Michael Cooke | North Carolina Agricultural and Technical State University (original) (raw)
Papers by Michael Cooke
SST/CSA 92, 1992
Static power system modeling methods, such as power pow and fault analyses, seek to describe stea... more Static power system modeling methods, such as power pow and fault analyses, seek to describe steady state operating conditions of the system. However, practical systems are not always in a steady state mode. Up loading and down loading units, system failures and naturally instigated surges are all situations where steady state analysis techniques fail to accurately describe the system. In these situations transients are introduced which can range from minor ripples to significant spikes with the potential to disrupt system stability or even damage to subsystems.To investigate these occurences, transient analysis techniques must be employed. This paper deals with the discrete-time modeling method which can be used t o perform the transient analysis. The derivation of this technique as well as possible implementations are discussed .
Power system analysis, which includes steady state, transient and dynamic, traditionally has been... more Power system analysis, which includes steady state, transient and dynamic, traditionally has been relegated to an off line or planning activity. This means that the system is only designed to respond to contingencies that have been adequately simulated prior to implementation of the design. Practically, this limits the adaptability of the system when confronted with new or unstudied phenomenon. The usual system response is to isolate the affected area temporarily until the system is stabilized or the disturbance is redirected out of the system. This down time or unmet demand time could be minimized if the system could predict the onset of contingencies and react in a more timely fashion. In order to accomplish this system monitoring, contingency analysis and control or correction must be in real time. The problem, of course, is that the steady state of the system must be known prior to performing a transient or dynamic analysis, since these former analyses determine the deviation from the steady state. Moreover, classical steady state determination or power flow analysis methods are time consuming and potentially unstable. In [1], a method for eliminating the possibility of non-convergence and significantly improving the processing time of power flow analysis is described. In this paper a method of performing steady state, transient and control analyses simultaneously is proposed. This method utilizes a parallel architecture of heterogeneous neural networks to perform power flow analysis, transient stability and fault analysis, and automatic generation control (AGC). The simulation is performed on a set of networked computers employing PVM (parallel virtual machines) technology for intermachine communication.
Today, there exist many examples of artificial neural network (ANN) technology implementations. B... more Today, there exist many examples of artificial neural network (ANN) technology implementations. By far the most successful of these have been with multilayer feedforward networks, primarily utilizing the backpropagation (BPN) paradigm. These networks are universal classifiers and as such are able to address various engineering problems. However, the designing and building of these networks is not well defined. In fact, there may not exist a practical step-by-step method of design which can be broadly applied since theoretically there are an infinite number of configurations which would have to be tested to identify the optimal design. Practically, if certain network parameters are bounded over a reasonable range it is possible to design an optimal network within these guidelines. In this paper, a BPN network is designed by applying this method. The results suggest the method is efficient, reliable and probably yields the absolute optimal network. The nonlinear systems modeling and simulation problem, power flow analysis, is undertaken with the BPN network being compared with the classical Newton-Raphson method.
The motivation for this research was to design a linear system optimal control signal generator, ... more The motivation for this research was to design a linear system optimal control signal generator, which would give a N-step solution for U*(k), the optimal control signal vector. Since this generator is to guarantee an optimal solution, it is based on a fixed Liapunov function. The control signal desired can be readily generated by conventional programming methods; however, the necessity of a complete system description for each control vector hampers this method resulting in excessive computation time. To overcome this, a neural network approach is explored. The network chosen, due to its curve fitting capabilities and fast convergence rate, is the generalized regression neural network (GRNN). The network performs generalized regression, which is a statistical method for approximating the curve of a continuously varying function, without direct knowledge of the function itself. Instead, the input data to the network consists of only parameters which affect the function curve. In this paper, the design and implementation of a GRNN based optimal control signal generator is detailed and its performance evaluated and compared with conventional programming.
Artificial neural networks (ANNs) are presently being utilized to solve many scientific and engin... more Artificial neural networks (ANNs) are presently being utilized to solve many scientific and engineering problems. The advantages include but are not limited to increased stability and processing speeds over classical approximation methods, simplicity of development and hardware implementability. The range of problems which may benefit from the use of ANNs seems unbounded, due to the various architectures and paradigms that may be employed. ANNs fall into two primary categories: 1) supervised networks that require output targets for training; and 2) unsupervised networks that 'learn' or stabilize to each input event. The supervised networks, which include the well known backpropagation (BPN) networks, are by far the most robust. Although the supervised networks can be applied to a greater number of problems they do not respond well in situations where the operational input-output relationships may vary from the previously learned. This is due to the necessity of off-line training. This shortfall usually regulates these powerful tools to off-line processing tasks. The objective of this research effort is to develop, design and implement an on-line processing and training (OPT) supervised ANN.
Today, the power flow or load flow algorithm may be defined as an iterative procedure for approxi... more Today, the power flow or load flow algorithm may be defined as an iterative procedure for approximating the steady state condition of a given power system. This methodology has sufficed for a long time and has been effective enough to meet power system planning demands, in most cases. However, a dilemma exists. The present basic power flow model, the Newton-Raphson, is not appreciably improvable in terms of speed of convergence and accuracy of solution. In fact, these two defining parameters oppose each other. The most successful attempts to improve upon the basic model either trade accuracy for speed (as in the Fast Decoupled Load Flow, which reduces the class of solvable problems) or vice versa (as in the Second Order Load Flow, which significantly complicates the algorithm. This paper proposes an alternative to the Newton-Raphson algorithm, namely an artificial neural network (ANN) method based on the backpropagation (BPN) learning algorithm. BPN, which has a feed forward network architecture and eliminates the divergence problems associated with iteration as well as significantly reducing the time of convergence. The accuracy of solution is determined by the range and amount of training provided; therefore, speed of convergence and accuracy of solution are independent of each other. A network architecture was developed and implemented and the results are detailed in this paper.
It is the authors intention to illustrate a method for power distribution that can make electric ... more It is the authors intention to illustrate a method for power distribution that can make electric vehicles viable for mass use. This novel method of power distribution has been coined Cellular Power Transmission and Distribution (CPTD)©. CPTD is a resurrection of Dr. Nikolai Tesla [10] research combined with modern telecommunication theory. CPTD blends wireless power transmission with contemporary cellular communication theory. The theory of CPTD will be presented along with its hypothesized benefits and drawbacks. The marriage of CPTD with electric vehicles will be discussed. It is the authors opinion that the combination of novel power systems like CPTD will revolutionize electric vehicles. EVs based on continuous power delivery system as proposed above shall be poised to be comparable to conventional vehicles (CVs). Traditionally, EVs have not compared well to CVs due to price, range of driving, speed, replenishment of primary fuel, etc. A cellular powered electric vehicle (CPEV)© will be poised to contend with CV's primarly by providing a constant energy source. The authors believe that the performance of CPEV's can approach that of CV's with respect to driving range, fuel cost per mile, and speed. Index terms-wireless power systems, cellular power transmission and distribution (CPTD), electric vehicle (EV), hybrid electric vehicles (HEV), cellular powered electric vehicle (CPEV)
Drafts by Michael Cooke
The full surface contact Twin Solar Cell (TSC) is in fact two cells in one; this means the cell c... more The full surface contact Twin Solar Cell (TSC) is in fact two cells in one; this means the cell can generate a greater output than that of a single cell of comparable dimension and construction at certain times during the solar insolation daily cycle. This can greatly improve the efficiency per unit area perpendicular to the insolation source. This is possible due to the cell's employment of full surface area collection at both the top and bottom plates. This twin cell configuration utilizes boron phosphorus (BoP) as a barrier between the top (front) and bottom (rear) junctions to allow for dual collection and distribution. Moreover, each cell can be connected to each adjacent cell in either a series or parallel manner. This flexibility extends the range of configurations that can be realized to the limit defined by the overall number of cells.
SST/CSA 92, 1992
Static power system modeling methods, such as power pow and fault analyses, seek to describe stea... more Static power system modeling methods, such as power pow and fault analyses, seek to describe steady state operating conditions of the system. However, practical systems are not always in a steady state mode. Up loading and down loading units, system failures and naturally instigated surges are all situations where steady state analysis techniques fail to accurately describe the system. In these situations transients are introduced which can range from minor ripples to significant spikes with the potential to disrupt system stability or even damage to subsystems.To investigate these occurences, transient analysis techniques must be employed. This paper deals with the discrete-time modeling method which can be used t o perform the transient analysis. The derivation of this technique as well as possible implementations are discussed .
Power system analysis, which includes steady state, transient and dynamic, traditionally has been... more Power system analysis, which includes steady state, transient and dynamic, traditionally has been relegated to an off line or planning activity. This means that the system is only designed to respond to contingencies that have been adequately simulated prior to implementation of the design. Practically, this limits the adaptability of the system when confronted with new or unstudied phenomenon. The usual system response is to isolate the affected area temporarily until the system is stabilized or the disturbance is redirected out of the system. This down time or unmet demand time could be minimized if the system could predict the onset of contingencies and react in a more timely fashion. In order to accomplish this system monitoring, contingency analysis and control or correction must be in real time. The problem, of course, is that the steady state of the system must be known prior to performing a transient or dynamic analysis, since these former analyses determine the deviation from the steady state. Moreover, classical steady state determination or power flow analysis methods are time consuming and potentially unstable. In [1], a method for eliminating the possibility of non-convergence and significantly improving the processing time of power flow analysis is described. In this paper a method of performing steady state, transient and control analyses simultaneously is proposed. This method utilizes a parallel architecture of heterogeneous neural networks to perform power flow analysis, transient stability and fault analysis, and automatic generation control (AGC). The simulation is performed on a set of networked computers employing PVM (parallel virtual machines) technology for intermachine communication.
Today, there exist many examples of artificial neural network (ANN) technology implementations. B... more Today, there exist many examples of artificial neural network (ANN) technology implementations. By far the most successful of these have been with multilayer feedforward networks, primarily utilizing the backpropagation (BPN) paradigm. These networks are universal classifiers and as such are able to address various engineering problems. However, the designing and building of these networks is not well defined. In fact, there may not exist a practical step-by-step method of design which can be broadly applied since theoretically there are an infinite number of configurations which would have to be tested to identify the optimal design. Practically, if certain network parameters are bounded over a reasonable range it is possible to design an optimal network within these guidelines. In this paper, a BPN network is designed by applying this method. The results suggest the method is efficient, reliable and probably yields the absolute optimal network. The nonlinear systems modeling and simulation problem, power flow analysis, is undertaken with the BPN network being compared with the classical Newton-Raphson method.
The motivation for this research was to design a linear system optimal control signal generator, ... more The motivation for this research was to design a linear system optimal control signal generator, which would give a N-step solution for U*(k), the optimal control signal vector. Since this generator is to guarantee an optimal solution, it is based on a fixed Liapunov function. The control signal desired can be readily generated by conventional programming methods; however, the necessity of a complete system description for each control vector hampers this method resulting in excessive computation time. To overcome this, a neural network approach is explored. The network chosen, due to its curve fitting capabilities and fast convergence rate, is the generalized regression neural network (GRNN). The network performs generalized regression, which is a statistical method for approximating the curve of a continuously varying function, without direct knowledge of the function itself. Instead, the input data to the network consists of only parameters which affect the function curve. In this paper, the design and implementation of a GRNN based optimal control signal generator is detailed and its performance evaluated and compared with conventional programming.
