Load flow analysis of radial and mesh distribution system using ZIP model

This paper presents a fault location method that can be used for radial or meshed distribution networks containing Distributed Energy Resources (DERs). The main advantage of the proposed approach is that it requires a minimum number of DFRs determined by the maximum degree among distribution network buses. Fault location is determined using the traveling wave based fault location algorithm. The IEEE 123 Node Test Feeder is used to validate the proposed method under different fault conditions using the EMTP RV and Matlab simulation tools.

Flexible alternatives to decoupled load flows at minimal computational costs

Fault analysis of unbalanced radial and meshed distribution system with inverter based distributed generation (IBDG)

A new efficient two-stage load flow method for meshed distribution networks is presented in this paper. First, a forward/backward sweep load flow method for radial distribution networks is reviewed. Based on the multi-port compensation theory, a two-stage method is then put forward to solve the load flow problem of meshed distribution networks. In the first stage, a meshed network is converted to a radial network by breaking the loops. The load flow of this radial network is calculated by applying the forward/backward sweep method. Breakpoint currents are worked out. In the second stage, injection currents of breakpoints are modified and the load flow of the radial network is calculated again, which is just the load flow of the original meshed network. Two example networks are employed to demonstrate the efficiency of the proposed method. Results are also given and compared with another method.

The electrical power is transmitted through a high-voltage transmission system to low-voltage consumers on the distribution side. Because of the high R/X ratio, high current, and low voltage in distribution systems, active power loss (TPL) is much higher as compared to transmission systems. The economic incentive for distribution power utilities is to reduce losses in their networks. This inducement is generally the cost differential between actual and standard losses. As a result, when actual losses exceed standard losses, the utilities are economically penalized, while when the reverse happens, they are getting benefitted. As a result, the loss minimization problem in distribution systems is an excellent research subject for academics. All the methods for the loss minimization are different from each other by the means of formulation of the problem, tools used to minimize the losses, constraints taken, solution methods, and results are obtained. In most of the cases, the distribution system is interconnected and due to increment in load demand and limitations of loading capacity of distribution lines, the idea of distribution side generation came to maintain the system parameters like voltage profile, power loss, cost of operation, etc. To minimize losses, several methods are available like distributed generation (DG) using renewable energy sources and combined heat and power systems, capacitor allocation, reconfiguration of the network, and D-FACTS devices like dynamic voltage restorer (DVR), distribution static compensator (D-STATCOM), unified power quality conditioner (UPQC) allocation, etc. In this chapter, 33 and 69 bus mesh distribution system analysis is proposed with implementation of multiple wind turbines (WT) and multiple D-STATCOM. Advantages of WT are cost-effective generation, clean fuel source, sustainability, and less land requirement. Here, the Artificial Bee Colony (ABC) algorithm is used for finding optimal size and location of WG and D-STATCOM. ABC is an optimization technique that offers population-based search procedure in which artificial bees change individual food locations over time, with the aim of the bees being to locate the places of food supply with the most nectar and recognize it as the one with the greatest nectar. Here direct approach-based load flow method is used to find load flow parameters like bus voltage, branch currents, and power loss. The outcome verifies effective improvement in the system voltage profile, reduction in distribution feeder line loss, and increment in cost savings.

Technical power losses in power systems are unavoidable, caused by factors such as transformer impedance, conductor resistance, equipment inefficiencies, line reactance, and phase imbalances. Reducing these losses is essential for improving system efficiency. This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. The key advantage of this methodology is its speed, allowing quick estimation of power loss curves both in normal and contingency conditions, whether mild or severe. ANN models excel at capturing the nonlinear behavior of power systems, eliminating the need for iterative methods commonly used in traditional approaches. The results showed that the ANN performed effectively, with a mean squared error during training below the specified threshold. For samples not included in the training set, the network accurately estimated 99% of the real and reactive power losses within the specified range, with residuals around 10−3 and an overall accuracy rate of 99% between the desired and obtained outputs. Additionally, a Graphical User Interface (GUI) was implemented to facilitate user interaction, allowing for easy visualization of power-loss predictions and real-time adjustments.

This paper presents an improved second order load flow method in rectangular coordinates which is distinctly superior to the existing second order methods from the point of view of both speed and storage. Further, the proposed method's memory requirement is comparable to that of the Fast Decoupled Load Flow (FULF) method. The new method would be considerably faster than the FOLF method for a number of systems for which the FDLF method requires a large number of iterations to converge. The proposed method is highly reliable and it gives solutions for a number of systems for which the FDLF method fails. In addition a novel procedure for handling generator bus Q limit violations is also presented.

Traditional Load Flow methods are considered as one of the most important tools for analyzing the operation of power system and to perform the planning studies. Since distribution systems exhibit characteristics like higher R/X ratio and loading of the feeders is higher, traditional load flow methods fail to analyze these networks and hence separate methods are developed to handle the situation. However, these distribution load flow methods restrict to calculating power flow at feeder level (> 11 kV) by representing load as a suitable model. The load segments i.e. 3-phase 4-wire LV network downstream to the 11kV/400V distribution transformer, is represented as time deterministic static loads while calculating distribution load flow. Due to the increase in demand for more situational awareness until the edge of the network and with large scale deployment of Advanced Metering Infrastructure (AMI) & Geographical Information System (GIS) technologies, it is possible to do load flow analysis on 400V 3-phase, 4-wire LV distribution network in phase-wise manner using smart meters, geographical & communication technologies. In this paper, we propose algorithm for such a load flow method and discuss its applications.

