Monkey algorithm (MA) is one of the evolution algorithms originally developed for optimization problems with continuous variables. In this paper, a discrete monkey algorithm (DMA) was proposed for transmission network expansion planning, one discrete optimization problem. It includes the representation of solution, the modification of objective function, climb process, watch-jump process, cooperation process, somersault process, stochastic perturbation mechanism and termination criteria. Large-step and small-step climb process are designed to avoid the disordered climb direction during the MA optimization process. Cooperation process and stochastic perturbation mechanism are also introduced to improve computational efficiency. The proposed method is applied to a 18-bus system and the IEEE 24-bus system. Numerical results demonstrate that DMA has powerful computational capability and is capable of solving different dimensions of expansion planning problems efficiently with small population size.
FACTS devices are recognized as a new damping resource for poorly damped, low frequency electromechanical oscillation, which usually takes place in a power system operating with long transmission lines under heavily loaded condition. Nevertheless, the damping effect of FACTS devices is known to be strongly influenced by their location and control system. This paper is motivated to cope with these two challenges. By ways of modal analysis, a location index for determining FACTS optimal location is proposed which is suitable and feasible for large scale power systems. A method for tuning parameters in the FACTS control system with a given configuration is presented simultaneously. Numerical results for a 7-machine power system has shown the validity and efficiency of the proposed method.
In this paper we will give a brief introduction on the study of security regions we have done, including steady-state security region to guarantee power flow security constraints and small disturbance stability, and dynamic security region to guarantee transient stability, both in injection space and in cut-set space.
The impact of the static shunt capacitor on power system damping is studied. First the Heffron-Phillips generator model is applied to the theoretical analysis of this problem. An explanation is made about the negative damping phenomenon observed in earlier simulation studies. Then, the simulation of a 10-bus system and a New England 10-generator-39-bus system is used to demonstrate the analytical results. Some useful suggestions for the small signal stability improvement of power systems are given based on the results of the study.
Power systems are large scale nonlinear systems with high complexity. Various optimization techniques and expert systems have been used in power system planning. However, there are always some factors that cannot be quantified, modeled, or even expressed by expert systems. Moreover, such planning problems are often large scale optimization problems. Although computational algorithms that are capable of handling large dimensional problems can be used, the computational costs are still very high. To solve these problems, in this paper, investigation is made to explore the efficiency and effectiveness of combining mathematic algorithms with human intelligence. It had been discovered that humans can join the decision making progresses by cognitive feedback. Based on cognitive feedback and genetic algorithm, a new algorithm called cognitive genetic algorithm is presented. This algorithm can clarify and extract human's cognition. As an important application of this cognitive genetic algorithm, a practical decision method for power distribution system planning is proposed. By using this decision method, the optimal results that satisfy human expertise can be obtained and the limitations of human experts can be minimized in the mean time.
Under extreme events, distribution systems may suffer blackouts and numerous restoration tasks will compete for limited crews and facilities. To enhance the resilience of distribution systems, multiple resources should be fully coordinated, coupling relationships between fault location, fault isolation, and service restoration should be considered, and flexible strategies should be formed for different situations faced by distribution systems. To this end, a comprehensive resilience-oriented fault location, fault isolation, and service restoration (RO-FLISR) method is proposed in this article, where, the fault location, fault isolation, and service restoration under extreme events are comprehensively considered at the same time for the first time, and multiple resources are coordinated. Three basic modules are formulated in the RO-FLISR method, i.e., the possible faulted zone determination (PFZD) module which could determine all the possible faulted zones when there are multiple faults and DGs, the line patrolling (LP) module which supports the optimal scheme for line paroling based on crews, and the fault isolation and service restoration (FISR) module in which the operation of MSs and RCSs are coordinated for comprehensive fault isolation and service restoration. The modules are formulated as MILP models. Effectiveness of the proposed method is verified with the IEEE 123-bus system.
A novel distribution network reconfiguration algorithm, named core schema genetic shortest-path algorithm (CSGSA) is proposed in this paper. It is based on the loads combination method. CSGSA consists of two steps: (1) searching for the optimal power supply paths for a sequence of loads one by one using shortest-path algorithm, and forming a core schema chromosome using core schema algorithm (CSA); and (2) searching possible core schema chromosomes for the optimal one using global optimization method-genetic algorithm. CSGSA is used for solving the loss minimum reconfiguration problem, and its global searching capability is greatly improved by using a proposed branch weight formulation, which considers the effect of residual branch capacities; CSGSA is also employed to solve the supply restoration problem and promising results have been obtained. The proposed method can be used as an efficient tool for distribution network loss minimization and supply restoration.
Deep learning models for nonintrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between datasets. For addressing such problems, self-supervised learning (SSL) is proposed in this article, where labeled appliance-level data from the target dataset or house are not required. Initially, only the aggregate power readings from target dataset are required to pretrain a general network via a self-supervised pretext task to map aggregate power sequences to derived representatives. Then, supervised downstream tasks are carried out for each appliance category to fine-tune the pretrained network, where the features learned in the pretext task are transferred. Utilizing labeled source datasets enables the downstream tasks to learn how each load is disaggregated, by mapping the aggregate to labels. Finally, the fine-tuned network is applied to load disaggregation for the target sites. For validation, multiple experimental cases are designed based on three publicly accessible REDD, U.K.-DALE, and REFIT datasets. Besides, the state-of-the-art neural networks are employed to perform NILM task in the experiments. Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any submetering data from the target datasets.
With the rapid development of renewable energy generation, the world is facing with a series of issues, such as how to integrate and make full use of the renewable energy. And smart grid is an effective strategy to deal with these challenges. As the development trend of power systems, smart grid has powerful functions, comprehensive benefits and broad prospects. But smart grid is not simply a technical matter, but associated with many fundamental and important concepts. It is vital to clarify these basic ideas for scientific and efficient implementation of smart grid, the technology innovation and industry development. The features, framework, associated technologies, short-term and long-term objectives of smart grid, as well as key issues of implementation of smart grid are illustrated in detail in the paper.
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