Pavel Hering

Transmission line identification using PMUs

The paper is devoted to the identification of transmission lines from synchronized measurements of current and voltage phasors provided by the phasor measurement units (PMUs). The series and shunt line parameters are recursively estimated using the extended Kalman filter (EKF). Actual line temperature and reference resistance are estimated instead of actual series resistance, which is then computed from these estimates. This approach can accommodate more information and, therefore, produce better estimates of the line resistance than the traditional approaches based on the weighted least squares method. Two case studies based on data from South Moravia and East Bohemia are presented to demonstrate the effectiveness of the proposed approach.

NEURAL NETWORK BASED BICRITERIAL DUAL CONTROL OF NONLINEAR SYSTEMS

Abstract A bicriterial dual controller for nonlinear stochastic systems is suggested. Two separate criterions are designed and used to introduce one of opposing aspects between estimation and control; caution and probing. A system is modelled using a multilayer perceptron network. Parameters of the network are estimated by the Gaussian sum method which allows to determine conditional probability density functions of the network weights. The proposed approach is compared with inovation dual control and the quality of the estimator and the regulator is analyzed by simulation and Monte Carlo analysis.

Sequential optimal experiment design for neural networks using multiple linearization

Design of an optimal input signal in system identification using a multi-layer perceptron network is treated. Neural networks of the same structure differing only in parameter values are able to approximate various nonlinear mappings. To ensure high quality of network parameter estimates, it is crucial to find a suitable input signal. It is shown that utilizing the conditional probability density function of parameters for design of the input signal provides better results than currently used procedures based on parameter point estimates only. The conditional probability density function of parameters is unknown and hence it is estimated using the Gaussian sum approach approximating arbitrary probability density function by a sum of normal distributions. This approach is less computationally demanding than the Markov Chain Monte Carlo method and achieves better results in comparison with the commonly used local prediction error methods. The properties of the proposed input signal designs are illustrated in numerical examples.

Identification of nonlinear non-gaussian systems by neural networks

Abstract Application of neural networks in identification of non linear nonGaussian systems is treated. Stress is laid on a parameter estimation of the networks. They are trained by the Gaussian sum method which is a global filtering method allowing to determine conditional probability density functions of network weights. Proposed approach to estimation of network weights (parameters) based on Gaussian sum filtering method overcomes commonly used prediction error methods and it is an interesting alternative to sequential Monte Carlo methods. The considered training approach is demonstrated by an illustration example.

On-line Ampacity Monitoring from Phasor Measurements

Abstract The knowledge of transmission line parameters involving series impedance and shunt admittance is crucial to power system analysis and power system state estimation. Particularly in areas of power systems protection it is critical to have accurate values of these parameters to determine the line ampacity. The line parameters change according to the load level and weather conditions, so it is necessary to identify the line parameters in real time. The paper deals with the on-line parameter identification of transmission lines from synchronized measurements of current and voltage phasors provided by the phasor measurement units. The series and shunt line parameters are recursively estimated from the phasor measurements and weather conditions using the extended Kalman filter. Contrary to the other works, an actual line temperature and reference resistance are estimated, which makes possible to monitor the actual state of the transmission line in order to determine an actual reserve or prediction of future transmission capacity. A case study based on data measured from a 110kV overhead transmission line is presented to demonstrate the effectiveness of the proposed approach.

Optimal scheduling of a pumped-storage hydro power plant operation

The paper presents an optimization technique for scheduling of pumped-storage power plant operation up to one year horizon. A pumped-storage power plant is an energy source with fast time response consisting of upper and lower reservoir, which has ability to store excess electric energy in the upper reservoir in form of potential energy of water, which is pumped from the lower reservoir. Its importance increases as a tool for transmission system operator to control of power system stability, load balancing and frequency control due to the increase of amount of power generated from renewable energy sources characterized by the fluctuation of power output such as photovoltaic and wind power sources. Usually, it has part of capacity reserved for needs of transmission system operator and the remaining capacity can be used by the plant operator in commercial manner to increase of profit. The aim of the paper is to propose fast algorithm based on dynamic programming to optimize free capacity while complying with all the technical and operational restrictions.

Functional Adaptive Control for Nonlinear Stochastic Systems in Presence of Outliers

Abstract This paper presents an enhancement of a functional adaptive control of nonlinear stochastic systems that renders it to be robust with respect to the occurrence of outliers in the plant measured output. Outliers are considered to be large deviations of a signal being measured, only occurring in a few percent of the observations. Therefore, although rare, the outliers cause poor parameter estimates and, consequently, heavily degrade control performance due to their large amplitude. A system is modelled using a multi-layer perceptron network and the measurement noise is modelled by a mixture of Gaussian distributions. One component of the mixture describes uncorrupted process data, while the others describe various types of outliers. Parameters of the network together with output prediction of the uncorrupted data component are estimated by an estimation method based on the mixture of Gaussian distributions. Control design is based on a bicriterial dual approach. The advantages of the proposed controller are illustrated in an example by simulation and Monte Carlo analysis.

Structure adaptation of multi-layer perceptron network for on-line system identification

Abstract Identification of nonlinear systems by a neural network is treated. The paper deals with a design of a suitable neural network structure to approximate a nonlinear function of the identified system. Contrary to the recent algorithms, the proposed structure adaptation algorithm can be applied on-line during the identification process. The designed algorithm consist of a statistical test for making decision about suitability of an a priori chosen network and then either a growing or a pruning according to the size of the network is applied. The acceptance or rejection of the model is realized by application of the statistical cumulative sum test from the decision making field. The growing part of the algorithm repeatedly utilizes principle of the learning methodology for detecting faults in nonlinear dynamical systems for adding neurons to the hidden layer. Finally, the pruning algorithm is based on a measure of sensitivity of the model output error to the removing of the network connections. The properties of the proposed structure adaptation algorithm are illustrated in a numerical example.