Yosuke Nakanishi

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of pieces of reactive power compensation equipment. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

A hybrid system for service restoration using expert system and genetic algorithm

This paper develops a hybrid system for solving a service restoration problem in distribution systems using expert systems and genetic algorithms. The main objective in service restoration procedure is to restore as many loads as possible by transferring de-energized loads via network reconfigurations to other supporting distribution feeders without violating operating and engineering constraints. The expert system determines switch operations to expand the supply margin of power sources in a case where the total power source capacity is not enough to restore the whole out-of-service areas. The genetic algorithm determines a part of out-of-service area for each power source. The feasibility of the developed algorithm for service restoration is demonstrated on a distribution network with promising results.

Development of power system simulator for research and education

An analog power-system simulator developed for research and education at Waseda University is discussed. It allows system dynamic analyses to be done easily and gives an overall understanding of the events comprising the analyses. Structurally, the simulator consists of a power system block and a digital control block. Programmable controllers are used for the digital control block, making it convenient to design control function blocks and to record events. >

Centralized control of clustered PV generations for loss minimization and power quality

This paper presents new approaches in order to solve problems related to clustered photovoltaic systems and evaluates the performance of the developed method. To cope with inappropriate phenomena caused by malfunctions of islanding detector and over voltages by reverse power flows from PV systems, we have developed a new islanding detector and two types of voltage control systems, and conducted experiments to verify the function of the developed systems. The test results obtained by this demonstrative implementation show that the proposed control system is able to maintain voltages within the regulated level and to equalize the efficiency in a PV system and also that the new method enables us to make total PV generation efficiency higher.

An application of reactive tabu search for service restoration in distribution systems and its comparison with the genetic algorithm and parallel simulated annealing

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine powersources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as branch and bounds method, expert system, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS) are noticed as efficient methods for solving large combinatorial optimization problems. Moreover, Reactive Tabu Search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search method for service restoration in distribution systems. This paper develops a reactive tabu search (RTS) for service restoration and compares RTS, GA, and PSA (Parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results.

Thermal unit scheduling for CO2 reduction including significant wind power penetration

In this paper, we present a method to determine the unit commitment schedules considering CO2 emission and cost including the frequency regulation capability of the units to mitigate fluctuation of wind power. The authors developed an extended procedure that obtains a trade-off solution of cost versus CO2 emission including a significant wind power penetration. The method has been tested on the 10-unit 24-hour model system using the estimated wind power curve derived from an actual wind farm. The results, such as the shadow prices of CO2 obtained by the trade-off analysis, may provide a basis of evaluating the equivalent cost of the wind farms and their contribution toward CO2 reduction.

Cost-minimum network planning in large wind farm using revised prim's algorithm

The traditional thermal and nuclear power plants are concentrated geographically. If such new power plants are planned, suitable routes which reach to nearest substations are suggested. The wind turbines in wind power plants, however, are scattered in a vast region. Its challenging to connect them and gather energy. However, there has been a few research of the method connecting wind turbines in a wind plant. In this paper, a planning method which optimizes the installation cost of power lines is developed. The target is an optimal design of power lines “in” large wind power plants. Specifically, we speculated a situation that installs roughly 50 wind generators (single unit at 3MW) in a vast region. Also, we have taken a consideration to establish an optimal network to reduce the transmission overload when power reaches to the aggregating point. Optimal placement of towers and/or link of wind farms are out of our scope.

A study on optimal locations and sizes of active filters as an additional function of distributed generation systems

We investigate the reduction in harmonics using a distributed generation system. In this study, we assume that the inverter of the distributed generation system also acts as an active filter. We propose a method to determine the optimal locations and sizes of the active filters as a tool to evaluate the effect on reduction in harmonics. In this method, the problem is formulated as a combinatorial optimization problem and solved using reactive tabu search, a combinatorial optimization method. The simulation results show that a considerable effect can be achieved if the distributed generation system with active filter works effectively.

An Improved Equivalent-Input-Disturbance Approach for Repetitive Control System With State Delay and Disturbance

An improved equivalent-input-disturbance (EID) approach is devised to enhance the disturbance-rejection performance for a strictly proper plant with a state delay in a modified repetitive-control system. A gain factor is introduced to construct an improved EID estimator. This increases the flexibility of system design and enables the adjustment of the dynamical performance of disturbance rejection. Moreover, the commutative condition, which is widely used for the conventional EID estimator, is avoided. Thus, it reduces the conservativeness of design by removing the constraints imposed by the commutative condition. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. For one subsystem, the delay information on both the modified repetitive controller and the plant is used to reduce the conservativeness of stability condition. The resulting linear matrix inequality (LMI) is used to find the gain of the state-feedback controller. Another LMI is derived to design the gains of the state observer and the improved EID estimator for the other subsystem. A case study on a metal-cutting system validates the superiority of the developed method.