Young Moon Park

A United Approach to Optimal Real and Reactive Power Dispatch

This paper presents a unified method for optimal real and reactive power dispatch for the economic operation of power systems. As in other methods, the problem is decomposed into a P-optimization module and a Q-optimization module, but in this method both modules use the same generation cost objective function. The control variables are generator real power outputs for the real power module; and generator reactive power outputs, shunt capacitors/reactors, and transformer tap settings for the reactive power module. The constraints are the operating limits of the control variables, power line flows, and bus voltages.

An optimal tracking neuro-controller for nonlinear dynamic systems

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function. The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC). The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant. The FFNC is designed using a novel inverse mapping concept by using a neuro-identifier. A generalized backpropagation-through-time (GBTT) algorithm is developed to minimize the general quadratic cost function for the FBNC training. The proposed methodology is useful as an off-line control method where the plant is first identified and then a controller is designed for it. A case study for a typical plant with nonlinear dynamics shows good performance of the proposed OTNC.

A self-organizing fuzzy logic controller for dynamic systems using a fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum. >

Optimal Real and Reactive Power Control Using Linear Programming

Abstract This paper develops an improved real and reactive power control technique using linear programming (LP) for an integrated power system. The problem is decomposed into two subproblems comprising real (P) and reactive power (Q) modules, and, using a unified approach, the real power generation, voltage magnitude, and transformer tap settings are optimized. The objective function is the fuel cost which is minimized in both the P and Q modules, subject to the operating constraints. The P-Q decomposition combined with the LP formulation improve the computation speed. The paper has another advantage of using the same cost objective function for both modules, unlike other conventional methods which use the power loss function for the Q module. The LP formulation is used for both the P and Q optimization modules, utilizing the revised simplex method which is normally available in a mainframe computer.

A logic based expert system (LBES) for fault diagnosis of power system

This paper proposes an expert system for fault diagnosis of a power system using a new inference method. Expertise is, in this paper, represented by logical implications and converted into a Boolean function. Unlike conventional rule-based expert systems, the expertise is converted into Prime Implicants (PIs) which are logically complete and sound. Therefore, off-line inference is possible by off-line identification of PIs, which reduces the on-line inference time considerably and makes it possible to utilize the proposed expert system in real-time environment. This paper also presents alarm verification and correction method for relay and circuit breaker (CB) using pre-identified PIs.

High impedance fault detection utilizing incremental variance of normalized even order harmonic power

A novel method for the detection of high-impedance faults is proposed which uses the incremental variance of a normalized even order ratio measure. Staged fault tests were extensively carried out in Korean electric power systems. From the analysis of the staged fault test data, it was found that there exists an intermittent arcing phenomenon in most high-impedance faults and that the waveforms of this arcing fault current have an asymmetrical shape in each cycle. Based on these facts, three criteria, (even-order power, even-order ratio, and even-order incremental variance) for fault detection are presented, all of which are based on the changes of normalized even-order harmonic power in fault currents. These criteria are compared through the analysis of staged fault data and normal switching event data. It is shown that the even-order incremental variance criterion is superior to the other two criteria and that, with this criterion, high-impedance faults can be distinguished from normal switching events, including special loads such as electric furnaces and subways. Microprocessor-based protective relays, which can detect high-impedance faults by using the proposed methods, have been constructed, installed in Korea Electric Power Corporation substations, and tested during the last two years. Details of these field tests are given. >

Design of reliable measurement system for state estimation

The authors propose an algorithm of optimal meter placement for the state estimation of power systems, which minimizes the total investment cost subject to a constraint of state estimation accuracy. The solution procedure is developed on the basis of the sequential meter addition/elimination method by reflecting the cost of individual meter installation and its contribution to the state estimation accuracy in the probabilistic sense. The sensitivity of the performance index to each meter installation is efficiently circulated with the use of recursive formulae. Selection ranking in the addition-elimination process is set up in terms of the performance index sensitivities. >

New Ananlytical Approach For Long-Term Generation Expansion Planning Based on Maximum Principle And Gaussian distribution Function

This paper presents a new approach for the optimal long-range generation planning. A completely new analytical approach for the production costing model and reliability measure is developed under the assumption of Gaussian probabilistic distribution of random load fluctuations and plant outages. The long-range generation investment problem is formulated as an optimal control problem to determine optimally the annual investment in new generation capacity. The Hamiltonian minimization is performed by using a version of the gradient projection method.

Optimal decentralized load frequency control

Abstract A new approach for optimal decentralized load frequency control of a multi-area interconnected power system is presented, which includes the optimal design of a decentralized load frequency controller, an observer for unmeasurable local states and load disturbances, and a quadratic estimator for tie-line power flow information transmitted at intervals. The optimal design of the decentralized controller is based on a modified application of the singular perturbation theory, and the decentralized Luenberger observer uses techniques of state augmentation for exponential disturbance functions and the representation of tie-line power flows as indirectly controlled inputs. The approach presented herein is tested numerically through a two-area load frequency system model, and the results demonstrate remarkable advantages over the conventional ones.

An Optimization Technique for Reactive Power Planning of Subtransmission Network under Normal Operation

This paper presents a new optimization method for reactive power planning of subtransmission network under normal operating conditions. The planning problem is divided into a master problem and two subproblems. The master problem is to determine yearly investment in reactive power compensation devices for expected load growth over a planning horizon. The subproblems are to determine the optimal operation of power system under normal conditions. The master problem is reduced to a yearly Hamiltonian minimization problem, thereby reducing the dimensionality of optimization algorithm. Two subproblems, P-optimization and Q-optimization, both minimize the fuel requirement using a unified approach.