Optimal Steady-State Voltage Control Using Gaussian Process Learning

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can't travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC).

A new approach to optimal reactive power and voltage control problem in a radial distribution system is proposed in this paper. The optimal control problem is to find a proper dispatch schedule for shunt capacitor banks and on-load tap changer at substation and shunt capacitor banks on feeders such that the power loss is minimized and the voltage profile is improved. To reduce computation burden, the whole problem is decomposed into two sub-problems, namely sub-problem on substation level and sub-problem on feeder level. A simplified dynamic programming and a fuzzy logic control algorithm are used to deal with the two sub-problems respectively. Coordination of the two algorithms provides an appropriate solution to the whole control problem. The numerical results demonstrate that the proposed approach is effective and feasible.

Summary A distribution network with distributed generation (DG) is usually called an active distribution network. The voltage quality in an active distribution network tends to degrade because of the bidirectional power flow in the network and the random output characteristic of DG. Existing voltage control methods cannot effectively address this issue. In this article, an improved differential evolution (DE) algorithm–based optimal series compensation voltage control method for an active distribution network is presented. It is assumed that the series compensating voltage is produced flexibly by modern power electronics equipment. The quantitative relationship connecting the node voltage deviation, network power loss and compensating voltage is analysed. The optimal control model, including the power loss and load node voltage, is constructed. Then an improved DE algorithm is proposed to solve the optimal model, which combines the standard DE approach with the simulated annealing and adaptive mutation operator method. The optimal control model can be solved effectively by the improved algorithm. A simulation model is developed based on the IEEE 33 standard distribution network with DG. Simulation results confirm the validity of the optimal control model and the advantages of the improved DE algorithm.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

In general, severe load imbalances in small AC micro-grid systems can degrade their operational performance and their maintenance. This is because the unbalanced load in the micro-grid affects the energy flow and the voltage regulation functions of each phase. In order to solve the voltage imbalance problem, several algorithms for the 3-phase 4-leg CVCF inverter have been proposed, but the control algorithms are not enough to operate the 4-leg CVCF inverter in a stable manner. Therefore, this paper proposes a single-phase voltage and power control algorithm for the 3-phase 4-leg CVCF inverter based on a dq control in order to improve the voltage imbalance problem caused by a severely unbalanced load, where the single phase voltage control algorithm is composed of an αβ-dq and a dq-αβ transformer, a voltage and a current controller, and an off-set controller and a PWM, and the single-phase power control algorithm is also composed of an αβ-dq and a dq-αβ transformer, an active/reactive power and a current controller, and an off-set controller and a PWM. Additionally, this paper performs modeling of the single-phase voltage and the power controller for a 4-leg CVCF inverter using the Matlab/Simulink S/W. From the simulation results, it is confirmed that the transient stability of the proposed single voltage control algorithm can be improved compared to the conventional control algorithm, and voltage control can also be maintained in a stable manner under extremely unbalanced conditions. Further, it is confirmed that 3-phase currents of the proposed single-phase power control algorithm are controlled in a stable manner under extremely unbalanced conditions.

Integration of large-scale distributed generators (DGs) (such as wind turbines, solar photo-voltaic, fuel cells, and so on) into the microgrid triggers voltage deviation which can give rise to desynchronization and distortion of power flow. To solve this problem, this article presents a distributed optimal secondary voltage control to synchronize the output voltage deviations of the DGs to zero. Instead of designing a distributed linear optimal control for the DGs linearized near an operating point as in the existing literature, a two-loop control structure comprising an inner-loop and outer loop is put in place. The inner-loop linearizes the nonlinear dynamics of each of the DGs. Then, the outer-loop which consists of the optimal distributed controller solves an optimization problem with the aim to minimize the voltage deviation of each DG by exchanging information with neighbouring DGs through a communication network. The proposed control technique will force the voltage deviations of the DGs to zero based on consensus. The efficacy of the proposed technique is confirmed by simulation studies.

