System level test selection based on combinatorial dependency matrix

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods are not suitable for system level test selection.The first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system level.The second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level tests.An innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is presented.The first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new matrix.The mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.