Application of Constrained Nonlinear Minimization to Estimate Parameters of Transmission Line in Power System

One of the essential components of power systems is the transmission line. It is used in the power system to transfer electricity energy from generation units to load units. Modern transmission line is a complex interconnected network. The mathematical model of transmission line is in nonlinear equations. This paper applied the constrained nonlinear minimization to estimate parameters of transmission line in power system. The great advantage in this proposed method is that the data of the Phase Measurement Unit (PMU) is not required. The ready data at power substations voltage magnitude(V), active (P) and reactive power (Q) is incorporated into mathematical model to estimate the parameters. The various methods of nonlinear iterative available in MATLAB toolbox such as fsolve, fmin, and fmincon are also compared in this paper

PMU Placement in Electric Transmission Networks for Reliable State Estimation against False Data Injection Attacks

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system (WAMS), resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system (ECPS) paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs

Frequency transfer via a two-way optical phase comparison on a multiplexed fiber network

We performed a two-way remote optical phase comparison on optical fiber. Two optical frequency signals were launched in opposite directions in an optical fiber and their phases were simultaneously measured at the other end. In this technique, the fiber noise was passively cancelled, and we compared two optical frequencies at the ultimate 1E-21 stability level. The experiment was performed on a 47 km fiber that is part of the metropolitan network for Internet traffic. The technique relies on the synchronous measurement of the optical phases at the two ends of the link, that is made possible by the use of digital electronics. This scheme offers several advantages with respect to active noise cancellation, and can be upgraded to perform more complex tasks

Comparisons of the execution times and memory requirements for high-speed discrete fourier transforms and fast fourier transforms, for the measurement of AC power harmonics

Conventional wisdom dictates that a Fast Fourier Transform (FFT) will be a more computationally effective method for measuring multiple harmonics than a Discrete Fourier Transform (DFT) approach. However, in this paper it is shown that carefully coded discrete transforms which distribute their computational load over many frames can be made to produce results in shorter execution times than the FFT approach, even for large number of harmonic measurement frequencies. This is because the execution time of the presented DFT actually rises with N and not the classical N2 value, while the execution time of the FFT rises with Nlog2N

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms