Decentralized adaptive neural network control of interconnected nonlinear dynamical systems with application to power system

Traditional nonlinear techniques cannot be directly applicable to control large scale interconnected nonlinear dynamic systems due their sheer size and unavailability of system dynamics. Therefore, in this dissertation, the decentralized adaptive neural network (NN) control of a class of nonlinear interconnected dynamic systems is introduced and its application to power systems is presented in the form of six papers. In the first paper, a new nonlinear dynamical representation in the form of a large scale interconnected system for a power network free of algebraic equations with multiple UPFCs as nonlinear controllers is presented. Then, oscillation damping for UPFCs using adaptive NN control is discussed by assuming that the system dynamics are known. Subsequently, the dynamic surface control (DSC) framework is proposed in continuous-time not only to overcome the need for the subsystem dynamics and interconnection terms, but also to relax the explosion of complexity problem normally observed in traditional backstepping. The application of DSC-based decentralized control of power system with excitation control is shown in the third paper. On the other hand, a novel adaptive NN-based decentralized controller for a class of interconnected discrete-time systems with unknown subsystem and interconnection dynamics is introduced since discrete-time is preferred for implementation. The application of the decentralized controller is shown on a power network. Next, a near optimal decentralized discrete-time controller is introduced in the fifth paper for such systems in affine form whereas the sixth paper proposes a method for obtaining the L2-gain near optimal control while keeping a tradeoff between accuracy and computational complexity. Lyapunov theory is employed to assess the stability of the controllers --Abstract, page iv

Secure High DER Penetration Power Distribution Via Autonomously Coordinated Volt/VAR Control

Traditionally voltage control in distribution power system (DPS) is performed through voltage regulating devices (VRDs) including on load tap changers (OLTCs), step voltage regulators (SVRs), and switched capacitor banks (SCBs). The recent IEEE 1547-2018 from March 2018 requires inverter fed distributed energy resources (DERs) to contribute reactive power to support the grid voltage. To accommodate VAR from DERs, well-organized control algorithm is required to use in this mode to avoid grid oscillations and unintended switching operations of VRDs. This paper presents two voltage control strategies (i) static voltage control considering voltage-reactive power mode (IEEE 1547-2018), (ii) dynamic and extensive voltage control with maximum utilization of DER capacity and system stability. Further, effective time-graded control is implemented between VRDs and DER units to reduce the simultaneous and negative operation. The proposed voltage control strategies are tested in a realistic 140-bus southern California distribution power system through extensive time-domain simulation studies. The results show that voltage quality in a distribution system is effectively achieved through the proposed voltage control strategies with a significantly reduction in the number of switching operations of VRDs. In addition, proposed voltage control strategies increase reliability and security of the DPS during unexpected failures

A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids

Cooperating interconnected microgrids with the Distribution System Operation (DSO) can lead to an improvement in terms of operation and reliability. This paper investigates the optimal operation and scheduling of interconnected microgrids highly penetrated by renewable energy resources (DERs). Moreover, an efficient stochastic framework based on the Unscented Transform (UT) method is proposed to model uncertainties associated with the hourly market price, hourly load demand and DERs output power. Prior to the energy management, a newly developed linearization technique is employed to linearize nodal equations extracted from the AC power flow. The proposed stochastic problem is formulated as a single-objective optimization problem minimizing the interconnected AC MGs cost function. In order to validate the proposed technique, a modified IEEE 69 bus network is studied as the test case

Novel Dynamic Representation and Control of Power Systems with FACTS Devices

FACTS devices have been shown to be useful in damping power system oscillations. However, in large power systems, the FACTS control design is complex due to the combination of differential and algebraic equations required to model the power system. In this paper, a new method to generate a nonlinear dynamic representation of the power network is introduced to enable more sophisticated control design. Once the new representation is obtained, a back stepping methodology for the UPFC is utilized to mitigate the generator oscillations. Finally, the neural network approximation property is utilized to relax the need for knowledge of the power system topology and to approximate the nonlinear uncertainties. The net result is a power system representation that can be used for the design of an enhanced FACTS control scheme. Simulation results are given to validate the theoretical conjectures

Novel Dynamic Representation and Control of Power Networks Embedded with FACTS Devices

FACTS devices have been shown to be powerful in damping power system oscillations caused by faults; however, in the multi machine control using FACTS, the control problem involves solving differential-algebraic equations of a power network which renders the available control schemes ineffective due to heuristic design and lack of know how to incorporate FACTS into the network. A method to generate nonlinear dynamic representation of a power system consisting of differential equations alone with universal power flow controller (UPFC) is introduced since differential equations are typically preferred for controller development. Subsequently, backstepping methodology is utilized to reduce the generator oscillations by using a FACTS device after a fault has occurred. Finally, we use neural networks to approximate the nonlinear network dynamics for controller design. The net result is a representation that could be potentially utilized for studying the placement and number of FACTS devices as well as to design a better control scheme for FACTS given a power network. Simulation results justify theoretical conjectures