Generalized three phase robust load-flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks

The distribution system is the final link between bulk power system and consumer end. A distinctive load flow solution method is used for analysis of the load flow of radial and weakly meshed network based on Kirchhoff’s Current Law (KCL) and KVL. This method has excellent convergence characteristics for both radial as well as weakly meshed structure and is based on bus injection to branch current and branch-current to bus-voltage matrix. The main contribution of the paper is: (i) an analysis has been carried out for a weekly mesh network considering number of loops addition and its impact on the losses, kW and kVAr requirements from a system, and voltage profile, (ii) different load models, realistic ZIP load model and load growth impact on losses, voltage profile, kVA and kVAr requirements, (iii) impact of addition of loops on losses, voltage profile, kVA and kVAr requirements from substation, and (iv) comparison of system performance with radial distribution system. Voltage stability is a major concern in planning and operation of power systems. This paper also includes identifying the closeness critical bus which is the most sensitive to the voltage collapse in radial distribution networks. Node having minimum value of voltage stability index is the most sensitive node. Voltage stability index values are computed for meshed network with number of loops added in the system. The results have been obtained for IEEE 33 and 69 bus test system. The results have also been obtained for radial distribution system for comparison.

Allocation of the power losses to distributed generators and consumers has been a challenging concern for decades in restructured power systems. This paper proposes a promising approach for loss allocation in power distribution systems based on a cooperative concept of game-theory, named Shapley Value allocation. The proposed solution is a generic approach, applicable to both radial and meshed distribution systems as well as those with high penetration of renewables and DG units. With several different methods for distribution system loss allocation, the suggested method has been shown to be a straight-forward and efficient criterion for performance comparisons. The suggested loss allocation approach is numerically investigated, the results of which are presented for two distribution systems and its performance is compared with those obtained by other methodologies.

Generalized Distribution Energy Systems (GDES) contribute to increasing the efficiency of energy use and renewable energy accommodation by coupling multiple energy sources. This article presents the network flow model for implementation of the optimal operation of the GDES. The special topological characteristic of the distribution system allows network constraints to be represented by Kirchhoff's current and voltage laws. The proposal uses a mixed—integer quadratic objective function and linear constraints to determine optimal power allocation by minimizing the distributed generation costs and transmission weakly meshed losses. The distribution components, capacity limits (transformers, lines and switching operations) are considerate. The robustness of the model is tested on 16—bus, 34—bus and 89—bus systems in radial or meshed distribution systems under different scenarios. The optimal Network Flow Model demonstrates the accuracy and efficiency in three tested systems.

This paper presents an Artificial Neural Network (ANN) approach for estimating total real and reactive power losses in electrical power systems. Three network architectures were explored: the Multilayer Perceptron (MLP), the Radial Basis Function (RBF) network, and the Generalized Regression Neural Network (GRNN). The main advantage of the proposed methodology lies in its ability to rapidly compute power loss values throughout the system. ANN models are especially effective due to their capacity to capture the nonlinear characteristics of power systems, thus eliminating the need for iterative procedures. The applicability and effectiveness of the approach were evaluated using the IEEE 14-bus test system and compared with the continuation power flow method, which estimates losses using conventional numerical techniques. The results indicate that the ANN-based models performed well, achieving mean squared error (MSE) values below the predefined threshold during both training and validation (0.001). Notably, the networks accurately estimated the total power losses within the expected range, with residuals on the order of 10−4. Among the models tested, the RBF network showed slightly superior performance in terms of error metrics, requiring fewer centers to meet the established criteria compared to the MLP and GRNN models (11 centers). However, the GRNN achieved the shortest processing time; even so, all three networks produced satisfactory and consistent results, particularly in identifying the critical points of electrical power systems, which is of fundamental importance for ensuring system stability and operational reliability.

This paper describes a critical evaluation of Non-Divergent Load flow methods in well, ill and unsolvable conditioned systems. The comparison studies deals Multiple Load Flow Solution (MLFS) based Second-Order Load-Flow (SOLF) in polar coordinate and Continuation Load Flow (CLF). The analytical bases, ability consideration of theses methods to return operation of power system from unsolvable to solvable region solution. Attention is given to the problems and techniques of to provide optimal recommendations of the parameters, that using in these Non-Divergent Load flow methods to lead to solvable region, based on inequality constraints of power system. A part of the survey, this paper also presents the comparison of numerical result using different type of aforesaid load flow methods for well and ill- conditioned systems. Accordingly, load flow simulation has been solved using the C++ programming.

This article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing the overall accuracy of fault localization. To validate the proposed methodology, extensive tests are conducted on various benchmark systems, including the IEEE 33-bus radial distribution system, the IEEE 33-bus meshed loop unbalanced distribution system, the IEEE 33-bus system with integrated renewable energy sources, and the IEEE 13-bus feeder test system. The results demonstrate a high fault classification accuracy of 99.08%, with an average localization error of just 1.2% of the total line length. The k-NN classifier exhibited a precision of 98.2% and a recall of 99.2%, underscoring the reliability and sensitivity of the proposed method. Additionally, the computational efficiency of the algorithm is evidenced by an average processing time of 0.0764 seconds per fault event, making it well-suited for real-time applications.