Dynamical Analysis and Stabilizing Control of Inclined Rotational Translational Actuator Systems

Rotational translational actuator (RTAC) system, whose motions occur in horizontal planes, is a benchmark for studying of control techniques. This paper presents dynamical analysis and stabilizing control design for the RTAC system on a slope. Based on Lagrange equations, dynamics of the inclined RTAC system is achieved by selecting cart position and rotor angle as the general coordinates and torque acting on the rotor as general force. The analysis of equilibriums and their controllability yields that controllability of equilibriums depends on inclining direction of the inclined RTAC system. To stabilize the system to its controllable equilibriums, a proper control Lyapunov function including system energy, which is used to show the passivity property of the system, is designed. Consequently, a stabilizing controller is achieved directly based on the second Lyapunov stability theorem. Finally, numerical simulations are performed to verify the correctness and feasibility of our dynamical analysis and control design

Adaptive Backstepping Integral Sliding Mode Control for 5DOF Barge-Type OFWT under Output Constraint

This article presents a new control solution for a dynamical model of a translational oscillator with a rotational actuator (TORA) based on multi-body dynamics for a barge-type offshore floating wind turbine (OFWT). TORA has been employed as an active structural control strategy. The solution of bounding the output movements of platform pitch and tower bending angle to a certain limit, along with mitigating the OFWT vibrations due to environmental disturbances and uncertainties, is presented in this novel control framework. This new control algorithm consists of a high-gain observer (HGO)-based adaptive backstepping integral sliding mode control (ISMC) and a barrier Lyapunov function (BLF). This guarantees satisfying the constraints on the states and effectively resolves the problem of the unavailability of the system states. The proposed control law based on the BLF has been compared with an adaptive backstepping ISMC to show the efficiency of the output-constraint control scheme. Through MATLAB/SIMULINK numerical simulations and their numeric error table, the effectiveness of the proposed control scheme has been examined. The results confirm the validity and efficiency of the proposed control approaches

Development of a Low Motion-Noise Humanoid Neck: Statics Analysis and Experimental Validation

Abstract-This paper presents our recently developed humanoid neck system that can effectively mimic motion of human neck with very low motion noises. The feature of low motion noises allows our system to work like a real human head/neck. Thus the level of acoustic noises from wearable equipments, such as donning respirators or chemical-resistant jackets, induced by human head motion can be simulated and investigated using such a system. The objective of this investigation is to facilitate using head-worn communication devices for the person who wears the protective equipment/uniform that usually produces communication-noise when the head/neck moves. Our low motion-noise humanoid neck system is based on the spring structure, which can generate 1 Degree of Freedom (DOF) jaw movement and 3DOF neck movement. To guarantee the low-noise feature, no noise-makers like gear and electrodriven parts are embedded in the head/neck structure. Instead, the motions are driven by seven cables, and the actuators pulling the cables are sealed in a sound insulation box. Furthermore, statics analysis of the system has been processed completely. Experimental results validate the analysis, and clearly show that the head/neck system can greatly mimic the motions of human head with an A-weighted noise level of 30 dB or below

Power Transmission Scheduling for Generators in a Deregulated Environment Based on a Game-Theoretic Approach

In a deregulated environment of the power market, in order to lower their energy price and guarantee the stability of the power network, appropriate transmission lines have to be considered for electricity generators to sell their energy to the end users. This paper proposes a game-theoretic power transmission scheduling for multiple generators to lower their wheeling cost. Based on the embedded cost method, a wheeling cost model consisting of congestion cost, cost of losses and cost of transmission capacity is presented. By assuming each generator behaves in a selfish and rational way, the competition among the multiple generators is formulated as a non-cooperative game, where the players are the generators and the strategies are their daily schedules of power transmission. We will prove that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game. Moreover, a distributed algorithm will be provided to realize the optimization in terms of minimizing the wheeling cost. Finally, simulations were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game approach for the generators in a deregulated environment

Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles

The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system’s imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts

Power Transmission Scheduling for Generators in a Deregulated Environment Based on a Game-Theoretic Approach

In a deregulated environment of the power market, in order to lower their energy price and guarantee the stability of the power network, appropriate transmission lines have to be considered for electricity generators to sell their energy to the end users. This paper proposes a game-theoretic power transmission scheduling for multiple generators to lower their wheeling cost. Based on the embedded cost method, a wheeling cost model consisting of congestion cost, cost of losses and cost of transmission capacity is presented. By assuming each generator behaves in a selfish and rational way, the competition among the multiple generators is formulated as a non-cooperative game, where the players are the generators and the strategies are their daily schedules of power transmission. We will prove that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game. Moreover, a distributed algorithm will be provided to realize the optimization in terms of minimizing the wheeling cost. Finally, simulations were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game approach for the generators in a deregulated environment

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach

Self-Insulating Joint Design for Live-Line Operation Based on the Cable-Driven Parallel-Series Mechanism

This paper proposes a self-insulating joint design based on the cable-driven parallel-series (CDPS) mechanism and electrical insulation analysis. The design provides the motions, mechanic support, and electrical insulation for robotic arms in live-line operation, which can maintain the equipment without manual intervention and power interruption. This CDPS mechanism can integrate four degrees of freedom (DOFs) motion in one joint, while the traditional series joint can only realize one DOF independently. The cable forces in the CDPS are calculated by the inverse kinematics to ensure the safe and flexible operation of the mechanism. The self-insulating joint has certain advantages over other designs because the electrical insulation is integrated into the joint instead of the traditional extra insulation layer. This integration reduces the weight of the arm mechanic structure. In addition, the structural complexity and weight are further reduced by separating the actuators and motors from the joint by using CDPS. Electric field distribution near the joint is calculated by the charge simulation method to analyze the insulation performance under the voltage of 35 kV. The cable forces and electric field distribution of the mechanism are measured to validate the simulation models. The inverse kinematics and insulation models of the self-insulating joint can provide detailed information for the mechanic and insulation design of the robotic arms