Energy Harvesting Using Piezoelectric Materials and High Voltage Scavenging Circuitry

Piezoelectric transducers are increasingly being used to harvest energy from environmental vibrations in order to power remote sensors or to charge batteries that power the sensors. In this paper, two modifications have been analyzed and tested to increase the harvested electrical power from a vibrating piezoelectric material. First, the voltage inversion method, which has recently been used in piezoelectricbased energy harvesting, and that shapes the voltage to be in phase with current in order to increase the harvested power is reviewed. By injecting additional current, a new voltage inversion scheme, referred as voltage compensation scheme, is introduced. This new scheme provides more than 14% increase in harvesting power over the parallel inversion method (parallel SSHI) alone and more than 50% in the case of series inversion method (series SSHI) alone. Second, the tapered cantilever beams were shown to be more effective in generating a uniform strain profile over rectangular and trapezoidal beams if they are precisely shaped. Using this modification, it is shown that a 300% increase in harvested power over available methods in the literature is obtainable

Energy Harvesting from Vibration with Alternate Scavenging Circuitry and Tapered Cantilever Beam

Piezoelectric transducers are increasingly being used to harvest energy from environmental vibrations in order to either power remote sensors or charge batteries that power the sensors. In this paper, a new voltage compensation scheme for high-voltage-based (\u3e 100 V ) energy harvesting is introduced, and its fundamental concepts, as well as the operation details, are elaborated. This scheme, when applied to the voltage inversion method [synchronized switch harvesting on inductor (SSHI)], provides an increase of over 14% in harvested power when compared to the parallel inversion method (parallel SSHI) alone and more than 50% in the case of series inversion method (series SSHI). Second, tapered cantilever beams were shown to be more effective in generating a uniform strain profile over rectangular and trapezoidal beams if they are precisely shaped, resulting in a significant increase in harvested power over available methods in the literature from laboratory experimental tests. In addition, a simplified method to design such a beam is introduced. Finally, a field test of the proposed tapered beam is conducted by using a dozer for earth-moving applications, and experimental results are discussed

Decentralized State Feedback and Near Optimal Adaptive Neural Network Control of Interconnected Nonlinear Discrete-Time Systems

In this paper, first a novel decentralized state feedback stabilization controller is introduced for a class of nonlinear interconnected discrete-time systems in affine form with unknown subsystem dynamics, control gain matrix, and interconnection dynamics by employing neural networks (NNs). Subsequently, the optimal control problem of decentralized nonlinear discrete-time system is considered with unknown internal subsystem and interconnection dynamics while assuming that the control gain matrix is known. For the near optimal controller development, the direct neural dynamic programming technique is utilized to solve the Hamilton-Jacobi-Bellman (HJB) equation forward-in-time. The decentralized optimal controller design for each subsystem utilizes the critic-actor structure by using NNs. All NN parameters are tuned online. By using Lyapunov techniques it is shown that all subsystems signals are uniformly ultimately bounded (UUB) for stabilization of such systems

Decentralized optimal control of a class of interconnected nonlinear discrete-time systems by using online Hamilton-Jacobi-Bellman formulation

In this paper, the direct neural dynamic programming technique is utilized to solve the Hamilton-Jacobi-Bellman equation forward-in-time for the decentralized near optimal regulation of a class of nonlinear interconnected discrete-time systems with unknown internal subsystem and interconnection dynamics, while the input gain matrix is considered known. Even though the unknown interconnection terms are considered weak and functions of the entire state vector, the decentralized control is attempted under the assumption that only the local state vector is measurable. The decentralized nearly optimal controller design for each subsystem consists of two neural networks (NNs), an action NN that is aimed to provide a nearly optimal control signal, and a critic NN which evaluates the performance of the overall system. All NN parameters are tuned online for both the NNs. By using Lyapunov techniques it is shown that all subsystems signals are uniformly ultimately bounded and that the synthesized subsystems inputs approach their corresponding nearly optimal control inputs with bounded error. Simulation results are included to show the effectiveness of the approach. © 2011 IEEE

Improved Sensorless Direct Torque Control Using Space Vector Modulation and Fuzzy Logic Controllers

Fast response, robust and simple structure of direct torque control (DTC) make it attractive in electric drive systems. Nonetheless, in sensorless applications, precise estimation of rotor speed and also motor torque and flux estimation are of utmost importance. An adaptive flux observer is presented considering stator current and flux vector components as state variables and rotor speed as an unknown parameter that is estimated simultaneously via an adaptive routine. Stator resistance value plays an important role in stator flux estimation. Similarly, stator resistance that is varying in different temperatures of motor can be updated through the adaptive scheme that provides more accurate performance of the system. Conversely, torque, flux, and current pulsations during steady state performance are disadvantages of the classical DTC method. A combined direct torque control and space vector modulation (DTC-SVM) strategy is presented using fuzzy logic control. Fuzzy logic controllers (FLCs) are designed such that obtain high accuracy during steady state, while preserve fast response in transients