Robert Fischl

The application of decision theory to contingency selection

This paper presents the theory and method for systematically finding the performance index (PI) which is used in Automatic Contingency Selection (ACS) algorithms. Since the ACS problem is a binary decision problem, then the choice of the PI is equivalent to the selection of a decision function which measures the impact of each contingency on the system performance in terms of giving out-of-limit conditions. This paper shows how to select the set of weighting coefficients in the currently used PIs for analyzing either the real power flow or node voltage magnitude problems in order to circumvent some of the contingency ranking problems. Even more important, it is shown how to select the threshold values of the PI which guarantee proper classification of the contingencies in terms of minimizing the probabilities of missing critical contingencies and false alarms. The approach taken is a set theoretic one which allows us to develop a method for finding the PI as a volume maximization problem which can be solved using standard SUMT methods. One such algorithm is given together with an illustrative example.

On the evaluation of voltage collapse criteria

The authors provide an approach that enables a power system operator or planner to evaluate his or her alternatives in selecting a certain voltage collapse criterion. Specifically, the selection of a criterion must guarantee that the risk of making a wrong decision is minimal for any operating point and disturbance. This is done by recasting the various voltage collapse criteria in terms of a decision framework. This framework is based on statistical decision theory and provides a method for evaluating the risk of making the wrong decision in terms of the probability of a missed voltage collapse or a false alarm. Some examples are given to illustrate the effectiveness of the proposed approach.<<ETX>>

Brief paper: Optimal control of a solar collector loop using a distributed-lumped model

This paper considers the optimal control of a solar collector loop described by a bilinear distributed parameter model for the collector fluid temperature and a bilinear lumped parameter model for the storage fluid temperature. The objective is to control the collector fluid velocity so as to maximize the net energy collected over a fixed time period. Necessary conditions for optimality, given by a set of equations whose solution yields the optimal control, are derived. It is shown that the optimal control is an open-loop, bang-bang control which depends on two terms: a measurable quantity which depends on the state of the collector fluid, and a quantity which depends on a future knowledge of the weather data. It is also shown that for the case in which only two switches occur during the period of operation, the optimal control depends only on the temperature difference across the collector. Thus, one can construct a feedback on/off controller for the system provided that it is known a priori that only two switches will occur during the time interval under consideration.

Direct position plus velocity feedback control of large flexible space structures

The authors demonstrate the feasibility of shape and position control of large flexible structures with collocated sensors and actuators using direct position-plus-velocity feedback. This result is important for large space structures where the number of modes is very large and eventually unknown. When the number of inputs equals the number of outputs, the stabilizing feedback gain stands for any positive definite matrix, including diagonal matrices. This result may explain the success of decentralized adaptive controllers when a reasonable number of sensors is used to satisfy the observability assumption. >

Screening power system contingencies using a back-propagation trained multiperceptron

The utility of trained neural networks in calculating the network state and classifying its security status under different load and contingency conditions is demonstrated. In particular, a two-layer multiperceptron is used to screen contingent branch overloads. The performance of this approach is evaluated using a six-bus example. The results indicate that the proposed tasks can be performed reliably by back-propagation-trained multiperceptrons.<<ETX>>

A framework to predict voltage collapse in power systems

A framework based on region-wise partitioning is presented which accounts for several of the mechanisms to predict voltage collapse and can serve as a basis for comparing the effectiveness of performance indices to predict on-line, voltage collapse problems in power systems. The basis of the framework is the voltage stability (VS) region, which accounts for both static and dynamic mechanisms of voltage collapse. Based on the VS region, static and dynamic performance indices, are defined to predict the static mechanisms of voltage collapse in the input or injection space and the dynamic mechanism in the post-contingency state space respectively. >

An improved Hopfield model for power system contingency classification

A method for designing neural networks (NNs) for classifying contingencies in terms of the number and type of limit violations is presented. Specifically, an optimization method (in contrast to a learning method) for finding the weights and thresholds of an associated Little-Hopfield NN is developed. This optimization method, which uses the linear programming technique, maximizes the probability of classifying the contingency correctly. The contingency classification problem is formulated into a pattern recognition problem. A NN to detect a prescribed set of patterns is then designed.<<ETX>>

Optimal and suboptimal control policies for a solar collector system

An optimal control problem to maximize the net energy gathered by a flat-plate solar collector system by controlling the collector fluid flow rate is investigated. The problem is formulated in terms of a distributed parameter system and solved using the method of characteristics. It is shown that if the pump of the collector loop is such that its pumping power is greater than a linear function of the fluid velocity, then the optimal control policy is one in which the fluid flow is instantly switched between zero and maximum rates. Necessary conditions that determine the optimal switching times are derived. Because the resultant switching function of the optimal policy is shown to be decomposable into two parts, one that depends on the state of the system and another that requires a priori knowledge of the solar intensity over the entire period of operation, a suboptimal control policy that can be implemented by an on/off feedback controller with hysteresis is proposed. When this suboptimal policy is compared with the optimal policy, it is shown that on a clear day with sufficient solar insolation to dictate a two-switch optimal policy, the two policies are identical. Under other weather conditions, the feedback suboptimal controller will keep the pump off for a slightly shorter period of time than the time dictated by the optimal control.

An inter-area transmission and voltage limitation (TVLIM) program

This paper presents a new power system planning tool, called the transmission and voltage limitation (TVLIM) program, which provides a graphical display of the set of all feasible and secure interarea megawatt power transfers. The TVLIM program identifies the voltage magnitude limits, thermal loading limits, and voltage stability limits that constrain power transfer between areas. These limits define the boundary of a security region, called the TVLIM region, in the space of area imports of real power. Two TVLIM regions are generated, one for the base case operating condition and the other for the next contingency set. The next contingency TVLIM region identifies the limiting contingencies in addition to the weak links in the power network. A 39-bus and a 300-bus test power system are used to illustrate the TVLIM display. >

Interconnected power system laboratory: fault analysis experiment

The primary goal of the project is to develop a fault analysis experiment which allows students to examine the effect of fault conditions on a power system in a realistic manner. This experiment is the first in a series of experiments to be implemented in Drexel Universitys Interconnected Power Systems Laboratory (IPSL). The IPSL provides a real-life power system network and a computer interface to the system in order to provide control and data capturing. The computer interface utilizes client/server and industry standard networking technology to help students visualize power system phenomena as seen by the system operator via an energy management system (EMS).