A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

A simple and novel approach in the design of an extended Kalman filter (EKF) for the measurement of power system frequency has been presented in this paper. The design principles and the validity of the model have been outlined. The performance of this filter has been compared with some of the existing methods for estimating the frequency of a signal under noisy conditions. The feasibility of the proposed filter has been tested in the laboratory under worst-case measurement and network conditions, which might occur in a typical power system. Also, the proof of the stability for the proposed filter has been discussed for a single sinusoid. It has been found that the proposed algorithm is suitable for real-time applications especially when the frequency changes are abrupt and the signal is corrupted with noise and other disturbances due to harmonics.

A novel Kalman filter for frequency estimation of distorted signals in power systems

Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

A critical review of detection and classification of power quality events

The paper proposes an extended complex Kalman filter and employs it for the estimation of power system frequency in the presence of random noise and distortions. From the discrete values of the 3-phase voltage signals of a power system, a complex voltage vector is formed using the well known /spl alpha//spl beta/-transform. A nonlinear state space formulation is then obtained for this complex signal and an extended Kalman filtering approach is used to compute the true state of the model iteratively with significant noise and harmonic distortions. As the frequency is modeled as a state, the estimation of the state vector yields the unknown power system frequency. Several computer simulations test results are presented in the paper to highlight the usefulness of this approach in estimating near nominal and off-nominal power system frequencies.

http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/67/1/pkd10.pdf

Frequency estimation of distorted power system signals using extended complex Kalman filter

Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

The design of an extended complex Kalman filter for the measurement of power system frequency has been presented in this paper. The design principles and the validity of the model have been outlined. A complex model has been developed to track a distorted signal that belongs to a power system. The model inherently takes care of the frequency measurement along with the amplitude and phase of the signals. The theory has been applied to standard test signals representing the worst-case measurement and network conditions in a typical power system. The proposed algorithm is suitable for real-time applications where the measurement noise and other disturbances are high. The complex quantities can be conveniently handled using a floating point processor. Comparison of the results of the proposed method with those obtained from a real extended Kalman filter reveals the superior performance of the former method.

/pdf/an-extended-complex-kalman-filter-for-frequency-measurement-198pwzkjnb.pdf

An extended complex Kalman filter for frequency measurement of distorted signals

AC microgrid protection – A review: Current and future prospective

http://dspace.nitrkl.ac.in/dspace/bitstream/2080/89/1/pkd19983.pdf

A new approach to monitoring electric power quality

MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid

A new approach for frequency relaying in power system is presented in this paper. The approach consists of passing the power system voltage signal through a two stage adaptive notch filter, which produces an estimate of the fundamental frequency of the voltage waveform and its enhanced amplitude. The adaptive of the notch filter coefficients are obtained by a recursive least mean squares algorithm. The performance of the proposed algorithm is computationally efficient and it produces an accurate estimate of the fundamental frequency in the presence of harmonics and noise, which is a prerequisite to frequency relaying in power systems. Several computer simulation results are presented in the paper to show the effectiveness of the algorithm. The algorithm is simple and suitable for real time implementation. Further the accuracy of this approach in presence of harmonics and noise is improved considerably.

Ranjan Kumar Jena

Papers

An extended complex Kalman filter for frequency measurement of distorted signals

AC microgrid protection – A review: Current and future prospective

A new approach to monitoring electric power quality

MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid

Estimation of power system frequency using adaptive notch filters