A hybrid power transfer allocation approach for deregulated power systems

Power transfer allocation is one of the major issues in deregulated power industry. This paper presents a hybrid technique for power transfer allocation. It is based on combining the existing power flow tracing methods that determines the power share from generators to line flows and loads. The advantages of the proposed method are demonstrated by the tests conducted on the IEEE 30-bus system and also on a practical bus equivalent power system of south Malaysia. The proposed method provides better reliability and minimizes the limitations of conventional power flow tracing methods

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

High Strength Concrete Beams Reinforced with Hooked Steel Fibers under Pure Torsion

A study of the behavior of fibers in high-strength reinforced concrete beams is presented in this paper. Twelve reinforced concrete beams were tested under a pure torsion load. Different compressive strengths (45.2, 64.7, and 84.8 MPa) and fiber volume fractions (0, 0.25, 0.5, and 0.75) with variable spacing between transverse reinforcements have been used. It was discovered that the maximum torque of a high-strength concrete beam is increased by about 20.3, 25.6, and 27.1% when the fractional volume of fiber is increased from 0 to 0.25, 0.5 and 0.75 respectively (when the compressive strength is 45.2 MPa and the transverse reinforcement spacing is 100 mm). The test results show that the ultimate torsional strength becomes higher when the concrete compressive strength increases, and this percentage increase becomes higher with increasing steel fiber volume fraction. When the spacing between transverse reinforcements decreases from 150 to 100 mm, the ultimate torque increases by 19.9%. When the spacing between transverse reinforcements decreases from 100 to 60 mm, the ultimate torque increases by 17.0%. In these beams, the fibers’ compressive strength and volume fraction were kept constant at 45.2 MPa and 0.75, respectively. Doi: 10.28991/CEJ-2022-08-01-07 Full Text: PD

Assessment of Rice Husk Biomass Potential for Power Generation in Pakistan

Rice husk is one of the utmost obtainable feedstock for renewable energy production and can contribute to resolving energy scarcity and environmental problems. Appropriate knowledge of the rice husk's physiochemical properties is essential for the approach of thermochemical conversion systems. The present study delivers data on proximate and ultimate analysis and heating values of rice husk collected from different regions of Sindh, Pakistan. Moisture content was found low ranging between 12.76% to 13.50% (Mean 12.98%), higher volatile matter in the range of 55.77% to 62.88% (Mean 61.19%) and ash particles of 14.50% to 16.48% (Mean 15.20%). The lower concentrations of nitrogen, 0.37% to 1.31%, (Mean 0.70%) and sulfur, 0.02% to 0.19%, (Mean 0.11%) environmentally deal with more appropriate fuel properties. The heating value of rice husk ranges varied from 5,276.33 to 6,237.13 Btu/lb (Mean 5,859.87 Btu/lb). The significant values of the rice husk samples indicated that the locally available renewable resources can be transformed into an extensive amount of energy products at a small level from active conversion techniques. Therefore, rice husk can be deliberated as appropriate fuel for energy generation and can be considered as an environmentally friendly and economically feasible fuel that helps to decline harmful pollutions

A Strategic Approach Using Representative LV Networks in the Assessment of Technical Losses on LV Network with Solar Photovoltaic

Low voltage network (LVN) forms an extensive part of the distribution network as it is used to connect electricity supply from utility substations to diverse segments of loads in different geographic locations. With the increasingly high penetration of solar PV in LVN, utility companies are finding it necessary to establish the contribution of these solar PV to the overall technical losses in the distribution network. This paper presents a strategic approach using representative LVN to determine the impact of solar PV on technical losses on the LVN. Five types of representative LVN characterized by different customer load segments (domestic, commercial and industrial) and peak load demand were developed. The impact on technical losses of solar PV connected to these representatives LVN were assessed on a statistical basis for a supply zone. The results obtained are consistent and could be applied to establish investment strategies on distribution network, tariff revision exercise and optimization of distribution network planning/design

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

Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error