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Accurate parameter identification plays an integral role in the modeling of an optimized solar module which in turn helps in the error-free prediction of its output. Five important parameters: the photoelectric current (Iph), ideality factor(α), saturation current (Io), series resistance (Rs), and shunt resistance (Rsh) are required for the accurate modeling of the PV cells/modules and need to be extracted as these parameters are not provided by the manufacturer in the datasheet. This paper proposes a new metaheuristic jellyfish optimization (JFO) algorithm for the parameter extraction of a solar module. The JFO algorithm achieves the optimal solution without being trapped in local solutions in less time. The parameter extraction using the JFO algorithm is done on two different solar modules i.e., Soltech-1STH-215P and PWP-201. The results are compared in terms of extracted parameters (Iph, α, Io, Rs, and Rsh) with the well-known optimization techniques like PSO, GA, and others available in the literature and with the manufacturer I–V and P–V characteristics. The proposed technique I–V and P–V characteristics are validated at different environmental conditions and are found to be similar to that of PSO and GA. It is also observed that the extracted parameters obtained using JFO are comparable with the other twenty-two techniques, and the proposed technique is one of the highly efficient techniques that can be utilized for parameter extraction of PV modules and to predict solar cell characteristics for all commercial modules without setting up any experimental measurements. MATLAB/simulation software is used for implementation and performance validation.

The wide area situational awareness attempts at the expeditious detection of imminent system abnormalities and alerting system operators to take appropriate measures. Because the critical situation may arise in a system due to faults on transmission lines spanning over a long distance, phasor measurement units (PMUs) have become an indispensable measuring device to provide a dynamic view of such a wide area system. In this paper, the perception about a 200 km long transmission line has been achieved with the help of phasor measurements from PMU, which has the capability of reporting 200 phasors per second. The comprehension about the perceived event is accomplished by computing the deviations of current phasor magnitude as well as phase angles derived from synchronized phasor measurements using the phaselet algorithm. Based on the comprehension of the perceived event, a specific type of fault has been predicted using the Gaussian Naïve Bayes approach. In order to validate the proposed methodology, it has been implemented on a laboratory setup.

As the electrical power system has increased in its geographical sprawl, adequate measures for reliability analysis for the wide area measurement system (WAMS) and phasor measurement units (PMUs) have become necessary. However, existing PMU reliability models are constrained by the assumption that PMU failures may be encountered either because of hardware failures or because of software failures only. Most modem safety critical systems, like the PMU are characterised by close proximity of hardware and software operations which leads to correlated failures. This is referred to as hardware-software interaction failure and is disregarded by contemporary PMU reliability models. In this paper, a modelling framework has been developed using Markov process that captures hardware-software interaction failures, apart from the hardware specific and software specific failures, and presents a Markov model-based unified PMU reliability model. This paper also offers a novel Monte Carlo simulation (MCS) technique to estimate PMU failure data to account for scanty PMU failure data from field installations. The novel algorithms for MCS based PMU failure estimation are expounded along with detailed methodologies for fitting the simulated failure data to the unified reliability model. The results presented herein demonstrate the improved accuracy of the proposed method.

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