconnect immediately when the island occurs. For this purpose, each DG must be equipped with islanding detection devices. In this research work the presented system is a two-step process, based on the collection of some of the key characteristics in the current and voltage signals to analyze second harmonic by the Fourier transform (FT).

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Section: Signal Processing Methods For Islanding Detectionmentioning

confidence: 99%

Machine Learning Based Islanding Detection During Blackout

Ghafoor

2022

IJSET

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“…After his cooperation with Photron Science Company, these patents were registered as ultra spectral modulation (USM), which has pretty high bandwidth efficiency. In some other literatures, from a signal bandwidth rather than power bandwidth point of view, such a system can be referred to as a carrier-synchronized ultra-wide band system (CS-UWB) [22], which can produce a high narrow carrier spectrum when the practical duty-cycle is low.…”

Section: Efficient Modulationmentioning

confidence: 99%

Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System

Chen

2013

Sensors

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The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

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Ultra-spectra communication system (USCS): a promising way to energy-efficient green communication networks

Cited by 2 publications

References 34 publications

Machine Learning Based Islanding Detection During Blackout

Machine Learning Based Islanding Detection During Blackout

Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System

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