This paper proposes optimal voltage control strategies for distribution systems that have multiple voltage regulator devices. The system must operate reaching desirable voltage profiles for scenarios that consider extreme loading (heavy and light), unbalanced loads and distributed generation connection. The problem is modeled as an optimization problem that ensures the attendance of the constraints related to voltage limits, power balance and control devices' capacity. The decision variables are the taps of the voltage control devices, which play an important role on Volt/Var control. A genetic algorithm implemented through a computer interface between Matlab and OpenDSS packages determines the number of control operations. It is assumed that the distribution system has a centralized control with all necessary communication infrastructure. To confirm the validity of the proposed method, simulations were performed by using the IEEE 34 node test feeder. The results showed that the strategies contributed to reduce the voltage deviation along the feeder, the voltage unbalance and the total feeder losses.

This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

This study presents the modelling and simulation of a single area powersystem network containing three different generating sources, solar thermalpower plant (STPP), thermal, and diesel power plants. The simultaneousfrequency and voltage control problem has been investigated using LinearQuadratic Regulator (LQR) and State Feedback based Sliding Mode Control(SF-SMC) design. Also, the integration of Particle Swarm Optimization(PSO) has been considered for the selection of LQR cost function weightmatrices and SMC control parameters. Proper selection of LQR and SF-SMCparameters allows the controllers to handle the complexities involved in avery efficient manner and also augments necessary robustness in the con-troller design. The transient performance of the PSO based LQR and SF-SMCcontrolled system has been assessed in terms of time-domain performanceindices, and it has been compared with the conventional PID and 2-DOF PIDcontrollers. Additionally, the robustness of the proposed controllers has beentested and verified after contemplating variation in model parameters

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

This paper proposes a method to find the optimal control scheme in the AC / DC converters considering the criteria of adjustment of voltage set points and the k-droop coefficient of each converter, with the objective of reduction losses in a HVDC grid. The differential evolution method is used for this optimization. To do this the control strategies and their characteristics is explained for each option of control system. The selection of the voltage control station (slack) is described together with the algorithm of selection of optimal voltage control scheme and the algorithm of optimization of voltage set points. Also, the CIGRE B4 DC grid test system benchmark is tested in steady and dynamic simulation to determine the advantages of the selected optimal control scheme and verify that the system maintains the stability when a fault has occurred.

Power systems can become unstable under transient periods such as short-circuit faults, leading to equipment damage and large scale blackouts. Power system stabilizers (PSS) can be designed to improve the stability of generators by quickly regulating the exciter field voltage to damp the swings of generator rotor angle and speed. The stability achieved through exciter field voltage control can be further improved with a relatively small, fast responding energy storage system (ESS) connected at the terminals of the generator that enables electrical power damping. PSS are designed and studied using a single-machine infinite-bus (SMIB) model. In this paper, we present a comprehensive optimal-control design for a flexible ac synchronous generator PSS using both exciter field voltage and ESS control including estimation of unmeasurable states. The controller is designed to minimize disturbances in rotor frequency and angle, and thereby improve stability. The design process is based on a linear quadratic regulator of the SMIB model with a test system linearized about different operating frequencies in the range 10 Hz to 60 Hz. The optimal performance of the PSS is demonstrated along with the resulting stability improvement.

The high penetration of distributed generation (DG) is changing the energy flows in the grid, as well as the energy losses and voltage profile of the system. To solve these problems, the impact of the connection of Renewable Energy Generators (REGs) on the grid is being studied. This work is intended to present the voltage variation of each node of the distribution grid before and after the insertion of a wind turbine or solar panel and the combination of both. The proposed method is self-adaptive regulation by combining settings in P/Q or P/V mode with a setpoint decision making block (setpoint adaptation block) based on fuzzy logic in order to maintain a stable and optimal voltage profile.The simulation results obtained using Matlab®/Simulink® clearly show the efficiency of the controllers used and their optimal control capacity.

This paper presents an optimal voltage control algorithm developed to increase the efficiency of operation of power systems and minimizing the reaction time for compensating voltage deviations due to load fluctuations. Because of the many variables of influence in the optimal voltage control (e.g. line power flow limits, bus voltage limits, etc.), the mathematical formulation is a constrained optimization problem with continuous and discrete variables, known as Optimal Power Flow (OPF), which is non-linear and non-convex. We propose a methodology to convexify the OPF problem based on Semidefinite Programming (SDP), with a novel formulation to include discrete decision variables such as shunt elements and tap changers positions as discrete variables. Finally, the SDP formulation of the case of a DC network is included.