Performance Assessment of Pareto and Non-Pareto Approaches for the Optimal Allocation of DG and DSTATCOM in the Distribution System

This paper proposes a Differential Evolution (DE) optimization algorithm and a Pareto-frontier Multi-Objective Differential Evolution (MODE) optimization algorithm for the optimal allocation of Distributed Generation (DG) and Distribution Static Compensator (DSTATCOM) in a radial distribution system. It considers the minimization of active power dissipation, voltage drop and the annual cost as the objectives of this optimization problem. The proposed techniques are tested on an IEEE 33 bus radial distribution system. To compare the performance of the MODE and DE, the weighted sum approach is carried out. This helps to select one solution from the Pareto front of the MODE. Case studies show that the allocation of both DG and DSTATCOM results in a noticeable reduction of system losses, voltage drop and annual cost. Comparative studies also show that the global convergence characteristics of MODE are better than several other optimization algorithms

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval

Application of ANN to Real and Reactive Power Allocation Scheme

This chapter describes the implementation of ANN for real and reactive power transfer allocation. The 25 bus equivalent power system of south Malaysia region and IEEE 118 bus system are used to demonstrate the applicability of the ANN output compared to that of the Modified Nodal Equations (MNE) which is used as trainers for real and reactive power allocation. The basic idea is to use supervised learning paradigm to train the ANN. Then the descriptions of inputs and outputs of the training data for the ANN are easily obtained from the load flow results and each method used as teachers respectively. The proposed ANN based method provides promising results in terms of accuracy and computation time. Artificial intelligence has been proven to be able to solve complex processes in deregulated power system such as loss allocation. So, it can be expected that the developed methodology will further contribute in improving the computation time of transmission usage allocation for deregulated system

Prediction of spot welding parameters using fuzzy logic controlling

The Resistance Spot Welding (RSW) represents one of the most important welding processes. The resistance spot welding quality depends on the process parameters like welding current, electrode force and welding time and their chosen levels. In this work, the experimental part is validated by the simulation part, where the last will be used later for predicting the results for new data with a very acceptable percentage of accuracy. This study presents an experimental work of the resistance spot welding for two similar sheets of Austenitic Stainless Steels (AISI 304) that are intended to be held together in one point by the pressure of the electrodes, with high magnitude of electrical current to be applied, where the resistance spot welding parameters (welding current and welding time) are changeable to show each of the parameter’s action on the welded material properties (The Maximum Shear Load that the metal can be subject to besides The Nugget Zone Diameter of the welded contact area). The experimental work in this study delivers genuine and important data that will be the basis for the Fuzzy Logic Controller (FLC), which will be set up then. The Artificial Intelligence (which is presented by the fuzzy logic controller) role is to predict the optimal welded material parameters for any given resistance spot welding parameters, and to discover the probability of expulsion, failure, or breaking in the welding process before it takes place or happens, where in this study, the FLC predicted the optimum value of the maximum shear load for RSW, which occurs at the welding time=20 cycle and the welding current=8 KA, while the estimated optimum value of the Nugget Diameter by FLC for RSW is found at welding time=20 cycle and welding current=8 KA.This prediction will save the metal parts and the electrodes of welding, besides saving the cost and the effor

Prediction of spot welding parameters using fuzzy logic controlling

The Resistance Spot Welding (RSW) represents one of the most important welding processes. The resistance spot welding quality depends on the process parameters like welding current, electrode force and welding time and their chosen levels. In this work, the experimental part is validated by the simulation part, where the last will be used later for predicting the results for new data with a very acceptable percentage of accuracy. This study presents an experimental work of the resistance spot welding for two similar sheets of Austenitic Stainless Steels (AISI 304) that are intended to be held together in one point by the pressure of the electrodes, with high magnitude of electrical current to be applied, where the resistance spot welding parameters (welding current and welding time) are changeable to show each of the parameter’s action on the welded material properties (The Maximum Shear Load that the metal can be subject to besides The Nugget Zone Diameter of the welded contact area). The experimental work in this study delivers genuine and important data that will be the basis for the Fuzzy Logic Controller (FLC), which will be set up then. The Artificial Intelligence (which is presented by the fuzzy logic controller) role is to predict the optimal welded material parameters for any given resistance spot welding parameters, and to discover the probability of expulsion, failure, or breaking in the welding process before it takes place or happens, where in this study, the FLC predicted the optimum value of the maximum shear load for RSW, which occurs at the welding time=20 cycle and the welding current=8 KA, while the estimated optimum value of the Nugget Diameter by FLC for RSW is found at welding time=20 cycle and welding current=8 KA.This prediction will save the metal parts and the electrodes of welding, besides saving the cost and the effor

An ANFIS approach for real power transfer allocation

This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results

Calibration of ZMPT101B voltage sensor module using polynomial regression for accurate load monitoring

Smart Electricity is quickly developing as the results of advancements in sensor technology. The accuracy of a sensing device is the backbone of every measurement and the fundamental of every electrical quantity measurement is the voltage and current sensing. The sensor calibration in the context of this research means the marking or scaling of the voltage sensor so that it can present accurate sampled voltage from the ADC output using appropriate algorithm. The peakpeak input voltage (measured with a standard FLUKE 115 meter) to the sensor is correlated with the peak-peak ADC output of the sensor using 1 to 5th order polynomial regression, in order to determine the best fitting relationship between them. The arduino microcontroller is used to receive the ADC conversion and is also programmed to calculate the root mean square value of the supply voltage. The analysis of the polynomials shows that the third order polynomial gives the best relationship between the analog input and ADC output. The accuracy of the algorithm is tested in measuring the root mean square values of the supply voltage using instantaneous voltage calculation and peak-peak voltage methods. The error in the measurement is less than 1% in the peak-peak method and less than 2.5% in the instantaneous method for voltage measurements above 50V AC, which is very good for measurements in utility. Therefore, the proposed calibration method will facilitate more accurate voltage and power computing for researchers and designers especially in load monitoring where the applied voltage is 240V or 120V ranges

Recent approaches and applications of non-intrusive load monitoring

The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)