Hong Tzer Yang

Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions

This paper develops an efficient, general economic dispatch (ED) algorithm for generating units with nonsmooth fuel cost functions. Based on the evolutionary programming (EP) technique, the new algorithm is capable of determining the global or near global optimal dispatch solutions in the cases where the classical Lagrangian based algorithms cease to be applicable. Effectiveness of the new algorithm is demonstrated on two example power systems and compared to that of the dynamic programming, simulated annealing, and genetic algorithms. Practical application of the developed algorithm is additionally verified on the Taiwan power (Taipower) system. Numerical results show that the proposed EP based ED algorithm can provide accurate dispatch solutions within reasonable time for any type of fuel cost functions.

Solving the capacitor placement problem in a radial distribution system using Tabu Search approach

In this paper, the capacitor placement problem in a radial distribution system is formulated and solved by a Tabu Search (TS) based solution algorithm. The capacitor placement problem considers practical operating constraints of capacitors, load growth, capacity of the feeder and the upper and lower bound constraints of voltage at different load levels to minimize the investment cost of capacitors and system energy loss. A sensitivity analysis method is used to select the candidate installation locations of the capacitors to reduce the search space of this problem a priori. Comparison results of the TS method with the simulated annealing (SA) show that the proposed TS method can offer the nearly optimal solution to the capacitor placement problem within reasonable computing time.

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

A de-noising scheme for enhancing wavelet-based power quality monitoring system

By means of the wavelet transform (WT), a power quality (PQ) monitoring system could easily and correctly detect and localize the disturbances in the power systems. However, the signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The performance of the WT in detecting the disturbance would be greatly degraded, due to the difficulty of distinguishing the noises and the disturbances. To enhance the capability of the WT-based PQ monitoring system, this paper proposes a de-noising approach to detection of transient disturbances in a noisy environment. In the proposed de-noising approach, a threshold of eliminating the influences of noises is determined adaptively according to the background noises. The abilities of the WT in detecting and localizing the disturbances can hence be restored. To test the effectiveness of the developed de-noising scheme, employed were diverse data obtained from the EMTP/ATP programs for the main transient disturbances in the power systems as well as from actual field tests. Using the approach proposed in this paper, remarkable efficiency of monitoring the PQ problems and high tolerance to the noises are approved.

Identification of ARMAX model for short term load forecasting: an evolutionary programming approach

This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

A new short-term load forecasting approach using self-organizing fuzzy ARMAX models

This paper proposes a new self-organizing model of fuzzy autoregressive moving average with exogenous input variables (FARMAX) for one day ahead hourly load forecasting of power systems. To achieve the purpose of self-organizing the FARMAX model, identification of the fuzzy model is formulated as a combinatorial optimization problem. Then a combined use of heuristics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy spaces and associated fuzzy membership functions. The proposed approach is verified by using diverse types of practical load and weather data for Taiwan Power (Taipower) systems. Comparisons are made of forecasting errors with the existing ARMAX model implemented by the commercial SAS package and an artificial neural networks (ANNs) method.

A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.

Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers

To enhance the fault diagnosis abilities for the dissolved gas analysis (DGA) of the power transformers, this paper proposes a novel adaptive fuzzy system for the incipient fault recognition through evolution enhanced design approach. Complying with the practical gas records and associated fault causes as much as possible, a fuzzy reasoning algorithm is presented to establish a preliminary fuzzy diagnosis system. In the system, an evolutionary optimization algorithm is further relied on to fine-tune the membership functions of the if-then inference rules. To make the diagnosis system intensively compact and the inference process more understandable, a pruning scheme is then developed to filter out the insignificant or redundant rules. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Taiwan Power Company (TPC).

A new thermal unit commitment approach using constraint logic programming

The authors propose a constraint logic programming (CLP) algorithm to solve the thermal unit commitment (UC) problem in this paper. The algorithm combines the characteristics of the logic programming with the constraint satisfaction as well as the depth-first branch and bound search techniques to provide an efficient and flexible approach to the UC problem. Through the constraint satisfaction techniques, the constraints, which consist of the upper bound on the objective value, are propagated as much as possible to actively reduce the search space of the UC problem in a priori way. Consequently, the optimal solution can be acquired in a very early stage. To demonstrate the effectiveness of the proposed approach, the practical thermal UC problem of Taiwan Power (Taipower) 38-unit system over a 24-hour period is solved by the CLP algorithm implemented in CHIP language. The results obtained are compared with those from the established methods of the dynamic programming, the Lagrangian relaxation as well as the simulated annealing.

A new neural networks approach to on-line fault section estimation using information of protective relays and circuit breakers

This paper proposes a new neural network diagnostic system for online power system fault section estimation using information of relays and circuit breakers. This system has a similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate fault section with its primary, local and/or remote protective relays and breakers. The diagnostic system can be applicable to the power system control center for single or multiple fault sections estimation, even in the cases of failure operation of relays and breakers, or error-existent data transmission. The proposed approach has been practically verified by testing on a model power system. The test results, although preliminary, suggest this system can be implemented by various electric utilities with relatively low customization effort. >