Artificial neural networks (ANNs) are presently being utilized to solve many scientific and engin... more Artificial neural networks (ANNs) are presently being utilized to solve many scientific and engineering problems. The advantages include but are not limited to increased stability and processing speeds over classical approximation methods, simplicity of development and hardware implementability. The range of problems which may benefit from the use of ANNs seems unbounded, due to the various architectures and paradigms that may be employed. ANNs fall into two primary categories: 1) supervised networks that require output targets for training; and 2) unsupervised networks that 'learn' or stabilize to each input event. The supervised networks, which include the well known backpropagation (BPN) networks, are by far the most robust. Although the supervised networks can be applied to a greater number of problems they do not respond well in situations where the operational input-output relationships may vary from the previously learned. This is due to the necessity of off-line training. This shortfall usually regulates these powerful tools to off-line processing tasks. The objective of this research effort is to develop, design and implement an on-line processing and training (OPT) supervised ANN.
Today, the power flow or load flow algorithm may be defined as an iterative procedure for approxi... more Today, the power flow or load flow algorithm may be defined as an iterative procedure for approximating the steady state condition of a given power system. This methodology has sufficed for a long time and has been effective enough to meet power system planning demands, in most cases. However, a dilemma exists. The present basic power flow model, the Newton-Raphson, is not appreciably improvable in terms of speed of convergence and accuracy of solution. In fact, these two defining parameters oppose each other. The most successful attempts to improve upon the basic model either trade accuracy for speed (as in the Fast Decoupled Load Flow, which reduces the class of solvable problems) or vice versa (as in the Second Order Load Flow, which significantly complicates the algorithm. This paper proposes an alternative to the Newton-Raphson algorithm, namely an artificial neural network (ANN) method based on the backpropagation (BPN) learning algorithm. BPN, which has a feed forward network architecture and eliminates the divergence problems associated with iteration as well as significantly reducing the time of convergence. The accuracy of solution is determined by the range and amount of training provided; therefore, speed of convergence and accuracy of solution are independent of each other. A network architecture was developed and implemented and the results are detailed in this paper.
It is the authors intention to illustrate a method for power distribution that can make electric ... more It is the authors intention to illustrate a method for power distribution that can make electric vehicles viable for mass use. This novel method of power distribution has been coined Cellular Power Transmission and Distribution (CPTD)©. CPTD is a resurrection of Dr. Nikolai Tesla [10] research combined with modern telecommunication theory. CPTD blends wireless power transmission with contemporary cellular communication theory. The theory of CPTD will be presented along with its hypothesized benefits and drawbacks. The marriage of CPTD with electric vehicles will be discussed. It is the authors opinion that the combination of novel power systems like CPTD will revolutionize electric vehicles. EVs based on continuous power delivery system as proposed above shall be poised to be comparable to conventional vehicles (CVs). Traditionally, EVs have not compared well to CVs due to price, range of driving, speed, replenishment of primary fuel, etc. A cellular powered electric vehicle (CPEV)© will be poised to contend with CV's primarly by providing a constant energy source. The authors believe that the performance of CPEV's can approach that of CV's with respect to driving range, fuel cost per mile, and speed. Index terms-wireless power systems, cellular power transmission and distribution (CPTD), electric vehicle (EV), hybrid electric vehicles (HEV), cellular powered electric vehicle (CPEV)
The full surface contact Twin Solar Cell (TSC) is in fact two cells in one; this means the cell c... more The full surface contact Twin Solar Cell (TSC) is in fact two cells in one; this means the cell can generate a greater output than that of a single cell of comparable dimension and construction at certain times during the solar insolation daily cycle. This can greatly improve the efficiency per unit area perpendicular to the insolation source. This is possible due to the cell's employment of full surface area collection at both the top and bottom plates. This twin cell configuration utilizes boron phosphorus (BoP) as a barrier between the top (front) and bottom (rear) junctions to allow for dual collection and distribution. Moreover, each cell can be connected to each adjacent cell in either a series or parallel manner. This flexibility extends the range of configurations that can be realized to the limit defined by the overall number of cells.